Paper Citation Record · LEDGER

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs

As of 19 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 1 inbound Pith citation observation for arXiv:2509.00096.

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

pith.paper-citation-record.v1
2509.00096 v2

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:52:43.601515Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-16T18:55:48.540435Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T18:58:19.022967Z

Reference resolution

44 of 44 outbound references displayed

verified exact0
verified fuzzy0
unresolved44
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cbdfc07c-f450-4deb-bc6a-564f1ae28d92 · outbound

This paper cites GPT-4 Technical Report.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.431424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62fe1c21-b473-41d5-91f1-4b73bf9debc5 · outbound

This paper cites The Internal State of an LLM Knows When It's Lying.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs The Internal State of an LLM Knows When It's Lying

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.436343Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d0c43ff-4d4a-4da6-b5d6-7e22706d5809 · outbound

This paper cites an unresolved cited work.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-15T16:52:44.038511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 34524cef-9230-4287-9642-fe97d20bba2d · outbound

This paper cites Is C4 Dataset Optimal for Pruning? An Investigation of Calibration Data for LLM Pruning.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs Is C4 Dataset Optimal for Pruning? An Investigation of Calibration Data for LLM Pruning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.445537Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be5832c4-ae36-4017-8006-a69ab5843a95 · outbound

This paper cites an unresolved cited work.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-15T16:52:44.027957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 443d3c2f-5b70-43fa-a59d-e8daa6308982 · outbound

This paper cites Truth is Universal: Robust Detection of Lies in LLMs.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs Truth is Universal: Robust Detection of Lies in LLMs

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.453058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2024af2-34f2-4ce9-8a81-8633c9bc3c79 · outbound

This paper cites Discovering Latent Knowledge in Language Models Without Supervision.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs Discovering Latent Knowledge in Language Models Without Supervision

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.457086Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68765867-9ba8-4777-872f-95a45f91cf9c · outbound

This paper cites Masked Thought: Simply Masking Partial Reasoning Steps Can Improve Mathematical Reasoning Learning of Language Models.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs Masked Thought: Simply Masking Partial Reasoning Steps Can Improve Mathematical Reasoning Learning of Language Models

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.460772Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8fb2bf4e-4c93-461c-a57a-b6743e723488 · outbound

This paper cites an unresolved cited work.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-15T16:52:44.017426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c216a6e1-12a9-48cc-9b5d-5b9d3e5d710f · outbound

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

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.468405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e4fee33-f1a1-442e-b748-491de414e8e9 · outbound

This paper cites A Survey on In-context Learning.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs A Survey on In-context Learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.472282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 771d00f2-1a96-4888-9ca4-af67c91d9118 · outbound

This paper cites The Llama 3 Herd of Models.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs The Llama 3 Herd of Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.476064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90fc427e-a763-42f9-8146-9e3670522d79 · outbound

This paper cites MaskLLM: Learnable Semi-Structured Sparsity for Large Language Models.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs MaskLLM: Learnable Semi-Structured Sparsity for Large Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.479651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83b51e3f-364b-4b9c-bde5-6fa8612fe995 · outbound

This paper cites an unresolved cited work.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs Unresolved cited work

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.484221Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b72bb8a5-7dcd-48df-82d2-414ae7fe9b04 · outbound

This paper cites DISP-LLM: Dimension-Independent Structural Pruning for Large Language Models.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs DISP-LLM: Dimension-Independent Structural Pruning for Large Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.488568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3204a3b-f0ce-48e3-8c4b-a504521dc962 · outbound

This paper cites an unresolved cited work.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs Unresolved cited work

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.492355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a04b4b2-bfc4-4bcc-93f3-e10afcaa5e58 · outbound

This paper cites an unresolved cited work.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs Unresolved cited work

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.496024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8737d069-3df2-4203-a1da-2667613cc0cd · outbound

This paper cites Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.499735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7eaaa16e-2c94-44f2-8059-7dbb7782ec26 · outbound

This paper cites Beware of Calibration Data for Pruning Large Language Models.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs Beware of Calibration Data for Pruning Large Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.503944Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0244f76c-2134-4eb6-9b72-fd626687e5dd · outbound

This paper cites Mistral 7B.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs Mistral 7B

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.508220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 017ac7e3-34d7-4957-8525-dcffa580f54f · outbound

This paper cites an unresolved cited work.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs Unresolved cited work

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.512266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 441e646a-f91c-4869-930b-507e3a82a205 · outbound

