Orionnnn/hub-stats huggingface dataset:
Large Language Models for Scientific Idea Generation: A Creativity-Centered Survey
Authors: name: Fatemeh Shahhosseini, name: Arash Marioriyad, name: Ali Momen, name: Mahdieh Soleymani Baghshah, name: Mohammad Hossein Rohban, name: Shaghayegh Haghjooy Javanmard
Published: 2025-11-05 07:50:43

Scientific idea generation lies at the heart of scientific discovery and has driven human progress-whether by solving unsolved problems or proposing novel hypotheses to explain unknown phenomena. Unlike standard scientific reasoning or general creative generation, idea generation in science is a multi-objective and open-ended task, where the novelty of a contribution is as essential as its empirical soundness. Large language models (LLMs) have recently emerged as promising generators of scientific ideas, capable of producing coherent and factual outputs with surprising intuition and acceptable reasoning, yet their creative capacity remains inconsistent and poorly understood. This survey provides a structured synthesis of methods for LLM-driven scientific ideation, examining how different approaches balance creativity with scientific soundness. We categorize existing methods into five complementary families: External knowledge augmentation, Prompt-based distributional steering, Inference-time scaling, Multi-agent collaboration, and Parameter-level adaptation. To interpret their contributions, we employ two complementary frameworks: Boden's taxonomy of Combinatorial, Exploratory and Transformational creativity to characterize the level of ideas each family expected to generate, and Rhodes' 4Ps framework-Person, Process, Press, and Product-to locate the aspect or source of creativity that each method emphasizes. By aligning methodological advances with creativity frameworks, this survey clarifies the state of the field and outlines key directions toward reliable, systematic, and transformative applications of LLMs in scientific discovery.
SpatialThinker: Reinforcing 3D Reasoning in Multimodal LLMs via Spatial Rewards
Authors: name: Hunar Batra, name: Haoqin Tu, name: Hardy Chen, name: Yuanze Lin, name: Cihang Xie, name: Ronald Clark
Published: 2025-11-10 18:52:47

Multimodal large language models (MLLMs) have achieved remarkable progress in vision-language tasks, but they continue to struggle with spatial understanding. Existing spatial MLLMs often rely on explicit 3D inputs or architecture-specific modifications, and remain constrained by large-scale datasets or sparse supervision. To address these limitations, we introduce SpatialThinker, a 3D-aware MLLM trained with RL to integrate structured spatial grounding with multi-step reasoning. The model simulates human-like spatial perception by constructing a scene graph of task-relevant objects and spatial relations, and reasoning towards an answer via dense spatial rewards. SpatialThinker consists of two key contributions: (1) a data synthesis pipeline that generates STVQA-7K, a high-quality spatial VQA dataset, and (2) online RL with a multi-objective dense spatial reward enforcing spatial grounding. SpatialThinker-7B outperforms supervised fine-tuning and the sparse RL baseline on spatial understanding and real-world VQA benchmarks, nearly doubling the base-model gain compared to sparse RL, and surpassing GPT-4o. These results showcase the effectiveness of combining spatial supervision with reward-aligned reasoning in enabling robust 3D spatial understanding with limited data and advancing MLLMs towards human-level visual reasoning.
AIonopedia: an LLM agent orchestrating multimodal learning for ionic liquid discovery
Authors: name: Yuqi Yin, name: Yibo Fu, name: Siyuan Wang, name: Peng Sun, name: Hongyu Wang, name: Xiaohui Wang, name: Lei Zheng, name: Zhiyong Li, name: Zhirong Liu, name: Jianji Wang, name: Zhaoxi Sun
Published: 2025-11-14 12:53:57

The discovery of novel Ionic Liquids (ILs) is hindered by critical challenges in property prediction, including limited data, poor model accuracy, and fragmented workflows. Leveraging the power of Large Language Models (LLMs), we introduce AIonopedia, to the best of our knowledge, the first LLM agent for IL discovery. Powered by an LLM-augmented multimodal domain foundation model for ILs, AIonopedia enables accurate property predictions and incorporates a hierarchical search architecture for molecular screening and design. Trained and evaluated on a newly curated and comprehensive IL dataset, our model delivers superior performance. Complementing these results, evaluations on literature-reported systems indicate that the agent can perform effective IL modification. Moving beyond offline tests, the practical efficacy was further confirmed through real-world wet-lab validation, in which the agent demonstrated exceptional generalization capabilities on challenging out-of-distribution tasks, underscoring its ability to accelerate real-world IL discovery.
UI2Code^N: A Visual Language Model for Test-Time Scalable Interactive UI-to-Code Generation
Authors: name: Zhen Yang, name: Wenyi Hong, name: Mingde Xu, name: Xinyue Fan, name: Weihan Wang, name: Jiele Cheng, name: Xiaotao Gu, name: Jie Tang
Published: 2025-11-11 13:00:09

User interface (UI) programming is a core yet highly complex part of modern software development. Recent advances in visual language models (VLMs) highlight the potential of automatic UI coding, but current approaches face two key limitations: multimodal coding capabilities remain underdeveloped, and single-turn paradigms make little use of iterative visual feedback. We address these challenges with an interactive UI-to-code paradigm that better reflects real-world workflows and raises the upper bound of achievable performance. Under this paradigm, we present UI2Code^N, a visual language model trained through staged pretraining, fine-tuning, and reinforcement learning to achieve foundational improvements in multimodal coding. The model unifies three key capabilities: UI-to-code generation, UI editing, and UI polishing. We further explore test-time scaling for interactive generation, enabling systematic use of multi-turn feedback. Experiments on UI-to-code and UI polishing benchmarks show that UI2Code^N establishes a new state of the art among open-source models and achieves performance comparable to leading closed-source models such as Claude-4-Sonnet and GPT-5. Our code and models are available at https://github.com/zai-org/UI2Code_N.
MarsRL: Advancing Multi-Agent Reasoning System via Reinforcement Learning with Agentic Pipeline Parallelism
Authors: name: Shulin Liu, name: Dong Du, name: Tao Yang, name: Yang Li, name: Boyu Qiu
Published: 2025-11-14 14:52:34

Recent progress in large language models (LLMs) has been propelled by reinforcement learning with verifiable rewards (RLVR) and test-time scaling. However, the limited output length of LLMs constrains the depth of reasoning attainable in a single inference process. Multi-agent reasoning systems offer a promising alternative by employing multiple agents including Solver, Verifier, and Corrector, to iteratively refine solutions. While effective in closed-source models like Gemini 2.5 Pro, they struggle to generalize to open-source models due to insufficient critic and correction capabilities. To address this, we propose MarsRL, a novel reinforcement learning framework with agentic pipeline parallelism, designed to jointly optimize all agents in the system. MarsRL introduces agent-specific reward mechanisms to mitigate reward noise and employs pipeline-inspired training to enhance efficiency in handling long trajectories. Applied to Qwen3-30B-A3B-Thinking-2507, MarsRL improves AIME2025 accuracy from 86.5% to 93.3% and BeyondAIME from 64.9% to 73.8%, even surpassing Qwen3-235B-A22B-Thinking-2507. These findings highlight the potential of MarsRL to advance multi-agent reasoning systems and broaden their applicability across diverse reasoning tasks.
Virtual Width Networks
Authors: name: Seed, name: Baisheng Li, name: Banggu Wu, name: Bole Ma, name: Bowen Xiao, name: Chaoyi Zhang, name: Cheng Li, name: Chengyi Wang, name: Chenyin Xu, name: Chi Zhang, name: Chong Hu, name: Daoguang Zan, name: Defa Zhu, name: Dongyu Xu, name: Du Li, name: Faming Wu, name: Fan Xia, name: Ge Zhang, name: Guang Shi, name: Haobin Chen, name: Hongyu Zhu, name: Hongzhi Huang, name: Huan Zhou, name: Huanzhang Dou, name: Jianhui Duan, name: Jianqiao Lu, name: Jianyu Jiang, name: Jiayi Xu, name: Jiecao Chen, name: Jin Chen, name: Jin Ma, name: Jing Su, name: Jingji Chen, name: Jun Wang, name: Jun Yuan, name: Juncai Liu, name: Jundong Zhou, name: Kai Hua, name: Kai Shen, name: Kai Xiang, name: Kaiyuan Chen, name: Kang Liu, name: Ke Shen, name: Liang Xiang, name: Lin Yan, name: Lishu Luo, name: Mengyao Zhang, name: Ming Ding, name: Mofan Zhang, name: Nianning Liang, name: Peng Li, name: Penghao Huang, name: Pengpeng Mu, name: Qi Huang, name: Qianli Ma, name: Qiyang Min, name: Qiying Yu, name: Renming Pang, name: Ru Zhang, name: Shen Yan, name: Shen Yan, name: Shixiong Zhao, name: Shuaishuai Cao, name: Shuang Wu, name: Siyan Chen, name: Siyu Li, name: Siyuan Qiao, name: Tao Sun, name: Tian Xin, name: Tiantian Fan, name: Ting Huang, name: Ting-Han Fan, name: Wei Jia, name: Wenqiang Zhang, name: Wenxuan Liu, name: Xiangzhong Wu, name: Xiaochen Zuo, name: Xiaoying Jia, name: Ximing Yang, name: Xin Liu, name: Xin Yu, name: Xingyan Bin, name: Xintong Hao, name: Xiongcai Luo, name: Xujing Li, name: Xun Zhou, name: Yanghua Peng, name: Yangrui Chen, name: Yi Lin, name: Yichong Leng, name: Yinghao Li, name: Yingshuan Song, name: Yiyuan Ma, name: Yong Shan, name: Yongan Xiang, name: Yonghui Wu, name: Yongtao Zhang, name: Yongzhen Yao, name: Yu Bao, name: Yuehang Yang, name: Yufeng Yuan, name: Yunshui Li, name: Yuqiao Xian, name: Yutao Zeng, name: Yuxuan Wang, name: Zehua Hong, name: Zehua Wang, name: Zengzhi Wang, name: Zeyu Yang, name: Zhengqiang Yin, name: Zhenyi Lu, name: Zhexi Zhang, name: Zhi Chen, name: Zhi Zhang, name: Zhiqi Lin, name: Zihao Huang, name: Zilin Xu, name: Ziyun Wei, name: Zuo Wang
Published: 2025-11-14 12:41:57

