Credit Where Credit Isn't Due: Pricing Reputation in Multi-Agent Shared Memory

Shared memory enables multi-agent systems to reuse information across tasks and learn source reputation from task feedback. However, when a reader agent answers correctly from its own knowledge even though the facts it retrieved are false, the success rewards the writer of those facts and raises its reputation. Evidence accumulated while a writer was reliable also outlives a change in its behavior, so a writer that turns unreliable keeps its reputation. Our experiments show that when writers turn from correct to false facts, retrieval ranked by reputation learned from raw task outcomes returns a correct fact on only 20.4% of reads, less than half the rate of random selection. To address these problems, we propose Priced Reputation Learning (PRL) for shared knowledge graph memory. PRL credits the writers of a successful task only in proportion to the probability that the same reader would have failed with no memory. PRL further adds a forgetting mechanism so that reputation can follow writers that change. Across six memory conditions and twelve readers from five model families, PRL selects correct facts where reputation learned from raw outcomes serves almost none, and reaches the writer oracle to within reader variation.