Pith Number

pith:UJUDHYFT

pith:2026:UJUDHYFTEFMULT2H5Q5TZP5ZIQ

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Agentic Reasoning for Large Language Models

Cheng Qian, Chi Wang, Dongqi Fu, Duo Zhou, Gaotang Li, Hanghang Tong, Heng Ji, Hui Liu, Jiaru Zou, Jiaxuan You, Jingrui He, Liri Fang, Mengting Ai, Ruizhong Qiu, Tianxin Wei, Ting-Wei Li, Wenxuan Bao, Xianfeng Tang, Xiangru Tang, Xiao Lin, Xuying Ning, Yin Xiao, Yuji Zhang, Yunzhe Li, Yu Wang, Ze Yang, Zhichen Zeng, Zhining Liu, Zihao Li

Agentic reasoning turns large language models into autonomous agents that plan, act, and adapt through interaction.

arxiv:2601.12538 v1 · 2026-01-18 · cs.AI · cs.CL

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Record completeness

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3 Author claim open · sign in to claim

4 Citations 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

This survey synthesizes agentic reasoning methods into a unified roadmap bridging thought and action, and outlines open challenges and future directions, including personalization, long-horizon interaction, world modeling, scalable multi-agent training, and governance for real-world deployment.

C2weakest assumption

The assumption that the three complementary dimensions—foundational agentic reasoning, self-evolving agentic reasoning, and collective multi-agent reasoning—provide a comprehensive and non-overlapping organization of the entire field of agentic reasoning for LLMs.

C3one line summary

The survey structures agentic reasoning for LLMs into foundational, self-evolving, and collective multi-agent layers while distinguishing in-context orchestration from post-training optimization and reviewing applications across domains.

References

300 extracted · 300 resolved · 56 Pith anchors

[1] Chain-of-thought prompting elicits reasoning in large language models.Advances in neural information processing systems, 35:24824–24837 2022

[2] Least-to-Most Prompting Enables Complex Reasoning in Large Language Models 2022 · arXiv:2205.10625

[3] Pal: Program-aided language models 2023

[4] Tree of thoughts: Deliberate problem solving with large language models.Advances in neural information processing systems, 36:11809–11822 2023

[5] React: Synergizing reasoning and acting in language models 2023

Formal links

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Cited by

20 papers in Pith

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ClawForge: Generating Executable Interactive Benchmarks for Command-Line Agents

Confidence Estimation in Automatic Short Answer Grading with LLMs

MAP: A Map-then-Act Paradigm for Long-Horizon Interactive Agent Reasoning

Receipt and verification

First computed	2026-05-17T23:38:13.728637Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

a26833e0b3215945cf47ec3b3cbfb94428e7cd1704bd2e5feb4d808470ee715f

Aliases

arxiv: 2601.12538 · arxiv_version: 2601.12538v1 · doi: 10.48550/arxiv.2601.12538 · pith_short_12: UJUDHYFTEFMU · pith_short_16: UJUDHYFTEFMULT2H · pith_short_8: UJUDHYFT

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/UJUDHYFTEFMULT2H5Q5TZP5ZIQ \
  | 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: a26833e0b3215945cf47ec3b3cbfb94428e7cd1704bd2e5feb4d808470ee715f

Canonical record JSON

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  "metadata": {
    "abstract_canon_sha256": "5013063d844cf6041cedf97e55bd43dfe3fb2ae2622cedb34cfb4c122915e95a",
    "cross_cats_sorted": [
      "cs.CL"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.AI",
    "submitted_at": "2026-01-18T18:58:23Z",
    "title_canon_sha256": "967c5045eca00d3ac7fe25a93633c3cd313726b3682652b641ae7f17ef71b777"
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  "source": {
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