GenAI4ELab/papercli-papers huggingface dataset:
SLA: Beyond Sparsity in Diffusion Transformers via Fine-Tunable Sparse–Linear Attention
Authors: Jintao Zhang; Haoxu Wang; Kai Jiang; Shuo Yang; Kaiwen Zheng; Haocheng Xi; Ziteng Wang; Hongzhou Zhu; Min Zhao; Ion Stoica; Joseph E. Gonzalez; Jianfei Chen; Jun Zhu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In Diffusion Transformer (DiT) models, particularly for video generation, attention latency is a major bottleneck due to the long sequence length and the quadratic complexity. Interestingly, we find that attention weights can be decoupled into two matrices: a small fraction of large weights with high rank and the remaining weights with very low rank. This naturally suggests applying sparse acceleration to the first part and low-rank acceleration to the second. Based on this finding, we propose SLA (**S**parse-**L**inear **A**ttention), a trainable attention method that fuses sparse and linear attention to accelerate diffusion models. SLA classifies attention weights into critical, marginal, and negligible, applying $\mathcal{O}(N^2)$ attention to critical weights, $\mathcal{O}(N)$ attention to marginal weights, and skipping negligible ones. SLA combines these computations into a single GPU kernel and supports both forward and backward passes. With only a few fine-tuning steps using SLA, DiT models achieve a $\textbf{20x}$ reduction in attention computation, resulting in significant acceleration without loss of generation quality. Experiments show that SLA reduces attention computation by $\textbf{95}$\% without degrading end-to-end generation quality, outperforming baseline methods. In addition, we implement an efficient GPU kernel for SLA, which yields a $\textbf{13.7x}$ speedup in attention computation and a $\textbf{2.2x}$ end-to-end speedup in video generation on Wan2.1-1.3B. The code is available at https://github.com/thu-ml/SLA.
EAST: Early Action Prediction Sampling Strategy with Token Masking
Authors: Iva Sović; Ivan Martinović; Marin Oršić
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Early action prediction seeks to anticipate an action before it fully unfolds, but limited visual evidence makes this task especially challenging. We introduce EAST, a simple and efficient framework that enables a model to reason about incomplete observations. In our empirical study, we identify key components when training early action prediction models. Our key contribution is a randomized training strategy that samples a time step separating observed and unobserved video frames, enabling a single model to generalize seamlessly across all test-time observation ratios. We further show that joint learning on both observed and future (oracle) representations significantly boosts performance, even allowing an encoder-only model to excel. To improve scalability, we propose a token masking procedure that cuts memory usage in half and accelerates training by 2× with no accuracy loss. Combined with a forecasting decoder, EAST sets a new state of the art on NTU60, SSv2, and UCF101, surpassing previous best work by 10.1, 7.7, and 3.9 percentage points, respectively.
InternSpatial: A Comprehensive Dataset for Spatial Reasoning in Vision-Language Models
Authors: Nianchen Deng; Lixin Gu; Shenglong Ye; Yinan He; Zhe Chen; Songze Li; Haomin Wang; Jinhui Yin; Qi Wei; Tianshuo Yang; Min Dou; Tong He; Wenqi Shao; Kaipeng Zhang; Yi Wang; Botian Shi; Yanting Zhang; Jifeng Dai; Yu Qiao; Wenhai Wang; Hongjie Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent benchmarks and datasets have been proposed to improve spatial reasoning in vision-language models (VLMs), yet existing open resources remain constrained by limited scale, narrow visual diversity, and restricted instruction expressiveness. To address these gaps, we present InternSpatial---the largest open-source dataset for spatial reasoning in VLMs---alongside InternSpatial-Bench, a comprehensive evaluation benchmark designed to assess spatial understanding across diverse instruction formats. InternSpatial contains 12 million question-answer(QA) pairs covering both single-view and multi-view scenarios, sourced from varied visual environments and supporting 19 distinct instruction formats that mirror real-world query patterns. InternSpatial-Bench aims to single-view assessment and also extends multi-view reasoning through a novel rotation estimation task. Experimental validation demonstrates that models trained on \trainset achieve substantial performance improvement of 12.1% on InternSpatial-Bench and 10.7% on VSI-Bench, while preserving competitive performance on general-purpose benchmarks. We expect these resources can advance the development of spatially-capable VLMs for practical applications in robotics and embodied AI systems. Our codes and datasets are publicly available at https://github.com/dengnianchen/intern-spatial.
