GenAI4ELab/papercli-papers huggingface dataset:
WSVD: Weighted Low-Rank Approximation for Fast and Efficient Execution of Low-Precision Vision-Language Models
Authors: Haiyu Wang; Yutong Wang; Jack Jiang; Sai Qian Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Singular Value Decomposition (SVD) has become an important technique for reducing the computational burden of Vision Language Models (VLMs), which play a central role in tasks such as image captioning and visual question answering. Although multiple prior works have proposed efficient SVD variants to enable low-rank operations, we find that in practice it remains difficult to achieve substantial latency reduction during model execution. To address this limitation, we introduce a new computational pattern and apply SVD at a finer granularity, enabling real and measurable improvements in execution latency. Furthermore, recognizing that weight elements differ in their relative importance, we adaptively allocate relative importance to each element during SVD process to better preserve accuracy, then extend this framework with quantization applied to both weights and activations, resulting in a highly efficient VLM. Collectively, we introduce Weighted SVD (WSVD), which outperforms other approaches by achieving over $1.8\times$ decoding speedup while preserving accuracy. We open source our code at: https://github.com/SAI-Lab-NYU/WSVD.
VGR: Visual Grounded Reasoning
Authors: Jiacong Wang; Zijian Kang; Haochen Wang; LiangXiao; Ya Wang; Jiawen Li; Bohong Wu; Ran Jiao; Haiyong Jiang; ChaoFeng; Jun Xiao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In the field of multimodal chain-of-thought (CoT) reasoning, existing approaches
predominantly rely on reasoning on pure linguistic space, which inherently suffers
from language bias and is largely confined to math or science domains. This narrow
focus limits their ability to handle complex visual reasoning tasks that demand comprehensive understanding of image details. To address these limitations, this paper
introduces VGR, a novel reasoning multimodal large language model (MLLM) that
can replay the visual memory during thinking just like humans. Unlike traditional
MLLMs, VGR first thinks the question and detects relevant regions that may help
solve problems, then, the visual memory from the critical area is extracted to assist
reasoning. To achieve this, we curate a large-scale SFT dataset called VGR-SFT
that contains reasoning data with mixed vision grounding and language deduction.
This teaches VGR to think and actively choose grounding areas for key information before answering, and we propose a dynamic visual memory replay stage to
integrates the corresponding information into the reasoning process, enhancing
multimodel comprehension. Experiments on the LLaVA-NeXT-7B baseline show
that VGR achieves superior performance on multimodal benchmarks requiring
comprehensive image detail understanding. Compared to the baseline, VGR uses
only 30% of the image token count while delivering scores of +4.1 on MMStar,
+7.1 on AI2D, and +12.9 improvement on ChartQA. The data is available at https://huggingface.co/BytedanceDouyinContent/VGR.
OR-PRM: A Process Reward Model for Algorithmic Problem in Operations Research
Authors: Yilin Wang; Heng Zhou; Dongxing Mao; Linjie Li; Jingru Tan; Haochen Han; Zhengyuan Yang; Alex Jinpeng Wang; Min Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) with Process Reward Models (PRMs) have shown strong reasoning ability, yet their potential in Operations Research (OR) remains unexplored. We present the first PRM tailored for OR, but find that directly training on mainstream datasets yields surprisingly weak performance.
To understand this gap, we conduct a systematic analysis and identify the primary bottleneck: the datasets themselves, where over 30\% of annotations are severely flawed.
To overcome these limitations, we first collect all existing synthetic datasets and apply a carefully designed filtering pipeline to construct a high-quality seed dataset. 
Building upon this seed, we then build OR-ProcessQA, the first large-scale dataset for OR with step-by-step supervision, where diverse solution pathways are generated via Monte Carlo Tree Search (MCTS) and each step is validated for logical consistency by GPT-4o.
Building on this foundation, we train OR-PRM, the first Process Reward Model in the OR domain, designed to evaluate and guide reasoning at every step rather than only the final outcome.
