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200
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Uncertainty Quantification for LLMs through Minimum Bayes Risk: Bridging Confidence and Consistency
|
[
"Roman Vashurin",
"Maiya Goloburda",
"Albina Ilina",
"Aleksandr Rubashevskii",
"Preslav Nakov",
"Artem Shelmanov",
"Maxim Panov"
] |
Uncertainty quantification (UQ) methods for Large Language Models (LLMs) encompass a variety of approaches, with two major types being particularly prominent: information-based, which focus on model confidence expressed as token probabilities, and consistency-based, which assess the semantic relationship between multiple outputs generated using repeated sampling. Several recent methods have combined these two approaches to boost UQ performance. However, they sometimes fail to outperform much simpler baseline methods. Our work discusses the fundamental approach to constructing uncertainty measures that directly links uncertainty with the minimum Bayes risks achieved by LLM decoding. Building on these findings, we propose a novel approach to integrating model confidence with output consistency, resulting in a family of efficient and robust UQ methods. We evaluate our approach across various tasks such as question answering, abstractive summarization, and machine translation, demonstrating sizable improvements over state-of-the-art UQ approaches.
|
poster
|
2502.04964
| null | null |
[] |
[] |
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] |
201
|
Hierachical Balance Packing: Towards Efficient Supervised Fine-tuning for Long-Context LLM
|
[
"Yongqiang Yao",
"Jingru Tan",
"Kaihuan Liang",
"Feizhao Zhang",
"Jiahao Hu",
"Yazhe Niu",
"Shuo Wu",
"Ruihao Gong",
"Dahua Lin",
"Ningyi Xu"
] |
Training Long-Context Large Language Models (LLMs) is challenging, as hybrid training with long-context and short-context data often leads to workload imbalances. Existing works mainly use data packing to alleviate this issue, but fail to consider imbalanced attention computation and wasted communication overhead. This paper proposes Hierarchical Balance Packing (HBP), which designs a novel batch-construction method and training recipe to address those inefficiencies. In particular, the HBP constructs multi-level data packing groups, each optimized with a distinct packing length. It assigns training samples to their optimal groups and configures each group with the most effective settings, including sequential parallelism degree and gradient checkpointing configuration. To effectively utilize multi-level groups of data, we design a dynamic training pipeline specifically tailored to HBP, including curriculum learning, adaptive sequential parallelism, and stable loss. Our extensive experiments demonstrate that our method significantly reduces training time over multiple datasets and open-source models while maintaining strong performance. For the largest DeepSeek-V2 (236B) MOE model, our method speeds up the training by 2.4$\times$ with competitive performance.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
202
|
EventMG: Efficient Multilevel Mamba-Graph Learning for Spatiotemporal Event Representation
|
[
"Sheng Wu",
"Lin Jin",
"Hui Feng",
"Bo Hu"
] |
Event cameras offer unique advantages in scenarios involving high speed, low light, and high dynamic range, yet their asynchronous and sparse nature poses significant challenges to efficient spatiotemporal representation learning. Specifically, despite notable progress in the field, effectively modeling the full spatiotemporal context, selectively attending to salient dynamic regions, and robustly adapting to the variable density and dynamic nature of event data remain key challenges. Motivated by these challenges, this paper proposes EventMG, a lightweight, efficient, multilevel Mamba-Graph architecture designed for learning high-quality spatiotemporal event representations. EventMG employs a multilevel approach, jointly modeling information at the micro (single event) and macro (event cluster) levels to comprehensively capture the multi-scale characteristics of event data. At the micro-level, it focuses on spatiotemporal details, employing State Space Model (SSM) based Mamba, to precisely capture long-range dependencies among numerous event nodes. Concurrently, at the macro-level, Component Graphs are introduced to efficiently encode the local semantics and global topology of dense event regions. Furthermore, to better accommodate the dynamic and sparse characteristics of data, we propose the Spatiotemporal-aware Event Scanning Technology (SEST), integrating the Attention-based Perturbation Network (APN) and Multidirectional Scanning Module (MSM), which substantially enhances the model's ability to perceive and focus on key spatiotemporal patterns. Experimental results demonstrate that EventMG achieves performance comparable or superior to state-of-the-art heavyweight models across multiple benchmarks, while maintaining an extremely low parameter count and linear complexity. This validates the effectiveness of the proposed architecture for efficient, multilevel spatiotemporal representation of event data. The code will be made publicly available upon acceptance.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
203
|
Exploring and Leveraging Class Vectors for Classifier Editing
|
[
"Jaeik Kim",
"Jaeyoung Do"
] |
Image classifiers play a critical role in detecting diseases in medical imaging and identifying anomalies in manufacturing processes. However, their predefined behaviors after extensive training make post hoc model editing difficult, especially when it comes to forgetting specific classes or adapting to distribution shifts. Existing classifier editing methods either focus narrowly on correcting errors or incur extensive retraining costs, creating a bottleneck for flexible editing. Moreover, such editing has seen limited investigation in image classification. To overcome these challenges, we introduce class vectors, which capture class-specific representation adjustments during fine-tuning. Whereas task vectors encode task-level changes in weight space, class vectors disentangle each class’s adaptation in the latent space. We show that class vectors capture each class’s semantic shift and that classifier editing can be achieved either by steering latent features along these vectors or by mapping them into weight space to update the decision boundaries. We also demonstrate that the inherent linearity and orthogonality of class vectors support efficient, flexible, and high-level concept editing via simple class arithmetic. Finally, we validate their utility in applications such as unlearning, environmental adaptation, adversarial defense, and adversarial trigger optimization.
|
poster
|
2510.11268
| null | null |
[] |
[] |
[] |
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] |
204
|
Semantic Retrieval Augmented Contrastive Learning for Sequential Recommendation
|
[
"Ziqiang Cui",
"Yunpeng Weng",
"Xing Tang",
"Xiaokun Zhang",
"Shiwei Li",
"Peiyang Liu",
"Bowei He",
"Dugang Liu",
"Weihong Luo",
"Xiuqiang He",
"Chen Ma"
] |
Contrastive learning has shown effectiveness in improving sequential recommendation models. However, existing methods still face challenges in generating high-quality contrastive pairs: they either rely on random perturbations that corrupt user preference patterns or depend on sparse collaborative data that generates unreliable contrastive pairs. Furthermore, existing approaches typically require predefined selection rules that impose strong assumptions, limiting the model's ability to autonomously learn optimal contrastive pairs. To address these limitations, we propose a novel approach named Semantic Retrieval Augmented Contrastive Learning (SRA-CL). SRA-CL leverages the semantic understanding and reasoning capabilities of LLMs to generate expressive embeddings that capture both user preferences and item characteristics. These semantic embeddings enable the construction of candidate pools for inter-user and intra-user contrastive learning through semantic-based retrieval. To further enhance the quality of the contrastive samples, we introduce a learnable sample synthesizer that optimizes the contrastive sample generation process during model training. SRA-CL adopts a plug-and-play design, enabling seamless integration with existing sequential recommendation architectures. Extensive experiments on four public datasets demonstrate the effectiveness and model-agnostic nature of our approach. Our code is available at https://anonymous.4open.science/r/SRA-CL/.
|
poster
|
2503.04162
| null | null |
[] |
[] |
[] |
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] |
205
|
WeatherPrompt: Multi-modality Representation Learning for All-Weather Drone Visual Geo-Localization
|
[
"Jiahao Wen",
"Hang Yu",
"Zhedong Zheng"
] |
Visual geo-localization for drones faces critical degradation under weather perturbations, \eg, rain and fog, where existing methods struggle with two inherent limitations: 1) Heavy reliance on limited weather categories that constrain generalization, and 2) Suboptimal disentanglement of entangled scene-weather features through pseudo weather categories.We present WeatherPrompt, a multi-modality learning paradigm that establishes weather-invariant representations through fusing the image embedding with the text context. Our framework introduces two key contributions: First, a Training-free Weather Reasoning mechanism that employs off-the-shelf large multi-modality models to synthesize multi-weather textual descriptions through human-like reasoning. It improves the scalability to unseen or complex weather, and could reflect different weather strength. Second, to better disentangle the scene and weather feature, we propose a multi-modality framework with the dynamic gating mechanism driven by the text embedding to adaptively reweight and fuse visual features across modalities. The framework is further optimized by the cross-modal objectives, including image-text contrastive learning and image-text matching, which maps the same scene with different weather conditions closer in the respresentation space. Extensive experiments validate that, under diverse weather conditions, our method achieves competitive recall rates compared to state-of-the-art drone geo-localization methods. Notably, it improves Recall@1 by +13.37\% under night conditions and by 18.69\% under fog and snow conditions.
|
poster
|
2508.09560
| null | null |
[] |
[] |
[] |
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] |
206
|
Continuous Simplicial Neural Networks
|
[
"Aref Einizade",
"Dorina Thanou",
"Fragkiskos Malliaros",
"Jhony H. Giraldo"
] |
Simplicial complexes provide a powerful framework for modeling high-order interactions in structured data, making them particularly suitable for applications such as trajectory prediction and mesh processing. However, existing simplicial neural networks (SNNs), whether convolutional or attention-based, rely primarily on discrete filtering techniques, which can be restrictive. In contrast, partial differential equations (PDEs) on simplicial complexes offer a principled approach to capture continuous dynamics in such structures. In this work, we introduce continuous sim}plicial neural networks (COSIMO), a novel SNN architecture derived from PDEs on simplicial complexes. We provide theoretical and experimental justifications of COSIMO's stability under simplicial perturbations. Furthermore, we investigate the over-smoothing phenomenon—a common issue in geometric deep learning—demonstrating that COSIMO offers better control over this effect than discrete SNNs. Our experiments on real-world datasets demonstrate that COSIMO achieves competitive performance compared to state-of-the-art SNNs in complex and noisy environments.
|
poster
|
2503.12919
| null | null |
[] |
[] |
[] |
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] |
207
|
Controllable 3D Molecular Generation for Structure-Based Drug Design Through Bayesian Flow Networks and Gradient Integration
|
[
"Seungyeon Choi",
"Hwanhee Kim",
"Chihyun Park",
"Dahyeon Lee",
"Seungyong Lee",
"Yoonju Kim",
"Hyoungjoon Park",
"Sein Kwon",
"Youngwan Jo",
"Sanghyun Park"
] |
Recent advances in Structure-based Drug Design (SBDD) have leveraged generative models for 3D molecular generation, predominantly evaluating model performance by binding affinity to target proteins. However, practical drug discovery necessitates high binding affinity along with synthetic feasibility and selectivity, critical properties that were largely neglected in previous evaluations. To address this gap, we identify fundamental limitations of conventional diffusion-based generative models in effectively guiding molecule generation toward these diverse pharmacological properties. We propose $\texttt{CByG}$, a novel framework extending Bayesian Flow Network into a gradient-based conditional generative model that robustly integrates property-specific guidance. Additionally, we introduce a comprehensive evaluation scheme incorporating practical benchmarks for binding affinity, synthetic feasibility, and selectivity, overcoming the limitations of conventional evaluation methods. Extensive experiments demonstrate that our proposed $\texttt{CByG}$, framework significantly outperforms baseline models across multiple essential evaluation criteria, highlighting its effectiveness and practicality for real-world drug discovery applications.
|
poster
|
2508.21468
| null | null |
[] |
[] |
[] |
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] |
208
|
Beyond Higher Rank: Token-wise Input-Output Projections for Efficient Low-Rank Adaptation
|
[
"Shiwei Li",
"Xiandi Luo",
"Haozhao Wang",
"Xing Tang",
"Ziqiang Cui",
"Dugang Liu",
"Yuhua Li",
"Xiuqiang He",
"Ruixuan Li"
] |
Low-rank adaptation (LoRA) is a parameter-efficient fine-tuning (PEFT) method widely used in large language models (LLMs). LoRA essentially describes the projection of an input space into a low-dimensional output space, with the dimensionality determined by the LoRA rank.In standard LoRA, all input tokens share the same weights and undergo an identical input-output projection.This limits LoRA's ability to capture token-specific information due to the inherent semantic differences among tokens.To address this limitation, we propose **Token-wise Projected Low-Rank Adaptation (TopLoRA)**, which dynamically adjusts LoRA weights according to the input token, thereby learning token-wise input-output projections in an end-to-end manner.Formally, the weights of TopLoRA can be expressed as $B\Sigma_X A$, where $A$ and $B$ are low-rank matrices (as in standard LoRA), and $\Sigma_X$ is a diagonal matrix generated from each input token $X$.Notably, TopLoRA does not increase the rank of LoRA weights but achieves more granular adaptation by learning token-wise LoRA weights (i.e., token-wise input-output projections).Extensive experiments across multiple models and datasets demonstrate that TopLoRA consistently outperforms LoRA and its variants. The code is available at the anonymous repository https://anonymous.4open.science/r/TopLoRA.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
209
|
CoTAn: Numerically Stable and Efficient Framework for Context-Aware Low-Rank Approximation
|
[
"Uliana Parkina",
"Maxim Rakhuba"
] |
Recent studies suggest that context-aware low-rank approximation is a useful tool for compression and fine-tuning of modern large-scale neural networks.In this type of approximation, a norm is weighted by a matrix of input activations, significantly improving metrics over the unweighted case. Nevertheless, existing methods for neural networks suffer from numerical instabilities due to their reliance on classical formulas involving explicit Gram matrix computation and their subsequent inversion. We demonstrate that this can degrade the approximation quality or cause numerically singular matrices.To address these limitations, we propose a novel _inversion-free regularized framework_ that is based entirely on stable decompositions and overcomes the numerical pitfalls of prior art. Our method can handle all possible challenging scenarios: (1) when calibration matrices exceed GPU memory capacity, (2) when input activation matrices are nearly singular, and even (3) when insufficient data prevents unique approximation.For the latter, we prove that our solution converges to a desired approximation and derive explicit error bounds.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
210
|
Beyond Oracle: Verifier-Supervision for Instruction Hierarchy in Reasoning and Instruction-Tuned LLMs
|
[
"Sian-Yao Huang",
"Li-Hsien Chang",
"Che-Yu Lin",
"Cheng-Lin Yang"
] |
Large language models (LLMs) are often prompted with multi-level directives—such as system instructions and user queries—that imply a hierarchy of authority. Yet models frequently fail to enforce this structure, especially in multi-step reasoning where errors propagate across intermediate steps. Existing methods rely on oracle completions but lack verifiable reward signals or intermediate traces, limiting their applicability. We introduce a unified supervision framework that embeds programmatically verifiable checkers into synthesized instruction-conflict instances. Each instance pairs a compliance directive with a conflicting one, along with an executable verifier that deterministically checks output adherence. This enables alignment without oracle labels or reasoning traces, supporting both instruction-tuned and reasoning models. The framework is instantiated via a synthesis pipeline that includes unit-test–based validation, LLM-assisted repair, and a probabilistic analysis of cleaning reliability. Fine-tuning on the resulting data improves instruction hierarchy adherence and boosts safety robustness—generalizing to adversarial safety benchmarks without task-specific supervision. This highlights verifiable supervision as a scalable foundation for robust alignment.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
211
|
HubGT: Fast Graph Transformer with Decoupled Hierarchy Labeling
|
[
"Ningyi Liao",
"Zihao Yu",
"Siqiang Luo",
"Gao Cong"
] |
Graph Transformer (GT) has recently emerged as a promising neural network architecture for learning graph-structured data. However, its global attention mechanism with quadratic complexity concerning the graph scale prevents wider application to large graphs. Effectively representing graph information while ensuring learning efficiency remains challenging, as our analysis reveals that current GT designs targeting scalability still suffer from the computational bottleneck related to graph-scale operations. In this work, we tackle the GT scalability issue by proposing HubGT, a scalable Graph Transformer boosted by fully decoupled graph processing and simplified learning. HubGT represents the graph by a novel hierarchical scheme exploiting hub labels, which is shown to be more informative than plain adjacency by offering global connections while promoting locality, and is particularly suitable for handling complex graph patterns such as heterophily. We also design algorithms for efficiently constructing and querying the hub label hierarchy tailored for the GT attention training in scalable deployments. Notably, the precomputation and training processes of HubGT achieve complexities linear to the number of graph edges and nodes, respectively, while the training stage completely removes graph-related computations, leading to favorable mini-batch capability and GPU utilization. Extensive experiments demonstrate that HubGT is efficient in terms of computational enhancement and mini-batch capability over existing GT designs on large-scale benchmarks, while achieving top-tier effectiveness on both homophilous and heterophilous graphs.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
212
|
ROGR: Relightable 3D Objects using Generative Relighting
|
[
"Jiapeng Tang",
"Matthew Levine",
"Dor Verbin",
"Stephan Garbin",
"Matthias Niessner",
"Ricardo Martin Brualla",
"Pratul Srinivasan",
"Philipp Henzler"
] |
We introduce ROGR, a novel approach that reconstructs a relightable 3D model of an object captured from multiple views, driven by a generative relighting model that simulates the effects of placing the object under novel environment illuminations. Our method samples the appearance of the object under multiple lighting environments, creating a dataset that is used to train a lighting-conditioned Neural Radiance Field (NeRF) that outputs the object's appearance under any input environmental lighting. The lighting-conditioned NeRF uses a novel dual-branch architecture to encode the general lighting effects and specularities separately. The optimized lighting-conditioned NeRF enables efficient feed-forward relighting under arbitrary environment maps without requiring per-illumination optimization or light transport simulation. We evaluate our approach on the established TensoIR and Stanford-ORB datasets, where it improves upon the state-of-the-art on most metrics, and showcase our approach on real-world object captures.
