GenAI4ELab/papercli-papers huggingface dataset:
One for Two: A Unified Framework for Imbalanced Graph Classification via Dynamic Balanced Prototype
Authors: Guanjun Wang; Binwu Wang; Jiaming Ma; Zhengyang Zhou; Pengkun Wang; Xu Wang; Yang Wang
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Graph Neural Networks (GNNs) have advanced graph classification, yet they remain vulnerable to graph-level imbalance, encompassing class imbalance and topological imbalance. To address both types of imbalance in a unified manner, we propose UniImb, a Unified framework for Imbalanced graph classification. Specifically, UniImb first captures multi-scale topological features and enhances data diversity via learnable personalized graph perturbations. It then employs a dynamic balanced prototype module to learn representative prototypes from graph instances, improving the quality of graph representations. Concurrently, a prototype load-balancing optimization term mitigates dominance by majority samples to equalize sample influence during training. We justify these design choices theoretically using the Information Bottleneck principle. Extensive experiments on 19 datasets-including a large-scale imbalanced air pollution graph dataset AirGraph released by us and 23 baselines demonstrate that UniImb has achieved dominant performance across various imbalanced scenarios. Our code is available at GitHub.
Healthcare Insurance Fraud Detection via Continual Fiedler Vector Graph Model
Authors: Yehan Zhang; Huaidong Zhang; Xuandi Luo; Shengfeng He
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Healthcare insurance fraud detection presents unique machine learning challenges: labeled data are scarce due to delayed verification processes, and fraudulent behaviors evolve rapidly, often manifesting in complex, graph-structured interactions. Existing methods struggle in such settings. Pretraining routines typically overlook structural anomalies under limited supervision, while online models often fail to adapt to changing fraud patterns without labeled updates.
To address these issues, we propose the Continual Fiedler Vector Graph model (ConFVG), a fraud detection framework designed for label-scarce and non-stationary environments. The framework comprises two key components. To mitigate label scarcity, we develop a Fiedler Vector-guided graph autoencoder that leverages spectral graph properties to learn structure-aware node representations. The Fiedler Vector, derived from the second smallest eigenvalue of the graph Laplacian, captures global topological signals such as community boundaries and connectivity bottlenecks, which are patterns frequently associated with collusive fraud. This enables the model to identify structurally anomalous nodes without relying on labels. To handle evolving graph streams, we propose a Subgraph Attention Fusion (SAF) module that constructs neighborhood subgraphs and applies attention-based reweighting to emphasize emerging high-risk structures. This design allows the model to adapt to new fraud patterns in real time. A Mean Teacher mechanism further stabilizes online updates and prevents forgetting of previously acquired knowledge.
Experiments on real-world medical fraud datasets demonstrate that the Continual Fiedler Vector Graph model outperforms state-of-the-art baselines in both low-label and distribution-shift scenarios, offering a scalable and structure-sensitive solution for real-time fraud detection.
Disentangled Robot Learning via Separate Forward and Inverse Dynamics Pretraining
Authors: Wenyao Zhang; Bozhou Zhang; Zekun Qi; Wenjun Zeng; Xin Jin; Li Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Vision-language-action (VLA) models have shown great potential in building generalist robots, but still face a dilemma–misalignment of 2D image forecasting and 3D action prediction. Besides, such a vision-action entangled training manner limits model learning from large-scale, action-free web video data. To address these issues, we propose DeFI, a novel framework that Decouples visual Forward and Inverse dynamics pretraining to exploit respective data sources, wherein video generation and action prediction are disentangled. We introduce the General Forward Dynamics Model (GFDM), pretrained on diverse human and robot videos for future prediction, and the General Inverse Dynamics Model (GIDM), trained via self-supervised learning to infer latent actions from unlabeled video transitions. These models are then integrated into a unified architecture for end-to-end finetuning on downstream tasks. In this manner, GFDM and GIDM first shine separately and then cooperate for mutual benefit. Extensive experiments on CALVIN ABC-D and SimplerEnv demonstrate state-of-the-art performance, with DeFI achieving an average task length of 4.51 for CALVIN, 51.2% success rate on SimplerEnv-Fractal benchmark and 81.3% success rate in real-world deployment, significantly outperforming prior methods.
