In-Context Credit Assignment via the Core

2026-05-07

AI Summary

This work addresses the challenge of fairly attributing credit among multiple creators of AI-generated content—such as code, news articles, or short videos—within a contextual window. To this end, we propose an incentive-compatible credit allocation mechanism grounded in cooperative game theory, specifically leveraging the least core to guarantee that no subset of contributors is significantly undervalued. As the first study to apply the least core to contextual credit assignment, we introduce an efficient approximation algorithm that integrates constraint seeding and constraint separation techniques, substantially reducing the number of large language model (LLM) queries required. Empirical evaluation on a web retrieval credit allocation task demonstrates that our method achieves a high-quality approximation of the least core with orders of magnitude fewer LLM calls compared to baseline approaches.

Abstract

We propose incentive-aligned mechanisms for in-context credit assignment: the task of assigning credit for AI-generated content (e.g. code, news articles, short-form videos) 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 are capable of approximating the least core using orders of magnitude fewer LLM calls compared to alternative methods.

Problem

• in-context credit assignment
• AI-generated content
• intellectual property
• cooperative game theory
• least core

Innovation

• in-context credit assignment
• least core
• cooperative game theory
• constraint separation
• LLM-efficient approximation