Paper Citation Record · LEDGER

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning

As of 23 August 2026, this Paper Citation Record lists 70 of 70 outbound references and 2 inbound Pith citation observations for arXiv:2510.04773.

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

pith.paper-citation-record.v1
2510.04773 v2

Coverage vector

measured 70 of 70 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T11:27:23.020889Z

measured 72 of 72 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:17:57.173259Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T21:52:48.286928Z

Reference resolution

70 of 70 outbound references displayed

verified exact0
verified fuzzy0
unresolved70
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 76d60a61-f8ad-4845-aa45-3f4f255b4314 · outbound

This paper cites Unlearning bias in language models by partitioning gradients.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Unlearning bias in language models by partitioning gradients

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:20.452230Z

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Observation a7549dde-221c-4458-ba98-927e18a0d670 · outbound

This paper cites Continual learning and private unlearning.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Continual learning and private unlearning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:20.531377Z

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Observation b2ff310c-a1d8-4d32-9566-473d2a7306c9 · outbound

This paper cites Who’s harry potter? approximate unlearning for llms.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Who’s harry potter? approximate unlearning for llms

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:20.622372Z

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Observation 8a903e82-8889-48cc-97a6-3f6827892dcf · outbound

This paper cites Knowledge Unlearning for Mitigating Privacy Risks in Language Models.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Knowledge Unlearning for Mitigating Privacy Risks in Language Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:20.679549Z

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Observation ab763c55-6373-4ee0-a0ec-7493f909c78e · outbound

This paper cites DEPN: Detecting and Editing Privacy Neurons in Pretrained Language Models.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning DEPN: Detecting and Editing Privacy Neurons in Pretrained Language Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:20.743364Z

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Observation d68c6b24-d6c9-4f77-b76d-b49076c2e403 · outbound

This paper cites Towards Safer Large Language Models through Machine Unlearning.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Towards Safer Large Language Models through Machine Unlearning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:20.798918Z

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Observation f5e3ef6c-75e9-43b7-aa67-8bd9060fcbaa · outbound

This paper cites Identifying and mitigating the security risks of generative ai.Foundations and Trends® in Privacy and Security, 6(1):1–52, 2023.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Identifying and mitigating the security risks of generative ai.Foundations and Trends® in Privacy and Security, 6(1):1–52, 2023

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:20.870907Z

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Observation a8f923d0-7a5c-4a63-8d89-43b3f4c3866f · outbound

This paper cites an unresolved cited work.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Unresolved cited work

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:20.943176Z

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Observation 773521d1-8438-45a1-8842-97cced60dc55 · outbound

This paper cites Assembly bill no.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Assembly bill no

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:21.028350Z

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Observation d06458fb-c61e-48ae-b485-bfdd3a2481b8 · outbound

This paper cites Large language model unlearning.Advances in Neural Information Processing Systems, 37:105425–105475, 2024.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Large language model unlearning.Advances in Neural Information Processing Systems, 37:105425–105475, 2024

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:21.177914Z

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Observation 67aec5fc-288a-4ef4-bf2e-9a71c26d0c3a · outbound

This paper cites Negative preference optimization: From catastrophic collapse to effective unlearning.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Negative preference optimization: From catastrophic collapse to effective unlearning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:21.305220Z

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Observation 62129147-0ca8-467b-bb76-2c52c5b57dc8 · outbound

This paper cites Direct preference optimization: Your language model is secretly a reward model.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Direct preference optimization: Your language model is secretly a reward model

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:21.398045Z

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Observation c84421ff-e3ad-45d2-82b8-10c0d87783c3 · outbound

This paper cites TOFU: A task of fictitious unlearning for LLMs.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning TOFU: A task of fictitious unlearning for LLMs

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:21.500262Z

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Observation b275dbd2-761a-43ec-9639-83242c7c3669 · outbound

This paper cites Smith, and Chiyuan Zhang.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Smith, and Chiyuan Zhang

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:21.569482Z

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Observation 8ad36c83-7027-4ce1-bd45-4a21c8f3cdf0 · outbound

This paper cites A Survey of Machine Unlearning.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning A Survey of Machine Unlearning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:21.659846Z

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Observation 95898f54-533a-4a64-93c7-bcc8af62b172 · outbound

This paper cites Towards making systems forget with machine unlearning.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Towards making systems forget with machine unlearning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:21.736197Z

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Observation bc7df84a-8f15-45c4-ba15-5fc45387676d · outbound

This paper cites Unrolling sgd: Understanding factors influencing machine unlearning.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Unrolling sgd: Understanding factors influencing machine unlearning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:21.822798Z

