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Paper Citation Record · LEDGER

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure

As of 10 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2607.04733.

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

pith.paper-citation-record.v1
2607.04733 v2

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T08:38:38.112327Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

24 of 24 outbound references displayed

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  • malformed identifier1
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External citation measurements

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Outbound references

Observation dad7608d-1bd7-405a-b3cc-707d053b5b18 · outbound

This paper cites On-policy distillation of language models: Learning from self-generated mistakes.

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure On-policy distillation of language models: Learning from self-generated mistakes

Reference 1

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source=pdf_text observed=2026-08-02T08:38:34.984658Z digest=sha256:a109bb535d9889ac3f60801d164e2efbd63ad98a7ec9179367ebdf18e5fe2acc

Observation 60bd8eb5-6209-4bbd-bcb3-2e5e9f3a1880 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure Evaluating Large Language Models Trained on Code

Reference 2

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source=pdf_text observed=2026-08-02T08:38:35.046480Z digest=sha256:9520d8efabd94ecd4c994f5eba02b529b23bef4578b4f687033c076e0d103027

Observation 4b7b6292-20d8-4cf5-b2ab-0b6ca3b5b8da · outbound

This paper cites UltraFeedback: Boosting Language Models with Scaled AI Feedback.

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure UltraFeedback: Boosting Language Models with Scaled AI Feedback

Reference 3

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source=pdf_text observed=2026-08-02T08:38:35.145791Z digest=sha256:7d5c1facc75286eeb8e014567d5b8815f3b40c27cbfc31752eeef2057b621296

Observation c73cf419-e4d7-43e9-bbdb-04b5eccb9fe9 · outbound

This paper cites Entropy-adaptive fine-tuning: Resolving confident conflicts to mitigate forgetting.

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure Entropy-adaptive fine-tuning: Resolving confident conflicts to mitigate forgetting

Reference 4

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source=pdf_text observed=2026-08-02T08:38:35.327688Z digest=sha256:49efd6d445c9a8833f5b863aea0e40b5adbefd8d42e74fc83cc3e52845f66424

Observation 26d01718-f7bd-4187-a9a0-a231356c441f · outbound

This paper cites The Llama 3 Herd of Models.

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure The Llama 3 Herd of Models

Reference 5

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source=pdf_text observed=2026-08-02T08:38:35.444962Z digest=sha256:c91e84480ac720b85b5473a1b3935763fde1e4e9c66c76fbb345ff1f3f37b0bc

Observation fa09bce4-11bf-47d0-ac11-fcc7820900cc · outbound

This paper cites Minillm: Knowledge distillation of large language models.

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure Minillm: Knowledge distillation of large language models

Reference 6

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source=pdf_text observed=2026-08-02T08:38:35.605399Z digest=sha256:1c4185820428bb2349b8cb020ff806624fc4a06bc7b8750b7710cc578225ee13

Observation 5c11e650-c073-43f7-ad02-07fba81649aa · outbound

This paper cites Measuring Massive Multitask Language Understanding.

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure Measuring Massive Multitask Language Understanding

Reference 7

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source=pdf_text observed=2026-08-02T08:38:35.723763Z digest=sha256:4206a7fb407b509187d3854deaeb3cd3e47525d3a865d90e7687fbf066b104f7

Observation e033b742-2224-4ccc-a669-2edd5a4e382d · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure Measuring Mathematical Problem Solving With the MATH Dataset

Reference 8

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source=pdf_text observed=2026-08-02T08:38:35.892003Z digest=sha256:c1b08d1c4e916c712df0627ea9675c1cfb3ed85d7b4ab38e829e7d26c449fac0

Observation 3c68a778-89c0-443d-8d61-c29bac1a8b0c · outbound

This paper cites Diversity and evenness: a unifying notation and its consequences.Ecology, 54(2):427–432, 1973.

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure Diversity and evenness: a unifying notation and its consequences.Ecology, 54(2):427–432, 1973

Reference 9

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source=pdf_text observed=2026-08-02T08:38:36.094767Z digest=sha256:b4f342db49660a445819502cd8a8669b21c40852b26eb65d6ef51f9244b1741c

Observation 4090c7d1-aaea-4e1b-bb4f-17d8fb7019f4 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure Distilling the Knowledge in a Neural Network

Reference 10

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source=pdf_text observed=2026-08-02T08:38:36.225463Z digest=sha256:892c6488970288d0692002fca99eddc91b20228b02872a3622215b978ad497fe

Observation f6597e67-a7cd-47c9-9960-f3f720b841db · outbound

This paper cites Entropy and diversity.Oikos, 113(2):363–375, 2006.

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure Entropy and diversity.Oikos, 113(2):363–375, 2006

Reference 11

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source=pdf_text observed=2026-08-02T08:38:36.363081Z digest=sha256:4d46dafe2cf2efd43748eaf007261acfa39a5e77e2d579b811b65d53301c81f0

Observation 2fc5fab5-817d-4f6e-9067-ab6801677e15 · outbound

This paper cites Numinamath: The largest public dataset in ai4maths with 860k pairs of competition math problems and solutions.Hugging Face repository, 13(9):9, 2024.

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure Numinamath: The largest public dataset in ai4maths with 860k pairs of competition math problems and solutions.Hugging Face repository, 13(9):9, 2024

Reference 12

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source=pdf_text observed=2026-08-02T08:38:36.572295Z digest=sha256:cd0446a7ce56f1d69150dea16c2232d978428237d0d5edff6b4f193bdad50766

Observation eb42ce25-3d59-4ce3-befe-ab9a607ef389 · outbound

This paper cites Preserving diversity in supervised fine-tuning of large language models.