|
spotlight
|
2510.03163
| null | null |
[] |
[] |
[] |
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] |
213
|
Deep Compositional Phase Diffusion for Long Motion Sequence Generation
|
[
"Ho Yin Au",
"Jie Chen",
"Junkun Jiang",
"Jingyu Xiang"
] |
Recent research on motion generation has shown significant progress in generating semantically aligned motion with singular semantics. However, when employing these models to create composite sequences containing multiple semantically generated motion clips, they often struggle to preserve the continuity of motion dynamics at the transition boundaries between clips, resulting in awkward transitions and abrupt artifacts. To address these challenges, we present Compositional Phase Diffusion, which leverages the Semantic Phase Diffusion Module (SPDM) and Transitional Phase Diffusion Module (TPDM) to progressively incorporate semantic guidance and phase details from adjacent motion clips into the diffusion process. Specifically, SPDM and TPDM operate within the latent motion frequency domain established by the pre-trained Action-Centric Motion Phase Autoencoder (ACT-PAE). This allows them to learn semantically important and transition-aware phase information from variable-length motion clips during training. Experimental results demonstrate the competitive performance of our proposed framework in generating compositional motion sequences that align semantically with the input conditions, while preserving phase transitional continuity between preceding and succeeding motion clips. Additionally, motion inbetweening task is made possible by keeping the phase parameter of the input motion sequences fixed throughout the diffusion process, showcasing the potential for extending the proposed framework to accommodate various application scenarios.
|
oral
|
2510.14427
| null | null |
[] |
[] |
[] |
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] |
214
|
Unraveling Metameric Dilemma for Spectral Reconstruction: A High-Fidelity Approach via Semi-Supervised Learning
|
[
"Xingxing Yang",
"Jie Chen",
"Zaifeng Yang"
] |
Spectral reconstruction from RGB images often suffers from a metameric dilemma, where distinct spectral distributions map to nearly identical RGB values, making them indistinguishable to current models and leading to unreliable reconstructions.In this paper, we present Diff-Spectra that integrates supervised physics-aware spectral estimation and unsupervised high-fidelity spectral regularization for HSI reconstruction.We first introduce an Adaptive illumiChroma Decoupling (AICD) module to decouple illumination and chrominance information, which learns intrinsic and distinctive feature distributions, thereby mitigating the metameric issue.Then, we incorporate the AICD into a learnable spectral response function (SRF) guided hyperspectral initial estimation mechanism to mimic the physical image formation and thus inject physics-aware reasoning into neural networks, turning an ill-posed problem into a constrained, interpretable task. We also introduce a metameric spectra augmentation method to synthesize comprehensive hyperspectral data to pre-train a Spectral Diffusion Module (SDM), which internalizes the statistical properties of real-world HSI data, enforcing unsupervised high-fidelity regularization on the spectral transitions via inner-loop optimization during inference.Extensive experimental evaluations demonstrate that our Diff-Spectra achieves SOTA performance on both Spectral reconstruction and downstream HSI classification. The code and dataset will be released.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
215
|
GenPO: Generative Diffusion Models Meet On-Policy Reinforcement Learning
|
[
"Shutong Ding",
"Ke Hu",
"Shan Zhong",
"Haoyang Luo",
"Weinan Zhang",
"Jingya Wang",
"Jun Wang",
"Ye Shi"
] |
Recent advances in reinforcement learning (RL) have demonstrated the powerful exploration capabilities and multimodality of generative diffusion-based policies. While substantial progress has been made in offline RL and off-policy RL settings, integrating diffusion policies into on-policy frameworks like PPO remains underexplored. This gap is particularly significant given the widespread use of large-scale parallel GPU-accelerated simulators, such as IsaacLab, which are optimized for on-policy RL algorithms and enable rapid training of complex robotic tasks. A key challenge lies in computing state-action log-likelihoods under diffusion policies, which is straightforward for Gaussian policies but intractable for flow-based models due to irreversible forward-reverse processes and discretization errors (e.g., Euler-Maruyama approximations). To bridge this gap, we propose GenPO, a generative policy optimization framework that leverages exact diffusion inversion to construct invertible action mappings. GenPO introduces a novel doubled dummy action mechanism that enables invertibility via alternating updates, resolving log-likelihood computation barriers. Furthermore, we also use the action log-likelihood for unbiased entropy and KL divergence estimation, enabling KL-adaptive learning rates and entropy regularization in on-policy updates. Extensive experiments on eight IsaacLab benchmarks, including legged locomotion (Ant, Humanoid, Anymal-D, Unitree H1, Go2), dexterous manipulation (Shadow Hand), aerial control (Quadcopter), and robotic arm tasks (Franka), demonstrate GenPO’s superiority over existing RL baselines. Notably, GenPO is the first method to successfully integrate diffusion policies into on-policy RL, unlocking their potential for large-scale parallelized training and real-world robotic deployment.
|
poster
|
2505.18763
| null | null |
[] |
[] |
[] |
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] |
216
|
Mesh-RFT: Enhancing Mesh Generation via Fine-grained Reinforcement Fine-Tuning
|
[
"Jian Liu",
"Jing Xu",
"Song Guo",
"Jing Li",
"Guojingfeng",
"Jiaao Yu",
"Haohan Weng",
"Biwen Lei",
"Xianghui Yang",
"Zhuo Chen",
"Fangqi Zhu",
"Tao Han",
"Chunchao Guo"
] |
Existing pretrained models for 3D mesh generation often suffer from data biases and produce low-quality results, while global reinforcement learning (RL) methods rely on object-level rewards that struggle to capture local structure details. To address these challenges, we present $\textbf{Mesh-RFT}$, a novel fine-grained reinforcement fine-tuning framework that employs Masked Direct Preference Optimization (M-DPO) to enable localized refinement via quality-aware face masking. To facilitate efficient quality evaluation, we introduce an objective topology-aware scoring system to evaluate geometric integrity and topological regularity at both object and face levels through two metrics: Boundary Edge Ratio (BER) and Topology Score (TS). By integrating these metrics into a fine-grained RL strategy, Mesh-RFT becomes the first method to optimize mesh quality at the granularity of individual faces, resolving localized errors while preserving global coherence. Experiment results show that our M-DPO approach reduces Hausdorff Distance (HD) by 24.6\% and improves Topology Score (TS) by 3.8\% over pre-trained models, while outperforming global DPO methods with a 17.4\% HD reduction and 4.9\% TS gain. These results demonstrate Mesh-RFT’s ability to improve geometric integrity and topological regularity, achieving new state-of-the-art performance in production-ready mesh generation.
|
spotlight
| null | null | null |
[] |
[] |
[] |
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] |
217
|
Conditioning Matters: Training Diffusion Policies is Faster Than You Think
|
[
"Zibin Dong",
"Yicheng Liu",
"Yinchuan Li",
"Hang Zhao",
"Jianye Hao"
] |
Diffusion policies have emerged as a mainstream paradigm for building vision-language-action (VLA) models. Although they demonstrate strong robot control capabilities, their training efficiency remains suboptimal. In this work, we identify a fundamental challenge in conditional diffusion policy training: when generative conditions are hard to distinguish, the training objective degenerates into modeling the marginal action distribution, a phenomenon we term loss collapse. To overcome this, we propose Cocos, a simple yet general solution that modifies the source distribution in the conditional flow matching to be condition-dependent. By anchoring the source distribution around semantics extracted from condition inputs, Cocos encourages stronger condition integration and prevents the loss collapse. We provide theoretical justification and extensive empirical results across simulation and real-world benchmarks. Our method achieves faster convergence and higher success rates than existing approaches, matching the performance of large-scale pre-trained VLAs using significantly fewer gradient steps and parameters. Cocos is lightweight, easy to implement, and compatible with diverse policy architectures, offering a general-purpose improvement to diffusion policy training.
|
poster
|
2505.11123
| null | null |
[] |
[] |
[] |
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] |
218
|
GeRaF: Neural Geometry Reconstruction from Radio Frequency Signals
|
[
"Jiachen Lu",
"Hailan Shanbhag",
"Haitham Al Hassanieh"
] |
GeRaF is the first method to use neural implicit learning for near-range 3D geometry reconstruction from radio frequency (RF) signals. Unlike RGB or LiDAR-based methods, RF sensing can see through occlusion but suffers from low resolution and noise due to its lens-less imaging nature. While lenses in RGB imaging constrain sampling to 1D rays, RF signals propagate through the entire space, introducing significant noise and leading to cubic complexity in volumetric rendering. Moreover, RF signals interact with surfaces via specular reflections requiring fundamentally different modeling. To address these challenges, GeRaF (1) introduces filter-based rendering to suppress irrelevant signals, (2) implements a physics-based RF volumetric rendering pipeline, and (3) proposes a novel lens-less sampling and lens-less alpha blending strategy that makes full-space sampling feasible during training. By learning signed distance functions, reflectiveness, and signal power through MLPs and trainable parameters, GeRaF takes the first step towards reconstructing millimeter-level geometry from RF signals in real-world settings.
|
spotlight
| null | null | null |
[] |
[] |
[] |
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] |
219
|
COLA: Towards Efficient Multi-Objective Reinforcement Learning with Conflict Objective Regularization in Latent Space
|
[
"Pengyi Li",
"Jianye Hao",
"Yifu Yuan",
"Hongyao Tang",
"Zibin Dong",
"YAN ZHENG"
] |
Many real-world control problems require continual policy adjustments to balance multiple objectives, which requires the acquisition of high-quality policies to cover diverse preferences. Multi-Objective Reinforcement Learning (MORL) provides a general framework to solve such problems. However, current MORL methods suffer from high sample complexity, primarily due to the neglect of efficient knowledge sharing and conflicts in optimization with different preferences. To this end, this paper introduces a novel framework, Conflict Objective Regularization in Latent Space (COLA). To enable efficient knowledge sharing, COLA establishes a shared latent representation space for common knowledge, which can avoid redundant learning under different preferences. Besides, COLA introduces a regularization term for the value function to mitigate the negative effects of conflicting preferences on the value function approximation, thereby improving the accuracy of value estimation. Experimental results across various multi-objective continuous control tasks demonstrate the significant superiority of COLA over the state-of-the-art MORL baselines.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
220
|
UFM: A Simple Path towards Unified Dense Correspondence with Flow
|
[
"Yuchen Zhang",
"Nikhil Keetha",
"Chenwei Lyu",
"Bhuvan Jhamb",
"Yutian Chen",
"Yuheng Qiu",
"Jay Karhade",
"Shreyas Jha",
"Yaoyu Hu",
"Deva Ramanan",
"Sebastian Scherer",
"Wenshan Wang"
] |
Dense image correspondence is central to many applications, such as visual odometry, 3D reconstruction, object association, and re-identification. Historically, dense correspondence has been tackled separately for wide-baseline scenarios and optical flow estimation, despite the common goal of matching content between two images. In this paper, we develop a Unified Flow \& Matching model (UFM), which is trained on unified data for pixels that are co-visible in both source and target images. UFM uses a simple, generic transformer architecture that directly regresses the $(u,v)$ flow. It is easier to train and more accurate for large flows compared to the typical coarse-to-find cost volumes in prior work. UFM is 28\% more accurate than state-of-the-art flow methods (Unimatch), while also having 62\% less error and 6.7x faster than dense wide-baseline matchers (RoMa). UFM is the first to demonstrate that unified training can outperform specialized approaches across both domains. This result enables fast, general-purpose correspondence and opens new directions for multi-modal, long-range, and real-time correspondence tasks.
|
poster
|
2506.09278
|
https://github.com/UniFlowMatch/UFM
|
https://uniflowmatch.github.io/
|
[
"infinity1096/UFM"
] |
[
"infinity1096/UFM-Base",
"infinity1096/UFM-Refine",
"infinity1096/UFM-Base-980",
"infinity1096/UFM-Refine-336",
"infinity1096/UFM-Refine-980"
] |
[] |
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] |
221
|
Pretraining a Shared Q-Network for Data-Efficient Offline Reinforcement Learning
|
[
"Jongchan Park",
"Mingyu Park",
"Donghwan Lee"
] |
Offline reinforcement learning (RL) aims to learn a policy from a static dataset without further interactions with the environment. Collecting sufficiently large datasets for offline RL is exhausting since this data collection requires colossus interactions with environments and becomes tricky when the interaction with the environment is restricted. Hence, how an agent learns the best policy with a minimal static dataset is a crucial issue in offline RL, similar to the sample efficiency problem in online RL. In this paper, we propose a simple yet effective plug-and-play pretraining method to initialize a feature of a $Q$-network to enhance data efficiency in offline RL. Specifically, we introduce a shared $Q$-network structure that outputs predictions of the next state and $Q$-value. We pretrain the shared $Q$-network through a supervised regression task that predicts a next state and trains the shared $Q$-network using diverse offline RL methods. Through extensive experiments, we empirically demonstrate that our method enhances the performance of existing popular offline RL methods on the D4RL, Robomimic and V-D4RL benchmarks. Furthermore, we show that our method significantly boosts data-efficient offline RL across various data qualities and data distributions trough D4RL and ExoRL benchmarks. Notably, our method adapted with only 10\% of the dataset outperforms standard algorithms even with full datasets.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
222
|
Robust Policy Expansion for Offline-to-Online RL under Diverse Data Corruption
|
[
"Longxiang He",
"Deheng Ye",
"Junbo Tan",
"Xueqian Wang",
"Li Shen"
] |
Pretraining a policy on offline data followed by fine-tuning through online interactions, known as Offline-to-Online Reinforcement Learning (O2O RL), has emerged as a promising paradigm for real-world RL deployment. However, both offline datasets and online interactions in practical environments are often noisy or even maliciously corrupted, severely degrading the performance of O2O RL. Existing works primarily focus on mitigating the conservatism of offline policies via online exploration, while the robustness of O2O RL under data corruption, including states, actions, rewards, and dynamics, is still unexplored. In this work, we observe that data corruption induces heavy-tailed behavior in the policy, thereby substantially degrading the efficiency of online exploration. To address this issue, we incorporate Inverse Probability Weighted (IPW) into the online exploration policy to alleviate heavy-tailedness, and propose a novel, simple yet effective method termed $\textbf{RPEX}$: $\textbf{R}$obust $\textbf{P}$olicy $\textbf{EX}$pansion. Extensive experimental results on D4RL datasets demonstrate that RPEX achieves SOTA O2O performance across a wide range of data corruption scenarios.
|
poster
|
2509.24748
|
https://github.com/felix-thu/RPEX
| null |
[] |
[] |
[] |
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0.006467089522629976
] |
223
|
Value Improved Actor Critic Algorithms
|
[
"Yaniv Oren",
"Moritz Zanger",
"Pascal van der Vaart",
"Mustafa Mert Çelikok",
"Matthijs Spaan",
"Wendelin Boehmer"
] |
To learn approximately optimal acting policies for decision problems, modern Actor Critic algorithms rely on deep Neural Networks (DNNs) to parameterize the acting policy and greedification operators to iteratively improve it. The reliance on DNNs suggests an improvement that is gradient based, which is per step much less greedy than the improvement possible by greedier operators such as the greedy update used by Q-learning algorithms.On the other hand, slow and steady changes to the policy can also be beneficial for the stability of the learning process, resulting in a tradeoff between greedification and stability.To address this tradeoff, we propose to extend the standard framework of actor critic algorithms with value-improvement: a second greedification operator applied only when updating the policy's value estimate.In this framework the agent can evaluate non-parameterized policies and perform much greedier updates while maintaining the steady gradient-based improvement to the parameterized acting policy.We prove that this approach converges in the popular analysis scheme of generalized Policy Iteration in the finite-horizon domain.Empirically, incorporating value-improvement into the popular off-policy actor-critic algorithms TD3 and SAC significantly improves or matches performance over their respective baselines, across different environments from the DeepMind continuous control domain, with negligible compute and implementation cost.
|
poster
|
2406.01423
| null | null |
[] |
[] |
[] |
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] |
224
|
DiEP: Adaptive Mixture-of-Experts Compression through Differentiable Expert Pruning
|
[
"Sikai Bai",
"Jie ZHANG",
"Haoxi Li",
"Zicong Hong",
"Song Guo"
] |
Despite the significant breakthrough of Mixture-of-Experts (MoE), the increasing scale of these MoE models presents huge memory and storage challenges. Existing MoE pruning methods, which involve reducing parameter size with a uniform sparsity across all layers, often lead to suboptimal outcomes and performance degradation due to varying expert redundancy in different MoE layers. To address this, we propose a non-uniform pruning strategy, dubbed Differentiable Expert Pruning (DiEP), which adaptively adjusts pruning rates at the layer level while jointly learning inter-layer importance, effectively capturing the varying redundancy across different MoE layers. By transforming the global discrete search space into a continuous one, our method handles exponentially growing non-uniform expert combinations, enabling adaptive gradient-based pruning. Extensive experiments on five advanced MoE models demonstrate the efficacy of our method across various NLP tasks. Notably, \textbf{DiEP} retains around 92\% of original performance on Mixtral 8$\times$7B with only half the experts, outperforming other pruning methods by up to 7.1% on the challenging MMLU dataset.
