GenAI4ELab/papercli-papers huggingface dataset:
Overthinking Reduction with Decoupled Rewards and Curriculum Data Scheduling
Authors: Shuyang Jiang; Yusheng Liao; Ya Zhang; Yanfeng Wang; Yu Wang
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: While large reasoning models trained with critic-free reinforcement learning and verifiable rewards (RLVR) represent the state-of-the-art, their practical utility is hampered by ``overthinking'', a critical issue where models generate excessively long reasoning paths without any performance benefit. Existing solutions that penalize length often fail, inducing performance degradation due to a fundamental misalignment between trajectory-level rewards and token-level optimization. In this work, we introduce a novel framework, DECS, built on our theoretical discovery of two previously unaddressed flaws in current length rewards: (1) the erroneous penalization of essential exploratory tokens and (2) the inadvertent rewarding of partial redundancy. Our framework's innovations include (i) a first-of-its-kind decoupled token-level reward mechanism that surgically distinguishes and penalizes redundant tokens, and (ii) a novel curriculum batch scheduling strategy to master the efficiency-efficacy equilibrium. Experimental results show DECS can achieve a dramatic reduction in reasoning tokens by over 50\% across seven benchmarks while simultaneously maintaining or even improving performance. It demonstrates conclusively that substantial gains in reasoning efficiency can be achieved without compromising a model's underlying reasoning power. Code is available at \url{https://github.com/pixas/DECS}.
Temporal Concept Dynamics in Diffusion Models via Prompt-Conditioned Interventions
Authors: Ada Görgün; Fawaz Sammani; Nikos Deligiannis; Bernt Schiele; Jonas Fischer
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Diffusion models are usually evaluated by their final outputs, gradually denoising random noise into meaningful images. 
Yet, generation unfolds along a trajectory, and understanding this dynamic process is crucial for explaining how controllable, reliable, and predictable these models are in terms of their success/failure modes. In this work, we ask the question: *when* does noise turn into a specific concept (e.g., age) and lock in the denoising trajectory? We propose PCI Prompt-Conditioned Intervention) to study this question. PCI is a training-free and model-agnostic framework for analyzing concept dynamics through diffusion time. The central idea is the analysis of *Concept Insertion Success* (CIS), defined as the probability that a concept inserted at a given timestep is preserved and reflected in the final image, offering a way to characterize the temporal dynamics of concept formation. Applied to several state-of-the-art text-to-image diffusion models and a broad taxonomy of concepts, PCI reveals diverse temporal behaviors across diffusion models, in which certain phases of the trajectory are more favorable to specific concepts even within the same concept type. These findings also provide actionable
insights for text-driven image editing, highlighting *when* interventions are most effective without requiring access to model internals or training, and yielding quantitatively stronger edits that achieve a balance of semantic accuracy and content preservation than strong baselines.
Harder Is Better: Boosting Mathematical Reasoning via Difficulty-Aware GRPO and Multi-Aspect Question Reformulation
Authors: Yanqi Dai; Yuxiang Ji; Xiao Zhang; Yong Wang; Xiangxiang Chu; Zhiwu Lu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) offers a robust mechanism for enhancing mathematical reasoning in large models.
However, we identify a systematic lack of emphasis on more challenging questions in existing methods from both algorithmic and data perspectives, despite their importance for refining underdeveloped capabilities.
Algorithmically, widely used Group Relative Policy Optimization (GRPO) suffers from an implicit imbalance where the magnitude of policy updates is lower for harder questions.
Data-wise, augmentation approaches primarily rephrase questions to enhance diversity without systematically increasing intrinsic difficulty.
To address these issues, we propose a two-dual MathForge framework to improve mathematical reasoning by targeting harder questions from both perspectives, which comprises a Difficulty-Aware Group Policy Optimization (DGPO) algorithm and a Multi-Aspect Question Reformulation (MQR) strategy.
Specifically, DGPO first rectifies the implicit imbalance in GRPO via difficulty-balanced group advantage estimation, and further prioritizes harder questions by difficulty-aware question-level weighting.
Meanwhile, MQR reformulates questions across multiple aspects to increase difficulty while maintaining the original gold answer.
