When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models | Paper Citation Record

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This paper cites Proceedings of the International Conference on Neural Information Processing Systems , pages =.

When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models Proceedings of the International Conference on Neural Information Processing Systems , pages =

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When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models Proceedings of the International Conference on Learning Representations , year =

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When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models Proceedings of the Annual Meeting of the Association for Computational Linguistics , pages =

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This paper cites AbstentionBench: Reasoning LLMs Fail on Unanswerable Questions.

When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models AbstentionBench: Reasoning LLMs Fail on Unanswerable Questions

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When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models AbsenceBench: Language Models Can't Tell What's Missing

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When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models Proceedings of the Conference on Empirical Methods in Natural Language Processing , pages =

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When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models ACM Computing Surveys , volume =

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This paper cites Proceedings of the International Semantic Web Conference , pages =.

When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models Proceedings of the International Semantic Web Conference , pages =

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When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models Proceedings of the ACM SIGMOD International Conference on Management of Data , pages =

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When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models Journal of Machine Learning Research , volume =

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When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models Proceedings of the International Conference on Neural Information Processing Systems , pages =

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When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models Language Models (Mostly) Know What They Know

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When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models Findings of the Association for Computational Linguistics: ACL 2023 , pages =

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When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models Proceedings of the Annual Meeting of the Association for Computational Linguistics , pages =

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When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models Proceedings of the Conference on Empirical Methods in Natural Language Processing , pages =

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When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models Proceedings of the International Conference on Learning Representations , year =

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When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models Unresolved cited work

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When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models Unresolved cited work

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When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models Proceedings of the Conference on Empirical Methods in Natural Language Processing , pages =

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When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models Proceedings of the Conference on Empirical Methods in Natural Language Processing , pages =

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When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models Unresolved cited work

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When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models Proceedings of the 3rd Workshop on Towards Knowledgeable Foundation Models , year =

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When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models Proceedings of the AAAI Conference on Artificial Intelligence , pages=

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When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models Clancey and Glenn Rennels , abstract =

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When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models Transfer of Rule-Based Expertise through a Tutorial Dialogue

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When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models The Engineering of Qualitative Models

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When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions

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When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models MemPrivacy: Privacy-Preserving Personalized Memory Management for Edge-Cloud Agents

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When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models Findings of the Association for Computational Linguistics: EMNLP 2025 , pages=

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When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models arXiv preprint arXiv:2412.04697 , year=

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When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models arXiv preprint arXiv:2602.14374 , year=

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When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models 48th Annual IEEE Symposium on Foundations of Computer Science , pages=

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:22:45.521865Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 61bb99c8-6f11-4703-ba88-2010fa62b769 · outbound

This paper cites 2014 , publisher=.

When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models 2014 , publisher=

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:22:45.506649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 31bb505e-cfef-476e-bc8a-f7996d052a16 · outbound

This paper cites Proceedings of the 37th International Conference on Machine Learning (ICML) , series=.

When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models Proceedings of the 37th International Conference on Machine Learning (ICML) , series=

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:22:45.490440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ca5fd62b-ca10-473f-a571-3d53dda337ad · outbound

This paper cites Individual Privacy Accounting via a R.

When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models Individual Privacy Accounting via a R

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:22:45.473672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8b15c1eb-eae1-4e86-af63-fbaf5e470924 · outbound

This paper cites an unresolved cited work.

When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models Unresolved cited work

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T21:22:45.050891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72262e97-aa63-4a4f-b084-4c70e56c493b · outbound

This paper cites Theory of Cryptography Conference (TCC) , pages=.

When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models Theory of Cryptography Conference (TCC) , pages=

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:22:45.446752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7a65af52-58fa-4174-926c-a7b31a084aaa · outbound

This paper cites Foundations of Software Science and Computational Structures , pages=.

When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models Foundations of Software Science and Computational Structures , pages=

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:22:45.431044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 48cc8c8c-9192-450e-bbd4-8212c5a6f8f3 · outbound

This paper cites 2012 IEEE 25th Computer Security Foundations Symposium , pages=.

When Absence Is Evidence: Evaluating Completeness-Sensitive Negative Reasoning in Large Language Models 2012 IEEE 25th Computer Security Foundations Symposium , pages=

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:22:45.415063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers