7 papers
EvoMemBench: Benchmarking Agent Memory from a Self-Evolving Perspective
Yuyao Wang, Zhongjian Zhang, Mo Chi +7
Recent benchmarks for Large Language Model (LLM) agents mainly evaluate reasoning, planning, and execution. However, memory is also essential for agents, as it enables them to stor…
Multi-Paradigm Agent Interaction in Practice:A Systematic Analysis of Generator-Evaluator, ReAct Loop,and Adversarial Evaluation in the buddyMe Framework
Xiaohua Wang, Chao Han, Kai Yu +2
The rapid evolution of Large Language Model (LLM) agents has produced diverse interaction paradigms, yet few production systems integrate multiple paradigms within a unified archit…
Artificial Intelligence-Assistant Cardiotocography: Unified Model for Signal Reconstruction, Fetal Heart Rate Analysis, and Variability Assessment
Xiaohua Wang, Kai Yu, XuXiao Liang +2
The monitoring of fetal heart rate (FHR) and the assessment of its variability are crucial for preventing fetal compromise and adverse outcomes. However, traditional methods encoun…
Good to Go: The LOOP Skill Engine That Hits 99% Success and Slashes Token Usage by 99% via One-Shot Recording and Deterministic Replay
Xiaohua Wang, Kai Yu, XuXiao Liang +2
Deploying AI agents for repetitive periodic tasks exposes a critical tension: Large Language Models (LLMs) offer unmatched flexibility in tool orchestration, yet their inherent sto…
No Action Without a NOD: A Heterogeneous Multi-Agent Architecture for Reliable Service Agents
Zixu Yang, Hang Zheng, Nan Jiang +5
Large language model (LLM) agents have increasingly advanced service applications, such as booking flight tickets. However, these service agents suffer from unreliability in long-h…
Does Pass Rate Tell the Whole Story? Evaluating Design Constraint Compliance in LLM-based Issue Resolution
Kai Yu, Zhenhao Zhou, Junhao Zeng +8
Repository-level issue resolution benchmarks have become a standard testbed for evaluating LLM-based agents, yet success is still predominantly measured by test pass rates. In prac…