1 citations · 1 across the 34 of their papers we have counts for
36 papers
REVEAL: Reverse Engineering Agentic Latents from Frontier-Model Traces
Yuetian Du, Meng Wang, Yucheng Wang +9
Frontier LLM agents perform complex interactive tasks, but their inference costs motivate transferring their experience to smaller open-source agents. Distillation and latent commu…
The Scientific Research Taste Benchmark (SciTaste-Bench): A Multi-Disciplinary Benchmark for Assessing Large Language Models' Judgment of Research Taste in Scientific Papers
Xinyi Gao, Xiaogang Li, Jialu Nie +9
Whether large language models can assess the innovativeness of academic publications matters for literature reviews, research funding screening, and academic recommendations. We in…
SWE-Test: Benchmarking LLM Vulnerability Discovery via Input Prediction
Yuanxiang Shi, Jiayi Lin, Xuanyong Lin +8
Vulnerability discovery is becoming an important ability of large language model (LLM) agents: agents that silently miss real defects leave critical software exposed. Rigorously me…
RubricRM: Generative Reward Modeling via Dynamic Rubrics for Image Generation and Editing
Zijian Kan, Wei Wang, Long Luo +6
Reward models play an essential role in aligning visual generative models, yet most existing visual reward models use a single scalar score or rely on fixed criteria that cannot ad…
The Illusion of : Evaluating the Breakdown of Counterfactual Reasoning in LLMs
Yucheng Wang, Yuetian Du, Zhengyi Liu +8
Counterfactual reasoning requires models to reason beyond the observed world and explain how altered conditions propagate through downstream consequences. Existing benchmarks large…
OmniJudge or OmniBias? Diagnosing Multimodal Judges through Balanced, Decoupled Lenses
Guangzheng Hu, Ziyue Jiang, Weixu Qiao +14
Multimodal understanding models that can jointly judge text-to-image (T2I), text-to-video (T2V) and text-to-speech (TTS) generation are increasingly used as "OmniJudges" for evalua…
Living-Harness Is an Interactive-Agent Evolver
Yuetian Du, Yucheng Wang, He Xu +9
Large language model (LLM) agents may recover from a failure within an episode or after a retry, yet the same execution failure can recur in later tasks because post-episode feedba…
Right Answer, Wrong Method: Shortcut Hacking Misleads the Evaluation of LLM Reasoning on Frontier Science Benchmarks
Xuan Ren, Weiqi Zhai, Tianle Pu +3
Scientific reasoning benchmarks typically evaluate large language models (LLMs) using final-answer accuracy. However, a correct answer does not necessarily demonstrate the reasonin…
Are the Financial Reasoning from LLMs Credible? A Real World Test over Long-Horizon Statements
Xinke Tong, Xuanming Zhang, Tianyi Tang +10
Do Large Language Models (LLMs) possess genuine structural reasoning, or merely rely on surface-level pattern matching? The financial domain, demanding numerical precision and mult…
Before the Action: Benchmarking LLMs on Prospective Hypothesis Discovery
Tianyun Zhong, Wangyi Jiang, Wei Wang +15
Large language models (LLMs) excel at answering pre-specified questions, yet their ability to navigate the open-ended, pre-conclusion stage of discovery remains largely unmeasured.…
Discovering Millions of Interpretable Features with Sparse Autoencoders
XinYang He, Wei Wang, Bing Zhao +5
Sparse autoencoders (SAEs) have emerged as a powerful tool for decomposing superposed language model representations into sparse and interpretable features. However, training SAEs…
Qwen-Image-Bench: From Generation to Creation in Text-to-Image Evaluation
Niantong Li, Guangzheng Hu, Weixu Qiao +35
Text-to-Image generation has evolved from basic image synthesis into a frequently used core capability in professional creative workflows, where simple text-image alignment can no…
Socratic-SWE: Self-Evolving Coding Agents via Trace-Derived Agent Skills
Chuan Xiao, Zhengbo Jiao, Shaobo Wang +5
LLM-driven software engineering agents have become a central testbed for real-world language-model capability, yet their training remains limited by the availability of high-qualit…
FeynmanBench: Benchmarking Multimodal LLMs on Diagrammatic Physics Reasoning
Zeyu Wang, Jingye Xu, Xiaogang Li +7
Current multimodal benchmarks for scientific reasoning primarily evaluate local information extraction -- models recognize symbols and values and then perform textual inference. Th…
BilliardPhys-Bench: Benchmarking Physical Reasoning and Visual Dynamics of Multimodal LLMs
Ben Wang, Xiaogang Li, Ruochen Gao +6
Current multimodal models handle static image recognition well, but intuitive physical reasoning remains a weakness. Predicting how objects will move and interact from a single ima…
MedPRMBench: A Fine-grained Benchmark for Process Reward Models in Medical Reasoning
Lingyan Wu, Xiang Zheng, Weiqi Zhai +5
Medical reasoning can reach a plausible answer while overlooking an unsafe or unsupported intermediate step. Process-Level Reward Models (PRMs) can verify such steps, yet existing…
ClinConsensus: A Physician-Calibrated Benchmark for Evaluating Clinical Rubric Coverage in Chinese Medical LLMs
Xiang Zheng, Han Li, Wenjie Luo +15
Open-ended medical LLM evaluation remains weakly grounded in physician-calibrated coverage of clinically relevant response criteria, especially in localized clinical settings. We i…
Thinking by Subtraction: Confidence-Driven Contrastive Decoding for LLM Reasoning
Lexiang Tang, Weihao Gao, Bingchen Zhao +4
Recent work on test-time scaling for large language model (LLM) reasoning typically assumes that allocating more inference-time computation uniformly improves correctness. However,…
DeepVision-103K: A Visually Diverse, Broad-Coverage, and Verifiable Mathematical Dataset for Multimodal Reasoning
Haoxiang Sun, Lizhen Xu, Bing Zhao +5
Reinforcement Learning with Verifiable Rewards (RLVR) has been shown effective in enhancing the visual reflection and reasoning capabilities of Large Multimodal Models (LMMs). Howe…
Human Tool: An MCP-Style Framework for Human-Agent Collaboration
Yuanrong Tang, Huiling Peng, Bingxi Zhao +5
Human-AI collaboration faces growing challenges as AI systems increasingly outperform humans on complex tasks, while humans remain responsible for orchestration, validation, and de…
Benchmark Health Index: A Systematic Framework for Benchmarking the Benchmarks of LLMs
Longyuan Zhu, Hairan Hua, Linlin Miao +1
Large Language Models (LLMs) are advancing rapidly, yet the benchmarks used to measure this progress are becoming increasingly unreliable. Score inflation and selective reporting h…
Credit Where It is Due: Cross-Modality Connectivity Drives Precise Reinforcement Learning for MLLM Reasoning
Zhengbo Jiao, Shaobo Wang, Zifan Zhang +4
Reinforcement Learning with Verifiable Rewards (RLVR) has significantly advanced the reasoning capabilities of Multimodal Large Language Models (MLLMs), yet how visual evidence is…
Beyond Quantity: Trajectory Diversity Scaling for Code Agents
Guhong Chen, Chenghao Sun, Cheng Fu +16
As code large language models (LLMs) evolve into tool-interactive agents via the Model Context Protocol (MCP), their generalization is increasingly limited by low-quality synthetic…
SCONE: A Practical, Constraint-Aware Plug-in for Latent Encoding in Learned DNA Storage
Cihan Ruan, Lebin Zhou, Rongduo Han +6
DNA storage has matured from concept to practical stage, yet its integration with neural compression pipelines remains inefficient. Early DNA encoders applied redundancy-heavy cons…