In-Context Credit Assignment via the Core

Abstract:We propose incentive-aligned mechanisms for in-context credit assignment: the task of assigning credit for AI-generated content among creators whose intellectual property appears in the context window. Our approach is based on the least core solution concept from cooperative game theory, which distributes value in a way that is as stable as possible by ensuring that no subset of creators is significantly under-compensated relative to the value they could generate on their own. We develop algorithms for approximating the least core, which leverage novel routines for constraint seeding and constraint separation. On a web retrieval credit assignment task, we find that our approaches approximate the least core using an order of magnitude fewer LLM calls compared to alternative methods.

Computer Science and Game Theory (cs.GT); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2605.06920 [cs.GT]

(or arXiv:2605.06920v2 [cs.GT] for this version)

https://doi.org/10.48550/arXiv.2605.06920 Focus to learn more arXiv-issued DOI via DataCite

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