Entity-Collision: A Stratified Protocol for Attributing Retrieval Lift in Agent Memory

📰 ArXiv cs.AI

Learn to attribute retrieval lift in agent memory using Entity-Collision, a stratified protocol that separates lexical leakage from tag-mixing, improving benchmark accuracy

advanced Published 29 May 2026
Action Steps
  1. Implement the Entity-Collision protocol in your agent-memory benchmark
  2. Stratify queries by discriminator tag to isolate tag-mixing effects
  3. Pin the BM25 floor by construction to control for lexical leakage
  4. Compare the retrieval lift of different retrievers using the Entity-Collision protocol
  5. Analyze the results to attribute the lift to specific factors
Who Needs to Know This

Researchers and developers working on agent-memory benchmarks and information retrieval systems can benefit from this protocol to improve the accuracy of their evaluations. It helps to identify the actual lift in retrieval performance, rather than being confounded by lexical leakage or tag-mixing

Key Insight

💡 Entity-Collision protocol helps to accurately attribute retrieval lift in agent memory by controlling for lexical leakage and tag-mixing

Share This
🚀 Improve agent-memory benchmarks with Entity-Collision, a protocol that separates lexical leakage from tag-mixing #informationretrieval #agentmemory

Key Takeaways

Learn to attribute retrieval lift in agent memory using Entity-Collision, a stratified protocol that separates lexical leakage from tag-mixing, improving benchmark accuracy

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