mlsanityData to 7 Oct 2026

MEMAUDIT: An Exact Package-Oracle Evaluation Protocol for Budgeted Long-Term LLM Memory Writing

arXiv 2605.02199 ↗First posted 4 May 2026cs.AIcited by 3

On the map: LLM agents and tool use → Agent memory systems

Abstract (arXiv)

Long-term LLM agents must compress streams of past interactions into persistent memory before future queries are known. Existing evaluations usually measure final question-answering accuracy, which entangles memory writing with retrieval, prompting, and reader reasoning. We introduce MEMAUDIT, an exact packageoracle evaluation protocol for budgeted long-term memory writing. A MEMAUDIT package fixes an experience stream, candidate memory representations, storage costs, semantic evidence units, future-query requirements, and a budget, turning write-time memory selection into a finite auditable optimization problem with a certified denominator. We instantiate this protocol with a concave-over-modular semantic coverage objective under storage and one-representation-per-experience constraints, and compute exact package optima using branch-and-bound with MILP certification. Across controlled exact packages, validity-heavy stress tests, human-audited natural support slices, and exported Mem0, A-Mem, and Letta stores, MEMAUDIT separates representation quality, validity-state preservation, and budget-aware selection effects that end-to-end QA cannot localize. The resulting artifact provides reusable package generators, certified solvers, natural package exports, external-system scorers, and cached reproducibility metadata for evaluating what memory writers actually preserve under fixed storage budgets.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Agent memory systems, one of 16 directions in LLM agents and tool use. Builds on 9 of its 15 references that are in the map. First posted on arXiv 4 May 2026.

Builds on

9 of 15 references.

Closest ancestors · 4

Same direction, newest first

  1. 2025Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory28 Apr
  2. 2025A-MEM: Agentic Memory for LLM Agents17 Feb
  3. 2024MemSim: A Bayesian Simulator for Evaluating Memory of LLM-based Personal Assistants30 Sep
  4. 2024Evaluating Very Long-Term Conversational Memory of LLM Agents27 Feb

Other references · 5

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
  3. 2023MemGPT: Towards LLMs as Operating Systemscited by 1,295
  4. 2024LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memorycited by 656
  5. 2023MemoryBank: Enhancing Large Language Models with Long-Term Memorycited by 539

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026Auditing Long-Term Memory Evaluation: Repeated Judging, Reader Variation, and Negative Controls
  2. 2026Zero-Mem: Zero-Token Memory Operations for LLM Agents
  3. 2026A-TMA: Decoupling State-Aware Memory Failures in Long-Term Agent Memory
  4. 2026Mem++: Non-Destructive Memory for Long-Term Organizational LLM Agents
  5. 2026MemOps: Benchmarking Lifecycle Memory Operations in Long-Horizon Conversations
  6. 2025MemR3: Memory Retrieval via Reflective Reasoning for LLM Agents
  7. 2025Memory in Large Language Models: Mechanisms, Evaluation and Evolution
  8. 2026Diagnosing Retrieval vs. Utilization Bottlenecks in LLM Agent Memory