This paper cites TruthfulQA: Measuring How Models Mimic Human Falsehoods.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs TruthfulQA: Measuring How Models Mimic Human Falsehoods

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.516820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe40b80b-3a04-4bac-ad2d-7a45617e0f91 · outbound

This paper cites SlimGPT: Layer-wise Structured Pruning for Large Language Models.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs SlimGPT: Layer-wise Structured Pruning for Large Language Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.520711Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f968116d-fde8-4cc3-972e-938739ddb704 · outbound

This paper cites The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.524534Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68d22262-afe3-4521-8a33-59c70e244bed · outbound

This paper cites How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated Questions.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs How to Catch an AI Liar: Lie Detection in Black-Box LLMs by Asking Unrelated Questions

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.528664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9078ba3-6a2b-403d-922e-39b1ff96a04f · outbound

This paper cites an unresolved cited work.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs Unresolved cited work

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.532630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c3d494f-a6ab-4c18-bd3d-35e55e807e95 · outbound

This paper cites an unresolved cited work.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-08-15T16:52:43.981204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 58d0893f-5703-4177-85ea-8cb43c64e446 · outbound

This paper cites an unresolved cited work.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-08-15T16:52:43.970447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b66adfa7-1c22-4d92-9cd2-0b566e16ccbc · outbound

This paper cites Large Language Models Meet NLP: A Survey.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs Large Language Models Meet NLP: A Survey

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.544021Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c68dc214-2f2f-43dd-8a6b-6df831811cba · outbound

This paper cites A Simple and Effective Pruning Approach for Large Language Models.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs A Simple and Effective Pruning Approach for Large Language Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.548071Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ef78972-89cf-4bfa-98e6-c79d90aeefa2 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.551815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06c58fcd-351b-4041-a6ab-fba2095b099e · outbound

This paper cites Self-Training with Direct Preference Optimization Improves Chain-of-Thought Reasoning.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs Self-Training with Direct Preference Optimization Improves Chain-of-Thought Reasoning

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.555748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5593c3c-77df-44f3-b7f0-325a56f0575d · outbound

This paper cites an unresolved cited work.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs Unresolved cited work

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.559640Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a23ab16-3fa7-4620-9405-ae7ba76eadd2 · outbound

This paper cites an unresolved cited work.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs Unresolved cited work

Reference 34

Resolution: unresolved; raw_fallback, observed 2026-08-15T16:52:43.952908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 67a9c5a5-2626-4655-87a9-94ce55d76194 · outbound

This paper cites On the Impact of Calibration Data in Post-training Quantization and Pruning.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs On the Impact of Calibration Data in Post-training Quantization and Pruning

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.567193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 673589bb-b221-463a-8b28-ea62daa7fd26 · outbound

This paper cites Beyond Perplexity: Multi-dimensional Safety Evaluation of LLM Compression.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs Beyond Perplexity: Multi-dimensional Safety Evaluation of LLM Compression

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.570991Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e180557-d46f-47e4-a24b-c577b55b8202 · outbound

This paper cites Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.574471Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9cb09dcf-659f-43c1-b75e-a99915ce1cb3 · outbound

This paper cites LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.578456Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 812fcf29-147d-4d96-b03a-bca4cc98ac56 · outbound

This paper cites TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.582292Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ceddc53-7fc4-453b-828f-c074cff33d88 · outbound

This paper cites Small Language Models Need Strong Verifiers to Self-Correct Reasoning.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs Small Language Models Need Strong Verifiers to Self-Correct Reasoning

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.585998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27f8626a-b36c-45e5-90d7-bdaed263e80b · outbound

This paper cites A Survey of Large Language Models.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs A Survey of Large Language Models

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.589521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4642824b-bfb0-4811-a3a6-adbc72b3d573 · outbound

This paper cites A Survey on Efficient Inference for Large Language Models.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs A Survey on Efficient Inference for Large Language Models

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.593508Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8ada4d3-7aec-4b92-8269-ac075cf3bb93 · outbound

This paper cites online" 'onlinestring :=.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs online" 'onlinestring :=

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.597597Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d5991e7-44de-437d-9bcf-81f18c72b528 · outbound

This paper cites write newline.

Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs write newline

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:43.601515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 9984d5ee-22f3-41d3-80b6-0797f120f9a5 · inbound

Fragile Knowledge, Robust Instruction-Following: The Width Pruning Dichotomy in Llama-3.2 cites this paper.

Fragile Knowledge, Robust Instruction-Following: The Width Pruning Dichotomy in Llama-3.2 Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-16T18:58:19.024693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.