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FlowNet: Modeling Dynamic Spatio-Temporal Systems via Flow Propagation
|
[
"Yutong Feng",
"Xu Liu",
"Yutong Xia",
"Yuxuan Liang"
] |
Accurately modeling complex dynamic spatio-temporal systems requires capturing flow-mediated interdependencies and context-sensitive interaction dynamics. Existing methods, predominantly graph-based or attention-driven, rely on similarity-driven connectivity assumptions, neglecting asymmetric flow exchanges that govern system evolution. We propose Spatio-Temporal Flow, a physics-inspired paradigm that explicitly models dynamic node couplings through quantifiable flow transfers governed by conservation principles. Building on this, we design FlowNet, a novel architecture leveraging flow tokens as information carriers to simulate source-to-destination transfers via Flow Allocation Modules, ensuring state redistribution aligns with physical laws. FlowNet dynamically adjusts the interaction radius through an Adaptive Spatial Masking module, suppressing irrelevant noise while enabling context-aware propagation. A cascaded architecture enhances scalability and nonlinear representation capacity. Experiments demonstrate that FlowNet significantly outperforms existing SOTA approaches on seven metrics in the modeling of three real-world systems, validating its efficiency and physical interpretability. We establish a principled methodology for modeling complex systems through spatio-temporal flow interactions.
|
poster
| null | null | null |
[] |
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] |
401
|
Understanding and Enhancing Mask-Based Pretraining for Universal Biological Representations
|
[
"Mingze Dong",
"Leda Wang",
"Yuval Kluger"
] |
Mask-based pretraining has become a cornerstone of modern large-scale models across natural language processing, computer vision, and recently biology. Despite its empirical success, its role and limits in learning data representations have been unclear. In this work, we show that the behavior of mask-based pretraining can be directly characterized by test risk in high-dimensional minimum-norm ("ridge-less") linear regression, without relying on further model specifications. Further analysis of linear models uncovers several novel aspects of mask-based pretraining. The theoretical framework and its implications have been validated across diverse neural architectures (including MLPs, CNNs, and Transformers) applied to both vision and language tasks. Guided by our theory, we propose an embarrassingly simple yet overlooked pretraining scheme named *Randomly Random Mask AutoEncoding* (**R$^2$MAE**), designed for learning universal representations capturing features of all scales from data. We implement **R$^2$MAE** in DNA sequence and single-cell self-supervised models, where it improves upon state-of-the-art models in terms of various performance metrics for MLP and transformer-based architectures, outperforming standard and more complicated mask pretraining schemes.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
402
|
Real-Time Execution of Action Chunking Flow Policies
|
[
"Kevin Black",
"Manuel Galliker",
"Sergey Levine"
] |
Modern AI systems, especially those interacting with the physical world, increasingly require real-time performance. However, the high latency of state-of-the-art generalist models, including recent vision-language-action models (VLAs), poses a significant challenge. While action chunking has enabled temporal consistency in high-frequency control tasks, it does not fully address the latency problem, leading to pauses or out-of-distribution jerky movements at chunk boundaries. This paper presents a novel inference-time algorithm that enables smooth asynchronous execution of action chunking policies. Our method, real-time chunking (RTC), is applicable to any diffusion- or flow-based VLA out of the box with no retraining. It generates the next action chunk while executing the current one, "freezing" actions guaranteed to execute and "inpainting" the rest. To test RTC, we introduce a new benchmark of 12 highly dynamic tasks in the Kinetix simulator, as well as evaluate 6 challenging real-world bimanual manipulation tasks. Results demonstrate that RTC is fast, performant, and uniquely robust to inference delay, significantly improving task throughput and enabling success in precise tasks --- such as lighting a match --- even in the presence of extreme latency.
|
poster
|
2506.07339
| null |
https://www.pi.website/research/real_time_chunking
|
[] |
[] |
[] |
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] |
403
|
Fractional Langevin Dynamics for Combinatorial Optimization via Polynomial-Time Escape
|
[
"Shiyue Wang",
"Ziao Guo",
"Changhong Lu",
"Junchi Yan"
] |
Langevin Dynamics (LD) and its discrete proposal have been widely applied in the field of Combinatorial Optimization (CO). Both sampling-based and data-driven approaches have benefited significantly from these methods. However, LD's reliance on Gaussian noise limits its ability to escape narrow local optima, requires costly parallel chains, and performs poorly in rugged landscapes or with non-strict constraints. These challenges have impeded the development of more advanced approaches. To address these issues, we introduce Fractional Langevin Dynamics (FLD) for CO, replacing Gaussian noise with $\alpha$-stable L\'evy noise. FLD can escape from local optima more readily via L\'evy flights, and in multiple-peak CO problems with high potential barriers it exhibits a polynomial escape time that outperforms the exponential escape time of LD. Moreover, FLD coincides with LD when $\alpha = 2$, and by tuning $\alpha$ it can be adapted to a wider range of complex scenarios in the CO fields. We provide theoretical proof that our method offers enhanced exploration capabilities and improved convergence. Experimental results on the Maximum Independent Set, Maximum Clique, and Maximum Cut problems demonstrate that incorporating FLD advances both sampling-based and data-driven approaches, achieving state-of-the-art (SOTA) performance in most of the experiments.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
404
|
Causal Discovery over Clusters of Variables in Markovian Systems
|
[
"Tara Anand",
"Adèle Ribeiro",
"Jin Tian",
"George Hripcsak",
"Elias Bareinboim"
] |
Causal discovery approaches are limited by scalability and interpretability, and are primarily for learning relationships among variables. Learning causal relationships among sets or clusters of variables is of interest as for some applications, relationships among variables grouped in semantically meaningful ways is the goal, and in others, clusters improve causal discovery in high-dimensions by reducing dimensionality. Here, we introduce an approach for learning over clusters in Markov causal systems. We develop a new graphical model to encode knowledge of relationships between user-defined clusters while fully representing independencies and dependencies over clusters, faithful to a specific distribution. Then we define and characterize a graphical equivalence class of these models that share cluster-level independence information. Lastly, we introduce an algorithm for causal discovery, leveraging these new representations, to soundly represent learnable causal relationships between clusters of variables.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
405
|
Multi-agent KTO: Reinforcing Strategic Interactions of Large Language Model in Language Game
|
[
"Rong Ye",
"Yongxin Zhang",
"yikai zhang",
"Haoyu Kuang",
"peng sun",
"zhongyu wei"
] |
Achieving Artificial General Intelligence (AGI) requires AI agents that can not only make strategic decisions but also engage in flexible and meaningful communication. Inspired by Wittgenstein's language game theory, we propose that language agents can learn through in-context interaction rather than traditional multi-stage frameworks that separate decision-making from language expression. Using Werewolf, a social deduction game that tests language understanding, strategic interaction, and adaptability, as a test bed, we develop the Multi-agent Kahneman-Tversky's Optimization (MaKTO). MaKTO engages diverse models in extensive gameplay to generate unpaired desirable and unacceptable responses, then employs KTO to refine the model's decision-making process. In 9-player Werewolf games, MaKTO achieves a 61% average win rate across various models, outperforming GPT-4o and two-stage RL agents by relative improvements of 23.0% and 10.9%, respectively. Notably, MaKTO also demonstrates human-like performance, winning 60% against expert players and showing only 48.9% detectability in Turing-style blind tests.
|
poster
|
2501.14225
| null | null |
[] |
[] |
[
"ReneeYe/werewolf_game_reasoning"
] |
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406
|
DAAC: Discrepancy-Aware Adaptive Contrastive Learning for Medical Time series
|
[
"Yifan WANG",
"Hongfeng Ai",
"ruiqi li",
"Maowei Jiang",
"Quangao Liu",
"Jiahua Dong",
"ruiyuan kang",
"Alan Liang",
"Zihang Wang",
"ruikai liu",
"Cheng Jiang",
"Chenzhong Li"
] |
Medical time-series data play a vital role in disease diagnosis but suffer from limited labeled samples and single-center bias, which hinder model generalization and lead to overfitting. To address these challenges, we propose DAAC (Discrepancy-Aware Adaptive Contrastive learning), a learnable multi-view contrastive framework that integrates external normal samples and enhances feature learning through adaptive contrastive strategies. DAAC consists of two key modules: (1) a Discrepancy Estimator, built upon a GAN-enhanced encoder-decoder architecture, captures the distribution of normal data and computes reconstruction errors as indicators of abnormality. These discrepancy features augment the target dataset to mitigate overfitting. (2) an Adaptive Contrastive Learner uses multi-head attention to extract discriminative representations by contrasting embeddings across multiple views and data granularities (subject, trial, epoch, and temporal levels), eliminating the need for handcrafted positive-negative sample pairs. Extensive experiments on three clinical datasets—covering Alzheimer’s disease, Parkinson’s disease, and myocardial infarction—demonstrate that DAAC significantly outperforms existing methods, even when only 10\% of labeled data is available, showing strong generalization and diagnostic performance.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
407
|
SPINT: Spatial Permutation-Invariant Neural Transformer for Consistent Intracortical Motor Decoding
|
[
"Trung Le",
"Hao Fang",
"Jingyuan Li",
"Tung Nguyen",
"Lu Mi",
"Amy L Orsborn",
"Uygar Sümbül",
"Eli Shlizerman"
] |
Intracortical Brain-Computer Interfaces (iBCI) aim to decode behavior from neural population activity, enabling individuals with motor impairments to restore motor functions and communication abilities. A central challenge in the long-term deployment of iBCIs is the nonstationarity of neural recordings, where instability of electrode recordings alters the composition and tuning of the recorded neural population across sessions. Existing approaches attempt to address this issue by explicit alignment techniques; however, they rely on fixed neural identities and require test-time labels and parameter updates, limiting their ability to generalize across sessions and imposing a computational burden during deployment. In this work, we introduce SPINT - a Spatial Permutation-Invariant Neural Transformer framework for behavioral decoding that operates directly on unordered sets of neural units. Central to our approach is a novel context-dependent positional embedding scheme that infers unit-specific identities dynamically, enabling flexible generalization across recording sessions. Our model supports inference on variable-size neural populations and allows few-shot, gradient-free adaptation using a small amount of unlabeled data from the new session. To further promote robustness to population variability, we introduce dynamic channel dropout, a regularization method for iBCI applications by simulating shifts in population composition during training. We evaluate our approach on three motor decoding tasks from the FALCON Benchmark, comprising multi-session datasets from human and non-human primates. Our approach demonstrates robust cross-session generalization, outperforming existing zero-shot and few-shot unsupervised baselines while eliminating the need for test-time alignment and fine-tuning. Our work contributes an initial step toward a flexible and practical framework for robust, scalable neural decoding in long-term iBCI applications.
|
poster
|
2507.08402
| null | null |
[] |
[] |
[] |
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] |
408
|
Linear Attention for Efficient Bidirectional Sequence Modeling
|
[
"Arshia Afzal",
"Elias Abad Rocamora",
"Leyla Candogan",
"Pol Puigdemont",
"Francesco Tonin",
"Yongtao Wu",
"Mahsa Shoaran",
"Volkan Cevher"
] |
Linear Transformers and State Space Models have emerged as efficient alternatives to softmax Transformers for causal sequence modeling, enabling parallel training via matrix multiplication and efficient RNN-style inference. However, despite their success in causal tasks, no unified framework exists for applying Linear Transformers to bidirectional sequence modeling.We introduce LION, the first framework to systematically extend Linear Transformers to the bidirectional setting. LION generalizes three core representations commonly used in the causal case—full Linear Attention, bidirectional RNN, and chunkwise parallel form—to the bidirectional setting. These forms are theoretically equivalent and enable models to exploit the strengths of each during training and inference.We prove that a broad class of Linear Transformers can be extended using LION and validate our framework via three core examples based on the choice of decay type: LION-Lit, the bidirectional extension of Katharopoulos et al., 2020; LION-D, based on Sun et al., 2023; and LION-S, a variant using selective decay. Across standard bidirectional tasks, LION enables models to match or exceed the performance of softmax Transformers, while offering significantly faster training and more efficient inference than existing State Space Models.
|
poster
|
2502.16249
| null | null |
[] |
[] |
[] |
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] |
409
|
Stackelberg Self-Annotation: A Robust Approach to Data-Efficient LLM Alignment
|
[
"Chu Xu",
"Zhixin Zhang",
"Tianyu Jia",
"Yujie Jin"
] |
Aligning large language models (LLMs) with human preferences typically demands vast amounts of meticulously curated data, which is both expensive and prone to labeling noise. We propose Stackelberg Game Preference Optimization (SGPO), a robust alignment framework that models alignment as a two-player Stackelberg game between a policy (leader) and a worst-case preference distribution (follower). The proposed SGPO guarantees $\mathcal{O}(\epsilon)$-bounded regret within an $\epsilon$-Wasserstein ball, offering formal robustness to (self-)annotation noise. We instantiate SGPO with Stackelberg Self-Annotated Preference Optimization (SSAPO), which uses minimal human-labeled “seed” preferences and iteratively self-annotates new prompts. In each iteration, SSAPO applies a distributionally robust reweighting of synthetic annotations, ensuring that noisy or biased self-labels do not derail training. Remarkably, using only 2K seed preferences—about 1/30 of standard human labels—SSAPO achieves strong win rates against GPT-4 across multiple benchmarks within three iterations. These results highlight that a principled Stackelberg formulation yields data-efficient alignment for LLMs, significantly reducing reliance on costly human annotations.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
410
|
OmniSync: Towards Universal Lip Synchronization via Diffusion Transformers
|
[
"Ziqiao Peng",
"Jiwen Liu",
"Haoxian Zhang",
"Xiaoqiang Liu",
"Songlin Tang",
"Pengfei Wan",
"Di ZHANG",
"Hongyan Liu",
"Jun He"
] |
Lip synchronization is the task of aligning a speaker’s lip movements in video with corresponding speech audio, and it is essential for creating realistic, expressive video content. However, existing methods often rely on reference frames and masked-frame inpainting, which limit their robustness to identity consistency, pose variations, facial occlusions, and stylized content. In addition, since audio signals provide weaker conditioning than visual cues, lip shape leakage from the original video will affect lip sync quality.In this paper, we present OmniSync, a universal lip synchronization framework for diverse visual scenarios. Our approach introduces a mask-free training paradigm using Diffusion Transformer models for direct frame editing without explicit masks, enabling unlimited-duration inference while maintaining natural facial dynamics and preserving character identity.During inference, we propose a flow-matching-based progressive noise initialization to ensure pose and identity consistency, while allowing precise mouth-region editing. To address the weak conditioning signal of audio, we develop a Dynamic Spatiotemporal Classifier-Free Guidance (DS-CFG) mechanism that adaptively adjusts guidance strength over time and space.We also establish the AIGC-LipSync Benchmark, the first evaluation suite for lip synchronization in diverse AI-generated videos. Extensive experiments demonstrate that OmniSync significantly outperforms prior methods in both visual quality and lip sync accuracy, achieving superior results in both real-world and AI-generated videos.
|
spotlight
|
2505.21448
| null | null |
[] |
[] |
[] |
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] |
411
|
Understanding while Exploring: Semantics-driven Active Mapping
|
[
"Liyan Chen",
"Huangying Zhan",
"Hairong Yin",
"Yi Xu",
"Philippos Mordohai"
] |
Effective robotic autonomy in unknown environments demands proactive exploration and precise understanding of both geometry and semantics. In this paper, we propose ActiveSGM, an active semantic mapping framework designed to predict the informativeness of potential observations before execution. Built upon a 3D Gaussian Splatting (3DGS) mapping backbone, our approach employs semantic and geometric uncertainty quantification, coupled with a sparse semantic representation, to guide exploration. By enabling robots to strategically select the most beneficial viewpoints, ActiveSGM efficiently enhances mapping completeness, accuracy, and robustness to noisy semantic data, ultimately supporting more adaptive scene exploration. Our experiments on the Replica and Matterport3D datasets highlight the effectiveness of ActiveSGM in active semantic mapping tasks.
|
poster
|
2506.00225
| null | null |
[] |
[] |
[] |
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412
|
MEGADance: Mixture-of-Experts Architecture for Genre-Aware 3D Dance Generation
|
[
"kaixing yang",
"Xulong Tang",
"Ziqiao Peng",
"Yuxuan Hu",
"Jun He",
"Hongyan Liu"
] |
Music-driven 3D dance generation has attracted increasing attention in recent years, with promising applications in choreography, virtual reality, and creative content creation. Previous research has generated promising realistic dance movement from audio signals. However, traditional methods underutilize genre conditioning, often treating it as auxiliary modifiers rather than core semantic drivers. This oversight compromises music-motion synchronization and disrupts dance genre continuity, particularly during complex rhythmic transitions, thereby leading to visually unsatisfactory effects. To address the challenge, we propose MEGADance, a novel architecture for music-driven 3D dance generation. By decoupling choreographic consistency into dance generality and genre specificity, MEGADance demonstrates significant dance quality and strong genre controllability. It consists of two stages: (1) High-Fidelity Dance Quantization Stage (HFDQ), which encodes dance motions into a latent representation by Finite Scalar Quantization (FSQ) and reconstructs them with kinematic-dynamic constraints, and (2) Genre-Aware Dance Generation Stage (GADG), which maps music into the latent representation by synergistic utilization of Mixture-of-Experts (MoE) mechanism with Mamba-Transformer hybrid backbone. Extensive experiments on the FineDance and AIST++ dataset demonstrate the state-of-the-art performance of MEGADance both qualitatively and quantitatively. Code will be released upon acceptance.
