Paper Citation Record · LEDGER

On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2405.16455.

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

pith.paper-citation-record.v1
2405.16455 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:16:40.186644Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T20:57:23.620580Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 3bf6d4d8-7973-42df-833b-da06b03a8c7a · inbound

Fundamental Limits of Game-Theoretic LLM Alignment: Smith Consistency and Preference Matching cites this paper.

Fundamental Limits of Game-Theoretic LLM Alignment: Smith Consistency and Preference Matching On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T14:16:40.186644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d05d8e8-2c3e-4b5a-81ae-0e73ee18c877 · inbound

Square$\chi$PO: Differentially Private and Robust $\chi^2$-Preference Optimization in Offline Direct Alignment cites this paper.

Square$\chi$PO: Differentially Private and Robust $\chi^2$-Preference Optimization in Offline Direct Alignment On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-07T13:45:03.350482Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f3c4032-9f7c-458e-964b-d3b0f333e6ba · inbound

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory cites this paper.

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T01:04:32.454213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87142bb4-165a-438c-894a-4ef8a4ece542 · inbound

Exploring a Gamified Personality Assessment Method through Interaction with LLM Agents Embodying Different Personalities cites this paper.

Exploring a Gamified Personality Assessment Method through Interaction with LLM Agents Embodying Different Personalities On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization

Reference 140

Resolution: verified exact; arxiv_id, observed 2026-05-19T06:37:07.620062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4c8b87d4-5aad-4da1-a322-545f90e4eb97 · inbound

Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges cites this paper.

Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization

Reference 221

Resolution: unresolved; no resolver link, observed 2026-08-06T14:13:07.104772Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 005718e4-27e6-48cc-8ea7-4e06d3fcd0e4 · inbound

The Fair Game: Auditing & Debiasing AI Algorithms Over Time cites this paper.

The Fair Game: Auditing & Debiasing AI Algorithms Over Time On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization

Reference 130

Resolution: unresolved; no resolver link, observed 2026-08-05T22:45:20.828281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c1c29ba-f599-4abb-8e1f-7192e8ac808e · inbound

Counterfactual Reward Model Training for Bias Mitigation in Multimodal Reinforcement Learning cites this paper.

Counterfactual Reward Model Training for Bias Mitigation in Multimodal Reinforcement Learning On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T15:44:38.622610Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a09d48ca-5a53-4333-928e-17f03e4641c8 · inbound

"GenAI Defaults to Bias!" Gamify AI Literacy Through Reflections on Prompts cites this paper.

"GenAI Defaults to Bias!" Gamify AI Literacy Through Reflections on Prompts On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-04T16:29:42.395542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38527641-703e-44af-98b8-5e64922fefbe · inbound

Curated Synthetic Data Doesn't Have to Collapse: A Theoretical Study of Generative Retraining with Pluralistic Preferences cites this paper.

Curated Synthetic Data Doesn't Have to Collapse: A Theoretical Study of Generative Retraining with Pluralistic Preferences On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization

Reference 101

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:30:54.396711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7b1e7592-25e7-48c9-bbd3-bbaaa9cc933b · inbound

PAFO: Pareto Fairness Optimization for Personalized Reward Modeling cites this paper.

PAFO: Pareto Fairness Optimization for Personalized Reward Modeling On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-02T20:57:23.622083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.