GenAI4ELab/papercli-papers huggingface dataset:
Battery Fault: A Comprehensive Dataset and Benchmark for Battery Fault Diagnosis
Authors: Qingdi Liu; Yan Fu; Lishuo LIU; Yanke LIN; Jin Xin; Jianfeng Zhang; Cheng Hao Liu; Lujia Pan; Dongxu Guo; Yuejiu Zheng; Qiang Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: With the accelerated popularization of electric vehicles (EV), battery safety issues have become an important research focus. Data-driven battery fault diagnosis algorithms, built on real-world operational data, are critical methods for reducing safety risks. However, existing battery datasets have limitations such as insufficient scale, coarse-grained labels, and lack of coverage of real-world operating conditions, which seriously restrict the development of data-driven fault diagnosis algorithms. To address these issues, this paper introduces a large-scale benchmark dataset named CH-BatteryGen, which is, to the best of our knowledge, the first EV battery system fault diagnosis dataset based on real-world operating conditions. This dataset integrates real on-board operation data with mechanism-constrained generative modeling technology, balancing authenticity and scalability. It covers two mainstream battery chemistries, namely nickel-cobalt-manganese (NCM) lithium batteries and lithium iron phosphate (LFP) batteries, and involves charging, discharging, and operation data of 1000 electric vehicles. It provides four fault labels (normal, self-discharge, high-resistance, low-capacity) and three severity level annotations, supporting two benchmark tasks: fault classification and fault grading. Through systematic validation using traditional machine learning methods (random forest (RF), support vector machine (SVM)) and deep learning models (long short-term memory (LSTM), convolutional neural network (CNN)), the results show that the CNN model performs best in the fault classification task, achieving an F1-score of 0.9280 in the LFP discharging scenario; in the fault grading task, the F1-score reaches 0.8813. The CH-BatteryGen dataset has been open-sourced, aiming to provide a standardized evaluation platform for battery fault diagnosis algorithms, promote research development in this field, and contribute to the transformation of sustainable transportation systems.
NI Sampling: Accelerating Discrete Diffusion Sampling by Token Order Optimization
Authors: Enshu Liu; Xuefei Ning; Yu Wang; Zinan Lin
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Discrete diffusion language models (dLLMs) have recently emerged as a promising alternative to traditional autoregressive approaches, offering the flexibility to generate tokens in arbitrary orders and the potential of parallel decoding. However, existing heuristic sampling strategies remain inefficient: they choose only a small part of tokens to sample at each step, leaving substantial room for improvement. In this work, we study the problem of token sampling order optimization and demonstrate its significant potential for acceleration. Specifically, we find that fully leveraging correct predictions at each step can reduce the number of sampling iterations by an order of magnitude without compromising accuracy. Based on this, we propose Neural Indicator Sampling (NI Sampling), a general sampling order optimization framework that utilize a neural indicator to decide which tokens should be sampled at each step. We further propose a novel trajectory-preserving objective to train the indicator. Experiments on LLaDA and Dream models across multiple benchmarks show that our method achieves up to 14.3$\times$ acceleration over full-step sampling with negligible performance drop, and consistently outperforms confidence threshold sampling in the accuracy–step trade-off.
Huxley-G\"odel Machine: Human-Level Coding Agent Development by an Approximation of the Optimal Self-Improving Machine
Authors: Wenyi Wang; Piotr Piękos; Li Nanbo; Firas Laakom; Yimeng Chen; Mateusz Ostaszewski; Mingchen Zhuge; Jürgen Schmidhuber
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Recent studies operationalize self-improvement through coding agents that edit their own codebases. They grow a tree of self-modifications through expansion strategies that favor higher software engineering benchmark performance, assuming that this implies more promising subsequent self-modifications. However, we identify a mismatch between the agent’s self-improvement potential (metaproductivity) and its coding benchmark performance, namely the Metaproductivity-Performance~Mismatch. Inspired by Huxley’s concept of clade, we propose a metric ($\mathrm{CMP}$) that aggregates the benchmark performances of the descendants of an agent as an indicator of its potential for self-improvement. We show that, in our self-improving coding agent development setting, access to the true CMP is sufficient to simulate how the Gödel Machine would behave under certain assumptions. We introduce the Huxley-G\"odel Machine (HGM), which, by estimating $\mathrm{CMP}$ and using it as guidance, searches the tree of self-modifications. On SWE-bench Verified and Polyglot, HGM outperforms prior self-improving coding agent development methods while using fewer allocated CPU hours. Last but not least, HGM demonstrates strong transfer to other coding datasets and LLMs. %large language models. 