|
poster
|
2505.17543
| null | null |
[] |
[] |
[] |
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] |
413
|
Multi-Objective Reinforcement Learning with Max-Min Criterion: A Game-Theoretic Approach
|
[
"woohyeon Byeon",
"Giseung Park",
"Jongseong Chae",
"Amir Leshem",
"Youngchul Sung"
] |
In this paper, we propose a provably convergent and practical framework for multi-objective reinforcement learning with max-min criterion. From a game-theoretic perspective, we reformulate max-min multi-objective reinforcement learning as a two-player zero-sum regularized continuous game and introduce an efficient algorithm based on mirror descent. Our approach simplifies the policy update while ensuring global last-iterate convergence. We provide a comprehensive theoretical analysis on our algorithm, including iteration complexity under both exact and approximate policy evaluations, as well as sample complexity bounds. To further enhance performance, we modify the proposed algorithm with adaptive regularization.Our experiments demonstrate the convergence behavior of the proposed algorithm in tabular settings, and our implementation for deep reinforcement learning significantly outperforms previous baselines in many MORL environments.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
414
|
Improving Target Sound Extraction via Disentangled Codec Representations with Privileged Knowledge Distillation
|
[
"Dail Kim",
"Joon-Hyuk Chang"
] |
Target sound extraction aims to isolate target sound sources from an input mixture using a target clue to identify the sounds of interest. To address the challenge posed by the wide variety of sounds, recent work has introduced privileged knowledge distillation (PKD), which utilizes privileged information (PI) about the target sound, available only during training. While PKD has shown promise, existing approaches often suffer from overfitting of the teacher model for the overly rich PI and ineffective knowledge transfer to the student model. In this paper, we propose Disentangled Codec Knowledge Distillation (DCKD) to mitigate these issues by regulating the amount and the flow of target sound information within the teacher model. We begin by extracting a compressed representation of the target sound using a neural audio codec to regulate the amount of PI. Disentangled representation learning is then applied to remove class information and extract fine-grained temporal information as PI. Subsequently, an n-hot vector as the target clue and the class-independent PI are used to condition the early and later layers of the teacher model, respectively, forming a regulated coarse-to-fine target information flow. The resulting representation is transferred to the student model through feature-level knowledge distillation. Experimental results show that DCKD consistently improves existing methods across model architectures under the multi-target selection condition.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
415
|
Noisy Multi-Label Learning through Co-Occurrence-Aware Diffusion
|
[
"Senyu Hou",
"Yuru Ren",
"Gaoxia Jiang",
"Wenjian Wang"
] |
Noisy labels often compel models to overfit, especially in multi-label classification tasks. Existing methods for noisy multi-label learning (NML) primarily follow a discriminative paradigm, which relies on noise transition matrix estimation or small-loss strategies to correct noisy labels. However, they remain substantial optimization difficulties compared to noisy single-label learning. In this paper, we propose a Co-Occurrence-Aware Diffusion (CAD) model, which reformulates NML from a generative perspective. We treat features as conditions and multi-labels as diffusion targets, optimizing the diffusion model for multi-label learning with theoretical guarantees. Benefiting from the diffusion model's strength in capturing multi-object semantics and structured label matrix representation, we can effectively learn the posterior mapping from features to true multi-labels. To mitigate the interference of noisy labels in the forward process, we guide generation using pseudo-clean labels reconstructed from the latent neighborhood space, replacing original point-wise estimates with neighborhood-based proxies. In the reverse process, we further incorporate label co-occurrence constraints to enhance the model's awareness of incorrect generation directions, thereby promoting robust optimization. Extensive experiments on both synthetic (Pascal-VOC, MS-COCO) and real-world (NUS-WIDE) noisy datasets demonstrate that our approach outperforms state-of-the-art methods.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
416
|
Trust, But Verify: A Self-Verification Approach to Reinforcement Learning with Verifiable Rewards
|
[
"Xiaoyuan Liu",
"Tian Liang",
"Zhiwei He",
"Jiahao Xu",
"Wenxuan Wang",
"Pinjia He",
"Zhaopeng Tu",
"Haitao Mi",
"Dong Yu"
] |
Large Language Models (LLMs) show great promise in complex reasoning, with Reinforcement Learning with Verifiable Rewards (RLVR) being a key enhancement strategy. However, a prevalent issue is ``superficial self-reflection'', where models fail to robustly verify their own outputs. We introduce RISE (Reinforcing Reasoning with Self-Verification), a novel online RL framework designed to tackle this. RISE explicitly and simultaneously trains an LLM to improve both its problem-solving and self-verification abilities within a single, integrated RL process. The core mechanism involves leveraging verifiable rewards from an outcome verifier to provide on-the-fly feedback for both solution generation and self-verification tasks. In each iteration, the model generates solutions, then critiques its own on-policy generated solutions, with both trajectories contributing to the policy update. Extensive experiments on diverse mathematical reasoning benchmarks show that RISE consistently improves model's problem-solving accuracy while concurrently fostering strong self-verification skills. Our analyses highlight the advantages of online verification and the benefits of increased verification compute. Additionally, RISE models exhibit more frequent and accurate self-verification behaviors during reasoning. These advantages reinforce RISE as a flexible and effective path towards developing more robust and self-aware reasoners.
|
poster
|
2505.13445
|
https://github.com/xyliu-cs/rise
| null |
[] |
[] |
[] |
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] |
417
|
Fused View-Time Attention and Feedforward Reconstruction for 4D Scene Generation
|
[
"Chaoyang Wang",
"Ashkan Mirzaei",
"Vidit Goel",
"Willi Menapace",
"Aliaksandr Siarohin",
"Michael Vasilkovsky",
"Ivan Skorokhodov",
"Vladislav Shakhrai",
"Sergei Korolev",
"Sergey Tulyakov",
"Peter Wonka"
] |
We propose the first framework capable of computing a 4D spatio-temporal grid of video frames and 3D Gaussian particles for each time step using a feed-forward architecture. Our architecture has two main components, a 4D video model and a 4D reconstruction model. In the first part, we analyze current 4D video diffusion architectures that perform spatial and temporal attention either sequentially or in parallel within a two-stream design. We highlight the limitations of existing approaches and introduce a novel fused architecture that performs spatial and temporal attention within a single layer. The key to our method is a sparse attention pattern, where tokens attend to others in the same frame, at the same timestamp, or from the same viewpoint.In the second part, we extend existing 3D reconstruction algorithms by introducing a Gaussian head, a camera token replacement algorithm, and additional dynamic layers and training. Overall, we establish a new state of the art for 4D generation, improving both visual quality and reconstruction capability.
|
poster
|
2506.18839
| null | null |
[] |
[] |
[] |
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] |
418
|
Negative Feedback Really Matters: Signed Dual-Channel Graph Contrastive Learning Framework for Recommendation
|
[
"Leqi Zheng",
"Chaokun Wang",
"Zixin Song",
"Cheng Wu",
"Shannan Yan",
"Jiajun Zhang",
"Ziyang Liu"
] |
Traditional recommender systems have relied heavily on positive feedback for learning user preferences, while the abundance of negative feedback in real-world scenarios remains underutilized. To address this limitation, recent years have witnessed increasing attention on leveraging negative feedback in recommender systems to enhance recommendation performance. However, existing methods face three major challenges: limited model compatibility, ineffective information exchange, and computational inefficiency. To overcome these challenges, we propose a model-agnostic Signed Dual-Channel Graph Contrastive Learning (SDCGCL) framework that can be seamlessly integrated with existing graph contrastive learning methods. The framework features three key components: (1) a Dual-Channel Graph Embedding that separately processes positive and negative graphs, (2) a Cross-Channel Distribution Calibration mechanism to maintain structural consistency, and (3) an Adaptive Prediction Strategy that effectively combines signals from both channels. Building upon this framework, we further propose a Dual-channel Feedback Fusion (DualFuse) model and develop a two-stage optimization strategy to ensure efficient training. Extensive experiments on four public datasets demonstrate that our approach consistently outperforms state-of-the-art baselines by substantial margins while exhibiting minimal computational complexity. Our source code and data are released at \url{https://anonymous.4open.science/r/SDCGCL-E56E/}.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
419
|
The First Few Tokens Are All You Need: An Efficient and Effective Unsupervised Prefix Fine-Tuning Method for Reasoning Models
|
[
"Ke Ji",
"Jiahao Xu",
"Tian Liang",
"Qiuzhi Liu",
"Zhiwei He",
"Xiaoyuan Liu",
"Xingyu Chen",
"Junying Chen",
"Benyou Wang",
"Zhaopeng Tu",
"Haitao Mi",
"Dong Yu"
] |
Improving the reasoning capabilities of large language models (LLMs) typically requires supervised fine-tuning with labeled data or computationally expensive sampling. We introduce Unsupervised Prefix Fine-Tuning (UPFT), which leverages the observation of Prefix Self-Consistency -- the shared initial reasoning steps across diverse solution trajectories -- to enhance LLM reasoning efficiency. By training exclusively on the initial prefix substrings (as few as 8 tokens), UPFT removes the need for labeled data or exhaustive sampling. Experiments on reasoning benchmarks show that UPFT matches the performance of supervised methods such as Rejection Sampling Fine-Tuning, while reducing training time by 75\% and sampling cost by 99\%. Further analysis reveals that errors tend to appear in later stages of the reasoning process and that prefix-based training preserves the model’s structural knowledge. This work demonstrates how minimal unsupervised fine-tuning can unlock substantial reasoning gains in LLMs, offering a scalable and resource-efficient alternative to conventional approaches.
|
poster
|
2503.02875
|
https://github.com/1KE-JI/UPFT
| null |
[] |
[] |
[] |
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] |
420
|
Bridging Human and LLM Judgments: Calibration, Alignment, and Bias Detection
|
[
"Felipe Maia Polo",
"Xinhe Wang",
"Mikhail Yurochkin",
"Gongjun Xu",
"Moulinath Banerjee",
"Yuekai Sun"
] |
LLM-as-a-judge (LLMJ) has become popular for scalably evaluating language model outputs on open-ended user queries. However, LLM judges do not always align with human annotators, exhibiting systematic and undesired differences, e.g., biases toward certain writing styles. In this paper, we introduce the first unified statistical framework that jointly models human and LLM judge ratings under both absolute and relative (e.g., pairwise comparisons) evaluation paradigms. We posit the existence of a latent human preference score that is shared between humans and LLM judges for each prompt–response pair, and allow LLM judgments to deviate systematically via a linear transformation of a covariate vector, which encodes potential sources of LLM judge biases. Our core contribution is a statistical model that links LLM-assigned scores to the latent human signal and a set of biasing covariates, for which we propose a fitting algorithm and establish asymptotic normality for resulting estimators, enabling formal hypothesis tests of human-LLM discrepancies. Empirically, our model (i) improves the probability calibration of LLM judgments, (ii) aligns LLM ratings more closely with human judgments, and (iii) quantifies and tests systematic LLM biases across evaluation scenarios. We verify the efficacy and provide insightful results using four LLM judges and queries from BigGen Bench and Chatbot Arena.
|
poster
| null | null | null |
[] |
[] |
[] |
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421
|
ESCA: Contextualizing Embodied Agents via Scene-Graph Generation
|
[
"Jiani Huang",
"Mayank Keoliya",
"Matthew Kuo",
"Neelay Velingker",
"Amish Sethi",
"JungHo Jung",
"Ziyang Li",
"Ser Nam Lim",
"Mayur Naik"
] |
Multi-modal large language models (MLLMs) are making rapid progress toward general-purpose embodied agents. However, current training pipelines primarily rely on high-level vision-sound-text pairs and lack fine-grained, structured alignment between pixel-level visual content and textual semantics. To overcome this challenge, we propose ESCA, a new framework for contextualizing embodied agents through structured spatial-temporal understanding. At its core is SGClip, a novel CLIP-based, open-domain, and promptable model for generating scene graphs. SGClip is trained on 87K+ open-domain videos via a neurosymbolic learning pipeline, which harnesses model-driven self-supervision from video-caption pairs and structured reasoning, thereby eliminating the need for human-labeled scene graph annotations. We demonstrate that SGClip supports both prompt-based inference and task-specific fine-tuning, excelling in scene graph generation and action localization benchmarks. ESCA with SGClip consistently improves both open-source and commercial MLLMs, achieving state-of-the-art performance across two embodied environments. Notably, it significantly reduces agent perception errors and enables open-source models to surpass proprietary baselines.
|
spotlight
| null | null | null |
[] |
[] |
[] |
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422
|
Sloth: scaling laws for LLM skills to predict multi-benchmark performance across families
|
[
"Felipe Maia Polo",
"Seamus Somerstep",
"Leshem Choshen",
"Yuekai Sun",
"Mikhail Yurochkin"
] |
Scaling laws for large language models (LLMs) predict model performance based on parameters like size and training data. However, differences in training configurations and data processing across model families lead to significant variations in benchmark performance, making it difficult for a single scaling law to generalize across all LLMs. On the other hand, training family-specific scaling laws requires training models of varying sizes for every family. In this work, we propose Skills Scaling Laws (SSLaws, pronounced as \texttt{Sloth}), a novel scaling law that leverages publicly available benchmark data and assumes LLM performance is driven by low-dimensional latent skills, such as reasoning and instruction following. These latent skills are influenced by computational resources like model size and training tokens, but with varying efficiencies across model families. \texttt{Sloth} exploits correlations across benchmarks to provide more accurate and interpretable predictions while alleviating the need to train multiple LLMs per family. We present both theoretical results on parameter identification and empirical evaluations on 12 prominent benchmarks, from Open LLM Leaderboard v1/v2, demonstrating that \texttt{Sloth} predicts LLM performance accurately and offers insights into scaling behaviors for complex downstream tasks, increased test-time compute, and compute-optimal scaling of skills.
|
poster
|
2412.06540
|
https://github.com/felipemaiapolo/sloth
| null |
[] |
[] |
[] |
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] |
423
|
Slow Transition to Low-Dimensional Chaos in Heavy-Tailed Recurrent Neural Networks
|
[
"Eva Xie",
"Stefan Mihalas",
"Łukasz Kuśmierz"
] |
Growing evidence suggests that synaptic weights in the brain follow heavy-tailed distributions, yet most theoretical analyses of recurrent neural networks (RNNs) assume Gaussian connectivity. We systematically study the activity of RNNs with random weights drawn from biologically plausible Lévy alpha-stable distributions. While mean-field theory for the infinite system predicts that the quiescent state is always unstable---implying ubiquitous chaos---our finite-size analysis reveals a sharp transition between quiescent and chaotic dynamics. We theoretically predict the gain at which the finite system transitions from quiescent to chaotic dynamics, and validate it through simulations. Compared to Gaussian networks, finite heavy-tailed RNNs exhibit a broader gain regime near the edge of chaos, namely, a slow transition to chaos. However, this robustness comes with a tradeoff: heavier tails reduce the Lyapunov dimension of the attractor, indicating lower effective dimensionality. Our results reveal a biologically aligned tradeoff between the robustness of dynamics near the edge of chaos and the richness of high-dimensional neural activity. By analytically characterizing the transition point in finite-size networks---where mean-field theory breaks down---we provide a tractable framework for understanding dynamics in realistically sized, heavy-tailed neural circuits.
|
poster
|
2505.09816
| null | null |
[] |
[] |
[] |
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424
|
Forging Time Series with Language: A Large Language Model Approach to Synthetic Data Generation
|
[
"Cécile Rousseau",
"Tobia Boschi",
"Giandomenico Cornacchia",
"Dhaval Salwala",
"Alessandra Pascale",
"Juan Moreno"
] |
SDForger is a flexible and efficient framework for generating high-quality multivariate time series using LLMs. Leveraging a compact data representation, SDForger provides synthetic time series generation from a few samples and low-computation fine-tuning of any autoregressive LLM. Specifically, the framework transforms univariate and multivariate signals into tabular embeddings, which are then encoded into text and used to fine-tune the LLM. At inference, new textual embeddings are sampled and decoded into synthetic time series that retain the original data's statistical properties and temporal dynamics. Across a diverse range of datasets, SDForger outperforms existing generative models in many scenarios, both in similarity-based evaluations and downstream forecasting tasks. By enabling textual conditioning in the generation process, SDForger paves the way for multimodal modeling and the streamlined integration of time series with textual information.
