GenAI4ELab/papercli-papers huggingface dataset:
AttTok: Marrying Attribute Tokens with Generative Pre-trained Vision-Language Models towards Medical Image Understanding
Authors: Hualiang Wang; Xinyue Xu; Lehan Wang; Bin Pu; Xiaomeng Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent generative pre-trained vision–language (GPTv) models have achieved remarkable success in multi-modal understanding, inspiring their adaptation to medical imaging tasks such as disease diagnosis and visual question answering (VQA). However, current instruction-tuned GPTv models suffer from two key challenges: (1) medical attributes (e.g., disease names, severity grades) are encoded as plain text tokens, collapsing semantically distinct concepts into nearly identical textual sequences; and (2) inadequate textual supervision weakens visual representation learning, leading to severe inter-attribute confusion and misaligned vision–language embeddings. To address these limitations, we introduce attribute tokens (AttTok), a set of pre‑defined special tokens that uniquely encode clinical attributes (e.g., imaging modality, diagnosis, severity) within a structured token space. Complemented by attribute‑centric embedding books, AttTok serves as anchor points for aligning both visual and textual modalities into a shared, discriminative representation space. Building on this foundation, we design two key components: an attribute‑centric cross attention (ACC) adapter, which breaks the vision‑to‑text information‑flow bottleneck and enriches the visual encoder with discriminative attribute knowledge, and an attribute‑centric matching (ACM) loss, which enforces robust multi‑modal alignment centered on the attribute tokens. Extensive experiments on five medical classification benchmarks and three VQA datasets demonstrate that AttTok substantially improves both discriminative accuracy and medical knowledge reasoning, establishing a new paradigm for medical GPTv models with clinically discriminative understanding.
RD-HRL: Generating Reliable Sub-Goals for Long-Horizon Sparse-Reward Tasks
Authors: Yixiang Shan; Haipeng Liu; Ting Long; Yi Chang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Long-horizon sparse-reward tasks, such as goal-conditioned or robot manipulation tasks, remain challenging in offline reinforcement learning due to the credit assignment problem. Hierarchical methods have been proposed to tackle this problem by introducing sub-goal planning guided by value functions, which in principle can shorten the effective planning horizon for both high-level and low-level planners, and thereby avoiding  the credit assignment problem. However, we demonstrate that the sub-goal selection mechanism is unreliable, as it relies on value functions suffering from generalization noise, which misguides value estimation and thus leads to sub-optimal sub-goals. In this work, to provide more reliable sub-goals, we novelly introduce a reliability-driven decision mechanism, and propose Reliability-Driven HRL (RD-HRL) as the solution. The reliability-driven decision mechanism provide decision-level targets for high-level policy, thereby providing noise-immune decision spaces for them, ensuring the reliability of sub-goals (which are termed as action-level targets in this paper). Comprehensive experimental results demonstrate that our approach RD-HRL outperforms baseline methods across multiple benchmarks, highlighting the competitive advantages of RD-HRL. Our code is anonymously available at \url{https://github.com/Looomo/RD-HRL-public}.
GEPO: Group Expectation Policy Optimization for Stable Heterogeneous Reinforcement Learning
Authors: Han Zhang; RuibinZheng; ZEXUAN YI; Zhuo Zhang; Hanyang Peng; Hui Wang; Jiayin Qi; Binxing Fang; Ruifeng Xu; Yue Yu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: As single-center computing approaches power constraints, decentralized training becomes essential. However, traditional Reinforcement Learning (RL) methods, crucial for enhancing large model post-training, cannot adapt to decentralized distributed training due to the tight coupling between parameter learning and rollout sampling. For this, we propose HeteroRL, a heterogeneous RL architecture that decouples these processes, enabling stable training across geographically distributed nodes connected via the Internet. The core component is Group Expectation Policy Optimization (GEPO), an asynchronous RL algorithm robust to latency caused by network delays or heterogeneity in computational resources. Our study reveals that high latency significantly increases KL divergence, leading to higher variance of importance weights and training instability. GEPO mitigates this issue by using group expectation weighting to exponentially reduce the variance of importance weights, with theoretical guarantees.  Experiments show GEPO achieves superior stability—only a 3\% performance drop from online to 1800s latency—and reduces the best-to-last gap by 85\% versus GSPO ($\Delta$=1.8 vs. 12.0) while attaining the highest scores, highlighting its effectiveness in decentralized, resource-heterogeneous environments.
