Curating Always-Loaded Context for LLM Agents: A Capacitated Assortment Model with Censored Feedback

The paper studies how to curate the fixed context files that large language model agents load at the start of each session. It models this as a capacitated assortment problem, proving bounds on optimal file size and showing that simply appending all positive‑value instructions can be far suboptimal. The authors analyze censored feedback from past sessions, derive evidence‑collection thresholds for adding instructions, bound regret under limited human review, and empirically demonstrate that irrelevant rules in real context files hurt model compliance.

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• llms
• agents