|
poster
|
2509.16105
| null | null |
[] |
[] |
[] |
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] |
225
|
RoMA: Scaling up Mamba-based Foundation Models for Remote Sensing
|
[
"Fengxiang Wang",
"Yulin Wang",
"Di Wang",
"Haotian Wang",
"Mingshuo Chen",
"Hongzhen Wang",
"Haiyan Zhao",
"Yangang Sun",
"Shuo Wang",
"Long Lan",
"Wenjing Yang",
"Jing Zhang"
] |
Recent advances in self-supervised learning for Vision Transformers (ViTs) have fueled breakthroughs in remote sensing (RS) foundation models. However, the quadratic complexity of self-attention poses a significant barrier to scalability, particularly for large models and high-resolution images. While the linear-complexity Mamba architecture offers a promising alternative, existing RS applications of Mamba remain limited to supervised tasks on small, domain-specific datasets. To address these challenges, we propose RoMA, a framework that enables scalable self-supervised pretraining of Mamba-based RS foundation models using large-scale, diverse, unlabeled data. RoMA enhances scalability for high-resolution images through a tailored auto-regressive learning strategy, incorporating two key innovations: 1) a rotation-aware pretraining mechanism combining adaptive cropping with angular embeddings to handle sparsely distributed objects with arbitrary orientations, and 2) multi-scale token prediction objectives that address the extreme variations in object scales inherent to RS imagery. Systematic empirical studies validate that Mamba adheres to RS data and parameter scaling laws, with performance scaling reliably as model and data size increase. Furthermore, experiments across scene classification, object detection, and semantic segmentation tasks demonstrate that RoMA-pretrained Mamba models consistently outperform ViT-based counterparts in both accuracy and computational efficiency. The source code and pretrained models have be released in Anonymous Github.
|
poster
|
2503.10392
|
https://github.com/MiliLab/RoMA
| null |
[] |
[
"initiacms/RoMA"
] |
[] |
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] |
226
|
Unveiling the Spatial-temporal Effective Receptive Fields of Spiking Neural Networks
|
[
"Jieyuan (Eric) Zhang",
"Xiaolong Zhou",
"Wenjie Wei",
"Hanwen Liu",
"Qian Sun",
"Shuai Wang",
"Malu Zhang",
"Yang Yang",
"Haizhou Li"
] |
Spiking Neural Networks (SNNs) offer promising biological plausibility, energy efficiency, and event-driven processing for neuromorphic computing. Despite architectural and training advances, their complex spatiotemporal dynamics complicate understanding of intrinsic learning behaviors. We introduce Spatio-Temporal Effective Receptive Field (ST-ERF), extending traditional receptive field analysis to accommodate SNN temporal dynamics. Our analysis reveals two key ST-ERF properties: Gaussian-like spatial distributions and power-law temporal decay patterns. Furthermore, examination of Transformer-based SNN architectures reveals significant receptive field variations, with several prominent models (SDT-V1, Meta-SDT, QKFormer) showing limited global receptive fields that impair visual long-sequence modeling. Identifying the channel-mixer component as the critical factor, we propose two novel designs: MLPixer and Splash-and-reconstruct Block (SR Block). These innovations enhance global spatial receptive fields in early network stages, significantly improving performance on challenging visual tasks. Experimental results validate our theoretical findings and demonstrate the effectiveness of our proposed architectures, advancing both theoretical understanding and practical applications of SNNs in complex visual processing.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
227
|
Object Concepts Emerge from Motion
|
[
"Haoqian Liang",
"Xiaohui Wang",
"Zhichao Li",
"Ya Yang",
"Naiyan Wang"
] |
Object concepts play a foundational role in human visual cognition, enabling perception, memory, and interaction in the physical world. Inspired by findings in developmental neuroscience—where infants are shown to acquire object understanding through observation of motion—we propose a biologically inspired framework for learning object-centric visual representations in an unsupervised manner.Our key insight is that motion boundary serves as a strong signal for object-level grouping, which can be used to derive pseudo instance supervision from raw videos. Concretely, we generate motion-based instance masks using off-the-shelf optical flow and clustering algorithms, and use them to train visual encoders via contrastive learning. Our framework is fully label-free and does not rely on camera calibration, making it scalable to large-scale unstructured video data.We evaluate our approach on three downstream tasks spanning both low-level (monocular depth estimation) and high-level (3D object detection and occupancy prediction) vision. Our models outperform previous supervised and self-supervised baselines and demonstrate strong generalization to unseen scenes. These results suggest that motion-induced object representations offer a compelling alternative to existing vision foundation models, capturing a crucial but overlooked level of abstraction: the visual instance.The corresponding code will be released upon paper acceptance.
|
poster
|
2505.21635
| null | null |
[] |
[] |
[] |
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] |
228
|
Optimism Without Regularization: Constant Regret in Zero-Sum Games
|
[
"John Lazarsfeld",
"Georgios Piliouras",
"Ryann Sim",
"Stratis Skoulakis"
] |
This paper studies the *optimistic* variant of Fictitious Play for learning in two-player zero-sum games. While it is known that Optimistic FTRL -- a *regularized* algorithm with a bounded stepsize parameter -- obtains constant regret in this setting, we show for the first time that similar, optimal rates are also achievable *without* regularization: we prove for two-strategy games that Optimistic Fictitious Play (using *any* tiebreaking rule) obtains only *constant regret*, providing surprising new evidence on the ability of *non*-no-regret algorithms for fast learning in games. Our proof technique leverages a geometric view of Optimistic Fictitious Play in the dual space of payoff vectors, where we show a certain *energy function* of the iterates remains bounded over time. Additionally, we also prove a regret *lower bound* of $\Omega(\sqrt{T})$ for *Alternating* Fictitious Play. In the unregularized regime, this separates the ability of optimism and alternation in achieving $o(\sqrt{T})$ regret.
|
poster
|
2506.16736
| null | null |
[] |
[] |
[] |
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] |
229
|
Learning to Insert for Constructive Neural Vehicle Routing Solver
|
[
"Fu Luo",
"Xi Lin",
"Mengyuan Zhong",
"Fei Liu",
"Zhenkun Wang",
"Jianyong Sun",
"Qingfu Zhang"
] |
Neural Combinatorial Optimisation (NCO) is a promising learning-based approach for solving Vehicle Routing Problems (VRPs) without extensive manual design. While existing constructive NCO methods typically follow an appending-based paradigm that sequentially adds unvisited nodes to partial solutions, this rigid approach often leads to suboptimal results. To overcome this limitation, we explore the idea of insertion-based paradigm and propose Learning to Construct with Insertion-based Paradigm (L2C-Insert), a novel learning-based method for constructive NCO. Unlike traditional approaches, L2C-Insert builds solutions by strategically inserting unvisited nodes at any valid position in the current partial solution, which can significantly enhance the flexibility and solution quality. The proposed framework introduces three key components: a novel model architecture for precise insertion position prediction, an efficient training scheme for model optimization, and an advanced inference technique that fully exploits the insertion paradigm's flexibility. Extensive experiments on both synthetic and real-world instances of the Travelling Salesman Problem (TSP) and Capacitated Vehicle Routing Problem (CVRP) demonstrate that L2C-Insert consistently achieves superior performance across various problem sizes.
|
poster
|
2505.13904
| null | null |
[] |
[] |
[] |
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] |
230
|
GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large Language Models to 8K Resolution
|
[
"Fengxiang Wang",
"Mingshuo Chen",
"Yueying Li",
"Di Wang",
"Haotian Wang",
"Zonghao Guo",
"Zefan Wang",
"Shan Boqi",
"Long Lan",
"Yulin Wang",
"Hongzhen Wang",
"Wenjing Yang",
"Bo Du",
"Jing Zhang"
] |
Ultra-high-resolution (UHR) remote sensing (RS) imagery offers valuable data for Earth observation but pose challenges for existing multimodal foundation models due to two key bottlenecks: (1) limited availability of UHR training data, and (2) token explosion caused by the large image size. To address data scarcity, we introduce **SuperRS-VQA** (avg. 8,376$\times$8,376) and **HighRS-VQA** (avg. 2,000$\times$1,912), the highest-resolution vision-language datasets in RS to date, covering 22 real-world dialogue tasks. To mitigate token explosion, our pilot studies reveal significant redundancy in RS images: crucial information is concentrated in a small subset of object-centric tokens, while pruning background tokens (e.g., ocean or forest) can even improve performance.Motivated by these findings, we propose two strategies: *Background Token Pruning* and *Anchored Token Selection*, to reduce the memory footprint while preserving key semantics.Integrating these techniques, we introduce **GeoLLaVA-8K**, the first RS-focused multimodal large language model capable of handling inputs up to 8K$\times$8K resolution, built on the LLaVA framework. Trained on SuperRS-VQA and HighRS-VQA, GeoLLaVA-8K sets a new state-of-the-art on the XLRS-Bench. Datasets and code will be released.
|
spotlight
| null | null | null |
[] |
[] |
[] |
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] |
231
|
Gradient-Guided Epsilon Constraint Method for Online Continual Learning
|
[
"Song Lai",
"Changyi Ma",
"Fei Zhu",
"Zhe Zhao",
"Xi Lin",
"GAOFENG MENG",
"Qingfu Zhang"
] |
Online Continual Learning (OCL) requires models to learn sequentially from data streams with limited memory. Rehearsal-based methods, particularly Experience Replay (ER), are commonly used in OCL scenarios. This paper revisits ER through the lens of $\epsilon$-constraint optimization, revealing that ER implicitly employs a soft constraint on past task performance, with its weighting parameter post-hoc defining a slack variable. While effective, ER's implicit and fixed slack strategy has limitations: it can inadvertently lead to updates that negatively impact generalization, and its fixed trade-off between plasticity and stability may not optimally balance current streaming with memory retention, potentially overfitting to the memory buffer. To address these shortcomings, we propose the \textbf{G}radient-Guided \textbf{E}psilon \textbf{C}onstraint (\textbf{GEC}) method for online continual learning. GEC explicitly formulates the OCL update as an $\epsilon$-constraint optimization problem, which minimize the loss on the current task data and transform the stability objective as constraints and propose a gradient-guided method to dynamically adjusts the update direction based on whether the performance on memory samples violates a predefined slack tolerance $\bar{\varepsilon}$: if forgetting exceeds this tolerance, GEC prioritizes constraint satisfaction; otherwise, it focuses on the current task while controlling the rate of increase in memory loss. Empirical evaluations on standard OCL benchmarks would demonstrate GEC's ability to achieve a superior trade-off, leading to improved overall performance.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
232
|
Why Popular MOEAs are Popular: Proven Advantages in Approximating the Pareto Front
|
[
"Mingfeng Li",
"Qiang Zhang",
"Weijie Zheng",
"Benjamin Doerr"
] |
Recent breakthroughs in the analysis of multi-objective evolutionary algorithms (MOEAs) are mathematical runtime analyses of those algorithms which are intensively used in practice. So far, most of these results show the same performance as previously known for simple algorithms like the GSEMO. The few results indicating advantages of the popular MOEAs share the same shortages: They consider the performance for the problem of computing the full Pareto front, (of some algorithms enriched with newly invented mechanisms,) and this on newly designed benchmarks. In this work, we overcome these shortcomings by analyzing how existing popular MOEAs approximate the Pareto front of the established LargeFront benchmark. We prove that all popular MOEAs, including NSGA-II (sequential version), NSGA-III, SMS-EMOA, and SPEA2, only need an expected time of $O(n^2 \log n)$ function evaluations to compute an additive $\varepsilon$-approximation of the Pareto front of the LargeFront benchmark. This contrasts with the already proven exponential runtime (with high probability) of the GSEMO on the same task. % (which we also extend to any dominance-only black-box MOEA). This result is the first mathematical runtime analysis showing and explaining the superiority of popular MOEAs over simple ones like the GSEMO for the central task of computing good approximations to the Pareto front.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
233
|
TS-MOF: Two-Stage Multi-Objective Fine-tuning for Long-Tailed Recognition
|
[
"Zhe Zhao",
"Zhiheng Gong",
"Pengkun Wang",
"HaiBin Wen",
"Cankun Guo",
"Bo Xue",
"Xi Lin",
"Zhenkun Wang",
"Qingfu Zhang",
"Yang Wang"
] |
Long-Tailed Recognition (LTR) presents a significant challenge due to extreme class imbalance, where existing methods often struggle to balance performance across head and tail classes. Directly applying multi-objective optimization (MOO) to leverage multiple LTR strategies can be complex and unstable. To address this, we propose TS-MOF (Two-Stage Multi-Objective Fine-tuning), a novel framework that strategically decouples feature learning from classifier adaptation. After standard pre-training, TS-MOF freezes the feature backbone and focuses on an efficient multi-objective fine-tuning of specialized classifier heads. The core of TS-MOF's second stage lies in two innovations: Refined Performance Level Agreement for adaptive task weighting based on real-time per-class performance, and Robust Deterministic Projective Conflict Gradient for stable gradient conflict resolution and constructive fusion. This approach enables effective synergy between diverse LTR strategies, leading to significant and balanced performance improvements. Extensive experiments on CIFAR100-LT, ImageNet-LT, and iNaturalist 2018 demonstrate that TS-MOF achieves state-of-the-art results, particularly enhancing tail class accuracy (e.g., +3.3\% on CIFAR100-LT IR=100 tail) while improving head class performance, all within a remarkably short fine-tuning period of 20 epochs.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
234
|
Novel Exploration via Orthogonality
|
[
"Andreas Theophilou",
"Özgür Şimşek"
] |
Discovering novel states or transitions efficiently requires effective policies or optionsthat direct the agent away from well-visited regions of the state space. This paper introducesa method, Novel Exploration via Orthogonality (NEO), that automatically uncoversnot only which state regions are novel but also how to reach them by leveraging Laplacian-basedrepresentations. Specifically, we use the eigenvectors of a modified graph Laplacian toinduce gradient flows from frequently visited (less novel) states to seldom visited (more novel)states. We show that modifying the Laplacian with weighted sinks yieldseigenvectors whose extreme values align with the most novel regions of the state space.We evaluate reinforcement learning agents with our option discovery method against related methods such as eigen-options and cover options in online, incremental settings. In summary we provide three key contributions, we provide bounds for our Laplacian matrices, we show that the smoothest eigenvectors with real eigenvalue below certain bounds provide guaranteed gradients to novel nodes for undirected and directed graphs, and we show that our option method NEO outperforms related state-of-the-art approaches in undirected and directed domains with varying structures.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
235
|
FedQS: Optimizing Gradient and Model Aggregation for Semi-Asynchronous Federated Learning
|
[
"Yunbo Li",
"Jiaping Gui",
"Zhihang Deng",
"Fanchao Meng",
"Yue Wu"
] |
Federated learning (FL) enables collaborative model training across multiple parties without sharing raw data, with semi-asynchronous FL (SAFL) emerging as a balanced approach between synchronous and asynchronous FL. However, SAFL faces significant challenges in optimizing both gradient-based (e.g., FedSGD) and model-based (e.g., FedAvg) aggregation strategies, which exhibit distinct trade-offs in accuracy, convergence speed, and stability. While gradient aggregation achieves faster convergence and higher accuracy, it suffers from pronounced fluctuations, whereas model aggregation offers greater stability but slower convergence and suboptimal accuracy. This paper presents FedQS, the first framework to theoretically analyze and address these disparities in SAFL. FedQS introduces a *divide-and-conquer strategy* to handle client heterogeneity by classifying clients into four distinct types and adaptively optimizing their local training based on data distribution characteristics and available computational resources. Extensive experiments on computer vision, natural language processing, and real-world tasks demonstrate that FedQS achieves the highest accuracy, attains the lowest loss, and ranks among the fastest in convergence speed, outperforming state-of-the-art baselines. Our work bridges the gap between aggregation strategies in SAFL, offering a unified solution for stable, accurate, and efficient federated learning. The code and datasets are available at https://anonymous.4open.science/r/FedQS-EDD6.
|
poster
|
2510.07664
| null | null |
[] |
[] |
[] |
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] |
236
|
FORLA:Federated Object-centric Representation Learning with Slot Attention
|
[
"Guiqiu Liao",
"Matjaz Jogan",
"Eric Eaton",
"Daniel Hashimoto"
] |
Learning efficient visual representations across heterogeneous unlabeled datasets remains a central challenge in federated learning. Effective federated representations require features that are jointly informative across clients while disentangling domain-specific factors without supervision. We introduce FORLA, a novel framework for federated object-centric representation learning and feature adaptation across clients using unsupervised slot attention. At the core of our method is a shared feature adapter, trained collaboratively across clients to adapt features from foundation models, and a shared slot attention module that learns to reconstruct the adapted features. To optimize this adapter, we design a two-branch student–teacher architecture. In each client, a student decoder learns to reconstruct full features from foundation models, while a teacher decoder reconstructs their adapted, low-dimensional counterpart. The shared slot attention module bridges cross-domain learning by aligning object-level representations across clients. Experiments in multiple real-world datasets show that our framework not only outperforms centralized baselines on object discovery but also learns a compact, universal representation that generalizes well across domains. This work highlights federated slot attention as an effective tool for scalable, unsupervised visual representation learning from cross-domain data with distributed concepts.