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure Preserving diversity in supervised fine-tuning of large language models

Reference 13

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source=pdf_text observed=2026-08-02T08:38:36.728501Z digest=sha256:8ba6792615c4ebc797f445d78c76fb7dcce04cb8d6165808c2b4feb837a79c6e

Observation d577f018-ef09-4022-a5c5-005acbd56acb · outbound

This paper cites an unresolved cited work.

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure Unresolved cited work

Reference 14

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source=pdf_text observed=2026-08-02T08:38:36.922842Z digest=sha256:13363080bfbdae4f03d956f7b8f790c06866a8a000e3b617460aef6b5483445d

Observation 77a38fad-a5fe-4a0c-8dff-7a06acf988fe · outbound

This paper cites An empirical study of catastrophic forgetting in large language models during continual fine-tuning.IEEE Transactions on Audio, Speech and Language Processing, 2025.

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure An empirical study of catastrophic forgetting in large language models during continual fine-tuning.IEEE Transactions on Audio, Speech and Language Processing, 2025

Reference 15

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source=pdf_text observed=2026-08-02T08:38:37.085780Z digest=sha256:716327a16e9171e79abb294d741b4de84d872b00aca28ff6588bf2e3c2736016

Observation a67413a1-35fa-4ed7-8cab-ed29276cde23 · outbound

This paper cites Training language models to follow instructions with human feedback.Advances in neural information processing systems, 35:27730–27744, 2022.

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure Training language models to follow instructions with human feedback.Advances in neural information processing systems, 35:27730–27744, 2022

Reference 16

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source=pdf_text observed=2026-08-02T08:38:37.163872Z digest=sha256:7fa690f859d451f8fab1a9d64641bb65d9738b98d3541a2dc2edfd9433999a0a

Observation f43f6738-ae72-41dd-a3a3-c2185b7810fe · outbound

This paper cites an unresolved cited work.

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure Unresolved cited work

Reference 17

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source=pdf_text observed=2026-08-02T08:38:37.314921Z digest=sha256:9e166a0e4273cecebae8a61684ba56d256cb3b45e54507282199f1e61fefb6c1

Observation 081ceec4-cb1c-44db-ad2d-5e71351963f9 · outbound

This paper cites Self-instruct: Aligning language models with self-generated instructions.

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure Self-instruct: Aligning language models with self-generated instructions

Reference 18

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source=pdf_text observed=2026-08-02T08:38:37.452964Z digest=sha256:a7951dbf5e17cb0ce622574db0aed8d48b18173ffd2aecdd712dd55675ebaa0d

Observation 9d59ce80-0e5b-4a26-9b82-fafcca550ba8 · outbound

This paper cites HE-SNR: Uncovering Latent Logic via Entropy for Guiding Mid-Training on SWE-bench.

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure HE-SNR: Uncovering Latent Logic via Entropy for Guiding Mid-Training on SWE-bench

Reference 19

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source=pdf_text observed=2026-08-02T08:38:37.532639Z digest=sha256:864dd0f2172aa39601693dc79d7517d3a83896142991b1d19e51f7ff732dff60

Observation 5a604c43-fab8-4055-bc57-229852a92f21 · outbound

This paper cites Magicoder: Empowering Code Generation with OSS-Instruct.

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure Magicoder: Empowering Code Generation with OSS-Instruct

Reference 20

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source=pdf_text observed=2026-08-02T08:38:37.623072Z digest=sha256:03952cf122edb31d495636f403e9f4086e2e8458b726b0ae481ddaba34199e7e

Observation b8f00bbc-86a1-4398-906c-23b5739303d6 · outbound

This paper cites Neural Text Generation with Unlikelihood Training.

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure Neural Text Generation with Unlikelihood Training

Reference 21

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source=pdf_text observed=2026-08-02T08:38:37.781693Z digest=sha256:572e597d3d66ac02e858b2d8d94253554c65e8dcfd3bb49e715fe110090d3e52

Observation 71f84657-bd2f-4f81-a023-059ce3b6b30d · outbound

This paper cites On the generalization of sft: A reinforcement learning perspective with reward rectification.arXiv preprint arXiv:2508.05629, 2025.

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure On the generalization of sft: A reinforcement learning perspective with reward rectification.arXiv preprint arXiv:2508.05629, 2025

Reference 22

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source=pdf_text observed=2026-08-02T08:38:37.881481Z digest=sha256:53f4a311d8ea1e93e2612f28fc39561341cf0a63d4c31b408e6b2f7ffe259d1b

Observation 66dc21ed-9028-458f-91fa-012871efa06a · outbound

This paper cites Qwen3 Technical Report.

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure Qwen3 Technical Report

Reference 23

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source=pdf_text observed=2026-08-02T08:38:37.944918Z digest=sha256:eaa6dda99de42dc2414214376d1f3590fb55f92cd2e5ab9c809455917595e359

Observation 58a8abad-04f2-48b1-84a7-a8805f96f5ad · outbound

This paper cites Risk perceptions and safety compliance of workers employed in agriculture, forestry, and fishing.

LP-SFT: Local-Preserving Supervised Fine-Tuning via Multimodal Entropy Structure Risk perceptions and safety compliance of workers employed in agriculture, forestry, and fishing

Reference 24

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source=pdf_text observed=2026-08-02T08:38:38.112327Z digest=sha256:73a6f6ca2ee092ece36c3638c80b4ade20a87bc2df8dbcf415afac09fd7536c1

Pith citing papers

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