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100
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LLM at Network Edge: A Layer-wise Efficient Federated Fine-tuning Approach
|
[
"Jinglong Shen",
"Nan Cheng",
"Wenchao Xu",
"Haozhao Wang",
"Yifan guo",
"Jiajie Xu"
] |
Fine-tuning large language models (LLMs) poses significant computational burdens, especially in federated learning (FL) settings. We introduce Layer-wise Efficient Federated Fine-tuning (LEFF), a novel method designed to enhance the efficiency of FL fine-tuning while preserving model performance and minimizing client-side computational overhead. LEFF strategically selects layers for fine-tuning based on client computational capacity, thereby mitigating the straggler effect prevalent in heterogeneous environments. Furthermore, LEFF incorporates an importance-driven layer sampling mechanism, prioritizing layers with greater influence on model performance. Theoretical analysis demonstrates that LEFF achieves a convergence rate of $\mathcal{O}(1/\sqrt{T})$. Extensive experiments on diverse datasets demonstrate that LEFF attains superior computational efficiency and model performance compared to existing federated fine-tuning methods, particularly under heterogeneous conditions.
|
poster
| null | null | null |
[] |
[] |
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] |
101
|
Generalizable Hand-Object Modeling from Unconstrained RGB Images via 3D Gaussians
|
[
"Xingyu Liu",
"Pengfei Ren",
"Qi Qi",
"Haifeng Sun",
"Zirui Zhuang",
"Jing Wang",
"Jianxin Liao",
"Jingyu Wang"
] |
Recent advances in hand–object interaction modeling have employed implicit representations, such as Signed Distance Functions (SDF), Neural Radiance Fields (NeRF), and 3D Gaussian Splatting (3DGS), to reconstruct hands and objects with arbitrary topology and photorealistic detail. However, these methods often rely on dense 3D surface annotations, or are tailored to short clips constrained in motion trajectories and scene contexts, limiting their generalization to diverse environments and movement patterns. In this work, we present HOGS, an adaptively perceptive 3DGS-based framework for generalizable hand–object modeling from unconstrained monocular RGB images. By integrating photometric cues from the visual modality with the physically grounded structure of 3D Gaussians, HOGS disentangles inherent geometry from transient lighting and motion-induced appearance changes. This endows implicit representations with the ability to generalize to unseen environments and dynamic motion patterns. Experiments on two challenging datasets demonstrate that HOGS outperforms state-of-the-art methods in monocular hand-object reconstruction and photo-realistic rendering.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
102
|
Characterizing Dataset Bias via Disentangled Visual Concepts
|
[
"Jinho Choi",
"Hyesu Lim",
"Steffen Schneider",
"Jaegul Choo"
] |
Dataset bias is ubiquitous in machine learning datasets. Yet, systematically identifying these biases is challenging without costly, fine-grained attribute annotations. We introduce ConceptScope, a framework for characterizing dataset bias by disentangling visual concepts using a Sparse Autoencoder. Our framework automatically discovers visual concepts present in datasets and distinguishes them into target, contextual, and bias concepts. We first validate our framework by accurately detecting six distinct types of visual concepts—object, texture, background, facial attributes, emotion, and action—achieving high accuracy on labeled datasets. We then extend our approach to discover both known biases in annotated datasets (such as CelebA) and novel biases in datasets without explicit bias annotations (such as ImageNet). Furthermore, we introduce a method to partition test data into subgroups based on the strength and presence of task-related versus bias concepts, introducing a practical use case of ConceptScope for model robustness diagnosis. Our approach leverages existing datasets without the need for additional bias annotations, providing valuable insights into how concept distributions affect model generalization under bias-induced distribution shifts.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
103
|
Dynamic Text Bundling Supervision for Zero-Shot Inference on Text-Attributed Graphs
|
[
"Yusheng Zhao",
"Qixin Zhang",
"Xiao Luo",
"Weizhi Zhang",
"Zhiping Xiao",
"Wei Ju",
"Philip S Yu",
"Ming Zhang"
] |
Large language models (LLMs) have been used in many zero-shot learning problems, with their strong generalization ability. Recently, adopting LLMs in text-attributed graphs (TAGs) has drawn increasing attention. However, the adoption of LLMs faces two major challenges: limited information on graph structure and unreliable responses. LLMs struggle with text attributes isolated from the graph topology. Worse still, they yield unreliable predictions due to both information insufficiency and the inherent weakness of LLMs (e.g., hallucination). Towards this end, this paper proposes a novel method named Dynamic Text Bundling Supervision (DENSE) that queries LLMs with bundles of texts to obtain bundle-level labels and uses these labels to supervise graph neural networks. Specifically, we sample a set of bundles, each containing a set of nodes with corresponding texts of close proximity. We then query LLMs with the bundled texts to obtain the label of each bundle. Subsequently, the bundle labels are used to supervise the optimization of graph neural networks, and the bundles are further refined to exclude noisy items. To justify our design, we also provide theoretical analysis of the proposed method. Extensive experiments across ten datasets validate the effectiveness of the proposed method.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
104
|
Do LVLMs Truly Understand Video Anomalies? Revealing Hallucination via Co-Occurrence Patterns
|
[
"Menghao Zhang",
"Pengfei Ren",
"Kangheng Lin",
"Qi Qi",
"Haifeng Sun",
"Huazheng Wang",
"Zirui Zhuang",
"Lei Zhang",
"Jianxin Liao",
"Jingyu Wang"
] |
Large Vision-Language Models (LVLMs) pretrained on large-scale multimodal data have shown promising capabilities in Video Anomaly Detection (VAD). However, their ability to reason about abnormal events based on scene semantics remains underexplored. In this paper, we investigate LVLMs’ behavior in VAD from a visual-textual co-occurrence perspective, focusing on whether their decisions are driven by statistical shortcuts between visual instances and textual phrases. By analyzing visual-textual co-occurrence in pretraining data and conducting experiments under different data settings, we reveal a hallucination phenomenon: LVLMs tend to rely on co-occurrence patterns between visual instances and textual phrases associated with either normality or abnormality, leading to incorrect predictions when these high-frequency objects appear in semantically mismatched contexts. To address this issue, we propose VAD-DPO, a direct preference optimization method supervised with counter-example pairs. By constructing visually similar but semantically contrasting video clips, VAD-DPO encourages the model to align its predictions with the semantics of scene rather than relying on co-occurrence patterns. Extensive experiments on six benchmark datasets demonstrate the effectiveness of VAD-DPO in enhancing both anomaly detection and reasoning performance, particularly in scene-dependent scenarios.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
105
|
Dual Prototype-Enhanced Contrastive Framework for Class-Imbalanced Graph Domain Adaptation
|
[
"Xin Ma",
"Yifan Wang",
"Siyu Yi",
"Wei Ju",
"Junyu Luo",
"Yusheng Zhao",
"Xiao Luo",
"Jiancheng Lv"
] |
Graph transfer learning, especially in unsupervised domain adaptation, focuses on transferring knowledge from a label-rich source domain graph to an unlabeled target domain graph. However, most existing methods assume a balanced label distribution in the source graph, often inconsistent with real-world scenarios where node labels are class-imbalanced. This presents significant challenges for graph transfer learning, as it is impacted by biased knowledge from the source graph and substantial domain distribution shifts. Given the effectiveness of class-specific prototypes in capturing domain semantics, in this paper, we propose a dual-branch prototype-enhanced contrastive framework for class-imbalanced graph domain adaptation. Specifically, we introduce a dual-branch graph encoder to capture both local and global information, generating class-specific prototypes from a distilled anchor set. Then, a prototype-enhance contrastive learning framework is introduced to address the aforementioned challenges. On the one hand, we encourage class alignment between the two branches based on constructed prototypes to mitigate the impact of class imbalance. On the other hand, we generate pseudo-labels for the target domain and align sample pairs across domains that share similar semantics to reduce domain discrepancies. Extensive experimental results show that our ImGDA outperforms the state-of-the-art methods across multiple datasets and settings.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
106
|
Vinci: Deep Thinking in Text-to-Image Generation using Unified Model with Reinforcement Learning
|
[
"wang lin",
"Wentao Hu",
"Liyu Jia",
"Kaihang Pan",
"Majun Zhang",
"Zhou Zhao",
"Fei Wu",
"Jingyuan Chen",
"Hanwang Zhang"
] |
With the continuous development of large language models and reasoning chain technologies, the potential of deep reasoning based on reinforcement learning has shown remarkable promise in multi-task scenarios. However, existing unified models have yet to achieve end-to-end integration in image generation and understanding tasks, limiting the model’s self-reflection ability and the realization of cross-modal reasoning chains. To address this, we propose Vinic, a novel framework designed to enable interleaved image generation and understanding through deep reasoning capabilities. We leverage a small amount of multimodal chain-of-thought (MCoT) data for cold-start and employ reinforcement learning to guide the integration of image generation and understanding tasks. Additionally, we introduce a momentum-based reward function, which dynamically adjusts the reward distribution by considering historical improvements, ensuring the stability of the model across multiple generations. Experimental results demonstrate that integrating MCoT can achieve a +22% improvement over the base model on Geneval, effectively enhancing both image generation quality and instruction alignment capabilities.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
107
|
PolyJuice Makes It Real: Black-Box, Universal Red-Teaming for Synthetic Image Detectors
|
[
"Sepehr Dehdashtian",
"Mashrur Mahmud Morshed",
"Jacob Seidman",
"Gaurav Bharaj",
"Vishnu Boddeti"
] |
Synthetic image detectors (SIDs) are a key defense against the risks posed by the growing realism of images from text-to-image (T2I) models. Red teaming improves SID’s effectiveness by identifying and exploiting their failure modes via misclassified synthetic images. However, existing red-teaming solutions (i) require white-box access to SIDs, which is infeasible for proprietary state-of-the-art detectors, and (ii) generate image-specific attacks through expensive online optimization. To address these limitations, we propose PolyJuice, the first black-box, image-agnostic red-teaming method for SIDs, based on an observed distribution shift in the T2I latent space between samples correctly and incorrectly classified by the SID. PolyJuice generates attacks by (i) identifying the direction of this shift through a lightweight offline process that only requires black-box access to the SID, and (ii) exploiting this direction by universally steering all generated images towards the SID’s failure modes. PolyJuice-steered T2I models are significantly more effective at deceiving SIDs (up to 84%) compared to their unsteered counterparts. We also show that the steering directions can be estimated efficiently at lower resolutions and transferred to higher resolutions using simple interpolation, reducing computational overhead. Finally, tuning SID models on PolyJuice-augmented datasets notably enhances the performance of the detectors (up to 30%).
|
poster
|
2509.15551
| null | null |
[] |
[] |
[] |
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] |
108
|
Unified 2D-3D Discrete Priors for Noise-Robust and Calibration-Free Multiview 3D Human Pose Estimation
|
[
"Geng Chen",
"Pengfei Ren",
"Xufeng Jian",
"Haifeng Sun",
"Menghao Zhang",
"Qi Qi",
"Zirui Zhuang",
"Jing Wang",
"Jianxin Liao",
"Jingyu Wang"
] |
Multi-view 3D human pose estimation (HPE) leverages complementary information across views to improve accuracy and robustness. Traditional methods rely on camera calibration to establish geometric correspondences, which is sensitive to calibration accuracy and lacks flexibility in dynamic settings. Calibration-free approaches address these limitations by learning adaptive view interactions, typically leveraging expressive and flexible continuous representations. However, as the multiview interaction relationship is learned entirely from data without constraint, they are vulnerable to noisy input, which can propagate, amplify and accumulate errors across all views, severely corrupting the final estimated pose.To mitigate this, we propose a novel framework that integrates a noise-resilient discrete prior into the continuous representation-based model. Specifically, we introduce the \textit{UniCodebook}, a unified, compact, robust, and discrete representation complementary to continuous features, allowing the model to benefit from robustness to noise while preserving regression capability.Furthermore, we further propose an attribute-preserving and complementarity-enhancing Discrete-Continuous Spatial Attention (DCSA) mechanism to facilitate interaction between discrete priors and continuous pose features.Extensive experiments on three representative datasets demonstrate that our approach outperforms both calibration-required and calibration-free methods, achieving state-of-the-art performance.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
109
|
Fix False Transparency by Noise Guided Splatting
|
[
"Aly El Hakie",
"Yiren Lu",
"Yu Yin",
"Michael Jenkins",
"Yehe Liu"
] |
3D Gaussian Splatting (3DGS) has demonstrated impressive capabilities in 3D reconstruction. However, its α-blending can induce ’false transparency’ artifacts, particularly where low point cloud density in sparse or low-texture regions causes foreground objects to appear improperly transparent. This issue stems from an ill-posed optimization. During training, background Gaussians blend with foreground ones, making them difficult to differentiate using only photometric loss, which leads to the observed transparency in these regions. This view-inconsistency issue is hard to detect in static renderings during training and validation, but becomes evident in object-centric reconstruction during interactive rotation. Although other causes of view-inconsistency (e.g., popping artifacts) have been explored recently, false transparency has not been explicitly identified. This paper proposes a novel explanation to the problem and a solution to remedy it by injecting opaque noise Gaussians in the object volume during training. Our strategy, Noise Guided Splatting ( NGS), encourages surface Gaussians to adopt higher opacity while minimally modifying the existing splatting process. To quantitatively evaluate the false transparency in static renderings, we propose a transmittance-based metric to characterize the extent of the false transparency problem. We also introduce a customized high-quality object-centric scan dataset exhibiting prominent transparency issues and supplement popular existing datasets (e.g., DTU) with new complementary infill noise specifically designed to evaluate false transparency handling in 3D reconstruction methods. Across various datasets, NGS substantially reduces surface transmittance while maintaining performance on standard rendering metrics (e.g., PSNR), demonstrating its effectiveness
|
poster
|
2510.15736
| null | null |
[] |
[] |
[] |
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] |
110
|
CADGrasp: Learning Contact and Collision Aware General Dexterous Grasping in Cluttered Scenes
|
[
"Jiyao Zhang",
"Zhiyuan Ma",
"Tianhao Wu",
"Zeyuan Chen",
"Hao Dong"
] |
Dexterous grasping in cluttered environments presents substantial challenges due to the high degrees of freedom of dexterous hands, occlusion, and potential collisions arising from diverse object geometries and complex layouts. To address these challenges, we propose CADGrasp, a two-stage algorithm for general dexterous grasping using single-view point cloud inputs. In the first stage, we predict a scene-decoupled, contact- and collision-aware representation—sparse IBS—as the optimization target. Sparse IBS compactly encodes the geometric and contact relationships between the dexterous hand and the scene, enabling stable and collision-free dexterous grasp pose optimization. To enhance the prediction of this high-dimensional representation, we introduce an occupancy-diffusion model with voxel-level conditional guidance and force closure score filtering. In the second stage, we develop several energy functions and ranking strategies for optimization based on sparse IBS to generate high-quality dexterous grasp poses. Extensive experiments in both simulated and real-world settings validate the effectiveness of our approach, demonstrating its capability to mitigate collisions while maintaining a high grasp success rate across diverse objects and complex scenes.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
111
|
Dynamic Test-Time Compute Scaling in Control Policy: Difficulty-Aware Stochastic Interpolant Policy
|
[
"Inkook Chun",
"Seungjae Lee",
"Michael Albergo",
"Saining Xie",
"Eric Vanden-Eijnden"
] |
Diffusion- and flow-based policies deliver state-of-the-art performance on long-horizon robotic manipulation and imitation-learning tasks. However, these controllers employ a fixed inference budget at every control step, regardless of task complexity, leading to computational inefficiency for simple subtasks while potentially underperforming on challenging ones. To address these issues, we introduce Difficulty-Aware Stochastic Interpolant Policy (DA-SIP), a framework that enables robotic controllers to adaptively adjust their integration horizon in real-time based on task difficulty. Our approach employs a difficulty classifier that analyzes RGB-D observations to dynamically select the step budget, the optimal solver variant, and ODE/SDE integration at each control cycle. DA-SIP builds upon the stochastic interpolant formulation to provide a unified framework that unlocks diverse training and inference configurations for diffusion- and flow-based policies. Through comprehensive benchmarks across diverse manipulation tasks, DA-SIP achieves 2.6-4.4× reduction in total computation time while maintaining task-success rates comparable to fixed maximum-computation baselines. By implementing adaptive computation within this framework, DA-SIP transforms generative robot controllers into efficient, task-aware systems that intelligently allocate inference resources where they provide the greatest benefit.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
112
|
Structure-Aware Cooperative Ensemble Evolutionary Optimization on Combinatorial Problems with Multimodal Large Language Models
|
[
"Jie Zhao",
"Kang Cheong"
] |
While evolutionary algorithms (EAs) have proven effective in exploring the vast solution spaces characteristic of such problems, traditional encoding schemes, such as binary or numerical representations, often fail to capture the intricate structural properties of networks. Through employing image-based encodings to preserve topological context, this study utilizes multimodal large language models (MLLMs) as evolutionary operators to facilitate structure-aware optimization over graph data. To address the visual clutter inherent in large-scale network visualizations, we leverage graph sparsification techniques to simplify representations while maintaining essential structural features. To further enhance robustness and mitigate biases introduced by any single sparsification view, we propose a cooperative evolutionary framework that operates across multiple sparsified variants from diverse structural perspectives to achieve knowledge transfer among various domains. Additionally, acknowledging the sensitivity of MLLMs to network layout, we introduce an ensemble strategy that aggregates outputs from various layout configurations through consensus voting. Finally, experiments on real-world networks, illustrated through the classical influence maximization task, demonstrate that our approach substantially improves both the quality and resilience of solutions in MLLM-driven evolutionary optimization.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
113
|
Uncertainty-Guided Exploration for Efficient AlphaZero Training
|
[
"Scott Cheng",
"Meng-Yu Tsai",
"Ding-Yong Hong",
"Mahmut T Kandemir"
] |
AlphaZero has achieved remarkable success in complex decision-making problems through self-play and neural network training. However, its self-play process remains inefficient due to limited exploration of high-uncertainty positions, the overlooked runner-up decisions in Monte Carlo Tree Search (MCTS), and high variance in value labels. To address these challenges, we propose and evaluate uncertainty-guided exploration by branching from high-uncertainty positions using our proposed Label Change Rate (LCR) metric, which is further refined by a Bayesian inference framework. Our proposed approach leverages runner-up MCTS decisions to create multiple variations, and ensembles value labels across these variations to reduce variance. We investigate three key design parameters for our branching strategy: where to branch, how many variations to branch, and which move to play in the new branch. Our empirical findings indicate that branching with 10 variations per game provides the best performance-exploration balance. Overall, our end-to-end results show an improved sample efficiency over the baseline by 58.5\% on 9x9 Go in the early stage of training and by 47.3\% on 19x19 Go in the late stage of training.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
114
|
Give Me FP32 or Give Me Death? Challenges and Solutions for Reproducible Reasoning
|
[
"Jiayi Yuan",
"Hao Li",
"Xinheng Ding",
"Wenya Xie",
"Yu-Jhe Li",
"Wentian Zhao",
"Kun Wan",
"Jing Shi",
"Xia Hu",
"Zirui Liu"
] |
Large Language Models (LLMs) are now integral across various domains and have demonstrated impressive performance. Progress, however, rests on the premise that benchmark scores are both accurate and reproducible. We demonstrate that the reproducibility of LLM performance is fragile: changing system configuration such as evaluation batch size, GPU count, and GPU version can introduce significant difference in the generated responses. This issue is especially pronounced in reasoning models, where minor rounding differences in early tokens can cascade into divergent chains of thought, ultimately affecting accuracy. For instance, under bfloat16 precision with greedy decoding, a reasoning model like DeepSeek-R1-Distill-Qwen-7B can exhibit up to $\textbf{9\\% accuracy variance due to differences in GPU count, type, and evaluation batch size used.}$We trace the root cause of this variability to the non-associative nature of floating-point arithmetic under limited numerical precision. Our work presents the first systematic investigation into how numerical precision affects reproducibility in LLM inference. Through carefully controlled experiments across various hardware, software, and precision settings, we quantify when and how model outputs diverge.Our analysis reveals that floating-point precision—while critical for reproducibility—is often neglected in evaluation practices. Inspired by this, we develop a lightweight inference pipeline, dubbed $\textit{LayerCast}$, that stores weights in 16-bit precision but performs all computations in FP32, balancing memory efficiency with numerical stability.