The agent optimized by HGM on SWE-bench Verified with GPT-5 mini and evaluated on SWE-bench Lite with GPT-5 achieves human-level performance, matching the best officially checked results of human-engineered coding agents. Our code is publicly available at https://github.com/metauto-ai/HGM.
Gaia2: Benchmarking LLM Agents on Dynamic and  Asynchronous Environments
Authors: Romain Froger; Pierre Andrews; Matteo Bettini; Amar Budhiraja; Ricardo Silveira Cabral; Virginie Do; Emilien Garreau; Jean-Baptiste Gaya; Hugo Laurençon; Maxime Lecanu; Kunal Malkan; Dheeraj Mekala; Pierre Menard; Gerard Moreno-Torres Bertran; Ulyana Piterbarg; Mikhail Plekhanov; Mathieu Rita; Andrey Rusakov; Vladislav Vorotilov; Mengjue Wang; Ian Yu; Amine Benhalloum; Grégoire Mialon; Thomas Scialom
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: We introduce **Gaia2**, a benchmark for evaluating large language model agents in realistic, asynchronous environments. Unlike prior static or synchronous evaluations, Gaia2 introduces scenarios where environments evolve independently of agent actions, requiring agents to operate under temporal constraints, adapt to noisy and dynamic events, resolve ambiguity, and collaborate with other agents. Each scenario is paired with a write-action verifier, enabling fine-grained, action-level evaluation and making Gaia2 directly usable for reinforcement learning from verifiable rewards. Our evaluation of state-of-the-art proprietary and open-source models shows that no model dominates across capabilities: GPT-5 (high) reaches the strongest overall score of 42% pass@1 but fails on time-sensitive tasks, Claude-4 Sonnet trades accuracy and speed for cost, Kimi-K2 leads among open-source models with 21% pass@1. These results highlight fundamental trade-offs between reasoning, efficiency, robustness, and expose challenges in closing the “sim2real” gap. Gaia2 is built on a consumer environment with the open-source **Agents Research Environments** platform and designed to be easy to extend. By releasing Gaia2 alongside the foundational ARE framework, we aim to provide the community with a flexible infrastructure for developing, benchmarking, and training the next generation of practical agent systems.
Group Verification-based Policy Optimization for Interactive Coding Agents
Authors: Silong Dai; Changzhi Sun; Haolun Wu; Huanran Zheng; Tao Ji; Junchi Yan; Yuanbin Wu; Dell Zhang; Xiaoling Wang; Xuelong Li
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advancements in reinforcement learning from verifiable rewards (RLVR), particularly through Group Relative Policy Optimization (GRPO), have significantly improved the capabilities of large language models (LLMs) for interactive coding agents.
However, these methods overlook process-verifiable environment feedback (e.g., code execution failures), leading to inaccurate advantage estimation at each reasoning step and insufficient learning.
To address this issue, we propose Group Verification-based Policy Optimization (GVPO), a novel RL algorithm that introduces an advantage shaping framework integrating both outcome-verifiable and process-verifiable signals.
While outcome-verifiable rewards ensure alignment with long-term task objectives, process-verifiable feedback derived from intermediate execution traces (e.g., syntax errors, runtime exceptions) serves as corrective shaping terms at the step level.
By jointly leveraging these two forms of verifiability, GVPO achieves more accurate credit assignment, balancing short-term process guidance with long-term outcome alignment.
This unified formulation yields more stable optimization, faster convergence, and stronger generalization in complex interactive environments.
A 32B-parameter agent trained with GVPO in the AppWorld environment outperforms OpenAI’s o1 agent by 12.7\% on the more challenging Test-C split and surpasses the strongest 32B RL-trained state-of-the-art baseline by 3.7\%.
