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Paper Citation Record · LEDGER

Enhancing LLM Safety via Constrained Direct Preference Optimization

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 inbound Pith citation observations for arXiv:2403.02475.

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

pith.paper-citation-record.v1
2403.02475 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 15 of 15 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:24:12.784378Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation e3322188-3172-48ef-b84e-540124df4156 · inbound

Jailbreak Attacks and Defenses Against Large Language Models: A Survey cites this paper.

Jailbreak Attacks and Defenses Against Large Language Models: A Survey Enhancing LLM Safety via Constrained Direct Preference Optimization

Reference 59

Resolution
verified exact
arxiv_id, observed 2026-05-15T02:20:44.762062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-15T02:20:44.368219Z digest=sha256:1e93cdf23f2fde49b9a8d27930909853f20f471288d77fc3424ca1016ab267ef

Observation df0fb837-b648-4b15-9f59-646e4f21287a · inbound

Clear Preferences Leave Traces: Reference Model-Guided Sampling for Preference Learning cites this paper.

Clear Preferences Leave Traces: Reference Model-Guided Sampling for Preference Learning Enhancing LLM Safety via Constrained Direct Preference Optimization

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-10T14:39:22.636132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:39:22.636132Z digest=sha256:1a7058d3a32cbf18bc6f15bac50458a80d305321ecdd252bd82ced1a2b98621c

Observation 8a1a9168-3123-4793-9419-953e9e725e33 · inbound

Vulnerability Mitigation for Safety-Aligned Language Models via Debiasing cites this paper.

Vulnerability Mitigation for Safety-Aligned Language Models via Debiasing Enhancing LLM Safety via Constrained Direct Preference Optimization

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-09T13:14:34.063868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T13:14:34.063868Z digest=sha256:931712b0177f3b8afae1264251dd6fff97da80aba980d912fcfc1fd75061fd2e

Observation c48394cd-17d8-4785-8765-b74899b2e30e · inbound

A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment cites this paper.

A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment Enhancing LLM Safety via Constrained Direct Preference Optimization

Reference 257

Resolution
unresolved
no resolver link, observed 2026-08-16T11:24:12.784378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:24:12.784378Z digest=sha256:0637d8c14dba130f03f330f3d2f8117fba812a6407c1e59b46c1d33c6d5380ec

Observation f3078b42-6be0-469d-88f3-a7f023a620f9 · inbound

Fight Fire with Fire: Defending Against Malicious RL Fine-Tuning via Reward Neutralization cites this paper.

Fight Fire with Fire: Defending Against Malicious RL Fine-Tuning via Reward Neutralization Enhancing LLM Safety via Constrained Direct Preference Optimization

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-15T23:30:24.239154Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T23:30:24.239154Z digest=sha256:d6d4632bf215106f523182e219c020e568227bfacacdffcb38be15bd34603f9e

Observation 7f1d5073-9f8a-45ce-be38-8a0bd2e8880e · inbound

Learning Safety Constraints for Large Language Models cites this paper.

Learning Safety Constraints for Large Language Models Enhancing LLM Safety via Constrained Direct Preference Optimization

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-07T12:30:16.012101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:30:16.012101Z digest=sha256:d6fdb24f2ac6a2299b9b2bfdc0bc76c4e5f2dcc915c5cbcb84f432399c9af1f4

Observation 094a5a8d-af98-4b91-83a0-bad6ed9f6a95 · inbound

Reinforcement Learning from Human Feedback with High-Confidence Safety Constraints cites this paper.

Reinforcement Learning from Human Feedback with High-Confidence Safety Constraints Enhancing LLM Safety via Constrained Direct Preference Optimization

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-07T05:21:58.706600Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T05:21:58.706600Z digest=sha256:e8d3a317de3b3ae60938686b0aacce94f56afe2b87a1db51840d6fd4326d660f

Observation a1939382-08e1-4005-bcf6-ae0c11ce47c8 · inbound

The Geometry of Harmfulness in LLMs through Subconcept Probing cites this paper.

