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REVIEW 3 major objections 5 minor 53 references

A frozen large language model can learn domain-specific extraction skills from its own unstable and missed predictions, and these evolved skills outperform static few-shot examples at constructing n-ary knowledge hypergraphs.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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2026-08-02 21:35 UTC pith:L7QLPIGX

load-bearing objection A plausible synthesis with consistent empirical direction, but the n-ary claims rest on an unreleased benchmark; deserves review, not acceptance. the 3 major comments →

arxiv 2602.19543 v2 pith:L7QLPIGX submitted 2026-02-23 cs.CL cs.IR

Hyper-KGGen: A Skill-Driven Knowledge Extractor for High-Quality Knowledge Hypergraph Generation

Rizhuo Huang , Yifan Feng , Rundong Xue , Shihui Ying , Jun-Hai Yong , Chuan Shi , Shaoyi Du , Yue Gao This is my paper
classification cs.CL cs.IR
keywords knowledge hypergraphn-ary relation extractionskill distillationlarge language modelsfew-shot promptingretrieval-augmented generationstability-based rewardHyperDocRED
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper argues that the bottleneck in n-ary knowledge hypergraph extraction is not the model's capacity but its lack of scenario-specific guidance. Hyper-KGGen addresses this by first building a structural skeleton of binary relations and then enriching it into n-ary hyperedges, and by maintaining a Global Skill Library distilled from the model's own execution history. The library is grown through a stability-based feedback loop: parallel rollouts identify relations the model extracts reliably, inconsistently, or not at all, and the latter two categories are turned into reusable trigger-action skills. The central empirical claim is that these evolved skills provide richer, more transferable guidance than static few-shot examples, shown by higher n-ary extraction F1, higher fact-verification accuracy, and better downstream RAG quality. A sympathetic reader would care because, if true, a frozen LLM can be adapted to new extraction domains without fine-tuning or hand-crafted prompts.

Core claim

The paper's central discovery is that a knowledge hypergraph extractor can improve itself by converting its own unstable and missed predictions into reusable skills. Hyper-KGGen freezes the LLM and instead learns a Global Skill Library, a set of textual trigger-action rules. During training, K parallel rollouts are aligned to gold hyperedges, and each gold relation is categorized as stable (always extracted), unstable (sometimes extracted), or missed (never extracted). The unstable set is processed by path induction over successful traces, and the missed set by hindsight reasoning from the gold relation; both produce skills that are added, merged, or pruned by a controller. The paper reports

What carries the argument

The load-bearing mechanism is the Global Skill Library, a textual memory of trigger-action extraction skills maintained by a Skill Library Controller. The supporting mechanism is the Stability-based Relative Reward loop: K parallel rollouts at non-zero temperature are aligned to gold hyperedges via semantic embedding similarity, and each gold relation is split into a stable, unstable, or missed set. Unstable relations are distilled into skills by path induction over successful reasoning traces, and missed relations by hindsight reasoning from the gold edge. The coarse-to-fine extraction pipeline (binary skeleton, qualified binary, general n-ary) supplies the structural substrate, so the skil

Load-bearing premise

The gold-standard n-ary annotations in the new HyperDocRED benchmark are consistent and unbiased; the paper gives no annotation protocol, no inter-annotator agreement, and no dataset release, so every skill-driven gain measured against it rests on that unverified benchmark.

