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Paper Citation Record · LEDGER

Aligning Large Language Models for Faithful Integrity Against Opposing Argument

As of 15 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:2501.01336.

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

pith.paper-citation-record.v1
2501.01336 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:35:43.990637Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T04:59:45.724855Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T10:29:25.181680Z

Reference resolution

39 of 39 outbound references displayed

  • verified exact0
  • verified fuzzy4
  • unresolved35
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 91a8b2a6-80a8-4544-9eea-6b348d776990 · outbound

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

Aligning Large Language Models for Faithful Integrity Against Opposing Argument The Internal State of an LLM Knows When It's Lying

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-10T22:35:43.832103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:35:43.832103Z digest=sha256:7f31fe1e1f8d6c43f620a6b01fee03b5fe1405a9a721ce74328a2d0279889531

Observation e5314165-2166-4a2e-8b7a-598961b242ea · outbound

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

Aligning Large Language Models for Faithful Integrity Against Opposing Argument Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback

Reference 2

Resolution
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no resolver link, observed 2026-08-10T22:35:43.837445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:35:43.837445Z digest=sha256:858bb69ce5dae73a30e6b70517dafa3034ed3e9eadfc6448ae00de1bfcc6de5f

Observation b5d1f2bd-0fbf-40f6-a8a3-8a4240a5301c · outbound

This paper cites Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness

Reference 3

Resolution
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no resolver link, observed 2026-08-10T22:35:43.841810Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:35:43.841810Z digest=sha256:1fa6c0c63dc017f37fab76e3185ba384a7fba71c26c2f51b9ca9bbd45ea7afad

Observation 176a7dec-d4fc-4c05-ab27-1517b08a5deb · outbound

This paper cites an unresolved cited work.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument Unresolved cited work

Reference 4

Resolution
unresolved
raw_fallback, observed 2026-08-10T22:35:44.444923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-10T22:35:43.846273Z digest=sha256:63968e675dd96bb0827b0f6475d97b970fcb57dc941d76429532dd1ae3af1b49

Observation 0b01f957-8f0e-4e44-9231-175522c4efa4 · outbound

This paper cites In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigation.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigation

Reference 5

Resolution
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no resolver link, observed 2026-08-10T22:35:43.850316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:35:43.850316Z digest=sha256:63d962c5d5edf7ed95c31ac3888815c724a30e20633928405a2a4a342b1e7f4e

Observation 0bbc7490-c9e7-4c25-b6f6-4f1edd5f40f4 · outbound

This paper cites E.; Stoica, I.; and Xing, E.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument E.; Stoica, I.; and Xing, E

Reference 6

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no resolver link, observed 2026-08-10T22:35:43.854429Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:35:43.854429Z digest=sha256:27c3ab08eeb445bb798bae68c9db1482f9a716a06dfcd59b09c5ea0ad7191c74

Observation 03790916-6628-4bc5-9d31-3d3c5b9cc316 · outbound

This paper cites Scaling Instruction-Finetuned Language Models.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument Scaling Instruction-Finetuned Language Models

Reference 7

Resolution
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no resolver link, observed 2026-08-10T22:35:43.858736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:35:43.858736Z digest=sha256:f2643bcf86d6be3b7ff45858c3285432aacb823f50e49ea84340d1de42461c61

Observation cc67af9a-9f03-488f-9208-ac80a66159df · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument Training Verifiers to Solve Math Word Problems

Reference 8

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no resolver link, observed 2026-08-10T22:35:43.862959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:35:43.862959Z digest=sha256:80938e8af5b8b61cb082f513adb42d8aa48aa30cc5136c59e66d2e569cd3443c

Observation 2f141340-f6e8-4343-b0c8-9f87e573c291 · outbound

This paper cites I don't know.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument I don't know

Reference 9

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-10T22:35:43.867018Z digest=sha256:3d390934330bb3a7f400084b15bf98222b8e433bcabca9e8ca644f9dadeba446

Observation f4c02857-e6ae-41ea-b514-d91fdd220ce5 · outbound

This paper cites Truthful AI: Developing and governing AI that does not lie.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument Truthful AI: Developing and governing AI that does not lie

Reference 10

Resolution
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no resolver link, observed 2026-08-10T22:35:43.870593Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:35:43.870593Z digest=sha256:da3371c68d81879782737ab2d709c44ffa3a026a21605f6ff65aaa76a801b555

Observation ecc3316a-2fdc-48b3-8099-be9a1dd6ca22 · outbound

This paper cites an unresolved cited work.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument Unresolved cited work

Reference 11

Resolution
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raw_fallback, observed 2026-08-10T22:35:44.414891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 1ae228e9-9f6a-4f20-a86c-00cf6b079273 · outbound

This paper cites an unresolved cited work.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument Unresolved cited work

Reference 12

Resolution
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raw_fallback, observed 2026-08-10T22:35:44.403161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-10T22:35:43.878444Z digest=sha256:996cb2095367972a04b9ce43d13f735cb638b6320a6667d84ae9600dfc149d59