Together, these advances enable OR-PRM to substantially improve LLMs’ reasoning capability, achieving a maximum absolute improvement of 12.5\% over the base model in Best-of-N settings, and highlighting the power of process-oriented supervision for reliable problem solving in operations research.
Coarse-to-Fine Learning of Dynamic Causal Structures
Authors: Dezhi Yang; Qiaoyu Tan; Carlotta Domeniconi; Jun Wang; Lizhen Cui; Guoxian Yu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Learning the dynamic causal structure is a difficult challenge in discovering causality from time series. Most existing studies rely on distributional or structural invariance to uncover the underlying causal dynamics, assuming stationary or partially stationary causality, which frequently conflicts with complex causal relationships in the real world. This boosts temporal causal discovery to encompass fully dynamic causality, where both instantaneous and lagged causal dependencies may change over time, bringing significant challenges to the efficiency and stability of causal discovery. To tackle these challenges, we introduce DyCausal, a dynamic causal structure learning framework that leverages convolutional networks to effectively model causal structures within coarse-grained time windows, and introduces linear interpolation to refine causal structures to each time step and recover time-varying causal graphs. In addition, we propose an acyclic constraint based on matrix norm scaling. It is more stable both theoretically and empirically, and constrains loops in dynamic causal structures with improved efficiency. Evaluations on both synthetic and real-world datasets prove that DyCausal significantly outperforms existing methods and identifies fully dynamic causal structures from coarse to fine.
HiPO: Self-Hint Policy Optimization for RLVR
Authors: Deng Qiyuan; Kehai Chen; Min Zhang; Zhongwen Xu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) is a promising method for enhancing the complex 
problem-solving abilities of large language models (LLMs). This is particularly evident in domains requiring 
long-horizon reasoning and precise execution, such as solving complex mathematical problems where solutions 
hinge on a fragile sequence of tool-based actions. However, current approaches are often crippled by two 
interconnected issues: the near-miss problem, where sparse rewards nullify the learning signal for 
almost-correct attempts, and the resulting exploration stagnation, which prevents the model from 
discovering better solutions. To address these challenges, we introduce HiPO (Hint-guided Policy Optimization), 
a novel RLVR framework that enables the agent to learn from its own rare successes. 
Our core insight is to capture an occasional successful trajectory within a training batch and
repurpose its initial correct steps as an on-policy “hint”. This process 
transforms a single, stochastically-found success into a dense contrastive learning signal, 
effectively allowing the model to teach itself how to overcome the near-miss 
problem and break exploration stagnation. On a challenging suite of five mathematical reasoning benchmarks, 
HiPO improves the average avg@32 by +5.0 percentage points (pp) over the strong GRPO baseline. 
This improvement is driven by substantial absolute point gains on challenging datasets, 
including +10.3 pp on CMIMC 2025, +4.9 pp on BRUMO 2025, +4.6 pp on AIME 2024, and +3.1 pp on AIME 2025.
Furthermore, HiPO demonstrates a new exploration paradigm, 
repurposing rare successes into reusable guidance to significantly accelerate skill acquisition for complex tasks, 
establishing a more efficient and scalable path for models to autonomously master intricate reasoning.
LLMs as Rules Oracles: Exploring Real-World Multimodal Reasoning in Tabletop Strategy Game Environments
Authors: Joseph J Peper; Sai Krishna Gandra; Yunxiang Zhang; Vaibhav Chennareddy; Shloki Jha; Ali Payani; Lu Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We introduce **LudoBench**, a multimodal reasoning benchmark that evaluates whether vision-enabled large language models (LMs) can acquire, integrate, and reason over heterogeneous game knowledge in mainstream analog tabletop games. Unlike prior works that emphasize deep strategic mastery, LudoBench targets an initial reasoning challenge uninitiated gamers face: *correctly comprehending a new tabletop strategy game for the first time*.  

We examine whether, given a visual depiction of a tabletop scene and a corresponding ruleset, a model can correctly answer grounded questions about the pictured scenario. Concretely, LudoBench tests three cumulative situated game-comprehension capabilities: (1) *Environment Perception*, (2) *Heterogeneous Rules Integration*, and (3) *Short-horizon Optimization*, to progressively stress-test the foundational reasoning required for real-world game comprehension. 

Evaluating frontier LMs on three diverse strategy games, we find that even the strongest models achieve only ~68% accuracy on simple environment perception tasks and fall below 10% on situated multi-step comprehension puzzles that hobbyist gamers can routinely solve.
Our extensive failure analysis and knowledge-ablation experiments reveal that *models largely fail to comprehend rich cross-modal reference knowledge* and are subsequently unable to apply this knowledge to messy and unfamiliar situated environments. Our findings highlight the many steps remaining for current methods to succeed on complex multimodal reasoning in the real world.
Addressing divergent representations from causal interventions on neural networks
Authors: Satchel Grant; Simon Jerome Han; Alexa R. Tartaglini; Christopher Potts
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: A common approach to mechanistic interpretability is to causally manipulate model representations via targeted interventions in order to understand what those representations encode. Here we ask whether such interventions create out-of-distribution (divergent) representations, and whether this raises concerns about how faithful their resulting explanations are to the target model in its natural state. First, we demonstrate theoretically and empirically that common causal intervention techniques often do shift internal representations away from the natural distribution of the target model. Then, we provide a theoretical analysis of two cases of such divergences: "harmless" divergences that occur in the behavioral null-space of the layer(s) of interest, and "pernicious" divergences that activate hidden network pathways and cause dormant behavioral changes. Finally, in an effort to mitigate the pernicious cases, we apply and modify the Counterfactual Latent (CL) loss from Grant (2025) allowing representations from causal interventions to remain closer to the natural distribution, reducing the likelihood of harmful divergences while preserving the interpretive power of the interventions. Together, these results highlight a path towards more reliable interpretability methods.
OmniMouse: Scaling properties of multi-modal, multi-task Brain Models on 150B Neural Tokens
Authors: Konstantin Friedrich Willeke; Polina Turishcheva; Alex Gilbert; Goirik Chakrabarty; Hasan Atakan Bedel; Paul G. Fahey; Yongrong Qiu; Marissa A. Weis; Michaela Vystrčilová; Taliah Muhammad; Lydia Ntanavara; Rachel E Froebe; Kayla Ponder; Zheng Huan Tan; Emin Orhan; Erick Cobos; Sophia Sanborn; Katrin Franke; Fabian H. Sinz; Alexander S. Ecker; Andreas S. Tolias
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Scaling data and artificial neural networks has transformed AI, driving breakthroughs in language and vision. Whether similar principles apply to modeling brain activity remains unclear. Here we leveraged a dataset of 3.1 million neurons from the visual cortex of 73 mice across 323 sessions, totaling more than 150 billion neural tokens recorded during natural movies, images and parametric stimuli, and behavior. We train multi-modal, multi-task models that support three regimes flexibly at test time: neural prediction, behavioral decoding, neural forecasting, or any combination of the three. OmniMouse achieves state-of-the-art performance, outperforming specialized baselines across nearly all evaluation regimes. We find that performance scales reliably with more data, but gains from increasing model size saturate. This inverts the standard AI scaling story: in language and computer vision, massive datasets make parameter scaling the primary driver of progress, whereas in brain modeling -- even in the mouse visual cortex, a relatively simple system -- models remain data-limited despite vast recordings. The observation of systematic scaling raises the possibility of phase transitions in neural modeling, where larger and richer datasets might unlock qualitatively new capabilities, paralleling the emergent properties seen in large language models. Code available at \url{https://github.com/enigma-brain/omnimouse}.