|
oral
|
2506.09501
|
https://github.com/nanomaoli/llm_reproducibility
| null |
[] |
[] |
[] |
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] |
115
|
Learning Principles from Multi-modal Human Preference
|
[
"Jiayi Zhou",
"Jiaming Ji",
"Boyuan Chen",
"Jiapeng Sun",
"wenqi chen",
"Donghai Hong",
"Sirui Han",
"Yike Guo",
"Yaodong Yang"
] |
Training multi-modal large language models (MLLMs) that align with human intentions is a long-term challenge. Traditional score-only reward models for alignment suffer from low accuracy, weak generalization, and poor interpretability, blocking the progress of alignment methods, \textit{e.g.,} reinforcement learning from human feedback (RLHF). Generative reward models (GRMs) leverage MLLMs' intrinsic reasoning capabilities to discriminate pair-wise responses, but their pair-wise paradigm makes it hard to generalize to learnable rewards. We introduce Generative RLHF-V, a novel alignment framework that integrates GRMs with multi-modal RLHF. We propose a two-stage pipeline: \textbf{multi-modal generative reward modeling from RL}, where RL guides GRMs to actively capture human intention, then predict the correct pair-wise scores; and \textbf{RL optimization from grouped comparison}, which enhances multi-modal RL scoring precision by grouped responses comparison. Experimental results demonstrate that, besides out-of-distribution generalization of RM discrimination, our framework improves 4 MLLMs' performance across 7 benchmarks by 18.1\%, while the baseline RLHF is only 5.3\%. We further validate that Generative RLHF-V achieves a near-linear improvement with an increasing number of candidate responses.
|
poster
|
2505.18531
| null |
https://generative-rlhf-v.github.io/
|
[] |
[] |
[] |
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] |
116
|
Small Singular Values Matter: A Random Matrix Analysis of Transformer Models
|
[
"Max Staats",
"Matthias Thamm",
"Bernd Rosenow"
] |
This work analyzes singular-value spectra of weight matrices in pretrained transformer models to understand how information is stored at both ends of the spectrum. Using Random Matrix Theory (RMT) as a zero information hypothesis, we associate agreement with RMT as evidence of randomness and deviations as evidence for learning. Surprisingly, we observe pronounced departures from RMT not only among the largest singular values -- the usual outliers -- but also among the smallest ones. A comparison of the associated singular vectors with the eigenvectors of the activation covariance matrices shows that there is considerable overlap wherever RMT is violated. Thus, significant directions in the data are captured by small singular values and their vectors as well as by the large ones. We confirm this empirically: zeroing out the singular values that deviate from RMT raises language-model perplexity far more than removing values from the bulk, and after fine-tuning the smallest decile can be the third most influential part of the spectrum. To explain how vectors linked to small singular values can carry more information than those linked to larger values, we propose a linear random-matrix model. Our findings highlight the overlooked importance of the low end of the spectrum and provide theoretical and practical guidance for SVD-based pruning and compression of large language models. Our findings highlight the overlooked importance of small singular values and provide theoretical and practical guidance for SVD-based pruning and compression of large language models.
|
poster
|
2410.17770
| null | null |
[] |
[] |
[] |
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] |
117
|
Fully Dynamic Algorithms for Chamfer Distance
|
[
"Gramoz Goranci",
"Shaofeng Jiang",
"Peter Kiss",
"Eva Szilagyi",
"Qiaoyuan Yang"
] |
We study the problem of computing Chamfer distance in the fully dynamic setting, where two set of points $A, B \subset \mathbb{R}^{d}$, each of size up to $n$, dynamically evolve through point insertions or deletions and the goal is to efficiently maintain an approximation to $dist_{\mathrm{CH}}(A,B) = \sum_{a \in A} \min_{b \in B} dist(a,b)$, where $dist$ is a distance measure. Chamfer distance is a widely used dissimilarity metric for point clouds, with many practical applications that require repeated evaluation on dynamically changing datasets, e.g., when used as a loss function in machine learning. In this paper, we present the first dynamic algorithm for maintaining an approximation of the Chamfer distance under the $\ell_p$ norm for $p \in$ {$1,2$}.Our algorithm reduces to approximate nearest neighbor (ANN) search with little overhead. Plugging in standard ANN bounds, we obtain $(1+\epsilon)$-approximation in $\tilde{O}(\epsilon^{-d})$ update time and $O(1/\epsilon)$-approximation in $\tilde{O}(d n^{\epsilon^2} \epsilon^{-4})$ update time.We evaluate our method on real-world datasets and demonstrate that it performs competitively against natural baselines.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
118
|
Safe RLHF-V: Safe Reinforcement Learning from Multi-modal Human Feedback
|
[
"Jiaming Ji",
"Xinyu Chen",
"Rui Pan",
"Han Zhu",
"Jiahao Li",
"Donghai Hong",
"Boyuan Chen",
"Jiayi Zhou",
"Kaile Wang",
"Juntao Dai",
"Chi-Min Chan",
"Sirui Han",
"Yike Guo",
"Yaodong Yang"
] |
Multimodal large language models (MLLMs) are essential for building general-purpose AI assistants; however, they pose increasing safety risks. How can we ensure safety alignment of MLLMs to prevent undesired behaviors? Going further, it is critical to explore how to fine-tune MLLMs to preserve capabilities while meeting safety constraints. Fundamentally, this challenge can be formulated as a min-max optimization problem. However, existing datasets have not yet disentangled single preference signals into explicit safety constraints, hindering systematic investigation in this direction. Moreover, it remains an open question whether such constraints can be effectively incorporated into the optimization process for multi-modal models. In this work, we present the first exploration of the Safe RLHF-V -- the first multimodal safety alignment framework. The framework consists of: (I) BeaverTails-V, the first open-source dataset featuring dual preference annotations for helpfulness and safety, supplemented with multi-level safety labels (minor, moderate, severe); (II) Beaver-Guard-V, a multi-level guardrail system to proactively defend against unsafe queries and adversarial attacks. Applying the guard model over five rounds of filtering and regeneration significantly enhances the precursor model’s overall safety by an average of 40.9%. (II) Based on dual preference, we initiate the first exploration of multi-modal safety alignment within a constrained optimization. Experimental results demonstrate that Safe RLHF effectively improves both model helpfulness and safety. Specifically, Safe RLHF-V enhances model safety by 34.2% and helpfulness by 34.3%.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
119
|
Under the Shadow: Exploiting Opacity Variation for Fine-grained Shadow Detection
|
[
"Xiaotian Qiao",
"Ke Xu",
"Xianglong Yang",
"Ruijie Dong",
"Xiaofang Xia",
"Jiangtao Cui"
] |
Shadow characteristics are of great importance for scene understanding.Traditionally, shadow regions are considered as binary masks, often resulting in imprecise detection and suboptimal performance in tasks like shadow removal.We demonstrate that such an assumption oversimplifies light-object interactions in the scene, as the scene details under either hard or soft shadows remain visible to a certain degree.Based on this insight, we aim to reformulate the shadow detection paradigm from the opacity perspective, and introduce a new fine-grained shadow detection method.In particular, given the input image, we first propose a shadow opacity augmentation module to generate realistic images with varied shadow opacities.We then introduce a shadow feature separation module to learn the shadow position and opacity representations separately, followed by an opacity mask prediction module that fuses these representations and predicts fine-grained shadow detection results.In addition, we construct a new dataset with opacity-annotated shadow masks across varied scenarios.Extensive experiments demonstrate that our method outperforms the baselines qualitatively and quantitatively, enhancing downstream shadow applications like shadow removal and editing.The dataset and code will be made publicly available.
|
poster
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[] |
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[] |
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] |
120
|
MODEM: A Morton-Order Degradation Estimation Mechanism for Adverse Weather Image Recovery
|
[
"Hainuo Wang",
"Qiming Hu",
"Xiaojie Guo"
] |
Restoring images degraded by adverse weather remains a significant challenge due to the highly non-uniform and spatially heterogeneous nature of weather-induced artifacts, \emph{e.g.}, fine-grained rain streaks versus widespread haze. Accurately estimating the underlying degradation can intuitively provide restoration models with more targeted and effective guidance, enabling adaptive processing strategies. To this end, we propose a Morton-Order Degradation Estimation Mechanism (MODEM) for adverse weather image restoration. Central to MODEM is the Morton-Order 2D-Selective-Scan Module (MOS2D), which integrates Morton-coded spatial ordering with selective state-space models to capture long-range dependencies while preserving local structural coherence. Complementing MOS2D, we introduce a Dual Degradation Estimation Module (DDEM) that disentangles and estimates both global and local degradation priors. These priors dynamically condition the MOS2D modules, facilitating adaptive and context-aware restoration. Extensive experiments and ablation studies demonstrate that MODEM achieves state-of-the-art results across multiple benchmarks and weather types, highlighting its effectiveness in modeling complex degradation dynamics. Our code will be released soon.
|
poster
|
2505.17581
|
https://github.com/hainuo-wang/MODEM
| null |
[] |
[] |
[] |
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] |
121
|
Dynamic Diameter in High-Dimensions against Adaptive Adversary and Beyond
|
[
"Kiarash Banihashem",
"Morteza Monemizadeh",
"MohammadTaghi Hajiaghayi",
"Peyman Jabbarzade",
"Samira Goudarzi",
"Jeff Giliberti"
] |
In this paper, we study the fundamental problems of maintaining the diameter and a $k$-center clustering of a dynamic point set $P \subset \mathbb{R}^d$, where points may be inserted or deleted over time and the ambient dimension $d$ is not constant and may be high. Our focus is on designing algorithms that remain effective even in the presence of an \emph{adaptive adversary}—an adversary that, at any time $t$, knows the entire history of the algorithm’s outputs as well as all the random bits used by the algorithm up to that point. We present a fully dynamic algorithm that maintains a $2$-approximate diameter with a \emph{worst-case} update time of $poly(d, \log n)$, where $n$ is the length of the stream. Our result is achieved by identifying a robust representative of the dataset that requires infrequent updates, combined with a careful deamortization. To the best of our knowledge, this is the first efficient fully-dynamic algorithm for diameter in high dimensions that \emph{simultaneously} achieves a $2$-approximation guarantee and robustness against an adaptive adversary. We also give an improved dynamic $(4+\epsilon)$-approximation algorithm for the $k$-center problem, also resilient to an adaptive adversary. Our clustering algorithm achieves an amortized update time of $k^{2.5} d \cdot poly(\epsilon^{-1}, \log n)$, improving upon the amortized update time of $k^6 d \cdot poly( \epsilon^{-1}, \log n)$ by Biabani et al. [NeurIPS'24].
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
122
|
VividFace: A Robost and High-Fidelity Video Face Swapping Framework
|
[
"Hao Shao",
"Shulun Wang",
"Yang Zhou",
"Guanglu Song",
"Dailan He",
"ZHUOFAN ZONG",
"Shuo Qin",
"Yu Liu",
"Hongsheng Li"
] |
Video face swapping has seen increasing adoption in diverse applications, yet existing methods primarily trained on static images struggle to address temporal consistency and complex real-world scenarios. To overcome these limitations, we propose the first video face swapping framework, VividFace, a robust and high-fidelity diffusion-based framework. VividFace employs a novel hybrid training strategy that leverages abundant static image data alongside temporal video sequences, enabling it to effectively model temporal coherence and identity consistency in videos.Central to our approach is a carefully designed diffusion model integrated with a specialized VAE, capable of processing image-video hybrid data efficiently. To further enhance identity and pose disentanglement, we introduce and release the Attribute-Identity Disentanglement Triplet (AIDT) dataset, comprising a large-scale collection of triplets where each set contains three face images—two sharing the same pose and two sharing the same identity. Augmented comprehensively with occlusion scenarios, AIDT significantly boosts the robustness of VividFace against occlusions.Moreover, we incorporate advanced 3D reconstruction techniques as conditioning inputs to address significant pose variations effectively. Extensive experiments demonstrate that VividFace achieves state-of-the-art performance in identity preservation, temporal consistency, and visual realism, surpassing existing methods while requiring fewer inference steps. Our framework notably mitigates common challenges such as temporal flickering, identity loss, and sensitivity to occlusions and pose variations.The AIDT dataset, source code, and pre-trained weights will be released to support future research. The code and weights are available in the supplementary materials.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
123
|
Multiverse: Your Language Models Secretly Decide How to Parallelize and Merge Generation
|
[
"Xinyu Yang",
"Yuwei An",
"Hongyi Liu",
"Tianqi Chen",
"Beidi Chen"
] |
Autoregressive Large Language Models (AR-LLMs) frequently exhibit implicit parallelism in sequential generation. Inspired by this, we introduce **Multiverse**, a new generative model that enables natively parallel generation. Multiverse internalizes a MapReduce paradigm, generating automatically through three stages: (i) a *Map* stage for adaptive task decomposition, (ii) a *Process* stage for parallel subtask execution, and (iii) a *Reduce* stage for lossless result synthesis. Next, we build a real-world Multiverse reasoning model with co-design of data, algorithm, and system, enabling rapid and seamless transfer from frontier AR-LLMs. Starting from sequential reasoning chains, we create **Multiverse 1K** by converting them into structured training data using an automated LLM-assisted pipeline, avoiding costly human annotations. Algorithmically, we design **Multiverse Attention** to separate parallel reasoning steps while keeping compatibility with causal attention for efficient training. Systematically, we implement **Multiverse Engine** to enable parallel inference. It features a dedicated scheduler that dynamically switches between sequential and parallel generation, triggered directly by the model. After a 3-hour fine-tuning with 1K examples, *our Multiverse-32B stands as the only non-AR model achieving performance on par with leading AR-LLMs of the same scale*, evidenced by AIME24 & 25 scores of 52% and 43%, respectively. Moreover, our budget control experiments show that Multiverse-3B exhibits superior scaling, outperforming AR-LLMs by 1.87% on average using the same context length. Such parallel scaling further leads to practical efficiency gain, achieving up to 2$\times$ speedup across varying batch sizes.
