3 papers
cs.CL2026
Learning to Negotiate: Multi-Agent Deliberation for Collective Value Alignment in LLMs
Panatchakorn Anantaprayoon, Nataliia Babina, Nima Asgharbeygi +1
LLM alignment has progressed in single-agent settings through paradigms such as RL with human feedback (RLHF), while recent work explores scalable alternatives such as RL with AI f…
cs.CL2026
Distilling Reasoning Without Knowledge: A Framework for Reliable LLMs
Auksarapak Kietkajornrit, Jad Tarifi, Nima Asgharbeygi
Fact-seeking question answering with large language models (LLMs) remains unreliable when answers depend on up-to-date or conflicting information. Although retrieval-augmented and…
cs.CL2025
Dynamic Alignment for Collective Agency: Toward a Scalable Self-Improving Framework for Open-Ended LLM Alignment
Panatchakorn Anantaprayoon, Nataliia Babina, Jad Tarifi +1
Large Language Models (LLMs) are typically aligned with human values using preference data or predefined principles such as helpfulness, honesty, and harmlessness. However, as AI s…