Observation e88ad706-6623-4818-a329-00eab7dac264 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument Measuring Massive Multitask Language Understanding

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:35:43.882222Z digest=sha256:e6ea05609bd8c35625a6c000539ca828a028fa083fd7be3fd8d01c1eb597f810

Observation 1e205330-ee55-4dbd-9c4c-153ad3c7672d · outbound

This paper cites J.; Shen, Y.; Wallis, P.; Allen - Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; and Chen, W.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument J.; Shen, Y.; Wallis, P.; Allen - Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; and Chen, W

Reference 14

Resolution
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no resolver link, observed 2026-08-10T22:35:43.886128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:35:43.886128Z digest=sha256:c4034617b7783955c316b97a6c5596b6672536f6c40b34964241ee2630dc81a2

Observation 88694f21-73e0-4901-8379-31f0b092e7d3 · outbound

This paper cites Language Models (Mostly) Know What They Know.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument Language Models (Mostly) Know What They Know

Reference 16

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no resolver link, observed 2026-08-10T22:35:43.894859Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:35:43.894859Z digest=sha256:8a34414cc77c37caaca69671d7ff6d13c098d4bfade3d7ccdc289ad24595821f

Observation 4026c363-2a47-47b6-a53d-203991980fb4 · outbound

This paper cites an unresolved cited work.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument Unresolved cited work

Reference 17

Resolution
unresolved
raw_fallback, observed 2026-08-10T22:35:44.384902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-10T22:35:43.898752Z digest=sha256:8bf6c2c1fbc5e5567d310c8cf74b91b48f2fd31a8a15257c6a1602e856d497b4

Observation 038376fa-9d20-42bb-b0ae-15e47cd2afac · outbound

This paper cites an unresolved cited work.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument Unresolved cited work

Reference 18

Resolution
unresolved
raw_fallback, observed 2026-08-10T22:35:44.372773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-10T22:35:43.902688Z digest=sha256:8a9103c7ee4a834cbc8695a1f641c36c61b67de09b264749f08ac005f75eb3eb

Observation 42c26fc9-00bb-4a81-9d00-79ab0d0cebcb · outbound

This paper cites an unresolved cited work.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument Unresolved cited work

Reference 19

Resolution
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raw_fallback, observed 2026-08-10T22:35:44.361348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-10T22:35:43.906934Z digest=sha256:7c2d6460bb1effdb11900965c140ff29b8f3b3488db8cfe36527dca1b2c3a210

Observation e9d4ebc1-84b6-48b1-9a0a-75605f433075 · outbound

This paper cites Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models

Reference 20

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:35:43.911703Z digest=sha256:5d347d29823e89179c223c902ef322546632d5e74aab2e0483eb7f45536951ff

Observation f2b64298-0869-42a9-b6b7-f8e923ccf5c7 · outbound

This paper cites an unresolved cited work.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument Unresolved cited work

Reference 21

Resolution
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raw_fallback, observed 2026-08-10T22:35:44.349924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-10T22:35:43.915716Z digest=sha256:b76bd4874e360c0cdeeb7273b2e341b74ea3c354559ffe1409e195aef372b3f1

Observation ea1f7a8b-b37d-42e5-bf27-6292e1a0577c · outbound

This paper cites The Llama 3 Herd of Models.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument The Llama 3 Herd of Models

Reference 22

Resolution
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no resolver link, observed 2026-08-10T22:35:43.919308Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:35:43.919308Z digest=sha256:d8b5fe7795c49a700e84621512e977e06ccc7359d5fc3189d332e157edc3b576

Observation 6f00b4b3-7b1f-4c0e-b442-71ec4a82bb25 · outbound

This paper cites J.; Szlam, A.; Dinan, E.; and Boureau, Y.-L.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument J.; Szlam, A.; Dinan, E.; and Boureau, Y.-L

Reference 23

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-10T22:35:43.923244Z digest=sha256:55cc442b5b064f4fe046a7f3dd3cdf27b03cc0d9838a9f86237f272fc6520f35

Observation 76140d93-4bb0-48cf-b00d-9373d4f0fbb5 · outbound

This paper cites J.; Szlam, A.; Dinan, E.; and Boureau, Y.-L.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument J.; Szlam, A.; Dinan, E.; and Boureau, Y.-L

Reference 24

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation fc470d14-7cfc-4398-ba57-fd8deee2134f · outbound

This paper cites an unresolved cited work.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument Unresolved cited work

Reference 25

Resolution
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raw_fallback, observed 2026-08-10T22:35:44.311903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-10T22:35:43.930788Z digest=sha256:3c13d84331dfbe02f0f90b928fc7877629344dd4ea34ae27fbccd1e1eec0af22

Observation 7316a3a2-fb92-43e3-9bc4-8abdd1828f80 · outbound

This paper cites an unresolved cited work.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument Unresolved cited work

Reference 26

Resolution
unresolved
raw_fallback, observed 2026-08-10T22:35:44.299753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 88b4f048-4e97-4b13-b09a-0352066c6f09 · outbound

