GenAI4ELab/papercli-papers huggingface dataset:
Unleashing Scientific Reasoning for Bio-experimental Protocol Generation via Structured Component-based Reward Mechanism
Authors: Haoran Sun; Yankai Jiang; Zhenyu Tang; Yaning Pan; Shuang Gu; Zekai Lin; Lilong Wang; Wenjie Lou; Lei Liu; LEI BAI; Xiaosong Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: The foundation of reproducible science lies in protocols that are precise, logically ordered, and executable. The autonomous generation of these protocols through natural language queries could greatly improve the efficiency of the reproduction process. However, current leading large language models (LLMs) often generate incomplete or inconsistent protocols, limiting their utility. To address this limitation, we first introduce SciRecipe, a large-scale dataset of over 12K structured protocols spanning 27 biological subfields and encompassing both comprehension and problem-solving tasks. To further improve protocol generation, we propose the "Sketch-and-Fill" paradigm, which separates analysis, structuring, and expression to ensure each step is explicit and verifiable. Complementing this, the structured component-based reward mechanism evaluates step granularity, action order, and semantic fidelity, aligning model optimization with experimental reliability. Building on these components, we develop Thoth, trained through a staged Knowledge-to-Action process that progresses from knowledge acquisition to operational reasoning and ultimately to robust, executable protocol generation. Across multiple benchmarks, Thoth consistently surpasses both proprietary and open-source LLMs, achieving significant improvements in step alignment, logical sequencing, and semantic accuracy. Our approach paves the way for reliable scientific assistants that bridge knowledge with experimental execution.
WeTok: Powerful Discrete Tokenization for High-Fidelity Visual Reconstruction
Authors: Shaobin Zhuang; Yiwei Guo; Fangyikang Wang; Canmiao Fu; Zhipeng Huang; Zeyue Tian; Xiaohui Li; Ying Zhang; Chen Li; Yali Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Visual tokenizer is a critical component for vision generation. However, the existing tokenizers often face unsatisfactory trade-off between compression ratios and reconstruction fidelity. To fill this gap, we introduce a powerful and concise WeTok tokenizer, which surpasses the previous leading tokenizers via two core innovations. (1) Group-wise lookup-free Quantization (GQ). We partition the latent features into groups, and perform lookup-free quantization for each group. As a result, GQ can efficiently overcome memory and computation limitations of prior tokenizers, while achieving a reconstruction breakthrough with more scalable codebooks. (2) Generative Decoding (GD). Different from prior tokenizers, we introduce a generative decoder with a prior of extra noise variable. In this case, GD can probabilistically model the distribution of visual data conditioned on discrete tokens, allowing WeTok to reconstruct visual details, especially at high compression ratios. On the ImageNet 50k validation set, at a high-fidelity setting, WeTok achieves a record-low zero-shot rFID of 0.12, outperforming leading continuous tokenizers like FLUX-VAE (0.18) and SD-VAE 3.5 (0.19) with 400% compression ratio. Furthermore, in a high-compression regime, WeTok achieves a zero-shot rFID of 3.49 at a 768× compression ratio, substantially surpassing Cosmos, which scores 4.57 at only 50% our compression ratio.
GIR-Bench: Versatile Benchmark for Generating Images with Reasoning
Authors: Hongxiang Li; Yaowei Li; Bin Lin; Yuwei Niu; Yuhang Yang; Xiaoshuang Huang; Jiayin Cai; Xiaolong Jiang; Yao Hu; Long Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Unified multimodal models integrate the reasoning capacity of large language models with both image understanding and generation, showing great promise for advanced multimodal intelligence. However, the community still lacks a rigorous reasoning-centric benchmark to systematically evaluate the alignment between understanding and generation, and their generalization potential in complex visual tasks.
To this end, we introduce \textbf{GIR-Bench}, a comprehensive benchmark that evaluates unified models across three complementary perspectives. 
Firstly, we explore whether models can consistently leverage the same knowledge for both understanding and generation (GIR-Bench-Uni). 
Secondly, we investigate whether models can perform reasoning-centric text-to-image generation that requires applying logical constraints and implicit knowledge to generate faithful visual content (GIR-Bench-T2I).