|
poster
|
2506.02964
| null | null |
[] |
[] |
[] |
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] |
237
|
Latent Policy Barrier: Learning Robust Visuomotor Policies by Staying In-Distribution
|
[
"Zhanyi Sun",
"Shuran Song"
] |
Visuomotor policies trained via behavior cloning are vulnerable to covariate shift, where small deviations from expert trajectories can compound into failure. Common strategies to mitigate this issue involve expanding the training distribution through human-in-the-loop corrections or synthetic data augmentation. However, these approaches are often labor-intensive, rely on strong task assumptions, or compromise the quality of imitation. We introduce Latent Policy Barrier, a framework for robust visuomotor policy learning. Inspired by Control Barrier Functions, LPB treats the latent embeddings of expert demonstrations as an implicit barrier separating safe, in-distribution states from unsafe, out-of-distribution (OOD) ones. Our approach decouples the role of precise expert imitation and OOD recovery into two separate modules: a base diffusion policy solely on expert data, and a dynamics model trained on both expert and suboptimal policy rollout data. At inference time, the dynamics model predicts future latent states and optimizes them to stay within the expert distribution. Both simulated and real-world experiments show that LPB improves both policy robustness and data efficiency, enabling reliable manipulation from limited expert data and without additional human correction or annotation. More details are on our anonymous project website https://latentpolicybarrier.github.io.
|
spotlight
|
2508.05941
| null | null |
[] |
[] |
[] |
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] |
238
|
Scaling Offline RL via Efficient and Expressive Shortcut Models
|
[
"Nicolas Espinosa-Dice",
"Yiyi Zhang",
"Yiding Chen",
"Bradley Guo",
"Owen Oertell",
"Gokul Swamy",
"Kianté Brantley",
"Wen Sun"
] |
Diffusion and flow models have emerged as powerful generative approaches capable of modeling diverse and multimodal behavior. However, applying these models to offline RL remains challenging due to the iterative nature of their noise sampling processes, making policy optimization difficult. In this paper, we introduce Scalable Offline Reinforcement Learning (SORL), a new offline RL algorithm that leverages shortcut models – a novel class of generative models – to scale both training and inference. SORL's policy can capture complex data distributions and can be trained simply and efficiently in a one-stage training procedure. At test time, SORL supports both sequential and parallel inference scaling by using the learned Q-function as a verifier. We demonstrate that SORL achieves strong performance across a range of offline RL tasks and exhibits positive scaling behavior with increased test-time compute.
|
poster
|
2505.22866
| null | null |
[] |
[] |
[] |
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] |
239
|
Confusion-Driven Self-Supervised Progressively Weighted Ensemble Learning for Non-Exemplar Class Incremental Learning
|
[
"Kai Hu",
"Zhang Yu",
"Yuan Zhang",
"Zhineng Chen",
"Xieping Gao"
] |
Non-exemplar class incremental learning (NECIL) aims to continuously assimilate new knowledge while retaining previously acquired knowledge in scenarios where prior examples are unavailable. A prevalent strategy within NECIL mitigates knowledge forgetting by freezing the feature extractor after training on the initial task. However, this freezing mechanism does not provide explicit training to differentiate between new and old classes, resulting in overlapping feature representations. To address this challenge, we propose a **C**onfusion-driven se**L**f-supervised pr**O**gressi**V**ely weighted **E**nsemble lea**R**ning (*CLOVER*) framework for NECIL. Firstly, we introduce a confusion-driven self-supervised learning approach that enhances representation extraction by guiding the model to distinguish between highly confusable classes, thereby reducing class representation overlap. Secondly, we develop a progressively weighted ensemble learning method that gradually adjusts weights to integrate diverse knowledge more effectively, further minimizing representation overlap. Finally, extensive experiments demonstrate that our proposed method achieves state-of-the-art results on the CIFAR100, TinyImageNet, and ImageNet-Subset NECIL benchmarks.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
240
|
Robust Neural Rendering in the Wild with Asymmetric Dual 3D Gaussian Splatting
|
[
"CHENGQI LI",
"Zhihao Shi",
"Yangdi Lu",
"Wenbo He",
"Xiangyu Xu"
] |
3D reconstruction from in-the-wild images remains a challenging task due to inconsistent lighting conditions and transient distractors. Existing methods typically rely on heuristic strategies to handle the low-quality training data, which often struggle to produce stable and consistent reconstructions, frequently resulting in visual artifacts. In this work, we propose Asymmetric Dual 3DGS, a novel framework that leverages the stochastic nature of these artifacts: they tend to vary across different training runs due to minor randomness. Specifically, our method trains two 3D Gaussian Splatting (3DGS) models in parallel, enforcing a consistency constraint that encourages convergence on reliable scene geometry while suppressing inconsistent artifacts. To prevent the two models from collapsing into similar failure modes due to confirmation bias, we introduce a divergent masking strategy that applies two complementary masks: a multi-cue adaptive mask and a self-supervised soft mask, which leads to an asymmetric training process of the two models, reducing shared error modes. In addition, to improve the efficiency of model training, we introduce a lightweight variant called Dynamic EMA Proxy, which replaces one of the two models with a dynamically updated Exponential Moving Average (EMA) proxy, and employs an alternating masking strategy to preserve divergence. Extensive experiments on challenging real-world datasets demonstrate that our method consistently outperforms existing approaches while achieving high efficiency. Codes and trained models will be released.
|
spotlight
|
2506.03538
| null |
https://steveli88.github.io/AsymGS/
|
[] |
[] |
[] |
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] |
241
|
\textsc{Sneekdoor}: Stealthy Backdoor Attacks against Distribution Matching-based Dataset Condensation
|
[
"Song Ma",
"Dongyi Lv",
"He Yang",
"Wei Xi",
"Jizhong Zhao"
] |
Dataset condensation aims to synthesize compact yet informative datasets that retain the training efficacy of full-scale data, offering substantial gains in efficiency. Recent studies reveal that the condensation process can be vulnerable to backdoor attacks, where malicious triggers are injected into the condensation dataset, manipulating model behavior during inference. While prior approaches have made progress in balancing attack success rate and clean test accuracy, they often fall short in preserving stealthiness, especially in concealing the visual artifacts of condensed data or the perturbations introduced during inference. To address this challenge, we introduce \textsc{Sneakdoor}, which enhances stealthiness without compromising attack effectiveness. \textsc{Sneakdoor} exploits the inherent vulnerability of class decision boundaries and incorporates a generative module that constructs input-aware triggers aligned with local feature geometry, thereby minimizing detectability. This joint design enables the attack to remain imperceptible to both human inspection and statistical detection. Extensive experiments across multiple datasets demonstrate that \textsc{Sneakdoor} achieves a compelling balance among attack success rate, clean test accuracy, and stealthiness, substantially improving the invisibility of both the synthetic data and triggered samples while maintaining high attack efficacy.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
242
|
Gradient Descent as Loss Landscape Navigation: a Normative Framework for Deriving Learning Rules
|
[
"John Vastola",
"Samuel J Gershman",
"Kanaka Rajan"
] |
Learning rules—prescriptions for updating model parameters to improve performance—are typically assumed rather than derived. Why do some learning rules work better than others, and under what assumptions can a given rule be considered optimal? We propose a theoretical framework that casts learning rules as policies for navigating (partially observable) loss landscapes, and identifies optimal rules as solutions to an associated optimal control problem. A range of well-known rules emerge naturally within this framework under different assumptions: gradient descent from short-horizon optimization, momentum from longer-horizon planning, natural gradients from accounting for parameter space geometry, non-gradient rules from partial controllability, and adaptive optimizers like Adam from online Bayesian inference of loss landscape shape. We further show that continual learning strategies like weight resetting can be understood as optimal responses to task uncertainty. By unifying these phenomena under a single objective, our framework clarifies the computational structure of learning and offers a principled foundation for designing adaptive algorithms.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
243
|
A Smooth Sea Never Made a Skilled $\texttt{SAILOR}$: Robust Imitation via Learning to Search
|
[
"Arnav Kumar Jain",
"Vibhakar Mohta",
"Subin Kim",
"Atiksh Bhardwaj",
"Juntao Ren",
"Yunhai Feng",
"Sanjiban Choudhury",
"Gokul Swamy"
] |
The fundamental limitation of the behavioral cloning (BC) approach to imitation learning is that it only teaches an agent what the expert did at states the expert visited. This means that when a BC agent makes a mistake which takes them out of the support of the demonstrations, they often don't know how to recover from it. In this sense, BC is akin to *giving the agent the fish* -- giving them dense supervision across a narrow set of states -- rather than teaching them *to fish*: to be able to reason independently about achieving the expert's outcome even when faced with unseen situations at test-time. In response, we explore *learning to search* (L2S) from expert demonstrations, i.e. learning the components required to, at test time, plan to match expert outcomes, even after making a mistake. These include *(1)* a world model and *(2)* a reward model. We carefully ablate the set of algorithmic and design decisions required to combine these and other components for stable and sample/interaction-efficient learning of recovery behavior without additional human corrections. Across a dozen visual manipulation tasks from three benchmarks, our approach $\texttt{SAILOR}$ consistently out-performs state-of-the-art Diffusion Policies trained via BC on the same data. Furthermore, scaling up the amount of demonstrations used for BC by 5-10$\times$ still leaves a performance gap. We find that $\texttt{SAILOR}$ can identify nuanced failures and is robust to reward hacking.
|
spotlight
| null | null | null |
[] |
[] |
[] |
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244
|
PRIMT: Preference-based Reinforcement Learning with Multimodal Feedback and Trajectory Synthesis from Foundation Models
|
[
"Ruiqi Wang",
"Dezhong Zhao",
"Ziqin Yuan",
"Tianyu Shao",
"Guohua Chen",
"Dominic Kao",
"Sungeun Hong",
"Byung-Cheol Min"
] |
Preference-based reinforcement learning (PbRL) has emerged as a promising paradigm for teaching robots complex behaviors without reward engineering. However, its effectiveness is often limited by two critical challenges: the reliance on extensive human input and the inherent difficulties in resolving query ambiguity and credit assignment during reward learning. In this paper, we introduce PRIMT, a PbRL framework designed to overcome these challenges by leveraging foundation models (FMs) for multimodal synthetic feedback and trajectory synthesis. Unlike prior approaches that rely on single-modality FM evaluations, PRIMT employs a hierarchical neuro-symbolic fusion strategy, integrating the complementary strengths of vision-language models (VLMs) and large language models (LLMs) in evaluating robot behaviors for more reliable and comprehensive feedback. PRIMT also incorporates foresight trajectory generation to warm-start the trajectory buffer with bootstrapped samples, reducing early-stage query ambiguity, and hindsight trajectory augmentation for counterfactual reasoning with a causal auxiliary loss to improve credit assignment. We evaluate PRIMT on 2 locomotion and 6 manipulation tasks on various benchmarks, demonstrating superior performance over FM-based and scripted baselines. Website at https://sites.google.com/view/PRIMT.
|
oral
|
2509.15607
| null | null |
[] |
[] |
[] |
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] |
245
|
Deep RL Needs Deep Behavior Analysis: Exploring Implicit Planning by Model-Free Agents in Open-Ended Environments
|
[
"Riley Simmons-Edler",
"Ryan Badman",
"Felix Berg",
"Raymond Chua",
"John Vastola",
"Joshua Lunger",
"William Qian",
"Kanaka Rajan"
] |
Understanding the behavior of deep reinforcement learning (DRL) agents—particularly as task and agent sophistication increase—requires more than simple comparison of reward curves, yet standard methods for behavioral analysis remain underdeveloped in deep RL.We apply tools from neuroscience and ethology to study DRL agents in a novel, complex, partially observable environment, ForageWorld, designed to capture key aspects of real-world animal foraging—including sparse, depleting resource patches, predator threats, and spatially extended arenas.We use this environment as a platform for applying joint behavioral and neural analysis to agents, revealing detailed, quantitatively grounded insights into agent strategies, memory, and planning.Contrary to common assumptions, we find that model-free RNN-based DRL agents can exhibit structured, planning-like behavior purely through emergent dynamics—without requiring explicit memory modules or world models.Our results show that studying DRL agents like animals—analyzing them with neuroethology-inspired tools that reveal structure in both behavior and neural dynamics—uncovers rich structure in their learning dynamics that would otherwise remain invisible.We distill these tools into a general analysis framework that links core behavioral and representational features to diagnostic methods, which can be reused for a wide range of tasks.As agents grow more complex and autonomous, bridging neuroscience, cognitive science, and AI will be essential—not just for understanding their behavior, but for ensuring safe alignment and maximizing desirable behaviors that are hard to measure via reward.We show how this can be done by drawing on lessons from how biological intelligence is studied.
|
poster
|
2506.06981
| null | null |
[] |
[] |
[] |
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-0.02944689244031906
] |
246
|
STaRFormer: Semi-Supervised Task-Informed Representation Learning via Dynamic Attention-Based Regional Masking for Sequential Data
|
[
"Maximilian Forstenhäusler",
"Daniel Külzer",
"Christos Anagnostopoulos",
"Shameem Puthiya Parambath",
"Natascha Weber"
] |
Understanding user intent is essential for situational and context-aware decision-making. Motivated by a real-world scenario, this work addresses intent predictions of smart device users in the vicinity of vehicles by modeling sequential spatiotemporal data. However, in real-world scenarios, environmental factors and sensor limitations can result in non-stationary and irregularly sampled data, posing significant challenges. To address these issues, we propose STaRFormer, a Transformer-based approach that can serve as a universal framework for sequential modeling. STaRFormer utilizes a new dynamic attention-based regional masking scheme combined with a novel semi-supervised contrastive learning paradigm to enhance task-specific latent representations. Comprehensive experiments on 19 datasets varying in types (including non-stationary and irregularly sampled), domains, sequence lengths, training samples, and applications demonstrate the efficacy of STaRFormer. We achieve notable improvements over state-of-the-art approaches. Code and data are available via https://anonymous.4open.science/r/STaRFormer-78D8/.
|
poster
|
2504.10097
| null | null |
[] |
[] |
[] |
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] |
247
|
SwS: Self-aware Weakness-driven Problem Synthesis in Reinforcement Learning for LLM Reasoning
|
[
"Xiao Liang",
"Zhong-Zhi Li",
"Yeyun Gong",
"Yang Wang",
"Hengyuan Zhang",
"yelong shen",
"Ying Nian Wu",
"Weizhu Chen"
] |
Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective for training large language models (LLMs) on complex reasoning tasks, such as mathematical problem solving. A prerequisite for the scalability of RLVR is a high-quality problem set with precise and verifiable answers. However, the scarcity of well-crafted human-labeled math problems and limited-verification answers in existing distillation-oriented synthetic datasets limit their effectiveness in RL. Additionally, most problem synthesis strategies indiscriminately expand the problem set without considering the model’s capabilities, leading to low efficiency in generating useful questions. To mitigate this issue, we introduce a Self-aware Weakness-driven problem Synthesis framework (SwS) that systematically identifies model deficiencies and leverages them for problem augmentation. Specifically, we define weaknesses as questions that the model consistently fails to learn through its iterative sampling during RL training. We then extract the core concepts from these failure cases and synthesize new problems to strengthen the model's weak areas in subsequent augmented training, enabling it to focus on and gradually overcome its weaknesses. Without relying on external knowledge distillation, our framework enables robust generalization by empowering the model to self-identify and address its weaknesses in RL, yielding average performance gains of 10% and 7.7% on 7B and 32B models across eight mainstream reasoning benchmarks. Our code and data are available at https://anonymous.4open.science/r/SwS-E6F5/
|
poster
|
2506.08989
|
https://github.com/MasterVito/SwS
|
https://mastervito.github.io/MasterVito.SwS.github.io/
|
[] |
[] |
[
"MasterVito/SwS-Demo-Dataset"
] |
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] |
248
|
Learning Dynamics of RNNs in Closed-Loop Environments
|
[
"Yoav Ger",
"Omri Barak"
] |
Recurrent neural networks (RNNs) trained on neuroscience-inspired tasks offer powerful models of brain computation. However, typical training paradigms rely on open-loop, supervised settings, whereas real-world learning unfolds in closed-loop environments. Here, we develop a mathematical theory describing the learning dynamics of linear RNNs trained in closed-loop contexts. We first demonstrate that two otherwise identical RNNs, trained in either closed- or open-loop modes, follow markedly different learning trajectories. To probe this divergence, we analytically characterize the closed-loop case, revealing distinct stages aligned with the evolution of the training loss. Specifically, we show that the learning dynamics of closed-loop RNNs, in contrast to open-loop ones, are governed by an interplay between two competing objectives: short-term policy improvement and long-term stability of the agent-environment interaction. Finally, we apply our framework to a realistic motor control task, highlighting its broader applicability. Taken together, our results underscore the importance of modeling closed-loop dynamics in a biologically plausible setting.
