GenAI4ELab/papercli-papers huggingface dataset:
From Sequential to Parallel: Reformulating Dynamic Programming as GPU Kernels for Large-Scale Stochastic Combinatorial Optimization
Authors: Jingyi Zhao; Linxin Yang; Haohua ZHANG; Qile He; Tian Ding
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Dynamic programming (DP) is central to combinatorial optimization, optimal control, and reinforcement learning, yet its perceived sequentiality has long hindered scalability. We introduce a general-purpose GPU framework that reformulates broad classes of forward DP recursions as batched min--plus matrix--vector products over layered DAGs, collapsing actions into masked state-to-state transitions that map directly to GPU kernels. This approach removes a major bottleneck in scenario-based stochastic programming (SP), where the use of DP has traditionally restricted the number of scenarios due to excessive computational cost.
Our framework exposes massive parallelism across scenarios, transition layers, and, when applicable, route or action options, via self-designed GPU kernels that implement Bellman updates with warp-/block-level reductions and numerically safe masking. In a single GPU pass, these kernels can process over $10^6$ uncertainty realizations, far beyond the capacity of prior scenario-based methods. We demonstrate the approach in two canonical SP applications: (i) a vectorized split operator for the capacitated vehicle routing problem with stochastic demand, exploiting **2D** parallelism (scenarios $\times$ transitions); and (ii) a forward inventory reinsertion DP under an order-up-to policy, exploiting **3D** parallelism (scenarios $\times$ inventory transitions $\times$ route options). Across benchmarks, the implementation scales nearly linearly in the number of scenarios and achieves one to three orders of magnitude speedups over multithreaded CPU baselines, yielding tighter SAA estimates and consistently stronger first-stage decisions under identical wall-clock budgets. Viewed as hardware-aware software primitives, our min--plus DP kernels offer a drop-in path to scalable, GPU-accelerated stochastic discrete optimization.
Local Entropy Search over Descent Sequences for Bayesian Optimization
Authors: David Stenger; Armin Lindicke; Alexander von Rohr; Sebastian Trimpe
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Searching large and complex design spaces for a global optimum can be infeasible and unnecessary. A practical alternative is to iteratively refine the neighborhood of an initial design using local optimization methods such as gradient descent. We propose local entropy search (LES), a Bayesian optimization paradigm that explicitly targets the solutions reachable by the descent sequences of iterative optimizers. The algorithm propagates the posterior belief over the objective through the optimizer, resulting in a probability distribution over descent sequences. It then selects the next evaluation by maximizing mutual information with that distribution, using a combination of analytic entropy calculations and Monte-Carlo sampling of descent sequences. Empirical results on high-complexity synthetic objectives and benchmark problems show that LES achieves strong sample efficiency compared to existing local and global Bayesian optimization methods.
Robust Adversarial Attacks Against Unknown Disturbance via Inverse Gradient Sample
Authors: Zhaoyang Zhang; Shen Wang; Runze Liu; Guopu Zhu; Fanghui Sun; Ye Lu; Zeyue Wang; Yihan Yan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Adversarial attacks have achieved widespread success in various domains, yet existing methods suffer from significant performance degradation when adversarial examples are subjected to even minor disturbances. In this paper, we propose a novel and robust attack called IGSA (**I**nverse **G**radient **S**ample-based **A**ttack), capable of generating adversarial examples that remain effective under diverse unknown disturbances. IGSA employs an iterative two-step framework: (i) inverse gradient sampling, which searches for the most disruptive direction within the neighborhood of adversarial examples, and (ii) disturbance-guided refinement, which updates adversarial examples via gradient descent along the identified disruptive disturbance. Theoretical analysis reveals that IGSA enhances robustness by increasing the likelihood of adversarial examples within the data distribution. Extensive experiments in both white-box and black-box attack scenarios demonstrate that IGSA significantly outperforms state-of-the-art attacks in terms of robustness against various unknown disturbances. Moreover, IGSA exhibits superior performance when attacking adversarially trained defense models. Code is available at https://github.com/nimingck/IGSA.
