Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-14T11:20:09.244925Z
Paper Citation Record · LEDGER
As of 9 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 0 inbound Pith citation observations for arXiv:2607.10491.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-14T11:20:09.244925Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
57 of 57 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation fb5505be-2bbd-4cd1-8f89-d89108f46193 · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks , booktitle =
Reference 1
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Observation 0d011df1-ceb4-4214-acf0-824d956a929c · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Proceedings of the 37th International Conference on Machine Learning , series =
Reference 2
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EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics , pages =
Reference 3
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Observation 4091b58b-513e-412e-bdee-a9b2eab246f0 · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Unresolved cited work
Reference 4
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Observation a14ebfdb-d89e-4d75-99db-2d86512eb964 · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing , pages =
Reference 5
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Observation a9f1fd19-ae63-41ac-82d3-303ae889735c · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Foundations and Trends in Information Retrieval , volume =
Reference 6
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Observation aa360eb8-0b47-4571-af63-0d65b05fad1b · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing , pages =
Reference 7
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Observation da70d744-3c5a-4597-bff9-fef51bf86ea7 · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval , pages =
Reference 8
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Observation 8e4ad323-80f5-4dae-89e9-2a0051eac8cb · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics , pages =
Reference 9
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Observation a7cb5f2e-aaad-4680-8329-1108e03a2392 · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Passage Re-ranking with BERT
Reference 10
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Observation f6eb9462-1436-4b5c-b126-380fcc74aa50 · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Retrieval-Augmented Generation for Large Language Models: A Survey
Reference 11
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Observation 640ce256-d74f-4a66-9c05-6e43fb5a19e1 · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning The Twelfth International Conference on Learning Representations , year =
Reference 12
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Observation 4f729f62-1353-4d0e-91e0-b16290f1ed8f · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Corrective Retrieval Augmented Generation
Reference 13
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Observation a17cdbb3-b928-464a-aaf1-c367b2f1d998 · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning CRAG -- Comprehensive RAG Benchmark
Reference 14
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Observation 854ff38e-08c8-403a-ab67-563f72f90c62 · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Exploring Knowledge Conflicts for Faithful LLM Reasoning: Benchmark and Method
Reference 15
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Observation 618d4e98-e00e-4ddf-8264-5dd5bbba71b4 · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Advances in Neural Information Processing Systems , year =
Reference 16
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Observation f482dca4-f79d-48b6-b20e-e6689febdf9b · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages =
Reference 17
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Observation 9a5c770f-9615-4de8-8d46-4d0feadde37f · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Transactions of the Association for Computational Linguistics , volume =
Reference 18
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Observation c4120f6b-406f-4dc7-8371-7c74d48451ad · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning and Salakhutdinov, Ruslan and Manning, Christopher D
Reference 19
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Observation a4195147-fe4e-4984-a1a3-ad415459c03a · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning and Uszkoreit, Jakob and Le, Quoc and Petrov, Slav , title =
Reference 20
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Observation 75c4e5e9-7ed5-45f6-a41c-03eec7380fbb · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing , pages =
Reference 21
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Observation f5580e5b-4707-4649-ae4a-a0e2bb8127b7 · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics , pages =
Reference 22
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Observation 1616b70a-1da4-4782-b084-fa03734433d2 · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages =
Reference 23
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Observation 29ba8272-1f7c-4a78-8d4d-246fee37d232 · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Unresolved cited work
Reference 24
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Observation d083f965-594b-4649-bd7b-cf2d0e124081 · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , pages =
Reference 25
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Observation 52234bd0-f47b-4f96-aeca-b974c45830bc · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics , pages =
Reference 26
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Observation 397f03fb-8662-4b57-8478-1c8e2169c7b2 · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning ACM Computing Surveys , volume =
Reference 27
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Observation da3f48ba-b0e2-4809-b972-991ee229cb40 · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , pages =