|
poster
|
2505.17103
| null | null |
[] |
[] |
[] |
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425
|
Temporal Logic-Based Multi-Vehicle Backdoor Attacks against Offline RL Agents in End-to-end Autonomous Driving
|
[
"Xuan Chen",
"Shiwei Feng",
"Zikang Xiong",
"Shengwei An",
"Yunshu Mao",
"Lu Yan",
"Guanhong Tao",
"Wenbo Guo",
"Xiangyu Zhang"
] |
Assessing the safety of autonomous driving (AD) systems against security threats, particularly backdoor attacks, is a stepping stone for real-world deployment. However, existing works mainly focus on pixel-level triggers which are impractical to deploy in the real world. We address this gap by introducing a novel backdoor attack against the end-to-end AD systems that leverage one or more other vehicles' trajectories as triggers. To generate precise trigger trajectories, we first use temporal logic (TL) specifications to define the behaviors of attacker vehicles. Configurable behavior models are then used to generate these trajectories, which are quantitatively evaluated and iteratively refined based on the TL specifications. We further develop a negative training strategy by incorporating patch trajectories that are similar to triggers but are designated not to activate the backdoor. It enhances the stealthiness of the attack and refines the system’s responses to trigger scenarios. Through extensive experiments on 5 offline reinforcement learning (RL) driving agents with 6 trigger patterns and target actions combinations, we demonstrate the flexibility and effectiveness of our proposed attack, showing the under-exploration of existing end-to-end AD systems' vulnerabilities to such trajectory-based backdoor attacks.Videos of our attack are available at: https://sites.google.com/view/tlbackdoor/home.
|
poster
|
2509.16950
| null | null |
[] |
[] |
[] |
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426
|
LoRO: Real-Time on-Device Secure Inference for LLMs via TEE-Based Low Rank Obfuscation
|
[
"Gaojian Xiong",
"Yu Sun",
"Jianhua Liu",
"Jian Cui",
"Jianwei Liu"
] |
While Large Language Models (LLMs) have gained remarkable success, they are consistently at risk of being stolen when deployed on untrusted edge devices. As a solution, TEE-based secure inference has been proposed to protect valuable model property. However, we identify a statistical vulnerability in existing protection methods, and furtherly compromise their security guarantees by proposed Model Stealing Attacks with Prior. To eliminate this vulnerability, LoRO is presented in this paper, which leverages dense mask to completely obfuscate parameters. LoRO includes two innovations: (1) Low Rank Mask, which uses low-rank factors to generate dense masks efficiently. The computing complexity in TEE is hence reduced by an exponential amount to achieve inference speed up, while providing robust model confidentiality. (2) Factors Multiplexing, which reuses several cornerstone factors to generate masks for all layers. Compared to one-mask-per-layer, the secure memory requirement is reduced from GB-level to tens of MB, hence avoiding the hundred-fold latency introduced by secure memory paging. Experimental results indicate that LoRO achieve a $0.94\times$ Model Stealing (MS) accuracy, while SOTA methods presents $3.37\times$ at least. The averaged inference latency of LoRO is only $1.49\times$, compared to the best $112\times$ of SOTA. Moreover, LoRO results no accuracy loss, and requires no re-training and structure modification. LoRO can solve the concerns regarding model thefts on edge devices in an efficient and secure manner, facilitating the wide edge application of LLMs.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
427
|
Bilevel ZOFO: Efficient LLM Fine-Tuning and Meta-Training
|
[
"Reza Shirkavand",
"Peiran Yu",
"Qi He",
"Heng Huang"
] |
Fine-tuning pre-trained Large Language Models (LLMs) for downstream tasks using First-Order (FO) optimizers presents significant computational challenges. Parameter-Efficient Fine-Tuning~(PEFT) methods have been proposed to address these challenges by freezing most model parameters and training only a small subset. While PEFT is efficient, it may not outperform full fine-tuning when high task-specific performance is required.Zeroth-Order (ZO) methods offer an alternative for fine-tuning the entire pre-trained model by approximating gradients using only the forward pass, thus eliminating the computational burden of back-propagation,% in first-order methods, but they converge painfully slowly and are very sensitive to the choice of task prompts.We bridge these worlds with Bilevel‑ZOFO, a penalty‑based bilevel formulation that treats adapter parameters as a lower‑level learner coupled to an upper‑level ZO optimizer of the full backbone. This double-loop optimization strategy only requires the gradient of the PEFT model and the forward pass of the base model. We provide theoretical convergence guarantees for Bilevel ZOFO. Empirically, we demonstrate that Bilevel-ZOFO significantly outperforms existing ZO methods, achieves 2–4$\times$ faster training, and reduces sensitivity to prompts. Bilevel-ZOFO also outperforms FO PEFT methods while maintaining similar memory efficiency. Additionally, we show its strong potential for meta learning.
|
poster
|
2502.03604
| null | null |
[] |
[] |
[] |
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] |
428
|
Cost-Aware Contrastive Routing for LLMs
|
[
"Reza Shirkavand",
"Shangqian Gao",
"Peiran Yu",
"Heng Huang"
] |
We study cost-aware routing for large language models across diverse and dynamic pools of models. Existing approaches often overlook prompt-specific context, rely on expensive model profiling, assume a fixed set of experts, or use inefficient trial-and-error strategies. We introduce Cost-Spectrum Contrastive Routing (CSCR), a lightweight framework that maps both prompts and models into a shared embedding space to enable fast, cost-sensitive selection. CSCR uses compact, fast-to-compute logit footprints for open-source models and perplexity fingerprints for black-box APIs. A contrastive encoder is trained to favor the cheapest accurate expert within adaptive cost bands. At inference time, routing reduces to a single $k$‑NN lookup via a FAISS index, requiring no retraining when the expert pool changes and enabling microsecond latency. Across multiple benchmarks, CSCR consistently outperforms baselines, improving the accuracy–cost tradeoff by up to 25\%, while generalizing robustly to unseen LLMs and out-of-distribution prompts.
|
spotlight
|
2508.12491
| null | null |
[] |
[] |
[] |
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429
|
LongMagpie: A Self-synthesis Method for Generating Large-scale Long-context Instructions
|
[
"Chaochen Gao",
"Xing W",
"Zijia Lin",
"Debing Zhang",
"Songlin Hu"
] |
High-quality long-context instruction data is essential for aligning long-context large language models (LLMs). Despite the public release of models like Qwen and Llama, their long-context instruction data remains proprietary. Human annotation is costly and challenging, while template-based synthesis methods limit scale, diversity, and quality. We introduce LongMagpie, a self-synthesis framework that automatically generates large-scale long-context instruction data. Our key insight is that aligned long-context LLMs, when presented with a document followed by special tokens preceding a user turn, auto-regressively generate contextually relevant queries. By harvesting these document-query pairs and the model's responses, LongMagpie produces high-quality instructions without human effort. Experiments on HELMET, RULER, and Longbench v2 demonstrate that LongMagpie achieves leading performance on long-context tasks while maintaining competitive performance on short-context tasks, establishing it as a simple and effective approach for open, diverse, and scalable long-context instruction data synthesis.
|
poster
|
2505.17134
| null | null |
[] |
[] |
[] |
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430
|
StableGuard: Towards Unified Copyright Protection and Tamper Localization in Latent Diffusion Models
|
[
"Haoxin Yang",
"Bangzhen Liu",
"Xuemiao Xu",
"Cheng Xu",
"Yuyang Yu",
"Zikai Huang",
"Yi Wang",
"Shengfeng He"
] |
The advancement of diffusion models has enhanced the realism of AI-generated content but also raised concerns about misuse, necessitating robust copyright protection and tampering localization. Although recent methods have made progress toward unified solutions, their reliance on post hoc processing introduces considerable application inconvenience and compromises forensic reliability. We propose StableGuard, a novel framework that seamlessly integrates a binary watermark into the diffusion generation process, ensuring copyright protection and tampering localization in Latent Diffusion Models through an end-to-end design. We develop a Multiplexing Watermark VAE (MPW-VAE) by equipping a pretrained Variational Autoencoder (VAE) with a lightweight latent residual-based adapter, enabling the generation of paired watermarked and watermark-free images. These pairs, fused via random masks, create a diverse dataset for training a tampering-agnostic forensic network. To further enhance forensic synergy, we introduce a Mixture-of-Experts Guided Forensic Network (MoE-GFN) that dynamically integrates holistic watermark patterns, local tampering traces, and frequency-domain cues for precise watermark verification and tampered region detection. The MPW-VAE and MoE-GFN are jointly optimized in a self-supervised, end-to-end manner, fostering a reciprocal training between watermark embedding and forensic accuracy. Extensive experiments demonstrate that StableGuard consistently outperforms state-of-the-art methods in image fidelity, watermark verification, and tampering localization.
|
poster
|
2509.17993
| null | null |
[] |
[] |
[] |
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431
|
The Future Unmarked: Watermark Removal in AI-Generated Images via Next-Frame Prediction
|
[
"Huming Qiu",
"Zhaoxiang Wang",
"Mi Zhang",
"Xiaohan Zhang",
"Xiaoyu You",
"Min Yang"
] |
Image watermarking embeds imperceptible signals into AI-generated images for deepfake detection and provenance verification. Although recent semantic-level watermarking methods demonstrate strong resistance against conventional pixel-level removal attacks, their robustness against more advanced removal strategies remains underexplored, raising concerns about their reliability in practical scenarios. Existing removal attacks primarily operate in the pixel domain without altering image semantics, which limits their effectiveness against semantic-level watermarks.In this paper, we propose Next Frame Prediction Attack (NFPA), the first semantic-level removal attack. Unlike pixel-level attacks, NFPA formulates watermark removal as a video generation task: it treats the watermarked image as the initial frame and aims to subtly manipulate the image semantics to generate the next-frame image, i.e., the unwatermarked image.We conduct a comprehensive evaluation on eight state-of-the-art semantic watermarking schemes, demonstrating that NFPA consistently outperforms nine removal attack baselines in both watermark removal and image quality preservation. Our results reveal the vulnerabilities of current image watermarking methods and highlight the urgent need for more robust watermarks.Code is available at https://anonymous.4open.science/r/NFPA/.
|
poster
| null | null | null |
[] |
[] |
[] |
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432
|
QSCA: Quantization with Self-Compensating Auxiliary for Monocular Depth Estimation
|
[
"Jincheol Yang",
"Jaemin Choi",
"Matti Zinke",
"Suk-Ju Kang"
] |
Monocular depth estimation has advanced significantly with foundation models like Depth Anything, leveraging large-scale transformer architectures for the superior generalization. However, the deployment on resource-constrained devices remains challenging due to the high computation and memory requirement. Existing quantization methods, such as post-training quantization and quantization-aware training, often face trade-offs between efficiency and accuracy, or require extensive labeled data for retraining.To address these limitations, we propose Quantization with Self-Compensating Auxiliary for Monocular Depth Estimation (QSCA), a novel framework for 4-bit post-training quantization of Monocular depth estimation models. Our method integrates a lightweight Self-Compensating Auxiliary (SCA) module into both transformer encoder and decoder blocks, enabling the quantized model to recover from performance degradation without requiring ground truth.This design enables fast adaptation while preserving structural and spatial consistency in predicted depth maps.To our knowledge, this is the first framework to successfully apply 4-bit quantization across all layers of large-scale monocular depth estimation models.Experimental results demonstrate that QSCA significantly improves quantized depth estimation performance. On the NYUv2 dataset, it achieves an 11\% improvement in $\delta_1$ accuracy over existing post-training quantization methods.
|
poster
| null | null | null |
[] |
[] |
[] |
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433
|
WebDancer: Towards Autonomous Information Seeking Agency
|
[
"Jialong Wu",
"Baixuan Li",
"Runnan Fang",
"Wenbiao Yin",
"Liwen Zhang",
"Zhenglin Wang",
"Zhengwei Tao",
"Ding-Chu Zhang",
"Zekun Xi",
"Robert Tang",
"Yong Jiang",
"Pengjun Xie",
"Fei Huang",
"Jingren Zhou"
] |
Addressing intricate real-world problems necessitates in-depth information seeking and multi-step reasoning. Recent progress in agentic systems, exemplified by Deep Research, underscores the potential for autonomous multi-step research. In this work, we present a cohesive paradigm for building end-to-end agentic information seeking agents from a data-centric and training-stage perspective.Our approach consists of four key stages: (1) browsing data construction, (2) trajectories sampling, (3) supervised fine-tuning for effective cold start, and (4) reinforcement learning for enhanced generalisation.We instantiate this framework in a web agent based on the ReAct format, WebDancer.Empirical evaluations on the challenging GAIA and WebWalkerQA benchmarks demonstrate the strong performance of WebDancer, achieving considerable results and highlighting the efficacy of our training paradigm. Further analysis of agent training provides valuable insights and actionable, systematic pathways for developing more capable agentic models.
|
poster
|
2505.22648
|
https://github.com/Alibaba-NLP/DeepResearch
| null |
[] |
[
"Alibaba-NLP/WebDancer-32B"
] |
[] |
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] |
434
|
Theoretical Guarantees for the Retention of Strict Nash Equilibria by Coevolutionary Algorithms
|
[
"Alistair Benford",
"Per Kristian Lehre"
] |
Most methods for finding a Nash equilibrium rely on procedures that operate over the entire action space, making them infeasible for settings with too many actions to be searched exhaustively. Randomised search heuristics such as coevolutionary algorithms offer benefits in such settings, however they lack many of the theoretical guarantees established for exhaustive methods such as zero-regret learning. We address this by developing a method for proving necessary and sufficient conditions for a coevolutionary algorithm to be stable, in the sense that it reliably retains a Nash equilibrium following discovery. As the method provides bounds that are adapted to both application and algorithm instance, it can be used as a practical tool for parameter configuration. We additionally show how bounds on regret may be deduced from our results and undertake corresponding empirical analysis.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
435
|
WHAT MAKES MATH PROBLEMS HARD FOR REINFORCEMENT LEARNING: A CASE STUDY
|
[
"Ali Shehper",
"Anibal Medina-Mardones",
"Lucas Fagan",
"Bartłomiej Lewandowski",
"Angus Gruen",
"Yang Qiu",
"Piotr Kucharski",
"Zhenghan Wang",
"Sergei Gukov"
] |
Using a long-standing conjecture from combinatorial group theory, we explore, from multiple perspectives, the challenges of finding rare instances carrying disproportionately high rewards. Based on lessons learned in the context defined by the Andrews--Curtis conjecture, we analyze how reinforcement learning agents handle problems of varying hardness. We also address many mathematical questions as a part of our study. Notably, we demonstrate the length reducibility of all but two presentations in the Akbulut--Kirby series (1981), and resolve various potential counterexamples in the Miller--Schupp series (1991), including three infinite subfamilies.
|
poster
|
2408.15332
| null | null |
[] |
[] |
[] |
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436
|
UGCFormer: A Simple yet Universal Graph Cross-attention Network
|
[
"Jiaming Zhuo",
"Ziyi Ma",
"Yintong Lu",
"Yuwei Liu",
"Kun Fu",
"Di Jin",
"Chuan Wang",
"Wu Wenning",
"Zhen Wang",
"Xiaochun Cao",
"Liang Yang"
] |
Graph Transformers (GTs), effective at capturing long-range dependencies and structural biases simultaneously, are gradually recognized as viable alternatives to traditional Graph Neural Networks (GNNs). Advanced methods for GTs to leverage topology information involve integrating the GNN modules and modulating node attributes using positional encodings. However, the potential mechanism for their effectiveness remains not fully explored and understood. Upon revisiting these two methods, a common mechanism—Cross Aggregation—that effectively captures the interrelation between graph topology and node attributes is revealed. Building on this insight, this paper proposes a universal GT architecture with linear complexity, named Universal Graph Cross-attention Transformer (UGCFormer). The idea is to interactively learn the representations of graph topology and node attributes via a linearized Dual Cross-attention (DCA) module. In theory, this module can adaptively capture interrelations between these two types of graph information, thus achieving effective aggregation. To mitigate the overfitting problem caused by the two-channel design, a consistency constraint is utilized to enhance their commonality, which prevents representation distortion. Extensive evaluations on benchmark datasets demonstrate the effectiveness and efficiency of UGCFormer.
