GenAI4ELab/papercli-papers huggingface dataset:
On the Tension Between Optimality and Adversarial Robustness in Policy Optimization
Authors: Haoran Li; Jiayu Lv; Congying Han; Zicheng Zhang; Anqi Li; Yan Liu; Tiande Guo; Nan Jiang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Achieving optimality and adversarial robustness in deep reinforcement learning has long been regarded as conflicting goals. Nonetheless, recent theoretical insights presented in CAR suggest a potential alignment, raising the important question of how to realize this in practice.
This paper first identifies a key gap between theory and practice by comparing standard policy optimization (SPO) and adversarially robust policy optimization (ARPO). Although they share theoretical consistency, *a fundamental tension between robustness and optimality arises in practical policy gradient methods*. SPO tends toward convergence to vulnerable first-order stationary policies (FOSPs) with strong natural performance, whereas ARPO typically favors more robust FOSPs at the expense of reduced returns. Furthermore, we attribute this tradeoff to the *reshaping effect of the strongest adversaries* in ARPO, which significantly complicates the global landscape by inducing *deceptive sticky FOSPs*. This improves robustness but makes navigation more challenging. To alleviate this, we develop the *BARPO*, a bilevel framework unifying SPO and ARPO by modulating adversary strength, thereby facilitating navigability while preserving global optima. Extensive empirical results demonstrate that BARPO consistently outperforms vanilla ARPO, providing a practical approach to reconcile theoretical and empirical performance.
DVD-Quant: Data-free Video Diffusion Transformers Quantization
Authors: Zhiteng Li; Hanxuan Li; Junyi Wu; Kai Liu; Haotong Qin; Linghe Kong; Guihai Chen; Yulun Zhang; Xiaokang Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Diffusion Transformers (DiTs) have emerged as the state-of-the-art architecture for video generation, yet their computational and memory demands hinder practical deployment. While post-training quantization (PTQ) presents a promising approach to accelerate Video DiT models, existing methods suffer from two critical limitations: (1) dependence on computation-heavy and inflexible calibration procedures, and (2) considerable performance deterioration after quantization.
To address these challenges, we propose DVD-Quant, a novel Data-free quantization framework for Video DiTs. Our approach integrates three key innovations:
(1) Bounded-init Grid Refinement (BGR) and 
(2) Auto-scaling Rotated Quantization (ARQ) for calibration data-free quantization error reduction, as well as
(3) $\delta$-Guided Bit Switching ($\delta$-GBS) for adaptive bit-width allocation.
Extensive experiments across multiple video generation benchmarks demonstrate that DVD-Quant achieves an approximately 2$\times$ speedup over full-precision baselines on  advanced DiT models while maintaining visual fidelity. Notably, DVD-Quant is the first to enable W4A4 PTQ for Video DiTs without compromising video quality. Code and models will be released to facilitate future research.
Seesaw: Accelerating Training by Balancing Batch Size and Learning Rate Scheduling
Authors: Alexandru Meterez; Depen Morwani; Jingfeng Wu; Costin-Andrei Oncescu; Cengiz Pehlevan; Sham M. Kakade
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Increasing the batch size during training --- a “batch ramp'' --- is a promising strategy to accelerate large language model pretraining. While for SGD, doubling the batch size can be equivalent to halving the learning rate, the optimal strategy for adaptive optimizers like Adam is less clear. As a result, any batch-ramp scheduling, if used at all, is typically tuned heuristically. This work develops a principled framework for batch-size scheduling and introduces Seesaw: whenever a standard scheduler would halve the learning rate, Seesaw instead multiplies it by $1/\sqrt{2}$ and doubles the batch size, preserving loss dynamics while reducing serial steps. Theoretically, we provide, to our knowledge, the first finite-sample proof of equivalence between learning-rate decay and batch-size ramp-up for SGD on noisy linear regression, and we extend this equivalence to normalized SGD, a tractable proxy for Adam, under a variance-dominated regime observed in practice. Empirically, on 150M/300M/600M-parameter models trained at Chinchilla scale using a constant (critical) batch size, Seesaw matches cosine decay at equal FLOPs while reducing wall-clock time by $\approx 36\%$, approaching the theoretical limit implied by our analysis.
