GenAI4ELab/papercli-papers huggingface dataset:
DragFlow: Unleashing DiT Priors with Region-Based Supervision for Drag Editing
Authors: Zihan Zhou; Shilin Lu; Shuli Leng; Shaocong Zhang; Zhuming Lian; Xinlei Yu; Adams Wai-Kin Kong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Drag-based image editing has long suffered from distortions in the target region, largely because the priors of earlier base models, Stable Diffusion, are insufficient to project optimized latents back onto the natural image manifold. With the shift from UNet-based DDPMs to more scalable DiT with flow matching (e.g., SD3.5, FLUX), generative priors have become significantly stronger, enabling advances across diverse editing tasks. However, drag-based editing has yet to benefit from these stronger priors. This work introduces DragFlow, the first framework to effectively harness FLUX’s rich prior via region-based supervision, enabling full use of its finer-grained, spatially precise features for drag-based editing and achieving substantial improvements over existing baselines. We first show that directly applying point-based drag editing to DiTs performs poorly: unlike the highly compressed features of UNets, DiT features are insufficiently structured to provide reliable guidance for point-wise motion supervision. To overcome this limitation, DragFlow introduces a region-based editing paradigm, where affine transformations enable richer and more consistent feature supervision. Additionally, we integrate pretrained open-domain personalization adapters (e.g., IP-Adapter) to enhance subject consistency, while preserving background fidelity through gradient mask-based hard constraints. Multimodal large language models (MLLMs) are further employed to resolve task ambiguities. For evaluation, we curate a novel Region-based Dragging benchmark (ReD Bench) featuring region-level dragging instructions. Extensive experiments on DragBench-DR and ReD Bench show that DragFlow surpasses both point-based and region-based baselines, setting a new state-of-the-art in drag-based image editing. Code and dataset are available at https://github.com/Edennnnnnnnnn/DragFlow.
Bridging Degradation Discrimination and Generation for Universal Image Restoration
Authors: JiaKui Hu; Zhengjian Yao; Lujia Jin; Yanye Lu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Universal image restoration is a critical task in low-level vision, requiring the model to remove various degradations from low-quality images to produce clean images with rich detail. The challenges lie in sampling the distribution of high-quality images and adjusting the outputs on the basis of the degradation. This paper presents a novel approach, Bridging Degradation discrimination and Generation (BDG), which aims to address these challenges concurrently. First, we propose the Multi-Angle and multi-Scale Gray Level Co-occurrence Matrix (MAS-GLCM) and demonstrate its effectiveness in performing fine-grained discrimination of degradation types and levels. Subsequently, we divide the diffusion training process into three distinct stages: generation, bridging, and restoration. The objective is to preserve the diffusion model's capability of restoring rich textures while simultaneously integrating the discriminative information from the MAS-GLCM into the restoration process. This enhances its proficiency in addressing multi-task and multi-degraded scenarios. Without changing the architecture, BDG achieves significant performance gains in all-in-one restoration and real-world super-resolution tasks, primarily evidenced by substantial improvements in fidelity without compromising perceptual quality.
RIG: Synergizing Reasoning and Imagination in End-to-End Generalist Policy
Authors: Zhonghan Zhao; Wenwei Zhang; Haian Huang; Kuikun Liu; Jianfei Gao; Gaoang Wang; Kai Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Reasoning before action and imagining potential outcomes (i.e., world models) are essential for embodied agents operating in complex open-world environments. Yet, prior work either incorporates only one of these abilities in an end-to-end agent or integrates multiple specialized models into an agent system, limiting the learning efficiency and generalization of the policy. 
Thus, this paper makes the first attempt to synergize Reasoning and Imagination in an end-to-end Generalist policy, termed RIG.
To train RIG in an end-to-end manner, we construct a data pipeline that progressively integrates and enriches the content of imagination and reasoning in the trajectories collected from existing agents. The joint learning of reasoning and next image generation explicitly models the inherent correlation between reasoning, action, and dynamics of environments. It thus exhibits more than $17\times$ sample efficiency improvements and generalization in comparison with previous works.