What would settle it

Take a random sample of HyperDocRED test documents, have two independent teams re-annotate them following a written protocol, and compare their hyperedges to the released gold set; if inter-annotator agreement is low or the gold hyperedges do not align with the source text, the reported F1 advantage of skill-driven extraction becomes uninterpretable.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • On the new HyperDocRED benchmark, the skill-augmented variant reaches micro F1 0.5600, more than double the strongest hypergraph-RAG baseline (0.2228), indicating that learned skills substantially improve n-ary extraction completeness.
  • On MINE fact verification, accuracy rises from 0.7540 (KGGen) to 0.8367 (Hyper-KGGen+), implying the constructed hypergraph retains more verifiable facts from source text.
  • On UltraDomain downstream RAG evaluation, Hyper-KGGen+ achieves the best average instructional quality across five dimensions on both Mix and Pathology, suggesting that higher-quality hypergraph substrates directly improve retrieval-augmented generation.
  • Retrieval efficiency analysis shows that top-40 retrieval with Hyper-KGGen+ matches or exceeds larger retrieval budgets of baselines, indicating that the generated graph has higher evidence density per node.
  • The skill library transfers across four LLM backbones (GPT-4o-mini, Gemini-2.5-Flash, Qwen3, DeepSeek-V3.2), so the improvement is not tied to one base model.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The stability-based relative reward is a general self-improvement signal; the same loop could be applied to event extraction, temporal fact checking, or schema induction without n-ary-specific design, because it only needs a success/failure dichotomy across rollouts.
  • Because the library is textual and the LLM is frozen, the approach is a cheap alternative to fine-tuning; a natural extension is to measure skill-library growth against annotation or compute cost, to see where the curve flattens.
  • The paper's own ablation (skills vs. few-shot) shows that few-shot gains plateau while skills keep accumulating; a testable extension is whether the library continues to improve past a size where few-shot examples stall, and whether skills can be composed across domains.
  • The HyperDocRED construction is a bottleneck: without a public release and a documented annotation protocol, the benchmark's validity cannot be independently checked, and all reported gains against it are provisional until the data are available.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper presents Hyper-KGGen, a skill-driven framework for document-level knowledge hypergraph extraction with frozen LLMs. It combines a coarse-to-fine extraction pipeline (chunking, entity extraction, binary → qualified binary → n-ary hyperedges, deduplication) with a 'Global Skill Library' learned through parallel rollouts. Relations are categorized as stable/unstable/missed by comparison with a gold hypergraph 𝒢∗; unstable cases induce skills via path induction, and missed cases via hindsight reasoning with the ground-truth relation injected. The library is updated with ADD/MODIFY/MERGE/KEEP operations. The authors introduce HyperDocRED, a manually restructured version of Re-DocRED, and report experiments showing Hyper-KGGen+ outperforms KGGen, RAG, and hypergraph-RAG baselines on n-ary extraction (Table 5), fact coverage on MINE (Table 3), and RAG quality on UltraDomain (Table 2), plus a skills-vs-few-shot analysis (Fig. 5).

Significance. If the results hold, the paper would provide a practical method for adapting a frozen LLM to new extraction domains through a textual skill library, avoiding fine-tuning. The coarse-to-fine decomposition and stability-based relative reward are well-motivated, and the paper includes a wide range of baselines and a new benchmark. However, the central empirical claims are currently not verifiable: the new benchmark lacks annotation protocol, IAA, and release; there are no error bars or significance tests; and the skill acquisition is supervised by the gold standard, so the 'self-improving' framing is inflated. The paper is therefore a potentially useful contribution whose current evidence does not support the abstract's claims.