Aligning Collaborative View Recovery and Tensorial Subspace Learning via Latent Representation for Incomplete Multi-View Clustering
Authors: Youqing Wang; Yu Cao; Jinlu Wang; Xiang Xu; Jiapu Wang; Tengfei Liu; Junbin Gao; Jipeng Guo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multi-view data usually suffer from partially missing views in open scenarios, which inevitably degrades clustering performance. The incomplete multi-view clustering (IMVC) has attracted increasing attention and achieved significant success. Although existing imputation-based IMVC methods perform well, they still face one crucial limitation, i.e., view recovery and subspace representation lack explicit alignment and collaborative interaction in exploring complementarity and consistency across multiple views. To this end, this study proposes a novel IMVC method to Align collaborative view Recovery and tensorial Subspace Learning via latent representation (ARSL-IMVC). Specifically, the ARSL-IMVC infers the complete view from view-shared latent representation and view-specific estimator with Hilbert-Schmidt Independence Criterion regularizer, reshaping the consistent and diverse information intrinsically embedded in original multi-view data. Then, the ARSL-IMVC learns the view-shared and view-specific subspace representations from latent feature and recovered views, and models high-order correlations at the global and local levels in the unified low-rank tensor space. Thus, leveraging the latent representation as a bridge in a unified framework, the ARSL-IMVC seamlessly aligns the complementarity and consistency exploration across view recovery and subspace representation learning, negotiating with each other to promote clustering. Extensive experimental results on seven datasets demonstrate the powerful capacity of ARSL-IMVC in complex incomplete multi-view clustering tasks under various view missing scenarios. The source code is publicly available at https://github.com/caoyu110/ARSL-IMVC.
Traceable Evidence Enhanced Visual Grounded Reasoning: Evaluation and Method
Authors: Haochen Wang; Xiangtai Li; Zilong Huang; Anran Wang; Jiacong Wang; Tao Zhang; Jiani zheng; Sule Bai; Zijian Kang; Jiashi Feng; Wang Zhuochen; Zhaoxiang Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Models like OpenAI-o3 pioneer visual grounded reasoning by dynamically ref-
erencing visual regions, just like human “thinking with images”. However, no
benchmark exists to evaluate these capabilities holistically. To bridge this gap, we
propose TreeBench (Traceable Evidence Evaluation Benchmark), a diagnostic
benchmark built on three principles: (1) focused visual perception of subtle targets
in complex scenes, (2) traceable evidence via bounding box evaluation, and (3)
second-order reasoning to test object interactions and spatial hierarchies beyond
simple object localization. Prioritizing images with dense objects, we initially
sample 1K high-quality images from SA-1B, and incorporate eight LMM experts
to manually annotate questions, candidate options, and answers for each image.
After three stages of quality control, TreeBench consists of 405 challenging vi-
sual question-answering pairs, even the most advanced models struggle with this
benchmark, where none of them reach 60% accuracy, e.g., OpenAI-o3 scores only
54.87. Furthermore, we introduce TreeVGR (Traceable Evidence Enhanced Visual
Grounded Reasoning), a training paradigm to supervise localization and reasoning
jointly with reinforcement learning, enabling accurate localizations and explainable
reasoning pathways. Initialized from Qwen2.5-VL-7B, it improves V* Bench
(+16.8), MME-RealWorld (+12.6), and TreeBench (+13.4), proving traceability is
key to advancing vision-grounded reasoning. The code and data will be released.
