EmoDistill decomposes emotional strategy into IQL-based selection and LoRA-based expression, yielding SLM agents with highest utility across four negotiation domains from offline data.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Stronger reasoning models in LLMs reduce behavioral negotiation by defaulting to authority outcomes in multi-agent settings, unlike structured scaffolds that enable concessions.
citing papers explorer
-
EmoDistill: Offline Emotion Skill Distillation for Language Model Agents in Adversarial Negotiation
EmoDistill decomposes emotional strategy into IQL-based selection and LoRA-based expression, yielding SLM agents with highest utility across four negotiation domains from offline data.
-
When Reasoning Models Hurt Behavioral Simulation: A Solver-Sampler Mismatch in Multi-Agent LLM Negotiation
Stronger reasoning models in LLMs reduce behavioral negotiation by defaulting to authority outcomes in multi-agent settings, unlike structured scaffolds that enable concessions.