JointAVBench: A Benchmark for Joint Audio-Visual Reasoning Evaluation
Authors: Jianghan Chao; jianzhang gao; Wenhui Tan; Yuchong Sun; Ruihua Song; Liyun Ru
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Understanding videos inherently requires reasoning over both visual and auditory information. To properly evaluate Omni-Large Language Models (Omni-LLMs), which are capable of processing multi-modal information including vision and audio, an effective benchmark must comprehensively cover three key aspects: (1) multi-modal dependency (i.e., questions that cannot be answered using vision or audio alone), (2) diverse audio information types (e.g., speech, sound events), and (3) varying scene spans.
However, existing datasets fall short in one or more of these dimensions, limiting strict and comprehensive evaluation.
To address this gap, we introduce JointAVBench, a novel benchmark with strict audio-video correlation, spanning five cognitive dimensions, four audio information types (speech, sound events, music, vocal traits), and three scene spans (single-, cross-, and full-scene).
Given the high cost of manual annotation, we propose an automated pipeline that leverages state-of-the-art vision-LLMs, audio-LLMs, and general-purpose LLMs to synthesize questions and answers that strictly require joint audio-visual understanding. 
We evaluate leading vision-only, audio-only, and Omni-LLMs on our dataset. Results show that even the best-performing Omni-LLM achieves only 65.3\% average accuracy, outperforming uni-modal baselines but revealing substantial room for improvement, especially in cross-scene reasoning.
Rainbow Padding: Mitigating Early Termination in Instruction-Tuned Diffusion LLMs
Authors: BumJun Kim; Dongjae Jeon; Dueun Kim; Wonje Jeung; Albert No
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Diffusion large language models (dLLMs) have emerged as a promising alternative to autoregressive models, offering flexible generation orders and strong performance on complex reasoning tasks. 
However, instruction-tuned dLLMs exhibit a critical vulnerability we term \<eos\> overflow: as allocated sequence length increases, responses paradoxically become shorter, collapsing into early termination or degenerating into streams of \<eos\> tokens. 
Although noticed in practice, this issue has not been systematically analyzed. We trace its root cause to the dual role of \<eos\> as both termination and padding, which concentrates probability mass on \<eos\> at later positions and propagates backward to trigger early termination. 
To address this, we introduce Rainbow Padding, a simple remedy that replaces repeated \<eos\> placeholders with a repeating cycle of distinct padding tokens, distributing probability mass and breaking \<eos\> dominance. 
Experiments show that Rainbow Padding substantially improves length robustness and output quality, with as few as seven padding tokens to prevent early termination. 
Moreover, the method integrates efficiently into existing instruction-tuned models: LoRA fine-tuning for a single epoch on minimal data yields significant improvements, making this solution highly practical.
The project is available at ~\url{https://ai-isl.github.io/rainbow-padding}
A Stitch in Time Saves Nine: Proactive Self-Refinement for Language Models
Authors: Jinyi Han; Xinyi Wang; Haiquan Zhao; tingyun li; Zishang Jiang; Sihang Jiang; Jiaqing Liang; Xin Alex Lin; Weikang Zhou; Zeye Sun; Fei Yu; Yanghua Xiao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in self-refinement have demonstrated significant potential for improving the outputs of large language models (LLMs) through iterative refinement. However, most existing self-refinement methods rely on a reactive process with a fixed number of iterations, making it difficult to determine the optimal timing and content of refinement based on the evolving generation context. 
Inspired by the way humans dynamically refine their thoughts during execution, we propose ProActive Self-Refinement (PASR), a novel method that enables LLMs to refine their outputs during the generation process. 
Unlike methods that regenerate entire responses, PASR proactively decides whether, when, and how to refine based on the model’s internal state and evolving context. We conduct extensive experiments on a diverse set of 10 tasks to evaluate the effectiveness of PASR. 