LinearSR: Unlocking Linear Attention for Stable and Efficient Image Super-Resolution
Authors: Xiaohui Li; Shaobin Zhuang; Shuo Cao; Yang Yang; Yuandong Pu; Qi Qin; Siqi Luo; Bin Fu; Yihao Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Generative models for Image Super-Resolution (SR) are increasingly powerful, yet their reliance on self-attention's quadratic complexity ($O(N^2)$) creates a major computational bottleneck. Linear Attention offers an $O(N)$ solution, but its promise for photorealistic SR has remained largely untapped, historically hindered by a cascade of interrelated and previously unsolved challenges. This paper introduces LinearSR, a holistic framework that, for the first time, systematically overcomes these critical hurdles. Specifically, we resolve a fundamental, training instability that causes catastrophic model divergence using our novel ''knee point''-based Early-Stopping Guided Fine-tuning (ESGF) strategy. Furthermore, we mitigate the classic perception-distortion trade-off with a dedicated SNR-based Mixture of Experts (MoE) architecture. Finally, we establish an effective and lightweight guidance paradigm, TAG, derived from our ''precision-over-volume'' principle. Our resulting LinearSR model simultaneously delivers state-of-the-art perceptual quality with exceptional efficiency. Its core diffusion forward pass (1-NFE) achieves SOTA-level speed, while its overall multi-step inference time remains highly competitive. This work provides the first robust methodology for applying Linear Attention in the photorealistic SR domain, establishing a foundational paradigm for future research in efficient generative super-resolution.
DETR-ViP: Detection Transformer with Robust Discriminative Visual Prompts
Authors: Bo Qian; Dahu Shi; Xing Wei
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Visual prompted object detection enables interactive and flexible definition of target categories, thereby facilitating open-vocabulary detection. Since visual prompts are derived directly from image features, they often outperform text prompts in recognizing rare categories. Nevertheless, research on visual prompted detection has been largely overlooked, and it is typically treated as a byproduct of training text prompted detectors, which hinders its development. To fully unlock the potential of visual-prompted detection, we investigate the reasons why its performance is suboptimal and reveal that the underlying issue lies in the absence of global discriminability in visual prompts. Motivated by these observations, we propose DETR-ViP, a robust object detection framework that yields class-distinguishable visual prompts. On top of basic image-text contrastive learning, DETR-ViP incorporates global prompt integration and visual-textual prompt relation distillation to learn more discriminative prompt representations. In addition, DETR-ViP employs a selective fusion strategy that ensures stable and robust detection. Extensive experiments on COCO, LVIS, ODinW, and Roboflow100 demonstrate that DETR-ViP achieves substantially higher performance in visual prompt detection compared to other state-of-the-art counterparts. A series of ablation studies and analyses further validate the effectiveness of the proposed improvements and shed light on the underlying reasons for the enhanced detection capability of visual prompts.
VisionTrim: Unified Vision Token Compression for Training-Free MLLM Acceleration
Authors: Hanxun Yu; Wentong Li; Xuan Qu; Song Wang; Junbo Chen; Jianke Zhu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multimodal large language models (MLLMs) suffer from high computational costs due to excessive visual tokens, particularly in high-resolution and video-based scenarios. Existing token reduction methods typically focus on isolated pipeline components and often neglect textual alignment, leading to performance degradation. In this paper, we propose VisionTrim, a unified framework for training-free MLLM acceleration, integrating two effective plug-and-play modules: 1) the Dominant Vision Token Selection (DVTS) module, which preserves essential visual tokens via global-local view, and 2) the Text-Guided Vision Complement (TGVC) module, which facilitates context-aware token merging guided by textual cues. Extensive experiments across diverse image and video multimodal benchmarks demonstrate the performance superiority of our VisionTrim, advancing practical MLLM deployment in real-world applications. The code is available at: https://github.com/hanxunyu/VisionTrim.
