GenAI4ELab/papercli-papers huggingface dataset:
CoCoDiff: Correspondence-Consistent Diffusion Model for Fine-grained Style Transfer
Authors: Wenbo Nie; Zixiang Li; Renshuai Tao; Bin WU; Yunchao Wei; Yao Zhao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Transferring visual style between images while preserving semantic correspondence between similar objects remains a central challenge in computer vision. While existing methods have made great strides, most of them operate at global level but overlook region-wise and even pixel-wise semantic correspondence. To address this, we propose **CoCoDiff**, a novel *training-free* and *low-cost* style transfer framework that leverages pretrained latent diffusion models to achieve fine-grained, semantically consistent stylization. We identify that correspondence cues within generative diffusion models are under-explored and that content consistency across semantically matched regions is often neglected. CoCoDiff introduces a pixel-wise semantic correspondence module that mines intermediate diffusion features to construct a dense alignment map between content and style images. Furthermore, a cycle-consistency module then enforces structural and perceptual alignment across iterations, yielding object and region level stylization that preserves geometry and detail. Despite requiring no additional training or supervision, CoCoDiff delivers state-of-the-art visual quality and strong quantitative results, outperforming methods that rely on extra training or annotations.
DeAltHDR: Learning HDR Video Reconstruction from Degraded Alternating Exposure Sequences
Authors: Shuohao Zhang; Zhilu Zhang; RongJian Xu; Xiaohe Wu; Wangmeng Zuo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: High dynamic range (HDR) video can be reconstructed from low dynamic range (LDR) sequences with alternating exposures. However, most existing methods overlook the degradations (e.g., noise and blur) in LDR frames, focusing only on the brightness and position differences between them. To address this gap, we propose DeAltHDR, a novel framework for high-quality HDR video reconstruction from degraded sequences. Our framework addresses two key challenges. First, noisy and blurry content complicate inter-frame alignment. To tackle this, we propose a flow-guided masked attention mechanism that leverages optical flow for a dynamic sparse cross-attention computation, achieving superior performance while maintaining efficiency. Notably, its controllable attention ratio allows for adaptive inference costs. Second, the lack of real-world paired data hinders practical deployment. We overcome this with a two-stage training paradigm: the model is first pre-trained on our newly introduced synthetic paired dataset and subsequently fine-tuned on unlabeled real-world videos via a proposed self-supervised method. Experiments show our method outperforms state-of-the-art ones. Code and data will be available at https://zhang-shuohao.github.io/DeAltHDR/.
LH-DECEPTION: Simulating and Understanding LLM Deceptive Behaviors in Long-Horizon Interactions
Authors: Yang Xu; Xuanming Zhang; Samuel Yeh; Jwala Dhamala; Ousmane Dia; Rahul Gupta; Sharon Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Deception is a pervasive feature of human communication and an emerging concern in large language models (LLMs). While recent studies document instances of LLM deception, most evaluations remain confined to single-turn prompts and fail to capture the long-horizon interactions in which deceptive strategies typically unfold. We introduce a new simulation framework, LH-Deception, for a systematic, empirical quantification of deception in LLMs under extended sequences of interdependent tasks and dynamic contextual pressures. LH-Deception is designed as a multi-agent system: a performer agent tasked with completing tasks and a supervisor agent that evaluates progress, provides feedback, and maintains evolving states of trust. An independent deception auditor then reviews full trajectories to identify when and how deception occurs. We conduct extensive experiments across 11 frontier models, spanning both closed-source and open-source systems, and find that deception is model-dependent, increases with event pressure, and consistently erodes supervisor trust. Qualitative analyses further reveal emergent, long-horizon phenomena, such as ``chains of deception", which are invisible to static, single-turn evaluations. Our findings provide a foundation for evaluating future LLMs in real-world, trust-sensitive contexts.
