Pith Number

pith:TPQWBXF7

pith:2026:TPQWBXF7ORZP7C4ZDJQFEANWNM

not attested not anchored not stored refs resolved

TimelineReasoner: Advancing Timeline Summarization with Large Reasoning Models

Liancheng Zhang, Xiaoxi Li, Zhicheng Dou

TimelineReasoner uses large reasoning models to actively track events globally and fill gaps through targeted retrieval, producing more accurate and coherent timelines than passive LLM approaches.

arxiv:2605.12518 v1 · 2026-04-03 · cs.CL · 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

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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

Experimental results on open-domain TLS datasets demonstrate that TimelineReasoner significantly outperforms existing LLM-based TLS methods in terms of timeline accuracy, coverage, and coherence.

C2weakest assumption

That the specialized mechanisms (Event Scraper, Timeline Updater, Supervisor) and the two-stage reasoning process can be implemented reliably on top of existing large reasoning models without introducing new hallucinations or retrieval errors that undermine the claimed gains.

C3one line summary

TimelineReasoner applies large reasoning models in a Global Cognition plus Detail Exploration loop to produce more accurate, complete, and coherent timelines from news than prior LLM-based methods.

References

44 extracted · 44 resolved · 14 Pith anchors

[1] James Allan, Rahul Gupta, and Vikas Khandelwal. 2001. Temporal Summaries of News Topics. InSIGIR 2001: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in 2001 · doi:10.1145/383952.383954

[2] Qwen Technical Report 2023 · doi:10.48550/arxiv.2309

[3] Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models 2025 · doi:10.48550/arxiv.2503.09567

[4] Xiuying Chen, Zhangming Chan, Shen Gao, Meng-Hsuan Yu, Dongyan Zhao, and Rui Yan. 2019. Learning towards Abstractive Timeline Summarization. In Proceedings of the Twenty-Eighth International Joint Con 2019 · doi:10.24963/ijcai.2019/686

[5] Xiuying Chen, Mingzhe Li, Shen Gao, Zhangming Chan, Dongyan Zhao, Xin Gao, Xiangliang Zhang, and Rui Yan. 2023. Follow the Timeline! Generating an Abstractive and Extractive Timeline Summary in Chrono 2023 · doi:10.1145/3517221

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-18T03:10:02.891762Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

9be160dcbf7472ff8b991a605201b66b3b1465c4be16648e64e8d007f4bc885a

Aliases

arxiv: 2605.12518 · arxiv_version: 2605.12518v1 · doi: 10.48550/arxiv.2605.12518 · pith_short_12: TPQWBXF7ORZP · pith_short_16: TPQWBXF7ORZP7C4Z · pith_short_8: TPQWBXF7

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/TPQWBXF7ORZP7C4ZDJQFEANWNM \
  | 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: 9be160dcbf7472ff8b991a605201b66b3b1465c4be16648e64e8d007f4bc885a

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "6dfa3210baed528db536bbd52e1ec16853b5e7a32dca7f89bce191ed2410e7ac",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CL",
    "submitted_at": "2026-04-03T12:30:15Z",
    "title_canon_sha256": "7b912831a2aac1f62b93f8fbbd19e92e18a3b7c3accb08fc1a253f03d3e45b79"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.12518",
    "kind": "arxiv",
    "version": 1
  }
}