major comments (3)
  1. [§5.1.1, Table 5] The n-ary benchmark HyperDocRED is the sole basis for the headline extraction claims and the skills-vs-few-shot comparison (Fig. 5), but it is not verifiable as described. The paper states that the authors 'manually restructure the original binary annotations into n-ary relations' without providing the annotation protocol, inter-annotator agreement, examples of the restructuring decisions, or a dataset URL. The Abstract calls HyperDocRED 'rigorously annotated,' yet no evidence of rigor appears in the manuscript. If the restructuring reflects the authors' own grouping intuitions, the large F1 gap in Table 5 (Hyper-KGGen+ 0.5600 vs. Hyper-RAG 0.2228) could be partly an artifact of matching that specific annotation style. This is a missing-evidence problem, not a theoretical flaw, but it must be fixed by releasing the data with a detailed protocol and IAA before the central claim can be ass
  2. [§5.2, Tables 3 and 5, Figs. 3–4] All quantitative comparisons are single point estimates. No standard errors, confidence intervals, or significance tests are reported for the n-ary F1, fact-verification accuracy, or RAG scores. The 100-document HyperDocRED test set and the 100-article MINE set are small; semantic matching thresholds add further variance. Claims such as 'significantly outperforms' (Abstract) and 'consistently outperforms' (§5.2) are therefore not supported by the reported evidence. Please report variance across rollout seeds/temperatures and apply appropriate significance tests (e.g., bootstrap or paired tests) to the headline comparisons.
  3. [§4.3.2, §4.3.3] The skill-acquisition loop is explicitly aligned with the gold standard 𝒢∗: Stable/Unstable/Miss categorization is defined by retrieval frequency against 𝒢∗, and Miss-set skills are generated by injecting the ground-truth relation into the context ('hindsight reasoning'). The Introduction's claim of a 'self-improving loop that minimizes domain blind spots' and 'without human intervention' (Sec. 1) is thus inflated—the system is supervised by gold annotations. More importantly, the paper does not ablate the stability mechanism. A reader cannot tell whether the gains come from the stability categorization or simply from hindsight distillation on all gold relations (or from adding any retrieved text). Please add ablations that isolate the reward signal, e.g., (i) skills from the Unstable set only, (ii) skills from the Miss set only, (iii) hindsight on a random subset of gold relations, and
minor comments (5)
  1. [Abstract, §6] The paper repeatedly states that HyperDocRED is 'released' (Abstract, Sec. 6), but no URL, license, or download instructions are given. Similarly, no code release is mentioned. Please provide URLs or state clearly that materials are not yet available.
  2. [Table 1] Column heading 'Sample Correlation' is unexplained; specify what correlation is computed and how it relates to the dataset construction.
  3. [Figs. 3 and 5] Figure captions do not state the semantic matching threshold used for the PR curves or the meaning of the horizontal axis in the skill-size plot; please clarify in captions or in the text.
  4. [§5.1.3] The precision-recall evaluation uses a Hungarian matching on cosine similarity from all-MiniLM-L6-v2. The thresholds 0.65/0.70/0.75 are mentioned in §5.3.1, but the default threshold used for Table 5 is not stated explicitly.
  5. [Appendix D] The case study appears to be a single positive example; please state how it was selected and whether similar patterns hold across the full test set.

Circularity Check

0 steps flagged

No significant circularity: skills are trained on gold labels but evaluated on held-out and external benchmarks; HyperDocRED construction is a validity concern, not a circular derivation.

full rationale

I inspected the claimed derivation chain. The system is trained by aligning K parallel rollouts with the gold-standard hypergraph G* (Sec. 4.3.2: 'By aligning the K candidate graphs with G*, we categorize the gold relations...' and 'We inject the ground-truth relation from G* into the context as a posterior condition'), and it is evaluated on a separate test split of HyperDocRED (Sec. 5.1.1: '50 training documents... 100 test documents') plus external MINE and UltraDomain benchmarks. This is ordinary supervised training/evaluation, not a definitional reduction. The semantic-similarity reward (Sec. 4.3.2) and the semantic-matching evaluation (Sec. 5.1.3) are aligned, but that is an intended train/test objective alignment, not a case where the predicted quantity equals the input by construction. The manual restructuring of HyperDocRED (Sec. 5.1.1) is a legitimate benchmark-validity concern (no protocol/IAA/release), but the paper's headline comparisons to KGGen/Hyper-RAG on external MINE (Table 3) and UltraDomain (Table 2) do not reduce to this dataset. The related-work citations to [8] and [11] are baseline descriptions, not load-bearing justifications of the core mechanism. No equation or fitted parameter is renamed as a prediction. Therefore no circular step is exhibited.

Axiom & Free-Parameter Ledger

4 free parameters · 5 axioms · 1 invented entities

The central empirical claims rest on several unproven domain assumptions: the reliability of the manually constructed HyperDocRED gold standard, the stability-based partition as a faithful proxy for general vs scenario-specific knowledge, the validity of embedding-similarity matching for n-ary relations, and the LLM-judge scoring of RAG outputs. None of these are independently established or ablated. The method also has unstated hyperparameters (K, T, chunk overlap) that can affect results.

free parameters (4)
  • Parallel rollout count K
    Equation (6) samples K candidate hypergraphs; K is never specified. The stable/unstable/miss partition depends on K, so the skill acquisition signal is sensitive to this unstated choice.
  • Sampling temperature T
    Equation (6) uses nonzero temperature T; the value is not reported. It controls rollout diversity, which defines what falls into the unstable set.
  • Semantic matching threshold = 0.65 / 0.70 / 0.75
    Used in PR evaluation (Fig. 3) to decide whether predicted and gold relations match; thresholds are chosen by hand and no single threshold or confidence interval is reported.
  • Chunk overlap size o
    Section 4.2.1 introduces overlapping windows with local context o; the size is not reported though it affects entity and relation recall.
axioms (5)
  • domain assumption An ideal ground-truth hypergraph 𝒢* exists and is comprehensive and precise (Section 3).
    The optimization objective and all evaluations assume the gold hypergraph is complete and correct; benchmark quality is not independently established.
  • domain assumption Extraction stability across K parallel rollouts cleanly separates general knowledge from scenario-specific knowledge (Section 4.3.2).
    The entire reward design treats 'retrieved in all samples' vs 'subset' vs 'never' as meaningful epistemic categories; no evidence or ablation supports this mapping.
  • domain assumption Embedding similarity of relation descriptions computed by all-MiniLM-L6-v2 is a valid proxy for relational equivalence (Section 5.1.3).
    The Hungarian matching evaluation treats cosine similarity of sentence embeddings as ground-truth matching; no human validation of this metric is provided.
  • domain assumption LLM judge scores along five dimensions reflect RAG response quality (Section 5.1.4).
    Downstream utility is measured by an LLM-based judge; no human correlation or inter-judge agreement is reported.
  • domain assumption LLMs possess latent domain skill that can be unlocked by textual skill libraries (Section 1 preliminary analysis).
    The premise of the Scenario Gap is asserted from preliminary analysis; the actual analysis is not shown.
invented entities (1)
  • Global Skill Library 𝒮 no independent evidence
    purpose: Evolving memory of reusable extraction skills (trigger/action pairs) retrieved at inference to adapt a frozen LLM to new domains.
    A new component introduced by the paper; its contents are generated by the model itself and there is no external benchmark for skill quality, only downstream extraction scores.

pith-pipeline@v1.3.0-alltime-deepseek · 20839 in / 17484 out tokens · 141979 ms · 2026-08-02T21:35:28.993214+00:00 · methodology

0 comments
read the original abstract

Knowledge hypergraphs surpass traditional binary knowledge graphs by encapsulating complex n-ary atomic facts, providing a more comprehensive paradigm for semantic representation. However, constructing high-quality hypergraphs remains challenging due to the scenario gap: generic extractors struggle to generalize across diverse domains with specific jargon, while existing methods often fail to balance structural skeletons with fine-grained details. To bridge this gap, we propose Hyper-KGGen, a skill-driven framework that reformulates extraction as a dynamic skill-evolving process. First, Hyper-KGGen employs a coarse-to-fine mechanism to systematically decompose documents, ensuring full-dimensional coverage from binary links to complex hyperedges. Crucially, it incorporates an adaptive skill acquisition module that actively distills domain expertise into a Global Skill Library. This is achieved via a stability-based feedback loop, where extraction stability serves as a relative reward signal to induce high-quality skills from unstable traces and missed predictions. Additionally, we present HyperDocRED, a rigorously annotated benchmark for document-level knowledge hypergraph extraction. Experiments demonstrate that Hyper-KGGen significantly outperforms strong baselines, validating that evolved skills provide substantially richer guidance than static few-shot examples in multi-scenario settings.