|
spotlight
|
2506.09991
| null |
https://multiverse4fm.github.io/
|
[] |
[] |
[] |
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] |
124
|
KORGym: A Dynamic Game Platform for LLM Reasoning Evaluation
|
[
"Jiajun Shi",
"Jian Yang",
"Jiaheng Liu",
"Xingyuan Bu",
"Jiangjie Chen",
"Junting Zhou",
"Kaijing Ma",
"Zhoufutu Wen",
"Bingli Wang",
"Yancheng He",
"Liang Song",
"Hualei Zhu",
"Shilong Li",
"Xingjian Wang",
"Wei Zhang",
"Ruibin Yuan",
"Yifan Yao",
"Wenjun Yang",
"Yunli Wang",
"Siyuan Fang",
"Siyu Yuan",
"Qianyu He",
"Robert Tang",
"Yingshui Tan",
"Wangchunshu Zhou",
"ZHAO-XIANG ZHANG",
"Zhoujun Li",
"Wenhao Huang",
"Ge Zhang"
] |
Recent advancements in large language models (LLMs) underscore the need for more comprehensive evaluation methods to accurately assess their reasoning capabilities. Existing benchmarks are often domain-specific and thus cannot fully capture an LLM’s general reasoning potential. To address this limitation, we introduce the **Knowledge Orthogonal Reasoning Gymnasium (KORGym)**, a dynamic evaluation platform inspired by KOR-Bench and Gymnasium. KORGym offers over fifty games in either textual or visual formats and supports interactive, multi-turn assessments with reinforcement learning scenarios. Using KORGym, we conduct extensive experiments on 19 LLMs and 8 VLMs, revealing consistent reasoning patterns within model families and demonstrating the superior performance of closed-source models. Further analysis examines the effects of modality, reasoning strategies, reinforcement learning techniques, and response length on model performance. We expect KORGym to become a valuable resource for advancing LLM reasoning research and developing evaluation methodologies suited to complex, interactive environments.
|
spotlight
|
2505.14552
|
https://github.com/multimodal-art-projection/korgym
| null |
[] |
[] |
[] |
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] |
125
|
Parameter Efficient Merging for Multimodal Large Language Models with Direction Robustness
|
[
"Fanhu Zeng",
"Haiyang Guo",
"Fei Zhu",
"Li Shen",
"Hao Tang"
] |
Fine-tuning pre-trained models with custom data leads to numerous expert models on specific tasks. Merging models into one universal model to empower multi-task ability refraining from data leakage has gained popularity. With the expansion in data and model size, parameter efficient tuning becomes the common practice for obtaining task-specific models efficiently. However, few methods are dedicated to efficient merging and existing methods designed for full fine-tuning merging fail under efficient merging. To address the issue, we analyze from low-rank decomposition and reveal that direction robustness during merging is crucial for merging efficient modules. We furthermore uncover that compensating for gap between stark singular values contributes to direction robustness. Therefore, we propose CoPA-Merging, a training-free parameter efficient merging method with complementary parameter adaptation to maintain directions. Specifically, we (1) prune parameters and scale coefficients from inter-parameter relation for singular values to maintain direction stability away from task interference and (2) perform cross-task normalization to enhance unseen task generalization. We establish a benchmark consisting of diverse multimodal tasks, on which we conduct experiments to certificate the outstanding performance and generalizability of our method. Additional study and extensive analyses further showcase the effectiveness.
|
spotlight
| null | null | null |
[] |
[] |
[] |
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] |
126
|
CAML: Collaborative Auxiliary Modality Learning for Multi-Agent Systems
|
[
"Rui Liu",
"Yu Shen",
"Peng Gao",
"Pratap Tokekar",
"Ming Lin"
] |
Multi-modal learning has become a crucial technique for improving the performance of machine learning applications across domains such as autonomous driving, robotics, and perception systems. However, in certain scenarios, particularly in resource-constrained environments, some modalities available during training may be absent during inference. While existing frameworks effectively utilize multiple data sources during training and enable inference with reduced modalities, they are primarily designed for single-agent settings. This poses a critical limitation in dynamic environments such as connected autonomous vehicles (CAV), where incomplete data coverage can lead to decision-making blind spots. Conversely, some works explore multi-agent collaboration but without addressing missing modality at test time. To overcome these limitations, we propose Collaborative Auxiliary Modality Learning (CAML), a novel multi-modal multi-agent framework that enables agents to collaborate and share multi-modal data during training, while allowing inference with reduced modalities during testing. Experimental results in collaborative decision-making for CAV in accident-prone scenarios demonstrate that CAML achieves up to a 58.1% improvement in accident detection. Additionally, we validate CAML on real-world aerial-ground robot data for collaborative semantic segmentation, achieving up to a 10.6% improvement in mIoU.
|
poster
|
2502.17821
| null | null |
[] |
[] |
[] |
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] |
127
|
Single-Step Operator Learning for Conditioned Time-Series Diffusion Models
|
[
"Hui Chen",
"Vikas Singh"
] |
Diffusion models have achieved significant success, yet their application to time series data, particularly with regard to efficient sampling, remains an active area of research. We describe an operator-learning approach for conditioned time-series diffusion models that gives efficient single-step generation by leveraging insights from the frequency-domain characteristics of both the time-series data and the diffusion process itself. The forward diffusion process induces a structured, frequency-dependent smoothing of the data's probability density function. However, this frequency smoothing is related (e.g., via likelihood function) to easily accessible frequency components of time-series data. This suggests that a module operating in the frequency space of the time-series can, potentially, more effectively learn to reverse the frequency-dependent smoothing of the data distribution induced by the diffusion process. We set up an operator learning task, based on frequency-aware building blocks, which satisfies semi-group properties, while exploiting the structure of time-series data. Evaluations on multiple datasets show that our single-step generation proposal achieves forecasting/imputation results comparable (or superior) to many multi-step diffusion schemes while significantly reducing inference costs.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
128
|
A compressive-expressive communication framework for compositional representations
|
[
"Rafael Elberg",
"Felipe del Río",
"Mircea Petrache",
"Denis Parra"
] |
Compositional generalization—the ability to interpret novel combinations of familiar elements—is a hallmark of human cognition and language. Despite recent advances, deep neural networks still struggle to acquire this property reliably. In this work, we introduce CELEBI (Compressive-Expressive Language Emergence through a discrete Bottleneck and Iterated learning), a novel self-supervised framework for inducing compositionality in learned representations from pre-trained models, through a reconstruction-based communication game between a sender and a receiver. Building on theories of language emergence, we integrate three mechanisms that jointly promote compressibility, expressivity, and efficiency in the emergent language. First, interactive decoding incentivizes intermediate reasoning by requiring the receiver to produce partial reconstructions after each symbol. Second, a reconstruction-based imitation phase, inspired by iterated learning, trains successive generations of agents to imitate reconstructions rather than messages, enforcing a tighter communication bottleneck. Third, pairwise distance maximization regularizes message diversity by encouraging high distances between messages, with formal links to entropy maximization. Our method significantly improves both the efficiency and compositionality of the learned messages on the Shapes3D and MPI3D datasets, surpassing prior discrete communication frameworks in both reconstruction accuracy and topographic similarity. This work provides new theoretical and empirical evidence for the emergence of structured, generalizable communication protocols from simplicity-based inductive biases.
|
poster
|
2501.19182
| null | null |
[] |
[] |
[] |
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] |
129
|
RIGNO: A Graph-based Framework For Robust And Accurate Operator Learning For PDEs On Arbitrary Domains
|
[
"Sepehr Mousavi",
"Shizheng Wen",
"Levi Lingsch",
"Maximilian Herde",
"Bogdan Raonic",
"Siddhartha Mishra"
] |
Learning the solution operators of PDEs on arbitrary domains is challenging due to the diversity of possible domain shapes, in addition to the often intricate underlying physics. We propose an end-to-end graph neural network (GNN) based neural operator to learn PDE solution operators from data on point clouds in arbitrary domains. Our multi-scale model maps data between input/output point clouds by passing it through a downsampled regional mesh. The approach includes novel elements aimed at ensuring spatio-temporal resolution invariance. Our model, termed RIGNO, is tested on a challenging suite of benchmarks composed of various time-dependent and steady PDEs defined on a diverse set of domains. We demonstrate that RIGNO is significantly more accurate than neural operator baselines and robustly generalizes to unseen resolutions both in space and in time.
|
poster
|
2501.19205
|
https://github.com/camlab-ethz/rigno
| null |
[] |
[] |
[
"shiwen0710/Datasets_for_GAOT"
] |
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] |
130
|
Reasoning Can Hurt the Inductive Abilities of Large Language Models
|
[
"Haibo Jin",
"Peiyan Zhang",
"Man Luo",
"Haohan Wang"
] |
Large Language Models (LLMs) have shown remarkable progress across domains, yet their ability to perform inductive reasoning—inferring latent rules from sparse examples—remains limited. It is often assumed that chain-of-thought (CoT) prompting, as used in Large Reasoning Models (LRMs), enhances such reasoning. We investigate this assumption with creating four controlled, diagnostic game-based tasks—chess, Texas Hold’em, dice games, and blackjack—with hidden human-defined rules. We find that CoT reasoning can degrade inductive performance, with LRMs often underperforming their non-reasoning counterparts.To explain this, we present a theoretical framework that reveals how reasoning steps can amplify error through three failure modes: incorrect sub-task decomposition, incorrect sub-task solving, and incorrect final answer summarization. Based on our theoretical and empirical analysis, we introduce structured interventions that adapt CoT generation according to our identified failure types. These interventions improve inductive accuracy without retraining. Our findings suggest that effective (CoT) reasoning depends not only on taking more steps but also on ensuring those steps are well-structured.
|
poster
|
2505.24225
| null | null |
[] |
[] |
[] |
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] |
131
|
Optimizing Anytime Reasoning via Budget Relative Policy Optimization
|
[
"Penghui Qi",
"Zichen Liu",
"Tianyu Pang",
"Chao Du",
"Wee Sun Lee",
"Min Lin"
] |
Scaling test-time compute is crucial for enhancing the reasoning capabilities of large language models (LLMs). Existing approaches typically employ reinforcement learning (RL) to maximize a verifiable reward obtained at the end of reasoning traces. However, such methods optimize only the final performance under a large and fixed token budget, which hinders efficiency in both training and deployment. In this work, we present a novel framework, **AnytimeReasoner**, to optimize *anytime reasoning performance*, which aims to improve token efficiency and the flexibility of reasoning under varying token budget constraints. To achieve this, we truncate the complete thinking process to fit within sampled token budgets from a prior distribution, compelling the model to summarize the optimal answer for each truncated thinking for verification. This introduces verifiable dense rewards into the reasoning process, facilitating more effective credit assignment in RL optimization. We then optimize the thinking and summary policies in a decoupled manner to maximize the cumulative reward. Additionally, we introduce a novel variance reduction technique, **Budget Relative Policy Optimization (BRPO)**, to enhance the robustness and efficiency of the learning process when reinforcing the thinking policy. Empirical results in mathematical reasoning tasks demonstrate that our method consistently outperforms GRPO across all thinking budgets under various prior distributions, enhancing both training and token efficiency.
|
poster
|
2505.13438
|
https://github.com/sail-sg/AnytimeReasoner
| null |
[] |
[] |
[] |
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] |
132
|
Principled Long-Tailed Generative Modeling via Diffusion Models
|
[
"Pranoy Das",
"Kexin Fu",
"Abolfazl Hashemi",
"Vijay Gupta"
] |
Deep generative models, particularly diffusion models, have achieved remarkable success across diverse domains but face significant challenges when trained on real-world, long-tailed datasets-where a few "head" classes dominate and many "tail" classes are underrepresented. This paper develops a rigorous theoretical framework for long-tailed learning via diffusion models through the lens of deep mutual learning. We introduce a novel regularized training objective that combines the standard diffusion loss with a mutual learning term, enabling balanced performance across all class labels, including the underrepresented tails. Our approach to learn via the proposed regularized objective is to formulated it as a multi-player game, with Nash equilibrium serving as the solution concept. We derive a non-asymptotic first-order convergence result for individual gradient descent algorithm to find the Nash equilibrium. We show that the Nash gap of the score network obtained from the algorithm is upper bounded by $\mathcal{O}(\frac{1}{\sqrt{T_{train}}}+\beta)$ where $\beta$ is the regularizing parameter and $T_{train}$ is the number of iterations of the training algorithm. Furthermore, we theoretically establish hyper-parameters for training and sampling algorithm that ensure we find conditional score networks (under our model) with a worst case sampling error $\mathcal{O}(\epsilon+1), \forall \epsilon>0$ across all class labels, which is further supported by numerical experiments. Our results offer new insights and guarantees for training diffusion models on imbalanced, long-tailed data, with implications for fairness, privacy, and generalization in real-world generative modeling scenarios.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
133
|
RvLLM: LLM Runtime Verification with Domain Knowledge
|
[
"Yedi Zhang",
"Sun Emma",
"Annabelle En",
"Jin Song Dong"
] |
Large language models (LLMs) have emerged as a dominant AI paradigm due to their exceptional text understanding and generation capabilities. However, their tendency to generate inconsistent or erroneous outputs challenges their reliability, especially in high-stakes domains requiring accuracy and trustworthiness. Existing research primarily focuses on detecting and mitigating model misbehavior in general-purpose scenarios, often overlooking the potential of integrating domain-specific knowledge. In this work, we advance misbehavior detection by incorporating domain knowledge. The core idea is to design a general specification language that enables domain experts to customize domain-specific predicates in a lightweight and intuitive manner, supporting later runtime verification of LLM outputs. To achieve this, we design a novel specification language ESL and introduce a runtime verification framework RvLLM to validate LLM output against domain-specific constraints defined in ESL. RvLLM operates in two main stages: interpretation and reasoning. During interpretation, it derives interpretations of the specification based on context, which then guide the reasoning process to identify inconsistencies. When new knowledge is derived, RvLLM issues a follow-up query to the LLM to further verify the consistency. We evaluate RvLLM on three representative tasks: violation detection against Singapore Rapid Transit Systems Act, numerical comparison, and inequality solving. Experimental results demonstrate that RvLLM effectively detects erroneous outputs across various LLMs in a lightweight and flexible manner. The results reveal that despite their impressive capabilities, LLMs remain prone to low-level errors due to limited interpretability and a lack of formal guarantees during inference, and our framework offers a potential long-term solution by leveraging expert domain knowledge to rigorously and efficiently verify LLM outputs.
|
poster
|
2505.18585
| null | null |
[] |
[] |
[] |
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] |
134
|
Dynamic Siamese Expansion Framework for Improving Robustness in Online Continual Learning
|
[
"Fei Ye",
"Yulong Zhao",
"Qihe Liu",
"Junlin Chen",
"Adrian G. Bors",
"Jingling sun",
"Rongyao Hu",
"shijie zhou"
] |
Continual learning requires the model to continually capture novel information without forgetting prior knowledge. Nonetheless, existing studies predominantly address the catastrophic forgetting, often neglecting enhancements in model robustness. Consequently, these methodologies fall short in real-time applications, such as autonomous driving, where data samples frequently exhibit noise due to environmental and lighting variations, thereby impairing model efficacy and causing safety issues. In this paper, we address robustness in continual learning systems by introducing an innovative approach, the Dynamic Siamese Expansion Framework (DSEF) that employs a Siamese backbone architecture, comprising static and dynamic components, to facilitate the learning of both global and local representations over time. Specifically, the proposed framework dynamically generates a lightweight expert for each novel task, leveraging the Siamese backbone to enable rapid adaptation. A novel Robust Dynamic Representation Optimization (RDRO) approach is proposed to incrementally update the dynamic backbone by maintaining all previously acquired representations and prediction patterns of historical experts, thereby fostering new task learning without inducing detrimental knowledge transfer. Additionally, we propose a novel Robust Feature Fusion (RFF) approach to incrementally amalgamate robust representations from all historical experts into the expert construction process. A novel mutual information-based technique is employed to derive adaptive weights for feature fusion by assessing the knowledge relevance between historical experts and the new task, thus maximizing positive knowledge transfer effects. A comprehensive experimental evaluation, benchmarking our approach against established baselines, demonstrates that our method achieves state-of-the-art performance even under adversarial attacks.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
135
|
Fading to Grow: Growing Preference Ratios via Preference Fading Discrete Diffusion for Recommendation
|
[
"Guoqing Hu",
"An Zhang",
"Shuchang Liu",
"Wenyu Mao",
"Jiancan Wu",
"Xun Yang",
"Xiang Li",
"Lantao Hu",
"Han Li",
"Kun Gai",
"Xiang Wang"
] |
Recommenders aim to rank items from a discrete item corpus in line with user interests, yet suffer from extremely sparse user preference data. Recent advances in diffusion models have inspired diffusion-based recommenders, which alleviate sparsity by injecting noise during a forward process to prevent collapse of perturbed preference distributions. However, current diffusion‑based recommenders predominantly rely on continuous Gaussian noise, which is intrinsically mismatched with the discrete nature of user preference data in recommendation. In this paper, building upon recent advances in discrete diffusion, we propose \textbf{PreferGrow}, a discrete diffusion-based recommender modeling preference ratios by fading and growing user preferences over the discrete item corpus. PreferGrow differs from existing diffusion-based recommenders in three core aspects: (1) Discrete modeling of preference ratios:PreferGrow models relative preference ratios between two items, where a positive value indicates a more preferred one over another less preferred. This formulation aligns naturally with the discrete and ranking-oriented nature of recommendation tasks.(2) Perturbing via preference fading: Instead of injecting continuous noise, PreferGrow fades user preferences by replacing the preferred item with alternatives---physically akin to negative sampling---thereby eliminating the need for any prior noise assumption.(3) Preference reconstruction via growing: PreferGrow reconstructs user preferences by iteratively growing the preference signal from the estimated ratios. We further provide theoretical analysis showing that PreferGrow preserves key properties of discrete diffusion processes.PreferGrow provides a well-defined matrix‑based formulation for discrete diffusion-based recommendation and empirically outperforms existing diffusion‑based recommenders across five benchmark datasets, underscoring its superior effectiveness.Our codes are available at \url{https://anonymous.4open.science/r/PreferGrow_Commit-2259/}.
