GenAI4ELab/papercli-papers huggingface dataset:
UniHand: A Unified Model for Diverse Controlled 4D Hand Motion Modeling
Authors: Zhihao Sun; Tong Wu; Ruirui Tu; Daoguo Dong; Zuxuan Wu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Hand motion plays a central role in human interaction, yet modeling realistic 4D hand motion (*i.e.*, 3D hand pose sequences over time) remains challenging. 
Research in this area is typically divided into two tasks: 
(1) Estimation approaches reconstruct precise motion from visual observations, but often fail under hand occlusion or absence; 
(2) Generation approaches focus on synthesizing hand poses by exploiting generative priors under multi-modal structured inputs and infilling motion from incomplete sequences.
However, this separation not only limits the effective use of heterogeneous condition signals that frequently arise in practice, but also prevents knowledge transfer between the two tasks.
We present **UniHand**, a unified diffusion-based framework that formulates both estimation and generation as conditional motion synthesis. 
UniHand integrates heterogeneous inputs by embedding structured signals into a shared latent space through a joint variational autoencoder, which aligns conditions such as MANO parameters and 2D skeletons.
Visual observations are encoded with a frozen vision backbone, while a dedicated hand perceptron extracts hand-specific cues directly from image features, removing the need for complex detection and cropping pipelines.
A latent diffusion model then synthesizes consistent motion sequences from these diverse conditions.
Extensive experiments across multiple benchmarks demonstrate that UniHand delivers robust and accurate hand motion modeling, maintaining performance under severe occlusions and temporally incomplete inputs.
SAM-Veteran: An MLLM-Based Human-like SAM Agent for Reasoning Segmentation
Authors: Tianyuan Du; Haopeng Li; Zhen Fan; Jiarui Zhang; Panwang Pan; Yang Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Significant progress has been made in reasoning segmentation by combining multi-modal large language models (MLLMs) with the Segment Anything Model (SAM): the former excel in reasoning and vision–language alignment, while the latter offers powerful pixel-level understanding. However, current paradigms fall short in exploiting SAM’s strengths, especially the ability to support iterative mask refinement by interactive segmentation, a process that human users can naturally perform. To bridge this gap, we introduce  **SAM-Veteran**, an experienced mask-aware SAM agent capable of emulating human interaction with SAM via a reasoning-driven segmentation workflow that integrates (i) generating bounding boxes given image–query pairs for SAM input, (ii) proposing refinement points based on SAM-generated masks, and (iii) adaptively terminating the process. Aiming for this goal, we propose a multi-task reinforcement learning framework based on Group Relative Policy Optimization (GRPO), which enhances the MLLM’s abilities in textual grounding and mask comprehension. Furthermore, we introduce a dynamic sampling strategy tailored for generating both boxes and points to stabilize training. Extensive experiments across diverse datasets show that SAM-Veteran achieves human-like interaction with SAM and establishes new state-of-the-art performance on both in-domain and out-of-domain benchmarks.
Teach to Reason Safely: Policy-Guided Safety Tuning for MLRMs
Authors: Jingyu Zhang; Kun Yang; Ming Wen; Zhuoer Xu; Zeyang Sha; shiwen cui; Zhaohui Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multimodal Large Reasoning Models (MLRMs) have exhibited remarkable capabilities in complex multimodal tasks.
However, our findings reveal a critical trade-off: reasoning-based models are more prone to generating harmful content, leading to degradation in safety performance.
This paper presents a large-scale analysis of this safety–reasoning trade-off, identifying two main mechanisms of safety degradation: (i) visual attention drift, which reduces the model’s reliance on visual grounding and thereby exacerbates overlooked risks in cross-modal interactions; (ii) unsafe reasoning patterns, including flawed reasoning initiation and chain-of-thought safety attenuation, which compromise the model’s safety awareness.
To mitigate these issues, we propose **P**olicy-guided **S**afety **T**uning (**PST**), a two-stage alignment framework. It first employs *Policy-Guided Supervised Fine-Tuning* to integrate explicit safety policies into the reasoning process, establishing a structured and interpretable foundation for safe decision-making. 