Figures

Figures reproduced from arXiv: 2602.19543 by Chuan Shi, Jun-Hai Yong, Rizhuo Huang, Rundong Xue, Shaoyi Du, Shihui Ying, Yifan Feng, Yue Gao.

Figure 1
Figure 1. Figure 1: Illustration of the Scenario Gap of general prompt [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The overall architecture of our proposed Hyper-KGGen framework for high quality knowledge hypergraph gener [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Precision-Recall Curves for 𝑛-ary Relation Extraction on the HyperDocRED Dataset [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Distribution of MINE scores across 100 articles for [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Performance Scaling with Few-Shot Setting and [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗

discussion (0)

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Reference graph

Works this paper leans on

53 extracted references · 17 linked inside Pith

  1. [1]

    Gabor Angeli, Melvin Jose Johnson Premkumar, and Christopher D Manning

  2. [2]

    Vahan Arsenyan, Spartak Bughdaryan, Fadi Shaya, Kent Wilson Small, and Davit Shahnazaryan. 2024. Large language models for biomedical knowledge graph construction: information extraction from EMR notes. InProceedings of the 23rd Workshop on Biomedical Natural Language Processing . 295–317

    2024

  3. [3]

    Yuzheng Cai, Siqi Cai, Yuchen Shi, Zihan Xu, Lichao Chen, Yulei Qin, Xiaoyu Tan, Gang Li, Zongyi Li, Haojia Lin, et al. 2025. Training-free group relative policy optimization. arXiv preprint arXiv:2510.08191 (2025)

    arXiv 2025

  4. [4]

    Adatasetforhyper-relationalextractionandacube-fillingapproach

    Yew Ken Chia, Lidong Bing, Sharifah Mahani Aljunied, Luo Si, and Soujanya Poria.2022. Adatasetforhyper-relationalextractionandacube-fillingapproach. arXiv preprint arXiv:2211.10018 (2022)

    Pith/arXiv arXiv 2022

  5. [5]

    Gheorghe Comanici, Eric Bieber, Mike Schaekermann, Ice Pasupat, Noveen Sachdeva, Inderjit Dhillon, Marcel Blistein, Ori Ram, Dan Zhang, Evan Rosen, et al. 2025. Gemini 2.5: Pushing the frontier with advanced reasoning, multi- modality, long context, and next generation agentic capabilities.arXiv preprint arXiv:2507.06261 (2025)

    Pith/arXiv arXiv 2025

  6. [6]

    Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, Dasha Metropolitansky, Robert Osazuwa Ness, and Jonathan Larson. 2024. From local to global: A graph rag approach to query- focused summarization. arXiv preprint arXiv:2404.16130 (2024)

    Pith/arXiv arXiv 2024

  7. [7]

    Bahare Fatemi, Perouz Taslakian, David Vazquez, and David Poole. 2019. Knowledge hypergraphs: Prediction beyond binary relations. arXiv preprint arXiv:1906.00137 (2019)

    Pith/arXiv arXiv 2019

  8. [8]

    Yifan Feng, Hao Hu, Xingliang Hou, Shiquan Liu, Shihui Ying, Shaoyi Du, Han Hu, and Yue Gao. 2025. Hyper-RAG: Combating LLM Hallucinations using Hypergraph-Driven Retrieval-Augmented Generation. arXiv preprint arXiv:2504.08758 (2025)

    Pith/arXiv arXiv 2025

  9. [9]

    Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yixin Dai, Jiawei Sun, Haofen Wang, and Haofen Wang. 2023. Retrieval-augmented generation for large language models: A survey.arXiv preprint arXiv:2312.10997 2, 1 (2023)

    Pith/arXiv arXiv 2023

  10. [10]

    Zirui Guo, Lianghao Xia, Yanhua Yu, Tu Ao, and Chao Huang. 2024. Lightrag: Simpleandfastretrieval-augmentedgeneration. arXiv preprint arXiv:2410.05779 (2024)