|
poster
|
2509.26063
| null | null |
[] |
[] |
[] |
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] |
136
|
Synthetic-powered predictive inference
|
[
"Meshi Bashari",
"Roy Maor Lotan",
"Yonghoon Lee",
"Edgar Dobriban",
"Yaniv Romano"
] |
Conformal prediction is a framework for predictive inference with a distribution-free, finite-sample guarantee. However, it tends to provide uninformative prediction sets when calibration data are scarce. This paper introduces Synthetic-powered predictive inference (SPPI), a novel framework that incorporates synthetic data---e.g., from a generative model---to improve sample efficiency. At the core of our method is a score transporter: an empirical quantile mapping that aligns nonconformity scores from trusted, real data with those from synthetic data. By carefully integrating the score transporter into the calibration process, SPPI provably achieves finite-sample coverage guarantees without making any assumptions about the real and synthetic data distributions. When the score distributions are well aligned, SPPI yields substantially tighter and more informative prediction sets than standard conformal prediction. Experiments on image classification and tabular regression demonstrate notable improvements in predictive efficiency in data-scarce settings.
|
poster
|
2505.13432
|
https://github.com/meshiba/sppi
| null |
[] |
[] |
[] |
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] |
137
|
Learning Chern Numbers of Multiband Topological Insulators with Gauge Equivariant Neural Networks
|
[
"Longde Huang",
"Oleksandr Balabanov",
"Hampus Linander",
"Mats Granath",
"Daniel Persson",
"Jan Gerken"
] |
Equivariant network architectures are a well-established tool for predicting invariant or equivariant quantities. However, almost all learning problems considered in this context feature a global symmetry, i.e. each point of the underlying space is transformed with the same group element, as opposed to a local *gauge* symmetry, where each point is transformed with a different group element, exponentially enlarging the size of the symmetry group. We use gauge equivariant networks to predict topological invariants (Chern numbers) of multiband topological insulators for the first time. The gauge symmetry of the network guarantees that the predicted quantity is a topological invariant. A major technical challenge is that the relevant gauge equivariant networks are plagued by instabilities in their training, severely limiting their usefulness. In particular, for larger gauge groups the instabilities make training impossible. We resolve this problem by introducing a novel gauge equivariant normalization layer which stabilizes the training. Furthermore, we prove a universal approximation theorem for our model. We train on samples with trivial Chern number only but show that our model generalizes to samples with non-trivial Chern number and provide various ablations of our setup.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
138
|
RadZero: Similarity-Based Cross-Attention for Explainable Vision-Language Alignment in Radiology with Zero-Shot Multi-Task Capability
|
[
"Jonggwon Park",
"Soobum Kim",
"Byungmu Yoon",
"Kyoyun Choi"
] |
Recent advancements in multi-modal models have significantly improved vision-language (VL) alignment in radiology. However, existing approaches struggle to effectively utilize complex radiology reports for learning and offer limited interpretability through attention probability visualizations. To address these challenges, we introduce $\textbf{RadZero}$, a novel framework for VL alignment in radiology with zero-shot multi-task capability. A key component of our approach is $\textbf{VL-CABS}$ ($\textbf{V}$ision-$\textbf{L}$anguage $\textbf{C}$ross-$\textbf{A}$ttention $\textbf{B}$ased on $\textbf{S}$imilarity),which aligns text embeddings with local image features for interpretable, fine-grained VL reasoning.RadZero leverages large language models to extract concise semantic sentences from radiology reports and employs multi-positive contrastive training to effectively capture relationships between images and multiple relevant textual descriptions.It uses a pre-trained vision encoder with additional trainable Transformer layers, allowing efficient high-resolution image processing. By computing similarity between text embeddings and local image patch features, VL-CABS enables zero-shot inference with similarity probability for classification, and pixel-level VL similarity maps for grounding and segmentation. Experimental results on public chest radiograph benchmarks show that RadZero outperforms state-of-the-art methods in zero-shot classification, grounding, and segmentation. Furthermore, VL similarity map analysis highlights the potential of VL-CABS for improving explainability in VL alignment. Additionally, qualitative evaluation demonstrates RadZero’s capability for open-vocabulary semantic segmentation, further validating its effectiveness in medical imaging.
|
poster
|
2504.07416
| null | null |
[] |
[] |
[] |
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] |
139
|
Beyond Accuracy: Dissecting Mathematical Reasoning for LLMs Under Reinforcement Learning
|
[
"Jiayu Wang",
"Yifei Ming",
"Zixuan Ke",
"Caiming Xiong",
"Shafiq Joty",
"Aws Albarghouthi",
"Frederic Sala"
] |
Reinforcement learning (RL) has become the dominant paradigm for endowing language models with advanced reasoning capabilities. Despite the substantial empirical gains demonstrated by RL-based training methods like GRPO, a granular understanding of their advantages is still lacking. To address this gap, we introduce a fine-grained analytic framework to dissect the impact of RL on reasoning. Our framework specifically investigates key elements that have been hypothesized to benefit from RL training: (1) plan-following and execution, (2) problem decomposition, and (3) improved reasoning and knowledge utilization. Using this framework, we gain insights beyond mere accuracy. For instance, providing models with explicit step-by-step plans surprisingly degrades performance on the most challenging benchmarks, yet RL-tuned models exhibit greater robustness, experiencing markedly smaller performance drops than their base counterparts. This suggests that RL may not primarily enhance the execution of external plans but rather empower models to formulate and follow internal strategies better suited to their reasoning processes. Conversely, we observe that RL enhances the model's capacity to integrate provided knowledge into its reasoning process, leading to performance improvements across diverse tasks. We also study difficulty, showing improved training by developing new ways to exploit hard problems. Our findings lay a foundation for more principled training and evaluation of reasoning models.
|
poster
|
2506.04723
| null | null |
[] |
[] |
[] |
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] |
140
|
SingRef6D: Monocular Novel Object Pose Estimation with a Single RGB Reference
|
[
"Jiahui Wang",
"Haiyue Zhu",
"Haoren Guo",
"Abdullah Al Mamun",
"Cheng Xiang",
"Tong Heng LEE"
] |
Recent 6D pose estimation methods demonstrate notable performance but still face some practical limitations. For instance, many of them rely heavily on sensor depth, which may fail with challenging surface conditions, such as transparent or highly reflective materials. In the meantime, RGB-based solutions provide less robust matching performance in low-light and texture-less scenes due to the lack of geometry information. Motivated by these, we propose **SingRef6D**, a lightweight pipeline requiring only a **single RGB** image as a reference, eliminating the need for costly depth sensors, multi-view image acquisition, or training view synthesis models and neural fields. This enables SingRef6D to remain robust and capable even under resource-limited settings where depth or dense templates are unavailable. Our framework incorporates two key innovations. First, we propose a token-scaler-based fine-tuning mechanism with a novel optimization loss on top of Depth-Anything v2 to enhance its ability to predict accurate depth, even for challenging surfaces. Our results show a 14.41% improvement (in $\delta_{1.05}$) on REAL275 depth prediction compared to Depth-Anything v2 (with fine-tuned head). Second, benefiting from depth availability, we introduce a depth-aware matching process that effectively integrates spatial relationships within LoFTR, enabling our system to handle matching for challenging materials and lighting conditions. Evaluations of pose estimation on the REAL275, ClearPose, and Toyota-Light datasets show that our approach surpasses state-of-the-art methods, achieving a 6.1% improvement in average recall.
|
poster
|
2509.21927
| null | null |
[] |
[] |
[] |
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] |
141
|
Memory-Augmented Potential Field Theory: A Framework for Adaptive Control in Non-Convex Domains
|
[
"Dongzhe Zheng",
"Wenjie Mei"
] |
Stochastic optimal control methods often struggle in complex non-convex landscapes, frequently becoming trapped in local optima due to their inability to learn from historical trajectory data. This paper introduces Memory-Augmented Potential Field Theory, a unified mathematical framework that integrates historical experience into stochastic optimal control. Our approach dynamically constructs memory-based potential fields that identify and encode key topological features of the state space, enabling controllers to automatically learn from past experiences and adapt their optimization strategy. We provide a theoretical analysis showing that memory-augmented potential fields possess non-convex escape properties, asymptotic convergence characteristics, and computational efficiency. We implement this theoretical framework in a Memory-Augmented Model Predictive Path Integral (MPPI) controller that demonstrates significantly improved performance in challenging non-convex environments. The framework represents a generalizable approach to experience-based learning within control systems (especially robotic dynamics), enhancing their ability to navigate complex state spaces without requiring specialized domain knowledge or extensive offline training.
|
poster
|
2509.19672
| null | null |
[] |
[] |
[] |
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] |
142
|
Bridging the Expressivity Gap: Provably Tractable SHAP Explanations for Tensor Networks
|
[
"Reda Marzouk",
"Shahaf Bassan",
"Guy Katz"
] |
Although Shapley additive explanations (SHAP) can be computed in polynomial time for simple models like decision trees, they unfortunately become NP-hard to compute for more expressive black-box models like neural networks - where generating explanations is often most critical. In this work, we analyze the problem of computing SHAP explanations for *Tensor Networks (TNs)*, a broader and more expressive class of models than those for which current exact SHAP algorithms are known to hold, and which is widely used for neural network abstraction and compression. First, we introduce a general framework for computing provably exact SHAP explanations for general TNs with arbitrary structures. Interestingly, we show that, when TNs are restricted to a *Tensor Train (TT)* structure, SHAP computation can be performed in *poly-logarithmic* time using *parallel* computation. Thanks to the expressiveness power of TTs, this complexity result can be generalized to many other popular ML models such as decision trees, tree ensembles, linear models, and linear RNNs, therefore tightening previously reported complexity results for these families of models. Finally, by leveraging reductions of binarized neural networks to Tensor Network representations, we demonstrate that SHAP computation can become *efficiently tractable* when the network’s *width* is fixed, while it remains computationally hard even with constant *depth*. This highlights an important insight: for this class of models, width - rather than depth - emerges as the primary computational bottleneck in SHAP computation.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
143
|
Smooth Regularization for Efficient Video Recognition
|
[
"Gil Goldman",
"Raja Giryes",
"Mahadev Satyanarayanan"
] |
We propose a smooth regularization technique that instillsa strong temporal inductive bias in video recognition models,particularly benefiting lightweight architectures.Our method encourages smoothness in the intermediate-layer embeddings ofconsecutive frames by modeling their changes as a Gaussian Random Walk (GRW).This penalizes abrupt representational shifts,thereby promoting low-acceleration solutions that better alignwith the natural temporal coherence inherent in videos.By leveraging this enforced smoothness, lightweight models can moreeffectively capture complex temporal dynamics.Applied to such models, our technique yields a 2.8%–6.0%accuracy improvement on Kinetics-600.Notably, the MoViNets model family trained with our smoothregularization improves the current state-of-the-art by 2.8%–3.4% withintheir respective FLOP constraints, while MobileNetV3 andthe MoViNets-Stream family achieve gains of 2.8%–6.0% overprior state-of-the-art models with comparable memory footprints.Code and models will be released upon acceptance.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
144
|
Reparameterized LLM Training via Orthogonal Equivalence Transformation
|
[
"Zeju Qiu",
"Simon Buchholz",
"Tim Xiao",
"Maximilian Dax",
"Bernhard Schölkopf",
"Weiyang Liu"
] |
While Large language models (LLMs) are driving the rapid advancement of artificial intelligence, effectively and reliably training these large models remains one of the field's most significant challenges. To address this challenge, we propose POET, a novel reparameterized training algorithm that uses orthogonal equivalence transformation to optimize neurons. Specifically, POET reparameterizes each neuron with two learnable orthogonal matrices and a fixed random weight matrix. Because of its provable preservation of spectral properties of weight matrices, POET can stably optimize the objective function with improved generalization. We further develop efficient approximations that make POET flexible and scalable for training large-scale neural networks. Extensive experiments validate the effectiveness and scalability of POET in training LLMs.
|
poster
|
2506.08001
|
https://github.com/Sphere-AI-Lab/poet
|
https://spherelab.ai/poet/
|
[] |
[] |
[] |
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] |
145
|
Does Representation Guarantee Welfare?
|
[
"Jakob de Raaij",
"Ariel Procaccia",
"Alexandros Psomas"
] |
A panel satisfies *descriptive representation* when its composition reflects the population. We examine the role of descriptive representation in collective decision making through an optimization lens, asking whether representative panels make decisions that maximize social welfare for the underlying population. Our main results suggest that, in general, representation with respect to *intersections* of two or more features guarantees higher social welfare than that achieved by the status quo of proportionally representing individual features. Moreover, an analysis of real data suggests that representation with respect to pairs of features is feasible in practice. These results have significant implications for the design of *citizens' assemblies*, which are gaining prominence in AI governance.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
146
|
Prot2Text-V2: Protein Function Prediction with Multimodal Contrastive Alignment
|
[
"Xiao Fei",
"Michail Chatzianastasis",
"Sarah Carneiro",
"Hadi Abdine",
"Lawrence Petalidis",
"Michalis Vazirgiannis"
] |
Predicting protein function from sequence is a central challenge in computational biology. While existing methods rely heavily on structured ontologies or similarity-based techniques, they often lack the flexibility to express structure-free functional descriptions and novel biological functions. In this work, we introduce Prot2Text-V2, a novel multimodal sequence-to-text model that generates free-form natural language descriptions of protein function directly from amino acid sequences. Our method combines a protein language model as a sequence encoder (ESM-3B) and a decoder-only language model (LLaMA-3.1-8B-Instruct) through a lightweight nonlinear modality projector.A key innovation is our Hybrid Sequence-level Contrastive Alignment Learning (H-SCALE), which improves cross-modal learning by matching mean- and std-pooled protein embeddings with text representations via contrastive loss. After the alignment phase, we apply instruction-based fine-tuning using LoRA on the decoder to teach the model how to generate accurate protein function descriptions conditioned on the protein sequence. We train Prot2Text-V2 on about 250K curated entries from SwissProt and evaluate it under low-homology conditions, where test sequences have low similarity with training samples. Prot2Text-V2 consistently outperforms traditional and LLM-based baselines across various metrics.
|
poster
|
2505.11194
|
https://github.com/colinfx/prot2text-v2
| null |
[] |
[
"xiao-fei/Prot2Text-V2-11B-Instruct-hf"
] |
[] |
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] |
147
|
Discrete Neural Flow Samplers with Locally Equivariant Transformer
|
[
"Zijing Ou",
"Ruixiang ZHANG",
"Yingzhen Li"
] |
Sampling from unnormalised discrete distributions is a fundamental problem across various domains. While Markov chain Monte Carlo offers a principled approach, it often suffers from slow mixing and poor convergence.In this paper, we propose Discrete Neural Flow Samplers (DNFS), a trainable and efficient framework for discrete sampling. DNFS learns the rate matrix of a continuous-time Markov chain such that the resulting dynamics satisfy the Kolmogorov equation.As this objective involves the intractable partition function, we then employ control variates to reduce the variance of its Monte Carlo estimation, leading to a coordinate descent learning algorithm.To further facilitate computational efficiency, we propose locally equivaraint Transformer, a novel parameterisation of the rate matrix that significantly improves training efficiency while preserving powerful network expressiveness.Empirically, we demonstrate the efficacy of DNFS in a wide range of applications, including sampling from unnormalised distributions, training discrete energy-based models, and solving combinatorial optimisation problems.