|
poster
|
2505.13567
| null | null |
[] |
[] |
[] |
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] |
249
|
MINGLE: Mixtures of Null-Space Gated Low-Rank Experts for Test-Time Continual Model Merging
|
[
"Zihuan Qiu",
"Yi Xu",
"Chiyuan He",
"Fanman Meng",
"Linfeng Xu",
"Qingbo Wu",
"Hongliang Li"
] |
Continual model merging integrates independently fine-tuned models sequentially without access to original training data, providing a scalable and efficient solution to continual learning. However, current methods still face critical challenges, notably parameter interference among tasks and limited adaptability to evolving test distributions. The former causes catastrophic forgetting of integrated tasks, while the latter hinders effective adaptation to new tasks. To address these, we propose MINGLE, a novel framework for test-time continual model merging, which leverages test-time adaptation using a small set of unlabeled test samples from the current task to dynamically guide the merging process.MINGLE employs a mixture-of-experts architecture composed of parameter-efficient, low-rank experts, enabling efficient adaptation and improving robustness to distribution shifts.To mitigate catastrophic forgetting, we propose Null-Space Constrained Gating, which restricts gating updates to subspaces orthogonal to prior task representations. This suppresses activations on old task inputs and preserves model behavior on past tasks.To further balance stability and adaptability, we design an Adaptive Relaxation Strategy, which dynamically adjusts the constraint strength based on interference signals captured during test-time adaptation.Extensive experiments on standard continual merging benchmarks demonstrate that MINGLE achieves robust generalization, reduces forgetting significantly, and consistently surpasses previous state-of-the-art methods by 7-9\% on average across diverse task orders.
|
poster
|
2505.11883
|
https://github.com/zihuanqiu/MINGLE
| null |
[] |
[] |
[] |
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] |
250
|
Stealthy Yet Effective: Distribution-Preserving Backdoor Attacks on Graph Classification
|
[
"Xiaobao Wang",
"Ruoxiao Sun",
"Yujun Zhang",
"Bingdao Feng",
"Dongxiao He",
"Luzhi Wang",
"Di Jin"
] |
Graph Neural Networks (GNNs) have demonstrated strong performance across tasks such as node classification, link prediction, and graph classification, but remain vulnerable to backdoor attacks that implant imperceptible triggers during training to control predictions. While node-level attacks exploit local message passing, graph-level attacks face the harder challenge of manipulating global representations while maintaining stealth. We identify two main sources of anomaly in existing graph classification backdoor methods: structural deviation from rare subgraph triggers and semantic deviation caused by label flipping, both of which make poisoned graphs easily detectable by anomaly detection models. To address this, we propose DPSBA, a clean-label backdoor framework that learns in-distribution triggers via adversarial training guided by anomaly-aware discriminators. DPSBA effectively suppresses both structural and semantic anomalies, achieving high attack success while significantly improving stealth. Extensive experiments on real-world datasets validate that DPSBA achieves a superior balance between effectiveness and detectability compared to state-of-the-art baselines.
|
poster
|
2509.26032
| null | null |
[] |
[] |
[] |
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] |
251
|
PPMStereo: Pick-and-Play Memory Construction for Consistent Dynamic Stereo Matching
|
[
"WANG Yun",
"Qiaole Dong",
"Yongjian Zhang",
"Tin Lun Lam",
"Yanwei Fu",
"Dapeng Wu",
"Junjie Hu"
] |
Temporally consistent depth estimation from stereo video is critical for real-world applications such as augmented reality, where inconsistent depth estimation disrupts the immersion of users.Despite its importance, this task remains challenging due to the difficulty in modeling long-term temporal consistency in a computationally efficient manner.Previous methods attempt to address this by aggregating spatio-temporal information but face a fundamental trade-off: limited temporal modeling provides only modest gains, whereas capturing long-range dependencies significantly increases computational cost.To address this limitation, we introduce a memory buffer for modeling long-range spatio-temporal consistency while achieving efficient dynamic stereo matching.Inspired by the two-stage decision-making process in humans, we propose a Pick-and-Play Memory (PPM) construction module for dynamic Stereo matching, dubbed as PPMStereo. PPM consists of a pick process that identifies the most relevant frames and a play process that weights the selected frames adaptively for spatio-temporal aggregation.This two-stage collaborative process maintains a compact yet highly informative memory buffer while achieving temporally consistent information aggregation.Extensive experiments validate the effectiveness of PPMStereo, demonstrating state-of-the-art performance in both accuracy and temporal consistency.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
252
|
Towards Unified and Lossless Latent Space for 3D Molecular Latent Diffusion Modeling
|
[
"Yanchen Luo",
"ZHIYUAN LIU",
"Yi Zhao",
"Sihang Li",
"Hengxing Cai",
"Kenji Kawaguchi",
"Tat-Seng Chua",
"Yang Zhang",
"Xiang Wang"
] |
3D molecule generation is crucial for drug discovery and material science, requiring models to process complex multi-modalities, including atom types, chemical bonds, and 3D coordinates. A key challenge is integrating these modalities of different shapes while maintaining SE(3) equivariance for 3D coordinates. To achieve this, existing approaches typically maintain separate latent spaces for invariant and equivariant modalities, reducing efficiency in both training and sampling.In this work, we propose **U**nified Variational **A**uto-**E**ncoder for **3D** Molecular Latent Diffusion Modeling (**UAE-3D**), a multi-modal VAE that compresses 3D molecules into latent sequences from a unified latent space, while maintaining near-zero reconstruction error. This unified latent space eliminates the complexities of handling multi-modality and equivariance when performing latent diffusion modeling. We demonstrate this by employing the Diffusion Transformer--a general-purpose diffusion model without any molecular inductive bias--for latent generation. Extensive experiments on GEOM-Drugs and QM9 datasets demonstrate that our method significantly establishes new benchmarks in both *de novo* and conditional 3D molecule generation, achieving leading efficiency and quality.
|
poster
|
2503.15567
|
https://github.com/lyc0930/UAE-3D
| null |
[] |
[] |
[] |
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] |
253
|
FRBNet: Revisiting Low-Light Vision through Frequency-Domain Radial Basis Network
|
[
"Fangtong Sun",
"Congyu Li",
"Ke Yang",
"Hanwen Yu",
"Yuchen Pan",
"Xichuan Zhang",
"Yiying Li"
] |
Low-light vision remains a fundamental challenge in computer vision due to severe illumination degradation, which significantly affects the performance of downstream tasks such as detection and segmentation. While recent state-of-the-art methods have improved performance through invariant feature learning modules, they still fall short due to incomplete modeling of low-light conditions. Therefore, we revisit low-light image formation and extend the classical Lambertian model to better characterize low-light conditions. By shifting our analysis to the frequency domain, we theoretically prove that the frequency-domain channel ratio can be leveraged to extract illumination-invariant features via a structured filtering process. We then propose a novel and end-to-end trainable module named \textbf{F}requency-domain \textbf{R}adial \textbf{B}asis \textbf{Net}work (\textbf{FRBNet}), which integrates the frequency-domain channel ratio operation with a learnable frequency domain filter for the overall illumination-invariant feature enhancement. As a plug-and-play module, FRBNet can be integrated into existing networks for low-light downstream tasks without modifying loss functions. Extensive experiments across various downstream tasks demonstrate that FRBNet achieves superior performance, including +2.2 mAP for dark object detection and +2.9 mIoU for nighttime segmentation. Code is available at \href{https://anonymous.4open.science/r/FRBNet_Anony}{FRBNet\_anony}.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
254
|
Flow-Based Policy for Online Reinforcement Learning
|
[
"Lei Lyu",
"Yunfei Li",
"Yu Luo",
"Fuchun Sun",
"Tao Kong",
"Jiafeng Xu",
"Xiao Ma"
] |
We present $\textbf{FlowRL}$, a novel framework for online reinforcement learning that integrates flow-based policy representation with Wasserstein-2-regularized optimization. We argue that in addition to training signals, enhancing the expressiveness of the policy class is crucial for the performance gains in RL. Flow-based generative models offer such potential, excelling at capturing complex, multimodal action distributions. However, their direct application in online RL is challenging due to a fundamental objective mismatch: standard flow training optimizes for static data imitation, while RL requires value-based policy optimization through a dynamic buffer, leading to difficult optimization landscapes. FlowRL first models policies via a state-dependent velocity field, generating actions through deterministic ODE integration from noise. We derive a constrained policy search objective that jointly maximizes Q through the flow polciy while bounding the Wasserstein-2 distance to a behavior-optimal policy implicitly derived from the replay buffer. This formulation effectively aligns the flow optimization with the RL objective, enabling efficient and value-aware policy learning despite the complexity of the policy class. Empirical evaluations on DMControl and Humanoidbench demonstrate that FlowRL achieves competitive performance in online reinforcement learning benchmarks.
|
poster
|
2506.12811
| null | null |
[] |
[] |
[] |
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] |
255
|
Feed-Forward Bullet-Time Reconstruction of Dynamic Scenes from Monocular Videos
|
[
"hanxue liang",
"Jiawei Ren",
"Ashkan Mirzaei",
"Antonio Torralba",
"Ziwei Liu",
"Igor Gilitschenski",
"Sanja Fidler",
"Cengiz Oztireli",
"Huan Ling",
"Zan Gojcic",
"Jiahui Huang"
] |
Recent advancements in static feed-forward scene reconstruction have demonstrated significant progress in high-quality novel view synthesis. However, these models often struggle with generalizability across diverse environments and fail to effectively handle dynamic content. We present BTimer (short for Bullet Timer), the first motion-aware feed-forward model for real-time reconstruction and novel view synthesis of dynamic scenes. Our approach reconstructs the full scene in a 3D Gaussian Splatting representation at a given target (‘bullet’) timestamp by aggregating information from all the context frames. Such a formulation allows BTimer to gain scalability and generalization by leveraging both static and dynamic scene datasets. Given a casual monocular dynamic video, BTimer reconstructs a bullet-time scene within 150ms while reaching state-of-the-art performance on both static and dynamic scene datasets, even compared with optimization-based approaches.
|
poster
|
2412.03526
| null | null |
[] |
[] |
[] |
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256
|
Failure by Interference: Language Models Make Balanced Parentheses Errors When Faulty Mechanisms Overshadow Sound Ones
|
[
"Daking Rai",
"Samuel Miller",
"Kevin Moran",
"Ziyu Yao"
] |
Despite remarkable advances in coding capabilities, language models (LMs) still struggle with simple syntactic tasks such as generating balanced parentheses. In this study, we investigate the underlying mechanisms behind the persistence of these errors across LMs of varying sizes (124M–7B) to both understand and mitigate the errors. Our study reveals that LMs rely on a number of components (attention heads and FF neurons) that independently make their own predictions. While some components reliably promote correct answers across a generalized range of inputs (i.e., implementing "sound mechanisms''), others are less reliable and introduce noise by promoting incorrect tokens (i.e., implementing "faulty mechanisms''). Errors occur when the faulty mechanisms overshadow the sound ones and dominantly affect the predictions. Motivated by this insight, we introduce RASteer, a steering method to systematically identify and increase the contribution of reliable components for improving model performance. RASteer substantially improves performance on balanced parentheses tasks, boosting accuracy of some models from $0$\% to around $100$\% without impairing the models' general coding ability. We further demonstrate its broader applicability in arithmetic reasoning tasks, achieving performance gains of up to around $20$\%.
|
poster
|
2507.00322
| null | null |
[] |
[] |
[] |
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] |
257
|
The Quotient Bayesian Learning Rule
|
[
"Mykola Lukashchuk",
"Raphaël Trésor",
"Wouter Nuijten",
"Ismail Senoz",
"Bert Vries"
] |
This paper introduces the Quotient Bayesian Learning Rule, an extension of natural-gradient Bayesian updates to probability models that fall outside the exponential family. Building on the observation that many heavy-tailed and otherwise non-exponential distributions arise as marginals of minimal exponential families, we prove that such marginals inherit a unique Fisher–Rao information geometry via the quotient-manifold construction. Exploiting this geometry, we derive the Quotient Natural Gradient algorithm, which takes steepest-descent steps in the well-structured covering space and projects them horizontally onto the quotient, thereby guaranteeing parametrization-invariant optimization in the target space. Empirical results on the Student-$t$ distribution confirm that our method converges more rapidly and attains higher-quality solutions than previous variants of the Bayesian Learning Rule. These findings position quotient geometry as a unifying tool for efficient and principled inference across a broad class of latent-variable models.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
258
|
Domain Adaptive Hashing Retrieval via VLM Assisted Pseudo-Labeling and Dual Space Adaptation
|
[
"Jingyao Li",
"Zhanshan Li",
"Shuai Lü"
] |
Unsupervised domain adaptive hashing has emerged as a promising approach for efficient and memory-friendly cross-domain retrieval. It leverages the model learned on labeled source domains to generate compact binary codes for unlabeled target domain samples, ensuring that semantically similar samples are mapped to nearby points in the Hamming space. Existing methods typically apply domain adaptation techniques to the feature space or the Hamming space, especially pseudo-labeling and feature alignment. However, the inherent noise of pseudo-labels and the insufficient exploration of complementary knowledge across spaces hinder the ability of the adapted model. To address these challenges, we propose a Vision-language model assisted Pseudo-labeling and Dual Space adaptation (VPDS) method. Motivated by the strong zero-shot generalization capabilities of pre-trained vision-language models (VLMs), VPDS leverages VLMs to calibrate pseudo-labels, thereby mitigating pseudo-label bias. Furthermore, to simultaneously utilize the semantic richness of high-dimensional feature space and preserve discriminative efficiency of low-dimensional Hamming space, we introduce a dual space adaptation approach that performs independent alignment within each space. Extensive experiments on three benchmark datasets demonstrate that VPDS consistently outperforms existing methods in both cross-domain and single-domain retrieval tasks, highlighting its effectiveness and superiority.
|
poster
| null | null | null |
[] |
[] |
[] |
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259
|
Audits Under Resource and Information Constraints: Scaling Laws for Fairness Pareto Frontiers
|
[
"Sarah Cen",
"Salil Goyal",
"Zaynah Javed",
"Ananya Karthik",
"Percy Liang",
"Daniel Ho"
] |
Model audits play a critical role in holding AI actors accountable, and one branch of the law for which model audits are particularly salient is discrimination law. Several areas of discrimination law (including but extending beyond employment) implicate what is known as the "less discriminatory alternatives" (LDA) framework, in which a policy (i.e., model) is defensible if no LDA can be found with a reasonable amount of effort. Notably, the burden of proving that an LDA exists typically falls on the claimant (the party alleging discrimination) rather than the defendant. This creates a significant hurdle, as claimants would seemingly need to produce a less discriminatory model, a task requiring resources and expertise beyond most litigants. Moreover, model developers generally shield any information about the current model and training data as trade secrets. In this work, we propose a novel toolkit enabling claimants to establish the existence of LDAs even if they lack significant compute, training data, or information about the current model. Our method allows claimants to demonstrate that (obtainable) less discriminatory models exist without producing them, requiring substantially fewer resources and (potentially proprietary) information than model development. We do so by establishing a scaling law for the loss-fairness Pareto frontier (PF). As our main result, we provide a closed-form upper bound for the PF, where we focus on fairness as demographic parity as an illustrative example. Using this expression, the claimant can fit a curve to the PF in the "low resource-information regime," then extrapolate the PF that applies for the (large) model being contested. The expression thus serves as a scaling law for loss-fairness PFs.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
260
|
Zero-Shot Detection of LLM-Generated Text via Implicit Reward Model
|
[
"Runheng Liu",
"Heyan Huang",
"Xingchen Xiao",
"Zhijing Wu"
] |
Large language models (LLMs) have demonstrated remarkable capabilities across various tasks. However, their ability to generate human-like text has raised concerns about potential misuse. This underscores the need for reliable and effective methods to detect LLM-generated text. In this paper, we propose IRM, a novel zero-shot approach that leverages Implicit Reward Models for LLM-generated text detection. Such implicit reward models can be derived from publicly available instruction-tuned and base models. Previous reward-based method relies on preference construction and task-specific fine-tuning. In comparison, IRM requires neither preference collection nor additional training.We evaluate IRM on the DetectRL benchmark and demonstrate that IRM can achieve superior detection performance, outperforms existing zero-shot and supervised methods in LLM-generated text detection.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
261
|
Direct Alignment with Heterogeneous Preferences
|
[
"Ali Shirali",
"Arash Nasr-Esfahany",
"Abdullah Alomar",
"Parsa Mirtaheri",
"Rediet Abebe",
"Ariel Procaccia"
] |
Alignment with human preferences is commonly framed using a universal reward function, even though human preferences are inherently heterogeneous. We formalize this heterogeneity by introducing user types and examine the limits of the homogeneity assumption.We show that aligning to heterogeneous preferences with a single policy is best achieved using the average reward across user types. However, this requires additional information about annotators. We examine improvements under different information settings, focusing on direct alignment methods. We find that minimal information can yield first-order improvements, while full feedback from each user type leads to consistent learning of the optimal policy. Surprisingly, however, no sample-efficient consistent direct loss exists in this latter setting. These results reveal a fundamental tension between consistency and sample efficiency in direct policy alignment.