Tracing and Reversing Edits in LLMs
Authors: Paul Youssef; Zhixue Zhao; Christin Seifert; Jörg Schlötterer
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Knowledge editing methods (KEs) are a cost-effective way to update the factual content of large language models (LLMs), but they pose a dual-use risk. While KEs are beneficial for updating outdated or incorrect information, they can be exploited maliciously to implant misinformation or bias. In order to defend against these types of malicious manipulation, we need robust techniques that can reliably detect, interpret, and mitigate malicious edits. To that end, we introduce the tasks of tracing and reversing edits. We propose a novel method to infer the edited object entity, solely based on the modified weights, without access to the editing prompt or any other semantically similar prompts, with up to 99\% accuracy. Further, we propose an effective and training-free method for reversing edits. Our method reverses up to 94\% of the edits, and helps regain the original model's output distribution without access to any information about the edit. This method can further be repurposed to distinguish between edited and unedited weights. Our findings highlight the feasibility of tracing and reversing edits based on the edited weights, opening a new research direction for safeguarding LLMs against adversarial manipulations.
Landing with the Score: Riemannian Optimization through Denoising
Authors: Andrey Kharitenko; Zebang Shen; Riccardo De Santi; Niao He; Florian Dorfler
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Under the \emph{data manifold hypothesis}, high-dimensional data concentrate near a low-dimensional manifold. We study Riemannian optimization when this manifold is only given implicitly through the data distribution, and standard geometric operations are unavailable. 
This formulation captures a broad class of data-driven design problems that are central to modern generative AI. Our key idea is a \emph{link function} that ties the data distribution to the geometric quantities needed for optimization: its gradient and Hessian recover the projection onto the manifold and its tangent space in the small-noise regime. This construction is directly connected to the score function in diffusion models, allowing us to leverage well-studied parameterizations, efficient training procedures, and even
pretrained score networks from the diffusion model literature to perform optimization. On top of this foundation, we develop two {efficient} inference-time algorithms for optimization over data manifolds: \emph{Denoising Landing Flow} (DLF) and \emph{Denoising Riemannian Gradient Descent} (DRGD). We provide theoretical guarantees for approximate feasibility (manifold adherence) and optimality (small Riemannian gradient norm). We demonstrate the effectiveness of our approach on finite-horizon reference tracking tasks in data-driven control, illustrating their potential for practical generative and design applications.
Agentic Reinforcement Learning with Implicit Step Rewards
Authors: Xiaoqian Liu; Ke Wang; Yuchuan Wu; Fei Huang; Yongbin Li; Jianbin Jiao; Junge Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) are increasingly developed as autonomous agents using reinforcement learning (agentic RL) that reason and act in interactive environments.
However, sparse and sometimes unverifiable rewards make it extremely challenging to assign credit when training LLM agents that serve as a policy.
Recent work attempts to integrate process supervision into RL but suffers from biased annotation, reward hacking, high-variance from overly fine-grained rewards or failures when state overlap is rare.
We therefore introduce implicit step rewards for agentic RL (**iStar**), a general credit-assignment strategy that integrates seamlessly with standard RL algorithms without relying on additional rollouts or explicit step labels.
Particularly, we alternatively optimize an implicit process reward model (PRM) with the policy model to generate step rewards for each action via a multi-turn DPO objective. Theoretical analysis shows that this learning objective produces a step-wise reward function learned from trajectory preferences.
Then the implicit step rewards are used to compute step-level advantages, which are combined with trajectory (or episode)-level advantages for policy updates, creating a self-reinforcing training loop.
We evaluate our method on three challenging agent benchmarks, including WebShop and VisualSokoban, as well as open-ended social interactions with unverifiable rewards in SOTOPIA. 
Crucially, our method shows superior performance over frontier LLMs and strong RL baselines across domains, achieving state-of-the-art results with higher sample-efficiency and training stability.
Further analysis also demonstrates efficient exploration by **iStar** with increased rewards in both step- and episode-level while maintaining fewer steps to achieve task success.