    Pith/arXiv arXiv 2024

  11. [11]

    Hao Hu, Yifan Feng, Ruoxue Li, Rundong Xue, Xingliang Hou, Zhiqiang Tian, Yue Gao, and Shaoyi Du. 2025. Cog-RAG: Cognitive-Inspired Dual- Hypergraph with Theme Alignment Retrieval-Augmented Generation. arXiv preprint arXiv:2511.13201 (2025)

    arXiv 2025

  12. [12]

    Haoyu Huang, Chong Chen, Zeang Sheng, Yang Li, and Wentao Zhang. 2025. CanLLMsbeGoodGraphJudgeforKnowledgeGraphConstruction?.In Proceed- ings of the 2025 Conference on Empirical Methods in Natural Language Processing . 10940–10959

    2025

  13. [13]

    Haoyu Huang, Yongfeng Huang, Junjie Yang, Zhenyu Pan, Yongqiang Chen, Kaili Ma, Hongzhi Chen, and James Cheng. 2025. Retrieval-Augmented Gen- eration with Hierarchical Knowledge.arXiv preprint arXiv:2503.10150 (2025)

    arXiv 2025

  14. [14]

    Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al. 2024. Gpt-4o system card.arXiv preprint arXiv:2410.21276 (2024)

    Pith/arXiv arXiv 2024

  15. [15]

    Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, Zhiyuan Zhang, Keshav Santhanam, Sri Vardhamanan, Saiful Haq, Ashutosh Sharma, Thomas T Joshi, Hanna Moazam, et al. 2023. Dspy: Compiling declarative language model calls into self-improving pipelines.arXiv preprint arXiv:2310.03714 (2023)

    Pith/arXiv arXiv 2023

  16. [16]

    Yassir Lairgi, Ludovic Moncla, Rémy Cazabet, Khalid Benabdeslem, and Pierre Cléau. 2024. itext2kg: Incremental knowledge graphs construction using large language models. InInternational Conference on Web Information Systems Engi- neering. Springer, 214–229

    2024

  17. [17]

    Aixin Liu, Aoxue Mei, Bangcai Lin, Bing Xue, Bingxuan Wang, Bingzheng Xu, Bochao Wu, Bowei Zhang, Chaofan Lin, Chen Dong, et al. 2025. Deepseek- v3. 2: Pushing the frontier of open large language models. arXiv preprint arXiv:2512.02556 (2025)

    Pith/arXiv arXiv 2025

  18. [18]

    Yu Liu, Quanming Yao, and Yong Li. 2020. Generalizing tensor decomposition for n-ary relational knowledge bases. InProceedings of the web conference 2020 . 1104–1114

    2020

  19. [19]

    Haoran Luo, Guanting Chen, Yandan Zheng, Xiaobao Wu, Yikai Guo, Qika Lin, Yu Feng, Zemin Kuang, Meina Song, Yifan Zhu, et al. 2025. HyperGraphRAG: Retrieval-AugmentedGenerationviaHypergraph-StructuredKnowledgeRepre- sentation. arXiv preprint arXiv:2503.21322 (2025)

    arXiv 2025

  20. [20]

    Haoran Luo, Yuhao Yang, Tianyu Yao, Yikai Guo, Zichen Tang, Wentai Zhang, Shiyao Peng, Kaiyang Wan, Meina Song, Wei Lin, et al. 2024. Text2nkg: Fine- grainedn-aryrelationextractionforn-aryrelationalknowledgegraphconstruc- tion. Advances in Neural Information Processing Systems 37(2024),27417–27439

    2024

  21. [21]

    Belinda Mo, Kyssen Yu, Joshua Kazdan, Joan Cabezas, Proud Mpala, Lisa Yu, Chris Cundy, Charilaos Kanatsoulis, and Sanmi Koyejo. 2025. Kggen: Extract- ing knowledge graphs from plain text with language models. arXiv preprint arXiv:2502.09956 (2025)

    arXiv 2025

  22. [22]

