10 papers
SeVeR: Selective Visual Exposure and Retrieval for 3D Medical Image Question Answering
Yaojun Hu, Danyang Tu, Yang Liu +8
Volumetric medical VQA requires reasoning over long and redundant 3D visual token sequences, especially in multi-sequence MRI where complementary modalities provide diverse diagnos…
PhysMRV: Physical Memory Retrieval and Verification for Physics Plausibility Reasoning
Wenyuan Wang, Lianyu Hu, Hao Wang +1
Video-language models (VLMs) have achieved remarkable performance on video understanding and visual question answering, yet they remain unreliable in reasoning about physical plaus…
Agents' Last Exam
Yiyou Sun, Xinyang Han, Weichen Zhang +378
Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically meaningful deployment across many professional d…
Agent Planning Benchmark: A Diagnostic Framework for Planning Capabilities in LLM Agents
Haoyu Sun, Wenxuan Wang, Mingyang Song +5
Planning is central to LLM agents: before acting, an agent must decompose goals, select tools, reason over constraints, and decide when a task is infeasible. Yet existing agent eva…
BreastGPT: A Multimodal Large Language Model for the Full Spectrum of Breast Cancer Clinical Routine
Yang Liu, Jiajin Zhang, Danyang Tu +8
Breast cancer remains a leading cause of cancer-related mortality among women. Its clinical management requires multimodal reasoning across a clinical workflow that spans \textit{s…
PhotoCraft: Agentic Reasoning with Hierarchical Self-Evolving Memory for Deep Image Search
Kailin Lyu, Zhiqiang Yuan, Jianwei He +9
Deep Image Search requires multi-step reasoning over rich contextual cues, such as time, location, and event relations. However, most existing LLM-based agents are stateless and re…
Video-Based Reward Modeling for Computer-Use Agents
Linxin Song, Jieyu Zhang, Huanxin Sheng +6
Computer-using agents (CUAs) are becoming increasingly capable; however, it remains difficult to scale evaluation of whether a trajectory truly fulfills a user instruction. In this…
KernelClaw: A Multi-Agent Framework for GPU Kernel Optimization with Dual-Memory Mechanisms
Qitong Sun, Jun Han, Tianlin Li +6
Improving GPU kernel efficiency is crucial for efficient AI systems. Recent work has explored leveraging large language models (LLMs) for GPU kernel generation and optimization. Ho…
From Failure to Mastery: Generating Hard Samples for Tool-use Agents
Bingguang Hao, Zengzhuang Xu, Yuntao Wen +11
The advancement of LLM agents with tool-use capabilities requires diverse and complex training corpora. Existing data generation methods, which predominantly follow a paradigm of r…
FunReason-MT Technical Report: Advanced Data Synthesis Solution for Real-world Multi-Turn Tool-use
Zengzhuang Xu, Bingguang Hao, Zechuan Wang +14
Function calling (FC) empowers large language models (LLMs) and autonomous agents to interface with external tools, a critical capability for solving complex, real-world problems.…