Human-Inspired Memory Architecture for LLM Agents

• URL: http://arxiv.org/abs/2605.08538v1
• Date: Fri, 08 May 2026 22:52:37 GMT
• Title: Human-Inspired Memory Architecture for LLM Agents
• Abstract summary: We present a biologically-grounded memory architecture comprising six cognitive mechanisms.<n>Each mechanism addresses a specific failure mode of naive memory accumulation.<n>At S-tier scale (50 sessions), dedup-based consolidation yields a +13.3 pp improvement in preference recall.
• Score: 0.9507070656654629
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current LLM agents lack principled mechanisms for managing persistent memory across long interaction horizons. We present a biologically-grounded memory architecture comprising six cognitive mechanisms: (1) sleep-phase consolidation, (2) interference-based forgetting, (3) engram maturation, (4) reconsolidation upon retrieval, (5) entity knowledge graphs, and (6) hybrid multi-cue retrieval. Each mechanism addresses a specific failure mode of naive memory accumulation. We introduce a synthetic calibration methodology that derives all pipeline thresholds without benchmark data exposure, eliminating a common source of evaluation leakage. We evaluate on two benchmarks. First, a VSCode issue-tracking dataset (13K issues, 120K events) where deduplication-based consolidation achieves 97.2% retention precision with 58% store reduction (+21.8 pp over baseline). Second, the LongMemEval personal-chat benchmark where we conduct the first streaming M-tier evaluation (475 sessions, ~540K unique turns). At a 200K-token context budget, our pipeline matches raw retrieval accuracy (70.1% vs. 71.2%, overlapping 95% CI) while exposing a tunable accuracy/store-size operating curve. At S-tier scale (50 sessions), dedup-based consolidation yields a +13.3 pp improvement in preference recall.

Related papers

• ReacTOD: Bounded Neuro-Symbolic Agentic NLU for Zero-Shot Dialogue State Tracking [2.2573512203799626]
  We propose ReacTOD, a bounded neuro-symbolic architecture that reformulates NLU as discrete tool calls within a self-correcting ReAct loop governed by deterministic validation.<n>A bounded ReAct loop enables iterative self-correction, improving accuracy by up to 9.3 percentage points over single-pass inference on MultiWOZ.
  arXiv  Detail & Related papers  (2026-05-18T20:06:04Z)
• Distributional Energy-Based Models for Uncertainty-Aware Structured LLM Reasoning [40.342912574072024]
  Large Language Models produce structured outputs such as travel plans or code solutions.<n>Individual reasoning steps may appear correct while the output as a whole violates budgets, fails test cases, or contradicts earlier deductions.<n>We propose a learned quality scorer with deterministic analytical constraint penalties for verifying structured LLM outputs.
  arXiv  Detail & Related papers  (2026-05-15T17:08:27Z)
• Echo: KV-Cache-Free Associative Recall with Spectral Koopman Operators [45.88028371034407]
  KV-cache-free associative recall architecture built around Spectral Koopman Attention (SKA)<n>We introduce Echo, a KV-cache-free associative recall architecture built around Spectral Koopman Attention (SKA)
  arXiv  Detail & Related papers  (2026-05-07T22:26:27Z)
• ZenBrain: A Neuroscience-Inspired 7-Layer Memory Architecture for Autonomous AI Systems [51.56484100374058]
  On LongMemEval-500, ZenBrain matches a long-context oracle's binary-judge accuracy to within 4.5 pp.<n> ZenBrain is a 7-layer neuroscience-inspired memory architecture.
  arXiv  Detail & Related papers  (2026-04-26T20:39:19Z)
• Open-Set Vein Biometric Recognition with Deep Metric Learning [0.0]
  We rigorously evaluate the computational boundaries of Deep Metric Learning (DML) under strict open-set constraints.<n>Our approach learns discnative L2-normalised embeddings and employs prototype-based matching with a similarity threshold.<n>On the large-scale MMCBNU 6000 benchmark, our best model (ResNet50-CBAM) achieves an OSCR of 0.9945, AUROC of 0.9974, and EER of 1.57%, maintaining high identification accuracy (99.6% Rank-1) while robustly rejecting unknown subjects.
  arXiv  Detail & Related papers  (2026-04-16T11:03:11Z)
• Drawing on Memory: Dual-Trace Encoding Improves Cross-Session Recall in LLM Agents [0.22917707112773592]
  LLM agents with persistent memory store information as flat factual records provide little context for temporal reasoning, change tracking, or cross-session aggregation.<n>Inspired by the drawing effect, we introduce dual-trace memory encoding.<n>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.
  arXiv  Detail & Related papers  (2026-04-14T16:45:06Z)
• ImplicitMemBench: Measuring Unconscious Behavioral Adaptation in Large Language Models [60.14219417402433]
  Existing memory benchmarks for LLM agents evaluate explicit recall of facts, yet overlook implicit memory where experience becomes automated behavior without conscious retrieval.<n>We introduce ImplicitMemBench, the first systematic benchmark evaluating implicit memory through three constructs drawn from standard cognitive-science accounts of non-declarative memory.<n>Our 300-item suite employs a unified Learning/Priming-Interfere-Test protocol with first-attempt scoring.
  arXiv  Detail & Related papers  (2026-04-09T10:26:32Z)
• MemMachine: A Ground-Truth-Preserving Memory System for Personalized AI Agents [2.541923091180284]
  Large Language Model (LLM) agents require persistent memory to maintain personalization, factual continuity, and long-horizon reasoning.<n>We present MemMachine, an open-source memory system that integrates short-term, long-term episodic, and profile memory.<n>MemMachine uses contextualized retrieval that expands nucleus matches with surrounding context, improving recall when relevant evidence spans multiple dialogue turns.
  arXiv  Detail & Related papers  (2026-04-06T16:57:06Z)
• Omni-SimpleMem: Autoresearch-Guided Discovery of Lifelong Multimodal Agent Memory [76.63021613850093]
  We deploy an autonomous research pipeline to discover Omni-SimpleMem, a unified multimodal memory framework for lifelong AI agents.<n>System achieves state-of-the-art on two benchmarks, improving F1 by +411% on LoCoMo and +214% on Mem-Gallery.<n>We provide a taxonomy of six discovery types and identify four properties that make multimodal memory particularly suited for autoresearch.
  arXiv  Detail & Related papers  (2026-04-01T15:06:23Z)
• Resource-Efficient Iterative LLM-Based NAS with Feedback Memory [49.44875022114861]
  Neural Architecture Search (NAS) automates network design, but conventional methods demand substantial computational resources.<n>We propose a closed-loop pipeline leveraging large language models (LLMs) to iteratively generate, evaluate, and refine convolutional neural network architectures.
  arXiv  Detail & Related papers  (2026-03-12T16:00:22Z)
• MLP Memory: A Retriever-Pretrained Memory for Large Language Models [39.890098399493986]
  NLPRAG Memory is a lightweight parametric module that learns to internalize retrieval patterns without explicit document access.<n>Our architecture integrates this pretrained Memory with Transformer decoders through simple probability, yielding 17.5% and 24.1% scaling gains on WikiText-103 and Web datasets, respectively.
  arXiv  Detail & Related papers  (2025-08-03T16:40:53Z)
• Benchmarking Reasoning Robustness in Large Language Models [76.79744000300363]
  We find significant performance degradation on novel or incomplete data.<n>These findings highlight the reliance on recall over rigorous logical inference.<n>This paper introduces a novel benchmark, termed as Math-RoB, that exploits hallucinations triggered by missing information to expose reasoning gaps.
  arXiv  Detail & Related papers  (2025-03-06T15:36:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.