2 citations · 4 across the 25 of their papers we have counts for
27 papers
CreepScope: Measuring Post-Use Authorization Exposure in Tool-Using Agents
Haoran Yu, Lifei Liu, Danping Zhang +4
Authorization checks at individual tool calls do not reveal how long earlier authority remains usable. We study this lifecycle failure as privilege creep: a capability stays reacha…
SafeCoEvo: Co-Evolving Safety Harnesses and Guards for LLM Agents at Test-Time
Yu Cheng, Yongkang Hu, Shuaijie Ma +12
LLM agents deployed in real-world environments continually encounter new tasks and safety risks, while execution feedback typically becomes available only after each task is comple…
Bilevel Coordinated Reflection: A Game-Theoretic Approach to Multi-Agent LLM Systems
Yihang Chen, Yuxiang Chen, Yuxuan Huang +3
Multi-agent LLM systems commonly use an orchestrator to decompose a task for a team of workers and then improve through textual reflection. Despite strong empirical results, these…
Large Discovery Models: Empirically-grounded Model-Based Open-Ended Search
Zhongwei Yu, Yan Song, Xue Yan +9
Scientific discovery often involves optimising expensive-to-evaluate objectives over vast, structured, and open-ended hypothesis spaces, such as molecules, protein sequences, and c…
Noise Floor Audit for Agent Benchmarks
Yihang Chen, Pin Qian, Su Wang +4
We audit measurement variability for 3 native tool-calling endpoints across 2 providers on the official BFCL multiple and parallel categories, using matched AST grading. At tempera…
Explicit State Elicitation Is Not Enough: A Controlled Audit of Memory-Policy Classification
Yihang Chen, Pin Qian, Su Wang +4
Personalized agents must decide whether retrieved user memory should be used, ignored, updated, or queried before it affects a current task. We use this setting to develop an empir…
The Path to Self-Evolving Clinical Systems: Scaling Medical Agents from Assistance to Autonomy
Chunzheng Zhu, Lei Tian, Bohan Tan +15
The growing ability of large language models and vision-language models to jointly interpret and reason over images and text is reshaping medical imaging AI, moving it from task-sp…
BAP-SQL: Budget-Aware Observation Planning for Agentic Text-to-SQL
Chong Peng, Pin Qian, Su Wang +2
Tool-using agents do not merely consume observations: their actions determine what arrives next. In agentic text-to-SQL, a broad query can spend context and database work before us…
When Should Active RAG Retrieve? A Budget-Aware Evaluation of Utility, Calibration, and Cost
Pin Qian, Su Wang, Chong Peng +5
Active RAG systems decide when to retrieve external knowledge during generation, making them a budget-sensitive case of agentic RAG and self-adaptive retrieval. Yet evaluations oft…
Toward User-Conditioned Evaluation of Personal LLM Agents under Temporal Interventions
Pin Qian, Su Wang, Yihang Chen +5
Personal agents maintain memories, learned skills, tool configurations, and policy state that evolve with each user. Existing agent benchmarks often evaluate these capabilities in…
Phantom Guardrails: When Self-Improving Agent Harnesses Fix Failures That Never Happened
Su Wang, Pin Qian, Yifan Lin +5
Self-improving AI agents are designed to learn from their mistakes. We show they can also hallucinate mistakes that never happened. We study this failure mode in automated harness…
Operational Reframing and Approval-Framed Delegation in Multi-Agent LLM Safety
Lifei Liu, Haoran Yu, Xiaochong Jiang +3
Safety evaluations of multi-agent LLM systems often compare a direct prompt with a planner-executor pipeline and report the difference as a single "pipeline effect." We argue that…
Habituation at the Gate: Rising Approval and Declining Scrutiny in Human Review of AI Agent Code
Haoran Yu, Lifei Liu, Xiaochong Jiang +4
As AI coding agents (e.g., GitHub Copilot, Devin, OpenAI Codex, Cursor) submit pull requests to open-source repositories at scale, a key question arises: do human reviewers gradual…
Beyond Simpson's Paradox: A Cascade of Confounders in AI Agent Pull-Request Co-Authorship
Haoran Yu, Xiaochong Jiang, Lifei Liu +3
Pooled across five AI coding agents, pull requests (PRs) with a human Co-Authored-By trailer merge less often than purely-autonomous ones (53.8% vs. 79.8%) -- yet this aggregate fi…
When Safe Skills Collide: Measuring Compositional Risk in Agent Skill Ecosystems
Su Wang, Pin Qian, Yihang Chen +6
LLM agents increasingly rely on community-contributed skills that expand an agent's operational capability set. We study a core safety problem in agentic AI systems: whether indivi…
Relevant Is Not Warranted: Evidence-Force Calibration for Cited RAG
Pin Qian, Su Wang, Xiaoyuan Wang +7
Cited RAG evaluation often treats visible sources as a grounding signal, but a real, topically relevant citation can still under-warrant the attached wording. We study this diagnos…
Holder Policy Optimisation
Yuxiang Chen, Dingli Liang, Yihang Chen +8
Group Relative Policy Optimisation (GRPO) enhances large language models by estimating advantages across a group of sampled trajectories. However, mapping these trajectory-level ad…
Does RAG Know When Retrieval Is Wrong? Diagnosing Context Compliance under Knowledge Conflict
Yihang Chen, Pin Qian, Su Wang +4
Retrieval-Augmented Generation (RAG) is usually evaluated by whether the final answer is correct. Under knowledge conflict, this hides a key question: did the model follow retrieve…
Web2BigTable: A Bi-Level Multi-Agent LLM System for Internet-Scale Information Search and Extraction
Yuxuan Huang, Yihang Chen, Zhiyuan He +6
Agentic web search increasingly faces two distinct demands: deep reasoning over a single target, and structured aggregation across many entities and heterogeneous sources. Current…
ProMMSearchAgent: A Generalizable Multimodal Search Agent Trained with Process-Oriented Rewards
Wentao Yan, Shengqin Wang, Huichi Zhou +4
Training multimodal agents via reinforcement learning for knowledge-intensive visual reasoning is fundamentally hindered by the extreme sparsity of outcome-based supervision and th…
DR-MMSearchAgent: Deepening Reasoning in Multimodal Search Agents
Shengqin Wang, Wentao Yan, Huichi Zhou +4
Agentic multimodal models have garnered significant attention for their ability to leverage external tools to tackle complex tasks. However, it is observed that such agents often m…
EE-MCP: Self-Evolving MCP-GUI Agents via Automated Environment Generation and Experience Learning
Tiantian He, Yihang Chen, Keyue Jiang +4
Computer-use agents that combine GUI interaction with structured API calls via the Model Context Protocol (MCP) show promise for automating software tasks. However, existing approa…
Memento-Skills: Let Agents Design Agents
Huichi Zhou, Siyuan Guo, Anjie Liu +14
We introduce \emph{Memento-Skills}, a generalist, continually-learnable LLM agent system that functions as an \emph{agent-designing agent}: it autonomously constructs, adapts, and…
TAME: A Trustworthy Test-Time Evolution of Agent Memory with Systematic Benchmarking
Yu Cheng, Yongkang Hu, Jiuan Zhou +8
Test-time evolution of agent memory represents a pivotal paradigm for advancing AGI, as it strengthens complex reasoning through experience accumulation without requiring parameter…