Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents | Paper Citation Record

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents Qwen2.5-1M Technical Report

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents arXiv preprint arXiv:2606.19348 , year=

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents Qwen3 Technical Report

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents Advances in neural information processing systems , volume=

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents Proceedings of the 2024 conference on empirical methods in natural language processing , pages=

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents FinanceBench: A New Benchmark for Financial Question Answering

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents Advances in neural information processing systems , volume=

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents Unresolved cited work

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents Findings of the Association for Computational Linguistics: ACL 2024 , pages=

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) , pages=

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents Journal of Artificial Intelligence Research , author=

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents The Eleventh International Conference on Learning Representations , year=

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents Advances in neural information processing systems , volume=

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents Gorilla: Large Language Model Connected with Massive APIs

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents International Conference on Learning Representations , volume=

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents AutoGen: Enabling Next-Gen

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents TaskWeaver: A Code-First Agent Framework

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents International Conference on Learning Representations , volume=

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents 27th USENIX Security Symposium (USENIX Security 18) , pages=

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents Findings of the Association for Computational Linguistics: EMNLP 2020 , pages=

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents IEEE Transactions on Software Engineering , volume=

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents ACM Computing Surveys (CSUR) , volume=

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents txt, a cheap Shallow Parsing approach for Regulatory texts , author=

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents Scientific data , volume=

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents arXiv preprint arXiv:2603.23519 , year=

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents Proceedings of the AAAI Conference on Artificial Intelligence , volume=

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents IEEE access , volume=

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents Proceedings of the 62nd annual meeting of the association for computational linguistics (volume 1: Long papers) , pages=

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents Qwen-Image Technical Report

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents Proceedings of the AAAI Conference on Artificial Intelligence , volume=

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents IndustryBench: Probing the Industrial Knowledge Boundaries of LLMs

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents Physical-Layer Signal Injection Attacks on EV Charging Ports: Bypassing Authentication via Electrical-Level Exploits

Reference 39

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents Chain-of-Thought Prompting Elicits Reasoning in Large Language Models , url =

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents The Eleventh International Conference on Learning Representations , year=

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents Training language models to follow instructions with human feedback , url =

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents L -Eval: Instituting Standardized Evaluation for Long Context Language Models

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents 2024 , url=

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

Reference 45

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents The Twelfth International Conference on Learning Representations , year=

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents Findings of the Association for Computational Linguistics: EMNLP 2021 , pages=

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents and Gardner, Matt

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents ACORD : An Expert-Annotated Retrieval Dataset for Legal Contract Drafting

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents Advances in Neural Information Processing Systems , volume=

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents Proceedings of the 2018 conference of the North American chapter of the Association for Computational Linguistics: Human language technologies, volume 1 (long papers) , pages=

Reference 51

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , pages=

Reference 52

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

Reference 53

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Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents D oc M ath-Eval: Evaluating Math Reasoning Capabilities of LLM s in Understanding Long and Specialized Documents

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Observation ef2ffef8-dae6-4c41-abe8-7f38a7643daa · outbound

This paper cites L ong D oc URL : a Comprehensive Multimodal Long Document Benchmark Integrating Understanding, Reasoning, and Locating.

Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents L ong D oc URL : a Comprehensive Multimodal Long Document Benchmark Integrating Understanding, Reasoning, and Locating

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:28.780194Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be39bc49-bc04-445a-889f-1b244acaad77 · outbound

This paper cites Marathon: A Race Through the Realm of Long Context with Large Language Models.

Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents Marathon: A Race Through the Realm of Long Context with Large Language Models

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:28.944679Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd222f99-07e2-42ec-a6ef-3e0c9ccdb251 · outbound

This paper cites LONGAGENT : Achieving Question Answering for 128k-Token-Long Documents through Multi-Agent Collaboration.

Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents LONGAGENT : Achieving Question Answering for 128k-Token-Long Documents through Multi-Agent Collaboration

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:29.124793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee9968ae-5863-4866-ba4b-33f3b17f2f0b · outbound

This paper cites Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 , pages =.

Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2 , pages =

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:29.276413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41ed5e38-1ea7-4f28-9cc1-7baf895ea063 · outbound

This paper cites 2021 , url=.

Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents 2021 , url=

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:49:32.021891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3d48fa26-701c-4809-9d04-b57dbd0d22fb · outbound

This paper cites Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence , pages=.

Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence , pages=

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:49:31.707433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 76d5f9b3-44d6-4c85-86d9-413eaba4932f · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , pages =.

Benchmarking and Enhancing LLMs for Rule-Intensive Review of National Standard Documents Proceedings of the AAAI Conference on Artificial Intelligence , pages =

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:49:31.439432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers