GenAI4ELab/papercli-papers huggingface dataset:
Gradient-Normalized Smoothness for Optimization with Approximate Hessians
Authors: Andrei Semenov; Martin Jaggi; Nikita Doikov
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In this work, we develop new optimization algorithms that use approximate second-order information combined with the gradient regularization technique to achieve fast global convergence rates for both convex and non-convex objectives. The key innovation of our analysis is a novel notion called Gradient-Normalized Smoothness, which characterizes the maximum radius of a ball around the current point that yields a good relative approximation of the gradient field. Our theory establishes a natural intrinsic connection between Hessian approximation and the linearization of the gradient. Importantly, Gradient-Normalized Smoothness does not depend on the specific problem class of the objective functions, while effectively translating local information about the gradient field and Hessian approximation into the global behavior of the method. This new concept equips approximate second-order algorithms with universal global convergence guarantees, recovering state-of-the-art rates for functions with Hölder-continuous Hessians and third derivatives, quasi-self-concordant functions, as well as smooth classes in first-order optimization. These rates are achieved automatically and extend to broader classes, such as generalized self-concordant functions. We demonstrate direct applications of our results for global linear rates in logistic regression and softmax problems with approximate Hessians, as well as in non-convex optimization using Fisher and Gauss-Newton approximations.
PA3FF:Learning Part-Aware Dense 3D Feature Field For Generalizable Articulated Object Manipulation
Authors: Yue Chen; Muqing Jiang; Kaifeng Zheng; Jiaqi Liang; Chenrui Tie; Haoran Lu; Ruihai Wu; Hao Dong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Articulated object manipulation is essential for various real-world robotic tasks, yet generalizing across diverse objects remains a major challenge. A key to generalization lies in understanding functional parts (e.g., door handles and knobs), which indicate where and how to manipulate across diverse object categories and shapes. Previous works attempted to achieve generalization by introducing foundation features, while these features are mostly 2D-based and do not specifically consider functional parts. When lifting these 2D features to geometry-profound 3D space, challenges arise, such as long runtimes, multi-view inconsistencies, and low spatial resolution with insufficient geometric information. To address these issues, we propose \textbf{Part-Aware 3D Feature Field (PA3FF)}, a novel dense 3D feature with part awareness for generalizable articulated object manipulation. PA3FF is trained by 3D part proposals from a large-scale labeled datasets, via a contrastive learning formulation. Given point clouds as input, PA3FF predicts a continuous 3D feature field in a feedforward manner, where the distance between point feature reflects the proximity of functional parts: points with similar features are more likely to belong to the same part. Building on this feature, we introduce the \textbf{Part-Aware Diffusion Policy (PADP)}, an imitation learning framework aimed at enhancing sample efficiency and generalization for robotic manipulation. We evaluate PADP on several simulated and real-world tasks, demonstrating that PA3FF consistently outperforms a range of 2D and 3D representations in manipulation scenarios, including CLIP, DINOv2, and Grounded-SAM, achieving state-of-the-art performance. Beyond imitation learning, PA3FF enables diverse downstream methods, including correspondence learning and segmentation task, making it a versatile foundation for robotic manipulation.  Project page: https://pa3ff.github.io/.
Frame Guidance: Training-Free Guidance for Frame-Level Control in Video Diffusion Models
Authors: Sangwon Jang; Taekyung Ki; Jaehyeong Jo; Jaehong Yoon; Soo Ye Kim; Zhe Lin; Sung Ju Hwang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Advancements in diffusion models have significantly improved video quality, directing attention to fine-grained controllability. However, many existing methods depend on fine-tuning large-scale video models for specific tasks, which becomes increasingly impractical as model sizes continue to grow. In this work, we present Frame Guidance, a training-free guidance for controllable video generation based on frame-level signals, such as keyframes, style reference images, sketches, or depth maps. By applying guidance to only a few selected frames, Frame Guidance can steer the generation of the entire video, resulting in a temporally coherent controlled video. To enable training-free guidance on large-scale video models, we propose a simple latent processing method that dramatically reduces memory usage, and apply a novel latent optimization strategy designed for globally coherent video generation. Frame Guidance enables effective control across diverse tasks, including keyframe guidance, stylization, and looping, without any training, and is compatible with any models. Experimental results show that Frame Guidance can produce high-quality controlled videos for a wide range of tasks and input signals.