Then, PST applies *Safety Reasoning Preference Optimization* to encourage the model to generate safe, helpful, and informative responses while reducing oversensitive  and homogeneous characteristics.
Extensive experiments demonstrate that PST significantly reduces harmful outputs across multiple multimodal safety benchmarks, while maintaining competitive performance on general tasks.
Customizing Visual Emotion Evaluation for MLLMs: An Open-vocabulary, Multifaceted, and Scalable Approach
Authors: Daiqing Wu; Dongbao Yang; Sicheng Zhao; Can Ma; Yu ZHOU
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recently, Multimodal Large Language Models (MLLMs) have achieved exceptional performance across diverse tasks, continually surpassing previous expectations regarding their capabilities. Nevertheless, their proficiency in perceiving emotions from images remains debated, with studies yielding divergent results in zero-shot scenarios. We argue that this inconsistency stems partly from constraints in existing evaluation methods, including the oversight of plausible responses, limited emotional taxonomies, neglect of contextual factors, and labor-intensive annotations. To facilitate customized visual emotion evaluation for MLLMs, we propose an Emotion Statement Judgment task that overcomes these constraints. Complementing this task, we devise an automated pipeline that efficiently constructs emotion-centric statements with minimal human effort. Through systematically evaluating prevailing MLLMs, our study showcases their stronger performance in emotion interpretation and context-based emotion judgment, while revealing relative limitations in comprehending perception subjectivity. When compared to humans, even top-performing MLLMs like GPT4o demonstrate remarkable performance gaps, underscoring key areas for future improvement. By developing a fundamental evaluation framework and conducting a comprehensive MLLM assessment, we hope this work contributes to advancing emotional intelligence in MLLMs. Project page: https://github.com/wdqqdw/MVEI.
Adaptive Nonlinear Compression for Large Foundation Models
Authors: Liang Xu; Shufan Shen; Qingming Huang; Yao Zhu; Xiangyang Ji; Shuhui Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Despite achieving superior performance, large foundation models (LFMs) have substantial memory requirements, leading to a growing demand for model compression methods.
While low-rank approximation presents a promising hardware-friendly solution, existing linear methods suffer significant information losses due to rank truncation. Nonlinear kernels can enhance expressiveness by operating in higher-dimensional spaces, yet most kernels introduce prohibitive overhead and struggle to support adaptive rank allocation across heterogeneous matrices.
In this paper, we propose a compression method called Nonlinear Low-Rank Approximation with Adaptive Budget Allocation (NLA). 
Instead of relying on linear products, we employ piecewise-linear kernels with a forward-pass optimization operator to approximate weight matrices, enhancing the recovery of high-rank weight matrices from low-rank matrices.
Moreover, considering the heterogeneous representation abilities and dynamic sensitivities of different weight matrices, we adaptively allocate the compression ratio of each weight matrix during the re-training process by cubic sparsity scheduling.
Through evaluations on large language models and vision models across various datasets, NLA demonstrates superior performance while achieving a higher compression ratio compared to existing methods.
DeepEyesV2: Toward Agentic Multimodal Model
Authors: Jack Hong; Chenxiao Zhao; ChengLIn Zhu; Weiheng Lu; Guohai Xu; XingYu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Agentic multimodal models should not only comprehend text and images, but also actively invoke external tools, such as code execution environments and web search, and integrate these operations into reasoning. In this work, we introduce DeepEyesV2 and explore how to build an agentic multimodal model from the perspectives of data construction, training methods, and model evaluation. We observe that direct reinforcement learning alone fails to induce robust tool-use behavior. This phenomenon motivates a two-stage training pipeline: a cold-start stage to establish tool-use patterns, and reinforcement learning stage to further refine tool invocation. We curate a diverse, moderately challenging training dataset, specifically including examples where tool use is beneficial. We validate DeepEyesV2 across real-world understanding, mathematical reasoning, and search-intensive benchmarks, demonstrating that systematic tool integration enables reliable and extensible multimodal reasoning behaviour. Moreover, DeepEyesV2 exhibits task-adaptive tool invocation, tending to use image operations for perception tasks and numerical computations for reasoning tasks. Reinforcement learning further enable complex tool combinations and allowing model to selectively invoke tools based on problem context. We hope our study can provide guidance for community in developing agentic multimodal models.