|
spotlight
| null | null | null |
[] |
[] |
[] |
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] |
437
|
Sparse Polyak: an adaptive step size rule for high-dimensional M-estimation
|
[
"Tianqi Qiao",
"Marie Maros"
] |
We propose and study Sparse Polyak, a variant of Polyak’s adaptive step size, designed to solve high-dimensional statistical estimation problems where the problem dimension is allowed to grow much faster than the sample size. In such settings, the standard Polyak step size performs poorly, requiring an increasing number of iterations to achieve optimal statistical precision-even when, the problem remains well conditioned and/or the achievable precision itself does not degrade with problem size. We trace this limitation to a mismatch in how smoothness is measured: in high dimensions, it is no longer effective to estimate the Lipschitz smoothness constant. Instead, it is more appropriate to estimate the smoothness restricted to specific directions relevant to the problem (restricted Lipschitz smoothnes constant). Sparse Polyak overcomes this issue by modifying the step size to estimate the restricted Lipschitz smoothness constant. We support our approach with both theoretical analysis and numerical experiments, demonstrating its improved performance.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
438
|
Towards Visualization-of-Thought Jailbreak Attack against Large Visual Language Models
|
[
"HongQiong Zhong",
"Qingyang Teng",
"Baolin Zheng",
"Guanlin Chen",
"Yingshui Tan",
"Zhendong Liu",
"Jiaheng Liu",
"Wenbo Su",
"Xiaoyong Zhu",
"Bo Zheng",
"Kaifu Zhang"
] |
As large Visual-Language Models (VLMs) continue to evolve, they have demonstrated increasingly sophisticated logical reasoning capabilities and multimodal thought generation, opening doors to widespread applications. However, this advancement raises serious concerns about content security, particularly when these models process complex multimodal inputs requiring intricate reasoning. When faced with these safety challenges, the critical competition between logical reasoning and safety objectives of VLMs is often overlooked in previous works. In this paper, we introduce Visualization-of-Thought Attack (VoTA), a novel and automated attack framework that strategically constructs chains of images with risky visual thoughts to challenge victim models. Our attack provokes the inherent conflict between the model's logical processing and safety protocols, ultimately leading to the generation of unsafe content. Through comprehensive experiments, VoTA achieves remarkable effectiveness, improving the average attack success rate (ASR) by 26.71\% (from 63.70\% to 90.41\%) on 9 open-source VLMs and 6 commercial VLMs, compared to the state-of-the-art methods. These results expose a critical vulnerability: current VLMs struggle to maintain safety guarantees when processing insecure multimodal visualization-of-thought inputs, highlighting the urgency and necessity of enhancing safety alignment.
|
poster
| null | null | null |
[] |
[] |
[] |
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439
|
Flatness is Necessary, Neural Collapse is Not: Rethinking Generalization via Grokking
|
[
"Ting Han",
"Linara Adilova",
"Henning Petzka",
"Jens Kleesiek",
"Michael Kamp"
] |
Neural collapse, i.e., the emergence of highly symmetric, class-wise clustered representations, is frequently observed in deep networks and is often assumed to reflect or enable generalization. In parallel, flatness of the loss landscape has been theoretically and empirically linked to generalization. Yet, the causal role of either phenomenon remains unclear: Are they prerequisites for generalization, or merely by-products of training dynamics? We disentangle these questions using grokking, a training regime in which memorization precedes generalization, allowing us to temporally separate generalization from training dynamics and we find that while both neural collapse and relative flatness emerge near the onset of generalization, only flatness consistently predicts it. Models encouraged to collapse or prevented from collapsing generalize equally well, whereas models regularized away from flat solutions exhibit delayed generalization, resembling grokking---even in architectures and datasets where it does not typically occur. Furthermore, we show theoretically that neural collapse implies relative flatness under classical assumptions, explaining their empirical co-occurrence. Our results support the view that relative flatness is a potentially necessary and more fundamental property for generalization, and demonstrate how grokking can serve as a powerful probe for isolating its geometric underpinnings.
|
poster
|
2509.17738
| null | null |
[] |
[] |
[] |
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440
|
Beyond Average Value Function in Precision Medicine: Maximum Probability-Driven Reinforcement Learning for Survival Analysis
|
[
"Jianqi Feng",
"Wei Zhao",
"Zhenke Wu",
"Chengchun Shi",
"Xiaodong Yan"
] |
Constructing multistage optimal decisions for alternating recurrent event data is critically important in medical and healthcare research. Current reinforcement learning (RL) methods have only been applied to time-to-event data, and maximize the expectation. However, recurrent event data exhibit a distinct structure and emphasize the probability of event occurrences. In this paper, we incorporate recurrent event data and for the first time propose a RL objective focused on maximizing probabilities. To apply recurrent event data within the RL framework, we formulate a Decision Process optimization framework. During the optimization, we address the challenge of heterogeneous stage counts across individuals by reformulating an auxiliary problem. The proposed optimal policy can be efficiently implemented using Bellman optimality operators. Additionally, we establish the equivalence properties of the optimal policy under the new objective and the unbiasedness of the estimated Q-function. Experiments show that proposed method can converge faster and reduce the variance, and achieve a larger probability compared with the traditional objective.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
441
|
Unveiling the Learning Mind of Language Models: A Cognitive Framework and Empirical Study
|
[
"Zhengyu Hu",
"Jianxun Lian",
"Zheyuan Xiao",
"Seraphina Zhang",
"Tianfu Wang",
"Nicholas Jing Yuan",
"Xing Xie",
"Hui Xiong"
] |
Large language models (LLMs) have shown impressive capabilities across tasks such as mathematics, coding, and reasoning, yet their learning ability, which is crucial for adapting to dynamic environments and acquiring new knowledge, remains underexplored. In this work, we address this gap by introducing a framework inspired by cognitive psychology and education. Specifically, we decompose general learning ability into three distinct, complementary dimensions: *Learning from Instructor* (acquiring knowledge via explicit guidance), *Learning from Concept* (internalizing abstract structures and generalizing to new contexts), and *Learning from Experience* (adapting through accumulated exploration and feedback). We conduct a comprehensive empirical study across the three learning dimensions and identify several insightful findings, such as (i) interaction improves learning; (ii) conceptual understanding is scale-emergent and benefits larger models; and (iii) LLMs are effective few-shot learners but not many-shot learners. Based on our framework and empirical findings, we introduce a benchmark that provides a unified and realistic evaluation of LLMs' general learning abilities across three learning cognition dimensions. It enables diagnostic insights and supports evaluation and development of more adaptive and human-like models.
|
poster
|
2506.13464
| null | null |
[] |
[] |
[] |
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] |
442
|
Are Pixel-Wise Metrics Reliable for Computerized Tomography Reconstruction?
|
[
"Tianyu Lin",
"Xinran Li",
"Chuntung Zhuang",
"Qi Chen",
"Yuanhao Cai",
"Kai Ding",
"Alan Yuille",
"Zongwei Zhou"
] |
Widely adopted evaluation metrics for sparse-view CT reconstruction---such as Structural Similarity Index Measure and Peak Signal-to-Noise Ratio---prioritize pixel-wise fidelity but often fail to capture the completeness of critical anatomical structures, particularly small or thin regions that are easily missed. To address this limitation, we propose a suite of novel anatomy-aware evaluation metrics designed to assess structural completeness across anatomical structures, including large organs, small organs, intestines, and vessels. Building on these metrics, we introduce CARE, a Completeness-Aware Reconstruction Enhancement framework that incorporates structural penalties during training to encourage anatomical preservation of significant regions. CARE is model-agnostic and can be seamlessly integrated into both analytical reconstruction methods and modern learning-based methods, such as Neural Radiance Fields and Gaussian Splatting. When applied to these methods, CARE substantially improves structural completeness in reconstructed CT scans, yielding performance gains of up to +32\% for large organs, +22\% for small organs, +40\% for intestines, and +36\% for vessels. Code has been attached as supplementary material for peer review and will be made publicly available.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
443
|
UMoE: Unifying Attention and FFN with Shared Experts
|
[
"Yuanhang Yang",
"Chaozheng Wang",
"Jing Li"
] |
Sparse Mixture of Experts (MoE) architectures have emerged as a promising approach for scaling Transformer models. While initial works primarily incorporated MoE into feed-forward network (FFN) layers, recent studies have explored extending the MoE paradigm to attention layers to enhance model performance. However, existing attention-based MoE layers require specialized implementations and demonstrate suboptimal performance compared to their FFN-based counterparts. In this paper, we aim to unify the MoE designs in attention and FFN layers by introducing a novel reformulation of the attention mechanism, revealing an underlying FFN-like structure within attention modules. Our proposed architecture, UMoE, achieves superior performance through attention-based MoE layers while enabling efficient parameter sharing between FFN and attention components.
|
spotlight
|
2505.07260
|
https://github.com/ysngki/UMoE
| null |
[] |
[] |
[] |
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] |
444
|
Point4Bit: Post Training 4-bit Quantization for Point Cloud 3D Detection
|
[
"Jianyu Wang",
"Yu Wang",
"Shengjie Zhao",
"Sifan Zhou"
] |
Voxel-based 3D object detectors have achieved remarkable performance in point cloud perception, yet their high computational and memory demands pose significant challenges for deployment on resource-constrained edge devices. Post-training quantization (PTQ) provides a practical means to compress models and accelerate inference; however, existing PTQ methods for point cloud detection are typically limited to INT8 and lack support for lower-bit formats such as INT4, which restricts their deployment potential. In this paper, we present Point4bit, the first general 4-bit PTQ framework tailored for voxel-based 3D object detectors. To tackle challenges in low-bit quantization, we propose two key techniques: (1) Foreground-aware Piecewise Activation Quantization (FA-PAQ), which leverages foreground structural cues to improve the quantization of sparse activations; and (2) Gradient-guided Key Weight Quantization (G-KWQ), which preserves task-critical weights through gradient-based analysis to reduce quantization-induced degradation. Extensive experiments demonstrate that Point4bit achieves INT4 quantization with minimal accuracy loss with less than 1.5\% accuracy drop. Moreover, we validate its generalization ability on point cloud classification and segmentation tasks, demonstrating broad applicability. Our method further advances the bit-width limitation of point cloud quantization to 4 bits, demonstrating strong potential for efficient deployment on resource-constrained edge devices.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
445
|
A Clean Slate for Offline Reinforcement Learning
|
[
"Matthew T Jackson",
"Uljad Berdica",
"Jarek Liesen",
"Shimon Whiteson",
"Jakob Foerster"
] |
Progress in offline reinforcement learning (RL) has been impeded by ambiguous problem definitions and entangled algorithmic designs, resulting in inconsistent implementations, insufficient ablations, and unfair evaluations. Although offline RL explicitly avoids environment interaction, prior methods frequently employ extensive, undocumented online evaluation for hyperparameter tuning, complicating method comparisons. Moreover, existing reference implementations differ significantly in boilerplate code, obscuring their core algorithmic contributions. We address these challenges by first introducing a rigorous taxonomy and a transparent evaluation protocol that explicitly quantifies online tuning budgets. To resolve opaque algorithmic design, we provide clean, minimalistic, single-file implementations of various model-free and model-based offline RL methods, significantly enhancing clarity and achieving substantial speed-ups. Leveraging these streamlined implementations, we propose Unifloral, a unified algorithm that encapsulates diverse prior approaches and enables development within a single, comprehensive hyperparameter space. Using Unifloral with our rigorous evaluation protocol, we develop two novel algorithms - TD3-AWR (model-free) and MoBRAC (model-based) - which substantially outperform established baselines. Our implementation is publicly available at https://anonymous.4open.science/r/Unifloral-NeurIPS-D1A2.
|
oral
|
2504.11453
|
https://github.com/EmptyJackson/unifloral
| null |
[] |
[] |
[] |
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] |
446
|
Sequence Modeling Is Not Evolutionary Reasoning
|
[
"Yasha Ektefaie",
"Andrew Shen",
"Lavik Jain",
"Maha Farhat",
"Marinka Zitnik"
] |
Protein language models (PLMs) are commonly assumed to capture evolutionary information by training on large protein sequence datasets. However, it remains unclear whether PLMs can reason about evolution—that is, infer evolutionary relationships between protein sequences. To test this capability, we introduce a benchmark for evolutionary reasoning and find that existing PLMs consistently fail to recover phylogenetic structure, despite strong performance on standard tasks such as masked token prediction and contact prediction. To address this limitation, we present Phyla. Phyla introduces a hybrid state-space and transformer architecture that jointly process multiple sequences and is trained using a tree-based objective over 3,000 phylogenies spanning diverse protein families. Phyla achieves state-of-the-art performance in evolutionary reasoning, outperforming the next-best model by 13\% on tree reconstruction and 10\% on taxonomic clustering. Beyond synthetic benchmarks, Phyla applies to real-world settings: it reconstructs biologically accurate branches of the tree of life and infers whole-genome evolutionary relationships from Mycobacterium tuberculosis genomes. These findings suggest that evolutionary reasoning is not an emergent property of large-scale sequence modeling. Instead, Phyla shows that models trained with phylogenetic supervision can reason about evolution more effectively, offering a biologically grounded path toward evolutionary foundation models.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
447
|
Empowering Decision Trees via Shape Function Branching
|
[
"Nakul Upadhya",
"Eldan Cohen"
] |
Decision trees are prized for their interpretability and strong performance on tabular data. Yet, their reliance on simple axis‑aligned linear splits often forces deep, complex structures to capture non‑linear feature effects, undermining human comprehension of the constructed tree. To address this limitation, we propose a novel generalization of a decision tree, the Shape Generalized Tree (SGT), in which each internal node applies a learnable axis‑aligned shape function to a single feature, enabling rich, non‑linear partitioning in one split. As users can easily visualize each node's shape functions, SGTs are inherently interpretable and provide intuitive, visual explanations of the model's decision mechanisms. To learn SGTs from data, we propose ShapeCART, an efficient induction algorithm for SGTs. We further extend the SGT framework to bivariate shape functions (S$^2$GT) and multi‑way trees (SGT$_K$), and present Shape$^2$CART and ShapeCART$_K$, extensions to ShapeCART for learning S$^2$GTs and SGT$_K$s, respectively. Experiments on various datasets show that SGTs achieve superior performance with reduced model size compared to traditional axis-aligned linear trees.
|
poster
| null | null | null |
[] |
[] |
[] |
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448
|
Finding and Reactivating Post-Trained LLMs' Hidden Safety Mechanisms
|
[
"Mingjie Li",
"Wai Man Si",
"Michael Backes",
"Yang Zhang",
"Yisen Wang"
] |
Despite the impressive performance of general-purpose large language models (LLMs), they often require fine-tuning or post-training to excel at specific tasks. For instance, large reasoning models (LRMs), such as the DeepSeek-R1 series, demonstrate strong reasoning capabilities after post-training different general large language models on diverse chain-of-thought (CoT) datasets. However, this additional training frequently comes at the cost of reduced safety, as the fine-tuned or post-trained models tend to exhibit more harmful behaviors compared with the regular LLMs before post-training or fine-tuning, potentially leading to harmful outcomes due to their enhanced capabilities. Taking LRMs as an example, we first investigate the underlying cause of this safety degradation in this paper. Our analysis reveals that post-training can mask the original safety mechanisms of the base LLM, while over-amplifying representations related to their post-training ability. But luckily, we also find that LRMs' safety mechanisms still exist instead of being removed during their post-training. Based on these findings, we propose a lightweight and cost-effective solution called SafeReAct that restores the suppressed safety behaviors by aligning with LoRA adapters on a few layers. Experiments on four state-of-the-art LRMs show that our method significantly improves safety on harmful prompts without compromising reasoning performance. Besides LRMs, additional results on other domain-specific LLMs, like medical models, further confirm the generality and effectiveness of our approach.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
449
|
Adjacent Words, Divergent Intents: Jailbreaking Large Language Models via Task Concurrency
|
[
"Yukun Jiang",
"Mingjie Li",
"Michael Backes",
"Yang Zhang"
] |
Despite their superior performance on a wide range of domains, large language models (LLMs) remain vulnerable to misuse for generating harmful content, a risk that has been further amplified by various jailbreak attacks.Existing jailbreak attacks mainly follow sequential logic, where LLMs understand and answer each given task one by one.However, concurrency, a natural extension of the sequential scenario, has been largely overlooked.In this work, we first propose a word-level method to enable task concurrency in LLMs, where adjacent words encode divergent intents.Although LLMs maintain strong utility in answering concurrent tasks, which is demonstrated by our evaluations on mathematical and general question-answering benchmarks, we notably observe that combining a harmful task with a benign one significantly reduces the probability of it being filtered by the guardrail, showing the potential risks associated with concurrency in LLMs.Based on these findings, we introduce $\texttt{JAIL-CON}$, an iterative attack framework that $\underline{\text{JAIL}}$breaks LLMs via task $\underline{\text{CON}}$currency. Experiments on widely-used LLMs demonstrate the strong jailbreak capabilities of $\texttt{JAIL-CON}$ compared to existing attacks.Furthermore, when the guardrail is applied as a defense, compared to the sequential answers generated by previous attacks, the concurrent answers in our $\texttt{JAIL-CON}$ exhibit greater stealthiness and are less detectable by the guardrail, highlighting the unique feature of task concurrency in jailbreaking LLMs.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
450
|
ItDPDM: Information-Theoretic Discrete Poisson Diffusion Model
|
[
"Sagnik Bhattacharya",
"Abhiram Gorle",
"Ahsan Bilal",
"Connor Ding",
"Amit Kumar Singh Yadav",
"Tsachy Weissman"
] |
Generative modeling of non-negative, discrete data, such as symbolic music, remains challenging due to two persistent limitations in existing methods. Firstly, many approaches rely on modeling continuous embeddings, which is suboptimal for inherently discrete data distributions. Secondly, most models optimize variational bounds rather than exact data likelihood, resulting in inaccurate likelihood estimates and degraded sampling quality. While recent diffusion-based models have addressed these issues separately, we tackle them jointly. In this work, we introduce the Information-Theoretic Discrete Poisson Diffusion Model (ItDPDM), inspired by photon arrival process, which combines exact likelihood estimation with fully discrete-state modeling. Central to our approach is an information-theoretic Poisson Reconstruction Loss (PRL) that has a provable exact relationship with the true data likelihood. ItDPDM achieves improved likelihood and sampling performance over prior discrete and continuous diffusion models on a variety of synthetic discrete datasets. Furthermore, on real-world datasets such as symbolic music and images, ItDPDM attains superior likelihood estimates and competitive generation quality—demonstrating a proof of concept for distribution-robust discrete generative modeling.
