The Geometry of Agent Memory: Forecasting Retrieval-Bound Memory Failures, and Characterizing Its Limits

Key points

• We evaluate four memory implementations with dense or hybrid retrieval and one observation-summary comparator.1
• A logistic classifier uses 25 features describing query proximity, memory-conversation correspondence, and conversation structure.1
• A fixed-store context-selection experiment finds higher accuracy with proximity-ranked memories than with random subsets, consistent with ordinary retrieval relevance.1
• The results characterize associations between memory representations and correctness, rather than establish a reliable deployment diagnostic.1

Sources (1)

• [1]The Geometry of Agent Memory: Forecasting Retrieval-Bound Memory Failures, and Characterizing Its Limits
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 01:56 PM

  We study whether geometric relationships between conversation turns, memory texts, and queries can predict answer correctness before generation.

  We evaluate four memory implementations with dense or hybrid retrieval and one observation-summary comparator.

Extractive summary: sentences quoted from the sources.