Optimizing ID Consistency in Multimodal Large Models:  Facial Restoration via Alignment, Entanglement, and Disentanglement
Authors: Yuran Dong; Hang Dai; Mang Ye
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multimodal editing large models have demonstrated powerful editing capabilities across diverse tasks. However, a persistent and long-standing limitation is the decline in facial identity (ID) consistency during realistic portrait editing. Due to the human eye’s high sensitivity to facial features, such inconsistency significantly hinders the practical deployment of these models. 
Current facial ID preservation methods struggle to achieve consistent restoration of both facial identity and edited element IP due to Cross-source Distribution Bias and Cross-source Feature Contamination.
To address these issues, we propose EditedID, an Alignment-Disentanglement-Entanglement framework for robust identity-specific facial restoration. By systematically analyzing diffusion trajectories, sampler behaviors, and attention properties, we introduce three key components: 1) Adaptive mixing strategy that aligns cross-source latent representations throughout the diffusion process. 2) Hybrid solver that disentangles source-specific identity attributes and details. 3) Attentional gating mechanism that selectively entangles visual elements. Extensive experiments show that EditedID achieves state-of-the-art performance in preserving original facial ID and edited element IP consistency.
As a training-free and plug-and-play solution, it establishes a new benchmark for practical and reliable single/multi-person facial identity restoration in open-world settings, paving the way for the deployment of multimodal editing large models in real-person editing scenarios.
The code is available at https://github.com/NDYBSNDY/EditedID.
NLI : Non-uniform Linear Interpolation Approximation of Nonlinear Operations for Efficient LLMs Inference
Authors: Jiangyong Yu; Xiaomeng Han; Xing Hu; Chen Xu; Zhe jiang; Dawei Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of tasks, but their deployment is often constrained by substantial memory footprints and computational costs. While prior work has achieved significant progress in compressing and accelerating linear layers, nonlinear layers—such as SiLU, RMSNorm, and Softmax—still heavily depend on high-precision floating-point operations. In this paper, we propose a calibration-free, dynamic-programming-optimal, and hardware-friendly framework called \underline{N}on-uniform \underline{L}inear \underline{I}nterpolation (NLI). NLI is capable of efficiently approximating a variety of nonlinear functions, enabling seamless integration into LLMs and other deep neural networks with almost no loss in accuracy. NLI ingeniously recasts cutpoint selection as a dynamic-programming problem, achieving the \emph{globally} minimal interpolation error in $\mathcal{O}(M \times N^2)$ time via Bellman’s optimality principle. Based on the NLI algorithm, we also design and implement a plug-and-play universal nonlinear computation unit. Hardware experiments demonstrate that the NLI Engine achieves more than 4× improvement in computational efficiency compared to the state-of-the-art designs.
Proximal Supervised Fine-Tuning
Authors: Wenhong Zhu; Ruobing Xie; Rui Wang; Xingwu Sun; Di Wang; Pengfei Liu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Supervised fine-tuning (SFT) of foundation models often leads to poor generalization, where prior capabilities deteriorate after tuning on specific tasks. Inspired by trust-region policy optimization (TRPO) and proximal policy optimization (PPO) in reinforcement learning (RL), we propose Proximal SFT (PSFT), a fine-tuning objective that incorporates the benefits of trust-region, effectively constraining policy drift during SFT while maintaining competitive tuning. By viewing SFT as a special case of policy gradient methods with constant positive advantages, we derive PSFT that stabilizes optimization and leads to generalization, while leaving room for further optimization in subsequent post-training stages. Experiments across mathematical, human-value, and multimodal domains show that PSFT matches standard SFT in-domain, outperforms it in out-of-domain generalization, remains stable under prolonged training without causing entropy collapse, and provides a stronger foundation for the subsequent optimization.