Experimental results show that PASR significantly enhances problem-solving performance. In particular, on Qwen3-8B, PASR reduces average token consumption by 41.6% compared to standard generation, while also achieving an 8.2% improvement in accuracy. 
Our code and baselines used in the paper are available in the GitHub.
Alignment-Enhanced Integration of Connectivity and Spectral Sparsity in Dynamic Sparse Training of LLM
Authors: Wenjing Wu; Yingtao Zhang; Jialin Zhao; Carlo Vittorio Cannistraci
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: With the rapid development of large language models (LLMs), identifying efficient strategies for training such large-scale systems has become increasingly critical. Although LLMs have achieved remarkable success across diverse applications, the necessity of maintaining full dense matrices during pre-training has been questioned, giving rise to parameter-efficient sparse pre-training methods which retains parameter-efficiency in both training and inference. These methods can be further divided into connectivity sparse training and spectral sparse training, with dynamic connectivity sparse training and low-rank factorization emerging as representative approaches for the two branches.
However, a unified framework that effectively combines the strengths of both has yet to be established. In this work, we observe that the cancellation effect between the sparse and low-rank branches may limit the expressivity of the model, manifesting as output conflicts when the two components are combined. To address this issue, we first quantify the cancellation effect using the overlap cancellation ratio (OCR) and then propose a novel scheme that integrates dynamic sparse training with low-rank training, introducing a simple yet effective **alignment loss** to mitigate the disagreement between the two branches and promote better collaboration. We validate this scheme by combining a representative dynamic sparse training method, CHTs, with low-rank training, resulting in a new parameter-efficient training approach termed **CHTsL**. The method is evaluated on LLaMA60M and LLaMA130M using the OpenWebText and C4 datasets, where only 10%, 20%, and 30% of the parameters are preserved compared to dense training. Experimental results demonstrate that our proposed scheme effectively alleviates the cancellation effect, especially in the Q and K matrices of the attention layers, and improves training stability and performance compared to the naive combination of sparse and low-rank components. Additionally, the new scheme enables CHTsL to consistently outperform other parameter-efficient sparse training methods under the same parameter budget, achieving performance closest to that of dense training.
Concept-based Adversarial Attack: a Probabilistic Perspective
Authors: Andi Zhang; Xuan Ding; Steven McDonagh; Samuel Kaski
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We propose a concept-based adversarial attack framework that extends beyond single-image perturbations by adopting a probabilistic perspective. Rather than modifying a single image, our method operates on an entire concept - represented by a distribution - to generate diverse adversarial examples. Preserving the concept is essential, as it ensures that the resulting adversarial images remain identifiable as instances of the original underlying category or identity. By sampling from this concept-based adversarial distribution, we generate images that maintain the original concept but vary in pose, viewpoint, or background, thereby misleading the classifier. Mathematically, this framework remains consistent with traditional adversarial attacks in a principled manner. Our theoretical and empirical results demonstrate that concept-based adversarial attacks yield more diverse adversarial examples and effectively preserve the underlying concept, while achieving higher attack efficiency.
Adjusting Prediction Model Through Wasserstein Geodesic for Causal Inference
Authors: Yuguang Yan; Haolin Yang; Zecong Chen; Weilin Chen; Ruichu Cai; Zhifeng Hao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Causal inference estimates the treatment effect by comparing the potential outcomes of the treated and control groups. Due to the existence of confounders, the distributions of treated and control groups are imbalanced, resulting in limited generalization ability of the outcome prediction model, \ie, the prediction model trained on one group cannot perform well on the other group. To tackle this, existing methods usually adjust confounders to learn balanced representations for aligning the distributions. However, these methods could suffer from the over-balancing issue that predictive information about outcomes is removed during adjustment. In this paper, we propose to adjust the outcome prediction model to improve its generalization ability on both groups simultaneously, so that the over-balancing issue caused by confounder adjustment can be avoided. To address the challenge of large distribution discrepancy between groups during model adjustment, we propose to generate intermediate groups through the Wasserstein geodesic, which smoothly connects the control and treated groups. Based on this, we gradually adjust the outcome prediction model between consecutive groups by a self-training paradigm. To further enhance the performance of the model, we filter the generated samples to select high-quality samples for learning. We provide the theoretical analysis regarding our method, and demonstrate the effectiveness of our method on several benchmark datasets in terms of multiple evaluation metrics.