Overall, MathForge forms a synergistic loop: MQR expands the data frontier, and DGPO effectively learns from the augmented data.
Extensive experiments show that MathForge significantly outperforms existing methods on various mathematical reasoning tasks.
The code and augmented data are all available at https://github.com/AMAP-ML/MathForge.
On the Generalization Capacities of MLLMs for Spatial Intelligence
Authors: Gongjie Zhang; Wenhao Li; Quanhao Qian; Jiuniu Wang; Deli Zhao; Shijian Lu; Ran Xu
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Multimodal Large Language Models (MLLMs) that directly process RGB inputs for tasks like 3D localization and navigation have shown remarkable potential. However, we argue that these ``RGB-only'' approaches are fundamentally flawed in their ability to generalize across cameras. By ignoring camera parameters, they entangle an object's physical properties with the camera's perspective, creating an irresolvable ambiguity. We show this leads MLLMs to overfit to the training camera distribution, rather than learning true and generalizable 3D geometric principles. To address this, we propose Camera-Aware MLLM framework for spatial MLLMs. It learns generalizable spatial reasoning by: (i) injecting camera intrinsics via a dense embedding that conditions each visual token; (ii) introducing a camera-aware data augmentation strategy that synthetically varies camera parameters, forcing the model to disentangle camera properties from scene content; and (iii) distilling geometric priors from a 3D vision foundation model. Extensive experiments demonstrate that camera-aware MLLMs substantially outperform their naive counterparts, particularly in cross-camera generalization tests on spatially-grounded tasks, indicating that camera-awareness is not only beneficial but also a prerequisite for robust and generalizable spatial intelligence in MLLMs.
Vivid-VR: Distilling Concepts from Text-to-Video Diffusion Transformer for Photorealistic Video Restoration
Authors: Haoran Bai; Xiaoxu Chen; Canqian Yang; Zongyao He; Sibin Deng; Ying Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We present Vivid-VR, a DiT-based generative video restoration method built upon an advanced T2V foundation model, where ControlNet is leveraged to control the generation process, ensuring content consistency. However, conventional fine-tuning of such controllable pipelines frequently suffers from distribution drift due to limitations in imperfect multimodal alignment, resulting in compromised texture realism and temporal coherence. To tackle this challenge, we propose a concept distillation training strategy that utilizes the pretrained T2V model to synthesize training samples with embedded textual concepts, thereby distilling its conceptual understanding to preserve texture and temporal quality. To enhance generation controllability, we redesign the control architecture with two key components: 1) a control feature projector that filters degradation artifacts from input video latents to minimize their propagation through the generation pipeline, and 2) a new ControlNet connector employing a dual-branch design. This connector synergistically combines MLP-based feature mapping with cross-attention mechanism for dynamic control feature retrieval, enabling both content preservation and adaptive control signal modulation. Extensive experiments show that Vivid-VR performs favorably against existing approaches on both synthetic and real-world benchmarks, as well as AIGC videos, achieving impressive texture realism, visual vividness, and temporal consistency. The codes and checkpoints are publicly available at https://github.com/csbhr/Vivid-VR.
Reading Images Like Texts: Sequential Image Understanding in Vision-Language Models
Authors: Yueyan Li; Chenggong Zhao; Zeyuan Zang; Caixia Yuan; Xiaojie Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Vision-Language Models (VLMs) have demonstrated remarkable performance across a variety of real-world tasks. However, existing VLMs typically process visual information by serializing images, a method that diverges significantly from the parallel nature of human vision. Moreover, their opaque internal mechanisms hinder both deeper understanding and architectural innovation. Inspired by the dual-stream hypothesis of human vision, which distinguishes the "what" and "where" pathways, we deconstruct the visual processing in VLMs into object recognition and spatial perception for separate study. For object recognition, we convert images into text token maps and find that the model's perception of image content unfolds as a two-stage process from shallow to deep layers, beginning with attribute recognition and culminating in semantic disambiguation. For spatial perception, we theoretically derive and empirically verify the geometric structure underlying the positional representation in VLMs. Based on these findings, we introduce an instruction-agnostic token compression algorithm based on a plug-and-play visual decoder to improve decoding efficiency, and a RoPE scaling technique to enhance spatial reasoning. Through rigorous experiments, our work validates these analyses, offering a deeper understanding of VLM internals and providing clear principles for designing more capable future architectures.