Thirdly, we evaluate whether models can handle multi-step reasoning in editing (GIR-Bench-Edit). 
For each subset, we carefully design different task-specific evaluation pipelines tailored for each task. This enables fine-grained and interpretable evaluation while mitigating biases from the prevalent MLLM-as-a-Judge paradigm.
Extensive ablations over various unified models and generation-only systems have shown that: Although unified models are more capable of reasoning-driven visual tasks, they still exhibit a persistent gap between understanding and generation. The data and code for GIR-Bench are available at 
\url{https://anonymous.4open.science/r/GIR-Bench-7E40}.
The Forecast After the Forecast: A Post-Processing Shift in Time Series
Authors: Daojun Liang; Qi Li; Yinglong Wang; Jing Chen; Hu Zhang; Xiaoxiao Cui; Qizheng Wang; Shuo Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Time series forecasting has long been dominated by advances in model architecture, with recent progress driven by deep learning and hybrid statistical techniques. However, as forecasting models approach diminishing returns in accuracy, a critical yet underexplored opportunity emerges: the strategic use of post-processing. In this paper, we address the last-mile gap in time-series forecasting, which is to improve accuracy and uncertainty without retraining or modifying a deployed backbone. We propose $\delta$-Adapter, a lightweight, architecture-agnostic way to boost deployed time series forecasters without retraining. $\delta$-Adapter learns tiny, bounded modules at two interfaces: input nudging (soft edits to covariates) and output residual correction. We provide local descent guarantees, $O(\delta)$ drift bounds, and compositional stability for combined adapters.
Meanwhile, it can act as a feature selector by learning a sparse, horizon-aware mask over inputs to select important features, thereby improving interpretability.
In addition, it can also be used as a distribution calibrator to measure uncertainty. Thus, we introduce a Quantile Calibrator and a Conformal Corrector that together deliver calibrated, personalized intervals with finite-sample coverage.  
Our experiments across diverse backbones and datasets show that $\delta$-Adapter improves accuracy and calibration with negligible compute and no interface changes.
Rethinking LLM Evaluation: Can We Evaluate LLMs with 200× Less Data?
Authors: Shaobo Wang; Cong Wang; Wenjie Fu; Yue Min; Mingquan Feng; Isabel Guan; Xuming Hu; Conghui He; Cunxiang Wang; Kexin Yang; Xingzhang Ren; Fei Huang; Dayiheng Liu; Linfeng Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Benchmark suites for large language models are growing faster than our ability to pay for them. Even when training is already expensive, many use cases require repeated evaluation across many checkpoints, variants, and competing systems, and the steady expansion of benchmark suites increasingly turns evaluation into a bottleneck in tokens and compute. This scale changes what ``useful data'' means. Instead of asking whether an instance is good for training one model, we ask **which instances are necessary to keep the collective ordering of many models stable.** We analyze redundancy at the instance level and find repetition in both the text and the ranking patterns induced across models. Based on this observation, we formulate benchmark compression as a subset optimization problem that targets accurate score reconstruction and ranking preservation at the same time. We propose EssenceBench, a coarse-to-fine framework with three stages: redundancy-aware filtering with text and ranking signals, fitness-driven subset search with an iterative genetic algorithm and a fixed surrogate predictor, and attribution-guided refinement for better coverage under tight budgets. Across multiple leaderboards, EssenceBench achieves lower reconstruction error and stronger ranking preservation than prior approaches while reducing selection time. On HellaSwag with 10K instances, EssenceBench preserves 95\% of model rankings within a 5\% shift using only 50 instances, a 200$\times$ compression.  The source code will be made available upon acceptance of the paper.
ODEBrain: Continuous-Time EEG Graph for Modeling Dynamic Brain Networks
Authors: Haohui Jia; Zheng Chen; Lingwei Zhu; Rikuto Kotoge; Jathurshan Pradeepkumar; Yasuko Matsubara; Jimeng Sun; Yasushi Sakurai; Takashi Matsubara
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Modeling neural population dynamics is crucial for foundational neuroscientific research and various clinical applications. Conventional latent variable methods typically model continuous brain dynamics through discretizing time with recurrent architecture, which necessarily results in compounded cumulative prediction errors and failure of capturing instantaneous, nonlinear characteristics of EEGs. We propose ODEBrain, a Neural ODE latent dynamic forecasting framework to overcome these challenges by integrating spatio-temporal-frequency features into spectral graph nodes, followed by a Neural ODE modeling the continuous latent dynamics. Our design ensures that the latent representations can capture stochastic variations of complex brain states at any given time point. Extensive experiments verify that ODEBrain can improve significantly over existing methods in forecasting EEG dynamics with enhanced robustness and generalization capabilities.