Journey to the Centre of Cluster: Harnessing Interior Nodes for A/B Testing under Network Interference
Authors: Qianyi Chen; Anpeng Wu; Bo Li; Lu Deng; Yong Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: A/B testing on platforms often faces challenges from network interference, where a unit's outcome depends not only on its own treatment but also on the treatments of its network neighbors. To address this, cluster-level randomization has become standard, enabling the use of network-aware estimators. These estimators typically trim the data to retain only a subset of informative units, achieving low bias under suitable conditions but often suffering from high variance. 
In this paper, we first demonstrate that the interior nodes—units whose neighbors all lie within the same cluster—constitute the vast majority of the post-trimming subpopulation. In light of this, we propose directly averaging over the interior nodes to construct the mean-in-interior (MII) estimator, which circumvents the delicate reweighting required by existing network-aware estimators and substantially reduces variance in classical settings. However, we show that interior nodes are often not representative of the full population, particularly in terms of network-dependent covariates, leading to notable bias. 
We then augment the MII estimator with a counterfactual predictor trained on the entire network, allowing us to adjust for covariate distribution shifts between the interior nodes and full population.
By rearranging the expression, we reveal that our augmented MII estimator embodies an analytical form of the point estimator within prediction-powered inference framework~\citep{angelopoulos2023prediction,angelopoulos2023ppi++}. This insight motivates a semi-supervised lens, wherein interior nodes are treated as labeled data subject to selection bias. Extensive and challenging simulation studies demonstrate the outstanding performance of our augmented MII estimator across various settings.
QuRL: Rubrics As Judge For Open-Ended Question Answering
Authors: Xiyu Wei; Qingwei Zong; Xiaoguang Li; Eugene J. Yu; Sujian Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) has significantly improved the performance of large language models (LLMs) on tasks with gold ground truth, such as code generation and mathematical reasoning. However, its application to open-ended question answering (QA) remains challenging, primarily due to the absence of reliable evaluation and verifiable reward signals. This difficulty is further compounded by the limitations of existing evaluation paradigms. Previous approaches typically rely on human feedback or LLM-as-judge strategies, which are costly, prone to reward hacking, and often fail to provide sufficiently discriminative or interpretable evaluation signals. To address these limitations, we introduce a schema for generating case-wise  rubrics that are  question-specific, content-based and stylistically sensitive, thereby evaluating both factual soundness and writing quality. Building on this schema, we propose QuRL (Open-Ended QA with Rubric-guided Reinforcement Learning), a framework that automatically mines rubrics for each question from easily accessible online sources and leverages them as reward signals. With these rubrics, QuRL employs the GRPO (Group Relative Policy Optimization) algorithm to guide the model in exploring the correct generation path. Extensive experiments show that our framework achieves significant improvements of total +17.0 points on evaluation benchmark, demonstrating the effectiveness of rubric-guided reinforcement learning for open-ended QA.
