Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-14T23:48:38.098404Z
Paper Citation Record · LEDGER
As of 8 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.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-14T23:48:38.098404Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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
41 of 41 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 9373afc9-699d-47b3-8222-2222d350525c · outbound
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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Observation b8db6094-8d93-409c-ae03-7a1866af076c · outbound
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
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
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
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
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Observation e5cfc2b2-6bd5-40e4-a59a-0dd0189cfcb3 · outbound
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
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Observation 7b56c64c-5860-47dc-8b85-483203744bda · outbound
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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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No inbound Pith citation observations are available.