CroCoDiLight: Repurposing Cross-View Completion Encoders for Relighting
Authors: Alistair J Foggin; William A P Smith
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Cross-view completion (CroCo) has proven effective as pre-training for geometric downstream tasks such as stereo depth, optical flow, and point cloud prediction. In this paper we show that it also learns photometric understanding due to training pairs with differing illumination. We propose a method to disentangle CroCo latent representations into a single latent vector representing illumination and patch-wise latent vectors representing intrinsic properties of the scene. To do so, we use self-supervised cross-lighting and intrinsic consistency losses on a dataset two orders of magnitude smaller than that used to train CroCo. This comprises pixel-wise aligned, paired images under different illumination. We further show that the lighting latent can be used and manipulated for tasks such as interpolation between lighting conditions, shadow removal, and albedo estimation. This clearly demonstrates the feasibility of using cross-view completion as pre-training for photometric downstream tasks where training data is more limited.
MME-Emotion: A Holistic Evaluation Benchmark for Emotional Intelligence in Multimodal Large Language Models
Authors: Fan Zhang; Zebang Cheng; Chong Deng; Haoxuan Li; Zheng Lian; Qian Chen; Huadai Liu; Wen Wang; YiFan Zhang; Renrui Zhang; Ziyu Guo; Zhihong Zhu; Hao Wu; Haixin Wang; Yefeng Zheng; Xiaojiang Peng; Xian Wu; Kun Wang; Xiangang Li; Jieping Ye; Pheng-Ann Heng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in multimodal large language models (MLLMs) have catalyzed transformative progress in affective computing, enabling models to exhibit emergent emotional intelligence. Despite substantial methodological progress, current emotional benchmarks remain limited, as it is still unknown: (a) the generalization abilities of MLLMs across distinct scenarios, and (b) their reasoning capabilities to identify the triggering factors behind emotional states. To bridge these gaps, we present MME-Emotion, a systematic benchmark that assesses both emotional understanding and reasoning capabilities of MLLMs, enjoying scalable capacity, diverse settings, and unified protocols. As the largest emotional intelligence benchmark for MLLMs, MME-Emotion contains over 6,000 curated video clips with task-specific questioning-answering (QA) pairs, spanning broad scenarios to formulate eight emotional tasks. It further incorporates a holistic evaluation suite with hybrid metrics for emotion recognition and reasoning, analyzed through a multi-agent system framework.
Through a rigorous evaluation of 20 advanced MLLMs, we uncover both their strengths and limitations, yielding several key insights: (1) Current MLLMs exhibit unsatisfactory emotional intelligence, with the best-performing model achieving only $39.3\%$ recognition score and $56.0\%$ Chain-of-Thought (CoT) score on our benchmark. (2) Generalist models (\emph{e.g.}, Gemini-2.5-Pro) derive emotional intelligence from generalized multimodal understanding capabilities, while specialist models (\emph{e.g.}, R1-Omni) can achieve comparable performance through domain-specific post-training adaptation. By introducing MME-Emotion, we hope that it can serve as a foundation for advancing MLLMs' emotional intelligence in the future.
Beyond Distributions: Geometric Action Control for Continuous Reinforcement Learning
Authors: Zhihao Lin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Gaussian policies have dominated continuous control in deep reinforcement learning (RL), yet they suffer from a fundamental mismatch: their unbounded support requires ad-hoc squashing functions that distort the geometry of bounded action spaces.
While von Mises-Fisher (vMF) distributions offer a theoretically grounded alternative on the sphere, their reliance on Bessel functions and rejection sampling hinders practical adoption.
We propose \textbf{Geometric Action Control (GAC)}, a novel action generation paradigm that preserves the geometric benefits of spherical distributions while \textit{simplifying computation}.
GAC decomposes action generation into a direction vector and a learnable concentration parameter, enabling efficient interpolation between deterministic actions and uniform spherical noise.
This design reduces parameter count from \(2d\) to \(d+1\), and avoids the \(O(dk)\) complexity of vMF rejection sampling, achieving simple \(O(d)\) operations.
