16 citations · 22 across the 16 of their papers we have counts for
20 papers
Agent Error Dataset: Scaling 50,000 Error--Diagnosis Pairs for Failure Analysis and Error-Aware Post-Training
Kunlun Zhu, Xuyan Ye, Yibo Li +3
An unsuccessful LLM agent rollout contains more information than its final reward: the observations available to the agent, the actions it chose, and the environment's responses. R…
Learning Meta-Skills for Agent Harness Design in Test-Time AI4AI
Cheng Qian, Kunlun Zhu, Beibin Li +2
Agent performance depends on both reasoning ability and the environment in which it acts. We study test-time AI-for-AI, asking how a Builder can learn to construct better execution…
Relic: From Multi-Agent Collaboration to Persistent Organizational Capability
Hongyi Du, Tianyi Zhang, Weijia Zhang +9
Multiple agents may often conflict in an organization: for example, one coding agent changes an interface in a repository, but another continues to develop on the old version where…
CUADebug: Diagnosing and Repairing Computer-Use Agent Failures
Weijia Zhang, Kunlun Zhu, Zeyi Liu +8
Computer-use agents (CUAs) interact with graphical interfaces through screenshots and low-level mouse and keyboard actions, yet the causal error may precede the terminal failure. W…
Apodex Discovery: Reality Benchmarks and Environments for Evaluating and Building Discoverative Artificial Intelligence
Brian Wang, Bin Feng, Xiaoman Pan +26
Apollo did not reach the Moon merely because its engineers could solve difficult equations. It succeeded by turning a distant ambition into a mission architecture of explicit objec…
LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers
Tao Feng, Fangxu Yu, Haozhen Zhang +9
No single large language model (LLM) is optimal across all queries and budget constraints, making model routing essential for cost-effective deployment. Existing routers adopt dive…
BioInsight: Multi-Agent Orchestration for Interactive Biomedical Knowledge Discovery
Jieyi Wang, Bingxuan Li, Nanyi Jiang +9
Biomedical deep-research systems increasingly retrieve and synthesize scientific evidence, but their outputs typically collapse heterogeneous evidence into static text, making prov…
AgentDebugX: An Open-Source Toolkit for Failure Observability, Attribution, and Recovery in LLM Agents
Kunlun Zhu, Xuyan Ye, Zhiguang Han +9
LLM agent failures are difficult to debug because the step where an error surfaces is often not the one that caused it. Existing observability tools replay execution traces but pro…
Advancing Creative Physical Intelligence in Large Multimodal Models
Cheng Qian, Hyeonjeong Ha, Jiayu Liu +10
Large multimodal models (LMMs) have rapidly advanced in perception and reasoning; however, it remains unclear whether these capabilities generalize to discovering visually grounded…
Augmenting Interface Usability Heuristics for Reliable Computer-Use Agents
Jiateng Liu, Rushi Wang, Bingxuan Li +6
Recent advances have enabled general computer-use agents that interpret screens and execute grounded actions from human instructions, yet they still struggle to generalize to unsee…
SWE-Bench Mobile: Can Large Language Model Agents Develop Industry-Level Mobile Applications?
Muxin Tian, Zhe Wang, Blair Yang +7
Can large language model agents develop industry-level mobile applications? We introduce \textbf{SWE-Bench Mobile}, a benchmark for evaluating coding agents on realistic software e…
SIRAJ: Diverse and Efficient Red-Teaming for LLM Agents via Distilled Structured Reasoning
Kaiwen Zhou, Ahmed Elgohary, A S M Iftekhar +1
The ability of LLM agents to plan and invoke tools exposes them to new safety risks, making a comprehensive red-teaming system crucial for discovering vulnerabilities and ensuring…
TinyScientist: An Interactive, Extensible, and Controllable Framework for Building Research Agents
Haofei Yu, Keyang Xuan, Fenghai Li +6
Automatic research with Large Language Models (LLMs) is rapidly gaining importance, driving the development of increasingly complex workflows involving multi-agent systems, plannin…
ProtocolBench: Which LLM MultiAgent Protocol to Choose?
Hongyi Du, Jiaqi Su, Jisen Li +6
As large-scale multi-agent systems evolve, the communication protocol layer has become a critical yet under-evaluated factor shaping performance and reliability. Despite the existe…
Where LLM Agents Fail and How They can Learn From Failures
Kunlun Zhu, Zijia Liu, Bingxuan Li +15
Large Language Model (LLM) agents, which integrate planning, memory, reflection, and tool-use modules, have shown promise in solving complex, multi-step tasks. Yet their sophistica…
SafeScientist: Toward Risk-Aware Scientific Discoveries by LLM Agents
Kunlun Zhu, Jiaxun Zhang, Ziheng Qi +6
Recent advancements in large language model (LLM) agents have significantly accelerated scientific discovery automation, yet concurrently raised critical ethical and safety concern…
Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems
Bang Liu, Xinfeng Li, Jiayi Zhang +45
The advent of large language models (LLMs) has catalyzed a transformative shift in artificial intelligence, paving the way for advanced intelligent agents capable of sophisticated…
MultiAgentBench: Evaluating the Collaboration and Competition of LLM agents
Kunlun Zhu, Hongyi Du, Zhaochen Hong +8
Large Language Models (LLMs) have shown remarkable capabilities as autonomous agents, yet existing benchmarks either focus on single-agent tasks or are confined to narrow domains,…
ResearchTown: Simulator of Human Research Community
Haofei Yu, Zhaochen Hong, Zirui Cheng +5
Large Language Models (LLMs) have demonstrated remarkable potential in scientific domains, yet a fundamental question remains unanswered: Can we simulate human research communities…
How Far Are We From AGI: Are LLMs All We Need?
Tao Feng, Chuanyang Jin, Jingyu Liu +5
The evolution of artificial intelligence (AI) has profoundly impacted human society, driving significant advancements in multiple sectors. AGI, distinguished by its ability to exec…