We introduce Virtual Width Networks (VWN), a framework that delivers the benefits of wider representations without incurring the quadratic cost of increasing the hidden size. VWN decouples representational width from backbone width, expanding the embedding space while keeping backbone compute nearly constant. In our large-scale experiment, an 8-times expansion accelerates optimization by over 2 times for next-token and 3 times for next-2-token prediction. The advantage amplifies over training as both the loss gap grows and the convergence-speedup ratio increases, showing that VWN is not only token-efficient but also increasingly effective with scale. Moreover, we identify an approximately log-linear scaling relation between virtual width and loss reduction, offering an initial empirical basis and motivation for exploring virtual-width scaling as a new dimension of large-model efficiency.
DiscoX: Benchmarking Discourse-Level Translation task in Expert Domains
Authors: name: Xiying Zhao, name: Zhoufutu Wen, name: Zhixuan Chen, name: Jingzhe Ding, name: Jianpeng Jiao, name: Shuai Li, name: Xi Li, name: Danni Liang, name: Shengda Long, name: Qianqian Liu, name: Xianbo Wu, name: Hongwan Gao, name: Xiang Gao, name: Liang Hu, name: Jiashuo Liu, name: Mengyun Liu, name: Weiran Shi, name: Chenghao Yang, name: Qianyu Yang, name: Xuanliang Zhang, name: Ge Zhang, name: Wenhao Huang
Published: 2025-11-14 06:09:37

The evaluation of discourse-level translation in expert domains remains inadequate, despite its centrality to knowledge dissemination and cross-lingual scholarly communication. While these translations demand discourse-level coherence and strict terminological precision, current evaluation methods predominantly focus on segment-level accuracy and fluency. To address this limitation, we introduce DiscoX, a new benchmark for discourse-level and expert-level Chinese-English translation. It comprises 200 professionally-curated texts from 7 domains, with an average length exceeding 1700 tokens. To evaluate performance on DiscoX, we also develop Metric-S, a reference-free system that provides fine-grained automatic assessments across accuracy, fluency, and appropriateness. Metric-S demonstrates strong consistency with human judgments, significantly outperforming existing metrics. Our experiments reveal a remarkable performance gap: even the most advanced LLMs still trail human experts on these tasks. This finding validates the difficulty of DiscoX and underscores the challenges that remain in achieving professional-grade machine translation. The proposed benchmark and evaluation system provide a robust framework for more rigorous evaluation, facilitating future advancements in LLM-based translation.
CATS-V2V: A Real-World Vehicle-to-Vehicle Cooperative Perception Dataset with Complex Adverse Traffic Scenarios
Authors: name: Hangyu Li, name: Bofeng Cao, name: Zhaohui Liang, name: Wuzhen Li, name: Juyoung Oh, name: Yuxuan Chen, name: Shixiao Liang, name: Hang Zhou, name: Chengyuan Ma, name: Jiaxi Liu, name: Zheng Li, name: Peng Zhang, name: KeKe Long, name: Maolin Liu, name: Jackson Jiang, name: Chunlei Yu, name: Shengxiang Liu, name: Hongkai Yu, name: Xiaopeng Li
Published: 2025-11-14 11:07:04