PT$^2$-LLM: Post-Training Ternarization for Large Language Models
Authors: Xianglong Yan; ChengZhu Bao; Zhiteng Li; Tianao Zhang; Kaicheng Yang; Haotong Qin; Ruobing Xie; Xingwu Sun; Yulun Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) have shown impressive capabilities across diverse tasks, but their large memory and compute demands hinder deployment. Ternarization has gained attention as a promising compression technique, delivering substantial size reduction and high computational efficiency. However, its potential in the post-training quantization (PTQ) setting remains underexplored, due to the challenge of training-free parameter optimization and the quantization difficulty posed by outliers and dispersed weights. To address these issues, we propose PT$^2$-LLM, a post-training ternarization framework tailored for LLMs. At its core is an Asymmetric Ternary Quantizer equipped with a two-stage refinement pipeline: (1) Iterative Ternary Fitting (ITF), which alternates between optimal ternary grid construction and flexible rounding to minimize quantization error, and (2) Activation-aware Grid Alignment (AGA), which further refines the ternary grid to better match full-precision outputs. In addition, we propose a plug-and-play Structural Similarity-based Reordering (SSR) strategy that leverages inter-column structural similarity to ease quantization and mitigate outlier effects, further enhancing overall performance. Extensive experiments demonstrate that PT$^2$-LLM delivers competitive performance against state-of-the-art (SOTA) 2-bit PTQ methods with lower memory cost, while also accelerating both prefill and decoding to achieve end-to-end speedup. The code and models will be available at \url{https://github.com/XIANGLONGYAN/PT2-LLM}.
Fine-tuning Done Right in Model Editing
Authors: Wanli Yang; Rui Tang; Hongyu Zang; Du Su; Qi Cao; Jingang Wang; Huawei Shen; Xueqi Cheng; Fei Sun
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Fine-tuning, a foundational method for adapting large language models, has long been considered ineffective for model editing.
Here, we challenge this belief, arguing that the reported failure arises not from the inherent limitation of fine-tuning itself, but from adapting it to the sequential nature of the editing task, a single-pass depth-first pipeline that optimizes each sample to convergence before moving on.
While intuitive, this depth-first pipeline coupled with sample-wise updating over-optimizes each edit and induces interference across edits.
Our controlled experiments reveal that simply restoring fine-tuning to the standard breadth-first (i.e., epoch-based) pipeline with mini-batch optimization substantially improves its effectiveness for model editing.
Moreover, fine-tuning in editing also suffers from suboptimal tuning parameter locations inherited from prior methods. 
Through systematic analysis of tuning locations, we derive LocFT-BF, a simple and effective localized editing method built on the restored fine-tuning framework.
Extensive experiments across diverse LLMs and datasets demonstrate that LocFT-BF outperforms state-of-the-art methods by large margins. 
Notably, to our knowledge, it is the first to sustain 100K edits and 72B-parameter models,10 $\times$ beyond prior practice, without sacrificing general capabilities.
By clarifying a long-standing misconception and introducing a principled localized tuning strategy, we advance fine-tuning from an underestimated baseline to a leading method for model editing, establishing a solid foundation for future research.
SPEED: Scalable, Precise, and Efficient Concept Erasure for Diffusion Models
Authors: Ouxiang Li; Yuan Wang; Xinting Hu; Houcheng Jiang; Tao Liang; Yanbin Hao; Guojun Ma; Fuli Feng
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Erasing concepts from large-scale text-to-image (T2I) diffusion models has become increasingly crucial due to the growing concerns over copyright infringement, privacy violations, and offensive content. In scalable erasure applications, fine-tuning-based methods are time-consuming to precisely erase multiple target concepts, while real-time editing-based methods often degrade the generation quality of non-target concepts due to conflicting optimization objectives.