Understanding the Mixture-of-Experts with Nadaraya-Watson Kernel
Authors: Chuanyang Zheng; Jiankai Sun; Yihang Gao; Enze Xie; Yuehao Wang; Peihao Wang; Ting Xu; Matthew Chang; Liliang Ren; Jingyao Li; Jing Xiong; Kashif Rasul; Mac Schwager; Anderson Schneider; Zhangyang Wang; Yuriy Nevmyvaka
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Mixture-of-Experts (MoE) has become a cornerstone in recent state-of-the-art large language models (LLMs). Traditionally, MoE relies on $\mathrm{Softmax}$ as the router score function to aggregate expert output, a designed choice that has persisted from the earliest MoE models to modern LLMs, and is now widely regarded as standard practice. However, the necessity of using $\mathrm{Softmax}$ to project router weights into a probability simplex remains an unchallenged assumption rather than a principled design choice. In this work, we first revisit the classical Nadaraya–Watson regression and observe that MoE shares the same mathematical formulation as Nadaraya–Watson regression. Furthermore, we show that both feed-forward neural network (FFN) and Mixture-of-Experts (MoE) can be interpreted as a special case of Nadaraya–Watson regression, where the kernel function corresponds to the input neurons of the output layer. Motivated by these insights, we propose the **zero-additional-cost** Kernel Inspired Router with Normalization ($\mathrm{KERN}$), an FFN-style router function, as an alternative to $\mathrm{Softmax}$. We demonstrate that this router generalizes both $\mathrm{Sigmoid}$- and $\mathrm{Softmax}$-based routers. **Based on empirical observations and established practices in FFN implementation, we recommend the use of $\mathrm{ReLU}$ activation and $\ell_2$-normalization in $\mathrm{KERN}$ router function.** Comprehensive experiments in MoE and LLM validate the effectiveness of the proposed FFN-style router function $\mathrm{KERN}$.
Retaining Suboptimal Actions to Follow Shifting Optima in Multi-Agent Reinforcement Learning
Authors: Yonghyeon Jo; Sunwoo Lee; Seungyul Han
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Value decomposition is a core approach for cooperative multi-agent reinforcement learning (MARL). However, existing methods still rely on a single optimal action and struggle to adapt when the underlying value function shifts during training, often converging to suboptimal policies. To address this limitation, we propose Successive Sub-value Q-learning (S2Q), which learns multiple sub-value functions to retain alternative high-value actions. Incorporating these sub-value functions into a Softmax-based behavior policy, S2Q encourages persistent exploration and enables $Q^{\text{tot}}$ to adjust quickly to the changing optima. Experiments on challenging MARL benchmarks confirm that S2Q consistently outperforms various MARL algorithms, demonstrating improved adaptability and overall performance. Our code is available at https://github.com/hyeon1996/S2Q.
Advancing Spatiotemporal Representations in Spiking Neural Networks via Parametric Invertible Transformation
Authors: Yinsong Yan; Yujie Wu; Jibin Wu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Spiking Neural Networks (SNNs) are regarded as energy-efficient neural architectures due to their event-driven, spike-based computation paradigm. However, existing SNNs suffer from two fundamental limitations: (1) the constrained representational space imposed by binary spike firing mechanisms, which restricts the network's capacity to encode complex spatiotemporal patterns, and (2) the ineffective design of surrogate gradient functions that leads to gradient mismatch issues and suboptimal learning dynamics. To address these challenges, we propose the Parametric Invertible Transformation (PIT), which operates in a conjugate manner with neuronal dynamics to achieve adaptive modulation and augmented spike representations simultaneously. Second, we design an auxiliary gradient correction term to mitigate the gradient mismatch issue and oscillation phenomena during training. Moreover, we introduce a theoretical framework for analyzing the spatiotemporal representation space of SNNs. Extensive experiments on both static and neuromorphic datasets demonstrate state-of-the-art performance with our proposed method. This approach lays the theoretical foundation for expanding the spatiotemporal representations of SNNs, offering a viable pathway for developing low-latency and high-performance neuromorphic processing systems in resource-constrained environments. The code is available at https://github.com/YinsongYan/ICLR26.