This paper cites Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 27

Resolution
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no resolver link, observed 2026-08-10T22:35:43.939107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:35:43.939107Z digest=sha256:11eeb35d787908383d017289ad89ab12549ceef13c8f77f8a8a05ee5151fec3c

Observation 9cb80008-0378-40c8-bbbb-7c0db87695b0 · outbound

This paper cites an unresolved cited work.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument Unresolved cited work

Reference 28

Resolution
unresolved
raw_fallback, observed 2026-08-10T22:35:44.286696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 7196e2a1-ed2f-4ba5-a328-6d361b0976e0 · outbound

This paper cites Fine-tuning Language Models for Factuality.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument Fine-tuning Language Models for Factuality

Reference 29

Resolution
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no resolver link, observed 2026-08-10T22:35:43.946844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation eeeb3b85-3d9b-4a46-a1a6-d032d70bc1b6 · outbound

This paper cites an unresolved cited work.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument Unresolved cited work

Reference 30

Resolution
unresolved
raw_fallback, observed 2026-08-10T22:35:44.275112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation e82d4b7e-059b-4de9-998e-a8a804365969 · outbound

This paper cites Emergent Abilities of Large Language Models.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument Emergent Abilities of Large Language Models

Reference 31

Resolution
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no resolver link, observed 2026-08-10T22:35:43.955277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 547b175b-7004-4a27-a6a2-9b5ab7260839 · outbound

This paper cites Know Your Limits: A Survey of Abstention in Large Language Models.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument Know Your Limits: A Survey of Abstention in Large Language Models

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-10T22:35:43.958668Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 34bf6d9f-3787-47af-9f5a-41dbe1bfa8c3 · outbound

This paper cites The Earth is Flat because...: Investigating LLMs' Belief towards Misinformation via Persuasive Conversation.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument The Earth is Flat because...: Investigating LLMs' Belief towards Misinformation via Persuasive Conversation

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-10T22:35:43.962521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation b9251df7-8615-412a-b98c-bfb29e4f8c4d · outbound

This paper cites Alignment for Honesty.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument Alignment for Honesty

Reference 34

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no resolver link, observed 2026-08-10T22:35:43.966546Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:35:43.966546Z digest=sha256:aa0a1fb359b11c95d4ffee4d40f479cd1c49a5244ec9c14f191a2f1900f8394f

Observation dd657a7e-3013-49d7-a417-c5466989cb65 · outbound

This paper cites R.; and Cao, Y.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument R.; and Cao, Y

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T22:35:44.263652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-10T22:35:43.970852Z digest=sha256:e7e2fc44477cf6799871e55108fe6eaa8f2fe4010120584ff9e2c6190db48df2

Observation 9b43a599-8375-4fad-bed7-6901bff0f610 · outbound

This paper cites Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension

Reference 36

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unresolved
no resolver link, observed 2026-08-10T22:35:43.974468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:35:43.974468Z digest=sha256:4631cc50d2dd5b34535ca8378ef8a21a96e43c78a7fd096b08a5776ab3e02820

Observation 29b22627-fb9d-4c62-b229-767c32551ffc · outbound

This paper cites Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation

Reference 37

Resolution
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no resolver link, observed 2026-08-10T22:35:43.978810Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T22:35:43.978810Z digest=sha256:0037a6e965287bf4a53e74b9ca8efea033ee7f099e902f9463a800a99567f9a0

Observation 80097e9c-0647-4a6e-a112-c43d8fb7b8b5 · outbound

This paper cites TrustScore: Reference-Free Evaluation of LLM Response Trustworthiness.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument TrustScore: Reference-Free Evaluation of LLM Response Trustworthiness

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-10T22:35:43.982737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 3d04a755-b052-493a-af0e-f7c7d08cd046 · outbound

This paper cites , " * write output.state after.block = add.period write newline.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument , " * write output.state after.block = add.period write newline

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-10T22:35:43.986388Z

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source=arxiv_source observed=2026-08-10T22:35:43.986388Z digest=sha256:64b54d91efb63efed49981012282f2eb791816bcd922549182f1ce979d22f9b7

Observation 3a0026a7-2303-48ae-825b-eb08c1c671b2 · outbound

This paper cites write newline.

Aligning Large Language Models for Faithful Integrity Against Opposing Argument write newline

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-10T22:35:43.990637Z

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source=arxiv_source observed=2026-08-10T22:35:43.990637Z digest=sha256:52a37439ec1cdd4d9b3368cb98845f770d3cd461d10b5eb89f60a8490769454b

Pith citing papers

Observation 33c8c479-00b1-473d-8b80-f0cd496a3bcc · inbound

Spatiotemporal Sycophancy: Negation-Based Gaslighting in Video Large Language Models cites this paper.

Spatiotemporal Sycophancy: Negation-Based Gaslighting in Video Large Language Models Aligning Large Language Models for Faithful Integrity Against Opposing Argument

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-05-10T10:29:25.183157Z

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