|
poster
|
2502.16320
| null | null |
[] |
[] |
[] |
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] |
262
|
ArchCAD-400K: A Large-Scale CAD drawings Dataset and New Baseline for Panoptic Symbol Spotting
|
[
"Ruifeng Luo",
"Zhengjie Liu",
"Tianxiao Cheng",
"Jie Wang",
"Tongjie Wang",
"Fei Cheng",
"Fu Chai",
"Yanpeng Li",
"Xingguang Wei",
"Haomin Wang",
"Shenglong Ye",
"Wenhai Wang",
"Zhang",
"Yu Qiao",
"Hongjie Zhang",
"Xianzhong Zhao"
] |
Recognizing symbols in architectural CAD drawings is critical for various advanced engineering applications. In this paper, we propose a novel CAD data annotation engine that leverages intrinsic attributes from systematically archived CAD drawings to automatically generate high-quality annotations, thus significantly reducing manual labeling efforts. Utilizing this engine, we construct ArchCAD-400K, a large-scale CAD dataset consisting of 413,062 chunks from 5538 highly standardized drawings, making it over 26 times larger than the largest existing CAD dataset. ArchCAD-400K boasts an extended drawing diversity and broader categories, offering line-grained annotations. Furthermore, we present a new baseline model for panoptic symbol spotting, termed Dual-Pathway Symbol Spotter (DPSS). It incorporates an adaptive fusion module to enhance primitive features with complementary image features, achieving state-of-the-art performance and enhanced robustness. Extensive experiments validate the effectiveness of DPSS, demonstrating the value of ArchCAD-400K and its potential to drive innovation in architectural design and construction.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
263
|
Towards Resilient Safety-driven Unlearning for Diffusion Models against Downstream Fine-tuning
|
[
"Boheng Li",
"Renjie Gu",
"Junjie Wang",
"Leyi Qi",
"Yiming Li",
"Run Wang",
"Zhan Qin",
"Tianwei Zhang"
] |
Text-to-image (T2I) diffusion models have achieved impressive image generation quality and are increasingly fine-tuned for personalized applications. However, these models often inherit unsafe behaviors from toxic pretraining data, raising growing safety concerns. While recent safety-driven unlearning methods have made promising progress in suppressing model toxicity, they are identified to be fragile to downstream fine-tuning, where we reveal that state-of-the-art methods largely fail to retain their effectiveness even when fine-tuned on entirely benign datasets. To mitigate this problem, in this paper, we propose ResAlign, a safety-driven unlearning framework with enhanced resilience against downstream fine-tuning. By modeling downstream fine-tuning as an implicit optimization problem with a Moreau Envelope-based reformulation, ResAlign enables efficient gradient estimation to minimize the recovery of harmful behaviors. Additionally, a meta-learning strategy is proposed to simulate a diverse distribution of fine-tuning scenarios to improve generalization. Extensive experiments across a wide range of datasets, fine-tuning methods, and configurations demonstrate that ResAlign consistently outperforms prior unlearning approaches in retaining safety after downstream fine-tuning while preserving benign generation capability well.
|
poster
|
2507.16302
| null | null |
[] |
[] |
[] |
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] |
264
|
Equivariance by Contrast: Identifiable Equivariant Embeddings from Unlabeled Finite Group Actions
|
[
"Tobias Schmidt",
"Steffen Schneider",
"Matthias Bethge"
] |
We propose Equivariance by Contrast (EbC) to learn equivariant embeddings from observation pairs $(x, g \cdot x)$, where $g$ is drawn from a finite group acting on the data. Our method jointly learns a latent space and a group representation in which group actions correspond to invertible linear maps—without relying on group-specific inductive biases. We validate our approach on the infinite dSprites dataset with structured transformations defined by the finite group $G:= (R_m \times \mathbb{Z}_n \times \mathbb{Z}_n)$, combining discrete rotations and periodic translations. The resulting embeddings exhibit high-fidelity equivariance, with group operations faithfully reproduced in latent space. On synthetic data, we further validate the approach on the non-Abelian orthogonal group $O(n)$ and the generalized linear group $GL(n)$. We also provide a theoretical proof for identifiability. While broad evaluation across diverse group types on real-world data remains future work, our results constitute the first successful demonstration of general-purpose equivariant learning from group action observations alone, including non-trivial non-abelian groups and a product group motivated by modeling affine equivariances in computer vision.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
265
|
Collective Counterfactual Explanations: Balancing Individual Goals and Collective Dynamics
|
[
"Ahmad-Reza Ehyaei",
"Ali Shirali",
"Samira Samadi"
] |
Counterfactual explanations provide individuals with cost-optimal recommendations to achieve their desired outcomes. However, when a significant number of individuals seek similar state modifications, this individual-centric approach can inadvertently create competition and introduce unforeseen costs. Additionally, disregarding the underlying data distribution may lead to recommendations that individuals perceive as unusual or impractical.To address these challenges, we propose a novel framework that extends standard counterfactual explanations by incorporating a population dynamics model. This framework penalizes deviations from equilibrium after individuals follow the recommendations, effectively mitigating externalities caused by correlated changes across the population. By balancing individual modification costs with their impact on others, our method ensures a more equitable and efficient outcome.We show how this approach reframes the counterfactual explanation problem from an individual-centric task to a collective optimization problem. Augmenting our theoretical insights, we design and implement scalable algorithms for computing collective counterfactuals, showcasing their effectiveness and advantages over existing recourse methods, particularly in aligning with collective objectives.
|
poster
|
2402.04579
| null | null |
[] |
[] |
[] |
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] |
266
|
Advancing Expert Specialization for Better MoE
|
[
"Hongcan Guo",
"Haolang Lu",
"Guoshun Nan",
"Bolun Chu",
"Jialin Zhuang",
"Yuan Yang",
"Wenhao Che",
"Xinye Cao",
"Sicong Leng",
"Qimei Cui",
"Xudong Jiang"
] |
Mixture-of-Experts (MoE) models enable efficient scaling of large language models (LLMs) by activating only a subset of experts per input. However, we observe that the commonly used auxiliary load balancing loss often leads to expert overlap and overly uniform routing, which hinders expert specialization and degrades overall performance during post-training.To address this, we propose a simple yet effective solution that introduces two complementary objectives: (1) an orthogonality loss to encourage experts to process distinct types of tokens, and (2) a variance loss to encourage more discriminative routing decisions.Gradient-level analysis demonstrates that these objectives are compatible with the existing auxiliary loss and contribute to optimizing the training process.Experimental results over various model architectures and across multiple benchmarks show that our method significantly enhances expert specialization. Notably, our method improves classic MoE baselines with auxiliary loss by up to 23.79\%, while also maintaining load balancing in downstream tasks, without any architectural modifications or additional components. We will release our code to contribute to the community.
|
oral
|
2505.22323
| null | null |
[] |
[] |
[] |
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] |
267
|
Taught Well Learned Ill: Towards Distillation-conditional Backdoor Attack
|
[
"Yukun Chen",
"Boheng Li",
"Yu Yuan",
"Leyi Qi",
"Yiming Li",
"Tianwei Zhang",
"Zhan Qin",
"Kui Ren"
] |
Knowledge distillation (KD) is a vital technique for deploying deep neural networks (DNNs) on resource-constrained devices by transferring knowledge from large teacher models to lightweight student models. While teacher models from third-party platforms may undergo security verification (e.g., backdoor detection), we uncover a novel and critical threat: distillation-conditional backdoor attacks (DCBAs). DCBA injects dormant and undetectable backdoors into teacher models, which become activated in student models via the KD process, even with clean distillation datasets. While the direct extension of existing methods is ineffective for DCBA, we implement this attack by formulating it as a bilevel optimization problem and proposing a simple yet effective method (i.e., SCAR). Specifically, the inner optimization simulates the KD process by optimizing a surrogate student model, while the outer optimization leverages outputs from this surrogate to optimize the teacher model for implanting the conditional backdoor. Our SCAR addresses this complex optimization utilizing an implicit differentiation algorithm with a pre-optimized trigger injection function. Extensive experiments across diverse datasets, model architectures, and KD techniques validate the effectiveness of our SCAR and its resistance against existing backdoor detection, highlighting a significant yet previously overlooked vulnerability in the KD process.
|
poster
|
2509.23871
|
https://github.com/WhitolfChen/SCAR
| null |
[] |
[] |
[] |
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] |
268
|
OmniConsistency: Learning Style-Agnostic Consistency from Paired Stylization Data
|
[
"Yiren Song",
"Cheng Liu",
"Mike Zheng Shou"
] |
Diffusion models have advanced image stylization significantly, yet two core challenges persist: (1) maintaining consistent stylization in complex scenes, particularly identity, composition, and fine details, and (2) preventing style degradation in image-to-image pipelines with style LoRAs. GPT-4o's exceptional stylization consistency highlights the performance gap between open-source methods and proprietary models. To bridge this gap, we propose \textbf{OmniConsistency}, a universal consistency plugin leveraging large-scale Diffusion Transformers (DiTs). OmniConsistency contributes: (1) an in-context consistency learning framework trained on aligned image pairs for robust generalization; (2) a two-stage progressive learning strategy decoupling style learning from consistency preservation to mitigate style degradation; and (3) a fully plug-and-play design compatible with arbitrary style LoRAs under the Flux framework. Extensive experiments show that OmniConsistency significantly enhances visual coherence and aesthetic quality, achieving performance comparable to commercial state-of-the-art model GPT-4o.
|
poster
|
2505.18445
|
https://github.com/showlab/omniconsistency
| null |
[
"yiren98/OmniConsistency"
] |
[
"showlab/OmniConsistency"
] |
[] |
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] |
269
|
OASIS: One-Shot Federated Graph Learning via Wasserstein Assisted Knowledge Integration
|
[
"Guancheng Wan",
"Jiaru Qian",
"Wenke Huang",
"Qilin Xu",
"Xianda Guo",
"Boheng Li",
"Guibin Zhang",
"Bo Du",
"Mang Ye"
] |
Federated Graph Learning (FGL) offers a promising framework for collaboratively training Graph Neural Networks (GNNs) while preserving data privacy. In resource-constrained environments, One-shot Federated Learning (OFL) emerges as an effective solution by limiting communication to a single round. Current OFL approaches employing generative models have attracted considerable attention; however, they face unresolved challenges: these methods are primarily designed for traditional image data and fail to capture the fine-grained structural information of local graph data. Consequently, they struggle to integrate the intricate correlations necessary and transfer subtle structural insights from each client to the global model. To address these issues, we introduce **OASIS**, an innovative one-shot FGL framework. In OASIS, we propose a Synergy Graph Synthesizer designed to generate informative synthetic graphs and introduce a Topological Codebook to construct a structural latent space. Moreover, we propose the Wasserstein-Enhanced Semantic Affinity Distillation (WESAD) to incorporate rich inter-class relationships and the Wasserstein-Driven Structural Relation Distillation (WDSRD) to facilitate the effective transfer of structural knowledge from the Topological Codebook. Extensive experiments on real-world tasks demonstrate the superior performance and generalization capability of OASIS. The code is available for anonymous access at https://anonymous.4open.science/r/OASIS-NeurIPS25.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
270
|
GRE Suite: Geo-localization Inference via Fine-Tuned Vision-Language Models and Enhanced Reasoning Chains
|
[
"Chun Wang",
"Xiaojun Ye",
"Xiaoran Pan",
"Zihao Pan",
"Haofan Wang",
"Yiren Song"
] |
Recent advances in Visual Language Models (VLMs) have demonstrated exceptional performance in visual reasoning tasks. However, geo-localization presents unique challenges, requiring the extraction of multigranular visual cues from images and their integration with external world knowledge for systematic reasoning. Current approaches to geo-localization tasks often lack robust reasoning mechanisms and explainability, limiting their effectiveness. To address these limitations, we propose the Geo Reason Enhancement (GRE) Suite, a novel framework that augments VLMs with structured reasoning chains for accurate and interpretable location inference. The GRE Suite is systematically developed across three key dimensions: dataset, model, and benchmark. First, we introduce GRE30K, a high-quality geo-localization reasoning dataset designed to facilitate fine-grained visual and contextual analysis. Next, we present the GRE model, which employs a multi-stage reasoning strategy to progressively infer scene attributes, local details, and semantic features, thereby narrowing down potential geographic regions with enhanced precision. Finally, we construct the Geo Reason Evaluation Benchmark (GREval-Bench), a comprehensive evaluation framework that assesses VLMs across diverse urban, natural, and landmark scenes to measure both coarse-grained (e.g., country, continent) and fine-grained (e.g., city, street) localization performance. Experimental results demonstrate that GRE significantly outperforms existing methods across all granularities of geo-localization tasks, underscoring the efficacy of reasoning-augmented VLMs in complex geographic inference. Code and data will be released at https://anonymous.4open.science/r/GRE-74C0.
|
poster
|
2505.18700
|
https://github.com/Thorin215/GRE
| null |
[] |
[] |
[] |
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] |
271
|
What does gradient variance tell us about the optimality of vector fields?
|
[
"Teodora Reu",
"Sixtine Dromigny",
"Michael Bronstein",
"Francisco Vargas"
] |
We investigate the underlying cause of common failure modes behind the state-of-the-art fixed-point generative models, Rectified Flows and Schr\"odinger Bridge Matching. First, we introduce a gradient‐variance diagnostic that directly measures how learned vector fields induce transport errors after integration. Contrary to common belief, we show that low‐variance (i.e., well‐aligned) interpolants can still yield high transport error. We also demonstrate that standard neural network architectures (e.g., MLPs or CNNs) are fundamentally limited, failing to exactly represent even Gaussian-to-Gaussian transport. While prior work on Rectified Flows notes that strict convergence to optimal couplings is not guaranteed, it overlooks a key limitation: with noiseless interpolants, further iterations do not improve transport. If the pairings are already straight, the process stagnates; if they are deterministic but not straight, the vector field simply memorizes these pairings without achieving true optimal transport. We prove the existence of such memorizing vector fields.
|
spotlight
| null | null | null |
[] |
[] |
[] |
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] |
272
|
TC-Light: Temporally Consistent Relighting for Dynamic Long Videos
|
[
"Yang Liu",
"Chuanchen Luo",
"Zimo Tang",
"Yingyan Li",
"yuran Yang",
"Yuanyong Ning",
"Lue Fan",
"Junran Peng",
"ZHAO-XIANG ZHANG"
] |
Editing illumination in long videos with complex dynamics has significant value in various downstream tasks, including visual content creation and manipulation, as well as data scaling up for embodied AI through sim2real and real2real transfer. Nevertheless, existing video relighting techniques are predominantly limited to portrait videos or fall into the bottleneck of temporal consistency and computation efficiency. In this paper, we propose **TC-Light**, a novel paradigm characterized by the proposed two-stage post optimization mechanism. Starting from the video preliminarily relighted by an inflated video relighting model, it optimizes appearance embedding in the first stage to align global illumination. Then it optimizes the proposed canonical video representation, i.e., **Unique Video Tensor (UVT)**, to align fine-grained texture and lighting in the second stage. To comprehensively evaluate performance, we also establish a long and highly dynamic video benchmark. Extensive experiments show that our method enables physically plausible relighting results with superior temporal coherence and low computation cost.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
273
|
Parameter-free Algorithms for the Stochastically Extended Adversarial Model
|
[
"Shuche Wang",
"Adarsh Barik",
"Peng Zhao",
"Vincent Tan"
] |
We develop the first parameter-free algorithms for the Stochastically Extended Adversarial (SEA) model, a framework that bridges adversarial and stochastic online convex optimization. Existing approaches for the SEA model require prior knowledge of problem-specific parameters, such as the diameter of the domain $D$ and the Lipschitz constant of the loss functions $G$, which limits their practical applicability. Addressing this, we develop parameter-free methods by leveraging the Optimistic Online Newton Step (OONS) algorithm to eliminate the need for these parameters. We first establish a comparator-adaptive algorithm for the scenario with unknown domain diameter but known Lipschitz constant, achieving an expected regret bound of $\tilde{O}\big(\Vert u\Vert_2^2 + \Vert u\Vert_2(\sqrt{\sigma^2_{1:T}} + \sqrt{\Sigma^2_{1:T}})\big)$, where $u$ is the comparator vector and $\sigma^2_{1:T}$ and $\Sigma^2_{1:T}$ represent the cumulative stochastic variance and cumulative adversarial variation, respectively. We then extend this to the more general setting where both $D$ and $G$ are unknown, attaining the comparator- and Lipschitz-adaptive algorithm. Notably, the regret bound exhibits the same dependence on $\sigma^2_{1:T}$ and $\Sigma^2_{1:T}$, demonstrating the efficacy of our proposed methods even when both parameters are unknown in the SEA model.
