Pith Number

pith:6NNSXFBK

pith:2025:6NNSXFBK6JBRECCHR6FPCG4WHA

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From Human Memory to AI Memory: A Survey on Memory Mechanisms in the Era of LLMs

Chen Zhang, Huifeng Guo, Ruiming Tang, Sheng Liang, Yaxiong Wu, Yichao Wang, Yong Liu, Yongyue Zhang

This survey connects categories of human memory to memory in LLM-based AI systems and introduces a three-dimension eight-quadrant framework to organize the field.

arxiv:2504.15965 v2 · 2025-04-22 · cs.IR

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Claims

C1strongest claim

we propose a comprehensive survey on the memory of LLM-driven AI systems. In particular, we first conduct a detailed analysis of the categories of human memory and relate them to the memory of AI systems. Second, we systematically organize existing memory-related work and propose a categorization method based on three dimensions (object, form, and time) and eight quadrants.

C2weakest assumption

That mapping human memory categories directly to AI memory systems and organizing the literature via the proposed three-dimension eight-quadrant scheme will yield actionable insights for building more powerful memory mechanisms in LLMs.

C3one line summary

The paper surveys human memory categories, maps them to LLM memory, and proposes a new three-dimension (object, form, time) categorization into eight quadrants to organize existing work and highlight open problems.

References

155 extracted · 155 resolved · 25 Pith anchors

[1] A survey on large language model based autonomous agents 2024

[2] Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security 2024 · arXiv:2401.05459

[3] A Survey of Large Language Models 2023 · arXiv:2303.18223

[4] A survey on evaluation of large language models 2024

[5] All roads lead to rome: Unveiling the trajectory of recommender systems across the llm era 2024

Cited by

23 papers in Pith

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Rethinking Agentic Reinforcement Learning In Large Language Models

The Landscape of Agentic Reinforcement Learning for LLMs: A Survey

Receipt and verification

First computed	2026-05-17T23:38:14.287425Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

f35b2b942af2431208478f8af11b96380278171da2b0364f3fa142ea963092bc

Aliases

arxiv: 2504.15965 · arxiv_version: 2504.15965v2 · doi: 10.48550/arxiv.2504.15965 · pith_short_12: 6NNSXFBK6JBR · pith_short_16: 6NNSXFBK6JBRECCH · pith_short_8: 6NNSXFBK

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/6NNSXFBK6JBRECCHR6FPCG4WHA \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: f35b2b942af2431208478f8af11b96380278171da2b0364f3fa142ea963092bc

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "9be892aae9b3f4d12be10420456503b63419743cb342f499aaf56b33bf96c06c",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.IR",
    "submitted_at": "2025-04-22T15:05:04Z",
    "title_canon_sha256": "c1117039836b0cd39e8974d289ea220b57779280da231b4218865445e95a4f0c"
  },
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    "kind": "arxiv",
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}