To address this dilemma, we introduce SPEED, a ***scalable, precise, and efficient*** concept erasure approach that directly edits model parameters. SPEED searches for a null space, a model editing space where parameter updates do not affect non-target concepts, to achieve scalable and precise erasure. To facilitate accurate null space optimization, we incorporate three complementary strategies: Influence-based Prior Filtering (IPF) to selectively retain the most affected non-target concepts, Directed Prior Augmentation (DPA) to enrich the filtered retain set with semantically consistent variations, and Invariant Equality Constraints (IEC) to preserve key invariants during the T2I generation process.
Extensive evaluations across multiple concept erasure tasks demonstrate that SPEED consistently outperforms existing methods in non-target preservation while achieving efficient and high-fidelity concept erasure, successfully erasing 100 concepts within only 5 seconds. Our code and models are available at: https://github.com/Ouxiang-Li/SPEED.
From Curiosity to Caution: Mitigating Reward Hacking for Best-of-$N$ with Pessimism
Authors: Zhuohao Yu; Steven Wu; Adam Block
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Inference-time compute scaling has emerged as a powerful paradigm for improving language model performance on a wide range of tasks, but the question of how best to use the additional compute remains open.  A popular approach is *Best-of-$N$* (BoN) sampling, where $N$ candidate responses are generated, scored according to a reward model, and the highest-scoring response is selected.  While this approach can improve performance, it is vulnerable to *reward hacking*, where performance degrades as $N$ increases due to the selection of responses that exploit imperfections in the reward model instead of genuinely improving generation quality. Prior attempts to mitigate reward hacking---via stronger reward models or heavy-handed distributional regularization---either fail to fully address over-optimization or are too conservative to exploit additional compute.  In this work, we explore the principle of *pessimism* in reinforcement learning (RL), which uses lower confidence bounds on value estimates to avoid out-of-distribution (OOD) actions with uncertain reward estimates. Our approach, termed as *caution*, can be seen as the *reverse* of *curiosity*: where curiosity (e.g., via Random Network Distillation, RND) rewards prediction error as a signal of novelty, caution penalizes prediction error as a signal of distributional uncertainty. Practically, caution trains an error model on typical responses and uses its prediction error to lower reward estimates for atypical ones. Our extensive empirical evaluation demonstrates that caution is a simple, computationally efficient approach that substantially mitigates reward hacking in BoN sampling.  We also provide a theoretical analysis in a simplified linear setting, which shows that caution provably improves over the standard BoN approach.  Together, our results not only establish caution as a practical solution to reward hacking, but also provide evidence that curiosity-based approaches can be a general OOD detection technique in LLM settings.
Figma2Code: Automating Multimodal Design to Code in the Wild
Authors: Yi Gui; Jiawan Zhang; Yina Wang; Tianran Ma; Yao Wan; Shilin He; Dongping Chen; Zhou Zhao; Wenbin Jiang; Xuanhua Shi; Hai Jin; Philip S. Yu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Front-end development constitutes a substantial portion of software engineering, yet converting design mockups into production-ready *User Interface* (UI) code remains tedious and time-costly.  
While recent work has explored automating this process with *Multimodal Large Language Models* (MLLMs), existing approaches typically rely solely on design images. As a result, they must infer complex UI details from images alone, often leading to degraded results.  
In real-world development workflows, however, design mockups are usually delivered as Figma files—a widely used tool for front-end design—that embed rich multimodal information (e.g., metadata and assets) essential for generating high-quality UI.  
To bridge this gap, we introduce Figma2Code, a new task that generalizes *design-to-code* into a multimodal setting and aims to automate *design-to-code*  in the wild.  
Specifically, we collect paired design images and their corresponding metadata files from the Figma community. We then apply a series of processing operations, including rule-based filtering, human and MLLM-based annotation and screening, and metadata refinement. This process yields 3,055 samples, from which designers curate a balanced dataset of 213 high-quality cases.  