|
poster
|
2505.05082
| null | null |
[] |
[] |
[] |
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] |
451
|
Towards Identifiability of Hierarchical Temporal Causal Representation Learning
|
[
"Zijian Li",
"Minghao Fu",
"Junxian Huang",
"Yifan Shen",
"Ruichu Cai",
"Yuewen Sun",
"Guangyi Chen",
"Kun Zhang"
] |
Modeling hierarchical latent dynamics behind time series data is critical for capturing temporal dependencies across multiple levels of abstraction in real-world tasks. However, existing temporal causal representation learning methods fail to capture such dynamics, as they fail to recover the joint distribution of hierarchical latent variables from \textit{single-timestep observed variables}. Interestingly, we find that the joint distribution of hierarchical latent variables can be uniquely determined using three conditionally independent observations. Building on this insight, we propose a Causally Hierarchical Latent Dynamic (CHiLD) identification framework. Our approach first employs temporal contextual observed variables to identify the joint distribution of multi-layer latent variables. Sequentially, we exploit the natural sparsity of the hierarchical structure among latent variables to identify latent variables within each layer. Guided by the theoretical results, we develop a time series generative model grounded in variational inference. This model incorporates a contextual encoder to reconstruct multi-layer latent variables and normalize flow-based hierarchical prior networks to impose the independent noise condition of hierarchical latent dynamics. Empirical evaluations on both synthetic and real-world datasets validate our theoretical claims and demonstrate the effectiveness of CHiLD in modeling hierarchical latent dynamics.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
452
|
Let a Neural Network be Your Invariant
|
[
"Mirco Giacobbe",
"Daniel Kroening",
"Abhinandan Pal",
"Michael Tautschnig"
] |
Safety verification ensures that a system avoids undesired behaviour, acrucial requirement for the design of reactive systems, especially hardwaresystems. Liveness complements safety, ensuring that the system alsoachieves its desired objectives. A complete specification of functionalcorrectness must combine both safety and liveness aspects. Proving withmathematical certainty that a system satisfies a safety property demandspresenting an appropriate inductive invariant of the system, whereasliveness requires showing a measure of progress also known as rankingfunctions. Neural model checking has recently introduced a data-drivenapproach to the formal verification of reactive systems, albeit focusing onranking functions and thus addressing only liveness properties. In thispaper, we extend and generalise neural model checking to additionallyencompass inductive invariants and thus safety properties as well. Given asystem and a linear temporal logic specification of safety and liveness, ourapproach alternates a learning and a checking component towards theconstruction of a provably sound neural certificate. Our new methodintroduces a neural certificate architecture that jointly representsinductive invariants as proofs of safety, and ranking functions as proofs ofliveness. Moreover, our new architecture is able to leverage constraintsolvers for training, accelerating cognate neural model checking work otherwise basedon gradient descent. We experimentally demonstrate that our method is several orders of magnitude faster than the state-of-the-art model checkers on pure liveness and combined safety and liveness verification tasks written in SystemVerilog, while enabling the verification of richer properties than was previously possible forneural model checking.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
453
|
Online Time Series Forecasting with Theoretical Guarantees
|
[
"Zijian Li",
"Changze Zhou",
"Minghao Fu",
"Sanjay Manjunath",
"Fan Feng",
"Guangyi Chen",
"Yingyao Hu",
"Ruichu Cai",
"Kun Zhang"
] |
This paper is concerned with online time series forecasting, where unknown distribution shifts occur over time, i.e., latent variables influence the mapping from historical to future observations. To develop an automated way of online time series forecasting, we propose a Theoretical framework for Online Time-series forecasting (TOT in short) with theoretical guarantees. Specifically, we prove that supplying a forecaster with latent variables tightens the Bayes risk—the benefit endures under estimation uncertainty of latent variables and grows as the latent variables achieve a more precise identifiability. To better introduce latent variables into online forecasting algorithms, we further propose to identify latent variables with minimal adjacent observations. Based on these results, we devise a model-agnostic blueprint by employing a temporal decoder to match the distribution of observed variables and two independent noise estimators to model the causal inference of latent variables and mixing procedures of observed variables, respectively. Experiment results on synthetic data support our theoretical claims. Moreover, plug-in implementations built on several baselines yield general improvement across multiple benchmarks, highlighting the effectiveness in real-world applications.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
454
|
Multi-scale Temporal Prediction via Incremental Generation and Multi-agent Collaboration
|
[
"Zhitao Zeng",
"Guojian Yuan",
"Junyuan Mao",
"Yuxuan Wang",
"Xiaoshuang Jia",
"Yueming Jin"
] |
Accurate temporal prediction is the bridge between comprehensive scene understanding and embodied artificial intelligence. However, predicting multiple fine-grained states of scene at multiple temporal scales is difficult for vision-language models.We formalize the Multi‐Scale Temporal Prediction (MSTP) task in general and surgical scene by decomposing multi‐scale into two orthogonal dimensions: the temporal scale, forecasting states of human and surgery at varying look‐ahead intervals, and the state scale, modeling a hierarchy of states in general and surgical scene. For instance in general scene, states of contacting relationship are finer-grained than states of spatial relationship. For instance in surgical scene, medium‐level steps are finer‐grained than high‐level phases yet remain constrained by their encompassing phase. To support this unified task, we introduce the first MSTP Benchmark, featuring synchronized annotations across multiple state scales and temporal scales. We further propose a novel method, Incremental Generation and Multi‐agent Collaboration (IG-MC), which integrates two key innovations. Firstly, we propose an plug-and-play incremental generation to keep high-quality temporal prediction that continuously synthesizes up-to-date visual previews at expanding temporal scales to inform multiple decision-making agents, ensuring decision content and generated visuals remain synchronized and preventing performance degradation as look‐ahead intervals lengthen.Secondly, we propose a decision‐driven multi‐agent collaboration framework for multiple states prediction, comprising generation, initiation, and multi‐state assessment agents that dynamically triggers and evaluates prediction cycles to balance global coherence and local fidelity. Extensive experiments on the MSTP Benchmark in general and surgical scene show that IG‐MC is a generalizable plug-and-play method for MSTP, demonstrating the effectiveness of incremental generation and the stability of decision‐driven multi‐agent collaboration.
|
poster
|
2509.17429
| null | null |
[] |
[] |
[] |
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455
|
Injecting Frame-Event Complementary Fusion into Diffusion for Optical Flow in Challenging Scenes
|
[
"Haonan Wang",
"Hanyu Zhou",
"Haoyue Liu",
"Luxin Yan"
] |
Optical flow estimation has achieved promising results in conventional scenes but faces challenges in high-speed and low-light scenes, which suffer from motion blur and insufficient illumination. These conditions lead to weakened texture and amplified noise and deteriorate the appearance saturation and boundary completeness of frame cameras, which are necessary for motion feature matching. In degraded scenes, the frame camera provides dense appearance saturation but sparse boundary completeness due to its long imaging time and low dynamic range. In contrast, the event camera offers sparse appearance saturation, while its short imaging time and high dynamic range gives rise to dense boundary completeness. Traditionally, existing methods utilize feature fusion or domain adaptation to introduce event to improve boundary completeness. However, the appearance features are still deteriorated, which severely affects the mostly adopted discriminative models that learn the mapping from visual features to motion fields and generative models that generate motion fields based on given visual features. So we introduce diffusion models that learn the mapping from noising flow to clear flow, which is not affected by the deteriorated visual features. Therefore, we propose a novel optical flow estimation framework Diff-ABFlow based on diffusion models with frame-event appearance-boundary fusion. Inspired by the appearance-boundary complementarity of frame and event, we propose an Attention-Guided Appearance-Boundary Fusion module to fuse frame and event. Based on diffusion models, we propose a Multi-Condition Iterative Denoising Decoder. Our proposed method can effectively utilize the respective advantages of frame and event, and shows great robustness to degraded input. In addition, we propose a dual-modal optical flow dataset for generalization experiments. Extensive experiments have verified the superiority of our proposed method. We will provide the code once the paper accepted.
|
spotlight
|
2510.10577
| null | null |
[] |
[] |
[] |
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] |
456
|
Accelerating Model-Free Optimization via Averaging of Cost Samples
|
[
"Guido Carnevale",
"Giuseppe Notarstefano"
] |
Model-free optimization methods typically rely on cost samples gathered by perturbing the current solution estimate along a finite and fixed set of directions. However, at each iteration, only current cost samples are used, while potentially informative, previously collected samples are discarded. In this work, we challenge this conventional approach by introducing a simple yet effective memory mechanism that maintains an auxiliary vector of cost samples whose components are iteratively updated. By leveraging this stored information, our method estimates descent directions through an averaging of all perturbing directions weighted by the auxiliary vector components. This results in a faster convergence without increasing the number of function queries. By interpreting the resulting algorithm as a time-varying dynamical system, we are able to establish its convergence properties in the strongly convex case. In particular, by using tools from system theory based on timescale separation, we are able to guarantee a linear convergence rate toward an arbitrarily small neighborhood of the optimal solution. Numerical simulations on regression problems demonstrate that the proposed approach sensibly outperforms existing model-free optimization methods.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
457
|
Spik-NeRF: Spiking Neural Networks for Neural Radiance Fields
|
[
"Gang Wan",
"Qinlong Lan",
"Zihan Li",
"Huimin Wang",
"Wu Yitian",
"wang zhen",
"Wanhua Li",
"Yufei Guo"
] |
Spiking Neural Networks (SNNs), as a biologically inspired neural network architecture, have garnered significant attention due to their exceptional energy efficiency and increasing potential for various applications. In this work, we extend the use of SNNs to neural rendering tasks and introduce Spik-NeRF (Spiking Neural Radiance Fields). We observe that the binary spike activation map of traditional SNNs lacks sufficient information capacity, leading to information loss and a subsequent decline in the performance of spiking neural rendering models. To address this limitation, we propose the use of ternary spike neurons, which enhance the information-carrying capacity in the spiking neural rendering model. With ternary spike neurons, Spik-NeRF achieves performance that is on par with, or nearly identical to, traditional ANN-based rendering models. Additionally, we present a re-parameterization technique for inference that allows Spik-NeRF with ternary spike neurons to retain the event-driven, multiplication-free advantages typical of binary spike neurons. Furthermore, to further boost the performance of Spik-NeRF, we employ a distillation method, using an ANN-based NeRF to guide the training of our Spik-NeRF model, which is more compatible with the ternary neurons compared to the standard binary neurons. We evaluate Spik-NeRF on both realistic and synthetic scenes, and the experimental results demonstrate that Spik-NeRF achieves rendering performance comparable to ANN-based NeRF models.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
458
|
Bridging Symmetry and Robustness: On the Role of Equivariance in Enhancing Adversarial Robustness
|
[
"Longwei Wang",
"Chaowei Zhang",
"Ifrat Ikhtear Uddin",
"Prof. KC Santosh (PhD)",
"Xiao Qin",
"Yang Zhou"
] |
Adversarial examples reveal critical vulnerabilities in deep neural networks by exploiting their sensitivity to imperceptible input perturbations. While adversarial training remains the predominant defense strategy, it often incurs significant computational cost and may compromise clean-data accuracy. In this work, we investigate an architectural approach to adversarial robustness by embedding group-equivariant convolutions—specifically, rotation- and scale-equivariant layers—into standard convolutional neural networks (CNNs). These layers encode symmetry priors that align model behavior with structured transformations in the input space, promoting smoother decision boundaries and greater resilience to adversarial attacks. We propose and evaluate two symmetry-aware architectures: a parallel design that processes standard and equivariant features independently before fusion, and a cascaded design that applies equivariant operations sequentially. Theoretically, we demonstrate that such models reduce hypothesis space complexity, regularize gradients, and yield tighter certified robustness bounds under the CLEVER (Cross Lipschitz Extreme Value for nEtwork Robustness) framework. Empirically, our models consistently improve adversarial robustness and generalization across CIFAR-10, CIFAR-100, and CIFAR-10C under both FGSM and PGD attacks, without requiring adversarial training. These findings underscore the potential of symmetry-enforcing architectures as efficient and principled alternatives to data augmentation-based defenses.
|
spotlight
| null | null | null |
[] |
[] |
[] |
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459
|
NavBench: Probing Multimodal Large Language Models for Embodied Navigation
|
[
"Yanyuan Qiao",
"Haodong Hong",
"Wenqi Lyu",
"Dong An",
"Siqi Zhang",
"Yutong Xie",
"Xinyu Wang",
"Qi Wu"
] |
Multimodal Large Language Models (MLLMs) have demonstrated strong generalization in vision-language tasks, yet their ability to understand and act within embodied environments remains underexplored. We present NavBench, a benchmark to evaluate the embodied navigation capabilities of MLLMs under zero-shot settings. NavBench consists of two components: (1) navigation comprehension, assessed through three cognitively grounded tasks including global instruction alignment, temporal progress estimation, and local observation-action reasoning, covering 3,200 question-answer pairs; and (2) step-by-step execution in 432 episodes across 72 indoor scenes, stratified by spatial, cognitive, and execution complexity. To support real-world deployment, we introduce a pipeline that converts MLLMs' outputs into robotic actions. We evaluate both proprietary and open-source models, finding that GPT-4o performs well across tasks, while lighter open-source models succeed in simpler cases. Results also show that models with higher comprehension scores tend to achieve better execution performance. Providing map-based context improves decision accuracy, especially in medium-difficulty scenarios. However, most models struggle with temporal understanding, particularly in estimating progress during navigation, which may pose a key challenge.
|
poster
|
2506.01031
| null | null |
[] |
[] |
[] |
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460
|
TAI3: Testing Agent Integrity in Interpreting User Intent
|
[
"Shiwei Feng",
"Xiangzhe Xu",
"Xuan Chen",
"Kaiyuan Zhang",
"Syed Ahmed",
"Zian Su",
"Mingwei Zheng",
"Xiangyu Zhang"
] |
LLM agents are increasingly deployed to automate real-world tasks by invoking APIs through natural language instructions. While powerful, they often suffer from misinterpretation of user intent, leading to the agent’s actions that diverge from the user’s intended goal, especially as external toolkits evolve. Traditional software testing assumes structured inputs and thus falls short in handling the ambiguity of natural language. We introduce TAI3, an API-centric stress testing framework that systematically uncovers intent integrity violations in LLM agents. Unlike prior work focused on fixed benchmarks or adversarial inputs, TAI3 generates realistic tasks based on toolkits’ documentation and applies targeted mutations to expose subtle agent errors while preserving user intent. To guide testing, we propose semantic partitioning, which organizes natural language tasks into meaningful categories based on toolkit API parameters and their equivalence classes. Within each partition, seed tasks are mutated and ranked by a lightweight predictor that estimates the likelihood of triggering agent errors. To enhance efficiency, TAI3 maintains a datatype-aware strategy memory that retrieves and adapts effective mutation patterns from past cases. Experiments on 80 toolkit APIs demonstrate that TAI3 effectively uncovers intent integrity violations, significantly outperforming baselines in both error-exposing rate and query efficiency. Moreover, TAI3 generalizes well to stronger target models using smaller LLMs for test generation, and adapts to evolving APIs across domains.
|
poster
|
2506.07524
| null | null |
[] |
[] |
[] |
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] |
461
|
Chain-of-Action: Trajectory Autoregressive Modeling for Robotic Manipulation
|
[
"Wenbo Zhang",
"Tianrun Hu",
"Yanyuan Qiao",
"Hanbo Zhang",
"Yuchu Qin",
"Yang Li",
"Jiajun Liu",
"Tao Kong",
"Lingqiao Liu",
"Xiao Ma"
] |
We present Chain-of-Action (CoA), a novel visuo-motor policy paradigm built upon Trajectory Autoregressive Modeling. Unlike conventional approaches that predict next step action(s) forward, CoA generates an entire trajectory by explicit backward reasoning with task-specific goals through an action-level Chain-of-Thought (CoT) process. This process is unified within a single autoregressive structure: (1) the first token corresponds to a stable keyframe action that encodes the task-specific goals; and (2) subsequent action tokens are generated autoregressively, conditioned on the initial keyframe and previously predicted actions. This backward action reasoning enforces a global-to-local structure, allowing each local action to be tightly constrained by the final goal. To further realize the action reasoning structure, CoA incorporates four complementary designs: continuous action token representation; dynamic stopping for variable-length trajectory generation; reverse temporal ensemble; and multi-token prediction to balance action chunk modeling with global structure. As a result, CoA gives strong spatial generalization capabilities while preserving the flexibility and simplicity of a visuo-motor policy. Empirically, we observe CoA achieves the state-of-the-art performance across 60 RLBench tasks and 8 real-world manipulation tasks.