Photon: Speedup Volume Understanding with Efficient Multimodal Large Language Models
Authors: Chengyu Fang; Heng Guo; Zheng Jiang; Chunming He; Xiu Li; Minfeng Xu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multimodal large language models are promising for clinical visual question answering tasks, but scaling to 3D imaging is hindered by high computational costs. Prior methods often rely on 2D slices or fixed-length token compression, disrupting volumetric continuity and obscuring subtle findings. We present Photon, a framework that represents 3D medical volumes with token sequences of variable length.  Photon introduces instruction-conditioned token scheduling and surrogate gradient propagation to adaptively reduce tokens during both training and inference, which lowers computational cost while mitigating the attention dilution caused by redundant tokens. It incorporates a custom backpropagation rule with gradient restoration to enable differentiable optimization despite discrete token drop. To stabilize token compression and ensure reliable use of visual evidence, Photon further applies regularization objectives that mitigate language-only bias and improve reliability. Experiments on diverse medical visual question answering tasks show that Photon achieves state-of-the-art accuracy while reducing resource usage and accelerating both training and inference.
PACE: Pretrained Audio Continual Learning
Authors: Chang Li; Kanglei Zhou; Liyuan Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Audio is a fundamental modality for analyzing speech, music, and environmental sounds. While pretrained audio models have significantly advanced audio understanding, they remain fragile in real-world scenarios where data distributions evolve over time. In this work, we present the first systematic benchmark for audio continual learning (CL) with pretrained models (PTMs) and provide a comprehensive analysis of its unique challenges. Unlike in the vision domain where parameter-efficient fine-tuning (PEFT) has proven effective for CL, directly applying such strategies to audio leads to poor performance. This is due to a fundamental property of audio backbones: they emphasize low-level spectral details rather than structured semantics, resulting in severe upstream–downstream misalignment. Through extensive empirical analysis, we identify a promising technical route based on analytic classifiers with first-session adaptation (FSA), but also uncover two major limitations: representation saturation in coarse-grained scenarios and representation shifts in fine-grained scenarios. To address these challenges, we propose **PACE**, an innovative method that improves FSA via a regularized analytic classifier and introduces multi-session adaptation through adaptive subspace-orthogonal PEFT for better semantic alignment. Additionally, we design spectrogram-based boundary-aware perturbations to mitigate representation overlap and improve stability. Experiments across six diverse audio CL benchmarks demonstrate that PACE substantially outperforms state-of-the-art baselines, representing a significant step toward robust and scalable audio CL with PTMs.
JailbreakLoRA: Your Downloaded LoRA from Sharing Platforms might be Unsafe
Authors: Fanjunduo Wei; Zhenheng Tang; Rongfei Zeng; Tongliang Liu; Chengqi Zhang; Xiaowen Chu; Bo Han
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Low-Rank Adaptation (LoRA) benefits from its plug-and-play nature, enabling large language models (LLMs) to achieve significant performance gains at low cost, has driven the development of LoRA-sharing platforms. However, the jailbreak and backdoor concerns associated with LoRA-sharing platforms remain underexplored. Existing LoRA-based attacks primarily focus on achieving high attack success rates, while neglecting the core reason why LoRA is adopted by user, i.e. to gain downstream task capabilities. However, achieving effective attacks while preserving strong multi-task performance remains challenging, as the largely unrelated objectives tend to interfere with each other during optimization. In this paper, we propose JailbreakLoRA, a multi-task jailbreak LoRA training method that balances task utility and attack capability, it resolves training interference by uncertainty-weighting losses and mitigating gradient conflicts. Additionally, JailbreakLoRA is designed to generate an affirmative prefix upon trigger activation, exploiting inference-time hallucinations to enhance the effectiveness of jailbreak. Experimental results demonstrate that our method outperforms SOTA LoRA-based attacks, achieving a 16.0\% improvement in attack success rate while also enhancing performance on multi-downstream tasks by 16.5\% in average. Our code is available at https://github.com/tmlr-group/JailbreakLoRA.