Using this dataset, we benchmark ten state-of-the-art open-source and proprietary MLLMs. Our results show that while proprietary models achieve superior visual fidelity, they remain limited in layout responsiveness and code maintainability.  
Further experiments across modalities and ablation studies corroborate this limitation, partly due to models’ tendency to directly map primitive visual attributes from Figma metadata.
DeepScientist: Advancing Frontier-Pushing Scientific Findings Progressively
Authors: Yixuan Weng; Minjun Zhu; Qiujie Xie; QiYao Sun; Zhen Lin; Sifan Liu; Yue Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While previous AI Scientist systems can generate novel findings, they often lack the focus to produce scientifically valuable contributions that address pressing human-defined challenges. We introduce DeepScientist, a system designed to overcome this by conducting goal-oriented, fully autonomous scientific discovery over month-long timelines. It formalizes discovery as a Bayesian Optimization problem, using a cumulative Findings Memory to intelligently balance the exploitation of promising avenues with the exploration of novel hypotheses. Consuming over 20,000 GPU hours, the system generated about 5,000 unique ideas and experimentally validated approximately 1100, ultimately surpassing human-designed 2025 state-of-the-art (SOTA) methods on three frontier AI tasks by 183.7\%, 1.9\%, and 7.9\%. Crucially, this was achieved by autonomously redesigning core methodologies, not merely recombining existing techniques. In a striking demonstration, the system achieved progress on AI text detection in just two weeks that is comparable to three years of cumulative human research. This work provides the first large-scale evidence of an AI achieving discoveries that progressively surpass human SOTA on scientific tasks, producing valuable findings that genuinely push the frontier forward.
NExCO: Native Solution Expansion for Diffusion-based Combinatorial Optimization
Authors: Yu Wang; Yang Li; Jiale Ma; Junchi Yan; Yi Chang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: One central challenge in Neural Combinatorial Optimization (NCO) is handling hard constraints efficiently. Beyond the two classic paradigms, i.e., Local Construction (LC), which sequentially builds feasible solutions but scales poorly, and Global Prediction (GP), which produces one-shot heatmaps yet struggles with constraint conflicts, the recently proposed Adaptive Expansion (AE) shares the advantages of both by progressively growing partial solutions with instance-wise global awareness.
However, existing realizations bolt AE onto external GP predictors, so their solution quality is bounded by the backbone and their inference cost scales with repeated global calls.
In this paper, we fundamentally rethink adaptive expansion and make it native to a generative model, acting as its intrinsic decoding principle  rather than an external wrapper.
We propose NEXCO, a CO-specific masked diffusion framework that turns adaptive expansion into the model’s own iterative unmasking process.
Specifically, it involves a solution-expansion training procedure with a time-agnostic GNN denoiser, which learns diffusion trajectories between fully masked solutions and ground-truth solutions.
With the trained time-agnostic denoiser, we introduce a novel solution expansion scheme at the solving stage, enabling adaptive control over the intermediate solution states. 
It is achieved by constructing candidate sets according to confidence scores and applying feasibility projection to expand the solution while respecting constraints. 
In this way, ``adaptive" is not an afterthought but the decoding itself: intermediate diffusion states are meaningful partial solutions and progress is instance-adaptive rather than schedule-bound.
Extensive experiments on representative CO problems show that NEXCO achieves approximately 50\% improvement in solution quality and up to $4\times$ faster inference compared to prior state-of-the-art solvers. The source code is publicly available at https://github.com/yuuuuwang/NExCO.