Natural Identifiers for Privacy and Data Audits in Large Language Models
Authors: Lorenzo Rossi; Bartłomiej Marek; Franziska Boenisch; Adam Dziedzic
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Assessing the privacy of large language models (LLMs) presents significant challenges. In particular, most existing methods for auditing *differential privacy* require the insertion of specially crafted canary data *during training*, making them impractical for auditing already-trained models without costly retraining. Additionally, *dataset inference*, which audits whether a suspect dataset was used to train a model, is *infeasible* without access to a private non-member held-out dataset. Yet, such held-out datasets are often unavailable or difficult to construct for real-world cases since they have to be from the same distribution (IID) as the suspect data. These limitations severely hinder the ability to conduct scalable, *post-hoc* audits. To enable such audits, this work introduces **natural identifiers (NIDs)** as a novel solution to the above-mentioned challenges. NIDs are structured random strings, such as cryptographic hashes and shortened URLs, naturally occurring in common LLM training datasets. Their format enables the generation of unlimited additional random strings from the same distribution, which can act as alternative canaries for audits and as same-distribution held-out data for dataset inference. Our evaluation highlights that indeed, using NIDs, we can facilitate post-hoc differential privacy auditing *without any retraining* and enable dataset inference for any suspect dataset containing NIDs without the need for a private non-member held-out dataset.
Grounding and Enhancing Informativeness and Utility in Dataset Distillation
Authors: Shaobo Wang; Yantai Yang; Guo Chen; Peiru Li; Kaixin Li; Yufa Zhou; Zhaorun Chen; Linfeng Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Dataset Distillation (DD) seeks to create a compact dataset from a large, real-world dataset. While recent methods often rely on heuristic approaches to balance efficiency and quality, the fundamental relationship between original and synthetic data remains underexplored. This paper revisits knowledge distillation-based dataset distillation within a solid theoretical framework. We introduce the concepts of Informativeness and Utility, capturing crucial information within a sample and essential samples in the training set, respectively. Building on these principles, we define \textit{optimal dataset distillation} mathematically. We then present InfoUtil, a framework that balances informativeness and utility in synthesizing the distilled dataset. InfoUtil incorporates two key components: (1) game-theoretic informativeness maximization using Shapley Value attribution to extract key information from samples, and (2) principled utility maximization by selecting globally influential samples based on Gradient Norm. These components ensure that the distilled dataset is both informative and utility-optimized. Experiments demonstrate that our method achieves a 6.1\% performance improvement over the previous state-of-the-art approach on ImageNet-1K dataset using ResNet-18.
M3CoTBench: Benchmark Chain-of-Thought of MLLMs in Medical Image Understanding
Authors: Juntao Jiang; Jiangning Zhang; Yali bi; BAI Jinsheng; Weixuan Liu; Weiwei Jin; Zhucun Xue; Yong Liu; Xiaobin Hu; Shuicheng YAN
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Chain-of-Thought (CoT) reasoning has proven effective in enhancing large language models by encouraging step-by-step intermediate reasoning, and recent advances have extended this paradigm to Multimodal Large Language Models (MLLMs). In the medical domain, where diagnostic decisions depend on nuanced visual cues and sequential reasoning, CoT aligns naturally with clinical thinking processes. However, current benchmarks for medical image understanding generally focus on the final answer while ignoring the reasoning path. An opaque process lacks reliable bases for judgment, making it difficult to assist doctors in diagnosis. 