EUBRL: Epistemic Uncertainty Directed Bayesian Reinforcement Learning
Authors: Jianfei Ma; Wee Sun Lee
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: At the boundary between the known and the unknown, an agent inevitably confronts the dilemma of whether to explore or to exploit. Epistemic uncertainty reflects such boundaries, representing systematic uncertainty due to limited knowledge. In this paper, we propose a Bayesian reinforcement learning (RL) algorithm, $\texttt{EUBRL}$, which leverages epistemic guidance to achieve principled exploration. This guidance adaptively reduces per-step regret arising from estimation errors. We establish nearly minimax-optimal regret and sample complexity guarantees for a class of sufficiently expressive priors in infinite-horizon discounted MDPs. Empirically, we evaluate $\texttt{EUBRL}$ on tasks characterized by sparse rewards, long horizons, and stochasticity. Results demonstrate that $\texttt{EUBRL}$ achieves superior sample efficiency, scalability, and consistency.
Pisces: Cryptography-based Private Retrieval-Augmented Generation with Dual-Path Retrieval
Authors: Xiaojian Liang; Lushan Song; Shishuai Du; Weicheng Zhu; Tan Li Hui Faith; Jun Jie Sim; Haibing Jin; Zhenghao Wu; Yingting Liu; Xin Zhang; Jiang-Ming Yang; Pu Duan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Retrieval-augmented generation (RAG) enhances the response quality of large language models (LLMs) when handling domain-specific tasks, yet raises significant privacy concerns. This is because both the user query and documents within the knowledge base often contain sensitive or confidential information. To address these concerns, we propose $\texttt{Pisces}$, the first practical cryptography-based RAG framework that supports dual-path retrieval, while protecting both the query and documents. Along the semantic retrieval path, we reduce computation and communication overhead by leveraging a coarse-to-fine strategy. Specifically, a novel oblivious filter is used to privately select a candidate set of documents to reduce the scale of subsequent cosine similarity computations. For the lexical retrieval path, to reduce the overhead of repeatedly invoking labeled PSI, we implement a multi-instance labeled PSI protocol to compute term frequencies for BM25 scoring in a single execution. $\texttt{Pisces}$ can also be integrated with existing privacy-preserving LLM inference frameworks to achieve end-to-end privacy. Experiments demonstrate that $\texttt{Pisces}$ achieves retrieval accuracy comparable to the plaintext baselines, within a 1.87% margin.
Neural+Symbolic Approaches for Interpretable Actor-Critic Reinforcement Learning
Authors: Yue Yang; Fan Yang; Yu Bai; Hao Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The integration of neural networks into actor-critic frameworks has been pivotal in advancing the field of reinforcement learning, enabling agents to perform complex tasks with greater efficiency and adaptability. However, neural network-based actor-critic models remain opaque ``black boxes,'' concealing their decision-making processes and hindering their use in critical applications where transparent and explainable reasoning is essential. This work introduces an innovative adaptation of the actor-critic framework that unites neural networks with rule ensembles to tackle key challenges in reinforcement learning. We harness the computational power, scalability, and adaptability of neural networks to model the critic, while integrating a rule ensemble system for the actor, ensuring transparency and interpretability for decision-making. Our study establishes a theoretical foundation for integrating rule ensembles into the Advantage Actor-Critic (A2C) framework. Experimental results from seven classic and complex environments demonstrate that our proposed method matches or exceeds the performance of representative RL models, including symbolic methods, while offering self-interpretability and transparency.