DualEdit: Mitigating Safety Fallback in LLM Backdoor Editing via Affirmation-Refusal Regulation
Authors: Houcheng Jiang; Zetong Zhao; Junfeng Fang; Haokai Ma; Ruipeng Wang; Xiang Wang; Xiangnan He; Yang Deng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Safety-aligned large language models (LLMs) remain vulnerable to backdoor attacks. Recent model editing-based approaches enable efficient backdoor injection by directly modifying a small set of parameters to map triggers to attacker-desired behaviors. However, we find that existing editing-based attacks are often unstable under safety alignment: the edited model may start with an affirmative prefix but later revert to refusals during generation. We term this phenomenon \textit{safety fallback}. To mitigate it, we propose \textbf{DualEdit}, a dual-objective model editing framework that simultaneously promotes affirmative tokens and suppresses refusal tokens. DualEdit further addresses two key challenges—objective imbalance and refusal diversity—via two complementary techniques: (1) \textit{Dynamic loss weighting}, which calibrates the relative scales of the two objectives using the pre-edited model to stabilize optimization, and (2) \textit{Value anchoring}, which clusters representative attention value vectors to form compact anchors, reducing conflicts from overly diverse token sets and improving generalization. Experiments on safety-aligned LLMs show that DualEdit improves attack success by 10\% and reduces safety fallback rate by 11\% over baselines. Our code is available at: \url{https://github.com/zhaozetong/DualEdit}.
Designing Time Series Experiments in A/B Testing with Transformer Reinforcement Learning
Authors: Xiangkun Wu; Qianglin Wen; Yingying Zhang; Hongtu Zhu; Ting Li; Chengchun Shi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: A/B testing has become a gold standard for modern technological companies to
conduct policy evaluation. Yet, its application to time series experiments, where
treatments are sequentially assigned over time, remains challenging. Existing designs suffer from two limitations: (i) they do not fully leverage the entire history for treatment allocation; (ii) they rely on strong assumptions to approximate the objective function (e.g., the mean squared error of the estimated treatment effect) for optimizing the design. We first establish an impossibility theorem showing
that failure to condition on the full history leads to suboptimal designs, due to the dynamic dependencies in time series experiments. To address both limitations simultaneously, we next propose a transformer reinforcement learning (RL) approach which leverages transformers to condition treatment allocation on the entire history and employs RL to directly optimize the MSE without relying on
restrictive assumptions. Empirical evaluations on synthetic data, a publicly available dispatch simulator, and a real-world ridesharing dataset demonstrate that our proposal consistently outperforms existing designs.
PACEbench: A Framework for Evaluating Practical AI Cyber-Exploitation Capabilities
Authors: Zicheng Liu; Lige Huang; Jie Zhang; Dongrui Liu; Yuan Tian; Jing Shao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The increasing autonomy of Large Language Models (LLMs) necessitates a rigorous evaluation of their potential to aid in cyber offense. Existing benchmarks often lack real-world complexity and are thus unable to accurately assess LLMs' cybersecurity capabilities. To address this gap, we introduce PACEbench, a practical AI cyber-exploitation benchmark built on the principles of realistic vulnerability difficulty, environmental complexity, and cyber defenses. Specifically, PACEbench comprises four scenarios spanning single, blended, chained, and defense vulnerability exploitations. To handle these complex challenges, we propose PACEagent, a novel agent that emulates human penetration testers by supporting multi-phase reconnaissance, analysis, and exploitation.
Extensive experiments with seven frontier LLMs demonstrate that current models struggle with complex cyber scenarios, and none can bypass defenses. These findings suggest that current models do not yet pose a generalized cyber offense threat. Nonetheless, our work provides a robust benchmark to guide the trustworthy development of future models.
Multi-state Protein Sequence Design with DynamicMPNN
Authors: Alex Abrudan; Sebastian Pujalte Ojeda; Chaitanya K. Joshi; Matthew Greenig; Felipe Engelberger; Alena Khmelinskaia; Jens Meiler; Michele Vendruscolo; Tuomas Knowles
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Structural biology has long been dominated by the one sequence, one structure, one function paradigm, yet many critical biological processes—from enzyme catalysis to membrane transport—depend on proteins that adopt multiple conformational states. Existing multi-state design approaches rely on post-hoc aggregation of single-state predictions, achieving poor experimental success rates compared to single-state design. We introduce DynamicMPNN, an inverse folding model explicitly trained to generate sequences compatible with multiple conformations through joint learning across conformational ensembles. Trained on 46,033 conformational pairs covering 75% of CATH superfamilies and evaluated using Alphafold 3, DynamicMPNN outperforms ProteinMPNN by up to 31% on decoy-normalized RMSD and by 12% on sequence recovery across our challenging multi-state protein benchmark.