    Idza Aisara Norabid and Fariza Fauzi. 2022. Rule-based text extraction for mul- timodal knowledge graph. International Journal of Advanced Computer Science and Applications 13, 5 (2022)

    2022

  23. [23]

    Sergio Oramas, Mohamed Sordo, and Luis Espinosa-Anke. 2015. A rule-based approach to extracting relations from music tidbits. InProceedings of the 24th International Conference on World Wide Web. 661–666

    2015

  24. [24]

    Hongjin Qian, Zheng Liu, Peitian Zhang, Kelong Mao, Defu Lian, Zhicheng Dou, and Tiejun Huang. 2025. Memorag: Boosting long context processing with global memory-enhanced retrieval augmentation. InProceedings of the ACM on Web Conference 2025. 2366–2377

    2025

  25. [25]

    Bo Qiao, Zhuoyang Zou, Yu Huang, Kui Fang, Xinghui Zhu, and Yiming Chen

  26. [26]

    Paolo Rosso, Dingqi Yang, and Philippe Cudré-Mauroux. 2020. Beyond triplets: hyper-relationalknowledgegraphembeddingforlinkprediction.In Proceedings of the web conference 2020. 1885–1896

    2020

  27. [27]

    Edward W Schneider. 1973. Course Modularization Applied: The Interface Sys- tem and Its Implications For Sequence Control and Data Analysis. (1973). Conference acronym ’XX, June 03–05, 2018, Woodstock, NY Rizhuo Huang et al

    1973

  28. [28]

    Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. 2023. Reflexion: Language agents with verbal reinforcement learn- ing. Advances in Neural Information Processing Systems 36 (2023), 8634–8652

    2023

  29. [29]

    Yago:acoreof semantic knowledge

    FabianMSuchanek,GjergjiKasneci,andGerhardWeikum.2007. Yago:acoreof semantic knowledge. InProceedings of the 16th international conference on World Wide Web. 697–706

    2007

  30. [30]

    Qingyu Tan, Lu Xu, Lidong Bing, Hwee Tou Ng, and Sharifah Mahani Aljunied

  31. [31]

    Minzheng Wang, Longze Chen, Fu Cheng, Shengyi Liao, Xinghua Zhang, Bingli Wu, Haiyang Yu, Nan Xu, Lei Zhang, Run Luo, et al. 2024. Leave no document behind:Benchmarkinglong-contextllmswithextendedmulti-docqa.In Proceed- ings of the 2024 Conference on Empirical Methods in Natural Language Processing . 5627–5646

    2024

  32. [32]

    Xinglin Wang, Jiayi Shi, Shaoxiong Feng, Peiwen Yuan, Yiwei Li, Yueqi Zhang, Chuyi Tan, Ji Zhang, Boyuan Pan, Yao Hu, et al. 2026. Do Not Waste Your Roll- outs:RecyclingSearchExperienceforEfficientTest-TimeScaling. arXiv preprint arXiv:2601.21684 (2026)

    Pith/arXiv arXiv 2026

  33. [33]

    arXiv preprint arXiv:2205.12696 (2022)

    Revisiting DocRED–Addressing the False Negative Problem in Relation Extraction. arXiv preprint arXiv:2205.12696 (2022)

    Pith/arXiv arXiv 2022

  34. [34]

    Qwen3technical report

    An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, BowenYu,ChangGao,ChengenHuang,ChenxuLv,etal.2025. Qwen3technical report. arXiv preprint arXiv:2505.09388 (2025)

    Pith/arXiv arXiv 2025

  35. [35]

    Yawen Zeng, Qin Jin, Tengfei Bao, and Wenfeng Li. 2023. Multi-modal knowl- edge hypergraph for diverse image retrieval. InProceedings of the AAAI confer- ence on artificial intelligence, Vol. 37. 3376–3383

    2023

  36. [36]