|
poster
|
2505.17741
| null | null |
[] |
[] |
[] |
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] |
148
|
Shapley-Coop: Credit Assignment for Emergent Cooperation in Self-Interested LLM Agents
|
[
"Yun Hua",
"Haosheng Chen",
"Shiqin Wang",
"Wenhao Li",
"Xiangfeng Wang",
"Jun Luo"
] |
Large Language Models (LLMs) are increasingly deployed as autonomous agents in multi-agent systems, and promising coordination has been demonstrated in handling complex tasks under predefined roles and scripted workflows.However, significant challenges remain in open-ended environments, where agents are inherently self-interested and explicit coordination guidelines are absent. In such scenarios, misaligned incentives frequently lead to social dilemmas and inefficient collective outcomes.Inspired by how human societies tackle similar coordination challenges—through temporary collaborations like employment or subcontracting—a cooperative workflow \textbf{Shapley-Coop} is proposed. This workflow enables self-interested Large Language Model (LLM) agents to engage in emergent collaboration by using a fair credit allocation mechanism to ensure each agent’s contributions are appropriately recognized and rewarded.Shapley-Coop introduces structured negotiation protocols and Shapley-inspired reasoning to estimate agents’ marginal contributions, thereby enabling effective task-time coordination and equitable post-task outcome redistribution. This results in effective coordination that fosters collaboration while preserving agent autonomy, through a rational pricing mechanism that encourages cooperative behavior.Evaluated in two multi-agent games and a software engineering simulation, Shapley-Coop consistently enhances LLM agent collaboration and facilitates equitable outcome redistribution, accurately reflecting individual contributions during the task execution process.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
149
|
LOPT: Learning Optimal Pigovian Tax in Sequential Social Dilemmas
|
[
"Yun Hua",
"Shang Gao",
"Wenhao Li",
"Haosheng Chen",
"Bo Jin",
"Xiangfeng Wang",
"Jun Luo",
"Hongyuan Zha"
] |
Multi-agent reinforcement learning (MARL) has emerged as a powerful framework for modeling autonomous agents that independently optimize their individual objectives. However, in mixed-motive MARL environments, rational self-interested behaviors often lead to collectively suboptimal outcomes situations commonly referred to as social dilemmas.A key challenge in addressing social dilemmas lies in accurately quantifying and representing them in a numerical form that captures how self-interested agent behaviors impact social welfare.To address this challenge, \textit{externalities} in the economic concept is adopted and extended to denote the unaccounted-for impact of one agent's actions on others, as a means to rigorously quantify social dilemmas.Based on this measurement, a novel method, \textbf{L}earning \textbf{O}ptimal \textbf{P}igovian \textbf{T}ax (\textbf{LOPT}) is proposed. Inspired by Pigovian taxes, which are designed to internalize externalities by imposing cost on negative societal impacts, LOPT employs an auxiliary tax agent that learns an optimal Pigovian tax policy to reshape individual rewards aligned with social welfare, thereby promoting agent coordination and mitigating social dilemmas. We support LOPT with theoretical analysis and validate it on standard MARL benchmarks, including Escape Room and Cleanup. Results show that by effectively internalizing externalities that quantify social dilemmas, LOPT aligns individual objectives with collective goals, significantly improving social welfare over state-of-the-art baselines.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
150
|
Beyond Last-Click: An Optimal Mechanism for Ad Attribution
|
[
"Nan An",
"Weian Li",
"Qi Qi",
"Changyuan Yu",
"Liang Zhang"
] |
Accurate attribution for multiple platforms is critical for evaluating performance-based advertising. However, existing attribution methods rely heavily on the heuristic methods, e.g., Last-Click Mechanism (LCM) which always allocates the attribution to the platform with the latest report, lacking theoretical guarantees for attribution accuracy. In this work, we propose a novel theoretical model for the advertising attribution problem, in which we aim to design the optimal dominant strategy incentive compatible (DSIC) mechanisms and evaluate their performance. We first show that LCM is not DSIC and performs poorly in terms of accuracy and fairness. To address this limitation, we introduce the Peer-Validated Mechanism (PVM), a DSIC mechanism in which a platform's attribution depends solely on the reports of other platforms. We then examine the accuracy of PVM across both homogeneous and heterogeneous settings, and provide provable accuracy bounds for each case. Notably, we show that PVM is the optimal DSIC mechanism in the homogeneous setting. Finally, numerical experiments are conducted to show that PVM consistently outperforms LCM in terms of attribution accuracy and fairness.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
151
|
LLM Interpretability with Identifiable Temporal-Instantaneous Representation
|
[
"Xiangchen Song",
"Jiaqi Sun",
"Zijian Li",
"Yujia Zheng",
"Kun Zhang"
] |
Despite Large Language Models' remarkable capabilities, understanding their internal representations remains challenging. Mechanistic interpretability tools such as sparse autoencoders (SAEs) were developed to extract interpretable features from LLMs but lack temporal dependency modeling, instantaneous relation representation, and more importantly theoretical guarantees—undermining both the theoretical foundations and the practical confidence necessary for subsequent analyses. While causal representation learning (CRL) offers theoretically-grounded approaches for uncovering latent concepts, existing methods cannot scale to LLMs' rich conceptual space due to inefficient computation. To bridge the gap, we introduce an identifiable temporal causal representation learning framework specifically designed for LLMs' high-dimensional concept space, capturing both time-delayed and instantaneous causal relations. Our approach provides theoretical guarantees and demonstrates efficacy on synthetic datasets scaled to match real-world complexity. By extending SAE techniques with our temporal causal framework, we successfully discover meaningful concept relationships in LLM activations. Our findings show that modeling both temporal and instantaneous conceptual relationships advances the interpretability of LLMs.
|
poster
|
2509.23323
| null | null |
[] |
[] |
[] |
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] |
152
|
AlignAb: Pareto-Optimal Energy Alignment for Designing Nature-Like Antibodies
|
[
"Yibo Wen",
"Chenwei Xu",
"Jerry Yao-Chieh Hu",
"Kaize Ding",
"Han Liu"
] |
We present a three-stage framework for training deep learning models specializing in antibody sequence-structure co-design.We first pre-train a language model using millions of antibody sequence data.Then, we employ the learned representations to guide the training of a diffusion model for joint optimization over both sequence and structure of antibodies. During the final alignment stage, we optimize the model to favor antibodies with low repulsion and high attraction to the antigen binding site, enhancing the rationality and functionality of the designs.To mitigate conflicting energy preferences, we extend AbDPO (Antibody Direct Preference Optimization) to guide the model toward Pareto optimality under multiple energy-based alignment objectives. Furthermore, we adopt an iterative learning paradigm with temperature scaling, enabling the model to benefit from diverse online datasets without requiring additional data.In practice, our proposed methods achieve high stability and efficiency in producing a better Pareto front of antibody designs compared to top samples generated by baselines and previous alignment techniques.Through extensive experiments, we showcase the superior performance of our methods in generating nature-like antibodies with high binding affinity.
|
poster
|
2412.20984
| null | null |
[] |
[] |
[] |
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] |
153
|
SpecEdge: Scalable Edge-Assisted Serving Framework for Interactive LLMs
|
[
"Jinwoo Park",
"Seunggeun Cho",
"Dongsu Han"
] |
Large language models (LLMs) power many modern applications, but serving them at scale remains costly and resource-intensive. Current server-centric systems overlook consumer-grade GPUs at the edge. We introduce SpecEdge, an edge-assisted inference framework that splits LLM workloads between edge and server GPUs using a speculative decoding scheme, exchanging only token outputs over the network. SpecEdge employs proactive edge drafting to overlap edge token creation with server verification and pipeline-aware scheduling that interleaves multiple user requests to increase server-side throughput. Experiments show SpecEdge enhances overall cost efficiency by **1.91×** through achieving **2.22x** server throughput, and reduces inter token latency by **11.24\%** compared to a server-only baseline, introducing a scalable, cost-effective paradigm for LLM serving.
|
spotlight
|
2505.17052
| null | null |
[] |
[] |
[] |
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] |
154
|
Relaxing partition admissibility in Cluster-DAGs: a causal calculus with arbitrary variable clustering
|
[
"Clément Yvernes",
"Emilie Devijver",
"Adèle Ribeiro",
"Marianne Clausel",
"Eric Gaussier"
] |
Cluster DAGs (C-DAGs) provide an abstraction of causal graphs in which nodes represent clusters of variables, and edges encode both cluster-level causal relationships and dependencies arisen from unobserved confounding. C-DAGs define an equivalence class of acyclic causal graphs that agree on cluster-level relationships, enabling causal reasoning at a higher level of abstraction. However, when the chosen clustering induces cycles in the resulting C-DAG, the partition is deemed inadmissible under conventional C-DAG semantics. In this work, we extend the C-DAG framework to support arbitrary variable clusterings by relaxing the partition admissibility constraint, thereby allowing cyclic C-DAG representations. We extend the notions of d-separation and causal calculus to this setting, significantly broadening the scope of causal reasoning across clusters and enabling the application of C-DAGs in previously intractable scenarios. Our calculus is both sound and atomically complete with respect to the do-calculus: all valid interventional queries at the cluster level can be derived using our rules, each corresponding to a primitive do-calculus step.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
155
|
Topology-Aware Conformal Prediction for Stream Networks
|
[
"Jifan Zhang",
"Fangxin Wang",
"Zihe Song",
"Philip S Yu",
"Kaize Ding",
"Shixiang Zhu"
] |
Stream networks, a unique class of spatiotemporal graphs, exhibit complex directional flow constraints and evolving dependencies, making uncertainty quantification a critical yet challenging task. Traditional conformal prediction methods struggle in this setting due to the need for joint predictions across multiple interdependent locations and the intricate spatio-temporal dependencies inherent in stream networks. Existing approaches either neglect dependencies, leading to overly conservative predictions, or rely solely on data-driven estimations, failing to capture the rich topological structure of the network. To address these challenges, we propose Spatio-Temporal Adaptive Conformal Inference (STACI), a novel framework that integrates network topology and temporal dynamics into the conformal prediction framework. STACI introduces a topology-aware nonconformity score that respects directional flow constraints and dynamically adjusts prediction sets to account for temporal distributional shifts. We provide theoretical guarantees on the validity of our approach and demonstrate its superior performance on both synthetic and real-world datasets. Our results show that STACI effectively balances prediction efficiency and coverage, outperforming existing conformal prediction methods for stream networks.
|
poster
|
2503.04981
| null | null |
[] |
[] |
[] |
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] |
156
|
On topological descriptors for graph products
|
[
"Mattie Ji",
"Amauri Souza",
"Vikas Garg"
] |
Topological descriptors have been increasingly used for capturing multiscale structural information in relational data. In this work, we consider various filtrations on (Cartesian) graph products and the effect on their outputs on the topological descriptors - the Euler characteristics (EC) and persistent homology (PH). In particular, we establish a complete characterization of the expressive power of EC on general color-based filtrations. We also show that the PH descriptors of (virtual) graph products contain strictly more information than the computation on individual graphs while EC does not. Additionally, we provide algorithms to compute the PH diagrams of the product of vertex- and edge-level filtrations on the graph product. Overall, this work paves way for powerful graph persistent descriptors via graph product filtrations.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
157
|
Graph-based Symbolic Regression with Invariance and Constraint Encoding
|
[
"Ziyu Xiang",
"Kenna Ashen",
"Xiaofeng Qian",
"Xiaoning Qian"
] |
Symbolic regression (SR) offers interpretable over `black-box' solutions by providing analytical expressions, enabling the identification of key functional dependencies in complex systems. In this paper, we identify two key limiting factors in existing SR methods: (a) redundant representations arising from the inability to capture expression equivalences and higher-order operand relations, which breaks permutation invariance and limits encoder effectiveness; and (b) sparse rewards caused by incomplete incorporation of constraints evaluated on full expressions (e.g. constant fitting for unit consistency and/or physical law verification) during the sampling process. To address these challenges, we propose an efficient graph-based SR method, **GSR**, which preserves the permutation invariance by encoding expression equivalences and higher-order structural features on an expression graph (EG), and mitigates reward sparsity via employing hybrid neural-guided Monte-Carlo tree search (hnMCTS) for complete constraint integration and improved exploration-exploitation trade-off. Experiments on synthetic and real-world scientific datasets demonstrate the efficiency and accuracy of GSR in discovering underlying expressions and adhering to physical laws, offering practical solutions for scientific discovery.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
158
|
Less is More: an Attention-free Sequence Prediction Modeling for Offline Embodied Learning
|
[
"Wei Huang",
"Jianshu Zhang",
"Leiyu Wang",
"Heyue Li",
"Luoyi Fan",
"Yichen Zhu",
"Nanyang Ye",
"Qinying Gu"
] |
Offline reinforcement learning (offline RL) is increasingly approached as a sequence modeling task, with methods leveraging advanced architectures like Transformers to capture trajectory dependencies. Despite significant progress, the mechanisms underlying their effectiveness and limitations remain insufficiently understood. We conduct a thorough analysis on the representative Decision Transformer (DT) model using an entropy analysis and identify the inconsistencies in state-action-reward ($\langle s, a, R \rangle$) distributions causing attention ``dispersal". To address this, we propose a hierarchical framework that decomposes sequence modeling into intra-step relational modeling—handled by a Token Merger that fuses each $\langle s, a, R \rangle$ triplet—and inter-step modeling—handled by a Token Mixer across timesteps. We investigate several Token Merger designs and validate their effectiveness across various offline RL methods. Furthermore, our theoretical analysis and experimental results suggest that while Token Mixers are important, lightweight architecture can also achieve even better performance to more complex ones. We therefore propose a parameter-free Average Pooling Token Mixer, which, combined with a convolutional Token Merger, forms our final model, Decision HiFormer (DHi). DHi achieves a \textbf{73.6\%} improvement in inference speed and an \textbf{9.3\%} gain in policy performance on the D4RL benchmark compared to DT. DHi also generalizes well to real-world robotic manipulation tasks, offering both practical benefits and insights into sequence-based policy design for offline RL.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
159
|
MEIcoder: Decoding Visual Stimuli from Neural Activity by Leveraging Most Exciting Inputs
|
[
"Jan Sobotka",
"Luca Baroni",
"Ján Antolík"
] |
Decoding visual stimuli from neural population activity is crucial for understanding the brain and for applications in brain-machine interfaces. However, such biological data is often scarce, particularly in primates or humans, where high-throughput recording techniques, such as two-photon imaging, remain challenging or impossible to apply. This, in turn, poses a challenge for deep learning decoding techniques. To overcome this, we introduce MEIcoder, a biologically informed decoding method that leverages neuron-specific most exciting inputs (MEIs), a structural similarity index measure loss, and adversarial training. MEIcoder achieves state-of-the-art performance in reconstructing visual stimuli from single-cell activity in primary visual cortex (V1), especially excelling on small datasets with fewer recorded neurons. Using ablation studies, we demonstrate that MEIs are the main drivers of the performance, and in scaling experiments, we show that MEIcoder can reconstruct high-fidelity natural-looking images from as few as 1,000-2,500 neurons and less than 1,000 training data points. We also propose a unified benchmark with over 160,000 samples to foster future research. Our results demonstrate the feasibility of reliable decoding in early visual system and provide practical insights for neuroscience and neuroengineering applications.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
160
|
Investigating and Mitigating Catastrophic Forgetting in Medical Knowledge Injection through Internal Knowledge Augmentation Learning
|
[
"Yuxuan Zhou",
"Xien Liu",
"Xiao Zhang",
"Chen Ning",
"Shijin Wang",
"Guoping Hu",
"Ji Wu"
] |
Large Language Models (LLMs) must possess sufficient and comprehensive medical knowledge to effectively address real-world clinical problems. Though domain-specific fine-tuning can effectively inject medical knowledge into LLMs, it often leads to catastrophic forgetting of previously acquired knowledge and instruction following abilities. In this paper, we investigate catastrophic forgetting in medical knowledge injection and uncover a notable proximity-dependent forgetting nature: knowledge that is closely related to the injected knowledge is more likely to be forgotten, whereas distant knowledge is less affected. Moreover, we observe that existing mitigation techniques fail to effectively address this proximity-dependent forgetting. Inspired by this finding as well as human learning process, we propose a novel \textbf{Inter}nal K\textbf{n}owledge \textbf{A}ugmentation \textbf{L}earning (\textbf{InternAL}) approach that mitigates catastrophic forgetting by exploiting LLM's internal knowledge. Specifically, we first extract internal knowledge from the target LLM that is directly related to the injected knowledge, using questions generated from the injected knowledge. Then we augment the original knowledge injection dataset with the extracted internal knowledge, and train the LLM on this augmented dataset to enhance the model's ability to retain prior knowledge that is closely related to the injected knowledge. Experimental results on several representative LLMs (Llama, Qwen) show that our method significantly alleviates the forgetting of prior knowledge, especially the knowledge that is closely related to the injected knowledge. The datasets and codes will be released for future research.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
161
|
It’s Hard to Be Normal: The Impact of Noise on Structure-agnostic Estimation
|
[
"Jikai Jin",
"Lester Mackey",
"Vasilis Syrgkanis"
] |
Structure-agnostic causal inference studies the statistical limits of treatment effect estimation, when given access to black-box ML models that estimate nuisance components of the data generating process, such as estimates of the outcome regression and the treatment propensity. Here, we find that the answer depends in a surprising way on the distribution of the treatment noise. Focusing on the partially linear outcome model of \citet{robinson1988root}, we first show that the widely adopted double machine learning (DML) estimator is minimax rate-optimal for Gaussian treatment noise, resolving an open problem of \citet{mackey2018orthogonal}. Meanwhile, for independent non-Gaussian treatment noise, we show that DML is always suboptimal by constructing new practical procedures with higher-order robustness to nuisance errors. These *ACE* procedures use structure-agnostic cumulant estimators to achieve $r$-th order insensitivity to nuisance errors whenever the $(r+1)$-st treatment cumulant is non-zero. We complement these core results with novel minimax guarantees for binary treatments in the partially linear outcome model. Finally, using synthetic demand estimation experiments, we demonstrate the practical benefits of our higher-order robust estimators.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
162
|
SHF: Symmetrical Hierarchical Forest with Pretrained Vision Transformer Encoder for High-Resolution Medical Segmentation
|
[
"Enzhi Zhang",
"Peng Chen",
"Rui Zhong",
"Du Wu",
"Jun Igarashi",
"Isaac Lyngaas",
"Xiao Wang",
"Masaharu Munetomo",
"Mohamed Wahib"
] |
This paper presents a novel approach to addressing the long-sequence problem in high-resolution medical images for Vision Transformers (ViTs). Using smaller patches as tokens can enhance ViT performance, but quadratically increases computation and memory requirements. Therefore, the common practice for applying ViTs to high-resolution images is either to: (a) employ complex sub-quadratic attention schemes or (b) use large to medium-sized patches and rely on additional mechanisms within the model to capture the spatial hierarchy of details. We propose Symmetrical Hierarchical Forest (SHF), a lightweight approach that adaptively patches the input image to increase token information density and encode hierarchical spatial structures into the input embedding. We then apply a reverse depatching scheme to the output embeddings of the transformer encoder, eliminating the need for convolution-based decoders. Unlike previous methods that modify attention mechanisms \wahib{or use a complex hierarchy of interacting models}, SHF can be retrofitted to any ViT model to allow it to learn the hierarchical structure of details in high-resolution images without requiring architectural changes. Experimental results demonstrate significant gains in computational efficiency and performance: on the PAIP WSI dataset, we achieved a 3$\sim$32$\times$ speedup or a 2.95\% to 7.03\% increase in accuracy (measured by Dice score) at a $64K^2$ resolution with the same computational budget, compared to state-of-the-art production models. On the 3D medical datasets BTCV and KiTS, training was 6$\times$ faster, with accuracy gains of 6.93\% and 5.9\%, respectively, compared to models without SHF.