UniUGG: Unified 3D Understanding and Generation via Geometric-Semantic Encoding
Authors: Yueming Xu; Jiahui Zhang; Ze Huang; Yurui Chen; Yanpeng Zhou; Zhenyu Chen; Yu-Jie Yuan; Pengxiang Xia; Guowei Huang; Xinyue Cai; Zhongang Qi; Xingyue Quan; Jianye HAO; Hang Xu; Li Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Despite the impressive progress on understanding and generating images shown by the recent unified architectures, the integration of 3D tasks remains challenging and largely unexplored. In this paper, we introduce UniUGG, the first unified understanding and generation framework for 3D modalities. Our unified framework employs an LLM to comprehend and decode sentences and 3D representations. At its core, we propose a spatial decoder leveraging a latent diffusion model to generate high-quality 3D representations. This allows for the generation and imagination of 3D scenes based on a reference image and an arbitrary view transformation, while remaining supports for spatial visual question answering (VQA) tasks. Additionally, we propose a geometric-semantic learning strategy to pretrain the vision encoder. This design jointly captures the input's semantic and geometric cues, enhancing both spatial understanding and generation. Extensive experimental results demonstrate the superiority of our method in visual representation, spatial understanding, and 3D generation.
WavePolyp: Video Polyp Segmentation via Hierarchical Wavelet-Based Feature Aggregation and Inter-Frame Divergence Perception
Authors: Yuhua Zhang; Guilian Chen; Yuanqin He; Huisi Wu; Jing Qin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Automatic polyp segmentation from colonoscopy videos is a crucial technique that assists clinicians in improving the accuracy and efficiency of diagnosis, preventing polyps from developing into cancer.
However, video polyp segmentation (VPS) is a challenging task due to (1) the significant inter-frame divergence in videos, (2) the high camouflage of polyps in normal colon structures and (3) the clinical requirement of real-time performance.
In this paper, we propose a novel segmentation network, WavePolyp, which consists of two innovative components: a hierarchical wavelet-based feature aggregation (HWFA) module and inter-frame divergence perception (IDP) blocks.
Specifically, HWFA excavates and amplifies discriminative information from high-frequency and low-frequency features decomposed by wavelet transform, hierarchically aggregating them into refined spatial representations within each frame.
This module enhances the representation capability of intra-frame spatial features, effectively addressing the high camouflage of polyps in normal colon structures.
Furthermore, IDP perceives and captures inter-frame polyp divergence through a temporal divergence perception mechanism, enabling accurate polyp tracking while mitigating temporal inconsistencies caused by the significant inter-frame variations across frames.
Extensive experiments conducted on the SUN-SEG and CVC-612 datasets demonstrate that our method outperforms other state-of-the-art methods.
Codes are available at \url{https://github.com/FishballZhang/WavePolyp.
Consistent Noisy Latent Rewards for Trajectory Preference Optimization in Diffusion Models
Authors: Xiaole Xian; Xilin He; Wenting Chen; Wenshuang Liu; wenqi mu; Yancheng He; Liang Li; Yi Zhang; Xiangyu Yue
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in diffusion models for visual generation have sparked interest in human preference alignment, similar to developments in Large Language Models. While reward model (RM) based approaches enable trajectory-aware optimization by evaluating intermediate timesteps, they face two critical challenges: unreliable reward estimation on noisy latents due to pixel-level models' sensitivity to noise interference, and single-timestep preference evaluation across sampling trajectories where single-timestep evaluations can yield inconsistent preference rankings depending on the selected timestep.
To address these limitations, we propose a comprehensive framework with targeted solutions for each challenge. To achieve noise compatibility for reliable reward estimation, we introduce the Score-based Latent Reward Model (SLRM), which leverages the complete diffusion model as a preference discriminator with learnable task tokens and a score enhancement mechanism that explicitly preserves noise compatibility by augmenting preference logits with the denoising score function. To ensure consistent preference evaluation across trajectories, we develop Trajectory Advantages Preference Optimization (TAPO), which strategically performs Stochastic Differential Equations sampling and reward evaluation at multiple timesteps to dynamically capture trajectory advantages while identifying preference inconsistencies and preventing erroneous trajectory selection.