WATS: Wavelet-Aware Temperature Scaling for Reliable Graph Neural Networks
Authors: Xiaoyang Li; Linwei Tao; Haohui Lu; Minjing Dong; Junbin Gao; Chang Xu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Graph Neural Networks (GNNs) have demonstrated strong predictive performance on relational data; however, their confidence estimates often misalign with actual predictive correctness, posing significant limitations for deployment in safety-critical settings. While existing graph-aware calibration methods seek to mitigate this limitation, they primarily depend on coarse one-hop statistics, such as neighbor-predicted confidence, or latent node embeddings, thereby neglecting the fine-grained structural heterogeneity inherent in graph topology. In this work, we propose Wavelet-Aware Temperature Scaling (WATS), a post-hoc calibration framework for node classification that assigns node-specific temperatures based on tunable heat-kernel graph wavelet features. Specifically, WATS harnesses the scalability and topology sensitivity of graph wavelets to refine confidence estimates, all without necessitating model retraining or access to neighboring logits or predictions. Extensive evaluations across nine benchmark datasets with varying graph structures and three GNN backbones demonstrate that WATS achieves the lowest Expected Calibration Error (ECE) among most of the compared methods, outperforming both classical and graph-specific baselines by up to 41.2\% in ECE and reducing calibration variance by 15.84\% on average compared with graph-specific methods. Moreover, WATS remains computationally efficient, scaling well across graphs of diverse sizes and densities. The implementation is available at \url{https://github.com/lxy1134/WATS.git}
Diffusion and Flow-based Copulas: Forgetting and Remembering Dependencies
Authors: David Huk; Theodoros Damoulas
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Copulas are a fundamental tool for modelling multivariate dependencies in data, forming the method of choice in diverse fields and applications. However, the adoption of existing models for multimodal and high-dimensional dependencies is hindered by restrictive assumptions and poor scaling. In this work, we present methods for modelling copulas based on the principles of diffusions and flows. We design two processes that progressively forget inter-variable dependencies while leaving dimension-wise distributions unaffected, provably defining valid copulas at all times. We show how to obtain copula models by learning to remember the forgotten dependencies from each process, theoretically recovering the true copula at optimality. The first instantiation of our framework focuses on direct density estimation, while the second specialises in expedient sampling. Empirically, we demonstrate the superior performance of our proposed methods over state-of-the-art copula approaches in modelling complex and high-dimensional dependencies from scientific datasets and images. Our work enhances the representational power of copula models, empowering applications and paving the way for their adoption on larger scales and more challenging domains.
Lost in the Non-convex Loss Landscape: How to Fine-tune the Large Time Series Model?
Authors: Xu Zhang; Peng Wang; Wei Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recently, large time series models (LTSMs) have gained increasing attention due to their similarities to large language models, including flexible context length, scalability, and task generality, outperforming advanced task-specific models. However, prior studies indicate that pre-trained LTSMs may exhibit a poorly conditioned non-convex loss landscape, leading to limited trainability. As a result, direct fine-tuning tends to cause overfitting and suboptimal performance, sometimes even worse than training from scratch, substantially diminishing the benefits of pre-training.
To overcome this limitation, we propose Smoothed Full Fine-tuning (SFF), a novel fine-tuning technology. Specifically, we construct an auxiliary LTSM via random initialization to obtain a smoother loss landscape, and then linearly interpolate its weights with those of the pre-trained model to smooth the original landscape. This process improves trainability while preserving pre-trained knowledge, thereby enabling more effective downstream fine-tuning. From an optimization perspective, SFF perturbs sharp minima without significantly harming flat regions, facilitating escape from poor local basins toward smoother and more generalizable solutions. Extensive experiments on benchmark datasets demonstrate consistent improvements across eight representative LTSMs, including Timer, TimesFM, MOMENT, UniTS, MOIRAI, Chronos, TTMs, and Sundial, on diverse downstream tasks. The code is available at the link: \url{https://github.com/Meteor-Stars/SFF}.