During inference, RIG first reasons about the next action, produces potential action, and then predicts the action outcomes, which offers the agent a chance to review and self-correct based on the imagination before taking real actions.
Experimental results show that the synergy of reasoning and imagination not only improves the robustness, generalization, and interoperability of generalist policy but also enables test-time scaling to enhance overall performance.
VerifyBench: Benchmarking Reference-based Reward Systems for Large Language Models
Authors: Yuchen Yan; Jin Jiang; Zhenbang Ren; Yijun Li; Xudong Cai; Yang Liu; Xin Xu; Mengdi Zhang; Jian Shao; Yongliang Shen; Jun Xiao; Yueting Zhuang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large reasoning models such as OpenAI o1 and DeepSeek-R1 have demonstrated remarkable performance in complex reasoning tasks. A critical component of their training is the incorporation of reference-based reward systems within reinforcement learning (RL), where model outputs are evaluated against ground truth references.  However, existing reward benchmarks focus on preference comparisons between responses rather than evaluating verification against ground truth references, leaving a critical gap in our ability to evaluate verification systems used in reasoning model training. In this paper, we introduce VerifyBench and its challenging variant VerifyBench-Hard, two benchmarks specifically designed to assess reference-based reward systems. These benchmarks are constructed through meticulous data collection and curation, followed by careful human annotation to ensure high quality. Our comprehensive evaluation reveals that while larger model-based verifiers show promise on standard cases, all current systems demonstrate substantial room for improvement on challenging instances. Through systematic analysis of performance patterns across reasoning tasks and error categories, we provide insights for advancing reference-based reward systems. These benchmarks establish a standardized framework for improving verification accuracy, ultimately enhancing reasoning capabilities in models trained via RL.
KBVQ-MoE: KLT-guided SVD with Bias-Corrected Vector Quantization for MoE Large Language Models
Authors: Zukang Xu; Zhixiong Zhao; Xing Hu; Zhixuan Chen; Dawei Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Mixture of Experts (MoE) models have achieved great success by significantly improving performance while maintaining computational efficiency through sparse expert activation. However, their enormous parameter sizes and memory demands pose significant challenges for deployment in resource-constrained environments.
Vector Quantization (VQ) offers a promising approach for ultra-low-bit compression in Large Language Models (LLMs) by constructing and leveraging a codebook—where weight vectors are mapped to the most similar discrete codewords within the codebook. 
However, its direct application to MoEs suffers from significant performance degradation caused by two critical obstacles:  (1) redundant representation among experts leads to VQ repeatedly quantizing similar representations for each expert, resulting in inefficient utilization of the limited codebook capacity; and (2) cumulative outputs bias, amplified by expert aggregation, leads to distributional shifts in the quantized outputs, resulting in degraded model accuracy.
To this end, we propose KBVQ-MoE, a novel VQ framework to enhance extremely low-bit quantization for MoE-based LLMs. 
KBVQ-MoE introduces two lightweight and offline techniques that introduce negligible runtime computational and memory overhead:
(1) Input-driven redundancy elimination, where a Karhunen–Loève Transform (KLT) guided singular value decomposition (SVD) extracts and shares dominant weight components across experts. 
(2) Bias-corrected output stabilization, where vector quantization is applied to expert-specific (i.e., non-redundant) representations and the quantized outputs are corrected with channel-wise affine compensation.
Experiments on various MoE LLMs demonstrate that KBVQ-MoE preserves accuracy substantially better than existing quantization methods. 
For instance, 3-bit quantization of Qwen1.5-MoE-A2.7B achieves an average accuracy of 67.99, nearly identical to the FP16 baseline of 68.07, underscoring the potential of KBVQ-MoE for efficient deployment on edge devices and other resource-constrained platforms.
