Beyond Downstream QA: Rethinking How We Evaluate Agent Memory

Key points

• We show this evaluation paradigm is incomplete: it conflates memory correctness with retrieval and generation quality, producing both false positives where correct responses mask broken memory and false negatives where incorrect responses blame correct memory for retrieval failures.1
• We introduce FEB-30, a benchmark of 30 fact-evolution trajectories spanning 6 diagnostic categories and 3 difficulty levels, constructed via paradigm inversion: documents are generated from known fact trajectories, yielding deterministic ground truth without human annotation.1
• Applying FEB-30 to a representative memory system, we demonstrate that intrinsic and extrinsic evaluation diverge by an average of 16 percentage points, with 44% of trajectories exceeding meaningful divergence thresholds.1
• We release the benchmark, document generation pipeline, and scoring framework to support reproducible evaluation of agent memory systems.1

Sources (1)

• [1]Beyond Downstream QA: Rethinking How We Evaluate Agent Memory
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 04:58 PM

  Agent memory benchmarks evaluate whether an agent's responses reflect correct knowledge, measuring downstream question-answering accuracy as a proxy for memory quality.

  We show this evaluation paradigm is incomplete: it conflates memory correctness with retrieval and generation quality, producing both false positives where correct responses mask broken memory and false negatives where incorrect responses blame correct memory for retrieval failures.

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