Pith Number

pith:UQOLUGOK

pith:2025:UQOLUGOKYGIVAULY22FW7O2EDV

not attested not anchored not stored refs resolved

A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence

Cheng Qian, Dongrui Liu, Han Xiao, Heng Ji, Hongru Wang, Hongzhang Liu, Huan-ang Gao, Huazheng Wang, Jiahao Qiu, Jiayi Geng, Jiayi Zhang, Jinyu Xiang, Mengdi Wang, Mengkang Hu, Minda Hu, Qihan Ren, Qingyun Wu, Qiwen Zhao, Shaokun Zhang, Shilong Liu, Wenyue Hua, Xinzhe Juan, Xuan Qi, Yiran Wu, Yixiong Fang, Yuhang Zhou, Zhenhailong Wang

Self-evolving agents adapt their internal components through ongoing interactions to move beyond static large language models toward artificial super intelligence.

arxiv:2507.21046 v4 · 2025-07-28 · cs.AI

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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

This survey provides the first systematic and comprehensive review of self-evolving agents, organizing the field around three foundational dimensions: what, when, and how to evolve.

C2weakest assumption

The forward-looking premise that self-evolving agents constitute the primary route to Artificial Super Intelligence, which rests on an unproven long-term vision rather than demonstrated evidence.

C3one line summary

The paper delivers the first systematic review of self-evolving agents, structured around what components evolve, when adaptation occurs, and how it is implemented.

References

297 extracted · 297 resolved · 47 Pith anchors

[1] Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems , author=. 2025 , eprint= 2025

[2] ISBN 9798400704314 2024 · doi:10.1145/3626772.3661381

[3] Toward a Theory of Agents as Tool-Use Decision-Makers , author=. 2025 , eprint= 2025

[4] A survey on large language model based autonomous agents · doi:10.1007/s11704-024-40231-1

[5] Large Language Model Agent: A Survey on Methodology, Applications and Challenges , author=. 2025 , eprint= 2025

Formal links

3 machine-checked theorem links

Cited by

48 papers in Pith

Dynamic Mixture of Latent Memories for Self-Evolving Agents

EvolveR: Self-Evolving LLM Agents through an Experience-Driven Lifecycle

Improve Large Language Model Systems with User Logs

SOLAR: A Self-Optimizing Open-Ended Autonomous Agent for Lifelong Learning and Continual Adaptation

Autogenesis: A Self-Evolving Agent Protocol

Receipt and verification

First computed	2026-05-17T23:39:21.475689Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

a41cba19cac191505178d68b6fbb441d69e044317dd655aadab9caa126e745e9

Aliases

arxiv: 2507.21046 · arxiv_version: 2507.21046v4 · doi: 10.48550/arxiv.2507.21046 · pith_short_12: UQOLUGOKYGIV · pith_short_16: UQOLUGOKYGIVAULY · pith_short_8: UQOLUGOK

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/UQOLUGOKYGIVAULY22FW7O2EDV \
  | 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: a41cba19cac191505178d68b6fbb441d69e044317dd655aadab9caa126e745e9

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "5e095e3391867160653090fa894da636081a6f3c49d79f60df93efe581ee041d",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2025-07-28T17:59:05Z",
    "title_canon_sha256": "5fd67d2ad00a3537bae407d414a8c2d5d4da0b3fb646347d6e313a0d26a0014f"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2507.21046",
    "kind": "arxiv",
    "version": 4
  }
}