Probing in the Dark: State Entropy Maximization for POMDPs
Authors: Yonatan Ashlag; Mirco Mutti; Aviv Tamar; Kfir Yehuda Levy
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Sample efficiency is one of the main bottlenecks for optimal decision making via reinforcement learning. Pretraining a policy to maximize the entropy of the state visitation can substantially speedup reinforcement learning of downstream tasks. It is still an open question how to maximize the state entropy in POMDPs, where the true states of the environment, or their entropy, are not observed. In this work, we propose to maximize the entropy of a sufficient statistic of the history, which is called an information state. First, we show that a recursive latent model that predicts future observations is an information state in this setting. Then, we provide a practical algorithm, called LatEnt, to simultaneously learn the latent model and a latent-based policy maximizing the corresponding entropy objective from reward-free interactions with the POMDP. We empirically show that our approach induces higher state entropy than existing methods, which translates to better performance on downstream tasks.  As a byproduct, we open-source PROBE, the first benchmark to test reward-free pretraining in POMDPs.
Exploratory Diffusion Model for Unsupervised Reinforcement Learning
Authors: Chengyang Ying; Huayu Chen; Xinning Zhou; Zhongkai Hao; Hang Su; Jun Zhu
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Unsupervised reinforcement learning (URL) pre-trains agents by exploring diverse states in reward-free environments, aiming to enable efficient adaptation to various downstream tasks. Without extrinsic rewards, prior methods rely on intrinsic objectives, but heterogeneous exploration data demand strong modeling capacity for both intrinsic reward design and policy learning. We introduce the **Ex**ploratory **D**iffusion **M**odel (**ExDM**), which leverages the expressive power of diffusion models to fit diverse replay-buffer distributions, thus providing accurate density estimates and a score-based intrinsic reward that drives exploration into under-visited regions. This mechanism substantially broadens state coverage and yields robust pre-trained policies. Beyond exploration, ExDM offers theoretical guarantees and practical algorithms for fine-tuning diffusion policies under limited interactions, overcoming instability and computational overhead from multi-step sampling. Extensive experiments on Maze2d and URLB show that ExDM achieves superior exploration and faster downstream adaptation, establishing new state-of-the-art results, particularly in environments with complex structure or cross-embodiment settings. The source code is provided at https://github.com/yingchengyang/ExDM.
DCFold: Efficient Protein Structure Generation with Single Forward Pass
Authors: Zhe Zhang; Yuanning Feng; Yuxuan Song; Keyue Qiu; Hao Zhou; Wei-Ying Ma
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: AlphaFold3 introduces a diffusion-based architecture that elevates protein structure prediction to all-atom resolution with improved accuracy. This state-of-the-art performance has established AlphaFold3 as a foundation model for diverse generation and design tasks. However, its iterative design substantially increases inference time, limiting practical deployment in downstream settings such as virtual screening and protein design. We propose DCFold, a single-step generative model that attains AlphaFold3-level accuracy. Our Dual Consistency training framework, which incorporates a novel Temporal Geodesic Matching (TGM) scheduler, enables DCFold to achieve a 15× acceleration in inference while maintaining predictive fidelity. We validate its effectiveness across both structure prediction and binder design benchmarks.
DeCo-DETR: Decoupled Cognition DETR for efficient Open-Vocabulary Object Detection
Authors: Siheng Wang; Yanshu Li; Bohan Hu; Zhengdao Li; HaiboZhan; Linshan Li; Weiming Liu; Ruizhi Qian; Guangxin Wu; Hao Zhang; Jifeng Shen; Piotr Koniusz; Zhengtao Yao; Junhao Dong; Qiang Sun
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Open-vocabulary object detection (OVOD) enables models to recognize objects beyond predefined categories, but existing approaches remain limited in practical deployment. On the one hand, multimodal designs often incur substantial computational overhead due to their reliance on text encoders at inference time. On the other hand, tightly coupled training objectives introduce a trade-off between closed-set detection accuracy and open-world generalization. Thus, we propose Decoupled Cognition DETR (DeCo-DETR), a vision-centric framework that addresses these challenges through a unified decoupling paradigm. Instead of depending on online text encoding, DeCo-DETR constructs a hierarchical semantic prototype space from region-level descriptions generated by pre-trained LVLMs and aligned via CLIP, enabling efficient and reusable semantic representation. Building upon this representation, the framework further disentangles semantic reasoning from localization through a decoupled training strategy, which separates alignment and detection into parallel optimization streams. Extensive experiments on standard OVOD benchmarks demonstrate that DeCo-DETR achieves competitive zero-shot detection performance while significantly improving inference efficiency. These results highlight the effectiveness of decoupling semantic cognition from detection, offering a practical direction for scalable OVOD systems.
