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20222026
most citedRisk Perspective Exploration in Distributional Reinforcement Learning

1 citations · 2 across the 9 of their papers we have counts for

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cs.LG2025★ 1 cited

LLM Agents for Bargaining with Utility-based Feedback

Jihwan Oh

Bargaining, a critical aspect of real-world interactions, presents challenges for large language models (LLMs) due to limitations in strategic depth and adaptation to complex human…

cs.LG2025

From Belief Entrenchment to Robust Reasoning in LLM Agents

Jihwan Oh, Minchan Jeong, Jongwoo Ko +1

Multi-Agent Debate (MAD) has emerged as a promising inference scaling method for Large Language Model (LLM) reasoning. However, it frequently suffers from belief entrenchment, wher…