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Observation 427056dd-9a0d-4e4c-8227-871e1f84d102 · outbound

This paper cites Approximate data deletion from machine learning models.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Approximate data deletion from machine learning models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:21.904258Z

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Observation 01f61631-3dca-403b-af87-b6b97882fd9d · outbound

This paper cites KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.042626Z

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Observation fc2b673b-77f5-4978-8004-fff3461956c9 · outbound

This paper cites Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.168083Z

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Observation 6c5ee71e-14f3-48c3-89c9-e8946a400926 · outbound

This paper cites Eternal sunshine of the spotless net: Selective forgetting in deep networks.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Eternal sunshine of the spotless net: Selective forgetting in deep networks

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.279809Z

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Observation b8d3606d-f684-4666-892b-9267d6c9c27b · outbound

This paper cites Machine unlearning.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Machine unlearning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.377946Z

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Observation 14fcf637-2e57-454e-9fba-a047c508a841 · outbound

This paper cites Model sparsity can simplify machine unlearning.Advances in Neural Information Processing Systems, 36:51584–51605, 2023.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Model sparsity can simplify machine unlearning.Advances in Neural Information Processing Systems, 36:51584–51605, 2023

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.494559Z

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Observation a8242260-85d3-480a-b60d-c4c9d9e20c15 · outbound

This paper cites SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.568461Z

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Observation 28d5263f-751c-465b-aa6f-563363a0a433 · outbound

This paper cites Towards un- bounded machine unlearning.Advances in neural information processing systems, 36:1957– 1987, 2023.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Towards un- bounded machine unlearning.Advances in neural information processing systems, 36:1957– 1987, 2023

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.581625Z

Source-reported events for the cited work

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Observation de3d0565-6a7d-4220-a9f1-38fce8a050b0 · outbound

This paper cites Making ai forget you: Data deletion in machine learning.Advances in neural information processing systems, 32, 2019.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Making ai forget you: Data deletion in machine learning.Advances in neural information processing systems, 32, 2019

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.592812Z

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Observation 6dcd6cc9-c133-43fc-94dc-6d83c3093a42 · outbound

This paper cites Erasing concepts from diffusion models.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Erasing concepts from diffusion models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.604414Z

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Observation 1760d722-3b95-469a-90ab-4ab668aa86b4 · outbound

This paper cites Forget-me- not: Learning to forget in text-to-image diffusion models.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Forget-me- not: Learning to forget in text-to-image diffusion models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.611862Z

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Observation e37871ed-e77b-42af-a9ce-72061651491b · outbound

This paper cites Fast federated machine unlearning with nonlinear functional theory.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Fast federated machine unlearning with nonlinear functional theory

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.617452Z

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Observation b68d6268-6e49-4123-9d44-72b01997e618 · outbound

This paper cites Federated unlearning with gradient descent and conflict mitigation.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Federated unlearning with gradient descent and conflict mitigation

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.627175Z

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Observation 305b3c49-1a8a-45af-8cfe-7bc3962e438d · outbound

This paper cites Efficient model updates for approximate un- learning of graph-structured data.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Efficient model updates for approximate un- learning of graph-structured data

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.632281Z

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Observation 31ba6a7a-0881-4468-9f1e-af7642a4c274 · outbound

This paper cites Certified edge unlearning for graph neural networks.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Certified edge unlearning for graph neural networks

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.641600Z

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Observation 8983a749-7907-49c8-b55f-673239cb3a4b · outbound

This paper cites Machine unlearning for recommendation systems: An insight.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Machine unlearning for recommendation systems: An insight

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.654558Z

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Observation 8a4c8eae-a8ab-4b0b-a1fc-2f36196842e4 · outbound

This paper cites Simplicity prevails: Rethinking negative preference optimization for llm unlearning.arXiv preprint arXiv:2410.07163, 2024.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Simplicity prevails: Rethinking negative preference optimization for llm unlearning.arXiv preprint arXiv:2410.07163, 2024

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.669615Z

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Observation 077c96d1-f879-4768-8784-4243f5c9211f · outbound

This paper cites Alternate Preference Optimization for Unlearning Factual Knowledge in Large Language Models.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Alternate Preference Optimization for Unlearning Factual Knowledge in Large Language Models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.676125Z

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Observation 0ba8b6cb-9b38-40dc-8b5f-19cabd0453e1 · outbound

This paper cites SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.687906Z

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Observation 35d2aee4-66cd-4d6d-962a-6095ba727c2c · outbound

This paper cites Can bad teaching induce forgetting? unlearning in deep networks using an incompetent teacher.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Can bad teaching induce forgetting? unlearning in deep networks using an incompetent teacher

