6 papers
Spider-Sense: Intrinsic Risk Sensing for Efficient Agent Defense with Hierarchical Adaptive Screening
Zhenxiong Yu, Zhi Yang, Zhiheng Jin +19
As large language models (LLMs) evolve into autonomous agents, their real-world applicability has expanded significantly, accompanied by new security challenges. Most existing agen…
EvoFSM: Controllable Self-Evolution for Deep Research with Finite State Machines
Shuo Zhang, Chaofa Yuan, Ryan Guo +11
While LLM-based agents have shown promise for deep research, most existing approaches rely on fixed workflows that struggle to adapt to real-world, open-ended queries. Recent work…
MemGovern: Enhancing Code Agents through Learning from Governed Human Experiences
Qihao Wang, Ziming Cheng, Shuo Zhang +12
While autonomous software engineering (SWE) agents are reshaping programming paradigms, they currently suffer from a "closed-world" limitation: they attempt to fix bugs from scratc…
REMA: A Unified Reasoning Manifold Framework for Interpreting Large Language Model
Bo Li, Guanzhi Deng, Ronghao Chen +5
Understanding how Large Language Models (LLMs) perform complex reasoning and their failure mechanisms is a challenge in interpretability research. To provide a measurable geometric…
GitTaskBench: A Benchmark for Code Agents Solving Real-World Tasks Through Code Repository Leveraging
Ziyi Ni, Huacan Wang, Shuo Zhang +15
Beyond scratch coding, exploiting large-scale code repositories (e.g., GitHub) for practical tasks is vital in real-world software development, yet current benchmarks rarely evalua…
RepoMaster: Autonomous Exploration and Understanding of GitHub Repositories for Complex Task Solving
Huacan Wang, Ziyi Ni, Shuo Zhang +11
The ultimate goal of code agents is to solve complex tasks autonomously. Although large language models (LLMs) have made substantial progress in code generation, real-world tasks t…