The Geometry of Harmfulness in LLMs through Subconcept Probing Enhancing LLM Safety via Constrained Direct Preference Optimization

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-06T15:01:47.808584Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:01:47.808584Z digest=sha256:61030af4940164eb7cbb95a35bf0de3d361c8d21eae990ce3cb0614a5448523e

Observation 4424de3d-0056-46e5-a14a-8c910b95c668 · inbound

Enhancing Speech Large Language Models through Reinforced Behavior Alignment cites this paper.

Enhancing Speech Large Language Models through Reinforced Behavior Alignment Enhancing LLM Safety via Constrained Direct Preference Optimization

Reference 37

Resolution
verified exact
arxiv_id, observed 2026-05-21T22:24:23.468405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-21T22:23:52.392075Z digest=sha256:8c490b8ae4bc0524aed2b8a1d5466469efa5c2ad93e83a457d6fa2e48251811a

Observation 964885b8-7802-4657-802f-425b10c0d969 · inbound

FlashEvaluator: Expanding Search Space with Parallel Sequence-Level Evaluation cites this paper.

FlashEvaluator: Expanding Search Space with Parallel Sequence-Level Evaluation Enhancing LLM Safety via Constrained Direct Preference Optimization

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-02T19:23:43.246430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T19:23:43.246430Z digest=sha256:8f184a946d5c4d366858a45cc4d2fba6510ac45078798bf49bca97ef7114f184

Observation 18defa3c-fc79-471c-a584-7aee7dcfa815 · inbound

MGDA-Decoupled: Geometry-Aware Multi-Objective Optimisation for DPO-based LLM Alignment cites this paper.

MGDA-Decoupled: Geometry-Aware Multi-Objective Optimisation for DPO-based LLM Alignment Enhancing LLM Safety via Constrained Direct Preference Optimization

Reference 54

Resolution
verified exact
arxiv_id, observed 2026-05-10T01:40:55.342322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-05-10T01:38:49.892824Z digest=sha256:ee0c7f1000c8c4991d151f4dc7e48e815fdea3595abe7b9f05fdfe405464d629

Observation 9416754a-97e6-429e-a76f-6904dcf0ae8a · inbound

Scalable First-Order Interior Point Trust Region Algorithms for Linearly Constrained Optimization cites this paper.

Scalable First-Order Interior Point Trust Region Algorithms for Linearly Constrained Optimization Enhancing LLM Safety via Constrained Direct Preference Optimization

Reference 32

Resolution
verified exact
arxiv_id, observed 2026-05-11T23:06:22.066349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-08T01:30:53.740345Z digest=sha256:f8b144c8923b98c752a54a455e963ec413d2b8e34dcb7f046460fe8fca37621c

Observation b5e38fd8-be65-4be7-a4e7-ea14c221efc2 · inbound

Beyond the Prompt: Jailbreaking Function-Calling LLMs via Simulated Moderation Traces cites this paper.

Beyond the Prompt: Jailbreaking Function-Calling LLMs via Simulated Moderation Traces Enhancing LLM Safety via Constrained Direct Preference Optimization

Reference 27

Resolution
verified exact
arxiv_id, observed 2026-07-02T11:46:54.874567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-07-02T11:37:02.538968Z digest=sha256:df8876a7946fa982e246880193787580508be1736e3303952d16746d4c2e2b2e

Observation 85cbb6ca-3aa2-4830-b59b-5f800a6df652 · inbound

Safe Inference-Time Alignment via Lagrangian Reward Augmentation cites this paper.

Safe Inference-Time Alignment via Lagrangian Reward Augmentation Enhancing LLM Safety via Constrained Direct Preference Optimization

Reference 104

Resolution
unresolved
no resolver link, observed 2026-07-12T07:05:47.150308Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T07:05:47.150308Z digest=sha256:a52f4cdfb4ad6bff477ebf8eff10a3f438cc9acf933efb745096e8c5b8857e64

Observation 5ad6ebef-9d33-4b6d-b1cb-bb49ba592132 · inbound

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints cites this paper.

Every Sample Counts: Supervised Fine-Tuning of Language Models with Pointwise Constraints Enhancing LLM Safety via Constrained Direct Preference Optimization

Reference 6

Resolution
unresolved
no resolver link, observed 2026-07-13T05:26:06.829382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T05:26:06.829382Z digest=sha256:8afdee43fd71ba54e8e3832841a7869ea32c85d0c5c33ffb8798b977affbdb8f