Video Scene Segmentation with Genre and Duration Signals
Authors: Jungu Cho; Seong Jong Ha; Hae-Gon Jeon
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Video scene segmentation aims to detect semantically coherent boundaries in long-form videos, bridging the gap between low-level visual signals and high-level narrative understanding.
However, existing methods primarily rely on visual similarity between adjacent shots, which makes it difficult to accurately identify scene boundaries, especially when semantic transitions do not align with visual changes.
In this paper, we propose a novel approach that incorporates production-level metadata, specifically genre conventions and shot duration patterns, into video scene segmentation.
Our main contributions are three-fold:
(1) we leverage textual genre definitions as semantic priors to guide shot-level representation learning during self-supervised pretraining, enabling better capture of narrative coherence;
(2) we introduce a duration-aware anchor selection strategy that prioritizes shorter shots based on empirical duration statistics, improving pseudo-boundary generation quality;
(3) we propose a test-time shot splitting strategy that subdivides long shots into segments for improved temporal modeling.
Experimental results demonstrate state-of-the-art performance on MovieNet-SSeg and BBC datasets.
We introduce MovieChat-SSeg, extending MovieChat-1K with manually annotated scene boundaries across 1,000 videos spanning movies, TV series, and documentaries.
Dynamic Early Exit in Reasoning Models
Authors: Chenxu Yang; Qingyi Si; Yongjie Duan; Zheliang Zhu; Chenyu Zhu; Qiaowei Li; Minghui Chen; Zheng Lin; Weiping Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in large reasoning language models (LRMs) rely on test-time scaling, which extends long chain-of-thought (CoT) generation to solve complex tasks. However, overthinking in long CoT not only slows down the efficiency of problem solving, but also risks accuracy loss due to the extremely detailed or redundant reasoning steps. We propose a simple yet effective method that allows LLMs to self-truncate CoT sequences by early exit during generation. Instead of relying on fixed heuristics, the proposed method monitors model behavior at potential reasoning transition points and dynamically terminates the next reasoning chain's generation when the model exhibits high confidence in a trial answer. Our method requires no additional training and can be seamlessly integrated into existing o1-like reasoning LLMs. Experiments on 10 reasoning benchmarks (e.g., GSM8K, MATH-500, AMC, GPQA, AIME and LiveCodeBench) show that the proposed method is consistently effective on 11 cutting-edge reasoning LLMs of varying series and sizes, reducing the length of CoT sequences by an average of 19.1% to 80.1% while improving accuracy by 0.3% to 5.0%.
Q-Learning with Fine-Grained Gap-Dependent Regret
Authors: Haochen Zhang; Zhong Zheng; Lingzhou Xue
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We study fine-grained gap-dependent regret bounds for model-free reinforcement learning in episodic tabular Markov Decision Processes. Existing model-free algorithms achieve minimax worst-case regret, but their gap-dependent bounds remain coarse and fail to fully capture the structure of suboptimality gaps. To address this limitation, we establish fine-grained gap-dependent regret guarantees for both UCB-based and non-UCB-based algorithms. In the UCB-based setting, we develop a novel analytical framework that explicitly separates the analysis of optimal and suboptimal state-action pairs, yielding the first fine-grained regret upper bound for UCB-Hoeffding (Jin et al., 2018). In the non-UCB-based setting, we revisit the only existing algorithm, AMB (Xu et al., 2021), and identify two issues in its design and analysis: improper truncation in the $Q$-updates and violation of the martingale difference condition in the concentration argument. To resolve these issues, we propose two refinements of AMB: the UCB-based ULCB-Hoeffding and the non-UCB-based Refined AMB. For ULCB-Hoeffding, we establish the same fine-grained regret bound as UCB-Hoeffding by applying our fine-grained framework, highlighting its broad applicability. For Refined AMB, we derive a rigorous fine-grained gap-dependent regret bound in the non-UCB setting and demonstrate consistent empirical improvements over the original AMB.