|
poster
| null | null | null |
[] |
[] |
[] |
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462
|
DynamicVerse: Physically-Aware Multimodal Modeling for Dynamic 4D Worlds
|
[
"Kairun Wen",
"Yuzhihuang",
"Runyu Chen",
"Hui Zheng",
"Yunlong Lin",
"Panwang Pan",
"Chenxin Li",
"Wenyan Cong",
"Jian Zhang",
"Junbin Lu",
"Chenguo Lin",
"Dilin Wang",
"Zhicheng Yan",
"Hongyu Xu",
"Justin Theiss",
"Yue Huang",
"Xinghao Ding",
"Rakesh Ranjan",
"Zhiwen Fan"
] |
Understanding the dynamic physical world, characterized by its evolving 3D structure, real-world motion, and semantic content with textual descriptions, is crucial for human-agent interaction and enables embodied agents to perceive and act within real environments with human‑like capabilities. However, existing datasets are often derived from limited simulators or utilize traditional Structure-from-Motion for up-to-scale annotation and offer limited descriptive captioning, which restricts the capacity of foundation models to accurately interpret real-world dynamics from monocular videos, commonly sourced from the internet.To bridge these gaps, we introduce DynamicVerse, a physical‑scale, multimodal 4D modeling framework for real-world video. We employ large vision, geometric, and multimodal models to interpret metric-scale static geometry, real-world dynamic motion, instance-level masks, and holistic descriptive captions. By integrating window-based Bundle Adjustment with global optimization, our method converts long real-world video sequences into a comprehensive 4D multimodal format. DynamicVerse delivers a large-scale dataset consists of 100K+ video with 800K+ annotated masks and 10M+ frames from internet videos. Experimental evaluations on three benchmark tasks---video depth estimation, camera pose estimation, and camera intrinsics estimation---validate that our 4D modeling achieves superior performance in capturing physical-scale measurements with greater global accuracy than existing methods.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
463
|
Uncertainty-Sensitive Privileged Learning
|
[
"Fan-Ming Luo",
"Lei Yuan",
"Yang Yu"
] |
Privileged learning efficiently tackles high-dimensional, partially observable decision-making problems by first training a privileged policy (PP) on low-dimensional privileged observations, and then deriving a deployment policy (DP) either by imitating the PP or coupling it with an observation encoder. However, since the DP relies on local and partial observations, a behavioral divergence (BD) often emerges between the DP and the PP, ultimately degrading deployment performance. A promising strategy is to train a PP to learn the optimal behaviors attainable under the DP’s observation space by applying reward penalties in regions with large BD. However, producing these behaviors is challenging for the PP because they rely on the DP’s information-gathering progress, which is invisible to the PP. In this paper, we quantify the DP’s information-gathering progress by estimating the prediction uncertainty of privileged observations reconstructed from partial observations, and accordingly propose the framework of Uncertainty-Sensitive Privileged Learning (USPL). USPL feeds this uncertainty estimation to the PP and combines reward transformation with privileged-observation blurring, driving the PP to choose actions that actively reduce uncertainty and thus gather the necessary information. Experiments across nine tasks demonstrate that USPL significantly reduces the behavioral discrepancies, achieving superior deployment performance compared to baselines. Additional visualization results show that the DP accurately quantifies its uncertainty, and the PP effectively adapts to uncertainty variations.
|
poster
| null | null | null |
[] |
[] |
[] |
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464
|
Learning to Clarify by Reinforcement Learning Through Reward-Weighted Fine-Tuning
|
[
"Subhojyoti Mukherjee",
"Viet Lai",
"Raghavendra Addanki",
"Ryan Rossi",
"Seunghyun Yoon",
"Trung Bui",
"Anup B. Rao",
"Jayakumar Subramanian",
"Branislav Kveton"
] |
Question answering (QA) agents automatically answer questions posed in natural language. In this work, we learn to ask clarifying questions in QA agents. The key idea in our method is to simulate conversations that contain clarifying questions and learn from them using reinforcement learning (RL). To make RL practical, we propose and analyze offline RL objectives that can be viewed as reward-weighted supervised fine-tuning (SFT) and easily optimized in large language models. Our work stands in a stark contrast to recently proposed methods, based on SFT and direct preference optimization, which have additional hyper-parameters and do not directly optimize rewards. We compare to these methods empirically and report gains in both optimized rewards and language quality.
|
poster
|
2506.06964
| null | null |
[] |
[] |
[] |
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] |
465
|
Memo: Training Memory-Efficient Embodied Agents with Reinforcement Learning
|
[
"Gunshi Gupta",
"Karmesh Yadav",
"Zsolt Kira",
"Yarin Gal",
"Rahaf Aljundi"
] |
To enable embodied agents to operate effectively over extended timeframes, it is crucial to develop models that form and access memories to stay contextualized in their environment.In the current paradigm of training transformer-based policies for embodied sequential decision-making tasks, visual inputs often overwhelm the context limits of transformers, while humans can maintain and utilize a lifetime of experience compressed as memories.Significant compression is possible in principle, as much of the input is irrelevant and can be abstracted. However, existing approaches predominantly focus on either recurrent models with fixed-size memory or transformers with full-context reliance.In this work, we propose Memo, a transformer-based architecture and training recipe for reinforcement learning (RL) on memory-intensive, long-horizon tasks. Memo incorporates the creation and retrieval of memory by interleaving periodic summarization tokens with the inputs of a model during training.We demonstrate Memo's effectiveness on a grid-world meta-RL benchmark and a multi-object navigation task in photo-realistic indoor settings. Memo outperforms naive long-context transformer baselines while being more compute and storage efficient. Additionally, Memo generalizes better to longer contexts at inference time and remains robust in streaming settings, where historical context must be truncated to fit inference constraints.
|
spotlight
| null | null | null |
[] |
[] |
[] |
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] |
466
|
Language Modeling by Language Models
|
[
"Junyan Cheng",
"Peter Clark",
"Kyle Richardson"
] |
*Can we leverage LLMs to model the process of discovering novel language model (LM) architectures?* Inspired by real research, we propose a multi-agent LLM approach that simulates the conventional stages of research, from ideation and literature search (proposal stage) to design implementation (code generation), generative pre-training, and downstream evaluation (verification). Using ideas from scaling laws, our system *Genesys* employs a *Ladder of Scales* approach; new designs are proposed, adversarially reviewed, implemented, and selectively verified at increasingly larger model scales (14M$\sim$350M parameters) with a narrowing budget (the number of models we can train at each scale). To help make discovery efficient and factorizable, Genesys uses a novel genetic programming backbone, which we show has empirical advantages over commonly used direct prompt generation workflows (e.g., $\sim$86\% percentage point improvement in successful design generation, a key bottleneck). We report experiments involving 1,162 newly discovered designs (1,062 fully verified) and find the best designs to be competitive with known architectures (e.g., outperform GPT2, Mamba2, etc., on 6/9 common benchmarks). We couple these results with comprehensive system-level ablations and formal results, which give broader insights into the design of effective autonomous discovery systems.
|
spotlight
|
2506.20249
|
https://github.com/allenai/genesys
| null |
[] |
[] |
[] |
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] |
467
|
GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers
|
[
"Guang Liang",
"Xinyao Liu",
"Jianxin Wu"
] |
Vision Transformers (ViTs) are essential in computer vision but are computationally intensive, too. Model quantization, particularly to low bit-widths like 4-bit, aims to alleviate this difficulty, yet existing Post-Training Quantization (PTQ) and Quantization-Aware Training (QAT) methods exhibit significant limitations. PTQ often incurs substantial accuracy drop, while QAT achieves high accuracy but suffers from prohibitive computational costs, limited generalization to downstream tasks, training instability, and lacking of open-source codebase. To address these challenges, this paper introduces General, Practical, and Lightning Quantization (GPLQ), a novel framework designed for efficient and effective ViT quantization. GPLQ is founded on two key empirical insights: the paramount importance of activation quantization and the necessity of preserving the model's original optimization basin to maintain generalization. Consequently, GPLQ employs a sequential activation-first, weights-later strategy. Stage 1 keeps weights in FP32 while quantizing activations with a feature mimicking loss in only 1 epoch to keep it stay in the same basin, thereby preserving generalization. Stage 2 quantizes weights using a PTQ method. As a result, GPLQ is 100x faster than existing QAT methods, lowers memory footprint to levels even below FP32 training, and achieves 4-bit model performance that is highly competitive with FP32 models in terms of both accuracy on ImageNet and generalization to diverse downstream tasks, including fine-grained visual classification and object detection. We will release an easy-to-use open-source toolkit supporting multiple vision tasks.
|
poster
|
2506.11784
| null | null |
[] |
[] |
[] |
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468
|
DISCOVER: Automated Curricula for Sparse-Reward Reinforcement Learning
|
[
"Leander Diaz-Bone",
"Marco Bagatella",
"Jonas Hübotter",
"Andreas Krause"
] |
Sparse-reward reinforcement learning (RL) can model a wide range of highly complex tasks.Solving sparse-reward tasks is RL's core premise — requiring efficient exploration coupled with long-horizon credit assignment — and overcoming these challenges is key for building self-improving agents with superhuman ability.We argue that solving complex and high-dimensional tasks requires solving simpler tasks that are *relevant* to the target task.In contrast, most prior work designs strategies for selecting exploratory tasks with the objective of solving *any* task, making exploration of challenging high-dimensional, long-horizon tasks intractable.We find that the sense of direction, necessary for effective exploration, can be extracted from existing reinforcement learning algorithms, without needing any prior information.Based on this finding, we propose a method for _**di**rected **s**parse-reward goal-**co**nditioned **ve**ry long-horizon **R**L_ (DISCOVER), which selects exploratory goals in the direction of the target task.We connect DISCOVER to principled exploration in bandits, formally bounding the time until the target task becomes achievable in terms of the agent's initial distance to the target, but independent of the volume of the space of all tasks.Empirically, we perform a thorough evaluation in high-dimensional simulated environments. We find that the directed goal selection of DISCOVER solves exploration problems that are beyond the reach of prior state-of-the-art exploration methods in RL.
|
poster
|
2505.19850
| null | null |
[] |
[] |
[] |
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] |
469
|
On the Edge of Memorization in Diffusion Models
|
[
"Sam Buchanan",
"Druv Pai",
"Yi Ma",
"Valentin De Bortoli"
] |
When do diffusion models reproduce their training data, and when are they able to generate samples beyond it? A practically relevant theoretical understanding of this interplay between memorization and generalization may significantly impact real-world deployments of diffusion models with respect to issues such as copyright infringement and data privacy. In this paper, we propose a scientific andmathematical “laboratory” for investigating memorization and generalization in diffusion models trained on fully synthetic or natural image-like structured data. Within this setting, we theoretically characterize a crossover point wherein the weighted training loss of a fully generalizing model becomes greater than that of an underparameterized memorizing model at a critical value of model (under)parameterization. We then demonstrate via carefully-designed experiments that the location of this crossover predicts a phase transition in diffusion models trained via gradient descent, as our theory enables us to analytically predict the model size at which memorization becomes predominant. Our work provides an analytically tractable and practically meaningful setting for future theoretical and empirical investigations. Code for our experiments will be made available online.
|
poster
|
2508.17689
|
https://github.com/DruvPai/diffusion_mem_gen
| null |
[] |
[] |
[] |
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] |
470
|
Beyond Scores: Proximal Diffusion Models
|
[
"Zhenghan Fang",
"Mateo Diaz",
"Sam Buchanan",
"Jeremias Sulam"
] |
Diffusion models have quickly become some of the most popular and powerful generative models for high-dimensional data. The key insight that enabled their development was the realization that access to the score---the gradient of the log-density at different noise levels---allows for sampling from data distributions by solving a reverse-time stochastic differential equation (SDE) via forward discretization, and that popular denoisers allow for unbiased estimators of this score. In this paper, we demonstrate that an alternative, backward discretization of these SDEs, using proximal maps in place of the score, leads to theoretical and practical benefits. We leverage recent results in _proximal matching_ to learn proximal operators of the log-density and, with them, develop Proximal Diffusion Models (`ProxDM`). Theoretically, we prove that $\widetilde{\mathcal O}(d/\sqrt{\varepsilon})$ steps suffice for the resulting discretization to generate an $\varepsilon$-accurate distribution w.r.t. the KL divergence.Empirically, we show that two variants of `ProxDM` achieve significantly faster convergence within just a few sampling steps compared to conventional score-matching methods.
|
poster
|
2507.08956
| null | null |
[] |
[] |
[] |
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] |
471
|
Modeling Microenvironment Trajectories on Spatial Transcriptomics with NicheFlow
|
[
"Kristiyan Sakalyan",
"Alessandro Palma",
"Filippo Guerranti",
"Fabian Theis",
"Stephan Günnemann"
] |
Understanding the evolution of cellular microenvironments is essential for deciphering tissue development and disease progression. While spatial transcriptomics now enables high-resolution mapping of tissue organization across space and time, current techniques that analyze cellular evolution operate at the single-cell level, overlooking critical spatial relationships. We introduce NicheFlow, a flow-based generative model that infers the temporal trajectory of cellular microenvironments across sequential spatial slides. By representing local cell neighborhoods as point clouds, NicheFlow jointly models the evolution of cell states and coordinates using optimal transport and Variational Flow Matching. Our approach successfully recovers both global spatial architecture and local microenvironment composition across diverse spatio-temporal datasets, from embryonic to brain development.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
472
|
Near-Optimal Quantum Algorithms for Computing (Coarse) Correlated Equilibria of General-Sum Games
|
[
"Tongyang Li",
"Xinzhao Wang",
"Yexin Zhang"
] |
Computing Nash equilibria of zero-sum games in classical and quantum settings is extensively studied. For general-sum games, computing Nash equilibria is PPAD-hard and the computing of a more general concept called correlated equilibria has been widely explored in game thoery. In this paper, we initiate the study of quantum algorithms for computing $\varepsilon$-approximate correlated equilibria (CE) and coarse correlated equilibria (CCE) in multi-player normal-form games. Our approach utilizes quantum improvements to the multi-scale Multiplicative Weight Update (MWU) method for CE calculations, achieving a query complexity of $\tilde{O}(m\sqrt{n})$ for fixed $\varepsilon$. For CCE, we extend techniques from quantum algorithms for zero-sum games to multi-player settings, achieving query complexity $\tilde{O}(m\sqrt{n}/\varepsilon^{2.5})$. Both algorithms demonstrate a near-optimal scaling in the number of players $m$ and actions $n$, as confirmed by our quantum query lower bounds.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
473
|
Towards Physics-informed Visual-Spatial Intelligence with Human Priors: An Autonomous Driving Study
|
[
"Guanlin (Frank) Wu",
"Boyan Su",
"Yang Zhao",
"Pu Wang",
"Yichen Lin",
"Hao Frank Yang"
] |
How to encode visual-spatial intelligence (VSI) into representative and informative features remain an open challenge. Instead of following traditional Visual Question Answering (VQA)-style representation, we introduce spatial intelligence grid (SIG): a structured, grid-based data schema that embeds geometrical spatial relationship among objects along with physical priors in human world. We further derive a set of SIG-based optimal evaluation metrics that rigorously quantify a model’s true VSI capabilities. In few-shot in-context learning experiments on state-of-the-art multimodal LLMs (e.g. GPT-4o, Gemini-2.5-Pro), SIG yields consistently larger, more stable, and more comprehensive improvements across all VSI metrics compared to VQA-style representations, demonstrating its potential as a novel data schema for learning VSI. Based on SIG, we create SIGBench, a benchmark containing 1.4K driving frames annotated with ground-truth SIG labels and human gaze attention, supporting both grid-based machine VSI tasks and human-like attention-driven VSI tasks in autonomous-driving scenarios.
|
spotlight
| null | null | null |
[] |
[] |
[] |
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] |
474
|
Mix Data or Merge Models? Balancing the Helpfulness, Honesty, and Harmlessness of Large Language Model via Model Merging
|
[
"Jinluan Yang",
"Dingnan Jin",
"Anke Tang",
"Li Shen",
"Didi Zhu",
"Zhengyu Chen",
"Ziyu Zhao",
"Daixin Wang",
"Qing Cui",
"Zhiqiang Zhang",
"Jun Zhou",
"Fei Wu",
"Kun Kuang"
] |
Achieving balanced alignment of large language models (LLMs) in terms of Helpfulness, Honesty, and Harmlessness (3H optimization) constitutes a cornerstone of responsible AI. Existing methods like data mixture strategies face limitations, including heavy reliance on expert knowledge and conflicting optimization signals. While model merging offers parameter-level conflict-resolution strategies through integrating specialized models' parameters, its potential for 3H optimization remains underexplored. This paper systematically compares the effectiveness of model merging and data mixture methods in constructing 3H-aligned LLMs for the first time, revealing previously overlooked collaborative and conflict relationships among the 3H dimensions and discussing the advantages and drawbacks of data mixture (\textit{data-level}) and model merging (\textit{parameter-level}) methods in mitigating the conflict for balanced 3H optimization. Specially, we propose a novel \textbf{R}eweighting \textbf{E}nhanced task \textbf{S}ingular \textbf{M}erging method, \textbf{RESM}, through outlier weighting and sparsity-aware rank selection strategies to address the challenges of preference noise accumulation and layer sparsity adaptation inherent in 3H-aligned LLM merging. Extensive evaluations can verify the effectiveness and robustness of RESM compared to previous data mixture (2\%-5\% gain) and model merging (1\%-3\% gain) methods in achieving balanced LLM alignment.