|
spotlight
| null | null | null |
[] |
[] |
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] |
163
|
Uni-MuMER: Unified Multi-Task Fine-Tuning of Vision-Language Model for Handwritten Mathematical Expression Recognition
|
[
"Yu Li",
"Jin Jiang",
"Jianhua Zhu",
"Shuai Peng",
"Baole",
"Yuxuan Zhou",
"Liangcai Gao"
] |
Handwritten Mathematical Expression Recognition (HMER) remains a persistent challenge in Optical Character Recognition(OCR) due to the inherent freedom of symbol layout and variability in handwriting styles. Prior methods have faced performance bottlenecks, proposing isolated architectural modifications that are difficult to integrate coherently into a unified framework. Meanwhile, recent advances in pretrained vision-language models (VLMs) have demonstrated strong cross-task generalization, offering a promising foundation for developing unified solutions. In this paper, we introduce Uni-MuMER, which fully fine-tunes the Qwen2.5-VL-3B model for the HMER task without modifying its architecture, effectively injecting domain-specific knowledge into a generalist framework. Our method integrates three data-driven tasks: Tree-Aware Chain-of-Thought (Tree-CoT) for structured spatial reasoning, Error-Driven Learning (EDL) for reducing confusion among visually similar characters, and Symbol Counting (SC) for improving recognition consistency in long expressions. Experiments on the CROHME and HME100K datasets show that Uni-MuMER achieves new state-of-the-art performance, surpassing the best lightweight specialized model SSAN by 16.31\% and the top-performing VLM Gemini2.5-flash by 24.42\% in the zero-shot setting.
|
spotlight
| null | null | null |
[] |
[] |
[] |
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] |
164
|
Graph Persistence goes Spectral
|
[
"Mattie Ji",
"Amauri Souza",
"Vikas Garg"
] |
Including intricate topological information (e.g., cycles) provably enhances the expressivity of message-passing graph neural networks (GNNs) beyond the Weisfeiler-Leman (WL) hierarchy. Consequently, Persistent Homology (PH) methods are increasingly employed for graph representation learning. In this context, recent works have proposed decorating classical PH diagrams with vertex and edge features for improved expressivity. However, due to their dependence on features, these methods still fail to capture basic graph structural information. In this paper, we propose SpectRe --- a new topological descriptor for graphs that integrates spectral information into PH diagrams. Notably, SpectRe is strictly more expressive than existing descriptors on graphs. We also introduce notions of global and local stability to analyze existing descriptors and establish that SpectRe is locally stable. Finally, experiments on synthetic and real-world datasets demonstrate the effectiveness of SpectRe and its potential to enhance the capabilities of graph models in relevant learning tasks.
|
poster
|
2506.06571
| null | null |
[] |
[] |
[] |
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] |
165
|
Chain of Execution Supervision Promotes General Reasoning in Large Language Models
|
[
"Nuo Chen",
"Zehua Li",
"Keqin Bao",
"Junyang Lin",
"Dayiheng Liu"
] |
Building robust and general reasoning ability is a central goal in the development of large language models (LLMs). Recent efforts increasingly turn to code as a rich training source, given its inherent logical structure and diverse reasoning paradigms—such as divide-and-conquer, topological ordering, and enumeration. However, reasoning in code is often expressed implicitly and entangled with syntactic or implementation noise, making direct training on raw code suboptimal. To address this, we introduce TraceMind, a large-scale corpus of 2.6 million samples that transforms code execution into explicit, step-by-step chain-of-thought style rationales, which we call Chain of Execution (CoE). The corpus spans domains including mathematics, classical algorithms and algorithmic competition, and is enriched with variable-tracing questions and code rewritings to enhance logical granularity and code diversity. We evaluate Tracepile using three training setups—continue-pretraining, instruction tuning after pretraining, and two-stage finetuning. Experiments across four base models (LLaMA 3, LLaMA 3.1, Qwen-2.5, and Qwen-2.5 Coder) and 20 benchmarks covering math, code, logic, and algorithms demonstrate consistent improvements. Notably, Tracepile boosts LLaMA3-8B by 9.2\% on average across nine math datasets and delivers clear gains on LiveCodeBench, CRUX, and Zebra Logic under two-stage finetuning.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
166
|
FocalCodec: Low-Bitrate Speech Coding via Focal Modulation Networks
|
[
"Luca Della Libera",
"Francesco Paissan",
"Cem Subakan",
"Mirco Ravanelli"
] |
Large language models have revolutionized natural language processing through self-supervised pretraining on massive datasets. Inspired by this success, researchers have explored adapting these methods to speech by discretizing continuous audio into tokens using neural audio codecs. However, existing approaches face limitations, including high bitrates, the loss of either semantic or acoustic information, and the reliance on multi-codebook designs when trying to capture both, which increases architectural complexity for downstream tasks. To address these challenges, we introduce FocalCodec, an efficient low-bitrate codec based on focal modulation that utilizes a single binary codebook to compress speech between 0.16 and 0.65 kbps. FocalCodec delivers competitive performance in speech resynthesis and voice conversion at lower bitrates than the current state-of-the-art, while effectively handling multilingual speech and noisy environments. Evaluation on downstream tasks shows that FocalCodec successfully preserves sufficient semantic and acoustic information, while also being well-suited for generative modeling. Demo samples and code are available at https://anonymous.4open.science/w/fc-web-CC74/.
|
poster
|
2502.04465
| null |
https://lucadellalib.github.io/focalcodec-web/
|
[] |
[
"lucadellalib/focalcodec_25hz",
"lucadellalib/focalcodec_12_5hz",
"lucadellalib/focalcodec_50hz",
"lucadellalib/focalcodec_50hz_65k_causal",
"lucadellalib/focalcodec_50hz_4k_causal",
"lucadellalib/focalcodec_50hz_2k_causal"
] |
[] |
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] |
167
|
Stable Matching with Ties: Approximation Ratios and Learning
|
[
"Shiyun Lin",
"Simon Mauras",
"Nadav Merlis",
"Vianney Perchet"
] |
We study matching markets with ties, where workers on one side of the market may have tied preferences over jobs, determined by their matching utilities. Unlike classical two-sided markets with strict preferences, no single stable matching exists that is utility-maximizing for all workers. To address this challenge, we introduce the \emph{Optimal Stable Share} (OSS)-ratio, which measures the ratio of a worker's maximum achievable utility in any stable matching to their utility in a given matching. We prove that distributions over only stable matchings can incur linear utility losses, i.e., an $\Omega (N)$ OSS-ratio, where $N$ is the number of workers. To overcome this, we design an algorithm that efficiently computes a distribution over (possibly non-stable) matchings, achieving an asymptotically tight $O (\log N)$ OSS-ratio. When exact utilities are unknown, our second algorithm guarantees workers a logarithmic approximation of their optimal utility under bounded instability. Finally, we extend our offline approximation results to a bandit learning setting where utilities are only observed for matched pairs. In this setting, we consider worker-optimal stable regret, design an adaptive algorithm that smoothly interpolates between markets with strict preferences and those with statistical ties, and establish a lower bound revealing the fundamental trade-off between strict and tied preference regimes.
|
poster
|
2411.03270
| null | null |
[] |
[] |
[] |
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] |
168
|
SPOT-Trip: Dual-Preference Driven Out-of-Town Trip Recommendation
|
[
"Yinghui Liu",
"Hao Miao",
"Guojiang Shen",
"Yan Zhao",
"Xiangjie Kong",
"Ivan Lee"
] |
Out-of-town trip recommendation aims to generate a sequence of Points of Interest (POIs) for users traveling from their hometowns to previously unvisited regions based on personalized itineraries, e.g., origin, destination, and trip duration. Modeling the complex user preferences--which often exhibit a two-fold nature of static and dynamic interests--is critical for effective recommendations. However, the sparsity of out-of-town check-in data presents significant challenges in capturing such user preferences. Meanwhile, existing methods often conflate the static and dynamic preferences, resulting in suboptimal performance. In this paper, we for the first time systematically study the problem of out-of-town trip recommendation. A novel framework SPOT-Trip is proposed to explicitly learns the dual static-dynamic user preferences. Specifically, to handle scarce data, we construct a POI attribute knowledge graph to enrich the semantic modeling of users’ hometown and out-of-town check-ins, enabling the static preference modeling through attribute relation-aware aggregation. Then, we employ neural ordinary differential equations (ODEs) to capture the continuous evolution of latent dynamic user preferences and innovatively combine a temporal point process to describe the instantaneous probability of each preference behavior. Further, a static-dynamic fusion module is proposed to merge the learned static and dynamic user preferences. Extensive experiments on real data offer insight into the effectiveness of the proposed solutions, showing that SPOT-Trip achieves performance improvement by up to 17.01%.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
169
|
GraphMaster: Automated Graph Synthesis via LLM Agents in Data-Limited Environments
|
[
"Enjun Du",
"Xunkai Li",
"Tian Jin",
"Zhihan Zhang",
"Rong-Hua Li",
"Guoren Wang"
] |
The era of foundation models has revolutionized AI research, yet Graph Foundation Models (GFMs) remain constrained by the scarcity of large-scale graph corpora. Traditional graph data synthesis techniques primarily focus on simplistic structural operations, lacking the capacity to generate semantically rich nodes with meaningful textual attributes—a critical limitation for real-world applications. While large language models (LLMs) demonstrate exceptional text generation capabilities, their direct application to graph synthesis is impeded by context window limitations, hallucination phenomena, and structural consistency challenges. To address these issues, we introduce \textbf{GraphMaster}—the first multi-agent framework specifically designed for graph data synthesis in data-limited environments. GraphMaster orchestrates four specialized LLM agents (Manager, Perception, Enhancement, and Evaluation) that collaboratively optimize the synthesis process through iterative refinement, ensuring both semantic coherence and structural integrity. To rigorously evaluate our approach, we create new data-limited “Sub” variants of six standard graph benchmarks, specifically designed to test synthesis capabilities under realistic constraints. Additionally, we develop a novel interpretability assessment framework that combines human evaluation with a principled Grassmannian manifold-based analysis, providing both qualitative and quantitative measures of semantic coherence. Experimental results demonstrate that GraphMaster significantly outperforms traditional synthesis methods across multiple datasets, establishing a strong foundation for advancing GFMs in data-scarce environments.
|
spotlight
|
2504.00711
| null | null |
[] |
[] |
[] |
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] |
170
|
Tight Bounds on the Distortion of Randomized and Deterministic Distributed Voting
|
[
"Marzieh Nilipour",
"Mohammad Abam",
"Davoud Kareshki",
"Masoud Seddighin",
"MohammadHossein Paydar"
] |
We investigate metric distortion in distributed voting, where voters are divided into groups that each select a local representative, and a final winner is chosen from these representatives. This model captures real-world systems such as the U.S. presidential elections, where state-level decisions determine the national outcome. Our analysis focuses on four fundamental cost-minimization objectives introduced by Anshelevich et al.~\cite{anshelevich2022distortion}: the average of averages ($\avgavg$), average of maxima ($\avgmax$), maximum of averages ($\maxavg$), and maximum of maxima ($\maxmax$). We provide improved bounds for both deterministic and randomized rules which provides a near-complete picture of metric distortion in distributed voting. For deterministic mechanisms, we tighten the distortion gaps: we establish a tight lower bound of 5 for $\maxavg$ (improving the previous $2+\sqrt{5}$), reduce the upper bound for $\avgmax$ from 11 to 9, and show a tight upper bound of 3 for $\maxmax$ (improving the previous bound of 5). For randomized rules, we study two classes: (i) $\randdet$, where only the second stage is randomized, and (ii) $\randrand$, where both stages may be randomized. For $\randdet$, we establish tight bounds for all objectives: $5-2/k$ for $\avgavg$, $3$ for $\avgmax$ and $\maxmax$, $5$ for $\maxavg$. For $\randrand$, we show tight bounds of $3$ for $\maxavg$ and $\maxmax$, along with nearly tight bounds for $\avgavg$ and $\avgmax$ in the ranges $[3-2/n, 3-2/(kn^*)]$ and $[3-2/n, 3]$, respectively, where $n$ is the number of voters, $n^*$ is the size of the largest group, and k is the number of groups.
|
poster
|
2509.17134
| null | null |
[] |
[] |
[] |
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] |
171
|
AdaPA-Agent: A Personalized Agent with Adaptive Preference Arithmetic for Dynamic Preference Modeling
|
[
"Hongyi Nie",
"Yaqing Wang",
"Mingyang Zhou",
"Feiyang Pan",
"Quanming Yao",
"Zhen Wang"
] |
As large language models (LLMs) are increasingly used as personalized user assistants, effectively adapting to users' evolving preferences is critical for delivering high-quality personalized responses. While user preferences are often stable in content, their relative strengths shift over time due to changing goals and contexts. Therefore, modeling these dynamic preference strengths can enable finer-grained personalization. However, current methods face two major challenges: (i) limited user feedback makes it difficult to estimate preference strengths accurately, and (ii) natural language ambiguity limits the controllability of preference-guided generation. To address these issues, we propose AdaPA-Agent, a LLM-agent personalization framework that models dynamic preference strengths via Adaptive Preference Arithmetic. First, instead of requiring additional user feedback, AdaPA-Agent employs an alignment-based strength estimation module to estimate the strength of user preferences from the existing user-agent interaction. Then, it guides controllable personalized generation by linearly combining next-token distributions, weighted by the estimated strengths of individual preferences. Experiments on two personalization tasks-conversational recommendation and personalized web interaction-demonstrate that AdaPA-Agent better aligning with users' changing intents, and has achieved over 18.9\% and 14.2\% improvements compared to ReAct, the widely-used agent framework.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
172
|
Repo2Run: Automated Building Executable Environment for Code Repository at Scale
|
[
"Ruida Hu",
"Chao Peng",
"XinchenWang",
"Junjielong Xu",
"Cuiyun Gao"
] |
Scaling up executable code data is significant for improving language models’ software engineerin capability. The intricate nature of the process makes it labor-intensive, time-consuming and expert-knowledge-dependent to build a large number of executable code repositories, limiting the scalability of existing work based on running tests. The primary bottleneck lies in the automated building of test environments for different repositories, which is an essential yet underexplored task. To mitigate the gap, we introduce Repo2Run, the first LLM-based agent aiming at automating the building of executable test environments for any repositories at scale. Specifically, given a code repository, Repo2Run iteratively builds the Docker image, runs unit tests based on the feedback of the building, and synthesizes the Dockerfile until the entire pipeline is executed successfully. The resulting Dockerfile can then be used to create Docker container environments for running code and tests. We created a benchmark containing 420 Python repositories with unit tests for evaluation. The results illustrate that Repo2Run achieves an 86.0% success rate, outperforming SWE-agent by 77.0%. The resources of Repo2Run are available at https://anonymous.4open.science/r/Repo2Run.
|
spotlight
|
2502.13681
|
https://github.com/bytedance/Repo2Run
| null |
[] |
[] |
[] |
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] |
173
|
EnzyControl: Adding Functional and Substrate-Specific Control for Enzyme Backbone Generation
|
[
"Chao Song",
"ZHIYUAN LIU",
"Han Huang",
"Liang Wang",
"Qiong Wang",
"Jian-Yu Shi",
"Hui Yu",
"Yihang Zhou",
"Yang Zhang"
] |
Designing enzyme backbones with substrate-specific functionality is a critical challenge in computational protein engineering. Current generative models excel in protein design but face limitations in binding data, substrate-specific control, and flexibility for de novo enzyme backbone generation. To address this, we introduce **EnzyBind**, a dataset with 11,100 experimentally validated enzyme-substrate pairs specifically curated from PDBbind. Building on this, we propose **EnzyControl**, a method that enables functional and substrate-specific control in enzyme backbone generation. Our approach generates enzyme backbones conditioned on MSA-annotated catalytic sites and their corresponding substrates, which are automatically extracted from curated enzyme-substrate data. At the core of EnzyControl is **EnzyAdapter**, a lightweight, modular component integrated into a pretrained motif-scaffolding model, allowing it to become substrate-aware. A two-stage training paradigm further refines the model's ability to generate accurate and functional enzyme structures. Experiments show that our EnzyControl achieves the best performance across structural and functional metrics on EnzyBind and EnzyBench benchmarks, with particularly notable improvements of 25% in designability and 15% in catalytic efficiency compared to the baseline models.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
174
|
Beyond Pairwise Connections: Extracting High-Order Functional Brain Network Structures under Global Constraints
|
[
"Ling Zhan",
"Junjie Huang",
"Xiaoyao Yu",
"Wenyu Chen",
"Tao Jia"
] |
Functional brain network (FBN) modeling often relies on local pairwise interactions, whose limitation in capturing high-order dependencies is theoretically analysed in this paper. Meanwhile, the computational burden and heuristic nature of current hypergraph modeling approaches hinder end-to-end learning of FBN structures directly from data distributions. To address this, we propose to extract high-order FBN structures under global constraints, and implement this as a Global Constraints oriented Multi-resolution (GCM) FBN structure learning framework. It incorporates 3 types of global constraint (expected edge numbers, data source, and data labels) to enable learning FBN structures for 4 distinct levels (sample/subject/group/project) of modeling resolution. Experimental results demonstrate that GCM achieves up to a 30.6% improvement in relative accuracy and a 96.3% reduction in computational time across 5 datasets and 2 task settings, compared to 7 baselines and 8 state-of-the-art methods. Extensive experiments validate the contributions of individual components and highlight the interpretability of GCM. This work offers a novel perspective on FBN structure learning and provides a foundation for interdisciplinary applications in cognitive neuroscience.