Master Skill Learning with Policy-Grounded Synergy of LLM-based Reward Shaping and Exploring
Authors: Yanbin Chang; Junfan Lin; Jie Jiang; Runhao Zeng; Changxin Huang; Jianqiang Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The acquisition of robotic skills via reinforcement learning (RL) is crucial for advancing embodied intelligence, but designing effective reward functions for complex tasks remains challenging. Recent methods using large language models (LLMs) can generate reward functions from language instructions, but they often produce overly goal-oriented rewards that neglect state exploration, causing robots to get stuck in local optima. Traditional RL addresses this by adding exploration bonuses, but these are typically generic and inefficient, wasting resources on exploring task-irrelevant areas. To address these limitations, we propose Policy-grounded Synergy of Reward Shaping and Exploration (PoRSE), a novel and unified framework that guides LLMs to generate task-aware reward functions while constructing an abstract affordance space for efficient exploration bonuses. Given the vast number of possible reward-bonus combinations, it is impractical to exhaustively train a policy from scratch for each configuration to identify the best one. Instead, PoRSE employs an in-policy-improvement grounding process, dynamically and continuously generating and filtering out reward-bonus pairs along the policy improvement process. This approach accelerates skill acquisition and fosters a mutually reinforcing relationship between reward shaping, exploration and policy enhancement through close feedback. Experiments show that PoRSE is highly effective, achieving significant improvement in average returns across all robotic tasks compared to previous state-of-the-art methods. It also achieves initial success in two highly challenging manipulation tasks, marking a significant breakthrough.
Exploring Mode Connectivity in Krylov Subspace for Domain Generalization
Authors: Aodi Li; Liansheng Zhuang; Xiao Long; Houqiang Li; Shafei Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: This paper explores the geometric characteristics of loss landscapes to enhance domain generalization (DG) in deep neural networks. 
Existing methods mainly leverage the local flatness around minima for improved generalization. However, recent theoretical studies indicate that flatness does not universally guarantee better generalization. Instead, this paper investigates a global geometrical property for domain generalization, i.e., \emph{mode connectivity}, the phenomenon where distinct local minima are connected by continuous low-loss pathways. Different from flatness, mode connectivity enables transitions from poor to superior generalization models without leaving low-loss regions. 
To navigate these connected pathways effectively, this paper proposes a novel Billiard Optimization Algorithm (BOA), which discovers superior models by mimicking billiard dynamics. 
During this process, BOA operates within a low-dimensional Krylov subspace, aiming to alleviate the curse of dimensionality caused by the high-dimensional parameter space of deep models. 
Furthermore, this paper reveals that oracle test gradients strongly align with the Krylov subspace constructed from training gradients across diverse datasets and architectures. 
This alignment offers a powerful tool to bridge training and test domains, enabling the efficient discovery of superior models with limited training domains.
Experiments on DomainBed demonstrate that BOA consistently outperforms existing sharpness-aware and DG methods across diverse datasets and architectures.
Impressively, BOA even surpasses the sharpness-aware minimization by 3.6\% on VLCS when using a ViT-B/16 backbone.
$\alpha$-DPO: Robust Preference Alignment for Diffusion Models via $\alpha$ Divergence
Authors: Yang Li; Songlin Yang; Wei Wang; Xiaoxuan Han; Jing Dong
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Diffusion models have demonstrated remarkable success in high-fidelity image generation, yet aligning them with human preferences remains challenging. Direct Preference Optimization (DPO) offers a promising framework, but its effectiveness is critically hindered by noisy data arising from mislabeled preference pairs and individual preference pairs. We theoretically show that existing DPO objectives are equivalent to minimizing the Forward Kullback–Leibler (KL) divergence, whose mass-covering nature makes it intrinsically sensitive to such noise. To address this limitation, we propose $\alpha$-DPO, which reformulates preference alignment through the lens of $\alpha$-divergence. This formulation promotes mode-seeking behavior and bounds the influence of outliers, thereby enhancing robustness. Furthermore, we introduce a dynamic scheduling mechanism that adaptively adjusts $\alpha$ according to the observed preference distribution, providing data-aware noise tolerance during training. Extensive experiments on synthetic and real-world datasets validate that $\alpha$-DPO consistently outperforms existing baselines, achieving superior robustness and preference alignment.