QVGen: Pushing the Limit of Quantized Video Generative Models
Authors: Yushi Huang; Ruihao Gong; Jing Liu; Yifu Ding; Chengtao Lv; Haotong Qin; Jun Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Video diffusion models (DMs) have enabled high-quality video synthesis. Yet, their substantial computational and memory demands pose serious challenges to real-world deployment, even on high-end GPUs. As a commonly adopted solution, quantization has proven notable success in reducing cost for image DMs, while its direct application to video DMs remains ineffective.  In this paper, we present *QVGen*, a novel quantization-aware training (QAT) framework tailored for high-performance and inference-efficient video DMs under extremely low-bit quantization (*e.g.*, $4$-bit or below). We begin with a theoretical analysis demonstrating that reducing the gradient norm is essential to facilitate convergence for QAT. To this end, we introduce auxiliary modules ($\Phi$) to mitigate large quantization errors, leading to significantly enhanced convergence. To eliminate the inference overhead of $\Phi$, we propose a *rank-decay* strategy that progressively eliminates $\Phi$. Specifically, we repeatedly employ singular value decomposition (SVD) and a proposed rank-based regularization $\mathbf{\gamma}$ to identify and decay low-contributing components. This strategy retains performance while zeroing out additional inference overhead. Extensive experiments across $4$ state-of-the-art (SOTA) video DMs, with parameter sizes ranging from $1.3\text{B}\sim14\text{B}$, show that QVGen is *the first* to reach full-precision comparable quality under $4$-bit settings. Moreover, it significantly outperforms existing methods. For instance, our $3$-bit CogVideoX-2B achieves improvements of $+25.28$ in Dynamic Degree and $+8.43$ in Scene Consistency on VBench. Code and models are available at https://github.com/ModelTC/QVGen.
Physics-informed learning under mixing: How physical knowledge speeds up learning
Authors: Anna Scampicchio; Leonardo Felipe Toso; Rahel Rickenbach; James Anderson; Melanie Zeilinger
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: A major challenge in physics-informed machine learning is to understand how the incorporation of prior domain knowledge affects learning rates when data are dependent. Focusing on empirical risk minimization with physics-informed regularization, we derive complexity-dependent bounds on the excess risk in probability and in expectation. We prove that, when the physical prior information is aligned, the learning rate improves from the (slow) Sobolev minimax rate to the (fast) optimal i.i.d. one without sample-size deflation due to data dependence.