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.699805Z

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Observation 622fe18a-75f4-44b6-958d-688eaa748e6b · outbound

This paper cites Reversing the forget-retain objectives: An efficient llm unlearning framework from logit difference.Advances in Neural Information Processing Systems, 37:12581–12611, 2024.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Reversing the forget-retain objectives: An efficient llm unlearning framework from logit difference.Advances in Neural Information Processing Systems, 37:12581–12611, 2024

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.706659Z

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Observation 7ca5c113-7c77-49a6-897f-1dcb16bc08ec · outbound

This paper cites Unlearn What You Want to Forget: Efficient Unlearning for LLMs.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Unlearn What You Want to Forget: Efficient Unlearning for LLMs

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.713758Z

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Observation 63d4d8b0-1a9f-4c96-91b4-2a8d6a87eda1 · outbound

This paper cites Machine Unlearning of Pre-trained Large Language Models.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Machine Unlearning of Pre-trained Large Language Models

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.723583Z

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Observation 1aaa2258-65ea-48e9-9abe-89fa5edc6385 · outbound

This paper cites Knowledge Sanitization of Large Language Models.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Knowledge Sanitization of Large Language Models

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.736643Z

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Observation 61cb8d93-3765-4446-b6e2-f45170fd1cbb · outbound

This paper cites Revisiting Who's Harry Potter: Towards Targeted Unlearning from a Causal Intervention Perspective.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Revisiting Who's Harry Potter: Towards Targeted Unlearning from a Causal Intervention Perspective

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.749242Z

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Observation 21f9fa8b-8f0f-490e-be1f-e310b73050a3 · outbound

This paper cites Guardrail Baselines for Unlearning in LLMs.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Guardrail Baselines for Unlearning in LLMs

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.757191Z

Source-reported events for the cited work

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Observation 65baeb04-f295-4b0d-9eb6-d9efbf38b680 · outbound

This paper cites In-Context Unlearning: Language Models as Few Shot Unlearners.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning In-Context Unlearning: Language Models as Few Shot Unlearners

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.765817Z

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Observation 6f10c97d-7c95-4ac6-93b1-d128acc176f5 · outbound

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

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Training language models to follow instructions with human feedback.Advances in neural information processing systems, 35:27730–27744, 2022

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.777586Z

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Observation 82f0f4b3-e1e8-4975-b743-c72a044b7cf6 · outbound

This paper cites ORPO: Monolithic Preference Optimization without Reference Model.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning ORPO: Monolithic Preference Optimization without Reference Model

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.792715Z

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Observation 442eb49d-2fa8-46e9-92b5-db9aec14a9dd · outbound

This paper cites Simpo: Simple preference optimization with a reference-free reward.Advances in Neural Information Processing Systems, 37:124198–124235, 2024.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Simpo: Simple preference optimization with a reference-free reward.Advances in Neural Information Processing Systems, 37:124198–124235, 2024

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.800337Z

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Observation 951663bd-9add-4f69-9910-0ce7a4bcc7e2 · outbound

This paper cites Beyond Reverse KL: Generalizing Direct Preference Optimization with Diverse Divergence Constraints.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Beyond Reverse KL: Generalizing Direct Preference Optimization with Diverse Divergence Constraints

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.818786Z

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Observation 3bdb80e2-0b17-4b4f-91f7-3dd1fcb56502 · outbound

This paper cites A general theoretical paradigm to understand learning from human preferences.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning A general theoretical paradigm to understand learning from human preferences

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.832649Z

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Observation c66c64f5-2641-4af1-ac1f-b91dc604551b · outbound

This paper cites Generalizing offline alignment theoretical paradigm with diverse divergence constraints.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Generalizing offline alignment theoretical paradigm with diverse divergence constraints

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.843643Z

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Observation 39310ff7-28d9-4fea-889c-7381e850af02 · outbound

This paper cites Token-level Direct Preference Optimization.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Token-level Direct Preference Optimization

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.852709Z

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Observation a09e114b-10a6-446d-82b4-e6338fb04a64 · outbound

This paper cites Contrastive Decoding: Open-ended Text Generation as Optimization.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Contrastive Decoding: Open-ended Text Generation as Optimization

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.860151Z

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Observation 533cbf42-75a5-410c-8900-f73a7152f965 · outbound

This paper cites DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.867379Z

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Observation d983ffd8-7f3c-4346-8ab4-2e721874330c · outbound

This paper cites DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.875112Z

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Observation b5b445b7-29a0-413b-bd58-efd3679a9c9f · outbound

This paper cites I don’t know.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning I don’t know

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.883918Z

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Observation 84bc9d9d-87d3-4783-b656-deb234dd806f · outbound

This paper cites It is set to the log-probability corresponding to the k-th rank when tokens are sorted by log-probability in descending order, wherek=max(1,⌊p k · |V|⌋).

