3 papers
cs.CL2026
Time to REFLECT: Can We Trust LLM Judges for Evidence-based Research Agents?
Leyao Wang, Yanan He, Peng Chen +5
Deep research agents increasingly automate complex information-seeking tasks, producing evidence-grounded reports via multi-step reasoning, tool use, and synthesis. Their growing r…
cs.CL2026
References Improve LLM Alignment in Non-Verifiable Domains
Kejian Shi, Yixin Liu, Peifeng Wang +3
While Reinforcement Learning with Verifiable Rewards (RLVR) has shown strong effectiveness in reasoning tasks, it cannot be directly applied to non-verifiable domains lacking groun…
cs.CL2025
On Evaluating LLM Alignment by Evaluating LLMs as Judges
Yixin Liu, Pengfei Liu, Arman Cohan
Alignment with human preferences is an important evaluation aspect of LLMs, requiring them to be helpful, honest, safe, and to precisely follow human instructions. Evaluating large…