Error Notebook-Guided, Training-Free Part Retrieval in 3D CAD Assemblies via Vision-Language Models
Authors: Yunqing Liu; Nan Zhang; Zhiming Tan
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Effective specification-aware part retrieval within complex CAD assemblies is essential for automated engineering tasks. However, using LLMs/VLMs for this task is challenging: the CAD model metadata sequences often exceed token budgets, and fine-tuning high-performing proprietary models (e.g., GPT or Gemini) is unavailable. Therefore, we need a framework that delivers engineering value by handling long, non-natural-language CAD model metadata using VLMs, but without training. We propose a 2-stage framework with inference-time adaptation that combines corrected Error Notebooks with RAG to substantially improve VLM-based part retrieval reasoning. Each Error Notebook is built by correcting initial CoTs through reflective refinement, and then filtering each trajectory using our proposed grammar-constraint (GC) verifier to ensure structural well-formedness. The resulting notebook forms a high-quality repository of specification-CoT-answer triplets, from which RAG retrieves specification-relevant exemplars to condition the model's inference. We additionally contribute a CAD dataset with human preference annotations. Experiments with proprietary models (GPT-4o, Gemini, etc) show large gains, with GPT-4o (Omni) achieving up to +23.4 absolute accuracy points on the human-preference benchmark. The proposed GC verifier can further produce up to +4.5 accuracy points. Our approach also surpasses other training-free baselines (standard few-shot learning, self-consistency) and yields substantial improvements also for open-source VLMs (Qwen2-VL-2B-Instruct, Aya-Vision-8B). Under the cross-model GC setting, where the Error Notebook is constructed using GPT-4o (Omni), the 2B model inference achieves performance that comes within roughly 4 points of GPT-4o mini.
Explainable $ K $-means Neural Networks for Multi-view Clustering
Authors: Yalan Qin; Xinpeng Zhang; Guorui Feng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Despite multi-view clustering has achieved great progress in past decades, it is still a challenge to balance the effectiveness, efficiency, completeness and consistency of nonlinearly separable clustering for the data from different views. To address this challenge, we show that multi-view clustering can be regarded as a three-level optimization problem. To be specific, we divide the multi-view clustering into three sub-problems based on $ K $-means or kernel $ K $-means, i.e., linear clustering on the original multi-view dataset, nonlinear clustering on the set of obtained linear clusters and multi-view clustering by integrating partition matrices from different views obtained by linear and nonlinear clustering based on reconstruction. We propose Explainable $ K $-means Neural Networks (EKNN) and present how to unify these three sub-problems into a framework based on EKNN. It is able to simultaneously consider the effectiveness, efficiency, completeness and consistency for the nonlinearly multi-view clustering and can be optimized by an iterative algorithm. EKNN is explainable since the effect of each layer is known. To the best of our knowledge, this is the first attempt to balance the effectiveness, efficiency, completeness and consistency by dividing the multi-view clustering into three different sub-problems. Extensive experimental results demonstrate the effectiveness and efficiency of EKNN compared with other methods for multi-view clustering on different datasets in terms of different metrics.
Online Prediction of Stochastic Sequences with High Probability Regret Bounds
Authors: Matthias Frey; Jonathan H. Manton; Jingge Zhu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We revisit the classical problem of universal prediction of stochastic sequences with a finite time horizon $T$ known to the learner. The question we investigate is whether it is possible to derive vanishing regret bounds that hold with high probability, complementing existing bounds from the literature that hold in expectation. We propose such high-probability bounds which have a very similar form as the prior expectation bounds. For the case of universal prediction of a stochastic process over a countable alphabet, our bound states a convergence rate of $\mathcal{O}(T^{-1/2} \delta^{-1/2})$ with probability as least $1-\delta$ compared to prior known in-expectation bounds of the order $\mathcal{O}(T^{-1/2})$. We also propose an impossibility result which proves that it is not possible to improve the exponent of $\delta$ in a bound of the same form without making additional assumptions.
