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Paper Citation Record · LEDGER

EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning

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.

pith.paper-citation-record.v1
2607.10491 v1

Coverage vector

measured 57 of 57 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T11:20:09.244925Z

measured 57 of 57 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

57 of 57 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved57
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fb5505be-2bbd-4cd1-8f89-d89108f46193 · outbound

This paper cites Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks , booktitle =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:7241b2e6ee13f61ca6f795b70d2478d725a1f6a6aab2a0c67fe706a781501cc1

Observation 0d011df1-ceb4-4214-acf0-824d956a929c · outbound

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

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:bf33fbffd9092c3ee33f119920bb6c2ea842b631471a364a564bd202c9af3e58

Observation c61dbdae-8067-4aae-bfca-879730f9519c · outbound

This paper cites Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics , pages =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:e1cfcba2113d8994b1f1863e8220f07c2d5dfaa12d9786001d104aea4307feed

Observation 4091b58b-513e-412e-bdee-a9b2eab246f0 · outbound

This paper cites an unresolved cited work.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:a1f54ac127653e55947208db378ee061fb59d7e9846390ba0bb407f17335972b

Observation a14ebfdb-d89e-4d75-99db-2d86512eb964 · outbound

This paper cites Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing , pages =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:9ce273e1eaca2e848a4c9c408f49d3e52d73f30280bdff85a058d50dd7f04d7a

Observation a9f1fd19-ae63-41ac-82d3-303ae889735c · outbound

This paper cites Foundations and Trends in Information Retrieval , volume =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:d7b276b4f405ebf1069d4369793c5f8514e24350df0ec39f1101ba7d57a5006d

Observation aa360eb8-0b47-4571-af63-0d65b05fad1b · outbound

This paper cites Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing , pages =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:aac582da028a29b3ac9d9eacf30f7a96fab2c0c85c2ae17d4848c8eb31479b79

Observation da70d744-3c5a-4597-bff9-fef51bf86ea7 · outbound

This paper cites Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval , pages =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:fdc2eaef39fce1471406fbb238f6eb3fa930b4b884ade261c5c6b5b07fb4ae9e

Observation 8e4ad323-80f5-4dae-89e9-2a0051eac8cb · outbound

This paper cites Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics , pages =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:42db9a4c531748ce5ed641dd4f42ad901406f43369d3e762590abb8c60296350

Observation a7cb5f2e-aaad-4680-8329-1108e03a2392 · outbound

This paper cites Passage Re-ranking with BERT.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:553f8f62ae7e51140d1cacdc5d3b1d71e26eeb44d73b9550291fb945ab9ae211

Observation f6eb9462-1436-4b5c-b126-380fcc74aa50 · outbound

This paper cites Retrieval-Augmented Generation for Large Language Models: A Survey.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:d71b4ea13591e5f74ad293b1f65e462caeca37b65b351b108addc1395667322c

Observation 640ce256-d74f-4a66-9c05-6e43fb5a19e1 · outbound

This paper cites The Twelfth International Conference on Learning Representations , year =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:fa39d4babae43236a95b5b83cdad377651a4e411be59ee7f67c18ad549ed3763

Observation 4f729f62-1353-4d0e-91e0-b16290f1ed8f · outbound

This paper cites Corrective Retrieval Augmented Generation.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:e5711612009e77ace12e98b74d06120e328dcfe495b50de6a97beaf0d0d060bd

Observation a17cdbb3-b928-464a-aaf1-c367b2f1d998 · outbound

This paper cites CRAG -- Comprehensive RAG Benchmark.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:027b69edd227f9f0449cffe5b20305b98716b320578038ec20fbcb53c3b3c916

Observation 854ff38e-08c8-403a-ab67-563f72f90c62 · outbound

This paper cites Exploring Knowledge Conflicts for Faithful LLM Reasoning: Benchmark and Method.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:4600a2c9e245db441eef17a402505f0710c51b575b565b766f9de68af8a884e0

Observation 618d4e98-e00e-4ddf-8264-5dd5bbba71b4 · outbound

This paper cites Advances in Neural Information Processing Systems , year =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:4c4a0a1a28256f9f6267907af41d8f24dfa847befad846f16cbe7ca09dbbbda2

Observation f482dca4-f79d-48b6-b20e-e6689febdf9b · outbound

This paper cites Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , pages =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:338441d1e41b91709493a727bb80babdb7376cf99ff203b7b6acb7b411279881

Observation 9a5c770f-9615-4de8-8d46-4d0feadde37f · outbound

This paper cites Transactions of the Association for Computational Linguistics , volume =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:c3f18e81fe57aedec580a324c277a8006909f1aea32d3bae90329b18cc9abf1e

Observation c4120f6b-406f-4dc7-8371-7c74d48451ad · outbound

This paper cites and Salakhutdinov, Ruslan and Manning, Christopher D.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:571c3956bf5dd5e7defd2d4b71bb50d58219e12be1d2b6f43a2441ed4271565d

