Pith Number

pith:LZ6OY7ZG

pith:2025:LZ6OY7ZG7SCVNYQAJRQHISO6OO

not attested not anchored not stored refs resolved

WebSailor: Navigating Super-human Reasoning for Web Agent

Baixuan Li, Dingchu Zhang, Fei Huang, Huifeng Yin, Jialong Wu, Jingren Zhou, Junkai Zhang, Kuan Li, Litu Ou, Liwen Zhang, Ming Yan, Pengjun Xie, Weizhou Shen, Wenbiao Yin, Xinyu Wang, Xixi Wu, Yong Jiang, Zhengwei Tao, Zhongwang Zhang

WebSailor equips open-source models with the ability to reduce extreme uncertainty in web navigation, allowing them to match proprietary agents on complex information-seeking tasks.

arxiv:2507.02592 v1 · 2025-07-03 · cs.CL · cs.AI

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\usepackage{pith}
\pithnumber{LZ6OY7ZG7SCVNYQAJRQHISO6OO}

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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

With this integrated pipeline, WebSailor significantly outperforms all opensource agents in complex information-seeking tasks, matching proprietary agents' performance and closing the capability gap.

C2weakest assumption

Their success hinges on a sophisticated reasoning pattern absent in open-source models: the ability to systematically reduce extreme uncertainty when navigating vast information landscapes.

C3one line summary

WebSailor trains open-source web agents to match proprietary performance on complex information-seeking tasks by generating high-uncertainty scenarios and using a new RL method called DUPO.

References

34 extracted · 34 resolved · 21 Pith anchors

[1] Concrete Problems in AI Safety · arXiv:1606.06565

[2] FireAct: Toward language agent fine-tuning.arXiv preprint arXiv:2310.05915

[3] SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models · arXiv:2504.11468

[4] Evaluating Large Language Models Trained on Code · arXiv:2107.03374

[5] SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training · arXiv:2501.17161

Formal links

3 machine-checked theorem links

Cited by

28 papers in Pith

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Scalable Environments Drive Generalizable Agents

ICRL: Learning to Internalize Self-Critique with Reinforcement Learning

Receipt and verification

First computed	2026-05-17T23:38:13.684893Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

5e7cec7f26fc8556e2004c607449de739f0c8b28b1a00a7814fc440c8c89c788

Aliases

arxiv: 2507.02592 · arxiv_version: 2507.02592v1 · doi: 10.48550/arxiv.2507.02592 · pith_short_12: LZ6OY7ZG7SCV · pith_short_16: LZ6OY7ZG7SCVNYQA · pith_short_8: LZ6OY7ZG

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/LZ6OY7ZG7SCVNYQAJRQHISO6OO \
  | 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: 5e7cec7f26fc8556e2004c607449de739f0c8b28b1a00a7814fc440c8c89c788

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "c08830c2337f29570f837f0009ab613f6836d0547c3ee775478b5746678a4a52",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/publicdomain/zero/1.0/",
    "primary_cat": "cs.CL",
    "submitted_at": "2025-07-03T12:59:07Z",
    "title_canon_sha256": "79e675065d2edfb74313555e238a8c2d9770828a0d4661060612556a3673e1bf"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2507.02592",
    "kind": "arxiv",
    "version": 1
  }
}