Reference 28
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Observation 1fec07ce-0ceb-42b9-9f23-877756d2f664 · outbound
Reference 29
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Observation abbafd75-f27d-4ad7-8a12-3ef2ad6777b9 · outbound
Reference 30
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Observation 2593d26e-3485-438d-b974-4f66aafc745a · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning and Nowozin, Sebastian and Dillon, Joshua V
Reference 31
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Observation b658be29-6513-4266-b34f-e63dd6f2d412 · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Advances in Neural Information Processing Systems , volume =
Reference 32
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Observation e7f73a2e-b848-4995-8e1b-15ec7fdde6c9 · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Advances in Neural Information Processing Systems , volume =
Reference 33
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Observation 9a829436-a1e1-4403-931b-817efaa56008 · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Proceedings of the 33rd International Conference on Machine Learning , pages =
Reference 34
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Observation a0b49747-ccd3-411c-9deb-c26b38a85c77 · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Advances in Neural Information Processing Systems , volume =
Reference 35
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Observation e6b17e08-c6a4-44b2-bdad-fa68d3960710 · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Subjective Logic: A Formalism for Reasoning Under Uncertainty , publisher =
Reference 36
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Observation 938c6cf0-24c6-437f-978c-bfb9b3c6d77b · outbound
Reference 37
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Observation 44ef74e8-0f49-4acf-88e0-eb0b253e08ed · outbound
Reference 38
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Observation 2b66b0e0-a78c-46b0-ac59-58a894b23bab · outbound
Reference 39
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Observation 6c40220e-21ef-4857-80d7-f410d9936e77 · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning IEEE Transactions on Pattern Analysis and Machine Intelligence , volume =
Reference 40
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Observation 5e8f596a-962f-4154-8be9-00e138f2ccd0 · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning and Kaiser, Lukasz and Polosukhin, Illia , title =
Reference 41
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Observation b8993771-d6b7-4cad-86b1-a5a917dc6e8f · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics , pages =
Reference 42
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Observation 908a5e92-1f12-41db-b19c-d2e1ec04ee19 · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Unresolved cited work
Reference 43
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Observation f8963e4d-fbf5-4156-b557-6fb2b70f33d6 · outbound
Reference 44
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Observation 1c0f09e4-c034-44ae-a4df-a0f7862e77c7 · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning LLaMA: Open and Efficient Foundation Language Models , journal =
Reference 45
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Observation cbf185ec-38d7-41a3-b373-bc1415a2084f · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning The Llama 3 Herd of Models
Reference 46
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Observation deb4a5ae-5873-4960-ae6a-648357c78281 · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Unresolved cited work
Reference 47
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Observation bc5888c0-7a7c-4298-a09c-52e2b9369440 · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning and Zhang, Hao and Stoica, Ion , title =
Reference 48
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Observation 98933681-217d-482b-bb25-3ecae645b6d0 · outbound
Reference 49
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Observation 64f8fe8f-d7b5-4128-b1c7-d7b9637c5efa · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning and Artzi, Yoav , title =
Reference 50
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Observation 695bab4e-93ee-48c4-9c1b-19ed28835bd3 · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Text Summarization Branches Out , pages =
Reference 51
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Observation 24d286b6-95af-4b07-ac0c-45f26632b251 · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , pages =
Reference 52
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Observation 34c552ed-42f2-4266-9d3d-fed8e263ee01 · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning The Twelfth International Conference on Learning Representations , year =
Reference 53
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Observation c28ca4f1-adf5-47d3-9e47-a9bdb07dc419 · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning RAGBench: Explainable Benchmark for Retrieval-Augmented Generation Systems
Reference 54
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Observation ec22a2d2-b48c-4541-be97-6767c170d3c9 · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Educational and Psychological Measurement , volume =
Reference 55
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Observation a3842e10-ca16-4328-a1c6-a2ace42ae3bd · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning The Annals of Statistics , volume =
Reference 56
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Observation e2e40cb1-2160-432f-ab55-4b53dda48293 · outbound
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning Findings of the Association for Computational Linguistics: ACL 2024 , pages =
Reference 57
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No inbound Pith citation observations are available.