Pith Number

pith:KQRAAU7L

pith:2023:KQRAAU7LVMMXOKM7446XBDC42V

not attested not anchored not stored refs resolved

Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models

Andy Zhou, Haohan Wang, Kai Yan, Michal Shlapentokh-Rothman, Yu-Xiong Wang

Language models use Monte Carlo tree search with self-reflections to plan and act as agents.

arxiv:2310.04406 v3 · 2023-10-06 · cs.AI · cs.CL · cs.CV · cs.LG

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{KQRAAU7LVMMXOKM7446XBDC42V}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

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

LATS achieves state-of-the-art pass@1 accuracy (92.7%) for programming on HumanEval with GPT-4 and demonstrates gradient-free performance (average score of 75.9) comparable to gradient-based fine-tuning for web navigation on WebShop with GPT-3.5.

C2weakest assumption

That language models can reliably serve as value functions and self-reflectors within Monte Carlo Tree Search using only in-context learning and external environment feedback, without needing task-specific training.

C3one line summary

LATS integrates Monte Carlo Tree Search with language models using in-context learning, value functions, and self-reflection to achieve 92.7% pass@1 on HumanEval and competitive web navigation performance.

References

13 extracted · 13 resolved · 7 Pith anchors

[1] Graph of thoughts: Solving elaborate problems with large language models

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

[3] Training Verifiers to Solve Math Word Problems · arXiv:2110.14168

[4] Mastering Diverse Domains through World Models · arXiv:2301.04104

[5] Reason for fu- ture, act for now: A principled framework for au- tonomous LLM agents with provable sample efficiency

Formal links

2 machine-checked theorem links

Cited by

31 papers in Pith

Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems

TokUR: Token-Level Uncertainty Estimation for Large Language Model Reasoning

GraphFlow: A Graph-Based Workflow Management for Efficient LLM-Agent Serving

Efficient Agentic Reasoning Through Self-Regulated Simulative Planning

ArborKV: Structure-Aware KV Cache Management for Scaling Tree-based LLM Reasoning

Receipt and verification

First computed	2026-05-17T23:38:46.239259Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

54220053ebab1977299fe73d708c5cd54b37022e9760061182be87743661d9f1

Aliases

arxiv: 2310.04406 · arxiv_version: 2310.04406v3 · doi: 10.48550/arxiv.2310.04406 · pith_short_12: KQRAAU7LVMMX · pith_short_16: KQRAAU7LVMMXOKM7 · pith_short_8: KQRAAU7L

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/KQRAAU7LVMMXOKM7446XBDC42V \
  | 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: 54220053ebab1977299fe73d708c5cd54b37022e9760061182be87743661d9f1

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "f2175a07ca90456aaed3ab5aa5f7ed2f45abc1456dee4d6c1f7af366abc59403",
    "cross_cats_sorted": [
      "cs.CL",
      "cs.CV",
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2023-10-06T17:55:11Z",
    "title_canon_sha256": "32f9679ff79944b542b9890ca1a452e4bbd2842c0b4f8062941fda58f1d86326"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2310.04406",
    "kind": "arxiv",
    "version": 3
  }
}