Pith Number

pith:RGWEH4FG

pith:2025:RGWEH4FGVULA7CDD7E433OUL65

not attested not anchored not stored refs resolved

A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems

Bin Wu, Guibin Zhang, Han Zhou, Jinyuan Fang, Nikos Aletras, Siwei Liu, Xinhao Yi, Xi Wang, Xi Zhang, Yanwen Peng, Yingxu Wang, Yi Xu, Zaiqiao Meng, Zhaochun Ren, Zihao Li

Self-evolving AI agents use interaction feedback to continuously improve beyond their initial foundation model capabilities.

arxiv:2508.07407 v2 · 2025-08-10 · cs.AI · cs.CL · cs.MA

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\usepackage{pith}
\pithnumber{RGWEH4FGVULA7CDD7E433OUL65}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Self-evolving AI agents bridge the static capabilities of foundation models with the continuous adaptability required by lifelong agentic systems.

C2weakest assumption

That existing agent evolution techniques can be meaningfully unified and compared under a single four-component framework of System Inputs, Agent System, Environment, and Optimisers.

C3one line summary

A comprehensive review of self-evolving AI agents that improve themselves over time, organized via a framework of inputs, agent system, environment, and optimizers, with domain-specific and safety discussions.

References

126 extracted · 126 resolved · 25 Pith anchors

[1] arXiv preprint arXiv:2502.02928 , year=

[2] PromptWizard: Task-aware prompt optimization framework

[3] arXiv preprint arXiv:2503.22678 , year=

[4] arXiv preprint arXiv:2402.09015 , year=

[5] 34 Derek Austin and Elliott Chartock.GRAD-SUM: Leveraging gradient summarization for optimal prompt engineering. arXiv preprint arXiv:2407.12865,

Formal links

2 machine-checked theorem links

Cited by

33 papers in Pith

PACE: Two-Timescale Self-Evolution for Small Language Model Agents

Differentiable Mixture-of-Agents Incentivizes Swarm Intelligence of Large Language Models

EvoMemBench: Benchmarking Agent Memory from a Self-Evolving Perspective

OEP: Poisoning Self-Evolving LLM Agents via Locally Correct but Non-Transferable Experiences

What Do Evolutionary Coding Agents Evolve?

Receipt and verification

First computed	2026-05-17T23:38:49.801954Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

89ac43f0a6ad160f8863f939bdba8bf77c45fcc3c05cde2e14e48be26f2ab773

Aliases

arxiv: 2508.07407 · arxiv_version: 2508.07407v2 · doi: 10.48550/arxiv.2508.07407 · pith_short_12: RGWEH4FGVULA · pith_short_16: RGWEH4FGVULA7CDD · pith_short_8: RGWEH4FG

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/RGWEH4FGVULA7CDD7E433OUL65 \
  | 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: 89ac43f0a6ad160f8863f939bdba8bf77c45fcc3c05cde2e14e48be26f2ab773

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "55ad295007f5f557520b869117bfd72bd9bafa05ae801cf19492c71eedd4c90c",
    "cross_cats_sorted": [
      "cs.CL",
      "cs.MA"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2025-08-10T16:07:32Z",
    "title_canon_sha256": "cfd55accbaf5ef516bb178e65117a2080703b8c5ed092eedd40834f0e30ae4b2"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2508.07407",
    "kind": "arxiv",
    "version": 2
  }
}