    On the representation and embedding of knowledge bases beyond binary relations

    JianfengWen,JianxinLi,YongyiMao,ShiniChen,andRichongZhang.2016. On the representation and embedding of knowledge bases beyond binary relations. arXiv preprint arXiv:1604.08642 (2016)

    Pith/arXiv arXiv 2016

  37. [37]

    Hairong Zhang, Jiaheng Si, Guohang Yan, Boyuan Qi, Pinlong Cai, Song Mao, Ding Wang, and Botian Shi. 2025. RAKG: Document-level Retrieval Augmented Knowledge Graph Construction.arXiv preprint arXiv:2504.09823 (2025)

    Pith/arXiv arXiv 2025

  38. [38]

    Qizheng Zhang, Changran Hu, Shubhangi Upasani, Boyuan Ma, Fenglu Hong, Vamsidhar Kamanuru, Jay Rainton, Chen Wu, Mengmeng Ji, Hanchen Li, et al

  39. [39]

    Bowen Zhang and Harold Soh. 2024. Extract, define, canonicalize: An llm-based framework for knowledge graph construction.arXiv preprint arXiv:2404.03868 (2024)

    Pith/arXiv arXiv 2024

  40. [43]

    If it’s just meaningless symbols, output: State: False

    First, determine whether the text contains meaningful information. If it’s just meaningless symbols, output: State: False

  41. [44]

    Extract all named entities from the text

  42. [45]

    Each entity should have three parts: - name: The entity name, - type: The type of this entity - description: A brief description of what this entity is

  43. [46]

    Output MUST be valid JSON only

    Output the extracted entities as a list of objects. Output MUST be valid JSON only. JSON format: {{”nodes”:[”name”:””,”type”:””,”description”:””]}} Relation Extraction Prompt You are an expert hypergraph extraction assistant. Extract complex relationships (hyperedges) that involve MULTIPLE entities simultaneously from the given text. **Extraction Strategy...

  44. [47]

    Identify ALL participants for each relationship

  45. [48]

    ONLY use entities from the provided ’Known nodes’ list

  46. [49]

    If an entity is not in the ’Known nodes’ list, exclude it from the hyperedge. Known nodes: {known nodes} Text: {text} Hyper-KGGen: A Skill-Driven Knowledge Extractor for High-Quality Knowledge Hypergraph Generation Conference acronym ’XX, June 03–05, 2018, Woodstock, NY Skill Update Prompt Template You are an expert knowledge manager for hyperedge extract...

    2018

  47. [50]

    **ADD**: Add it if it provides unique, high-impact guidance not covered by existing experiences

  48. [51]

    **MERGE**: If it overlaps with existing experiences, merge into ONE clearer, more reusable experience; specify which existing IDs to merge with

  49. [52]

    **SKIP**: If fully covered / redundant / too vague

  50. [53]

    … instantiate <edge type>

    **DELETE**: If it reveals an existing experience is misleading/useless; delete the old one. Constraint Rules - Keep the flat structure: each experience has only ‘trigger‘, ‘action‘. - TRIGGER may be either: (a) a scenario cue (domain setting / discourse pattern / evidence style), OR (b) an anchor cue (event/state + role/discourse binding), but NOT a keywo...

    2018

  51. [2015]

    Leveraging linguistic structure for open domain information extraction. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Pro- cessing (Volume 1: Long Papers). 344–354

  52. [2022]

    A joint model for entity and relation extraction based on BERT.Neural Computing and Applications 34, 5 (2022), 3471–3481

    2022

  53. [2025]

    arXiv preprint arXiv:2510.04618 (2025)

    Agentic context engineering: Evolving contexts for self-improving lan- guage models. arXiv preprint arXiv:2510.04618 (2025). Hyper-KGGen: A Skill-Driven Knowledge Extractor for High-Quality Knowledge Hypergraph Generation Conference acronym ’XX, June 03–05, 2018, Woodstock, NY A Baselines In performance comparison, we consider the state-of-the-art base- l...

    Pith/arXiv arXiv 2025