Pragma-VL: Towards a Pragmatic Arbitration of Safety and Helpfulness in MLLMs
Authors: Ming Wen; Kun Yang; Xin Chen; Jingyu Zhang; DINGDING HAN; shiwen cui; Yuedong Xu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multimodal Large Language Models (MLLMs) pose critical safety challenges, as they are susceptible not only to adversarial attacks such as jailbreaking but also to inadvertently generating harmful content for benign users. While internal safety alignment via Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) is a primary mitigation strategy, current methods often face a safety-utility trade-off: they either refuse benign queries out of excessive caution or overlook latent risks in cross-modal interactions. To resolve this, we introduce Pragma-VL, an end-to-end alignment algorithm that enables MLLMs to pragmatically arbitrate between safety and helpfulness. First, we enhance visual risk perception with a novel cold-start SFT stage. This is achieved by applying risk-aware clustering to the visual encoder and using an interleaved dataset of risk descriptions and high-quality data. Second, we introduce a theoretically-guaranteed reward model that leverages synergistic learning. We train it with a novel data augmentation method that assigns dynamic weights based on the queries, enabling contextual arbitration between safety and helpfulness. Extensive experiments show that Pragma-VL effectively balances safety and helpfulness, outperforming baselines by 5% to 20% on most multimodal safety benchmarks while preserving its general capabilities in areas such as mathematics and knowledge reasoning.
InfoScan: Information-Efficient Visual Scanning via Resource-Adaptive Walks
Authors: Yifeng Wu; Huimin Huang; Shangjie Zhou; Yawen Huang; Hao Zheng; Yun Chen; Xian Wu; Ruize Han; Guanhua Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: High-resolution visual representation learning remains challenging due to the quadratic complexity of Vision Transformers and the limitations of existing efficient approaches, where fixed scanning patterns in recent Mamba-based models hinder content-adaptive perception.
To address these limitations, a novel Information-aware Scanning mechanism (InfoScan) tailored for state-space visual backbones is proposed, which dynamically allocates computational resources to the most salient regions of an image. 
Specifically, InfoScan rigorously assesses the informativeness of image patches by integrating entropy with local structural analyses, formulates a joint optimization objective balancing fine-grained detail preservation and broader contextual coherence, and learns an adaptive scanning policy via reinforcement learning. 
Built upon the innovative Visual Information State Space (VISS) block, InfoScan establishes a new family of models that achieve superior efficiency-accuracy trade-offs across diverse tasks. 
Extensive empirical evaluation in different downstream vision tasks demonstrates that our information-driven dynamic scanning paradigm offers a robust and principled alternative to fixed or global-first traversal methods. 
Collectively, our work positions adaptive, content-aware processing as a promising and effective new paradigm for efficient high-resolution visual representation.
Inheriting Generalizable Knowledge from LLMs to Diverse Vertical Tasks
Authors: Chang Liu; boyu shi; Xu Yang; Qiufeng Wang; Xin Geng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) have demonstrated remarkable generalization across diverse tasks, suggesting the existence of task-agnostic, generalizable knowledge encoded within them. However, how to systematically extract and evaluate this knowledge remains unexplored. In this work, we innovatively propose MASA (Matrix-level Alignment and Scalable Adaptation), a unified framework for extracting and transferring generalizable knowledge from LLMs. MASA first introduces a lightweight set of gene matrices trained with a dual alignment strategy, combining output alignment and spectral alignment, to capture the generalizable knowledge encoded in the feed-forward networks (FFNs) of LLM. It then employs scalable adaptation to flexibly reshape these gene matrices to match the parameter dimensions of lightweight dense models of various sizes, enabling direct initialization of their FFN layers. To evaluate the inherited knowledge, we measure the downstream performance of lightweight models initialized with MASA across language understanding and dialogue generation tasks spanning diverse vertical domains. Experiments on both dense and Mixture-of-Experts (MoE) source LLMs show that MASA consistently outperforms baselines such as random initialization, pruning, and distillation, yielding lightweight models that achieve stronger performance, require less pre-training data, and converge faster. These results establish MASA as an effective and general framework for extracting and leveraging the generalizable knowledge within LLMs.
ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving
Authors: Yongkang Li; Kaixin Xiong; Xiangyu Guo; Fang Li; Sixu Yan; Gangwei Xu; Lijun Zhou; Long Chen; Haiyang Sun; BING WANG; Kun Ma; Guang Chen; Hangjun Ye; Wenyu Liu; Xinggang Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent studies have explored leveraging the world knowledge and cognitive capabilities of Vision-Language Models (VLMs) to address the long-tail problem in end-to-end autonomous driving. However, existing methods typically formulate trajectory planning as a language modeling task, where physical actions are output in the language space, potentially leading to issues such as format-violating outputs, infeasible actions, and slow inference speeds. In this paper, we propose ReCogDrive, a novel **Re**inforced **Cog**nitive framework for end-to-end autonomous **Driv**ing, unifying driving understanding and planning by integrating an autoregressive model with a diffusion planner. First, to instill human driving cognition into the VLM, we introduce a hierarchical data pipeline that mimics the sequential cognitive process of human drivers through three stages: generation, refinement, and quality control. Building on this cognitive foundation, we then address the language-action mismatch by injecting the VLM's learned driving priors into a diffusion planner to efficiently generate continuous and stable trajectories. Furthermore, to enhance driving safety and reduce collisions, we introduce a Diffusion Group Relative Policy Optimization (DiffGRPO) stage, reinforcing the planner for enhanced safety and comfort. Extensive experiments on the NAVSIM and Bench2Drive benchmarks demonstrate that ReCogDrive achieves state-of-the-art performance. Additionally, qualitative results across diverse driving scenarios and DriveBench highlight the model's scene comprehension.
DanceTogether: Generating Interactive Multi-Person Video without Identity Drifting
Authors: Junhao Chen; Mingjin Chen; Jianjin Xu; Xiang Li; Junting Dong; Mingze Sun; Puhua Jiang; Hongxiang Li; Yuhang Yang; Hao Zhao; Xiao-Xiao Long; Ruqi Huang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Controllable video generation (CVG) has advanced rapidly, yet current systems falter when more than one actor must move, interact, and exchange positions under noisy control signals. We address this gap with DanceTogether, the first end-to-end diffusion framework that turns a single reference image plus independent pose-mask streams into long, photorealistic videos while strictly preserving every identity. A novel MaskPoseAdapter binds “who” and “how” at every denoising step by fusing robust tracking masks with semantically rich but noisy pose heat maps, eliminating the identity drift and appearance bleeding that plague frame-wise pipelines. To train and evaluate at scale, we introduce (i) PairFS-4K, 26 h of dual-skater footage with more than 7 000 distinct IDs, (ii) HumanRob-300, a one-hour humanoid-robot interaction set for rapid cross-domain transfer, and (iii) TogetherVideoBench, a three-track benchmark centred on the DanceTogEval-100 test suite covering dance, boxing, wrestling, yoga, and figure skating. On TogetherVideoBench, DanceTogether outperforms the prior arts by a significant margin. Moreover, we show that a one-hour fine-tune yields convincing human-robot videos, underscoring broad generalisation to embodied-AI and HRI tasks. Extensive ablations confirm that persistent identity-action binding is critical to these gains. Together, our model, datasets, and benchmark lift CVG from single-subject choreography to compositionally controllable, multi-actor interaction, opening new avenues for digital production, simulation, and embodied intelligence.
Rethinking Model Calibration through Spectral Entropy Regularization in Medical Image Segmentation
Authors: Kun Cheng; Yukun Zhang; William Henry Nailon; Tonggang Zhao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Deep neural networks for medical image segmentation often produce overconfident predictions, posing clinical risks due to miscalibrated uncertainty estimates. In this work, we rethink model calibration from a frequency-domain perspective and identify two critical factors causing miscalibration: spectral bias, where models overemphasize low-frequency components, and confidence saturation, which suppresses overall power spectral density in confidence maps. To address these challenges, we propose a novel frequency-aware calibration framework integrating spectral entropy regularization and power spectral smoothing. The spectral entropy term promotes a balanced frequency spectrum and enhances overall spectral power,  enabling better modeling of high-frequency boundary and low-frequency structural uncertainty. The smoothing module stabilizes frequency-wise statistics across training batches, reducing sample-specific fluctuations. Extensive experiments on six public medical imaging datasets and multiple segmentation architectures demonstrate that our approach consistently improves calibration metrics without sacrificing segmentation accuracy.