To address this gap, we introduce a new M3CoTBench benchmark specifically designed to evaluate the correctness, efficiency, impact, and consistency of CoT reasoning in medical image understanding. M3CoTBench features  (1) a diverse, multi-level difficulty dataset covering 24 examination types, (2) 13 varying-difficulty tasks,  (3) a suite of CoT-specific evaluation metrics (correctness, efficiency, impact, and consistency) tailored to clinical reasoning,  and (4) a performance analysis of multiple MLLMs. M3CoTBench systematically evaluates CoT reasoning across diverse medical imaging tasks, revealing current limitations of MLLMs in generating reliable and clinically interpretable reasoning, and aims to foster the development of transparent, trustworthy, and diagnostically accurate AI systems for healthcare.
GenCompositor: Generative Video Compositing with Diffusion Transformer
Authors: Shuzhou Yang; Xiaoyu Li; Xiaodong Cun; Guangzhi Wang; Lingen Li; Ying Shan; Jian Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Video compositing combines live-action footage to create video production, serving as a crucial technique in video creation and film production. Traditional pipelines require intensive labor efforts and expert collaboration, resulting in lengthy production cycles and high manpower costs. To address this issue, we automate this process with generative models, called generative video compositing. This new task strives to adaptively inject identity and motion information of foreground video to the target video in an interactive manner, allowing users to customize the size, motion trajectory, and other attributes of the dynamic elements added in final video. Specifically, we designed a novel Diffusion Transformer (DiT) pipeline based on its intrinsic properties. To maintain consistency of the target video before and after editing, we revised a light-weight DiT-based background preservation branch with masked token injection. As to inherit dynamic elements from other sources, a DiT fusion block is proposed using full self-attention, along with a simple yet effective foreground augmentation for training. Besides, for fusing background and foreground videos with different layouts based on user control, we developed a novel position embedding, named Extended Rotary Position Embedding (ERoPE). Finally, we curated a dataset comprising 61K sets of videos for our new task, called VideoComp. This data includes complete dynamic elements and high-quality target videos. Experiments demonstrate that our method effectively realizes generative video compositing, outperforming existing possible solutions in fidelity and consistency. Project is available at https://gencompositor.github.io/
FaithCoT-Bench: Benchmarking Instance-Level Faithfulness of Chain-of-Thought Reasoning
Authors: Xu Shen; Song Wang; Zhen Tan; Laura Yao; Xinyu Zhao; Kaidi Xu; Xin Wang; Tianlong Chen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large language models (LLMs) increasingly rely on Chain-of-Thought (CoT) prompting to improve problem-solving and provide seemingly transparent explanations. However, growing evidence shows that CoT often fail to faithfully represent the underlying reasoning process, raising concerns about their reliability in high-risk applications. Although prior studies have focused on mechanism-level analyses showing that CoTs can be unfaithful, they leave open the practical challenge of deciding whether a specific trajectory is faithful to the internal reasoning of the model. To address this gap, we introduce FaithCoT-Bench, a unified benchmark for instance-level CoT unfaithfulness detection. Our framework establishes a rigorous task formulation that formulates unfaithfulness detection as a discriminative decision problem, and provides FINE-CoT (Faithfulness instance evaluation for Chain-of-Thought), an expert-annotated collection of over 1,000 trajectories generated by four representative LLMs across four domains, including more than 300 unfaithful instances with fine-grained causes and step-level evidence. We further conduct a systematic evaluation of eleven representative detection methods spanning counterfactual, logit-based, and LLM-as-judge paradigms, deriving empirical insights that clarify the strengths and weaknesses of existing approaches and reveal the increased challenges of detection in knowledge-intensive domains and with more advanced models.
To the best of our knowledge, FaithCoT-Bench establishes the first comprehensive benchmark for instance-level CoT faithfulness, setting a solid basis for future research toward more interpretable and trustworthy reasoning in LLMs.
