ERGO: Entropy-guided Resetting for Generation Optimization in Multi-turn
  Language Models
Authors: name: Haziq Mohammad Khalid, name: Athikash Jeyaganthan, name: Timothy Do, name: Yicheng Fu, name: Sean O'Brien, name: Vasu Sharma, name: Kevin Zhu

Large Language Models (LLMs) suffer significant performance degradation in
multi-turn conversations when information is presented incrementally. Given
that multi-turn conversations characterize everyday interactions with LLMs,
this degradation poses a severe challenge to real world usability. We
hypothesize that abrupt increases in model uncertainty signal misalignment in
multi-turn LLM interactions, and we exploit this insight to dynamically realign
conversational context. We introduce ERGO (Entropy-guided Resetting for
Generation Optimization), which continuously quantifies internal uncertainty
via Shannon entropy over next token distributions and triggers adaptive prompt
consolidation when a sharp spike in entropy is detected. By treating
uncertainty as a first class signal rather than a nuisance to eliminate, ERGO
embraces variability in language and modeling, representing and responding to
uncertainty. In multi-turn tasks with incrementally revealed instructions, ERGO
yields a 56.6% average performance gain over standard baselines, increases
aptitude (peak performance capability) by 24.7%, and decreases unreliability
(variability in performance) by 35.3%, demonstrating that uncertainty aware
interventions can improve both accuracy and reliability in conversational AI.