|
poster
|
2510.04685
| null | null |
[] |
[] |
[] |
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] |
274
|
Approximately Aligned Decoding
|
[
"Daniel Melcer",
"Sujan Kumar Gonugondla",
"Pramuditha Perera",
"Haifeng Qian",
"Wen-Hao Chiang",
"Yanjun Wang",
"Nihal Jain",
"Pranav Garg",
"Xiaofei Ma",
"Anoop Deoras"
] |
It is common to reject undesired outputs of Large Language Models (LLMs); however, current methods to do so require an excessive amount of computation to re-sample after a rejection, or distort the distribution of outputs by constraining the output to highly improbable tokens.We present a method, Approximately Aligned Decoding (AprAD), to balance the distortion of the output distribution with computational efficiency, inspired by algorithms from the speculative decoding literature.AprAD allows for the generation of long sequences of text with difficult-to-satisfy constraints, while amplifying low probability outputs much less compared to existing methods.We show through a series of experiments that the task-specific performance of AprAD is comparable to methods that do not distort the output distribution, while being much more computationally efficient.
|
poster
|
2410.01103
| null | null |
[] |
[] |
[] |
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] |
275
|
Tight analyses of first-order methods with error feedback
|
[
"Daniel Berg Thomsen",
"Adrien Taylor",
"Aymeric Dieuleveut"
] |
Communication between agents often constitutes a major computational bottleneck in distributed learning. One of the most common mitigation strategies is to compress the information exchanged, thereby reducing communication overhead. To counteract the degradation in convergence associated with compressed communication, error feedback schemes—most notably EF and EF21—were introduced. In this work, we provide a tight analysis of both of these methods. Specifically, we find the Lyapunov function that yields the best possible convergence rate for each method—with matching lower bounds. This principled approach yields sharp performance guarantees and enables a rigorous, apples-to-apples comparison between EF, EF21, and Compressed Gradient Descent. Our analysis is carried out in a simplified yet representative setting, which allows for clean theoretical insights and fair comparison of the underlying mechanisms.
|
poster
|
2506.05271
| null | null |
[] |
[] |
[] |
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] |
276
|
Model-Guided Dual-Role Alignment for High-Fidelity Open-Domain Video-to-Audio Generation
|
[
"Kang Zhang",
"Trung X. Pham",
"Suyeon Lee",
"Axi Niu",
"Arda Senocak",
"Joon Son Chung"
] |
We present MGAudio, a novel flow-based framework for open-domain video-to-audio generation, which introduces model-guided dual-role alignment as a central design principle. Unlike prior approaches that rely on classifier-based or classifier-free guidance, MGAudio enables the generative model to guide itself through a dedicated training objective designed for video-conditioned audio generation. The framework integrates three main components: (1) a scalable flow-based Transformer denoiser, (2) a dual-role alignment mechanism where the audio-visual encoder serves both as a conditioning module and as a feature aligner to improve generation quality, and (3) a model-guided objective that enhances cross-modal coherence and audio realism.MGAudio achieves state-of-the-art performance on VGGSound, reducing FAD to 0.40, substantially surpassing the best classifier-free guidance baselines, and consistently outperforms existing methods across FD, IS, and alignment metrics. It also generalizes well to the challenging UnAV-100 benchmark. These results highlight model-guided dual-role alignment as a powerful and scalable paradigm for conditional video-to-audio generation.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
277
|
Median Selection with Noisy and Structural Information
|
[
"Mingyu Kang",
"Chenglin Fan"
] |
We study the problem of computing the exact median using unreliable comparison oracles, motivated by settings such as crowdsourced or noisy decision-making. We analyze the complexity of this problem under both the classical and partially ordered input settings. In the classical setting, we introduce a modified LazySelect algorithm that combines weak comparisons with occasional strong comparisons through majority voting. We show that this hybrid strategy has near-linear running time and can achieve high-probability correctness using only sublinear strong comparisons, even when the weak oracle is only slightly better than random guessing. Our theoretical results hold under the persistent comparison model, where resampling will not amplify the probability of correctness. In the partially ordered setting, we generalize the notion of median to directed acyclic graphs (DAGs) and show that the complexity of median selection depends heavily on the DAG's width. We complement our analysis with extensive experiments on synthetic data.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
278
|
Local Learning for Covariate Selection in Nonparametric Causal Effect Estimation with Latent Variables
|
[
"Zheng Li",
"Feng Xie",
"Xichen Guo",
"Yan Zeng",
"Hao Zhang",
"Zhi Geng"
] |
Estimating causal effects from nonexperimental data is a fundamental problem in many fields of science. A key component of this task is selecting an appropriate set of covariates for confounding adjustment to avoid bias. Most existing methods for covariate selection often assume the absence of latent variables and rely on learning the global causal structure among variables. However, identifying the global structure can be unnecessary and inefficient, especially when our primary interest lies in estimating the effect of a treatment variable on an outcome variable. To address this limitation, we propose a novel local learning approach for covariate selection in nonparametric causal effect estimation, which accounts for the presence of latent variables. Our approach leverages testable independence and dependence relationships among observed variables to identify a valid adjustment set for a target causal relationship, ensuring both soundness and completeness under standard assumptions. We validate the effectiveness of our algorithm through extensive experiments on both synthetic and real-world data.
|
poster
|
2411.16315
| null | null |
[] |
[] |
[] |
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] |
279
|
Cycle-Sync: Robust Global Camera Pose Estimation through Enhanced Cycle-Consistent Synchronization
|
[
"Shaohan Li",
"Yunpeng Shi",
"Gilad Lerman"
] |
We introduce Cycle-Sync, a robust and global framework for estimating camera poses (both rotations and locations). Our core innovation is a location solver that adapts message-passing least squares (MPLS) - originally developed for group synchronization - to the camera localization setting. We modify MPLS to emphasize cycle-consistent information, redefine cycle consistencies using estimated distances from previous iterations, and incorporate a Welsch-type robust loss. We establish the strongest known deterministic exact-recovery guarantee for camera location estimation, demonstrating that cycle consistency alone enables the lowest sample complexity to date. To further boost robustness, we introduce a plug-and-play outlier rejection module inspired by robust subspace recovery, and we fully integrate cycle consistency into MPLS for rotation averaging. Our global approach avoids the need for bundle adjustment. Experiments on synthetic and real datasets show that Cycle-Sync consistently outperforms leading pose estimators, including full structure-from-motion pipelines with bundle adjustment.
|
spotlight
| null | null | null |
[] |
[] |
[] |
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] |
280
|
GOATex: Geometry & Occlusion-Aware Texturing
|
[
"Hyunjin Kim",
"Kunho Kim",
"Wonkwang Lee",
"Adam Lee"
] |
We present GOATex, a diffusion-based method for 3D mesh texturing that generates high-quality textures for both exterior and interior surfaces. While existing methods perform well on visible regions, they inherently lack mechanisms to handle occluded interiors, resulting in incomplete textures and visible seams. To address this, we introduce an occlusion-aware texturing framework based on the concept of hit levels, which quantify the relative depth of mesh faces via multi-view ray casting. This allows us to partition mesh faces into ordered visibility layers, from outermost to innermost. We then apply a two-stage visibility control strategy that progressively reveals interior regions with structural coherence, followed by texturing each layer using a pretrained diffusion model. To seamlessly merge textures obtained across layers, we propose a soft UV-space blending technique that weighs each texture’s contribution based on view-dependent visibility confidence. Empirical results demonstrate that GOATex consistently outperforms existing methods, producing seamless, high-fidelity textures across both visible and occluded surfaces. Unlike prior works, GOATex operates entirely without costly fine-tuning of a pretrained diffusion model and allows separate prompting for exterior and interior mesh regions, enabling fine-grained control over layered appearances.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
281
|
Activation Control for Efficiently Eliciting Long Chain-of-thought Ability of Language Models
|
[
"Zekai Zhao",
"Qi Liu",
"Kun Zhou",
"Zihan Liu",
"Yifei Shao",
"Zhiting Hu",
"Biwei Huang"
] |
Despite the remarkable reasoning performance, eliciting the long chain-of-thought(CoT) ability in large language models(LLMs) typically requires costly reinforcement learning or supervised fine-tuning on high-quality distilled data. We investigate the internal mechanisms behind this capability and show that a small set of high-impact activations in the last few layers, greatly govern the long-form reasoning attributes, e.g. output length and self-reflection. Through simply amplifying these activations and adding ``wait'' tokens, the long CoT ability can be invoked without training, leading to significantly increased self-reflection rate and accuracy. In addition, we also find that the activation changes follow predictable trajectories, i.e. a sharp rise after special tokens and a subsequent exponential decay. Based on these insights, we introduce a general training-free activation control technique. It utilizes a few contrastive examples to identify the relevant activations, and then incorporates simple analytic functions to adjust their values at inference time to elicit long CoTs. Extensive experiments have verified the effectiveness of our methods in efficiently eliciting the long CoT ability of LLMs and improving the performance. Besides, we further propose a parameter-efficient fine-tuning method that trains only the last-layer activation amplification module and a few LoRA layers, outperforming LoRA on reasoning benchmarks with much fewer parameters. Our code and data will be fully public released.
|
spotlight
|
2505.17697
| null | null |
[] |
[] |
[] |
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] |
282
|
Your Pre-trained LLM is Secretly an Unsupervised Confidence Calibrator
|
[
"Beier Luo",
"Shuoyuan Wang",
"Sharon Li",
"Hongxin Wei"
] |
Post-training of large language models is essential for adapting pre-trained language models (PLMs) to align with human preferences and downstream tasks. While PLMs typically exhibit well-calibrated confidence, post-trained language models (PoLMs) often suffer from over-confidence, assigning high confidence to both correct and incorrect outputs, which can undermine reliability in critical applications.A major obstacle in calibrating PoLMs is the scarcity of labeled data for individual downstream tasks. To address this, we propose Disagreement-Aware Confidence Alignment (DACA), a novel unsupervised method to optimize the parameters (e.g., temperature $\tau$) in post-hoc confidence calibration. Our method is motivated by the under-confidence issue caused by prediction disagreement between the PLM and PoLM while aligning their confidence via temperature scaling. Theoretically, the PLM's confidence underestimates PoLM's prediction accuracy on disagreement examples, causing a larger $\tau$ and producing under-confident predictions. DACA mitigates this by selectively using only agreement examples for calibration, effectively decoupling the influence of disagreement.In this manner, our method avoids an overly large $\tau$ in temperature scaling caused by disagreement examples, improving calibration performance.Extensive experiments demonstrate the effectiveness of our method, improving the average ECE of open-sourced and API-based LLMs (e.g. GPT-4o) by up to 15.08$\%$ on common benchmarks.
|
poster
|
2505.16690
| null | null |
[] |
[] |
[] |
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] |
283
|
Policy Compatible Skill Incremental Learning via Lazy Learning Interface
|
[
"Daehee Lee",
"TaeYoon Kwack",
"Dongsu Lee",
"Wonje Choi",
"Honguk Woo"
] |
Skill Incremental Learning (SIL) is the process by which an embodied agent expands and refines its skill set over time by leveraging experience gained through interaction with its environment or by the integration of additional data. SIL facilitates efficient acquisition of hierarchical policies grounded in reusable skills for downstream tasks. However, as the skill repertoire evolves, it can disrupt compatibility with existing skill-based policies, limiting their reusability and generalization. In this work, we propose SIL-C, a novel framework that ensures skill-policy compatibility, allowing improvements in incrementally learned skills to enhance the performance of downstream policies without requiring policy re-training or structural adaptation. SIL-C employs a bilateral lazy learning-based mapping technique to dynamically align the subtask space referenced by policies with the skill space decoded into agent behaviors. This enables each subtask, derived from the policy's decomposition of a complex task, to be executed by selecting an appropriate skill based on trajectory distribution similarity. We evaluate SIL-C across diverse SIL scenarios and demonstrate that it maintains compatibility between evolving skills and downstream policies while ensuring efficiency throughout the learning process.
|
spotlight
|
2509.20612
| null | null |
[] |
[] |
[] |
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] |
284
|
Inference-time Alignment in Continuous Space
|
[
"Yige Yuan",
"Teng Xiao",
"Li Yunfan",
"Bingbing Xu",
"Shuchang Tao",
"Yunqi Qiu",
"Huawei Shen",
"Xueqi Cheng"
] |
Aligning large language models with human feedback at inference time has received increasing attention due to its flexibility. Existing methods rely on generating multiple responses from the base policy for search using a reward model, which can be considered as searching in a discrete response space. However, these methods struggle to explore informative candidates when the base policy is weak or the candidate set is small, resulting in limited effectiveness. In this paper, to address this problem, we propose Simple Energy Adaptation ($\textbf{SEA}$), a simple yet effective algorithm for inference-time alignment. In contrast to expensive search over the discrete space, SEA directly adapts original responses from the base policy toward the optimal one via gradient-based sampling in continuous latent space. Specifically, SEA formulates inference as an iterative optimization procedure on an energy function over actions in the continuous space defined by the optimal policy, enabling simple and effective alignment. For instance, despite its simplicity, SEA outperforms the second-best baseline with a relative improvement of up to $ \textbf{77.51\%}$ on AdvBench and $\textbf{16.36\%}$ on MATH. Code is publicly available at [this link](https://github.com/SEA-NeurIPS/Code).
|
poster
|
2505.20081
|
https://github.com/yuanyige/sea
| null |
[] |
[] |
[] |
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] |
285
|
TabDPT: An Open Tabular Foundation Model
|
[
"Junwei Ma",
"Valentin Thomas",
"Rasa Hosseinzadeh",
"Alex Labach",
"Jesse Cresswell",
"Keyvan Golestan",
"Guangwei Yu",
"Anthony L Caterini",
"Maks Volkovs"
] |
Tabular data is one of the most ubiquitous sources of information worldwide, spanning a wide variety of domains. This inherent heterogeneity has slowed the development of Tabular Foundation Models (TFMs) capable of fast generalization to unseen datasets. In-Context Learning (ICL) has recently emerged as a promising solution for TFMs, enabling dynamic adaptation to new tasks without additional tuning. While many studies have attempted to re-purpose large language models for tabular ICL, they have had limited success, so recent works have focused on developing tabular-specific foundation models. In this work, we propose an approach to combine ICL-based retrieval with self supervised learning to train tabular foundation models. We also investigate the utility of real vs. synthetic data for model pre-training, and show that real data can contain useful signal not easily captured in synthetic training. Specifically, we show that incorporating real data during the pre-training phase can lead to significantly faster training and better downstream generalization to unseen data. Our resulting model, TabDPT, achieves top performance on both regression (CTR23) and classification (CC18) benchmarks. Importantly, we also demonstrate that with our pre-training procedure, scaling both model and data size leads to consistent performance improvements that follow power laws. This echoes scaling laws in LLMs and other foundation models, and suggests that Internet-scale TFMs can be achievable. We open-source our full pipeline: inference code including trained model weights can be found \href{https://anonymous.4open.science/r/TabDPT-inference-6D8C/README.md}{here}, and the training code to reproduce experiments can be found \href{https://anonymous.4open.science/r/TabDPT-8586/README.md}{here}.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
286
|
ShapeCraft: LLM Agents for Structured, Textured and Interactive 3D Modeling
|
[
"Shuyuan Zhang",
"ChenHan Jiang",
"Zuoou Li",
"Jiankang Deng"
] |
Constructing structured 3D shapes is essential for gaming, virtual reality, and embodied AI. However, this process typically demands significant expertise and manual effort using conventional 3D modeling software. To make 3D content creation more accessible, we present ShapeCraft, a novel system that leverages large language model (LLM) agents to autonomously generate structured 3D shapes from natural language instructions. At the core of ShapeCraft is a graph-based procedural shape (GPS) representation, whose nodes store corresponding program snippets and layout information, facilitating efficient programmatic updates and flexible structure manipulation. This representation allows for post-modeling execution as structured geometry while preserving editability for artists. In the ShapeCraft workflow, LLM agents first hierarchically parses user's input and initialize GPS representation, then iteratively refines the procedural modeling and texturing steps to produce structured, textured, and interactive 3D assets. Our experiments show that ShapeCraft significantly outperforms existing LLM-based agents in generating geometrically accurate and semantically rich 3D models. Moreover, it achieves higher shape fidelity compared to optimization-based text-to-3D generation methods. We further demonstrate the versatility of ShapeCraft through examples of animated and user-customized editing, highlighting its potential for broader interactive applications.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
287
|
Towards Predicting Any Human Trajectory In Context
|
[
"Ryo Fujii",
"Hideo Saito",
"Ryo Hachiuma"
] |
Predicting accurate future trajectories of pedestrians is essential for autonomous systems but remains a challenging task due to the need for adaptability in different environments and domains. A common approach involves collecting scenario-specific data and performing fine-tuning via backpropagation. However, this process is often impractical on edge devices due to constrained computational resources. To address this challenge, we introduce TrajICL an In-Context Learning (ICL) framework for pedestrian trajectory prediction that enables rapid adaptation without fine-tuning on the scenario-specific data. We propose a spatio-temporal similarity-based example selection (STES) method that selects relevant examples from previously observed trajectories within the same scene by identifying similar motion patterns at corresponding locations. To further refine this selection, we introduce prediction-guided example selection (PG-ES), which selects examples based on both the past trajectory and the predicted future trajectory, rather than relying solely on the past trajectory. This approach allows the model to account for long-term dynamics when selecting examples. Finally, instead of relying on small real-world datasets with limited scenario diversity, we train our model on a large-scale synthetic dataset to enhance its prediction ability by leveraging in-context examples. Extensive experiments demonstrate that TrajICL achieves remarkable adaptation across both in-domain and cross-domain scenarios, outperforming even fine-tuned approaches across multiple public benchmarks. We will release the code and pretrained model upon acceptance.
