Paper Citation Record · LEDGER

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data

As of 22 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2603.19294.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2603.19294 v5

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T23:48:38.098404Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

41 of 41 outbound references displayed

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verified fuzzy0
unresolved41
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9373afc9-699d-47b3-8222-2222d350525c · outbound

This paper cites Detecting Stance in Tweets : A Signed Network based Approach.

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data Detecting Stance in Tweets : A Signed Network based Approach

Reference 1

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Observation a301a0d1-0888-4169-8816-376d46bf10ef · outbound

This paper cites IEEE Access9, 106907–106917 (2021).

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data IEEE Access9, 106907–106917 (2021)

Reference 2

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Observation 4ce35bdf-189c-4336-9808-cb68794b4590 · outbound

This paper cites In: 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM), pp.

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data In: 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM), pp

Reference 3

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Observation 24003b8d-8922-4d07-a8d8-3c5ca343ac5f · outbound

This paper cites In: Proceedings of the ACM Web Conference 2022, pp.

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data In: Proceedings of the ACM Web Conference 2022, pp

Reference 4

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Observation 755c4a05-1fe2-4973-aba3-959e65941c6b · outbound

This paper cites LLM-GAN: Construct Generative Adversarial Network Through Large Language Models For Explainable Fake News Detection.

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data LLM-GAN: Construct Generative Adversarial Network Through Large Language Models For Explainable Fake News Detection

Reference 5

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Observation 584cb8d0-bdf6-493a-9f90-bf6908e0178b · outbound

This paper cites Debunk and Infer: Multimodal Fake News Detection via Diffusion-Generated Evidence and LLM Reasoning.

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data Debunk and Infer: Multimodal Fake News Detection via Diffusion-Generated Evidence and LLM Reasoning

Reference 6

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Observation a07bc452-84a1-4ede-b49e-a68cd449309b · outbound

This paper cites CoRR (2024).

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data CoRR (2024)

Reference 7

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Observation 6297d440-6eee-4c90-ad3c-7d4f2713c7d8 · outbound

This paper cites Engineering Applications of Artificial Intelligence142, 109931 (2025).

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data Engineering Applications of Artificial Intelligence142, 109931 (2025)

Reference 8

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Observation 409ef8c2-a576-4f48-ad73-9ab5657c18b6 · outbound

This paper cites arXiv preprint arXiv:2510.05839 (2025) LLM-MRD 15.

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data arXiv preprint arXiv:2510.05839 (2025) LLM-MRD 15

Reference 9

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Observation e4a96c25-4360-431e-9832-34f27412cb19 · outbound

This paper cites International Journal of Web Information Systems21(2), 139–157 (2025).

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data International Journal of Web Information Systems21(2), 139–157 (2025)

Reference 10

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Observation 7891e531-69b9-4490-8f0e-732233ef1a97 · outbound

This paper cites Social Network Analysis and Mining 13(1), 101 (2023).

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data Social Network Analysis and Mining 13(1), 101 (2023)

Reference 11

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Observation ac39d8d8-0fd6-40b1-a646-e84fe9c680da · outbound

This paper cites In: 2024 7th In- ternational Conference on Data Science and Information Technology (DSIT), pp.

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data In: 2024 7th In- ternational Conference on Data Science and Information Technology (DSIT), pp

Reference 12

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Observation 69485951-533b-410b-acbe-cf448993ca93 · outbound

This paper cites Advances in Neural Information Processing Systems35, 24824–24837 (2022).

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data Advances in Neural Information Processing Systems35, 24824–24837 (2022)

Reference 13

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Observation 0496af6c-e3b4-401b-bfa7-f0c42feeb94b · outbound

This paper cites IEEE Transactions on Circuits and Systems for Video Technology 35(7), 6413–6423 (2025).

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data IEEE Transactions on Circuits and Systems for Video Technology 35(7), 6413–6423 (2025)

Reference 14

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Observation fd647de1-8faa-40ee-8206-3a0422179517 · outbound

This paper cites In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Reference 15

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Observation fc91434a-0a4c-4811-a557-37066eaa4055 · outbound

This paper cites arXiv preprint arXiv:2503.10200 (2025).

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data arXiv preprint arXiv:2503.10200 (2025)

Reference 16

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Observation c1261886-8151-4c2e-af4f-28b7eeed0150 · outbound

This paper cites High-Confidence Computing4(2), 100211 (2024).

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data High-Confidence Computing4(2), 100211 (2024)

Reference 17

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Observation 7122fad6-ea26-4e2e-aa9c-9e184722e92c · outbound

This paper cites The Stepwise Deception: Simulating the Evolution from True News to Fake News with LLM Agents.

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data The Stepwise Deception: Simulating the Evolution from True News to Fake News with LLM Agents

Reference 18

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Observation 07196efe-6bc1-4098-9b5d-04b195c88656 · outbound

This paper cites In: 2024 20th IEEE International Colloquium on Signal Processing & Its Applications (CSPA), pp.

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data In: 2024 20th IEEE International Colloquium on Signal Processing & Its Applications (CSPA), pp

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-14T23:48:38.098404Z

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Observation b8db6094-8d93-409c-ae03-7a1866af076c · outbound

This paper cites In: Proceedings of the IEEE/ACM 46th Interna- tional Conference on Software Engineering, pp.

