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Interaction Dynamics as a Reward Signal for LLMs

arXiv — cs.LG•Wednesday, November 12, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
The recent submission of 'Interaction Dynamics as a Reward Signal for LLMs' to arXiv presents a significant advancement in the field of conversational AI. The paper introduces TRACE, a novel reward model that leverages the geometric properties of dialogue, termed 'conversational geometry.' This approach reveals that the dynamics of interaction are as predictive of success as the content of the dialogue itself. The findings indicate that a reward model based solely on these structural signals achieved a pairwise accuracy of 68.20%, closely rivaling the 70.04% accuracy of a powerful baseline LLM that analyzes full transcripts. Furthermore, a hybrid model that integrates both interaction dynamics and textual analysis achieved the highest performance at 80.17%. This research underscores the importance of how agents communicate, suggesting that enhancing interaction dynamics could lead to more effective conversational agents and providing a new framework for aligning and diagnosing AI commu…
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