Fused-Planes: Why Train a Thousand Tri-Planes When You Can Share?
Authors: Karim Kassab; Antoine Schnepf; Jean-Yves Franceschi; Laurent Caraffa; Flavian Vasile; Jeremie Mary; Andrew I. Comport; Valerie Gouet-Brunet
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Tri-Planar NeRFs enable the application of powerful 2D vision models for 3D tasks, by representing 3D objects using 2D planar structures.
This has made them the prevailing choice to model large collections of 3D objects.
However, training Tri-Planes to model such large collections is computationally intensive and remains largely inefficient.
This is because the current approaches independently train one Tri-Plane per object, hence overlooking structural similarities in large classes of objects. 
In response to this issue, we introduce Fused-Planes, a novel object representation that improves the resource efficiency of Tri-Planes when reconstructing object classes, all while retaining the same planar structure.
Our approach explicitly captures structural similarities across objects through a latent space and a set of globally shared base planes.
Each individual Fused-Planes is then represented as a decomposition over these base planes, augmented with object-specific features.
Fused-Planes showcase state-of-the-art efficiency among planar representations, demonstrating $7.2 \times$ faster training and $3.2 \times$ lower memory footprint than Tri-Planes while maintaining rendering quality.
An ultra-lightweight variant further cuts per-object memory usage by $1875 \times$ with minimal quality loss.
Our project page can be found at https://fused-planes.github.io .
TABLET: A Large-Scale Dataset for Robust Visual Table Understanding
Authors: Iñigo Alonso; Imanol Miranda; Eneko Agirre; Mirella Lapata
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While table understanding increasingly relies on pixel-only settings, current benchmarks predominantly use synthetic renderings that lack the complexity and visual diversity of real-world tables. Additionally, existing visual table understanding (VTU) datasets offer fixed examples with single visualizations and pre-defined instructions, providing no access to underlying serialized data for reformulation. We introduce TABLET, a large-scale VTU dataset with 4 million examples across 21 tasks, grounded in 2 million unique tables where 88% preserve original visualizations. To evaluate whether models are able to jointly reason over tabular and visual content, we also introduce VisualTableQA, a benchmark requiring both visual perception and table understanding. Fine-tuning vision-language models like Qwen2.5-VL-7B and Gemma 3-4B on TABLET improves performance on seen and unseen VTU tasks while increasing robustness on real-world table visualizations. By preserving original visualizations and maintaining example traceability in a unified large-scale collection, TABLET establishes a foundation for robust training and extensible evaluation of future VTU models.
Selective Expert Guidance for Effective and Diverse Exploration in Reinforcement Learning of LLMs
Authors: Zishang Jiang; Jinyi Han; tingyun li; Xinyi Wang; Sihang Jiang; Zhaoqian Dai; Ma Shuguang; Fei Yu; Jiaqing Liang; Yanghua Xiao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become a widely adopted technique for enhancing the reasoning ability of Large Language Models (LLMs). However, the effectiveness of RLVR strongly depends on the capability of base models. This issue arises because it requires the model to have sufficient capability to perform high-quality exploration, which involves both effectiveness and diversity. Unfortunately, existing methods address this issue by imitating expert trajectories, which improve effectiveness but neglect diversity. To address this, we argue that the expert only needs to  provide guidance only at critical decision points rather than the entire reasoning path. Based on this insight, we propose MENTOR: Mixed-policy Expert Navigation for Token-level Optimization of Reasoning, a framework that provides expert guidance only at critical decision points to perform effective and diverse exploration in RLVR. Extensive experiments show that MENTOR enables models capture the essence of expert strategies rather than surface imitation, thereby performing high-quality exploration and achieving superior overall performance. Our code is available online.