InternSVG: Towards Unified SVG Tasks with Multimodal Large Language Models
Authors: Haomin Wang; Jinhui Yin; Qi Wei; Wenguang Zeng; Lixin Gu; Shenglong Ye; Zhangwei Gao; Yaohui Wang; Yanting Zhang; Yuanqi Li; Yanwen Guo; Wenhai Wang; Kai Chen; Yu Qiao; Hongjie Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: General SVG modeling remains challenging due to fragmented datasets, limited transferability of methods across tasks, and the difficulty of handling structural complexity. In response, we leverage the strong transfer and generalization capabilities of multimodal large language models (MLLMs) to achieve unified modeling for SVG understanding, editing, and generation. We present the InternSVG family, an integrated data–benchmark–model suite. At its core is SAgoge, the largest and most comprehensive multimodal dataset for SVG tasks, encompassing both static graphics and dynamic animations. It covers icons, long-sequence illustrations, scientific diagrams, and dynamic animations, supporting tasks of varied difficulty levels and providing deeper hierarchies with richer attributes compared to previous datasets. Based on this resource, we introduce SArena, a companion benchmark with comprehensive task definitions and standardized evaluation that aligns with the domains and difficulty spectrum covered by SAgoge. Building on these foundations, we propose InternSVG, a unified MLLM for SVG understanding, editing, and generation with SVG-specific special tokens, subword-based embedding initialization, and a two-stage training strategy that progresses from short static SVGs to long-sequence illustrations and complex animations. This unified formulation induces positive transfer and improves overall performance. Experiments on \benchset and prior benchmark confirm that InternSVG achieves substantial gains and consistently outperforms leading open and proprietary counterparts.
Data Provenance for Image Auto-Regressive Generation
Authors: Bihe Zhao; Louis Kerner; Michel Meintz; Tameem Bakr; Franziska Boenisch; Adam Dziedzic
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Image autoregressive models (IARs) have recently demonstrated remarkable capabilities in visual content generation, achieving photorealistic quality and rapid synthesis through the next-token prediction paradigm adapted from large language models. As these models become widely accessible, robust data provenance is required to reliably trace IAR-generated images to the source model that synthesized them. This is critical to prevent the spread of misinformation, detect fraud, and attribute harmful content. We find that although IAR-generated images often appear visually identical to real images, their generation process introduces characteristic patterns in their outputs, which serves as a reliable provenance signal for the generated images. Leveraging this, we present a post-hoc framework that enables the robust detection of such patterns for provenance tracing. Notably, our framework does not require modifications of the generative process or outputs. Thereby, it is applicable in contexts where prior watermarking methods cannot be used, such as for generated content that is already published without additional marks and for models that do not integrate watermarking.  We demonstrate the effectiveness of our approach across a wide range of IARs, highlighting its high potential for robust data provenance tracing in autoregressive image generation.
Grasp Any Region: Towards Precise, Contextual Pixel Understanding for Multimodal LLMs
Authors: Haochen Wang; Yuhao Wang; Tao Zhang; Yikang Zhou; Yanwei Li; Jiacong Wang; Jiani zheng; Ye Tian; Jiahao Meng; Zilong Huang; Guangcan Mai; Anran Wang; Yunhai Tong; Zhuochen Wang; Xiangtai Li; Zhaoxiang Zhang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: While Multimodal Large Language Models (MLLMs) excel at holistic understanding, they struggle with the dense world, i.e., complex scenes requiring fine-grained analysis of intricate details and object inter-relationships. Region-level MLLMs have been a promising step. However, previous attempts are generally optimized to understand given regions in isolation, neglecting crucial global contexts. To address this, we introduce Grasp Any Region (GAR) for comprehensive region-level visual understanding. Empowered by an effective RoI-aligned feature replay technique, GAR supports (1) precise perception by leveraging necessary global contexts, and (2) modeling interactions between multiple prompts. Together, it then naturally achieves (3) advanced compositional reasoning to answer specific free-form questions about any region, shifting the paradigm from passive description to active dialogue. Moreover, we construct GARBench, which not only provides a more accurate evaluation of single-region comprehension, but also, more importantly, measures interactions and complex reasoning across multiple regions. Empirically, GAR-1B not only maintains the state-of-the-art captioning capabilities, e.g.,
outperforming DAM-3B +4.5 on DLC-Bench, but also excels at modeling relationships between multiple prompts with advanced comprehension capabilities, even surpassing InternVL3-78B on GARBench-VQA. More importantly, our zero-shot
GAR-8B even outperforms in-domain VideoRefer-7B on VideoRefer-BenchQ, indicating its strong comprehension capabilities can be easily transferred to videos. Code and data will be released to the community.