CreatiDesign: A Unified Multi-Conditional Diffusion Transformer for Creative Graphic Design
Authors: Hui Zhang; Dexiang Hong; Maoke Yang; Yutao Cheng; Zhao Zhang; Weidong Chen; Jie Shao; Xinglong Wu; Zuxuan Wu; Yu-Gang Jiang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Graphic design plays a vital role in visual communication across advertising, marketing, and multimedia entertainment. Prior work has explored automated graphic design generation using diffusion models, aiming to streamline creative workflows and democratize design capabilities. However, complex graphic design scenarios require accurately adhering to design intent specified by multiple heterogeneous user-provided elements (\eg images, layouts, and texts), which pose multi-condition control challenges for existing methods. Specifically, previous single-condition control models demonstrate effectiveness only within their specialized domains but fail to generalize to other conditions, while existing multi-condition methods often lack fine-grained control over each sub-condition and compromise overall compositional harmony. To address these limitations, we introduce CreatiDesign, a systematic solution for automated graphic design covering both model architecture and dataset construction. First, we design a unified multi-condition driven architecture that enables flexible and precise integration of heterogeneous design elements with minimal architectural modifications to the base diffusion model. Furthermore, to ensure that each condition precisely controls its designated image region and to avoid interference between conditions, we propose a multimodal attention mask mechanism. Additionally, we develop a fully automated pipeline for constructing graphic design datasets, and introduce a new dataset with 400K samples featuring multi-condition annotations, along with a comprehensive benchmark. Experimental results show that CreatiDesign outperforms existing models by a clear margin in faithfully adhering to user intent.
FREAK: A Fine-grained Hallucination Evaluation Benchmark for Advanced MLLMs
Authors: Zhihan Yin; Jianxin Liang; Yueqian Wang; Yifeng Yao; Huishuai Zhang; Dongyan Zhao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Multimodal Large Language Models (MLLMs) suffer from hallucinations. Existing hallucination evaluation benchmarks are often limited by over-simplified tasks leading to saturated metrics, or insufficient diversity that fails to adequately assess the hallucination extent in state-of-the-art multimodal models. To address this gap, we propose FREAK, a comprehensive multimodal benchmark designed for fine-grained hallucination assessment in MLLMs. Through high-quality photorealistic images featuring fine-grained counter-commonsense edits, FREAK innovatively evaluates hallucination phenomena in detailed visual perception of MLLMs. Extensive experiments on FREAK show severe hallucination issues in SOTA models regarding detailed visual perception. To enable deeper investigation, we curate a controlled subset to indirectly evaluate the model’s ability to perceive target detailed information. Through systematic evaluation of prevailing Chain-of-Thought
(CoT) prompting techniques within this task, we reveal critical insights regarding hallucination patterns and model reasoning processes.
Theoretical Guarantees for Causal Discovery on Large Random Graphs
Authors: Mathieu Chevalley; Arash Mehrjou; Patrick Schwab
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We investigate theoretical guarantees for the \emph{false-negative rate} (FNR)—the fraction of true causal edges whose orientation is not recovered, under single-variable random interventions and an $\epsilon$-interventional faithfulness assumption that accommodates latent confounding. For sparse Erdős--Rényi directed acyclic graphs, where the edge probability scales as $p_e = \Theta(1/d)$, we show that the FNR concentrates around its mean at rate $O\bigl(\tfrac{\log d}{\sqrt d}\bigr)$, implying that large deviations above the expected error become exponentially unlikely as dimensionality increases. This concentration ensures that derived upper bounds hold with high probability in large-scale settings. Extending the analysis to generalized Barabási--Albert graphs reveals an even stronger phenomenon: when the degree exponent satisfies $\gamma > 3$, the deviation width scales as $O\bigl(d^{\beta - \frac{1}{2}}\bigr)$ with $\beta = 1/(\gamma - 1) < \frac{1}{2}$, and hence vanishes in the limit. This demonstrates that heterogeneous, heavy-tailed degree structures commonly observed in empirical networks can intrinsically regularize causal discovery by reducing variability in orientation error. These finite-dimension results provide the first dimension-adaptive, faithfulness-robust guarantees for causal structure recovery, and challenge the intuition that high dimensionality and network heterogeneity necessarily hinder accurate discovery. Our simulation results corroborate these theoretical predictions, showing that the FNR indeed concentrates and often vanishes in practice as dimensionality grows.