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data In: Proceedings of the IEEE/ACM 46th Interna- tional Conference on Software Engineering, pp

Reference 20

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Observation 72a06ea3-fb1f-4e15-ac0c-b02cf4e4487a · outbound

This paper cites In: MILCOM 2024-2024 IEEE Military Communications Conference (MILCOM), pp.

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data In: MILCOM 2024-2024 IEEE Military Communications Conference (MILCOM), pp

Reference 21

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Observation 7fd83911-a496-4819-8270-19cf35f22087 · outbound

This paper cites Future Gener- ation Computer Systems, 107877 (2025).

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data Future Gener- ation Computer Systems, 107877 (2025)

Reference 22

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Observation b6329bd4-bdfb-4f73-85ae-cf9a14fcd720 · outbound

This paper cites Social Network Analysis and Mining15(1), 1–16 (2025).

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data Social Network Analysis and Mining15(1), 1–16 (2025)

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-14T23:48:38.098404Z

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Observation e5cfc2b2-6bd5-40e4-a59a-0dd0189cfcb3 · outbound

This paper cites In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-14T23:48:38.098404Z

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Observation 7b56c64c-5860-47dc-8b85-483203744bda · outbound

This paper cites SAFE: Similarity-Aware Multi-Modal Fake News Detection.

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data SAFE: Similarity-Aware Multi-Modal Fake News Detection

Reference 25

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Observation 62812ffd-95f3-4638-b44c-a165b6bcb64b · outbound

This paper cites In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Reference 26

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Observation 823fb206-547a-4044-b506-8d2cf9998a31 · outbound

This paper cites SEER: Semantic Enhancement and Emotional Reasoning Network for Multimodal Fake News Detection.

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data SEER: Semantic Enhancement and Emotional Reasoning Network for Multimodal Fake News Detection

Reference 27

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Observation d591e100-c6de-4c1f-ac6c-b0a018583531 · outbound

This paper cites In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp.

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp

Reference 28

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Observation 55a44331-8529-44c5-9b5c-ea8eb52702c4 · outbound

This paper cites In: 2023 IEEE International Conference on Multimedia and Expo (ICME), pp.

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data In: 2023 IEEE International Conference on Multimedia and Expo (ICME), pp

Reference 29

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Observation 8cd1d0d8-21ab-49a3-b703-a77053119d4b · outbound

This paper cites In: Proceedings of the ACM on Web Conference 2025, pp.

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data In: Proceedings of the ACM on Web Conference 2025, pp

Reference 30

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Observation 247fa738-5dca-47a8-a19c-b0459335579c · outbound

This paper cites KEN: Knowledge Augmentation and Emotion Guidance Network for Multimodal Fake News Detection.

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data KEN: Knowledge Augmentation and Emotion Guidance Network for Multimodal Fake News Detection

Reference 31

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Observation 3a5b701c-9684-4dc1-b101-42b0126b743b · outbound

This paper cites Synergizing LLMs with Global Label Propagation for Multimodal Fake News Detection.

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data Synergizing LLMs with Global Label Propagation for Multimodal Fake News Detection

Reference 32

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Observation 7c2f91b2-b7ac-481c-b60c-bb07a5c49879 · outbound

This paper cites Large Language Model Agent for Fake News Detection.

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data Large Language Model Agent for Fake News Detection

Reference 33

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Observation 389d8043-4021-49b5-adc0-7a2253b94978 · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition, pp.

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data In: Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition, pp

Reference 34

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Observation a368f919-2d1d-4deb-ad5e-a0b558c46896 · outbound

This paper cites In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol.

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol

Reference 35

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Observation 3e4c9c18-d267-4f27-ba16-c75e4d7cad2a · outbound

This paper cites In: International Conference on Machine Learning, pp.

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data In: International Conference on Machine Learning, pp

Reference 36

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Observation 14ec2737-114e-4706-9913-3eb083145973 · outbound

This paper cites Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese.

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese

Reference 37

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Observation 68398b25-ce66-4d48-b627-b5081a75b765 · outbound

This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 38

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Observation 14f9f0a0-74e8-4664-8ea1-c697297e10bd · outbound

This paper cites In: Proceedings of the AAAI Conference on Artificial Intel- ligence, vol.

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data In: Proceedings of the AAAI Conference on Artificial Intel- ligence, vol

Reference 39

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Observation 1fb3c06f-4d12-4286-96bc-ca99e33fa2b4 · outbound

This paper cites In: Proceedings of the 33rd ACM International Conference on Multimedia, pp.

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data In: Proceedings of the 33rd ACM International Conference on Multimedia, pp

Reference 40

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Observation e1d7aea4-cea9-4f5b-8d65-ee753e757426 · outbound

This paper cites In: Proceedings of the 32nd ACM International Conference on Multimedia, pp.

Maximizing Mutual Information Between Prompt and Response Improves LLM Performance With No Additional Data In: Proceedings of the 32nd ACM International Conference on Multimedia, pp

Reference 41

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Pith citing papers

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