Observation a4195147-fe4e-4984-a1a3-ad415459c03a · outbound

This paper cites and Uszkoreit, Jakob and Le, Quoc and Petrov, Slav , title =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:80180382c485ea6fa513e86f15ce45b33622d43d810e289f37f28cf01e27b33a

Observation 75c4e5e9-7ed5-45f6-a41c-03eec7380fbb · outbound

This paper cites Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing , pages =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:81f05ac5880ee9a6ae186b9244da0ac0c41e4b91e2d0470d1cbfc2a0e5646e32

Observation f5580e5b-4707-4649-ae4a-a0e2bb8127b7 · outbound

This paper cites Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics , pages =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:5eb4f9dc6241ea825d6065b484a6ecc01d7b1d0c62102f44aced5d6eec4019c7

Observation 1616b70a-1da4-4782-b084-fa03734433d2 · outbound

This paper cites Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:40c41b12dac23c7a6dcf9e89a841604e73e84f7756f1e35d5c27cdb480b5bd92

Observation 29ba8272-1f7c-4a78-8d4d-246fee37d232 · outbound

This paper cites an unresolved cited work.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:4c269864643d2302c02b8d766d1a287bb2abd18a8bf3d5838f39a1586db76f4b

Observation d083f965-594b-4649-bd7b-cf2d0e124081 · outbound

This paper cites Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , pages =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:520e1eb5b7ec739390a29f1d92d7a90f5ac9b75f682c3263ccd0924d2312f4db

Observation 52234bd0-f47b-4f96-aeca-b974c45830bc · outbound

This paper cites Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics , pages =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:5db32fcab0fec2c6e7ee87ce579b3eb1947a70daba108c55dbc7fbe5c3e45690

Observation 397f03fb-8662-4b57-8478-1c8e2169c7b2 · outbound

This paper cites ACM Computing Surveys , volume =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:30c5a4ec1ec5424f399fff03dd97933a9a61aeb42096778d354ef6275ffde871

Observation da3f48ba-b0e2-4809-b972-991ee229cb40 · outbound

This paper cites Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , pages =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:1135831624f3db65e787877fade68081e51bcde1f6b5a3ff826e09078d797cf3

Observation 1fec07ce-0ceb-42b9-9f23-877756d2f664 · outbound

This paper cites , title =.

EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning , title =

Reference 29

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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:f712df9e6cdd10820fe59bc94fa612ccca4e0ae14bce7f19f6f49f50fa109cfd

Observation abbafd75-f27d-4ad7-8a12-3ef2ad6777b9 · outbound

This paper cites , title =.

EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning , title =

Reference 30

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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:bb2885ec22e0f872dcb09c88b403491cedb8beee98150e8ac964478bc552e3e9

Observation 2593d26e-3485-438d-b974-4f66aafc745a · outbound

This paper cites and Nowozin, Sebastian and Dillon, Joshua V.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:7a4985e0471e953cf071802753bfe88d866e63488f3a7c46b1ef30028e5da82b

Observation b658be29-6513-4266-b34f-e63dd6f2d412 · outbound

This paper cites Advances in Neural Information Processing Systems , volume =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:a8b8087f3740d8e477cfe826b8167e54e3843904208bcee0c9e4cf312264a2fe

Observation e7f73a2e-b848-4995-8e1b-15ec7fdde6c9 · outbound

This paper cites Advances in Neural Information Processing Systems , volume =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:70667beaabc6f34f121557223363f1a492daec3e206ac9b97efac234617646bf

Observation 9a829436-a1e1-4403-931b-817efaa56008 · outbound

This paper cites Proceedings of the 33rd International Conference on Machine Learning , pages =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:7418151900fc600a5cfe94c287817d1e1fc079c760bd2e782dda12c700604f73

Observation a0b49747-ccd3-411c-9deb-c26b38a85c77 · outbound

This paper cites Advances in Neural Information Processing Systems , volume =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:e114d87887990546d6f617950bb8eafdb4e6f1d277864e77a03711d44cbbe9c6

Observation e6b17e08-c6a4-44b2-bdad-fa68d3960710 · outbound

This paper cites Subjective Logic: A Formalism for Reasoning Under Uncertainty , publisher =.

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 =

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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:b646c20af1896a0f52e1b44fcf7d707ebb1ab8097442bd6949f7e60cf010856d

Observation 938c6cf0-24c6-437f-978c-bfb9b3c6d77b · outbound

This paper cites , title =.

EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning , title =

Reference 37

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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:46049dcee0a4bac308764b782db614c9c2b28917ffb71371c4fcaf4f80a80b67

Observation 44ef74e8-0f49-4acf-88e0-eb0b253e08ed · outbound

This paper cites 1976 , url =.

EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning 1976 , url =

Reference 38

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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:8c7fac0033c9ef9691d48173a907f4731d7918caabb4e1220fefcdadc4ed34c6

Observation 2b66b0e0-a78c-46b0-ac59-58a894b23bab · outbound

This paper cites , title =.

EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning , title =

Reference 39

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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:56774ac70771ffc2146184101efb33fc994cac4c785bd5e33047147ec5e3c6dd

Observation 6c40220e-21ef-4857-80d7-f410d9936e77 · outbound

This paper cites IEEE Transactions on Pattern Analysis and Machine Intelligence , volume =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:1699657e396266f0df5169ecbd79b64fccf58d8fa075426b9598eac553a1a44c

Observation 5e8f596a-962f-4154-8be9-00e138f2ccd0 · outbound

This paper cites and Kaiser, Lukasz and Polosukhin, Illia , title =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:ba68a73524ee6d654ee644f4c5b9e31c0d6aee413b08ba30a53355afbb2e416f

Observation b8993771-d6b7-4cad-86b1-a5a917dc6e8f · outbound

This paper cites Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics , pages =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:7462f2a42f81a27942bd7b35ddd29339c4cc551d8f5219e7c4bf25d9a5850b34

Observation 908a5e92-1f12-41db-b19c-d2e1ec04ee19 · outbound

This paper cites an unresolved cited work.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:96275e34e9ab9553515d4f673098aebb9a9dd2d1f3dd6bd53572c63daf3e878d

Observation f8963e4d-fbf5-4156-b557-6fb2b70f33d6 · outbound

This paper cites 2023 , doi =.

EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning 2023 , doi =

Reference 44

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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:ea22dc5a8dec1537788cd58f1b8e35e8f3aacef1c2a76ad8e95862ee9703eda5

Observation 1c0f09e4-c034-44ae-a4df-a0f7862e77c7 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models , journal =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:56338915ec9e5dcc91271feb0788b6138a09af864ad075a3fe57dca8f241743c

Observation cbf185ec-38d7-41a3-b373-bc1415a2084f · outbound

This paper cites The Llama 3 Herd of Models.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:c7f4664eddbc1cf4d2ff57c48e45c751c48d649e5b05aa33d1ec5efcffe3192c

Observation deb4a5ae-5873-4960-ae6a-648357c78281 · outbound

This paper cites an unresolved cited work.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:ba7ab053b999b19aa5fc96cd1b2ae1bbc30f081930c0231234e2a47a6cc7c608

Observation bc5888c0-7a7c-4298-a09c-52e2b9369440 · outbound

This paper cites and Zhang, Hao and Stoica, Ion , title =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:75813ef3533732e9f12d83124f24498fa4d5abc818c3df09f065f4c98310f204

Observation 98933681-217d-482b-bb25-3ecae645b6d0 · outbound

This paper cites , title =.

EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning , title =

Reference 49

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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:21e48f9cc9e48c5e2ade67ffe9b7623760b695574cecb6dfa37322bbc163488c

Observation 64f8fe8f-d7b5-4128-b1c7-d7b9637c5efa · outbound

This paper cites and Artzi, Yoav , title =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:73af14410537ae4083d13f85f4def227b66b0385eecf792e43510a951ddf3665

Observation 695bab4e-93ee-48c4-9c1b-19ed28835bd3 · outbound

This paper cites Text Summarization Branches Out , pages =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:9d35519d20906c76b85d07416031a24bff956c4027f7e4d06f3e72cdab7263ad

Observation 24d286b6-95af-4b07-ac0c-45f26632b251 · outbound

This paper cites Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , pages =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:96ca1b83b74a8ca9fec5f4690d992a8cebd52e2aeb4d47612eee263da6b240d0

Observation 34c552ed-42f2-4266-9d3d-fed8e263ee01 · outbound

This paper cites The Twelfth International Conference on Learning Representations , year =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:c0101eed9894c254c61c78b550759f773c43df83a9c020061e7e787de2cdd5b0

Observation c28ca4f1-adf5-47d3-9e47-a9bdb07dc419 · outbound

This paper cites RAGBench: Explainable Benchmark for Retrieval-Augmented Generation Systems.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:2f2ed91ed8287da17a118bf45ea1eb58dc0164d933422dcff5032355b662c965

Observation ec22a2d2-b48c-4541-be97-6767c170d3c9 · outbound

This paper cites Educational and Psychological Measurement , volume =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:944a93f67344aa88c620f7093c3d78d7e650edb0a4232e877ea58997c52320ea

Observation a3842e10-ca16-4328-a1c6-a2ace42ae3bd · outbound

This paper cites The Annals of Statistics , volume =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:e760e75b29dbcc28d949781e0f18c353819672c542c20b8c518267a4eb196e85

Observation e2e40cb1-2160-432f-ab55-4b53dda48293 · outbound

This paper cites Findings of the Association for Computational Linguistics: ACL 2024 , pages =.

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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source=arxiv_source observed=2026-07-14T11:20:09.244925Z digest=sha256:8a9fd8bb35d92aefb57c7f1a469e4f8202749b7d4ca66fba9df5d121f7cc1de2

Pith citing papers

No inbound Pith citation observations are available.