LogicReward: Incentivizing LLM Reasoning via Step-Wise Logical Supervision
Authors: Jundong Xu; Hao Fei; Huichi Zhou; Xin Quan; Qijun Huang; Shengqiong Wu; William Yang Wang; Mong-Li Lee; Wynne Hsu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Although LLMs exhibit strong reasoning capabilities, existing training methods largely depend on outcome-based feedback, which can produce correct answers with flawed reasoning.
Prior work introduces supervision on intermediate steps but still lacks guarantees of logical soundness, which is crucial in high-stakes scenarios where logical consistency is paramount. 
To address this, we propose LogicReward, a novel reward system that guides model training by enforcing step-level logical correctness with a theorem prover.
We further introduce Autoformalization with Soft Unification, which reduces natural language ambiguity and improves formalization quality, enabling more effective use of the theorem prover.
An 8B model trained on data constructed with LogicReward surpasses GPT-4o and o4-mini by 11.6\% and 2\% on natural language inference and logical reasoning tasks with simple training procedures. 
Further analysis shows that LogicReward enhances reasoning faithfulness, improves generalizability to unseen tasks such as math and commonsense reasoning, and provides a reliable reward signal even without ground-truth labels.
The code and data are available at https://llm-symbol.github.io/LogicReward.
From Observations to Events: Event-Aware World Models for Reinforcement Learning
Authors: Zhao-Han Peng; Shaohui Li; Zhi Li; Shulan Ruan; Yu LIU; You He
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While model-based reinforcement learning (MBRL) improves sample efficiency by learning world models from raw observations, existing methods struggle to generalize across structurally similar scenes and remain vulnerable to spurious variations such as textures or color shifts. From a cognitive science perspective, humans segment continuous sensory streams into discrete events and rely on these key events for decision-making. Motivated by this principle, we propose the Event-Aware World Model (EAWM), a general framework that learns event-aware representations to streamline policy learning without requiring handcrafted labels. EAWM employs an automated event generator to derive events from raw observations and introduces a Generic Event Segmentor (GES) to identify event boundaries, which mark the start and end time of event segments. Through event prediction, the representation space is shaped to capture meaningful spatio-temporal transitions. Beyond this, we present a unified formulation of seemingly distinct world model architectures and show the broad applicability of our methods. Experiments on Atari 100K, Craftax 1M, and DeepMind Control 500K, DMC-GB2 500K demonstrate that EAWM consistently boosts the performance of strong MBRL baselines by 10\%–45\%, setting new state-of-the-art results across benchmarks. Our code is released at [https://github.com/MarquisDarwin/EAWM](https://github.com/MarquisDarwin/EAWM).