|
poster
|
2502.06876
| null | null |
[] |
[] |
[] |
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] |
475
|
Controlling the Flow: Stability and Convergence for Stochastic Gradient Descent with Decaying Regularization
|
[
"Sebastian Kassing",
"Simon Weissmann",
"Leif Döring"
] |
The present article studies the minimization of convex, $L$-smooth functions defined on a separable real Hilbert space. We analyze regularized stochastic gradient descent (reg-SGD), a variant of stochastic gradient descent that uses a Tikhonov regularization with time-dependent, vanishing regularization parameter. We prove strong convergence of reg-SGD to the minimum-norm solution of the original problem without additional boundedness assumptions. Moreover, we quantify the rate of convergence and optimize the interplay between step-sizes and regularization decay. Our analysis reveals how vanishing Tikhonov regularization controls the flow of SGD and yields stable learning dynamics, offering new insights into the design of iterative algorithms for convex problems, including those that arise in ill-posed inverse problems. We validate our theoretical findings through numerical experiments on image reconstruction and ODE-based inverse problems.
|
poster
|
2505.11434
| null | null |
[] |
[] |
[] |
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] |
476
|
Dynamic Regret Reduces to Kernelized Static Regret
|
[
"Andrew Jacobsen",
"Alessandro Rudi",
"Francesco Orabona",
"Nicolò Cesa-Bianchi"
] |
We study dynamic regret in online convex optimization, where the objective is to achieve low cumulative loss relative to an arbitrary benchmark sequence. By observing that competing with an arbitrary sequence of comparators $u_{1},\ldots,u_{T}$ in $\mathcal{W}\subseteq\mathbb{R}^{d}$ is equivalent to competing with a *fixed* comparator *function* $u:[1,T]\to \mathcal{W}$, we frame dynamic regret minimization as a *static* regret problem in a *function space*. By carefully constructing a suitable function space in the form of a Reproducing Kernel Hilbert Space (RKHS), our reduction enables us to recover the optimal $R_{T}(u_{1},\ldots,u_{T}) = \mathcal{O}(\sqrt{\sum_{t}\\|u_{t}-u_{t-1}\\|T})$ dynamic regret guarantee in the setting of linear losses, and yields new scale-free and directionally-adaptive dynamic regret guarantees. Moreover, unlike prior dynamic-to-static reductions---which are valid only for linear losses---our reduction holds for *any* sequence of losses, allowing us to recover $\mathcal{O}\big(\\|u\\|^2+d_{\mathrm{eff}}(\lambda)\ln T\big)$ bounds in exp-concave and improper linear regression settings, where $d_{\mathrm{eff}}(\lambda)$ is a measure of complexity of the RKHS. Despite working in an infinite-dimensional space, the resulting reduction leads to algorithms that are computable in practice, due to the reproducing property of RKHSs.
|
poster
|
2507.05478
| null | null |
[] |
[] |
[] |
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] |
477
|
Multi-Modal View Enhanced Large Vision Models for Long-Term Time Series Forecasting
|
[
"ChengAo Shen",
"Wenchao Yu",
"Ziming Zhao",
"Dongjin Song",
"Wei Cheng",
"Haifeng Chen",
"Jingchao Ni"
] |
Time series, typically represented as numerical sequences, can also be transformed into images and texts, offering multi-modal views (MMVs) of the same underlying signal. These MMVs can reveal complementary patterns and enable the use of powerful pre-trained large models, such as large vision models (LVMs), for long-term time series forecasting (LTSF). However, as we identified in this work, applying LVMs to LTSF poses an inductive bias towards "forecasting periods". To harness this bias, we propose DMMV, a novel decomposition-based multi-modal view framework that leverages trend-seasonal decomposition and a novel backcast-residual based adaptive decomposition to integrate MMVs for LTSF. Comparative evaluations against 14 state-of-the-art (SOTA) models across diverse datasets show that DMMV outperforms single-view and existing multi-modal baselines, achieving the best mean squared error (MSE) on 6 out of 8 benchmark datasets. The code for reproducing our work is available in the supplementary material.
|
poster
|
2505.24003
| null | null |
[] |
[] |
[] |
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] |
478
|
Plan Your Travel and Travel with Your Plan: Wide-Horizon Planning and Evaluation via LLM
|
[
"Dongjie Yang",
"Chengqiang Lu",
"Qimeng Wang",
"Xinbei Ma",
"Yan Gao",
"Yao Hu",
"Hai Zhao"
] |
Travel planning is a complex task requiring the integration of diverse real-world information and user preferences. While LLMs show promise, existing methods with long-horizon thinking struggle with handling multifaceted constraints and preferences in the context, leading to suboptimal itineraries. We formulate this as an $L^3$ planning problem, emphasizing long context, long instruction, and long output. To tackle this, we introduce Multiple Aspects of Planning (MAoP), enabling LLMs to conduct wide-horizon thinking to solve complex planning problems. Instead of direct planning, MAoP leverages the strategist to conduct pre-planning from various aspects and provide the planning blueprint for planning models, enabling strong inference-time scalability for better performance. In addition, current benchmarks overlook travel's dynamic nature, where past events impact subsequent journeys, failing to reflect real-world feasibility. To address this, we propose Travel-Sim, an agent-based benchmark assessing plans via real-world travel simulation. This work advances LLM capabilities in complex planning and offers novel insights for evaluating sophisticated scenarios through agent-based simulation.
|
spotlight
| null | null | null |
[] |
[] |
[] |
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] |
479
|
Learning Theory for Kernel Bilevel Optimization
|
[
"Fares El Khoury",
"Edouard Pauwels",
"Samuel Vaiter",
"Michael Arbel"
] |
Bilevel optimization has emerged as a technique for addressing a wide range of machine learning problems that involve an outer objective implicitly determined by the minimizer of an inner problem. While prior works have primarily focused on the parametric setting, a learning-theoretic foundation for bilevel optimization in the nonparametric case remains relatively unexplored. In this paper, we take a first step toward bridging this gap by studying Kernel Bilevel Optimization (KBO), where the inner objective is optimized over a reproducing kernel Hilbert space. This setting enables rich function approximation while providing a foundation for rigorous theoretical analysis. In this context, we derive novel finite-sample generalization bounds for KBO, leveraging tools from empirical process theory. These bounds further allow us to assess the statistical accuracy of gradient-based methods applied to the empirical discretization of KBO. We numerically illustrate our theoretical findings on a synthetic instrumental variable regression task.
|
poster
|
2502.08457
| null | null |
[] |
[] |
[] |
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] |
480
|
Refining Norms: A Post-hoc Framework for OOD Detection in Graph Neural Networks
|
[
"Jiawei Gu",
"Ziyue Qiao",
"Zechao Li"
] |
Graph Neural Networks (GNNs) are increasingly deployed in mission-critical tasks, yet they often encounter inputs that lie outside their training distribution, leading to unreliable or overconfident predictions. To address this limitation, we present RAGNOR (Robust Aggregation Graph Norm for Outlier Recognition), a post-hoc approach that leverages embedding norms for robust out-of-distribution (OOD) detection on both node-level and graph-level tasks. Unlike previous methods designed primarily for image domains, RAGNOR directly tackles the relational challenges intrinsic to graphs: local contamination by anomalous neighbors, disparate norm scales across classes or roles, and insufficient references for boundary or low-degree nodes. By combining global Z-score normalization, median-based local aggregation, and multi-hop blending, RAGNOR effectively refines raw norm signals into robust OOD scores while incurring minimal overhead and requiring no retraining of the original GNN. Experimental evaluations on multiple benchmarks demonstrate that RAGNOR not only achieves competitive or superior detection performance compared to alternative techniques, but also provides an intuitive, modular design that can be readily integrated into existing graph pipelines.
|
poster
| null | null | null |
[] |
[] |
[] |
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481
|
From Experts to a Generalist: Toward General Whole-Body Control for Humanoid Robots
|
[
"Yuxuan Wang",
"Ming Yang",
"Weishuai Zeng",
"Yu Zhang",
"Xinrun Xu",
"Haobin Jiang",
"Gang Ding",
"Zongqing Lu"
] |
Achieving general agile whole-body control on humanoid robots remains a major challenge due to diverse motion demands and data conflicts. While existing frameworks excel in training single motion-specific policies, they struggle to generalize across highly varied behaviors due to conflicting control requirements and mismatched data distributions. In this work, we propose BUMBLEBEE (BB), an expert-generalist learning framework that combines motion clustering and sim-to-real adaptation to overcome these challenges. BB first leverages an autoencoder-based clustering method to group behaviorally similar motions using motion features and motion descriptions. Expert policies are then trained within each cluster and refined with real-world data through iterative delta action modeling to bridge the sim-to-real gap. Finally, these experts are distilled into a unified generalist controller that preserves agility and robustness across all motion types. Experiments on two simulations and a real humanoid robot demonstrate that BB achieves state-of-the-art general whole-body control, setting a new benchmark for agile, robust, and generalizable humanoid performance in the real world.
|
spotlight
|
2506.12779
| null |
https://beingbeyond.github.io/BumbleBee/
|
[] |
[] |
[] |
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] |
482
|
Revitalizing SVD for Global Covariance Pooling: Halley’s Method to Overcome Over-Flattening
|
[
"Jiawei Gu",
"Ziyue Qiao",
"Xinming Li",
"Zechao Li"
] |
Global Covariance Pooling (GCP) has garnered increasing attention in visual recognition tasks, where second-order statistics frequently yield stronger representations than first-order approaches. However, two main streams of GCP---Newton--Schulz-based iSQRT-COV and exact or near-exact SVD methods---struggle at opposite ends of the training spectrum. While iSQRT-COV stabilizes early learning by avoiding large gradient explosions, it over-compresses significant eigenvalues in later stages, causing an \emph{over-flattening} phenomenon that stalls final accuracy. In contrast, SVD-based methods excel at preserving the high-eigenvalue structure essential for deep networks but suffer from sensitivity to small eigenvalue gaps early on. We propose \textbf{Halley-SVD}, a high-order iterative method that unites the smooth gradient advantages of iSQRT-COV with the late-stage fidelity of SVD. Grounded in Halley's iteration, our approach obviates explicit divisions by $(\lambda_i - \lambda_j)$ and forgoes threshold- or polynomial-based heuristics. As a result, it prevents both early gradient explosions and the excessive compression of large eigenvalues. Extensive experiments on CNNs and transformer architectures show that Halley-SVD consistently and robustly outperforms iSQRT-COV at large model scales and batch sizes, achieving higher overall accuracy without mid-training switches or custom truncations. This work provides a new solution to the long-standing dichotomy in GCP, illustrating how high-order methods can balance robustness and spectral precision to fully harness the representational power of modern deep networks.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
483
|
Learning to Flow from Generative Pretext Tasks for Neural Architecture Encoding
|
[
"Sunwoo Kim",
"Hyunjin Hwang",
"Kijung Shin"
] |
The performance of a deep learning model on a specific task and dataset depends heavily on its neural architecture, motivating considerable efforts to rapidly and accurately identify architectures suited to the target task and dataset. To achieve this, researchers use machine learning models—typically neural architecture encoders—to predict the performance of a neural architecture. Many state-of-the-art encoders aim to capture information flow within a neural architecture, which reflects how information moves through the forward pass and backpropagation, via a specialized model structure. However, due to their complicated structures, these flow-based encoders are significantly slower to process neural architectures compared to simpler encoders, presenting a notable practical challenge. To address this, we propose FGP, a novel pre-training method for neural architecture encoding that trains an encoder to capture the information flow without requiring specialized model structures. FGP trains an encoder to reconstruct a flow surrogate, our proposed representation of the neural architecture's information flow. Our experiments show that FGP boosts encoder performance by up to 106\% in Precision@1\%, compared to the same encoder trained solely with supervised learning.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
484
|
Coupling Generative Modeling and an Autoencoder with the Causal Bridge
|
[
"Ruolin Meng",
"Ming-Yu Chung",
"Dhanajit Brahma",
"Ricardo Henao",
"Lawrence Carin"
] |
We consider inferring the causal effect of a treatment (intervention) on an outcome of interest in situations where there is potentially an unobserved confounder influencing both the treatment and the outcome. This is achievable by assuming access to two separate sets of control (proxy) measurements associated with treatment and outcomes, which are used to estimate treatment effects through a function termed the *causal bridge* (CB). We present a new theoretical perspective, associated assumptions for when estimating treatment effects with the CB is feasible, and a bound on the average error of the treatment effect when the CB assumptions are violated. From this new perspective, we then demonstrate how coupling the CB with an autoencoder architecture allows for the sharing of statistical strength between observed quantities (proxies, treatment, and outcomes), thus improving the quality of the CB estimates. Experiments on synthetic and real-world data demonstrate the effectiveness of the proposed approach in relation to the state-of-the-art methodology for proxy measurements.
|
poster
|
2509.25599
| null | null |
[] |
[] |
[] |
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485
|
Fourier Clouds: Fast Bias Correction for Imbalanced Semi-Supervised Learning
|
[
"Jiawei Gu",
"Yidi Wang",
"Qingqiang Sun",
"Xinming Li",
"Xiao Luo",
"Ziyue Qiao"
] |
Pseudo-label-based Semi-Supervised Learning (SSL) often suffers from classifier bias, particularly under class imbalance, as inaccurate pseudo-labels tend to exacerbate existing biases towards majority classes. Existing methods, such as \textit{CDMAD}\cite{cdmad}, utilize simplistic reference inputs—typically uniform or blank-colored images—to estimate and correct this bias. However, such simplistic references fundamentally ignore realistic statistical information inherent to real datasets, specifically typical color distributions, texture details, and frequency characteristics. This lack of \emph{statistical representativeness} can lead the model to inaccurately estimate its inherent bias, limiting the effectiveness of bias correction, particularly under severe class imbalance or substantial distribution mismatches between labeled and unlabeled datasets. To overcome these limitations, we introduce the \textbf{FARAD} (Fourier-Adapted Reference for Accurate Debiasing) System. This system utilizes random-phase images, constructed by preserving the amplitude spectrum of real data while randomizing the phase spectrum. This strategy ensures two critical properties: (1) \textbf{Semantic Irrelevance}, as randomizing phase removes any structural or recognizable semantic cues, and (2) \textbf{Statistical Representativeness}, as preserving the amplitude spectrum maintains realistic textures, color distributions, and frequency characteristics. Grounded theoretically in classical Fourier analysis, the FARAD System provides a robust, accurate estimation of per-class biases. Furthermore, computational efficiency is enhanced through optimized real-to-complex (R2C) batched Fast Fourier Transforms (FFTs). Comprehensive experiments demonstrate that our approach, significantly improves minority-class accuracy and overall SSL performance, particularly under challenging imbalance scenarios, compared with existing reference-based bias correction methods.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
486
|
Distillation Robustifies Unlearning
|
[
"Alex Infanger",
"Bruce W, Lee",
"Addie Foote",
"Leni Shor",
"Harish Kamath",
"Bryce Woodworth",
"Alex Cloud",
"Alexander Turner",
"Jacob Goldman-Wetzler"
] |
Large language models acquire undesired capabilities during pretraining, such as knowledge that could support the development of weapons of mass destruction. Machine unlearning methods aim to remove these capabilities but typically achieve only behavioral suppression: the model appears safe but retains latent capabilities that can be easily recovered through adversarial finetuning. To address this limitation, we show that distilling an unlearned model into a randomly initialized student yields a model that is significantly more robust to such relearning attacks. Next, we introduce Unlearn-Noise-Distill-on-Outputs (UNDO), which approximates network reinitialization by copying and noising the weights of an unlearned teacher model. UNDO attains most of the robustness of gold-standard data filtering at a fraction of the compute cost. In extensive comparisons to unlearning baselines on language and arithmetic tasks, UNDO achieves Pareto optimal robustness and performance. On the Weapons of Mass Destruction Proxy (WMDP) benchmark, UNDO robustifies unlearning to achieve competitive performance. Since LLM developers already apply distillation to reduce inference costs, our results show they can obtain robustness essentially “for free” by incorporating suppression before distillation. This insight offers a practical path to safer model deployment and open-weight release.