Real-Time Reasoning Agents in Evolving Environments
Authors: Yule Wen; Yixin Ye; Yanzhe Zhang; Diyi Yang; Hao Zhu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Agents in the real world must make not only logical but also *timely* judgments. This requires continuous awareness of the dynamic environment: hazards emerge, opportunities arise, and other agents act, while the agent's reasoning is still unfolding. Despite advances in language model reasoning, existing approaches fail to account for this dynamic nature. We introduce *real-time reasoning* as a new problem formulation for agents in evolving environments and build **Real-time Reasoning Gym** to demonstrate it. We study two paradigms for deploying language models in agents: (1) reactive agents, which employ language models with *bounded reasoning computation for rapid responses*, and (2) planning agents, which allow *extended reasoning computation for complex problems*. Our experiments show that even state-of-the-art models struggle with making logical and timely judgments in either paradigm. To address this limitation, we propose **AgileThinker**, which simultaneously engages *both reasoning paradigms*. AgileThinker consistently outperforms agents engaging only one reasoning paradigm as the task difficulty and time pressure rise, effectively balancing reasoning depth and response latency. Our work establishes real-time reasoning as a critical testbed for developing practical agents and provides a foundation for  research in temporally constrained AI systems, highlighting a path toward real-time capable agents.
CoFact: Conformal Factuality Guarantees for Language Models under Covariate Shift
Authors: Zirui Hu; Zheng Zhang; Yingjie Wang; Leszek Rutkowski; Dacheng Tao
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Large Language Models (LLMs) excel in natural language processing (NLP) tasks but often generate false or misleading information, known as hallucinations, raising reliability concerns in high-stakes applications. To provide statistical guarantees on the factuality of LLM outputs, conformal prediction based techniques have been proposed.  Despite their strong theoretical guarantees, they rely heavily on the exchangeability assumption between calibration and test data, which is frequently violated in real-world scenarios with dynamic covariate shifts.  To overcome this limitation, we introduce \textbf{CoFact}, a conformal prediction framework that uses online density ratio estimation to adaptively reweigh calibration data, ensuring alignment with evolving test distributions.  With this approach, CoFact bypasses the exchangeability requirement and provides robust factuality guarantees under non-stationary conditions. To theoretically justify CoFact, we establish an upper bound on the gap between the actual hallucination rate and the target level $\alpha$, demonstrating that the bound asymptotically approaches zero as the number of rounds and calibration samples increase.
Empirically, CoFact is evaluated on MedLFQA, WikiData, and the newly introduced \textbf{WildChat+} dataset, which captures real-world covariate shifts through user-generated prompts. Results demonstrate that CoFact consistently outperforms existing methods, maintaining reliability even under dynamic conditions.
RePrompt: Reasoning-Augmented Reprompting for Text-to-Image Generation via Reinforcement Learning
Authors: Mingrui Wu; Lu Wang; Pu Zhao; Fangkai Yang; Jianjin Zhang; Jianfeng Liu; Yuefeng Zhan; Weihao Han; Hao Sun; Jiayi Ji; Xiaoshuai Sun; Qingwei Lin; Weiwei Deng; Dongmei Zhang; Feng Sun; Rongrong Ji
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Despite recent progress in text-to-image (T2I) generation, existing models often struggle to faithfully capture user intentions from short and under-specified prompts. While prior work has attempted to enhance prompts using large language models (LLMs), these methods frequently generate stylistic or unrealistic content due to insufficient grounding in visual semantics and real-world composition. Inspired by recent advances in reasoning for language model, we propose RePrompt, a novel reprompting framework that introduces explicit reasoning into the prompt enhancement process via reinforcement learning. Instead of relying on handcrafted rules or stylistic rewrites, our method trains a language model to generate structured, self-reflective prompts by optimizing for image-level outcomes. The tailored reward models assesse the generated images in terms of human preference, semantic alignment, and visual composition, providing indirect supervision to refine prompt generation. Our approach enables end-to-end training without human-annotated data. Experiments on GenEval and T2I-Compbench show that RePrompt significantly boosts spatial layout fidelity and compositional generalization across diverse T2I backbones, establishing new state-of-the-art results. Code: https://github.com/microsoft/DKI_LLM/tree/main/RePrompt.
