3 papers
cs.CL2026
DRIP-R: A Benchmark for Decision-Making and Reasoning Under Real-World Policy Ambiguity in the Retail Domain
Hsuvas Borkakoty, Sebastian Pohl, Cheng Wang +2
LLM-based agents are increasingly deployed for routine but consequential tasks in real-world domains, where their behavior is governed by inherently ambiguous domain policies that…
cs.MA2026
Towards Self-Improving Error Diagnosis in Multi-Agent Systems
Jiazheng Li, Emine Yilmaz, Bei Chen +1
Large Language Model (LLM)-based Multi-Agent Systems (MAS) enable complex problem-solving but introduce significant debugging challenges, characterized by long interaction traces,…
cs.CL2024
Efficient Performance Tracking: Leveraging Large Language Models for Automated Construction of Scientific Leaderboards
Furkan Şahinuç, Thy Thy Tran, Yulia Grishina +3
Scientific leaderboards are standardized ranking systems that facilitate evaluating and comparing competitive methods. Typically, a leaderboard is defined by a task, dataset, and e…