Online Navigation Refinement: Achieving Lane-Level Guidance by Associating Standard-Definition and Online Perception Maps
Authors: Jiaxu Wan; Xu Wang; Mengwei Xie; Xinyuan Chang; Xinran Liu; Zheng Pan; Mu Xu; Hong Zhang; Ding Yuan; Yifan Yang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Lane-level navigation is critical for geographic information systems and navigation-based tasks, offering finer-grained guidance than road-level navigation by standard definition (SD) maps. However, it currently relies on expansive global HD maps that cannot adapt to dynamic road conditions. Recently, online perception (OP) maps have become research hotspots, providing real-time geometry as an alternative, but lack the global topology needed for navigation. To address these issues, Online Navigation Refinement (ONR), a new mission is introduced that refines SD-map-based road-level routes into accurate lane-level navigation by associating SD maps with OP maps. The map-to-map association to handle many-to-one lane-to-road mappings under two key challenges: (1) no public dataset provides lane-to-road correspondences; (2) severe misalignment from spatial fluctuations, semantic disparities, and OP map noise invalidates traditional map matching. For these challenges, We contribute: (1) Online map association dataset (OMA), the first ONR benchmark with 30K scenarios and 2.6M annotated lane vectors; (2) MAT, a transformer with path-aware attention to aligns topology despite spatial fluctuations and semantic disparities and spatial attention for integrates noisy OP features via global context; and (3) NR P-R, a metric evaluating geometric and semantic alignment. Experiments show that MAT outperforms existing methods at 34 ms latency, enabling low-cost and up-to-date lane-level navigation.
Equilibrium Language Models
Authors: Yikun Jiang; Huanyu Wang; Tianhong Ding; Wenhu Zhang; Yiming Wu; Hanbin Zhao; John C.S. Lui
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) excel across diverse applications but remain impractical for edge deployment due to severe memory bottlenecks at the edge devices. We propose Equilibrium Language Models (ELMs), a novel compression framework that replaces groups of Transformer layers with a lightweight fixed-point network, reinterpreting deep computation as solving for an equilibrium state. To achieve ELMs, We introduce *Group Pruning Policy Optimization*, which automatically learns optimal pruning intervals. Moreover, we propose *One-Step KV-Cache*, which drastically reduces memory overhead by storing only the final iteration cache without compromising the accuracy, to enable effective deployment at the edge devices. Across different tasks such as common sense reasoning, mathematical problem solving, and code generation, ELMs prune 28\% of parameters while retaining 99\% of the accuracy of dense fine-tuned LLMs, establishing a new direction for memory-efficient edge deployment of large models.
SesaHand: Enhancing 3D Hand Reconstruction via Controllable Generation with Semantic and Structural Alignment
Authors: Zhuoran Zhao; Xianghao Kong; Linlin Yang; Zheng WEI; Pan Hui; Anyi Rao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent studies on 3D hand reconstruction have demonstrated the effectiveness of synthetic training data to improve estimation performance. However, most methods rely on game engines to synthesize hand images, which often lack diversity in textures and environments, and fail to include crucial components like arms or interacting objects. Generative models are promising alternatives to generate diverse hand images, but still suffer from misalignment issues. In this paper, we present SesaHand, which enhances controllable hand image generation from both semantic and structural alignment perspectives for 3D hand reconstruction. Specifically, for semantic alignment, we propose a pipeline with Chain-of-Thought inference to extract human behavior semantics from image captions generated by the Vision-Language Model. This semantics suppresses human-irrelevant environmental details and ensures sufficient human-centric contexts for hand image generation. For structural alignment, we introduce hierarchical structural fusion to integrate structural information with different granularity for feature refinement to better align the hand and the overall human body in generated images. We further propose a hand structure attention enhancement method to efficiently enhance the model's attention on hand regions. Experiments demonstrate that our method not only outperforms prior work in generation performance but also improves 3D hand reconstruction with the generated hand images.