Learning from Algorithm Feedback: One-Shot SAT Solver Guidance with GNNs
Authors: Jan Tönshoff; Martin Grohe
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Boolean Satisfiability (SAT) solvers are foundational to computer science, yet their performance typically hinges on hand-crafted heuristics. This work introduces Reinforcement Learning from Algorithm Feedback (RLAF) as a paradigm for learning to guide SAT solver branching heuristics with Graph Neural Networks (GNNs). Central to our approach is a novel and generic mechanism for injecting inferred variable weights and polarities into the branching heuristics of existing SAT solvers. In a single forward pass, a GNN assigns these parameters to all variables. Casting this one-shot guidance as a reinforcement learning problem lets us train the GNN with off-the-shelf policy-gradient methods, such as GRPO, directly using the solver's computational cost as the sole reward signal. Extensive evaluations demonstrate that RLAF-trained policies significantly reduce the mean solve times of different base solvers across diverse SAT problem distributions, achieving more than a 2x speedup in some cases, while generalizing effectively to larger and harder problems after training. Notably, these policies consistently outperform expert-supervised approaches based on learning handcrafted weighting heuristics, offering a promising path towards data-driven heuristic design in combinatorial optimization.
SketchingReality: From Freehand Scene Sketches to Photorealistic Images
Authors: Ahmed Bourouis; Mikhail Bessmeltsev; Yulia Gryaditskaya
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent years have witnessed remarkable progress in generative AI, with natural language emerging as the most common conditioning input. As underlying models grow more powerful, researchers are exploring increasingly diverse conditioning signals -- such as depth maps, edge maps, camera parameters, and reference images -- to give users finer control over generation. Among different modalities, sketches constitute a natural and long-standing form of human communication, enabling rapid expression of visual concepts. Yet algorithms that effectively handle true freehand sketches -- with their inherent abstraction and distortions -- remain largely unexplored.
In this work, we distinguish between edge maps, often regarded as “sketches” in the literature, and genuine freehand sketches. We pursue the challenging goal of balancing photorealism with sketch adherence when generating images from freehand input. A key obstacle is the absence of ground-truth, pixel-aligned images: by their nature, freehand sketches do not have a single correct alignment. To address this, we propose a modulation-based approach that prioritizes semantic interpretation of the sketch over strict adherence to individual edge positions. We further introduce a novel loss that enables training on freehand sketches without requiring ground-truth pixel-aligned images.  We show that our method outperforms existing approaches in both semantic alignment with freehand sketch inputs and in the realism and overall quality of the generated images.
Non-Collaborative User Simulators for Tool Agents
Authors: Jeonghoon Shim; Woojung Song; Cheyon Jin; Seungwon Kook; Yohan Jo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Tool agents interact with users through multi-turn dialogues to accomplish various tasks. Recent studies have adopted user simulation methods to develop these agents in multi-turn settings. However, existing user simulators tend to be agent-friendly, exhibiting only cooperative behaviors, failing to train and test agents against non-collaborative users in the real world.
We propose a novel user simulator architecture that simulates four categories of non-collaborative behaviors: requesting unavailable services, digressing into tangential conversations, expressing impatience, and providing incomplete utterances. Our user simulator can simulate challenging and natural non-collaborative behaviors while reliably delivering all intents and information necessary to accomplish the task.
Our experiments on MultiWOZ and $\tau$-bench reveal significant performance degradation in state-of-the-art tool agents when encountering non-collaborative users, as well as agent weaknesses under each non-collaborative condition such as escalated hallucinations and dialogue breakdowns. 
Our findings point to the need for methods that can improve agent robustness to the wide range of user behaviors encountered in deployment. We release the extensible simulation framework to help the community develop and stress-test tool agents under realistic conditions within their own service domains.