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning It is set to the log-probability corresponding to the k-th rank when tokens are sorted by log-probability in descending order, wherek=max(1,⌊p k · |V|⌋)

Reference 57

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Observation aa2f7269-327e-4504-af77-d181ba846109 · outbound

This paper cites The final threshold used for filtering is the minimum of these two: τ=min(τ k,τ rel).

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning The final threshold used for filtering is the minimum of these two: τ=min(τ k,τ rel)

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.906273Z

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Observation e7987f28-a8fd-44db-8d94-22865e0676e4 · outbound

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Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Unresolved cited work

Reference 59

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Observation 8636b27f-491c-4d6e-b016-55fb9c0ea1a1 · outbound

This paper cites D.4.2 Evaluation Metrics MUSE evaluates unlearning across six criteria.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning D.4.2 Evaluation Metrics MUSE evaluates unlearning across six criteria

Reference 60

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Observation ab678436-6c6b-44db-ae6e-36a55acfb520 · outbound

This paper cites Quantified by VerbMem-f, which measures the ROUGE-L F1 score between model-generated continuations and true continuations fromD forget.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Quantified by VerbMem-f, which measures the ROUGE-L F1 score between model-generated continuations and true continuations fromD forget

Reference 61

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Observation 58365d12-b059-44af-af1f-d0c1ab031d7f · outbound

This paper cites Quantified by KnowMem-f, averaging ROUGE scores between model answers and ground-truth answers for QA pairs derived fromD forget.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Quantified by KnowMem-f, averaging ROUGE scores between model answers and ground-truth answers for QA pairs derived fromD forget

Reference 62

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Observation c48ed92a-a869-4276-8ef7-bd3726db3fad · outbound

This paper cites an unresolved cited work.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Unresolved cited work

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.962003Z

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Observation e7eccd9b-9864-4885-b52c-75c1e912e191 · outbound

This paper cites This is typically measured using theKnowMem-rmetric applied toD retain: KnowMem-r(f unlearn,D retain).

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning This is typically measured using theKnowMem-rmetric applied toD retain: KnowMem-r(f unlearn,D retain)

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.970398Z

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Observation 86721fe3-4bff-478b-9026-c4c8f0199470 · outbound

This paper cites an unresolved cited work.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Unresolved cited work

Reference 65

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Observation 3076dcc5-c606-4456-bc56-1cb4ae2c3002 · outbound

This paper cites an unresolved cited work.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Unresolved cited work

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.987509Z

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Observation 3097a34c-01b2-44dc-8ac5-94b0e7e4b5ca · outbound

This paper cites The combined objective is then expressed asL=L DiPO-f(θ) +λLDiPO-r(θ).

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning The combined objective is then expressed asL=L DiPO-f(θ) +λLDiPO-r(θ)

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:22.997014Z

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Observation bce5f7da-24f5-4e68-babc-27420bf57f32 · outbound

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Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Unresolved cited work

Reference 68

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Observation 0bd42952-c11d-41e6-a935-0147f48d2034 · outbound

This paper cites an unresolved cited work.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Unresolved cited work

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:23.012495Z

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Observation 086c3d83-9bac-4b22-9317-29964f111ebf · outbound

This paper cites Additionally, we discuss DiPO-Forget (using only LDiPO-f without any retain loss).

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Additionally, we discuss DiPO-Forget (using only LDiPO-f without any retain loss)

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:23.020889Z

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Observation 51f2608d-4c44-46eb-80b4-5544db1bae1f · outbound

This paper cites an unresolved cited work.

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning Unresolved cited work

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-04T11:27:21.088646Z

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Pith citing papers

Observation 584746ad-b1e1-4758-b4e4-2bbffacf8029 · inbound

Distinguishable Deletion: Unifying Knowledge Erasure and Refusal for Large Language Model Unlearning cites this paper.

Distinguishable Deletion: Unifying Knowledge Erasure and Refusal for Large Language Model Unlearning Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning

Reference 120

Resolution: verified exact; arxiv_id, observed 2026-06-25T02:17:31.129069Z

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Observation ad72ebad-475a-4e0b-81a9-969dc3ec1968 · inbound

Measure, Don't Optimize: Forecasting Recovery in LLM Unlearning cites this paper.

Measure, Don't Optimize: Forecasting Recovery in LLM Unlearning Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning

Reference 191

Resolution: unresolved; no resolver link, observed 2026-08-15T14:17:57.173259Z

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