Automatic Image-Level Morphological Trait Annotation for Organismal Images
Authors: Vardaan Pahuja; Samuel Stevens; Alyson East; Sydne Record; Yu Su
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Morphological traits are physical characteristics of biological organisms that provide vital clues on how organisms interact with their environment. Yet extracting these traits remains a slow, expert-driven process, limiting their use in large-scale ecological studies. A major bottleneck is the absence of high-quality datasets linking biological images to trait-level annotations. In this work, we demonstrate that sparse autoencoders trained on foundation-model features yield monosemantic, spatially grounded neurons that consistently activate on meaningful morphological parts. Leveraging this property, we introduce a trait annotation pipeline that localizes salient regions and uses vision-language prompting to generate interpretable trait descriptions. Using this approach, we construct Bioscan-Traits, a dataset of 80K trait annotations spanning 19K insect images from BIOSCAN-5M. Human evaluation confirms the biological plausibility of the generated morphological descriptions. We assess design sensitivity through a comprehensive ablation study, systematically varying key design choices and measuring their impact on the quality of the resulting trait descriptions. By annotating traits with a modular pipeline rather than prohibitively expensive manual efforts, we offer a scalable way to inject biologically meaningful supervision into foundation models, enable large-scale morphological analyses, and bridge the gap between ecological relevance and machine-learning practicality.
Towards a Foundation Model for Crowdsourced Label Aggregation
Authors: Hao Liu; Jiacheng Liu; Feilong Tang; Long Chen; Jiadi Yu; Yanmin Zhu; Qiwen Dong; Yichuan Yu; Xiaofeng Hou
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Inferring ground truth from noisy, crowdsourced labels is a fundamental challenge in machine learning. For decades, the dominant paradigm has relied on dataset-specific parameter estimation, a non-scalable method that fails to transfer knowledge.  Recent efforts toward universal aggregation models do not account for the structural and behavioral complexities of human-annotated crowdsourcing, resulting in poor real-world performance. To address this gap, we introduce CrowdFM, a foundation model for crowdsourced label aggregation. At its core, CrowdFM is a bipartite graph neural network that is pre-trained on a vast, domain-randomized synthetic dataset to learn diverse behavioral patterns. By leveraging a size-invariant initialization and attention-based message passing, it learns universal principles of collective intelligence and generalizes to new, unseen datasets. Extensive experiments on 22 real-world benchmarks show that our single, fixed model consistently matches or surpasses bespoke, per-dataset methods in both accuracy and efficiency. Furthermore, the representations learned by CrowdFM readily support diverse downstream applications, such as worker assessment and task assignment. Codes are available at https://github.com/liiuhaao/CrowdFM.
MathNet: A Global Multimodal Benchmark for Mathematical Reasoning and Retrieval
Authors: Shaden Alshammari; Kevin Wen; Abrar Zainal; Mark Hamilton; Navid Safaei; Sultan Albarakati; William T. Freeman; Antonio Torralba
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Mathematical problem solving remains a challenging test of reasoning for large language and multimodal models, yet existing benchmarks are limited in size, language coverage, and task diversity. We introduce **MathNet**, a high-quality, large-scale, multimodal, and multilingual dataset of Olympiad-level math problems together with a benchmark for evaluating mathematical reasoning in generative models and mathematical retrieval in embedding-based systems. **MathNet** spans 47 countries, 16 languages, and two decades of competitions, comprising **30,676 expert-authored problems with solutions** across diverse domains. In addition to the core dataset, we construct a retrieval benchmark consisting of mathematically equivalent and structurally similar problem pairs curated by human experts.

**MathNet** supports three tasks: (i) mathematical problem solving, (ii) problem retrieval, and (iii) retrieval-augmented problem solving (math RAG). Experimental results show that even state-of-the-art reasoning models (**78.4% for `Gemini-3.1-Pro` and 69.3% for `GPT-5`**) remain challenged, while embedding models struggle to retrieve equivalent problems. We further show that RAG performance is highly sensitive to retrieval quality; for example, `DeepSeek-V3.2-Speciale` achieves gains of up to **12%**, obtaining the highest scores on the benchmark. **MathNet** provides the largest high-quality Olympiad dataset together with the first benchmark for evaluating mathematical problem retrieval, and we publicly release both the dataset and benchmark at [https://mathnet.mit.edu](https://mathnet.csail.mit.edu).