Cross-Domain Lossy Compression via Rate- and Classification-Constrained Optimal Transport
Authors: Nam Nguyen; Thinh Nguyen; Bella Bose
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: We study cross-domain lossy compression, where the encoder observes a degraded source while the decoder reconstructs samples from a distinct target distribution. The problem is formulated as constrained optimal transport with two constraints on compression rate and classification loss. With shared common randomness, the one-shot setting reduces to a deterministic transport plan, and we derive closed-form distortion-rate-classification (DRC) and rate-distortion-classification (RDC) tradeoffs for Bernoulli sources under Hamming distortion. In the asymptotic regime, we establish analytic DRC/RDC expressions for Gaussian models under mean-squared error. The framework is further extended to incorporate perception divergences (Kullback-Leibler and squared Wasserstein), yielding closed-form distortion-rate-perception-classification (DRPC) functions. To validate the theory, we develop deep end-to-end compression models for super-resolution (MNIST), denoising (SVHN, CIFAR-10, ImageNet, KODAK), and inpainting (SVHN) problems, demonstrating the consistency between the theoretical results and empirical performance.
Spatial Reasoning with Vision-Language Models in Ego-Centric Multi-View Scenes
Authors: Mohsen Gholami; Ahmad Rezaei; Zhou Weimin; Sitong Mao; Shunbo Zhou; Yong Zhang; Mohammad Akbari
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Understanding 3D spatial relationships remains a major limitation of current Vision-Language Models (VLMs). Prior work has addressed this issue by creating spatial question-answering (QA) datasets based on single images or indoor videos. However, real-world embodied AI agents—such as robots and self-driving cars—typically rely on ego-centric, multi-view observations. To this end, we introduce Ego3D-Bench, a new benchmark designed to evaluate the spatial reasoning abilities of VLMs using ego-centric, multi-view outdoor data. Ego3D-Bench comprises over 8,600 QA pairs, created with significant involvement from human annotators to ensure quality and diversity. We benchmark 16 SOTA VLMs, including GPT-4o, Gemini1.5-Pro, InternVL3, and Qwen2.5-VL. Our results reveal a notable performance gap between human level scores and VLM performance, highlighting that current VLMs still fall short of human level spatial understanding (SU). To bridge this gap, we propose Ego3D-VLM, a post-training framework that enhances 3D spatial reasoning of VLMs. Ego3D-VLM generates cognitive map based on estimated global 3D coordinates, resulting in 12% and 56% average improvements on multi-choice QA and absolute distance estimation, respectively. Ego3D-VLM can be integrated with any existing VLM. Together, Ego3D-Bench and Ego3D-VLM offer valuable tools for advancing toward human level SU in real-world, multi-view environments. Code is available in the supplementary materials.
Adaptive Mamba Neural Operators
Authors: Zeyuan Song; Zheyu Jiang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Accurately solving partial differential equations (PDEs) on arbitrary geometries and a variety of meshes is an important task in science and engineering applications. In this paper, we propose Adaptive Mamba Neural Operators (AMO), which integrates reproducing kernels for state-space models (SSMs) rather than the kernel integral formulation of SSMs. This is achieved by constructing Takenaka-Malmquist systems for the PDEs. AMO offers new representations that align well with the adaptive Fourier decomposition (AFD) theory and can approximate the solution manifold of PDEs on a wide range of geometries and meshes. In several challenging benchmark PDE problems in the fields of fluid physics, solid physics, and finance on point clouds, structured meshes, regular grids, and irregular domains, AMO consistently outperforms state-of-the-art solvers in terms of relative $L^2$ error. Overall, this work presents a new paradigm for designing explainable neural operator frameworks. The code is available at https://github.com/checlams/AMO.
PEAR: Phase Entropy Aware Reward for Efficient Reasoning
Authors: Chen Huang; Wei Lu; Wenxuan Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Reasoning Models (LRMs) have achieved impressive performance on complex reasoning tasks by generating detailed chain-of-thought (CoT) explanations. However, these responses are often excessively long, containing redundant reasoning steps that inflate inference cost and reduce usability. Controlling the length of generated reasoning without sacrificing accuracy remains an open challenge. 
