Pith Number

pith:OQ64JU6F

pith:2025:OQ64JU6F5TDZ6XRD5FHHR4LIHX

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

Botian Shi, Cheng Yang, Daocheng Fu, Jianbiao Mei, Licheng Wen, Pinlong Cai, Rong Wu, Xiaoman Wang, Xuemeng Yang, Yufan Shen, Yuxin Wang

EvolveR lets LLM agents self-improve by distilling their own interaction trajectories into reusable strategic principles and then reinforcing policies in a closed loop.

arxiv:2510.16079 v3 · 2025-10-17 · cs.CL · cs.AI

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4 Citations open

5 Replications open

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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

We demonstrate the effectiveness of EvolveR on complex multi-hop question-answering benchmarks, where it achieves superior performance over strong agentic baselines.

C2weakest assumption

That distilling raw interaction trajectories into abstract reusable strategic principles will produce guidance that generalizes across tasks and that the policy reinforcement mechanism will produce genuine iterative improvement rather than superficial or unstable changes.

C3one line summary

EvolveR proposes a closed-loop self-evolution system for LLM agents that distills experiences into principles offline and applies reinforcement during online task interactions to achieve better performance on multi-hop QA tasks.

Formal links

2 machine-checked theorem links

Cited by

36 papers in Pith

SkillOpt: Executive Strategy for Self-Evolving Agent Skills

From Raw Experience to Skill Consumption: A Systematic Study of Model-Generated Agent Skills

SPIRAL: Self-Evolving Action-Conditioned Video Generation via Reflective Planning Agents

Ratchet: A Minimal Hygiene Recipe for Self-Evolving LLM Agents

Mem-$\pi$: Adaptive Memory through Learning When and What to Generate

Receipt and verification

First computed	2026-05-20T00:02:57.448614Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

743dc4d3c5ecc79f5e23e94e78f1683dd13e96e1506f0d0918cb3c5972a866e6

Aliases

arxiv: 2510.16079 · arxiv_version: 2510.16079v3 · doi: 10.48550/arxiv.2510.16079 · pith_short_12: OQ64JU6F5TDZ · pith_short_16: OQ64JU6F5TDZ6XRD · pith_short_8: OQ64JU6F

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/OQ64JU6F5TDZ6XRD5FHHR4LIHX \
  | 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: 743dc4d3c5ecc79f5e23e94e78f1683dd13e96e1506f0d0918cb3c5972a866e6

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "3083a336ee43f983d3fe891a0a6538b55e9271247773dc43de7c0eefe69b35ac",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CL",
    "submitted_at": "2025-10-17T12:03:16Z",
    "title_canon_sha256": "d4825c1c87b5ebd256232f1f3a1cedfe6979962acbe37e9f9157ec0667035bda"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2510.16079",
    "kind": "arxiv",
    "version": 3
  }
}