|
poster
|
2510.09175
| null | null |
[] |
[] |
[] |
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] |
175
|
TopoPoint: Enhance Topology Reasoning via Endpoint Detection in Autonomous Driving
|
[
"Yanping Fu",
"Xinyuan Liu",
"Tianyu Li",
"Yike Ma",
"Yucheng Zhang",
"Feng Dai"
] |
Topology reasoning, which unifies perception and structured reasoning, plays a vital role in understanding intersections for autonomous driving. However, its performance heavily relies on the accuracy of lane detection, particularly at connected lane endpoints. Existing methods often suffer from lane endpoints deviation, leading to incorrect topology construction. To address this issue, we propose TopoPoint, a novel framework that explicitly detects lane endpoints and jointly reasons over endpoints and lanes for robust topology reasoning. During training, we independently initialize point and lane query, and proposed Point-Lane Merge Self-Attention to enhance global context sharing through incorporating geometric distances between points and lanes as an attention mask . We further design Point-Lane Graph Convolutional Network to enable mutual feature aggregation between point and lane query. During inference, we introduce Point-Lane Geometry Matching algorithm that computes distances between detected points and lanes to refine lane endpoints, effectively mitigating endpoint deviation. Extensive experiments on the OpenLane-V2 benchmark demonstrate that TopoPoint achieves state-of-the-art performance in topology reasoning (48.8 on OLS). Additionally, we propose DET$_p$ to evaluate endpoint detection, under which our method significantly outperforms existing approaches (52.6 v.s. 45.2 on DET$_p$). The codes will be released soon.
|
poster
|
2505.17771
|
https://github.com/Franpin/TopoPoint
| null |
[] |
[] |
[] |
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] |
176
|
Semi-off-Policy Reinforcement Learning for Vision-Language Slow-thinking Reasoning
|
[
"Junhao Shen",
"Haiteng Zhao",
"Yuzhe Gu",
"Songyang Gao",
"Kuikun Liu",
"Haian Huang",
"Jianfei Gao",
"Dahua Lin",
"Wenwei Zhang",
"Kai Chen"
] |
Enhancing large vision-language models (LVLMs) with visual slow-thinking reasoning is crucial for solving complex multimodal tasks. However, since LVLMs are mainly trained with vision-language alignment, it is difficult to adopt on-policy reinforcement learning (RL) to develop the slow thinking ability because the rollout space is restricted by its initial abilities. Off-policy RL offers a way to go beyond the current policy, but directly distilling trajectories from external models may cause visual hallucinations due to mismatched visual perception abilities across models. To address these issues, this paper proposes **SOPHIA**, a simple and scalable **S**emi-**O**ff-**P**olicy RL for vision-language slow-t**HI**nking re**A**soning. SOPHIA builds a semi-off-policy behavior model by combining on-policy visual understanding from a trainable LVLM with off-policy slow-thinking reasoning from a language model, assigns outcome-based rewards to reasoning, and propagates visual rewards backward. Then LVLM learns slow-thinking reasoning ability from the obtained reasoning trajectories using propagated rewards via off-policy RL algorithms. Extensive experiments with InternVL2.5 and InternVL3.0 with 8B and 38B sizes show the effectiveness of SOPHIA. Notably, SOPHIA improves InternVL3.0-38B by 8.50\% in average, reaching state-of-the-art performance among open-source LVLMs on multiple multimodal reasoning benchmarks, and even outperforms some closed-source models (e.g., GPT-4.1) on the challenging MathVision and OlympiadBench, achieving 49.08\% and 49.95\% pass@1 accuracy, respectively. Analysis shows SOPHIA outperforms supervised fine-tuning and direct on-policy RL methods, offering a better policy initialization for further on-policy training.
|
poster
|
2507.16814
| null | null |
[] |
[] |
[] |
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] |
177
|
Scalable and Cost-Efficient de Novo Template-Based Molecular Generation
|
[
"Piotr Gaiński",
"Oussama Boussif",
"Andrei Rekesh",
"Dmytro Shevchuk",
"Ali Parviz",
"Mike Tyers",
"Robert Batey",
"Michał Koziarski"
] |
Template-based molecular generation offers a promising avenue for drug design by ensuring generated compounds are synthetically accessible through predefined reaction templates and building blocks. In this work, we tackle three core challenges in template-based GFlowNets: (1) minimizing synthesis cost, (2) scaling to large building block libraries, and (3) effectively utilizing small fragment sets. We propose **Recursive Cost Guidance**, a backward policy framework that employs auxiliary machine learning models to approximate synthesis cost and viability. This guidance steers generation toward low-cost synthesis pathways, significantly enhancing cost-efficiency, molecular diversity, and quality, especially when paired with an **Exploitation Penalty** that balances the trade-off between exploration and exploitation. To enhance performance in smaller building block libraries, we develop a **Dynamic Library** mechanism that reuses intermediate high-reward states to construct full synthesis trees. Our approach establishes state-of-the-art results in template-based molecular generation.
|
poster
|
2506.19865
| null | null |
[] |
[] |
[] |
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] |
178
|
One-Step Offline Distillation of Diffusion-based Models via Koopman Modeling
|
[
"Nimrod Berman",
"Ilan Naiman",
"Moshe Eliasof",
"Hedi Zisling",
"Omri Azencot"
] |
Diffusion-based generative models have demonstrated exceptional performance, yet their iterative sampling procedures remain computationally expensive. A prominent strategy to mitigate this cost is *distillation*, with *offline distillation* offering particular advantages in terms of efficiency, modularity, and flexibility. In this work, we identify two key observations that motivate a principled distillation framework: (1) while diffusion models have been viewed through the lens of dynamical systems theory, powerful and underexplored tools can be further leveraged; and (2) diffusion models inherently impose structured, semantically coherent trajectories in latent space. Building on these observations, we introduce the *Koopman Distillation Model* (KDM), a novel offline distillation approach grounded in Koopman theory - a classical framework for representing nonlinear dynamics linearly in a transformed space. KDM encodes noisy inputs into an embedded space where a learned linear operator propagates them forward, followed by a decoder that reconstructs clean samples. This enables single-step generation while preserving semantic fidelity. We provide theoretical justification for our approach: (1) under mild assumptions, the learned diffusion dynamics admit a finite-dimensional Koopman representation; and (2) proximity in the Koopman latent space correlates with semantic similarity in the generated outputs, allowing for effective trajectory alignment. Empirically, KDM achieves state-of-the-art performance across standard *offline distillation* benchmarks - improving FID scores by up to 40% in a single generation step.
|
poster
|
2505.13358
|
https://github.com/azencot-group/KDM
|
https://sites.google.com/view/koopman-distillation-model
|
[] |
[] |
[] |
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] |
179
|
PASS: Path-selective State Space Model for Event-based Recognition
|
[
"Jiazhou Zhou",
"Kanghao Chen",
"Lei Zhang",
"Lin Wang"
] |
Event cameras are bio-inspired sensors that capture intensity changes asynchronously with distinct advantages, such as high temporal resolution. Existing methods for event-based object/action recognition predominantly sample and convert event representation at every fixed temporal interval (or frequency). However, they are constrained to processing a limited number of event lengths and show poor frequency generalization, thus not fully leveraging the event's high temporal resolution. In this paper, we present our PASS framework, exhibiting superior capacity for spatiotemporal event modeling towards a larger number of event lengths and generalization across varying inference temporal frequencies. Our key insight is to learn adaptively encoded event features via the state space models (SSMs), whose linear complexity and generalization on input frequency make them ideal for processing high temporal resolution events. Specifically, we propose a Path-selective Event Aggregation and Scan (PEAS) module to encode events into features with fixed dimensions by adaptively scanning and selecting aggregated event presentation. On top of it, we introduce a novel Multi-faceted Selection Guiding (MSG) loss to minimize the randomness and redundancy of the encoded features during the PEAS selection process. Our method outperforms prior methods on five public datasets and shows strong generalization across varying inference frequencies with less accuracy drop (ours -8.62% v.s. -20.69% for the baseline). Moreover, our model exhibits strong long spatiotemporal modeling for a broader distribution of event length (1-10^9), precise temporal perception, and effective generalization for real-world scenarios. Code and checkpoints will be released upon acceptance.
|
poster
|
2409.16953
| null | null |
[] |
[] |
[] |
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] |
180
|
Right for the Right Reasons: Avoiding Reasoning Shortcuts via Prototype-Augmented Neurosymbolic AI
|
[
"Luca Andolfi",
"Eleonora Giunchiglia"
] |
Neurosymbolic methods combine neural perception with symbolic reasoning, but recent studies have shown that state-of-the-art approaches can fall prey of reasoning shortcuts---spurious but predictive associations between learned concepts and target labels that satisfy constraints syntactically, yet violate the intended semantics. In this paper, we investigate the use of prototype-augmented neural architectures to mitigate reasoning shortcuts. By adopting prototypical networks as a backbone, we introduce a method that anchors intermediate concepts in interpretable embeddings and guides their training using the available limited supervision and the symbolic background knowledge. Extensive experiments demonstrate that our approach significantly improves semantic alignment while maintaining predictive performance across multiple benchmarks.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
181
|
Segment then Splat: Unified 3D Open-Vocabulary Segmentation via Gaussian Splatting
|
[
"Yiren Lu",
"Yunlai Zhou",
"Yiran Qiao",
"Chaoda Song",
"Tuo Liang",
"Jing Ma",
"Huan Wang",
"Yu Yin"
] |
Open-vocabulary querying in 3D space is crucial for enabling more intelligent perception in applications such as robotics, autonomous systems, and augmented reality. However, most existing methods rely on 2D pixel-level parsing, leading to multi-view inconsistencies and poor 3D object retrieval. Moreover, they are limited to static scenes and struggle with dynamic scenes due to the complexities of motion modeling. In this paper, we propose Segment then Splat, a 3D-aware open vocabulary segmentation approach for both static and dynamic scenes based on Gaussian Splatting. Segment then Splat reverses the long established approach of ``segmentation after reconstruction" by dividing Gaussians into distinct object sets before reconstruction. Once the reconstruction is complete, the scene is naturally segmented into individual objects, achieving true 3D segmentation. This approach not only eliminates Gaussian-object misalignment issues in dynamic scenes but also accelerates the optimization process, as it eliminates the need for learning a separate language field. After optimization, a CLIP embedding is assigned to each object to enable open-vocabulary querying. Extensive experiments demonstrate the effectiveness of our proposed method in both static and dynamic scenarios.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
182
|
VR-Drive: Viewpoint-Robust End-to-End Driving with Feed-Forward 3D Gaussian Splatting
|
[
"Hoonhee Cho",
"Jae-Young Kang",
"Giwon Lee",
"Hyemin Yang",
"Heejun Park",
"Seokwoo Jung",
"Kuk-Jin Yoon"
] |
End-to-end autonomous driving (E2E-AD) has emerged as a promising paradigm that unifies perception, prediction, and planning into a holistic, data-driven framework. However, achieving robustness to varying camera viewpoints, a common real-world challenge due to diverse vehicle configurations, remains an open problem. In this work, we propose VR-Drive, a novel E2E-AD framework that addresses viewpoint generalization by jointly learning 3D scene reconstruction as an auxiliary task to enable planning-aware view synthesis. Unlike prior scene-specific synthesis approaches, VR-Drive adopts a feed-forward inference strategy that supports online training-time augmentation from sparse views without additional annotations. To further improve viewpoint consistency, we introduce a viewpoint-mixed memory bank that facilitates temporal interaction across multiple viewpoints and a viewpoint-consistent distillation strategy that transfers knowledge from original to synthesized views. Trained in a fully end-to-end manner, VR-Drive effectively mitigates synthesis-induced noise and improves planning under viewpoint shifts. In addition, we release a new benchmark dataset to evaluate E2E-AD performance under novel camera viewpoints, enabling comprehensive analysis. Our results demonstrate that VR-Drive is a scalable and robust solution for the real-world deployment of end-to-end autonomous driving systems. Our code and datasets will be made publicly available.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
183
|
Point-MaDi: Masked Autoencoding with Diffusion for Point Cloud Pre-training
|
[
"Xiaoyang Xiao",
"Runzhao Yao",
"Zhiqiang Tian",
"Shaoyi Du"
] |
Self-supervised pre-training is essential for 3D point cloud representation learning, as annotating their irregular, topology-free structures is costly and labor-intensive. Masked autoencoders (MAEs) offer a promising framework but rely on explicit positional embeddings, such as patch center coordinates, which leak geometric information and limit data-driven structural learning. In this work, we propose Point-MaDi, a novel Point cloud Masked autoencoding Diffusion framework for pre-training that integrates a dual-diffusion pretext task into an MAE architecture to address this issue. Specifically, we introduce a center diffusion mechanism in the encoder, noising and predicting the coordinates of both visible and masked patch centers without ground-truth positional embeddings. These predicted centers are processed using a transformer with self-attention and cross-attention to capture intra- and inter-patch relationships. In the decoder, we design a conditional patch diffusion process, guided by the encoder's latent features and predicted centers to reconstruct masked patches directly from noise. This dual-diffusion design drives comprehensive global semantic and local geometric representations during pre-training, eliminating external geometric priors. Extensive experiments on ScanObjectNN, ModelNet40, ShapeNetPart, S3DIS, and ScanNet demonstrate that Point-MaDi achieves superior performance across downstream tasks, surpassing Point-MAE by 5.51\% on OBJ-BG, 5.17\% on OBJ-ONLY, and 4.34\% on PB-T50-RS for 3D object classification on the ScanObjectNN dataset.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
184
|
DeepBLNet: Bilevel Network Learning via Hierarchically Structured Sparsity
|
[
"Jiayi Fan",
"Jingyuan Yang",
"Shuangge Ma",
"Mengyun Wu"
] |
Accurate network estimation serves as the cornerstone for understanding complex systems across scientific domains, spanning from decoding gene regulatory pathways in systems biology to identifying social interaction patterns in computational sociology. Modern applications demand methods that simultaneously address two critical challenges: capturing nonlinear interdependencies between variables and recovering inherent hierarchical structures where higher-level entities coordinate lower-level components (e.g., functional pathways organizing gene clusters). Traditional Gaussian graphical models fundamentally fail in these aspects due to their restrictive linear assumptions and flat network representations. We propose DeepBLNet, a deep learning framework for bi-level network inference. The core innovation lies in hierarchical selection layers that enforce structural consistency between high-level interaction groups and their constituent low-level connections via adaptive sparsity constraints. This architecture is integrated with compositional deep architectures that learn cross-level interaction patterns through constrained nonlinear transformations, explicitly preserving hierarchical dependencies while overcoming the representational limitations of linear methods. Crucially, we establish formal theoretical guarantees for consistent recovery of both high-level interactions and their internal low-level structures under general statistical regimes. Extensive validation demonstrates DeepBLNet's effectiveness across synthetic and real-world scenarios, achieving superior F1 scores compared to competitive methods and particularly benefiting complex systems analysis through its interpretable bilevel structure discovery.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
185
|
Are Large Reasoning Models Good Translation Evaluators? Analysis and Performance Boost
|
[
"Runzhe Zhan",
"Zhihong Huang",
"Xinyi Yang",
"Lidia Chao",
"Min Yang",
"Derek Wong"
] |
Recent advancements in large reasoning models (LRMs) have introduced the "slow thinking" paradigm, which leverages their inherent strengths to enhance reasoning capabilities for complex downstream tasks. However, the potential of LRMs as evaluators for machine translation (MT) quality remains underexplored. We provides the first systematic analysis of LRM-as-a-judge in MT evaluation. We identify key challenges, revealing LRMs require tailored evaluation materials, tend to ``overthink'' simpler instances and have issues with scoring mechanisms leading to overestimation. To address these, we propose to calibrate LRM thinking by training them on synthetic, human-like thinking trajectories. Our experiments on WMT24 Metrics benchmarks demonstrate that this approach largely reduces thinking budgets by ~35x while concurrently improving evaluation performance across different LRM scales from 7B to 32B (e.g., R1-Distill-Qwen-7B achieves a +8.7 correlation point improvement). These findings highlight the substantial potential of efficiently calibrated LRMs to advance human-centric MT evaluation.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
186
|
Vision Transformers with Self-Distilled Registers
|
[
"Zipeng Yan",
"Yinjie Chen",
"Chong Zhou",
"Bo Dai",
"Andrew Luo"
] |
Vision Transformers (ViTs) have emerged as the dominant architecture for visual processing tasks, demonstrating excellent scalability with increased training data and model size.However, recent work has identified the emergence of artifact tokens in ViTs that are incongruous with the local semantics.These anomalous tokens degrade ViT performance in tasks that require fine-grained localization or structural coherence. An effective mitigation of this issue is to the addition of register tokens to ViTs, which implicitly "absorb" the artifact term during training.Given the availability of various large-scale pre-trained ViTs,in this paper we aim at equipping them with such register tokens without the need of re-training them from scratch, which are infeasible considering their size.Specifically, we propose Post Hoc Registers (**PH-Reg**), an efficient self-distillationmethod that integrates registers into an existing ViT without requiring additional labeled data and full retraining. PH-Reg initializes both teacher and student networks from the same pre-trained ViT. The teacher remains frozen and unmodified, while the student is augmented with randomly initialized register tokens. By applying test-time augmentation to the teacher’s inputs, we generate denoised dense embeddings free of artifacts, which are then used to optimize only a small subset of unlocked student weights. We show that our approach can effectively reduce the number of artifact tokens, improving the segmentation and depth prediction of the student ViT under zero-shot and linear probing.