GranViT: A Fine-Grained Vision Model For Autoregressive Multimodal Large Language Models
Authors: Guanghao Zheng; Bowen Shi; Mingxing Xu; Ruoyu Sun; Peisen Zhao; ZhiboZhang; Wenrui Dai; Junni Zou; Hongkai Xiong; XIAOPENG ZHANG; Qi Tian
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Vision encoders are indispensable for allowing impressive performance of Multimodal Large Language Models (MLLMs) in vision–language tasks such as visual question answering and reasoning. However, existing vision encoders focus on global image representations but overlook fine-grained regional analysis. They are limited in fine-grained perception due to the scarcity of fine-grained annotated data and the lack of a fine-grained pre-training paradigm. In this paper, we propose GranViT, a novel Vision Transformer that integrates fine-grained feature extraction with semantic alignment to Large Language Models (LLMs) via region-level autoregressive training. We first construct Gran-29M, a dataset comprising 29 million natural and OCR images paired with over 180 million high-quality region-level annotations, to enable large-scale fine-grained pretraining. Consequently, we develop a pretraining-adaptation framework along with a self-distillation mechanism to train fine-grained GranViT on Gran-29M. We sufficiently exploit the fine-grained annotations from Gran-29M to resort to bounding-box-to-caption regression to enhance localized visual representation of the vision encoder in the pretraining and caption-to-bounding-box regression to improve vision feature utilization and localization for LLM in the adaptation. We further incorporate a self-distillation mechanism that imposes explicit localization constraints on the vision encoder to strengthen its regional reasoning capability. Extensive experiments show that GranViT surpasses existing vision encoders and attains strong transferability to varying LLMs. Remarkably, it achieves state-of-the-art results on fine-grained recognition, multimodal VQA, and OCR understanding.
WinT3R: Window-Based Streaming Reconstruction with Camera Token Pool
Authors: Zizun Li; Jianjun Zhou; Yifan Wang; Haoyu Guo; Wenzheng Chang; Yang Zhou; Haoyi Zhu; Junyi Chen; Chunhua Shen; Tong He
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We present WinT3R, a feed-forward reconstruction model capable of online prediction of precise camera poses and high-quality point maps.
Previous methods suffer from a trade-off between reconstruction quality and real-time performance.
To address this, we first introduce a sliding window mechanism that ensures sufficient information exchange among frames within the window, thereby improving the quality of geometric predictions without introducing a large amount of extra computation.
In addition, we leverage a compact representation of cameras and maintain a global camera token pool, which enhances the reliability of camera pose estimation without sacrificing efficiency.
These designs enable WinT3R to achieve state-of-the-art performance in terms of online reconstruction quality, camera pose estimation, and reconstruction speed, as validated by extensive experiments on diverse datasets.
Cambrian-S: Towards Spatial Supersensing in Video
Authors: Shusheng Yang; Jihan Yang; Pinzhi Huang; Ellis L Brown II; Zihao Yang; Yue Yu; Shengbang Tong; Zihan Zheng; Yifan Xu; Muhan Wang; Rob Fergus; Yann LeCun; Li Fei-Fei; Saining Xie
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We argue that progress in true multimodal intelligence calls for a shift from reactive, task-driven systems and brute-force long context towards a broader paradigm of supersensing. We frame spatial supersensing as four stages beyond linguistic-only understanding: semantic perception (naming what is seen), streaming event cognition (maintaining memory across continuous experiences), implicit 3D spatial cognition (inferring the world behind pixels), and predictive world modeling (creating internal models that filter and organize information). Current benchmarks largely test only the early stages, offering narrow coverage of spatial cognition and rarely challenging models in ways that require true world modeling. To drive progress in spatial supersensing, we present VSI-SUPER, a two-part benchmark: VSR (long-horizon visual spatial recall) and VSC (continual visual spatial counting). These tasks require arbitrarily long video inputs yet are resistant to brute-force context expansion. We then test data scaling limits by curating VSI-590K and training Cambrian-S, achieving +30% absolute improvement on VSI-Bench without sacrificing general capabilities. Yet performance on VSI-SUPER remains limited, indicating that scale alone is insufficient for spatial supersensing. We propose predictive sensing as a path forward, presenting a proof-of-concept in which a self-supervised next-latent-frame predictor leverages surprise (prediction error) to drive memory and event segmentation. On VSI-SUPER, this approach substantially outperforms leading proprietary baselines, showing that spatial supersensing requires models that not only see but also anticipate, select, and organize experience.