On the Thinking-Language Modeling Gap in Large Language Models
Authors: Chenxi Liu; Yongqiang Chen; Tongliang Liu; James Cheng; Bo Han; Kun Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) demonstrate remarkable capabilities in solving complicated reasoning tasks by imitating the human thinking process from human languages. However, even the most capable LLMs can still fail in tasks that are simple for humans. To understand the gap, we construct structural causal models of next-token predictors in human languages. As language is primarily a tool for humans to share knowledge instead of thinking, modeling human thinking from languages can integrate language expression biases into LLMs. More specifically, we show that LLMs can fail to understand implicit expressions -- expression patterns occur less frequently during training. Consequently, LLMs can easily overlook critical information when biased by implicit expressions. We verify our theoretical claims with carefully constructed realistic datasets containing implicit expressions. Furthermore, we also propose a prompt-level intervention to instruct LLMs to carefully expand and focus on all the expressions available. The empirical success of the prompt-level intervention across 11 tasks and 4 representative LLMs, along with the improvements over general reasoning tasks, reaffirms our findings. Our code is publicly available at the project website: https://causalcoat.github.io/lot
Efficient Learning on Large Graphs using a Densifying Regularity Lemma
Authors: Jonathan Kouchly; Ben Finkelshtein; Michael M. Bronstein; Ron Levie
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Learning on large graphs presents significant challenges, with traditional Message Passing Neural Networks suffering from computational and memory costs scaling linearly with the number of edges. We introduce the Intersecting Block Graph (IBG), a low-rank factorization of large directed graphs based on combinations of intersecting bipartite components, each consisting of a pair of communities, for source and target nodes. By giving less weight to non-edges, we show how an IBG can efficiently approximate any graph, sparse or dense. Specifically, we prove a constructive version of the weak regularity lemma: for any chosen accuracy, every graph can be approximated by a dense IBG whose rank depends only on that accuracy. This improves over prior versions of the lemma, where the rank depended on the number of nodes for sparse graphs. Our method allows for efficient approximation of large graphs that are both directed and sparse, a crucial capability for many real-world applications. We then introduce a graph neural network architecture operating on the IBG representation of the graph and demonstrating competitive performance on node classification, spatio-temporal graph analysis, and knowledge graph completion, while having memory and computational complexity linear in the number of nodes rather than edges.
OccDriver: Future Occupancy Guided Dual-branch Trajectory Planner in Autonomous Driving
Authors: Zhao Huang; Bowen Zhang; Zhongzhu Li; Di Lin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Trajectory planning for autonomous driving is challenging due to agents' behavioral uncertainty and intricate multi-agent interaction modeling. Most existing studies generate trajectories without explicitly exploiting possible scene evolution, while world models predict consequences from ego behavior, enabling more informed planning decisions. Inspired by the world model, we propose OccDriver, a novel rasterized-to-vectorized dual-branch framework for trajectory planning. This pipeline performs a coarse-to-fine trajectory decoding process: The vectorized branch first generate multimodal coarse trajectories; Then the rasterized branch predicts future scene evolutions conditioned on each coarse trajectory via occupancy flow prediction; Lastly, the vectorized branch leverages intuitive future interaction evolution of each modality from the rasterized branch and produces refined trajectories. Several cross-modality (occupancy and trajectory) losses are further introduced to improve the consistency between trajectory and occupancy prediction. Additionally, we apply a contingency objective in occupancy space, considering marginal and joint occupancy distributions in different planning scopes. Our model is assessed on the large-scale real-world nuPlan dataset and its associated planning benchmark. Experiments show that OccDriver achieves state-of-the-art in both Non-Reactive and Reactive closed-loop performance.
EgoBrain: Synergizing Minds and Eyes For Human Action Understanding
Authors: Nie Lin; Yansen Wang; Dongqi Han; Weibang Jiang; Jingyuan Li; Ryosuke Furuta; Yoichi Sato; Dongsheng Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The integration of brain-computer interfaces (BCIs), in particular electroencephalography (EEG), with artificial intelligence (AI) has shown tremendous promise in decoding human cognition and behavior from neural signals. In particular, the rise of multimodal AI models have brought new possibilities that have never been imagined before. Here, we present \data --the world's first large-scale, temporally aligned multimodal dataset that synchronizes first-person (egocentric) vision and EEG of human brain over extended periods of time, establishing a new paradigm for human-centered behavior analysis. This dataset comprises 61 hours of synchronized 32-channel EEG recordings and first-person video from 40 participants engaged in 29 categories of daily activities. We then developed a muiltimodal learning framework to fuse EEG and vision for action understanding, validated across both cross-subject and cross-environment challenges, achieving an action recognition accuracy of 66.70\%. EgoBrain paves the way toward a unified framework for multimodal and egocentric brain–computer interfaces, bridging neural signals and first-person perception. Our dataset and code are publicly available at: https://huggingface.co/datasets/ut-vision/EgoBrain and https://github.com/ut-vision/EgoBrain.