|
spotlight
|
2505.21501
| null | null |
[] |
[] |
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] |
187
|
HybridNorm: Towards Stable and Efficient Transformer Training via Hybrid Normalization
|
[
"Zhijian Zhuo",
"Yutao Zeng",
"Ya Wang",
"Sijun Zhang",
"Xiaoqing Li",
"Jian Yang",
"zhou Xun",
"Jinwen Ma"
] |
Transformers have become the de facto architecture for a wide range of machine learning tasks, particularly in large language models (LLMs). Despite their remarkable performance, challenges remain in training deep transformer networks, especially regarding the position of layer normalization. While Pre-Norm structures facilitate more stable training owing to their stronger identity path, they often lead to suboptimal performance compared to Post-Norm. In this paper, we propose **HybridNorm**, a simple yet effective hybrid normalization strategy that integrates the advantages of both Pre-Norm and Post-Norm. Specifically, HybridNorm employs QKV normalization within the attention mechanism and Post-Norm in the feed-forward network (FFN) of each transformer block. We provide both theoretical insights and empirical evidence demonstrating that HybridNorm improves gradient flow and model robustness. Extensive experiments on large-scale transformer models, including both dense and sparse variants, show that HybridNorm consistently outperforms both Pre-Norm and Post-Norm approaches across multiple benchmarks. These findings highlight the potential of HybridNorm as a more stable and effective technique for improving the training and performance of deep transformer models.
|
poster
|
2503.04598
|
https://github.com/BryceZhuo/HybridNorm
|
https://github.com/BryceZhuo/HybridNorm
|
[] |
[] |
[] |
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] |
188
|
A Generalized Label Shift Perspective for Cross-Domain Gaze Estimation
|
[
"HAORAN YANG",
"Xiaohui Chen",
"Chuan-Xian Ren"
] |
Aiming to generalize the well-trained gaze estimation model to new target domains, Cross-domain Gaze Estimation (CDGE) is developed for real-world application scenarios. Existing CDGE methods typically extract the domain-invariant features to mitigate domain shift in feature space, which is proved insufficient by Generalized Label Shift (GLS) theory. In this paper, we introduce a novel GLS perspective to CDGE and modelize the cross-domain problem by label and conditional shift problem. A GLS correction framework is presented and a feasible realization is proposed, in which a importance reweighting strategy based on truncated Gaussian distribution is introduced to overcome the continuity challenges in label shift correction. To embed the reweighted source distribution to conditional invariant learning, we further derive a probability-aware estimation of conditional operator discrepancy. Extensive experiments on standard CDGE tasks with different backbone models validate the superior generalization capability across domain and applicability on various models of proposed method.
|
poster
|
2505.13043
| null | null |
[] |
[] |
[] |
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] |
189
|
UI-Genie: A Self-Improving Approach for Iteratively Boosting MLLM-based Mobile GUI Agents
|
[
"Han Xiao",
"Guozhi Wang",
"Yuxiang Chai",
"Zimu Lu",
"Weifeng Lin",
"Hao He",
"Lue Fan",
"Liuyang Bian",
"Rui Hu",
"Liang Liu",
"Shuai Ren",
"yafei wen",
"xiaoxin chen",
"Aojun Zhou",
"Hongsheng Li"
] |
In this paper, we introduce UI-Genie, a self-improving framework addressing two key challenges in GUI agents: verification of trajectory outcome is challenging and high-quality training data are not scalable. These challenges are addressed by a reward model and a self-improving pipeline, respectively. The reward model, UI-Genie-RM, features an image-text interleaved architecture that efficiently processes historical context and unifies action-level and task-level rewards. To support the training of UI-Genie-RM, we develop deliberately-designed data generation strategies including rule-based verification, controlled trajectory corruption, and hard negative mining. To address the second challenge, a self-improvement pipeline progressively expands solvable complex GUI tasks by enhancing both the agent and reward models through reward-guided exploration and outcome verification in dynamic environments. For training the model, we generate UI-Genie-RM-517k and UI-Genie-Agent-16k, establishing the first reward-specific dataset for GUI agents while demonstrating high-quality synthetic trajectory generation without manual annotation. Experimental results show that UI-Genie achieves state-of-the-art performance across multiple GUI agent benchmarks with three generations of data-model self-improvement. We will open-source our complete framework implementation to facilitate further research.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
190
|
A Physics-preserved Transfer Learning Method for Differential Equations
|
[
"HAORAN YANG",
"Chuan-Xian Ren"
] |
While data-driven methods such as neural operator have achieved great success in solving differential equations (DEs), they suffer from domain shift problems caused by different learning environments (with data bias or equation changes), which can be alleviated by transfer learning (TL). However, existing TL methods adopted in DEs problems lack either generalizability in general DEs problems or physics preservation during training. In this work, we focus on a general transfer learning method that adaptively correct the domain shift and preserve physical relation within the equation. Mathematically, we characterize the data domain as product distribution and the essential problems as distribution bias and operator bias. A Physics-preserved Optimal Tensor Transport (POTT) method that simultaneously admits generalizability to common DEs and physics preservation of specific problem is proposed to adapt the data-driven model to target domain, utilizing the pushforward distribution induced by the POTT map. Extensive experiments in simulation and real-world datasets demonstrate the superior performance, generalizability and physics preservation of the proposed POTT method.
|
poster
|
2505.01281
| null | null |
[] |
[] |
[] |
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] |
191
|
Robust Reinforcement Learning in Finance: Modeling Market Impact with Elliptic Uncertainty Sets
|
[
"Shaocong Ma",
"Heng Huang"
] |
In financial applications, reinforcement learning (RL) agents are commonly trained on historical data, where their actions do not influence prices. However, during deployment, these agents trade in live markets where their own transactions can shift asset prices, a phenomenon known as market impact. This mismatch between training and deployment environments can significantly degrade performance. Traditional robust RL approaches address this model misspecification by optimizing the worst-case performance over a set of uncertainties, but typically rely on symmetric structures that fail to capture the directional nature of market impact. To address this issue, we develop a novel class of elliptic uncertainty sets. We establish both implicit and explicit closed-form solutions for the worst-case uncertainty under these sets, enabling efficient and tractable robust policy evaluation. Experiments on single-asset and multi-asset trading tasks demonstrate that our method achieves superior Sharpe ratio and remains robust under increasing trade volumes, offering a more faithful and scalable approach to RL in financial markets.
|
poster
| null | null | null |
[] |
[] |
[] |
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-0.013282905332744122
] |
192
|
GraphChain: Large Language Models for Large-scale Graph Analysis via Tool Chaining
|
[
"Chunyu Wei",
"Wenji Hu",
"Xingjia Hao",
"Xin Wang",
"Yifan Yang",
"Yunhai Wang",
"Yang Tian",
"Yueguo Chen"
] |
Large Language Models (LLMs) face significant limitations when applied to large-scale graphs, struggling with context constraints and inflexible reasoning. We introduce GraphChain, a novel framework enabling LLMs to analyze large graphs by orchestrating dynamic sequences of specialized tools, mimicking human exploratory processes. GraphChain incorporates two core technical contributions: (1) Progressive Graph Distillation, a reinforcement learning approach that learns to generate tool sequences balancing task relevance and intermediate state compression, thereby overcoming LLM context limitations. (2) Structure-aware Test-Time Adaptation (STTA), a mechanism using a lightweight, self-supervised adapter conditioned on graph spectral properties to efficiently adapt a frozen LLM policy to diverse graph structures via soft prompts without retraining. Experiments show GraphChain significantly outperforms prior methods, enabling scalable and adaptive LLM-driven graph analysis.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
193
|
Efficient RAW Image Deblurring with Adaptive Frequency Modulation
|
[
"Wenlong Jiao",
"Binglong Li",
"Wei Shang",
"Ping Wang",
"Dongwei Ren"
] |
Image deblurring plays a crucial role in enhancing visual clarity across various applications. Although most deep learning approaches primarily focus on sRGB images, which inherently lose critical information during the image signal processing pipeline, RAW images, being unprocessed and linear, possess superior restoration potential but remain underexplored. Deblurring RAW images presents unique challenges, particularly in handling frequency-dependent blur while maintaining computational efficiency. To address these issues, we propose Frequency Enhanced Network (FrENet), a framework specifically designed for RAW-to-RAW deblurring that operates directly in the frequency domain. We introduce a novel Adaptive Frequency Positional Modulation module, which dynamically adjusts frequency components according to their spectral positions, thereby enabling precise control over the deblurring process. Additionally, frequency domain skip connections are adopted to further preserve high-frequency details. Experimental results demonstrate that FrENet surpasses state-of-the-art deblurring methods in RAW image deblurring, achieving significantly better restoration quality while maintaining high efficiency in terms of reduced MACs. Furthermore, FrENet's adaptability enables it to be extended to sRGB images, where it delivers comparable or superior performance compared to methods specifically designed for sRGB data. The source code will be publicly available.
|
poster
|
2505.24407
|
https://github.com/WenlongJiao/FrENet
| null |
[] |
[] |
[] |
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] |
194
|
Lifelong Safety Alignment for Language Models
|
[
"Haoyu Wang",
"Zeyu Qin",
"Yifei Zhao",
"Chao Du",
"Min Lin",
"Xueqian Wang",
"Tianyu Pang"
] |
LLMs have made impressive progress, but their growing capabilities also expose them to highly flexible jailbreaking attacks designed to bypass safety alignment. While many existing defenses focus on known types of attacks, it is more critical to prepare LLMs for *unseen* attacks that may arise during deployment. To address this, we propose a **lifelong safety alignment** framework that enables LLMs to continuously adapt to new and evolving jailbreaking strategies. Our framework introduces a competitive setup between two components: a **Meta-Attacker**, trained to actively discover novel jailbreaking strategies, and a **Defender**, trained to resist them. To effectively warm up the Meta-Attacker, we first leverage the GPT-4o API to extract key insights from a large collection of jailbreak-related research papers. Through iterative training, the first iteration Meta-Attacker achieves a 73% attack success rate (ASR) on RR and a 57% transfer ASR on LAT using only *single-turn* attacks. Meanwhile, the Defender progressively improves its robustness and ultimately reduces the Meta-Attacker's success rate to just 7%, enabling safer and more reliable deployment of LLMs in open-ended environments.
|
poster
|
2505.20259
|
https://github.com/sail-sg/LifelongSafetyAlignment
| null |
[] |
[] |
[] |
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] |
195
|
Hierarchical Optimization via LLM-Guided Objective Evolution for Mobility-on-Demand Systems
|
[
"Yi Zhang",
"Yushen Long",
"Liping Huang",
"Yun Ni",
"Xiaohong Wang",
"Jun Liu"
] |
Online ride-hailing platforms aim to deliver efficient mobility-on-demand services, often facing challenges in balancing dynamic and spatially heterogeneous supply and demand. Existing methods typically fall into two categories: reinforcement learning (RL) approaches, which suffer from data inefficiency, oversimplified modeling of real-world dynamics, and difficulty enforcing operational constraints; or decomposed online optimization methods, which rely on manually designed high-level objectives that lack awareness of low-level routing dynamics. To address this issue, we propose a novel hybrid framework that integrates large language model (LLM) with mathematical optimization in a dynamic hierarchical system: (1) it is training-free, removing the need for large-scale interaction data as in RL, and (2) it leverages LLM to bridge cognitive limitations caused by problem decomposition by adaptively generating high-level objectives. Within this framework, LLM serves as a meta-optimizer, producing semantic heuristics that guide a low-level optimizer responsible for constraint enforcement and real-time decision execution. These heuristics are refined through a closed-loop evolutionary process, driven by harmony search, which iteratively adapts the LLM prompts based on feasibility and performance feedback from the optimization layer. Extensive experiments based on scenarios derived from both the New York and Chicago taxi datasets demonstrate the effectiveness of our approach, achieving an average improvement of 16% compared to state-of-the-art baselines.
|
poster
|
2510.10644
| null | null |
[] |
[] |
[] |
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196
|
TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster
|
[
"Kanghui Ning",
"Zijie Pan",
"Yu Liu",
"Yushan Jiang",
"James Zhang",
"Kashif Rasul",
"Anderson Schneider",
"Lintao Ma",
"Yuriy Nevmyvaka",
"Dongjin Song"
] |
Large Language Models (LLMs) and Foundation Models (FMs) have recently become prevalent for time series forecasting tasks. While fine-tuning LLMs enables domain adaptation, they often struggle to generalize across diverse and unseen datasets. Moreover, existing Time Series Foundation Models (TSFMs) still face challenges in handling non-stationary dynamics and distribution shifts, largely due to the lack of effective mechanisms for adaptation. To this end, we present TS-RAG, a retrieval-augmented generation framework for time series forecasting that enhances the generalization and interpretability of TSFMs. Specifically, TS-RAG leverages pre-trained time series encoders to retrieve semantically relevant segments from a dedicated knowledge base, enriching the contextual representation of the input query. Furthermore, we propose an Adaptive Retrieval Mixer (ARM) module that dynamically fuses the retrieved patterns with the TSFM's internal representation, improving forecasting accuracy without requiring task-specific fine-tuning. Thorough empirical studies on seven public benchmark datasets demonstrate that TS-RAG achieves state-of-the-art zero-shot forecasting performance, outperforming the existing TSFMs by up to 6.84\% across diverse domains while also providing desirable interpretability. To support reproducibility, anonymized code and data are available at: https://anonymous.4open.science/r/TS-RAG-F4DB.
|
poster
| null | null | null |
[] |
[] |
[] |
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197
|
AltLoRA: Towards Better Gradient Approximation in Low-Rank Adaptation with Alternating Projections
|
[
"Xin Yu",
"Yujia Wang",
"Jinghui Chen",
"Lingzhou Xue"
] |
Low-Rank Adaptation (LoRA) has emerged as an effective technique for reducing memory overhead in fine-tuning large language models. However, it often suffers from sub-optimal performance compared with full fine-tuning since the update is constrained in the low-rank space. Recent variants such as LoRA-Pro attempt to mitigate this by adjusting the gradients of the low-rank matrices to approximate the full gradient. However, LoRA-Pro's solution is not unique, and different solutions can lead to significantly varying performance in ablation studies. Besides, to incorporate momentum or adaptive optimization design, approaches like LoRA-Pro must first compute the equivalent gradient, causing a higher memory cost close to full fine-tuning. A key challenge remains in integrating momentum properly into the low-rank space with lower memory cost. In this work, we propose AltLoRA, an alternating projection method that avoids the difficulties in gradient approximation brought by the joint update design, meanwhile integrating momentum without higher memory complexity. Our theoretical analysis provides convergence guarantees and further shows that AltLoRA enables stable feature learning and robustness to transformation invariance. Extensive experiments across multiple tasks demonstrate that AltLoRA outperforms LoRA and its variants, narrowing the gap toward full fine-tuning while preserving superior memory efficiency.
|
poster
|
2505.12455
| null | null |
[] |
[] |
[] |
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] |
198
|
Sherlock: Self-Correcting Reasoning in Vision-Language Models
|
[
"Yi Ding",
"Ruqi Zhang"
] |
Reasoning Vision-Language Models (VLMs) have shown promising performance on complex multimodal tasks. However, they still face significant challenges: they are highly sensitive to reasoning errors, require large volumes of annotated data or accurate verifiers, and struggle to generalize beyond specific domains.To address these limitations, we explore self-correction as a strategy to enhance reasoning VLMs. We first conduct an in-depth analysis of reasoning VLMs’ self-correction abilities and identify key gaps. Based on our findings, we introduce Sherlock, a self-correction and self-improvement training framework. \emph{Sherlock} introduces a trajectory-level self-correction objective, a preference data construction method based on visual perturbation, and a dynamic $\beta$ for preference tuning. Once the model acquires self-correction capabilities using only 20k randomly sampled annotated data, it continues to self-improve without external supervision.Built on the Llama3.2-Vision-11B model, \emph{Sherlock Iter2} achieves remarkable results across eight benchmarks, reaching an average accuracy of 64.1 with direct generation and 65.4 after self-correction. It outperforms LLaVA-CoT (63.2), Mulberry (60.4), and LlamaV-o1 (63.4) while only using less than 20\% of the annotated data.
|
poster
|
2505.22651
|
https://github.com/DripNowhy/Sherlock
|
https://dripnowhy.github.io/Sherlock/
|
[] |
[
"Tuwhy/Llama-3.2V-11B-Sherlock-iter2",
"Tuwhy/Llama-3.2V-11B-Sherlock-iter1",
"Tuwhy/Llama-3.2V-11B-Sherlock-Offline",
"Tuwhy/Llama-3.2V-11B-Sherlock-SFT"
] |
[] |
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] |
199
|
Role-aware Multi-agent Reinforcement Learning for Coordinated Emergency Traffic Control
|
[
"Ming Cheng",
"Hao Chen",
"Zhiqing Li",
"Jia Wang",
"Senzhang Wang"
] |
Emergency traffic control presents an increasingly critical challenge, requiring seamless coordination among emergency vehicles, regular vehicles, and traffic lights to ensure efficient passage for all vehicles. Existing models primarily only focus on traffic light control, leaving emergency and regular vehicles prone to delay due to the lack of navigation strategies. To address this issue, we propose the ***R*ole-aware *M*ulti-agent *T*raffic *C*ontrol (RMTC)** framework, which dynamically assigns appropriate roles to traffic components for better cooperation by considering their relations with emergency vehicles and adaptively adjusting their policies. Specifically, RMTC introduces a *Heterogeneous Temporal Traffic Graph (HTTG)* to model the spatial and temporal relationships among all traffic components (traffic lights, regular and emergency vehicles) at each time step. Furthermore, we develop a *Dynamic Role Learning* model to infer the evolving roles of traffic lights and regular vehicles based on HTTG. Finally, we present a *Role-aware Multi-agent Reinforcement Learning* approach that learns traffic policies conditioned on the dynamically roles. Extensive experiments across four public traffic scenarios show that RMTC outperforms existing traffic light control methods by significantly reducing emergency vehicle travel time, while effectively preserving traffic efficiency for regular vehicles. The code is released at [https://anonymous.4open.science/r/RMTC-5E28](https://anonymous.4open.science/r/RMTC-5E28).
|
poster
| null | null | null |
[] |
[] |
[] |
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