AuthorsBenjamin Stern, Peter Nadel
TL;DR
Drawing on Memory’s dual-trace encoding pairs fact traces with scene traces and yields 73.7% vs 53.5% accuracy (+20.2pp) on LongMemEval-S.
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THE PROBLEM
LLM agents typically store flat factual records, discarding when, where, and why information was learned, which cripples temporal reasoning and aggregation.
On LongMemEval-S, a strong fact-only baseline (C7-control) reaches only 53.5% accuracy, leaving complex temporal, update, and multi-session questions frequently unanswered or wrong.
HOW IT WORKS
Drawing on Memory introduces dual-trace memory encoding, combining fact traces, scene traces, an evidence scoring gate, and a three-state retrieval protocol on top of Letta’s archival memory.
Think of fact traces as index cards and scene traces as richly illustrated postcards; the evidence scoring gate decides which postcards to keep, and the retrieval protocol knows when to trust them.
This KEY_MECHANISM lets Drawing on Memory reconstruct temporal sequences, updates, and cross-session aggregates that a plain context window or flat vector store cannot support.
DIAGRAM
This diagram shows how Drawing on Memory answers a recall question using its three-state retrieval protocol over fact and scene traces.
DIAGRAM
This diagram shows how Drawing on Memory is trained on 4,575 sessions then evaluated on 100 LongMemEval-S questions with GPT-4o judging.
PROCESS
Dual-trace memory encoding
Drawing on Memory processes a conversation and creates paired fact traces and scene traces, tagging them with shared anchors and YAML metadata.
Evidence scoring
Drawing on Memory scores relevance, specificity, and explicitness from 0–2 each, routing sessions to DROP or FULL encoding based on the 0–2 versus 3–6 threshold.
Three-state retrieval protocol
When queried, Drawing on Memory searches archival entries and classifies each question into State A, B, or C depending on fact and scene availability.
LongMemEval-S recall evaluation
Drawing on Memory answers 100 benchmark questions, with GPT-4o grading correctness across single-session, multi-session, update, temporal, and abstention categories.
KEY CONTRIBUTIONS
Dual-trace memory encoding protocol
Drawing on Memory defines a deployable dual-trace protocol with fact traces, scene traces, an evidence scoring gate, and a three-state retrieval protocol for Letta’s archival memory.
LongMemEval-S experimental evidence
Drawing on Memory achieves 73.7% accuracy versus 53.5% for C7-control on 99 shared LongMemEval-S questions, a +20.2pp gain with p < 0.0001.
Coding agent adaptation design
Drawing on Memory sketches a dual-trace adaptation for Letta Code, targeting debugging incidents, design decisions, and learning progressions with preliminary pilot validation.
RESULTS
Overall accuracy
73.7%
+20.2pp over C7-control
Temporal reasoning
65%
+40pp over C7-control
Multi-session aggregation
50%
+30pp over C7-control
Knowledge-update tracking
80%
+25pp over C7-control
On LongMemEval-S, which spans 4,575 sessions and 100 recall questions, Drawing on Memory shows that dual-trace encoding materially improves cross-session temporal, aggregation, and update reasoning over a fact-only baseline. The +20.2 percentage point overall gain demonstrates that richer encoding, not just more storage, changes long-term recall behavior.
BENCHMARK
On LongMemEval-S, which spans 4,575 sessions and 100 recall questions, Drawing on Memory shows that dual-trace encoding materially improves cross-session temporal, aggregation, and update reasoning over a fact-only baseline. The +20.2 percentage point overall gain demonstrates that richer encoding, not just more storage, changes long-term recall behavior.
BENCHMARK
Overall accuracy (%) on LongMemEval-S across memory conditions.
KEY INSIGHT
Drawing on Memory shows zero gain on single-session fact retrieval, with both C6-draw and C7-control at 75% accuracy despite dual-trace encoding.
This is surprising because many assume richer memories help everywhere, but Drawing on Memory reveals that scene traces matter only when temporal or cross-session context is required.
WHY IT MATTERS
Drawing on Memory unlocks practical, token-efficient episodic memory for agents, enabling temporal reasoning, knowledge-update tracking, and multi-session aggregation at scale.
Builders can now design agents that remember evolving user histories and project narratives across thousands of sessions, without changing models or paying extra token costs.
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Agentic Memory (AgeMem) exposes memory management tools, a three-stage progressive RL strategy, and step-wise GRPO directly inside the agent policy to jointly control long-term and short-term memory. On Qwen3-4B-Instruct, AgeMem attains 54.31% average performance across ALFWorld, SciWorld, PDDL, BabyAI, and HotpotQA, exceeding the best baseline A-Mem at 45.74%.
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All-Mem organizes long-term agent memory through Online/Offline Decoupling, Agentic Topology Consolidation, and Topology-Aware Retrieval over a topology-structured memory bank. On LoCoMo, All-Mem reaches 54.63 4o-J versus Mem0’s 48.91, and on LongMemEval-S All-Mem reaches 60.20 4o-J versus Mem0’s 55.80.
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