NC-Bench and NCfold: A Benchmark and Closed-Loop Framework for RNA Non-Canonical Base-Pair Prediction
Authors: Heqin Zhu; Ruifeng Li; Ao Chang; Mingqian Li; Hongyang Chen; Peng Xiong; S Kevin Zhou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: RNA secondary structure forms the basis for folding and function, with non-canonical (NC) interactions indispensable for catalysis, regulation, and molecular recognition.
Despite their importance, predicting NC base pairs remains challenging due to the absence of a standardized benchmark for systematic evaluation.
To address this, we introduce NC-Bench, the first benchmark dedicated to NC base-pair prediction. NC-Bench provides 925 curated RNA sequences with 6,708 high-quality NC annotations, fine-grained edge and orientation classification tasks, and IsoScore-based embedding evaluation, offering a rigorous foundation for systematic assessment.
Building on this, we propose NCfold, a dual-branch framework that couples sequence features with structural priors derived from RNA foundation models (RFMs) via Representative Embedding Fusion (REF) and REF-weighted self-attention.
The closed-loop design iteratively refines sequence and structure representations, alleviating data sparsity and enhancing predictive accuracy. 
Experiments on NC-Bench show that NCfold outperforms existing methods, with zero-shot and ablation studies confirming its effectiveness and underscoring the need for NC-specific benchmarks.
Together, NC-Bench and NCfold establish a systematic foundation for NC base-pair prediction, advancing our understanding of RNA structure and enabling next-generation RNA-centric applications. The datasets and codes are publicly available at https://github.com/heqin-zhu/NCBench.
Semi-Supervised Preference Optimization with Limited Feedback
Authors: Seonggyun Lee; Sungjun Lim; Seojin Park; Soeun Cheon; Kyungwoo Song
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: The field of preference optimization has made outstanding contributions to the alignment of language models with human preferences. Despite these advancements, recent methods still rely heavily on substantial paired (labeled) feedback data, leading to substantial resource expenditures. To address these challenges, we study the problem of Semi-Supervised Preference Optimization in which the idea is to learn from both a small number of pairwise preference labels and a large pool of unpaired samples simultaneously. Our key theoretical contribution proves the existence of an optimal reward threshold capable of separating winning and losing responses with high probability, which enables a principled pseudo-labeling of unpaired data. By leveraging these pseudo-labels, SSPO effectively distills latent preferences from large-scale unpaired data, thus maintaining human alignment while drastically reducing acquisition costs. Extensive experiments across datasets validate this remarkable data efficiency; for instance, SSPO trained with Mistral-7B-Instruct on just 1% of UltraFeedback consistently surpasses strong baselines trained on 10% of UltraFeedback.
AVoCaDO: An Audiovisual Video Captioner Driven by Temporal Orchestration
Authors: Xinlong Chen; Yue Ding; Weihong Lin; Jingyun Hua; Linli Yao; Yang Shi; Bozhou Li; Qiang Liu; Yuanxing Zhang; Pengfei Wan; Liang Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Audiovisual video captioning aims to generate semantically rich descriptions with temporal alignment between visual and auditory events, thereby benefiting both video understanding and generation. In this paper, we present **AVoCaDO**, a powerful audiovisual video captioner driven by the temporal orchestration between audio and visual modalities. We propose a two-stage post-training pipeline: (1) **AVoCaDO SFT**, which fine-tunes the model on a newly curated dataset of 107K high-quality, temporally-aligned audiovisual captions; and (2) **AVoCaDO GRPO**, which leverages tailored reward functions to further enhance temporal coherence and dialogue accuracy while regularizing caption length and reducing collapse. Experimental results demonstrate that AVoCaDO significantly outperforms existing open-source models across four audiovisual video captioning benchmarks, and also achieves competitive performance on the VDC benchmark under visual-only settings. The model will be made publicly available to facilitate future research in audiovisual video understanding and generation.