Empirically, GAC consistently matches or exceeds state-of-the-art methods across six MuJoCo benchmarks, achieving 37.6\% improvement over SAC on Ant-v4 and up to 112\% on complex DMControl tasks, demonstrating strong performance across diverse benchmarks.
Our ablation studies reveal that both \textbf{spherical normalization} and \textbf{adaptive concentration control} are essential to GAC's success.
These findings suggest that robust and efficient continuous control does not require complex distributions, but a principled respect for the geometry of action spaces. Code and pretrained models are available in supplementary materials.
Cannistraci-Hebb Training on Ultra-Sparse Spiking Neural Networks
Authors: Yuan Hua; Jilin Zhang; Yingtao Zhang; Leyi You; Baobo Xiong; Carlo Vittorio Cannistraci; Hong Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Inspired by the brain's spike-based computation, spiking neural networks (SNNs) inherently possess temporal activation sparsity. However, when it comes to the sparse training of SNNs in the structural connection domain, existing methods fail to achieve ultra-sparse network structures without significant performance loss, thereby hindering progress in energy-efficient neuromorphic computing. This limitation presents a critical challenge: how to achieve high levels of structural connection sparsity while maintaining performance comparable to fully connected networks. To address this challenge, we propose the Cannistraci-Hebb Spiking Neural Network (CH-SNN), a novel and generalizable dynamic sparse training framework for SNNs consisting of four stages. First, we propose a sparse spike correlated topological initialization (SSCTI) method to initialize a sparse network based on node correlations. Second, temporal activation sparsity and structural connection sparsity are integrated via a proposed sparse spike weight initialization (SSWI) method. Third, a hybrid link removal score (LRS) is applied to prune redundant weights and inactive neurons, improving information flow. Finally, the CH3-L3 network automaton framework inspired by Cannistraci-Hebb learning theory is incorporated to perform link prediction for potential synaptic regrowth. These mechanisms enable CH-SNN to achieve sparsification across all linear layers. We have conducted extensive experiments on six datasets including CIFAR-10 and CIFAR-100, evaluating various network architectures such as spiking convolutional neural networks and Spikformer. The proposed method achieves a maximum sparsity of 97.75% and outperforms the fully connected (FC) network by 0.16% in accuracy. Furthermore, we apply CH-SNN within an SNN training algorithm deployed on an edge neuromorphic processor. The experimental results demonstrate that, compared to the FC baseline without CH-SNN, the sparse CH-SNN architecture achieves up to 98.84% sparsity, an accuracy improvement of 2.27%, and a 97.5$\times$ reduction in synaptic operations, and the energy consumption is reduced by an average of 55$\times$ across four datasets. Our code is available at https://github.com/HuaGuaiGuai/CH-SNN.
Stop Guessing: Choosing the Optimization-Consistent Uncertainty Measurement for Evidential Deep Learning
Authors: Linye Li; Yufei Chen; Xiaodong Yue; Xujing Zhou; Qunjie Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Evidential Deep Learning (EDL) has emerged as a promising framework for uncertainty estimation in classification tasks by modeling predictive uncertainty with a Dirichlet prior. Despite its empirical success, prior work has primarily focused on the probabilistic properties of the Dirichlet distribution, leaving the role of optimization dynamics during training underexplored.  In this paper, we revisit EDL through the lens of optimization and establish a non-trivial connection: minimizing the expected cross-entropy loss over the Dirichlet prior implicitly encourages solutions akin to multi-class Support Vector Machines, maximizing decision margins. Motivated by this observation, we introduce the \emph{optimization-consistency principle}, which deems an uncertainty measure valid if its value decreases as samples approach the global optimum of the training objective. This principle provides a new criterion for evaluating and designing uncertainty measures that are consistent with the optimization dynamics. Building on this foundation, we further propose a novel measure, \emph{Margin-aware Predictive Uncertainty (MPU)}, which directly captures the separation between target and non-target evidence. Extensive experiments on out-of-distribution detection and classification-with-rejection benchmarks demonstrate the effectiveness of our propositions.