Lookahead Tree-Based Rollouts for Enhanced Trajectory-Level Exploration in Reinforcement Learning with Verifiable Rewards
Authors: Shangyu Xing; Siyuan Wang; Chenyuan Yang; Xinyu Dai; Xiang Ren
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reinforcement Learning with Verifiable Rewards (RLVR), particularly with algorithms like Group Relative Policy Optimization (GRPO), has proven highly effective in enhancing the reasoning capabilities of large language models. However, a critical bottleneck in current pipelines lies in the limited diversity of sampled trajectories during group rollouts. Homogeneous trajectories and their associated rewards would diminish the return signals for policy updates, thereby hindering effective policy learning. This lack of diversity stems primarily from token-level stochastic sampling, where local variations are likely to collapse into near-identical reasoning paths. To address this limitation, we propose Lookahead Tree-Based Rollouts (LATR), a novel rollout strategy designed to explicitly promotes trajectory-level diversity by enforcing branching into different candidate tokens likely to yield distinct continuations. Specifically, LATR iteratively operates in three stages: (1) branching at high-uncertainty generation steps, (2) performing lookahead simulation for each new branch, and (3) pruning branches that exhibits prolonged similarity during simulation. Compared with Stochastic Sampling, LATR accelerates policy learning by 131% on average and improves final pass@1 performance by 4.2% on both GRPO and Dynamic sAmpling Policy Optimization (DAPO) algorithms across different reasoning tasks. Our code and data are available at https://github.com/starreeze/latr.
Flow Actor-Critic for Offline Reinforcement Learning
Authors: Jongseong Chae; Jongeui Park; Yongjae Shin; Gyeongmin Kim; Seungyul Han; Youngchul Sung
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The dataset distributions in offline reinforcement learning (RL) often exhibit complex and multi-modal distributions, necessitating expressive policies to capture such distributions beyond widely-used Gaussian policies. To handle such complex and multi-modal datasets, in this paper, we propose Flow Actor-Critic, a new actor-critic method for offline RL, based on recent flow policies. The proposed method not only uses the flow model for actor as in previous flow policies but also exploits the expressive flow model for conservative critic acquisition to prevent Q-value explosion in out-of-data regions. To this end, we propose a new form of critic regularizer based on the flow behavior proxy model obtained as a byproduct of flow-based actor design. Leveraging the flow model in this joint way, we achieve new state-of-the-art performance for test datasets of offline RL including the D4RL and recent OGBench benchmarks.
T1: One-to-One Channel-Head Binding for Multivariate Time-Series Imputation
Authors: Dongik Park; Hyunwoo Ryu; Suahn Bae; Keondo Park; Hyung-Sin Kim
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Imputing missing values in multivariate time series remains challenging, especially under diverse missing patterns and heavy missingness. Existing methods suffer from suboptimal performance as corrupted temporal features hinder effective cross-variable information transfer, amplifying reconstruction errors. Robust imputation requires both extracting temporal patterns from sparse observations within each variable and selectively transferring information across variables—yet current approaches excel at one while compromising the other. We introduce T1 (Time series imputation with 1-to-1 channel-head binding), a CNN-Transformer hybrid architecture that achieves robust imputation through Channel-Head Binding—a mechanism creating one-to-one correspondence between CNN channels and attention heads. This design enables selective information transfer: when missingness corrupts certain temporal patterns, their corresponding attention pathways adaptively down-weight based on remaining observable patterns while preserving reliable cross-variable connections through unaffected channels. Experiments on 11 benchmark datasets demonstrate that T1 achieves state-of-the-art performance, reducing MSE by 46% on average compared to the second-best baseline, with particularly strong gains under extreme sparsity (70% missing ratio). The model generalizes to unseen missing patterns without retraining and uses a consistent hyperparameter configuration across all datasets. The code is available at https://github.com/Oppenheimerdinger/T1.