Vehicle-to-Vehicle (V2V) cooperative perception has great potential to enhance autonomous driving performance by overcoming perception limitations in complex adverse traffic scenarios (CATS). Meanwhile, data serves as the fundamental infrastructure for modern autonomous driving AI. However, due to stringent data collection requirements, existing datasets focus primarily on ordinary traffic scenarios, constraining the benefits of cooperative perception. To address this challenge, we introduce CATS-V2V, the first-of-its-kind real-world dataset for V2V cooperative perception under complex adverse traffic scenarios. The dataset was collected by two hardware time-synchronized vehicles, covering 10 weather and lighting conditions across 10 diverse locations. The 100-clip dataset includes 60K frames of 10 Hz LiDAR point clouds and 1.26M multi-view 30 Hz camera images, along with 750K anonymized yet high-precision RTK-fixed GNSS and IMU records. Correspondingly, we provide time-consistent 3D bounding box annotations for objects, as well as static scenes to construct a 4D BEV representation. On this basis, we propose a target-based temporal alignment method, ensuring that all objects are precisely aligned across all sensor modalities. We hope that CATS-V2V, the largest-scale, most supportive, and highest-quality dataset of its kind to date, will benefit the autonomous driving community in related tasks.
GGBench: A Geometric Generative Reasoning Benchmark for Unified Multimodal Models
Authors: name: Jingxuan Wei, name: Caijun Jia, name: Xi Bai, name: Xinglong Xu, name: Siyuan Li, name: Linzhuang Sun, name: Bihui Yu, name: Conghui He, name: Lijun Wu, name: Cheng Tan
Published: 2025-11-14 10:07:53

The advent of Unified Multimodal Models (UMMs) signals a paradigm shift in artificial intelligence, moving from passive perception to active, cross-modal generation. Despite their unprecedented ability to synthesize information, a critical gap persists in evaluation: existing benchmarks primarily assess discriminative understanding or unconstrained image generation separately, failing to measure the integrated cognitive process of generative reasoning. To bridge this gap, we propose that geometric construction provides an ideal testbed as it inherently demands a fusion of language comprehension and precise visual generation. We introduce GGBench, a benchmark designed specifically to evaluate geometric generative reasoning. It provides a comprehensive framework for systematically diagnosing a model's ability to not only understand and reason but to actively construct a solution, thereby setting a more rigorous standard for the next generation of intelligent systems. Project website: https://opendatalab-raiser.github.io/GGBench/.
EmoVid: A Multimodal Emotion Video Dataset for Emotion-Centric Video Understanding and Generation
Authors: name: Zongyang Qiu, name: Bingyuan Wang, name: Xingbei Chen, name: Yingqing He, name: Zeyu Wang
Published: 2025-11-14 06:44:21

Emotion plays a pivotal role in video-based expression, but existing video generation systems predominantly focus on low-level visual metrics while neglecting affective dimensions. Although emotion analysis has made progress in the visual domain, the video community lacks dedicated resources to bridge emotion understanding with generative tasks, particularly for stylized and non-realistic contexts. To address this gap, we introduce EmoVid, the first multimodal, emotion-annotated video dataset specifically designed for creative media, which includes cartoon animations, movie clips, and animated stickers. Each video is annotated with emotion labels, visual attributes (brightness, colorfulness, hue), and text captions. Through systematic analysis, we uncover spatial and temporal patterns linking visual features to emotional perceptions across diverse video forms. Building on these insights, we develop an emotion-conditioned video generation technique by fine-tuning the Wan2.1 model. The results show a significant improvement in both quantitative metrics and the visual quality of generated videos for text-to-video and image-to-video tasks. EmoVid establishes a new benchmark for affective video computing. Our work not only offers valuable insights into visual emotion analysis in artistically styled videos, but also provides practical methods for enhancing emotional expression in video generation.
MM-CRITIC: A Holistic Evaluation of Large Multimodal Models as Multimodal Critique
Authors: name: Gailun Zeng, name: Ziyang Luo, name: Hongzhan Lin, name: Yuchen Tian, name: Kaixin Li, name: Ziyang Gong, name: Jianxiong Guo, name: Jing Ma
Published: 2025-11-12 07:43:26