Through a systematic empirical analysis, we reveal a consistent positive correlation between model entropy and response length at different reasoning stages across diverse LRMs: the thinking phase exhibits higher entropy, reflecting exploratory behavior of longer responses, while the final answer phase shows lower entropy, indicating a more deterministic solution. This observation suggests that entropy at different reasoning stages can serve as a control knob for balancing conciseness and performance. Based on this insight, this paper introduces Phase Entropy Aware Reward (PEAR), a reward mechanism that incorporating phase-dependent entropy into the reward design. Instead of treating all tokens uniformly, PEAR penalize excessive entropy during the thinking phase and allowing moderate exploration at the final answer phase, which encourages models to generate concise reasoning traces that retain sufficient flexibility to solve the task correctly. This enables adaptive control of response length without relying on explicit length targets or rigid truncation rules. 
Extensive experiments across six benchmarks demonstrate that PEAR consistently reduces response length while sustaining competitive accuracy across model scales. In addition, PEAR demonstrates strong out-of-distribution (OOD) robustness beyond the training distribution.Our code is available at: https://github.com/iNLP-Lab/PEAR.
Revisiting the Past: Data Unlearning with Model State History
Authors: Keivan Rezaei; Mehrdad Saberi; Abhilasha Ravichander; Soheil Feizi
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models are trained on massive corpora of web data, which may include private data, copyrighted material, factually inaccurate data, or data that degrades model performance. Eliminating the influence of such problematic datapoints on a model through complete retraining---by repeatedly pretraining the model on datasets that exclude these specific instances---is computationally prohibitive. To address this, unlearning algorithms have been proposed, that aim to eliminate the influence of particular datapoints at a low computational cost, while leaving the rest of the model intact. However, precisely unlearning the influence of data on a large language model has proven to be a major challenge. In this work, we propose a new algorithm, MSA (**M**odel **S**tate **A**rithmetic), for unlearning datapoints in large language models. MSA utilizes prior model checkpoints--- artifacts that record model states at different stages of pretraining--- to estimate and counteract the effect of targeted datapoints. Our experimental results show that MSA achieves competitive performance and often outperforms existing machine unlearning algorithms across multiple benchmarks, models, and evaluation metrics, suggesting that MSA could be an effective approach towards more flexible large language models that are capable of data erasure.
Sparse but Critical: A Token-Level Analysis of Distributional Shifts in RLVR Fine-Tuning of LLMs
Authors: Haoming Meng; Kexin Huang; Shaohang Wei; Chiyu Ma; Shuo Yang; Xue Wang; Guoyin Wang; Bolin Ding; Jingren Zhou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reinforcement learning with verifiable rewards (RLVR) has significantly improved reasoning in large language models (LLMs), yet the token-level mechanisms underlying these improvements remain unclear. We present a systematic empirical study of RLVR’s distributional effects organized around three main analyses: (1) token-level characterization of distributional shifts between base and RL models, (2) the impact of token-level distributional shifts on reasoning performance through cross-sampling interventions, and (3) fine-grained mechanics of these shifts at the token level. We find that RL fine-tuning induces highly sparse and targeted changes, with only a small fraction of token distributions exhibiting meaningful divergence. We further characterize the structure of these shifts through analyses of token entropy, positional concentration, and reallocation of probability mass. To assess the functional importance of these sparse changes, we conduct cross-sampling experiments that selectively swap token choices between the base and RL models. Inserting only a small fraction of RL-sampled tokens into base generations progressively recovers RL performance gains, while injecting a similarly small number of base token choices into RL-generated responses collapses performance to base levels, isolating a sparse set of token-level decisions directly responsible for RLVR’s improvements. Finally, we explore divergence-weighted variants of the advantage signal as a diagnostic intervention, finding that they can yield improvements over baselines. Together, our results shed light on the distributional changes induced by RLVR and provide a fine-grained, token-level lens for understanding RLVR as a targeted refinement process.