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Paper Citation Record · LEDGER

Influence Functions for Preference Dataset Pruning

As of 13 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2507.14344.

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

pith.paper-citation-record.v1
2507.14344 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:09:23.499555Z

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

17 of 17 outbound references displayed

  • verified exact0
  • verified fuzzy1
  • unresolved15
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 15f1a7b2-3520-4bc9-baf5-666ea60c85ae · outbound

This paper cites Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback.

Influence Functions for Preference Dataset Pruning Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-06T16:09:23.411674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T16:09:23.411674Z digest=sha256:ff359b0af673fb6136ebc1084626570acb35de214f373d66bf16f81677670abf

Observation a6cf324b-4a72-416b-b084-966272757874 · outbound

This paper cites Gradient Similarity: An Explainable Approach to Detect Adversarial Attacks against Deep Learning.

Influence Functions for Preference Dataset Pruning Gradient Similarity: An Explainable Approach to Detect Adversarial Attacks against Deep Learning

Reference 3

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unresolved
no resolver link, observed 2026-08-06T16:09:23.417032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T16:09:23.417032Z digest=sha256:cd2089424800b0ea45248cad322d8379aa11e90a5e9888dbd1ce4b1956fd3b0d

Observation a3a8c523-4eb3-47c0-aee9-23fb7feeb443 · outbound

This paper cites Impact of Preference Noise on the Alignment Performance of Generative Language Models.

Influence Functions for Preference Dataset Pruning Impact of Preference Noise on the Alignment Performance of Generative Language Models

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-06T16:09:23.422447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T16:09:23.422447Z digest=sha256:ff8b3b83327854367f569b5613d3bdd20b99982d5df371d6d64857a9ffa9cd3b

Observation b0878fca-1a38-480c-89c8-7f52b10465a4 · outbound

This paper cites Studying Large Language Model Generalization with Influence Functions.

Influence Functions for Preference Dataset Pruning Studying Large Language Model Generalization with Influence Functions

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-06T16:09:23.428168Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T16:09:23.428168Z digest=sha256:888dbf510ca2d63e5f2b0e166572b65ffcbb73035fce6773eb7db80f719fd4af

Observation 7b753112-eac8-4516-9d07-e8a05368a152 · outbound

This paper cites Lora: Low-rank adaptation of large language models.

Influence Functions for Preference Dataset Pruning Lora: Low-rank adaptation of large language models

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-06T16:09:23.433776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T16:09:23.433776Z digest=sha256:d0ef09b4cedbc59d5d0fa1a6d019633fe6ce07826b5e9c335aa2b1de16ad0d2a

Observation 32499b97-b4a9-407c-a51b-96e6efd9b7c2 · outbound

This paper cites Understanding black-box predictions via influence functions.

Influence Functions for Preference Dataset Pruning Understanding black-box predictions via influence functions

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-06T16:09:23.439602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T16:09:23.439602Z digest=sha256:bd7c100fee6c67f2fb6122f1675497b028ac2f40c62dec03ea1e7ec3f6cef8a4

Observation 685b9411-9efc-4cae-8004-a92971097cfa · outbound

This paper cites DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models.

Influence Functions for Preference Dataset Pruning DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-06T16:09:23.444743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T16:09:23.444743Z digest=sha256:7f4caf914bbb29de4958ebcea4e60fad1f8117dd0ce16ceea77d8d0170cf0db3

Observation 66061f8f-4e7c-463b-907a-718713182124 · outbound

This paper cites Deep learning via hessian-free optimization.

Influence Functions for Preference Dataset Pruning Deep learning via hessian-free optimization

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T16:09:23.743955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-06T16:09:23.450136Z digest=sha256:2d6f7f3d364dcc704c6a96307a7384b464902a47693d15bd21acc598456a6fb5

Observation 60614bd7-f504-4242-98de-fba72ad1a421 · outbound

This paper cites Filtered Direct Preference Optimization.

Influence Functions for Preference Dataset Pruning Filtered Direct Preference Optimization

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-06T16:09:23.456274Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T16:09:23.456274Z digest=sha256:9abaa8d69ea2c961fd856348510542cfd06c00ffbe5b38a4ccd02b1d5e5b7bb2

Observation 70913170-d709-4fe3-b351-0bb53113d922 · outbound

This paper cites Fast exact multiplication by the hessian.

Influence Functions for Preference Dataset Pruning Fast exact multiplication by the hessian

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-06T16:09:23.461819Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T16:09:23.461819Z digest=sha256:a5dabcb5fd3228f83e269da6798ede6f62ed830275da4d22f8db973c94443cbf

Observation 9e99f1e6-ecc3-49d8-8acf-c6c6ee38c5ef · outbound

This paper cites Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul Christiano.

Influence Functions for Preference Dataset Pruning Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul Christiano

Reference 12

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unresolved
no resolver link, observed 2026-08-06T16:09:23.466946Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T16:09:23.466946Z digest=sha256:bf5133deb0f1647ba8b5b69eeaea70b0b27adc925c95cf7fbb57cb1d8f43bb34

Observation c2a8d3a5-f29c-4e6c-a723-76737d06d775 · outbound

This paper cites Dataset Pruning: Reducing Training Data by Examining Generalization Influence.

Influence Functions for Preference Dataset Pruning Dataset Pruning: Reducing Training Data by Examining Generalization Influence

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-06T16:09:23.472912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T16:09:23.472912Z digest=sha256:7bdd5bb97a581694a49e2207d1fe62baef246c0aac9ac4cf566af7e6a3036327

Observation 6c0b5006-ba21-4c3d-a80d-6978d4a8fc5c · outbound

This paper cites Star: Bootstrapping reasoning with reasoning.

Influence Functions for Preference Dataset Pruning Star: Bootstrapping reasoning with reasoning

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-06T16:09:23.478292Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T16:09:23.478292Z digest=sha256:380d30244c0d130922ac053e5633aa3f66a170c1570da2cddab5f44395834f4b

Observation 9674b528-a6df-4b4b-a15d-47c94d4be2ba · outbound

This paper cites write newline.

Influence Functions for Preference Dataset Pruning write newline

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-06T16:09:23.482874Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T16:09:23.482874Z digest=sha256:cd7fdb6e3534088c6820c56e44348a2e3612691caf65b3b98b420923c9dd1648

Observation 5f03e44a-c85d-4a68-a2b3-38d4d368d968 · outbound

This paper cites @esa (Ref.

Influence Functions for Preference Dataset Pruning @esa (Ref

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-06T16:09:23.488911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T16:09:23.488911Z digest=sha256:67eda43a6ecea0a42974d4b3c135b0ab6133d58def8a4b35004bda0cf923e645

Observation fdda32f0-efe7-46ce-9c58-671280c6a987 · outbound

This paper cites an unresolved cited work.

Influence Functions for Preference Dataset Pruning Unresolved cited work

Reference 17

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unresolved
no resolver link, observed 2026-08-06T16:09:23.494825Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T16:09:23.494825Z digest=sha256:8147a442d5313a4b87924b6ecfc9d57018bda966ba1883d0f4923c7eeb37f619

Observation 2953b2bc-0af4-4720-ae3c-0dd193cb6fff · outbound

This paper cites """""""".

Influence Functions for Preference Dataset Pruning """"""""

Reference 18

Resolution
malformed identifier
raw_fallback, observed 2026-08-06T16:09:23.668961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-06T16:09:23.499555Z digest=sha256:b35e4a52edf1cdff7c2b5ffa7fc90aecb9da08a00d9bd16ea0eab3ffad7bd600

Pith citing papers

No inbound Pith citation observations are available.