Dynamic-dLLM: Dynamic Cache-Budget and Adaptive Parallel Decoding for Training-Free Acceleration of Diffusion LLM
Authors: Tianyi Wu; Xiaoxi Sun; Yanhua Jiao; Yulin Li; Yixin Chen; Yun-Hao Cao; Yi-Qi Hu; Zhuotao Tian
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Diffusion Large Language Models (dLLMs) offer a promising alternative to autoregressive models, excelling in text generation tasks due to their bidirectional attention mechanisms. However, their computational complexity, scaling as $\mathcal{O}(L^3)$ with sequence length $L$, poses significant challenges for long-sequence and real-time applications, primarily due to the lack of compatibility with key-value caching and the non-autoregressive nature of denoising steps. Existing acceleration methods rely on static caching or parallel decoding strategies, which fail to account for the dynamic behavior of token properties across layers and decoding steps. We propose \textbf{Dynamic-dLLM}, a training-free framework that enhances dLLM inference efficiency through two components: Dynamic Cache Updating (DCU), which adaptively allocates cache-update budgets based on layer-wise token dynamics, and Adaptive Parallel Decoding (APD), which dynamically calibrates decoding thresholds to balance generation quality and efficiency. Extensive experiments on models like LLaDA-8B-Instruct, LLaDA-1.5, and Dream-v0-7B-Instruct across benchmarks such as MMLU, GSM8K, and HumanEval demonstrate that Dynamic-dLLM significantly improves inference speed, attaining an average speedup of exceeding 3$\times$ while maintaining performance. Dynamic-dLLM outperforms state-of-the-art acceleration methods and provides a plug-and-play solution for efficient dLLM deployment without compromising performance. Code and models will be made publicly available.
PCB-Bench: Benchmarking LLMs for Printed Circuit Board Placement and Routing
Authors: Jindong Li; Lianrong Chen; Bin Yang; Jiadong Zhu; Ying Wang; Yuzhe Ma; Menglin Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in Large Language Models (LLMs) have enabled impressive capabilities across diverse reasoning and generation tasks. However, their ability to understand and operate on real-world engineering problems, such as Printed Circuit Board (PCB) placement and routing, remains underexplored due to the lack of standardized benchmarks and high-fidelity datasets. To address this gap, we introduce PCB-Bench, the first comprehensive benchmark designed to systematically evaluate LLMs in the context of PCB design. PCB-Bench spans three complementary task settings: (1) text-based reasoning with approximately 3,700 expert-annotated instances, consisting of over 1,800 question-answer pairs and their corresponding choice question versions, covering component placement, routing strategies, and design rule compliance; (2) multimodal image-text reasoning with approximately 500 problems requiring joint interpretation of PCB visuals and technical specifications, including component identification, function recognition, and visual trace reasoning; (3) real-world design comprehension using over 170 complete PCB projects with schematics, placement files, and design documentation. We design structured evaluation protocols to assess both generative and discriminative capabilities, and conduct extensive comparisons across state-of-the-art LLMs. Our results reveal substantial gaps in current models’ ability to reason over spatial placements, follow domain-specific constraints, and interpret professional engineering artifacts. PCB-Bench establishes a foundational resource for advancing research toward more capable engineering AI, with implications extending beyond PCB design to broader structured reasoning domains. Data and code are available at https://github.com/digailab/PCB-Bench.
Adversarially Pretrained Transformers May Be Universally Robust In-Context Learners
Authors: Soichiro Kumano; Hiroshi Kera; Toshihiko Yamasaki
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Adversarial training is one of the most effective defenses against adversarial attacks, but it incurs a high computational cost. In this study, we present the first theoretical analysis suggesting that adversarially pretrained transformers can serve as universally robust foundation models—models that can adapt robustly to diverse downstream tasks with only lightweight tuning. Specifically, we demonstrate that single-layer linear transformers, after adversarial pretraining across a variety of classification tasks, can generalize robustly to unseen classification tasks through in-context learning from clean demonstrations (i.e., without requiring additional adversarial training or examples). This universal robustness stems from the model's ability to adaptively focus on robust features within given tasks. We also identify two open challenges for attaining robustness: the accuracy–robustness trade-off and sample-hungry training. This study initiates the discussion on the utility of universally robust foundation models. While their training is expensive, the investment would prove worthwhile as downstream tasks can obtain adversarial robustness for free. The code is available at https://github.com/s-kumano/universally-robust-in-context-learner.