DVLA-RL: Dual-Level Vision-Language Alignment with Reinforcement Learning Gating for Few-Shot Learning
Authors: Wenhao Li; Xianjing Meng; Qiangchang Wang; Zhongyi Han; Zhibin Wu; Yilong Yin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Few-shot learning (FSL) aims to generalize to novel categories with only a few samples. Recent approaches incorporate large language models (LLMs) to enrich visual representations with semantic embeddings derived from class names. However, they overlook progressive and adaptive alignment between vision and language from low-level to high-level semantics, resulting in limited semantic gains. To address these challenges, we propose Dual-level Vision–Language Alignment with Reinforcement Learning gating (DVLA-RL), which consists of Dual-level Semantic Construction (DSC) and RL-gated Attention (RLA). Specifically, DSC conditions LLMs on both class names and support samples to generate discriminative attributes, progressively selects the most relevant ones, and then synthesizes them into coherent class descriptions. This process provides complementary low-level attributes and high-level descriptions, enabling both fine-grained grounding and holistic class understanding. To dynamically integrate dual-level semantics along with the visual network layers, RLA formulates cross-modal fusion as a sequential decision process. A lightweight policy trained with episodic REINFORCE adaptively adjusts the contributions of self-attention and cross-attention to integrate textual and visual tokens. As a result, shallow layers refine local attributes and deep layers emphasize global semantics, enabling more precise cross-modal alignment. This achieves class-specific discrimination and generalized representations with merely a few support samples. DVLA-RL achieves new state-of-the-art performance across nine benchmarks in three diverse FSL scenarios.
Reinforcement Learning via Value Gradient Flow
Authors: Haoran Xu; Kaiwen Hu; Somayeh Sojoudi; Amy Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We study behavior-regularized reinforcement learning (RL), where regularization toward a reference distribution (the dataset in offline RL or the base model in LLM RL finetuning) is essential to prevent value over-optimization caused by erroneous out-of-distribution extrapolation. Existing methods either rely on reparameterized policy gradient, which are difficult to scale to large generative models, or on reject sampling, which can be overly conservative when attempting to move beyond the behavior support. In this paper, we propose Value Gradient Flow (VGF), a scalable new paradigm for behavior-regularized RL. VGF casts behavior-regularized RL as an optimal transport problem that maps the reference distribution to the value-induced optimal policy distribution. We solve this transport problem via discrete gradient flow, where value gradients guide particles initialized from the reference distribution. Our analysis shows that VGF imposes regularization implicitly by controlling the transport budget. VGF eliminates explicit policy parameterization while remaining expressive and flexible, this enables adaptive test-time scaling by adjusting the transport budget. Extensive experiments demonstrate that VGF significantly outperforms prior methods, achieving state-of-the-art results on offline RL benchmarks (D4RL, OGBench) and LLM RL tasks.
I-DRUID: Layout to image generation via instance-disentangled representation and unpaired data
Authors: Fengxiang Yang; Tianyi Zheng; Bangjie Yin; Shice Liu; Jinwei Chen; Peng-Tao Jiang; Bo Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Layout-to-Image (L2I) generation, aiming at coherently generating multiple instances conditioned on the given layouts and instance captions, has raised substantial attention in the recent research. The primary challenges of L2I stem from 1) attribute leakage due to the entangled instance features within attention and 2) limited generalization to novel scenes caused by insufficient image-text paired data. To address these issues, we propose I-DRUID, a novel framework that leverages instance-disentanglement representations (IDR) and unpaired data (UID) to improve L2I generation. IDR are extracted with our instance disentanglement modules, which utilizes information among instances to obtain semantic-related features while suppressing spurious parts. To facilitate disentangling, we require semantic-related features to trigger more accurate attention maps than spurious ones, formulating the instance-disentangled constraint to avoid attribute leakage. Moreover, to improve L2I generalization, we adapt L2I with unpaired, prompt-only data (UID) to novel scenes via reinforcement learning. Specifically, we enforce L2I model to learn from unpaired, prompt-only data by encouraging / rejecting the rational / implausible generation trajectories based on AI feedback, avoiding the need for paired data collection. Finally, our empirical observations show that IDM and RL cooperate synergistically to further enhance L2I accuracies. Extensive experiments  demonstrate the efficacy of our method.