3 citations · 4 across the 6 of their papers we have counts for
12 papers · 1 filter
EvoArena: Tracking Memory Evolution for Robust LLM Agents in Dynamic Environments
Jundong Xu, Qingchuan Li, Jiaying Wu +11
Large language model (LLM) agents have achieved strong performance on a wide range of benchmarks, yet most evaluations assume static environments. In contrast, real-world deploymen…
MAS-ZERO: Designing Multi-Agent Systems with Zero Supervision
Zixuan Ke, Austin Xu, Yifei Ming +4
Multi-agent systems (MAS) leveraging the impressive capabilities of Large Language Models (LLMs) hold significant potential for tackling complex tasks. However, most current MAS de…
Foundational Automatic Evaluators: Scaling Multi-Task Generative Evaluator Training for Reasoning-Centric Domains
Austin Xu, Xuan-Phi Nguyen, Yilun Zhou +3
Finetuning specialized generative evaluators has emerged as a popular paradigm to meet the increasing demand for scalable evaluation during both training and test-time. However, re…
Demystifying Domain-adaptive Post-training for Financial LLMs
Zixuan Ke, Yifei Ming, Xuan-Phi Nguyen +2
Domain-adaptive post-training of large language models (LLMs) has emerged as a promising approach for specialized domains such as medicine and finance. However, significant challen…
Synthesizing Agentic Data for Web Agents with Progressive Difficulty Enhancement Mechanisms
Shrey Pandit, Xuan-Phi Nguyen, Yifei Ming +4
Web-based 'deep research' agents aim to solve complex question - answering tasks through long-horizon interactions with online tools. These tasks remain challenging, as the underly…
Direct Judgement Preference Optimization
Peifeng Wang, Austin Xu, Yilun Zhou +2
Auto-evaluation is crucial for assessing response quality and offering feedback for model development. Recent studies have explored training large language models (LLMs) as generat…