mlsanityData to 7 Oct 2026

Drawing on Memory: Dual-Trace Encoding Improves Cross-Session Recall in LLM Agents

arXiv 2604.12948 ↗First posted 14 Apr 2026cs.AIcited by 0

On the map: LLM reasoning → Reasoning for agents, planning and code

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Abstract (arXiv)

LLM agents with persistent memory store information as flat factual records, providing little context for temporal reasoning, change tracking, or cross-session aggregation. Inspired by the drawing effect [3], we introduce dual-trace memory encoding. In this method, each stored fact is paired with a concrete scene trace, a narrative reconstruction of the moment and context in which the information was learned. The agent is forced to commit to specific contextual details during encoding, creating richer, more distinctive memory traces. Using the LongMemEval-S benchmark (4,575 sessions, 100 recall questions), we compare dual-trace encoding against a fact-only control with matched coverage and format over 99 shared questions. Dual-trace achieves 73.7% overall accuracy versus 53.5%, a +20.2 percentage point (pp) gain (95% CI: [+12.1, +29.3], bootstrap p < 0.0001). Gains concentrate in temporal reasoning (+40pp), knowledge-update tracking (+25pp), and multi-session aggregation (+30pp), with no benefit for single-session retrieval, consistent with encoding specificity theory [8]. Token analysis shows dual-trace encoding achieves this gain at no additional cost. We additionally sketch an architectural design for adapting dual-trace encoding to coding agents, with preliminary pilot validation.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 5 of its 11 references that are in the map. First posted on arXiv 14 Apr 2026.

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5 of 11 references.

References · 5

  1. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
  2. 2023MemGPT: Towards LLMs as Operating Systemscited by 1,295
  3. 2024Evaluating Very Long-Term Conversational Memory of LLM Agentscited by 800
  4. 2025Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memorycited by 722
  5. 2024LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memorycited by 656

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  1. 2026Less Context, More Accuracy: A Bi-Temporal Memory Engine for LLM Agents Where a Lean Retrieved Context Beats the Full History
  2. 2026Evaluating Memory Structure in LLM Agents
  3. 2026RaMem: Contextual Reinstatement for Long-term Agentic Memory
  4. 2026Persistent Memory in Multi-Agent LLM Inference: What It Costs, What It Buys, and When You Can Tell
  5. 2026When Does Memory Help? A Cost-Aware Evaluation of Long-Term Memory in Tool-Using LLM Agents
  6. 2026Memory Beyond Recall: A Dual-Process Cognitive Memory System for Self-Evolving LLM Agents
  7. 2026MemTrace: Probing What Final Accuracy Misses in Long-Term Memory
  8. 2026From Recall to Forgetting: Benchmarking Long-Term Memory for Personalized Agents