|
spotlight
|
2506.06278
| null | null |
[] |
[] |
[] |
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487
|
JADE: Joint Alignment and Deep Embedding for Multi-Slice Spatial Transcriptomics
|
[
"Yuanchuan Guo",
"Jun Liu",
"Huimin Cheng",
"Ying Ma"
] |
As spatial transcriptomics (ST) datasets increasingly span multiple adjacent or replicated slices, effective joint analysis across slices is needed to reconstruct tissue structures and identify consistent spatial gene expression patterns. This requires resolving spatial correspondences between slices while capturing shared transcriptomic features, two tasks that are typically addressed in isolation. Multi-slice analysis remains challenging due to physical distortions, technical variability, and batch effects. To address these challenges, we introduce Joint Alignment and Deep Embedding for multi-slice ST (JADE), a unified computational framework that simultaneously learns spot-wise alignments and shared low-dimensional embeddings across tissue slices. Unlike existing methods, JADE adopts a roundtrip framework in which each iteration alternates between alignment and embedding refinement. To infer alignment, we employ attention mechanisms that dynamically assess and weight the importance of different embedding dimensions, allowing the model to focus on the most alignment-relevant features while suppressing noise. To the best of our knowledge, JADE is the first method that jointly optimizes alignment and representation learning in a shared latent space, enabling robust multi-slice integration. We demonstrate that JADE outperforms existing alignment and embedding methods across multiple evaluation metrics in the 10x Visium human dorsolateral prefrontal cortex (DLPFC) and Stereo-seq axolotl brain datasets. By bridging spatial alignment and feature integration, JADE provides a scalable and accurate solution for cross-slice analysis of ST data.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
488
|
ComPO: Preference Alignment via Comparison Oracles
|
[
"Peter Chen",
"Xi Chen",
"Wotao Yin",
"Tianyi Lin"
] |
Direct alignment methods are increasingly used for aligning large language models (LLMs) with human preferences. However, these methods suffer from the issues of \textit{verbosity} and \textit{likelihood displacement}, which can be driven by the noisy preference pairs that induce similar likelihood for preferred and dispreferred responses. The contributions of this paper are two-fold. First, we propose a new preference alignment method based on comparison oracles and provide the convergence guarantee for its basic scheme. Second, we improve our method using some heuristics and conduct the experiments to demonstrate the flexibility and compatibility of practical scheme in improving the performance of LLMs using noisy preference pairs. Evaluations are conducted across multiple base and instruction-tuned models (Mistral-7B, Llama-3-8B and Gemma-2-9B) with benchmarks (AlpacaEval 2, MT-Bench and Arena-Hard). Experimental results show the effectiveness of our method as an alternative to addressing the limitations of existing direct alignment methods. A highlight of our work is that we evidence the importance of designing specialized methods for preference pairs with distinct likelihood margin, which complements the recent findings in Razin et al., [71].
|
poster
|
2505.05465
|
https://github.com/PeterLauLukChen/ComparisonPO
| null |
[] |
[] |
[] |
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] |
489
|
Co-Reinforcement Learning for Unified Multimodal Understanding and Generation
|
[
"Jingjing Jiang",
"Chongjie Si",
"Jun Luo",
"Hanwang Zhang",
"Chao Ma"
] |
This paper presents a pioneering exploration of reinforcement learning (RL) via group relative policy optimization for unified multimodal large language models (ULMs), aimed at simultaneously reinforcing generation and understanding capabilities. Through systematic pilot studies, we uncover the significant potential of ULMs to enable the synergistic co-evolution of dual capabilities within a shared policy optimization framework. Building on this insight, we introduce \textbf{CoRL}, a co-reinforcement learning framework comprising a unified RL stage for joint optimization and a refined RL stage for task-specific enhancement. With the proposed CoRL, our resulting model, \textbf{ULM-R1}, achieves average improvements of \textbf{7\%} on three text-to-image generation datasets and \textbf{23\%} on nine multimodal understanding benchmarks. These results demonstrate the effectiveness of CoRL and highlight the substantial benefit of reinforcement learning in facilitating cross-task synergy and optimization for ULMs.
|
spotlight
|
2505.17534
|
https://github.com/mm-vl/ULM-R1
| null |
[] |
[] |
[] |
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] |
490
|
Grounded Reinforcement Learning for Visual Reasoning
|
[
"Gabriel Sarch",
"Snigdha Saha",
"Naitik Khandelwal",
"Ayush Jain",
"Michael Tarr",
"Aviral Kumar",
"Katerina Fragkiadaki"
] |
While reinforcement learning (RL) over chains of thought has significantly advanced language models in tasks such as mathematics and coding, visual reasoning introduces added complexity by requiring models to direct visual attention, interpret perceptual inputs, and ground abstract reasoning in spatial evidence. We introduce ViGoRL (Visually Grounded Reinforcement Learning), a vision-language model trained with RL to explicitly anchor each reasoning step to specific visual coordinates. Inspired by human visual decision-making, ViGoRL learns to produce spatially grounded reasoning traces, guiding visual attention to task-relevant regions at each step. Across a diverse set of visual reasoning benchmarks—including SAT-2 and BLINK for spatial reasoning, and ScreenSpot and VisualWebArena for web-based grounding—ViGoRL consistently outperforms both supervised fine-tuning and conventional RL baselines that lack explicit grounding mechanisms. Incorporating multi-turn RL with visual feedback further improves ViGoRL’s performance on localizing small elements. Additionally, we find that grounding amplifies other visual behaviors such as region exploration, visual subgoal setting, and verification. Finally, human evaluations show that the model’s visual references are not only spatially accurate but also helpful for understanding model reasoning steps. Our results show that visually grounded RL is a strong paradigm for imbuing models with general-purpose visual reasoning.
|
poster
|
2505.23678
|
https://github.com/Gabesarch/grounded-rl
|
https://visually-grounded-rl.github.io/
|
[] |
[] |
[] |
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491
|
NFL-BA: Near-Field Light Bundle Adjustment for SLAM in Dynamic Lighting
|
[
"Andrea Dunn Beltran",
"Daniel Rho",
"Marc Niethammer",
"Roni Sengupta"
] |
Simultaneous Localization and Mapping (SLAM) systems typically assume static, distant illumination; however, many real-world scenarios, such as endoscopy, subterranean robotics, and search & rescue in collapsed environments, require agents to operate with a co-located light and camera in the absence of external lighting. In such cases, dynamic near-field lighting introduces strong, view-dependent shading that significantly degrades SLAM performance. We introduce Near-Field Lighting Bundle Adjustment Loss (NFL-BA) which explicitly models near-field lighting as a part of Bundle Adjustment loss and enables better performance for scenes captured with dynamic lighting. NFL-BA can be integrated into neural rendering-based SLAM systems with implicit or explicit scene representations. Our evaluations mainly focus on endoscopy procedure where SLAM can enable autonomous navigation, guidance to unsurveyed regions, blindspot detections, and 3D visualizations, which can significantly improve patient outcomes and endoscopy experience for both physicians and patients. Replacing Photometric Bundle Adjustment loss of SLAM systems with NFL-BA leads to significant improvement in camera tracking, 37% for MonoGS and 14% for EndoGSLAM, and leads to state-of-the-art camera tracking and mapping performance on the C3VD colonoscopy dataset. Further evaluation on indoor scenes captured with phone camera with flashlight turned on, also demonstrate significant improvement in SLAM performance due to NFL-BA.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
492
|
Lifelong Test-Time Adaptation via Learning in Tracked Low-Dimensional Subspace
|
[
"Dexin Duan",
"Rui Xu",
"Peilin Liu",
"Fei Wen"
] |
Test-time adaptation (TTA) aims to adapt a source model to a target domain using only test data. Existing methods predominantly rely on unsupervised entropy minimization or its variants, which suffer from degeneration, leading to trivial solutions with low-entropy but inaccurate predictions. In this work, we identify *entropy-deceptive* (ED) samples, instances where the model makes highly confident yet incorrect predictions, as the underlying cause of degeneration. Further, we reveal that the gradients of entropy minimization in TTA have an intrinsic low-dimensional structure, driven primarily by *entropy-truthful* (ET) samples whose gradients are highly correlated. In contrast, ED samples have scattered, less correlated gradients. Leveraging this observation, we show that the detrimental impact of ED samples can be suppressed by constraining model updates within the principal subspace of backward gradients. Building on this insight, we propose LCoTTA, a lifelong continual TTA method that tracks the principal subspace of gradients online and utilizes their projections onto this subspace for adaptation. Further, we provide theoretical analysis to show that the proposed subspace-based method can enhance the robustness against detrimental ED samples. Extensive experiments demonstrate that LCoTTA effectively overcomes degeneration and significantly outperforms existing methods in long-term continual adaptation scenarios. Code is available online.
|
poster
| null | null | null |
[] |
[] |
[] |
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] |
493
|
Learning to Steer: Input-dependent Steering for Multimodal LLMs
|
[
"Jayneel Parekh",
"Pegah KHAYATAN",
"Mustafa Shukor",
"Arnaud Dapogny",
"Alasdair Newson",
"Matthieu Cord"
] |
Steering has emerged as a practical approach to enable post-hoc guidance of LLMs towards enforcing a specific behavior. However, it remains largely underexplored for multimodal LLMs (MLLMs); furthermore, existing steering techniques, such as \textit{mean} steering, rely on a single steering vector, applied independently of the input query. This paradigm faces limitations when the desired behavior is dependent on the example at hand. For example, a safe answer may consist in abstaining from answering when asked for an illegal activity, or may point to external resources or consultation with an expert when asked about medical advice. In this paper, we investigate a fine-grained steering that uses an input-specific linear shift. This shift is computed using contrastive input-specific prompting. However, the input-specific prompts required for this approach are not known at test time. Therefore, we propose to train a small auxiliary module to predict the input-specific steering vector. Our approach, dubbed as L2S (Learn-to-Steer), demonstrates that it reduces hallucinations and enforces safety in MLLMs, outperforming other static baselines. We will open-source our code.
|
poster
|
2508.12815
| null | null |
[] |
[] |
[] |
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] |
494
|
Can Large Language Models Master Complex Card Games?
|
[
"Wei Wang",
"Fuqing Bie",
"Junzhe Chen",
"Dan Zhang",
"Shiyu Huang",
"Evgeny Kharlamov",
"Jie Tang"
] |
Complex games have long been an important benchmark for testing the progress of artificial intelligence algorithms. AlphaGo, AlphaZero, and MuZero have defeated top human players in Go and Chess, garnering widespread societal attention towards artificial intelligence. Concurrently, large language models (LLMs) have exhibited remarkable capabilities across various tasks, raising the question of whether LLMs can achieve similar success in complex games. In this paper, we explore the potential of LLMs in mastering complex card games. We systematically assess the learning capabilities of LLMs across eight diverse card games, evaluating the impact of fine-tuning on high-quality gameplay data, and examining the models' ability to retain general capabilities while mastering these games. Our findings indicate that: (1) LLMs can approach the performance of strong game AIs through supervised fine-tuning on high-quality data, (2) LLMs can master multiple complex card games simultaneously, with performance augmentation for games with similar rules and conflicts for dissimilar ones, and (3) LLMs experience a decline in general capabilities when mastering complex games, but this decline can be mitigated by integrating a certain amount of general instruction data. The evaluation results demonstrate strong learning ability and versatility of LLMs. The code is available at https://anonymous.4open.science/r/LLM4CardGame-D834
|
poster
|
2509.01328
|
https://github.com/THUDM/LLM4CardGame
| null |
[] |
[] |
[] |
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495
|
Adaptive Prediction-Powered AutoEval with Reliability and Efficiency Guarantees
|
[
"Sangwoo Park",
"Matteo Zecchin",
"Osvaldo Simeone"
] |
Selecting artificial intelligence (AI) models, such as large language models (LLMs), from multiple candidates requires accurate performance estimation. This is ideally achieved through empirical evaluations involving abundant real-world data. However, such evaluations are costly and impractical at scale. To address this challenge, autoevaluation methods leverage synthetic data produced by automated evaluators, such as LLMs-as-judges, reducing variance but potentially introducing bias. Recent approaches have employed semi-supervised prediction-powered inference ($\texttt{PPI}$) to correct for the bias of autoevaluators. However, the use of autoevaluators may lead in practice to a degradation in sample efficiency compared to conventional methods using only real-world data. In this paper, we propose $\texttt{R-AutoEval+}$, a novel framework that provides finite-sample reliability guarantees on the model evaluation, while also ensuring an enhanced (or at least no worse) sample efficiency compared to conventional methods. The key innovation of $\texttt{R-AutoEval+}$ is an adaptive construction of the model evaluation variable, which dynamically tunes its reliance on synthetic data, reverting to conventional methods when the autoevaluator is insufficiently accurate. Experiments on the use of LLMs-as-judges for the optimization of quantization settings for the weights of an LLM, and for prompt design in LLMs confirm the reliability and efficiency of $\texttt{R-AutoEval+}$.
|
spotlight
|
2505.18659
| null | null |
[] |
[] |
[] |
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] |
496
|
Non-Adaptive Adversarial Face Generation
|
[
"Sunpill Kim",
"Seunghun Paik",
"Chanwoo Hwang",
"Minsu Kim",
"Jae Hong Seo"
] |
Adversarial attacks on face recognition systems (FRSs) pose serious security and privacy threats, especially when these systems are used for identity verification. In this paper, we propose a novel method for generating adversarial faces—synthetic facial images that are visually distinct yet recognized as a target identity by the FRS. Unlike iterative optimization-based approaches (e.g., gradient descent or other iterative solvers), our method leverages the structural characteristics of the FRS feature space. We figure out that individuals sharing the same attribute (e.g., gender or race) form an attributed subsphere. By utilizing such subspheres, our method achieves both non-adaptiveness and a remarkably small number of queries. This eliminates the need for relying on transferability and open-source surrogate models, which have been a typical strategy when repeated adaptive queries to commercial FRSs are impossible. Despite requiring only a single non-adaptive query consisting of 100 face images, our method achieves a high success rate of over 93% against AWS’s CompareFaces API at its default threshold. Furthermore, unlike many existing attacks that perturb a given image, our method can deliberately produce adversarial faces that impersonate the target identity while exhibiting high-level attributes chosen by the adversary.
|
poster
|
2507.12107
| null | null |
[] |
[] |
[] |
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] |
497
|
Prediction with expert advice under additive noise
|
[
"Alankrita Bhatt",
"Victoria Kostina"
] |
Prediction with expert advice serves as a fundamental model in online learning and sequential decision-making. However, in many real-world settings, this classical model proves insufficient as the feedback available to the decision-maker is often subject to noise, errors, or communication constraints. This paper provides fundamental limits on performance, quantified by the regret, in the case when the feedback is corrupted by an additive noise. Our general analysis achieves sharp regret bounds for canonical examples of such additive noise as the Gaussian distribution, the uniform distribution, and a general noise with a log-concave density. This analysis demonstrates how different noise characteristics affect regret bounds and identifies how the regret fundamentally scales as a function of the properties of the noise distribution.
|
poster
| null | null | null |
[] |
[] |
[] |
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498
|
Emergent Risk Awareness in Rational Agents under Resource Constraints
|
[
"Daniel Jarne Ornia",
"Nicholas Bishop",
"Joel Dyer",
"Wei-Chen Lee",
"Anisoara Calinescu",
"Doyne Farmer",
"Michael Wooldridge"
] |
Advanced reasoning models with agentic capabilities (AI agents) are deployed to interact with humans and to solve sequential decision‑making problems under (often approximate) utility functions and internal models. When such problems have resource or failure constraints where action sequences may be forcibly terminated once resources are exhausted, agents face implicit trade‑offs that reshape their utility-driven (rational) behaviour. Additionally, since these agents are typically commissioned by a human principal to act on their behalf, asymmetries in constraint exposure can give rise to previously unanticipated misalignment between human objectives and agent incentives. We formalise this setting through a survival bandit framework, provide theoretical and empirical results that quantify the impact of survival‑driven preference shifts, identify conditions under which misalignment emerges and propose mechanisms to mitigate the emergence of risk-seeking or risk-averse behaviours. As a result, this work aims to increase understanding and interpretability of emergent behaviours of AI agents operating under such survival pressure, and offer guidelines for safely deploying such AI systems in critical resource‑limited environments.
|
poster
|
2505.23436
| null | null |
[] |
[] |
[] |
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499
|
MIBP-Cert: Certified Training against Data Perturbations with Mixed Integer Bilinear Programs
|
[
"Tobias Lorenz",
"Marta Kwiatkowska",
"Mario Fritz"
] |
Data errors, corruptions, and poisoning attacks during training pose a major threat to the reliability of modern AI systems. While extensive effort has gone into empirical mitigations, the evolving nature of attacks and the complexity of data require a more principled, provable approach to robustly learn on such data - and to understand how perturbations influence the final model. Hence, we introduce MIBP-Cert, a novel certification method based on Mixed Integer Bilinear Programming (MIBP) that computes sound, deterministic bounds to provide provable robustness even under complex threat models. By computing the set of parameters reachable through perturbed or manipulated data, we can predict all possible outcomes and guarantee robustness. To make solving this optimization problem tractable, we propose a novel relaxation scheme that bounds each training step without sacrificing soundness. We demonstrate the applicability of our approach to continuous and discrete data, as well as different threat models - including complex ones that were previously out of reach.
|
poster
| null | null | null |
[] |
[] |
[] |
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