|
poster
|
2506.00871
| null |
https://fujiry0.github.io/TrajICL-project-page/
|
[] |
[] |
[] |
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] |
288
|
Generate, but Verify: Reducing Hallucination in Vision-Language Models with Retrospective Resampling
|
[
"Tsung-Han (Patrick) Wu",
"Heekyung Lee",
"Jiaxin Ge",
"Joseph Gonzalez",
"Trevor Darrell",
"David Chan"
] |
Vision-Language Models (VLMs) excel at visual understanding but often suffer from visual hallucinations, where they generate descriptions of nonexistent objects, actions, or concepts, posing significant risks in safety-critical applications. Existing hallucination mitigation methods typically follow one of two paradigms: generation adjustment, which modifies decoding behavior to align text with visual inputs, and post-hoc verification, where external models assess and correct outputs. While effective, generation adjustment methods often rely on heuristics and lack correction mechanisms, while post-hoc verification is complicated, typically requiring multiple models and tending to reject outputs rather than refine them. In this work, we introduce REVERSE, a unified framework that integrates hallucination-aware training with on-the-fly self-verification. By leveraging a new hallucination-verification dataset containing over 1.3M semi-synthetic samples, along with a novel inference-time retrospective resampling technique, our approach enables VLMs to both detect hallucinations during generation and dynamically revise those hallucinations. Our evaluations show that REVERSE achieves state-of-the-art hallucination reduction, outperforming the best existing methods by up to 12% on CHAIR-MSCOCO and 34% on HaloQuest.
|
poster
|
2504.13169
|
https://github.com/tsunghan-wu/reverse_vlm
|
https://reverse-vlm.github.io/
|
[] |
[
"tsunghanwu/reverse_llava_v15",
"tsunghanwu/reverse_llava_more",
"tsunghanwu/reverse_qwen25_vl"
] |
[
"tsunghanwu/reverse-instruct-1.3m"
] |
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] |
289
|
NeurIPT: Foundation Model for Neural Interfaces
|
[
"Zitao Fang",
"Chenxuan Li",
"Hongting Zhou",
"Shuyang Yu",
"Guodong DU",
"Ashwaq Qasem",
"Yang Lu",
"Jing Li",
"Junsong Zhang",
"Sim Kuan Goh"
] |
Electroencephalography (EEG) has wide-ranging applications, from clinical diagnosis to brain-computer interfaces (BCIs). With the increasing volume and variety of EEG data, there has been growing interest in establishing foundation models (FMs) to scale up and generalize neural decoding. Despite showing early potential, applying FMs to EEG remains challenging due to substantial inter-subject, inter-task, and inter-condition variability, as well as diverse electrode configurations across recording setups. To tackle these open challenges, we propose **NeurIPT**, a foundation model tailored for diverse EEG-based **Neur**al **I**nterfaces with a **P**re-trained **T**ransformer by capturing both homogeneous and heterogeneous spatio-temporal characteristics inherent in EEG signals. Temporally, we introduce Amplitude-Aware Masked Pretraining (AAMP), masking based on signal amplitude rather than random intervals, to learn robust representations across varying signal intensities beyond local interpolation. Moreover, this temporal representation is enhanced by a progressive Mixture-of-Experts (MoE) architecture, where specialized expert subnetworks are progressively introduced at deeper layers, adapting effectively to the diverse temporal characteristics of EEG signals. Spatially, NeurIPT leverages the 3D physical coordinates of electrodes, enabling effective transfer across varying EEG settings, and develops Intra-Inter Lobe Pooling (IILP) during fine-tuning to efficiently exploit regional brain features. Empirical evaluations across nine downstream BCI datasets, via fine-tuning and training from scratch, demonstrated NeurIPT consistently achieved state-of-the-art performance, highlighting its broad applicability and robust generalization. Our work pushes forward the state of FMs in EEG and offers insights into scalable and generalizable neural information processing systems.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
290
|
Alignment of Large Language Models with Constrained Learning
|
[
"Botong Zhang",
"Shuo Li",
"Ignacio Hounie",
"Osbert Bastani",
"Dongsheng Ding",
"Alejandro Ribeiro"
] |
We study the problem of computing an optimal large language model (LLM) policy for a constrained alignment problem, where the goal is to maximize a primary reward objective while satisfying constraints on secondary utilities. Despite the popularity of Lagrangian-based LLM policy search in constrained alignment, iterative primal-dual methods often fail to converge, and non-iterative dual-based methods do not achieve optimality in the LLM parameter space. To address these challenges, we employ Lagrangian duality to develop an iterative dual-based alignment method that alternates between updating the LLM policy via Lagrangian maximization and updating the dual variable via dual descent. In theory, we characterize the primal-dual gap between the primal value in the distribution space and the dual value in the LLM parameter space. We further quantify the optimality gap of the learned LLM policies at near-optimal dual variables with respect to both the objective and the constraint functions. These results prove that dual-based alignment methods can find an optimal constrained LLM policy, up to an LLM parametrization gap. We demonstrate the effectiveness and merits of our approach through extensive experiments conducted on the PKU-SafeRLHF dataset.
|
poster
|
2505.19387
| null | null |
[] |
[] |
[] |
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] |
291
|
Quantifying Uncertainty in Error Consistency: Towards Reliable Behavioral Comparison of Classifiers
|
[
"Thomas Klein",
"Sascha Meyen",
"Wieland Brendel",
"Felix A. Wichmann",
"Kristof Meding"
] |
Benchmarking models is a key factor for the rapid progress in machine learning (ML) research. Thus, further progress depends on improving benchmarking metrics. A standard metric to measure the behavioral alignment between ML models and human observers is error consistency (EC). EC allows for more fine-grained comparisons of behavior than other metrics such as e.g. accuracy, and has been used in the influential Brain-Score benchmark to rank different DNNs by their behavioral consistency with humans. Previously, EC values have been reported without confidence intervals. However, empirically measured EC values are typically noisy - thus, without confidence intervals, valid benchmarking conclusions are problematic. Here we improve on standard EC in two ways: First, we show how to obtain confidence intervals for EC using a bootstrapping technique, allowing us to derive significance tests for EC. Second, we propose a new computational model relating the EC between two classifiers to the implicit probability that one of them copies responses from the other. This view of EC allows us to give practical guidance to scientists regarding the number of trials required for sufficiently powerful, conclusive experiments.Finally, we use our methodology to revisit popular NeuroAI-results. We find that while the general trend of behavioral differences between humans and machines holds up to scrutiny, many reported differences between deep vision models are statistically insignificant. Our methodology enables researchers to design adequately powered experiments that can reliably detect behavioral differences between models, providing a foundation for more rigorous benchmarking of behavioral alignment.
|
poster
|
2507.06645
| null | null |
[] |
[] |
[] |
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] |
292
|
Photography Perspective Composition: Towards Aesthetic Perspective Recommendation
|
[
"Lujian Yao",
"Siming Zheng",
"Xinbin Yuan",
"Zhuoxuan Cai",
"Pu Wu",
"Jinwei Chen",
"Bo Li",
"Peng-Tao Jiang"
] |
Traditional photography composition approaches are dominated by 2D cropping-based methods. However, these methods fall short when scenes contain poorly arranged subjects. Professional photographers often employ perspective adjustment as a form of 3D recomposition, modifying the projected 2D relationships between subjects while maintaining their actual spatial positions to achieve better compositional balance. Inspired by this artistic practice, we propose photography perspective composition (PPC), extending beyond traditional cropping-based methods. However, implementing the PPC faces significant challenges: the scarcity of perspective transformation datasets and undefined assessment criteria for perspective quality. To address these challenges, we present three key contributions: (1) An automated framework for building PPC datasets through expert photographs. (2) A video generation approach that demonstrates the transformation process from suboptimal to optimal perspectives. (3) A perspective quality assessment (PQA) model constructed based on human performance. Our approach is concise and requires no additional prompt instructions or camera trajectories, helping and guiding ordinary users to enhance their composition skills.
|
poster
|
2505.20655
| null | null |
[] |
[] |
[] |
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] |
293
|
ExGra-Med: Extended Context Graph Alignment for Medical Vision-Language Models
|
[
"Duy M. H. Nguyen",
"Nghiem Diep",
"Trung Nguyen",
"Hoang-Bao Le",
"Tai Nguyen",
"Anh-Tien Nguyen",
"TrungTin Nguyen",
"Nhat Ho",
"Pengtao Xie",
"Roger Wattenhofer",
"Daniel Sonntag",
"James Zou",
"Mathias Niepert"
] |
State-of-the-art medical multi-modal LLMs (med-MLLMs), such as LLaVA-Med and BioMedGPT, primarily depend on scaling model size and data volume, with training driven largely by autoregressive objectives. However, we reveal that this approach can lead to weak vision-language alignment, making these models overly dependent on costly instruction-following data. To address this, we introduce ExGra-Med, a novel multi-graph alignment framework that jointly aligns images, instruction responses, and extended captions in the latent space, advancing semantic grounding and cross-modal coherence. To scale to large LLMs (e.g., LLaMa-7B), we develop an efficient end-to-end training scheme using black-box gradient estimation, enabling fast and scalable optimization. Empirically, ExGra-Med matches LLaVA-Med’s performance using just 10\% of pre-training data, achieving a 20.13\% gain on VQA-RAD and approaching full-data performance. It also outperforms strong baselines like BioMedGPT and RadFM on visual chatbot and zero-shot classification tasks, demonstrating its promise for efficient, high-quality vision-language integration in medical AI.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
294
|
EchoShot: Multi-Shot Portrait Video Generation
|
[
"Jiahao Wang",
"Hualian Sheng",
"Sijia Cai",
"Weizhan Zhang",
"Caixia Yan",
"Yachuang Feng",
"Bing Deng",
"Jieping Ye"
] |
Video diffusion models substantially boost the productivity of artistic workflows with high-quality portrait video generative capacity. However, prevailing pipelines are primarily constrained to single-shot creation, while real-world applications urge for multiple shots with identity consistency and flexible content controllability. In this work, we propose EchoShot, a native and scalable multi-shot framework for portrait customization built upon a foundation video diffusion model. To start with, we propose shot-aware position embedding mechanisms within video diffusion transformer architecture to model inter-shot variations and establish intricate correspondence between multi-shot visual content and their textual descriptions. This simple yet effective design enables direct training on multi-shot video data without introducing additional computational overhead. To facilitate model training within multi-shot scenario, we construct PortraitGala, a large-scale and high-fidelity human-centric video dataset featuring cross-shot identity consistency and fine-grained captions such as facial attributes, outfits, and dynamic motions. To further enhance applicability, we extend EchoShot to perform reference image-based personalized multi-shot generation and long video synthesis with infinite shot counts. Extensive evaluations demonstrate that EchoShot achieves superior identity consistency as well as attribute-level controllability in multi-shot portrait video generation. Notably, the proposed framework demonstrates potential as a foundational paradigm for general multi-shot video modeling. All the models and the dataset will be made open-source upon acceptance.
|
poster
|
2506.15838
| null | null |
[] |
[
"JonneyWang/EchoShot"
] |
[] |
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] |
295
|
Error Forcing in Recurrent Neural Networks
|
[
"A Sağtekin",
"Colin Bredenberg",
"Cristina Savin"
] |
How should feedback influence recurrent neural network (RNN) learning? One way to address the known limitations of backpropagation through time is to directly adjust neural activities during the learning process. However, it remains unclear how to effectively use feedback to shape RNN dynamics. Here, we introduce error forcing (EF), where the network activity is guided orthogonally toward the zero-error manifold during learning. This method contrasts with alternatives like teaching forcing, which impose stronger constraints on neural activity and thus induce larger feedback influence on circuit dynamics. Furthermore, EF can be understood from a Bayesian perspective as a form of approximate dynamic inference. Empirically, EF consistently outperforms other learning algorithms across several tasks and its benefits persist when additional biological constraints are taken into account. Overall, EF is a powerful temporal credit assignment mechanism and a promising candidate model for learning in biological systems.
|
spotlight
| null | null | null |
[] |
[] |
[] |
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] |
296
|
Dynamical Properties of Tokens in Self-Attention and Effects of Positional Encoding
|
[
"Thieu Vo",
"Duy-Tung Pham",
"An Nguyen The",
"Viet-Hoang Tran",
"Nhan-Phu Chung",
"Xin Tong",
"Tan Nguyen"
] |
This paper investigates the dynamical properties of tokens in pre-trained transformer models and explores their application to improving Transformers. To this end, we analyze the dynamical system governing the continuous-time limit of the pre-trained model and characterize the asymptotic behavior of its solutions. Specifically, we characterize when tokens move closer to or farther from one another over time, depending on the model parameters. We provide sufficient conditions, based on these parameters, to identify scenarios where tokens either converge to zero or diverge to infinity. Unlike prior works, our conditions are broader in scope and more applicable to real-world models. Furthermore, we investigate how different forms of positional encoding - specifically absolute and rotary - affect these dynamical regimes. Empirical evidence reveals that the convergence scenario adversely impacts model performance. Motivated by these insights, we propose simple refinements to Transformer architectures that mitigate convergence behavior in models with absolute or rotary positional encoding. These findings support theoretical foundations and design principles for improving Transformer models.
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poster
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[] |
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] |
297
|
Stability and Sharper Risk Bounds with Convergence Rate $\tilde{O}(1/n^2)$
|
[
"Bowei Zhu",
"Shaojie Li",
"Mingyang Yi",
"Yong Liu"
] |
Prior work establishes at most $O\left(\log (n)/n\right)$ excess risk bounds via algorithmic stability for strongly convex learners with high probability. We show that under the same common assumptions—strong convexity, smoothness, and Lipschitz continous for losses— rates of $O\left(\log^2(n)/n^2\right)$ are at most achievable. To our knowledge, our analysis also provides the tightest high-probability bounds for gradient-based generalization gaps in nonconvex settings.
|
poster
|
2410.09766
| null | null |
[] |
[] |
[] |
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] |
298
|
Missing Data Imputation by Reducing Mutual Information with Rectified Flows
|
[
"Jiahao Yu",
"Qizhen Ying",
"Leyang Wang",
"Ziyue Jiang",
"Song Liu"
] |
This paper introduces a novel iterative method for missing data imputation that sequentially reduces mutual information between imputed data and their corresponding missing mask. Inspired by GAN-based approaches, which train generators to decrease the predictability of missingness patterns, our method explicitly targets the reduction of mutual information.Specifically, our algorithm iteratively minimizes the KL divergence between the joint distribution of the imputed data and missing mask, and the product of their marginals from the previous iteration. We show that the optimal imputation under this framework corresponds to solving an ODE, whose velocity field minimizes a rectified flow training objective. We further illustrate that some existing imputation techniques can be interpreted as approximate special cases of our mutual-information-reducing framework. Comprehensive experiments on synthetic and real-world datasets validate the efficacy of our proposed approach, demonstrating superior imputation performance.
|
poster
|
2505.11749
| null | null |
[] |
[] |
[] |
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] |
299
|
MAGNET: A Multi-agent Framework for Finding Audio-Visual Needles by Reasoning over Multi-Video Haystacks
|
[
"Sanjoy Chowdhury",
"Mohamed Elmoghany",
"Yohan Abeysinghe",
"Junjie Fei",
"Sayan Nag",
"Salman Khan",
"Mohamed Elhoseiny",
"Dinesh Manocha"
] |
Large multimodal models (LMMs) have shown remarkable progress in audio-visual understanding, yet they struggle with real-world scenarios that requirecomplex reasoning across extensive video collections. Existing benchmarks forvideo question answering remain limited in scope, typically involving one clipper query, which falls short of representing the challenges of large-scale, audio-visual retrieval and reasoning encountered in practical applications. To bridgethis gap, we introduce a novel task named AV-HaystacksQA, where the goalis to identify salient segments across different videos in response to a query andlink them together to generate the most informative answer. To this end, wepresent AVHaystacks, an audio-visual benchmark comprising 3100 annotated QApairs designed to assess the capabilities of LMMs in multi-video retrieval andtemporal grounding task. Additionally, we propose a model-agnostic, multi-agentframework MAGNET to address this challenge, achieving up to 89% and 65%relative improvements over baseline methods on BLEU@4 and GPT evaluationscores in QA task on our proposed AVHaystacks. To enable robust evaluation ofmulti-video retrieval and temporal grounding for optimal response generation, weintroduce two new metrics, STEM, which captures alignment errors between aground truth and a predicted step sequence and MTGS, to facilitate balanced andinterpretable evaluation of segment-level grounding performance. Our code anddataset will be released publicly
|
poster
|
2506.07016
| null |
https://schowdhury671.github.io/magnet_project/
|
[] |
[] |
[] |
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-0.02517690137028694
] |
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