Extensive experiments on Text-to-Image and Text-to-Video generation tasks demonstrate significant improvements on noisy latent evaluation and alignment performance.
Uncertainty Estimation via Hyperspherical Confidence Mapping
Authors: Eunseo Choi; Ho-Yeon Kim; Jaewon Lee; Taeyong jo; Myungjun lee; Heejin Ahn
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Quantifying uncertainty in neural network predictions is essential for deploying models in high-stakes domains such as autonomous driving, healthcare, and manufacturing.  While conventional approaches often depend on costly sampling or parametric distributional assumptions, we propose Hyperspherical Confidence Mapping (HCM), a simple yet principled framework for uncertainty estimation that is both sampling-free and distribution-free. HCM decomposes model outputs into a magnitude and a normalized direction vector constrained to lie on a unit hypersphere, enabling a novel interpretation of uncertainty as the degree of violation of a geometric constraint.  Grounded in this geometric constraint formulation, our method provides deterministic and interpretable uncertainty estimates applicable to both regression and classification. We validate the effectiveness of HCM across diverse benchmarks and real-world industrial tasks, demonstrating competitive or superior performance to ensemble and evidential approaches, while significantly reducing inference cost and ensuring strong confidence–error alignment. Our results highlight the value of geometric structure in uncertainty estimation and position HCM as a versatile alternative to conventional techniques.
Geometric Image Editing via Effects-Sensitive In-Context Inpainting with Diffusion Transformers
Authors: Shuo Zhang; Wenzhuo Wu; Huayu Zhang; Jiarong Cheng; Xianghao Zang; Chao Ban; Hao Sun; Zhongjiang He; Tianwei Cao; Kongming Liang; Zhanyu Ma
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in diffusion models have significantly improved image editing. However, challenges persist in handling geometric transformations, such as translation, rotation, and scaling, particularly in complex scenes. Existing approaches suffer from two main limitations: (1) difficulty in achieving accurate geometric editing of object translation, rotation, and scaling; (2) inadequate modeling of intricate lighting and shadow effects, leading to unrealistic results. To address these issues, we propose GeoEdit, a framework that leverages in-context generation through a diffusion transformer module, which integrates geometric transformations for precise object edits. Moreover, we introduce Effects-Sensitive Attention, which enhances the modeling of intricate lighting and shadow effects for improved realism. To further support training, we construct RS-Objects, a large-scale geometric editing dataset containing over 120,000 high-quality image pairs, enabling the model to learn precise geometric editing while generating realistic lighting and shadows. Extensive experiments on public benchmarks demonstrate that GeoEdit consistently outperforms state-of-the-art methods in terms of visual quality, geometric accuracy, and realism.
Omni-Weather: A Unified Multimodal Model for Weather Radar Understanding and Generation
Authors: Zhiwang Zhou; Yuandong Pu; Xuming He; Yidi Liu; Yixin Chen; Junchao Gong; Xiang Zhuang; Wanghan Xu; Qinglong Cao; SHIXIANG TANG; Yihao Liu; Wenlong Zhang; LEI BAI
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Weather modeling requires both accurate prediction and mechanistic interpretation, yet existing methods treat these goals in isolation, separating generation from understanding. To address this gap, we present Omni-Weather, the first multimodal foundation model that unifies weather generation and understanding within a single architecture. 
Omni-Weather integrates a radar encoder for weather generation tasks, followed by unified processing using a shared self-attention mechanism.
Moreover, we construct a Chain-of-Thought dataset for causal reasoning in weather generation, enabling interpretable outputs and improved perceptual quality.
Extensive experiments show Omni-Weather achieves state-of-the-art performance in both weather generation and understanding.
Our findings further indicate that generative and understanding tasks in the weather domain can mutually enhance each other.
Omni-Weather also demonstrates the feasibility and value of unifying weather generation and understanding.