Robust Adaptive Multi-Step Predictive Shielding
Authors: Tanmay Ambadkar; Darshan Chudiwal; Greg Anderson; Abhinav Verma
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reinforcement learning for safety-critical tasks requires policies that are both high-performing and safe throughout the learning process. While model-predictive shielding is a promising approach, existing methods are often computationally intractable for the high-dimensional, nonlinear systems where deep RL excels, as they typically rely on a patchwork of local models. We introduce **RAMPS**, a scalable shielding framework that overcomes this limitation by leveraging a learned, linear representation of the environment's dynamics. This model can range from a linear regression in the original state space to a more complex operator learned in a high-dimensional feature space. The key is that this linear structure enables a robust, look-ahead safety technique based on a *multi-step Control Barrier Function (CBF)*. By moving beyond myopic one-step formulations, **RAMPS** accounts for model error and control delays to provide reliable, real-time interventions. The resulting framework is minimally invasive, computationally efficient, and built upon robust control-theoretic foundations. Our experiments demonstrate that **RAMPS** significantly reduces safety violations compared to existing safe RL methods while maintaining high task performance in complex control environments.
Triple-BERT: Do We Really Need MARL for Order Dispatch on Ride-Sharing Platforms?
Authors: Zijian Zhao; Sen Li
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: On-demand ride-sharing platforms, such as Uber and Lyft, face the intricate real-time challenge of bundling and matching passengers—each with distinct origins and destinations—to available vehicles, all while navigating significant system uncertainties. Due to the extensive observation space arising from the large number of drivers and orders, order dispatching, though fundamentally a centralized task, is often addressed using Multi-Agent Reinforcement Learning (MARL). However, independent MARL methods fail to capture global information and exhibit poor cooperation among workers, while Centralized Training Decentralized Execution (CTDE) MARL methods suffer from the curse of dimensionality. To overcome these challenges, we propose Triple-BERT, a centralized  Single Agent Reinforcement Learning (MARL) method designed specifically for large-scale order dispatching on ride-sharing platforms. Built on a variant TD3, our approach addresses the vast action space through an action decomposition strategy that breaks down the joint action probability into individual driver action probabilities. To handle the extensive observation space, we introduce a novel BERT-based network, where parameter reuse mitigates parameter growth as the number of drivers and orders increases, and the attention mechanism effectively captures the complex relationships among the large pool of driver and orders. We validate our method  using a real-world ride-hailing dataset from Manhattan. Triple-BERT achieves approximately an 11.95% improvement over current state-of-the-art methods, with a 4.26% increase in served orders and a 22.25% reduction in pickup times. Our code, trained model parameters, and processed data are publicly available at https://github.com/RS2002/Triple-BERT .
Controlling Repetition in Protein Language Models
Authors: Jiahao Zhang; ZEQING ZHANG; Di Wang; Lijie Hu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Protein language models (PLMs) have enabled advances in structure prediction and de novo protein design, yet they frequently collapse into pathological repetition during generation. Unlike in text, where repetition merely reduces readability, in proteins it undermines structural confidence and functional viability. To unify this problem, we present the first systematic study of repetition in PLMs. We first propose quantitative metrics to characterize motif-level and homopolymer repetition and then demonstrate their negative impact on folding reliability. To address this challenge, we propose UCCS (Utility-Controlled Contrastive Steering), which steers protein generation with a constrained dataset.
Instead of naively contrasting high- vs. low-repetition sequences, we construct contrastive sets that maximize differences in repetition while tightly controlling for structural utility. This disentanglement yields steering vectors that specifically target repetition without degrading foldability. Injected at inference, these vectors consistently reduce repetition without retraining or heuristic decoding. Experiments with ESM-3 and ProtGPT2 in CATH, UniRef50, and SCOP show that our method outperforms decoding penalties and other baselines, substantially lowering repetition while preserving AlphaFold confidence scores. Our results establish repetition control as a central challenge for PLMs and highlight dataset-guided steering as a principled approach for reliable protein generation.