The ability of critique is vital for models to self-improve and serve as reliable AI assistants. While extensively studied in language-only settings, multimodal critique of Large Multimodal Models (LMMs) remains underexplored despite their growing capabilities in tasks like captioning and visual reasoning. In this work, we introduce MM-CRITIC, a holistic benchmark for evaluating the critique ability of LMMs across multiple dimensions: basic, correction, and comparison. Covering 8 main task types and over 500 tasks, MM-CRITIC collects responses from various LMMs with different model sizes and is composed of 4471 samples. To enhance the evaluation reliability, we integrate expert-informed ground answers into scoring rubrics that guide GPT-4o in annotating responses and generating reference critiques, which serve as anchors for trustworthy judgments. Extensive experiments validate the effectiveness of MM-CRITIC and provide a comprehensive assessment of leading LMMs' critique capabilities under multiple dimensions. Further analysis reveals some key insights, including the correlation between response quality and critique, and varying critique difficulty across evaluation dimensions. Our code is available at https://github.com/MichealZeng0420/MM-Critic.
CC30k: A Citation Contexts Dataset for Reproducibility-Oriented Sentiment Analysis
Authors: name: Rochana R. Obadage, name: Sarah M. Rajtmajer, name: Jian Wu
Published: 2025-11-11 03:13:17

Sentiments about the reproducibility of cited papers in downstream literature offer community perspectives and have shown as a promising signal of the actual reproducibility of published findings. To train effective models to effectively predict reproducibility-oriented sentiments and further systematically study their correlation with reproducibility, we introduce the CC30k dataset, comprising a total of 30,734 citation contexts in machine learning papers. Each citation context is labeled with one of three reproducibility-oriented sentiment labels: Positive, Negative, or Neutral, reflecting the cited paper's perceived reproducibility or replicability. Of these, 25,829 are labeled through crowdsourcing, supplemented with negatives generated through a controlled pipeline to counter the scarcity of negative labels. Unlike traditional sentiment analysis datasets, CC30k focuses on reproducibility-oriented sentiments, addressing a research gap in resources for computational reproducibility studies. The dataset was created through a pipeline that includes robust data cleansing, careful crowd selection, and thorough validation. The resulting dataset achieves a labeling accuracy of 94%. We then demonstrated that the performance of three large language models significantly improves on the reproducibility-oriented sentiment classification after fine-tuning using our dataset. The dataset lays the foundation for large-scale assessments of the reproducibility of machine learning papers. The CC30k dataset and the Jupyter notebooks used to produce and analyze the dataset are publicly available at https://github.com/lamps-lab/CC30k .
Solving a Million-Step LLM Task with Zero Errors
Authors: name: Elliot Meyerson, name: Giuseppe Paolo, name: Roberto Dailey, name: Hormoz Shahrzad, name: Olivier Francon, name: Conor F. Hayes, name: Xin Qiu, name: Babak Hodjat, name: Risto Miikkulainen
Published: 2025-11-12 06:27:55

LLMs have achieved remarkable breakthroughs in reasoning, insights, and tool use, but chaining these abilities into extended processes at the scale of those routinely executed by humans, organizations, and societies has remained out of reach. The models have a persistent error rate that prevents scale-up: for instance, recent experiments in the Towers of Hanoi benchmark domain showed that the process inevitably becomes derailed after at most a few hundred steps. Thus, although LLM research is often still benchmarked on tasks with relatively few dependent logical steps, there is increasing attention on the ability (or inability) of LLMs to perform long range tasks. This paper describes MAKER, the first system that successfully solves a task with over one million LLM steps with zero errors, and, in principle, scales far beyond this level. The approach relies on an extreme decomposition of a task into subtasks, each of which can be tackled by focused microagents. The high level of modularity resulting from the decomposition allows error correction to be applied at each step through an efficient multi-agent voting scheme. This combination of extreme decomposition and error correction makes scaling possible. Thus, the results suggest that instead of relying on continual improvement of current LLMs, massively decomposed agentic processes (MDAPs) may provide a way to efficiently solve problems at the level of organizations and societies.
Hail to the Thief: Exploring Attacks and Defenses in Decentralised GRPO
Authors: name: Nikolay Blagoev, name: Oğuzhan Ersoy, name: Lydia Yiyu Chen
Published: 2025-11-12 22:29:07

Group Relative Policy Optimization (GRPO) has demonstrated great utilization in post-training of Large Language Models (LLMs). In GRPO, prompts are answered by the model and, through reinforcement learning, preferred completions are learnt. Owing to the small communication volume, GRPO is inherently suitable for decentralised training as the prompts can be concurrently answered by multiple nodes and then exchanged in the forms of strings. In this work, we present the first adversarial attack in decentralised GRPO. We demonstrate that malicious parties can poison such systems by injecting arbitrary malicious tokens in benign models in both out-of-context and in-context attacks. Using empirical examples of math and coding tasks, we show that adversarial attacks can easily poison the benign nodes, polluting their local LLM post-training, achieving attack success rates up to 100% in as few as 50 iterations. We propose two ways to defend against these attacks, depending on whether all users train the same model or different models. We show that these defenses can achieve stop rates of up to 100%, making the attack impossible.