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Paper Citation Record · LEDGER

Learn Your Reference Model for Real Good Alignment

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2404.09656.

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

pith.paper-citation-record.v1
2404.09656 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:17:34.339008Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T03:45:17.579700Z

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 5adf27ae-e16d-4764-acd0-451d9711114c · inbound

Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization cites this paper.

Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization Learn Your Reference Model for Real Good Alignment

Reference 29

Resolution
verified exact
arxiv_id, observed 2026-05-16T09:16:17.362296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-16T09:16:17.150383Z digest=sha256:ecd79dbda8a06cf44eb3eb28cbc3895e862ddcebae75c119c00d6877e351833f

Observation d1395922-ed26-410d-b851-477679cdf230 · inbound

How to Merge Your Multimodal Models Over Time? cites this paper.

How to Merge Your Multimodal Models Over Time? Learn Your Reference Model for Real Good Alignment

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-11T19:24:43.058270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T19:24:43.058270Z digest=sha256:25ac5fccaa4543987539b74190985d565a81571a65ee52e01f1753280c4bf782

Observation 16b50905-91c6-44a7-9897-cb7ca96cdeb5 · inbound

Merging Models on the Fly Without Retraining: A Sequential Approach to Scalable Continual Model Merging cites this paper.

Merging Models on the Fly Without Retraining: A Sequential Approach to Scalable Continual Model Merging Learn Your Reference Model for Real Good Alignment

Reference 2013

Resolution
unresolved
no resolver link, observed 2026-08-10T20:03:11.084510Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:03:11.084510Z digest=sha256:810466058e77759998bc76f6f5892a51ec07c74bc027c650a56b02d74e009247

Observation fe31d83a-e06a-440a-8617-2220db8220f1 · inbound

The Differences Between Direct Alignment Algorithms are a Blur cites this paper.

The Differences Between Direct Alignment Algorithms are a Blur Learn Your Reference Model for Real Good Alignment

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-05-23T03:52:29.429125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-23T03:50:03.720389Z digest=sha256:492d7cebf51b40762af598e33f3ee8fdf6e0a277af60476a2f72c9ce68c67fee

Observation 71b9274c-3a20-4e44-b5f7-1f630de68c98 · inbound

On the Interplay of Human-AI Alignment,Fairness, and Performance Trade-offs in Medical Imaging cites this paper.

On the Interplay of Human-AI Alignment,Fairness, and Performance Trade-offs in Medical Imaging Learn Your Reference Model for Real Good Alignment

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-15T21:17:34.339008Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:17:34.339008Z digest=sha256:ca93609ab5fb2eb99719d40be1bc0e8fd76860e6af7cb3e32db87768f68ce9e0

Observation 9bf454c0-d0c9-470b-99ac-b9b245bc4f18 · inbound

Explicit Preference Optimization: No Need for an Implicit Reward Model cites this paper.

Explicit Preference Optimization: No Need for an Implicit Reward Model Learn Your Reference Model for Real Good Alignment

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-07T05:40:17.894671Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T05:40:17.894671Z digest=sha256:301ef980324de972bfe08473f1a0ab1896d251f976a508ffba91286d9c4f8ec0

Observation cad5854e-c103-4d4f-8d73-ebc7127bc9aa · 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 Learn Your Reference Model for Real Good Alignment

Reference 2006

Resolution
unresolved
no resolver link, observed 2026-08-07T01:04:30.926721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T01:04:30.926721Z digest=sha256:7bed64cb02d93c9673b4b96eed49fba4c587bab541f2b61567eda46f49c85327

Observation baeca0d5-9899-458f-8bbf-98d5bbf932e0 · inbound

Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution cites this paper.

Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution Learn Your Reference Model for Real Good Alignment

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-16T06:37:28.523968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-16T06:35:30.479542Z digest=sha256:eb078c34f6debd1f4b231d684b590d31fa4454f0c41d3a0ed8187255c4a84eae

Observation d6a6a91b-0d07-4461-819f-b2bb0e42711a · inbound

Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution cites this paper.

Provably avoiding over-optimization in Direct Preference Optimization without knowing the data distribution Learn Your Reference Model for Real Good Alignment

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-21T13:10:10.461804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-21T13:06:54.002248Z digest=sha256:8bb7dd03f75f41203ebf9939e7f0da9612ab765d8afb2afb8ebd21872a4e2478

Observation 8e0fb4a1-3870-4b13-9907-f1cfd2811068 · inbound

Intrinsic Mutual Information as a Modulator for Preference Optimization cites this paper.

Intrinsic Mutual Information as a Modulator for Preference Optimization Learn Your Reference Model for Real Good Alignment

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-11T21:41:19.142242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-08T04:30:48.824816Z digest=sha256:c83dbb0b6ac9f07ef49ec749389cc8d5a59cd6dc833b0566f63d2cd26e63c200

Observation 7cae48a0-bef3-4259-b815-4b029bd91f98 · inbound

TPMM-DPO: Trajectory-aware Preference-guided Model Merging for Iterative Direct Preference Optimization cites this paper.

TPMM-DPO: Trajectory-aware Preference-guided Model Merging for Iterative Direct Preference Optimization Learn Your Reference Model for Real Good Alignment

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-05-25T03:45:17.582152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-25T03:41:52.859647Z digest=sha256:9e012c4febdf2be647a475dcc785dba32b792f34f06e8539a63086f6656488c2

Observation 98799437-320c-4df1-a69c-b16943e07d18 · inbound

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration cites this paper.

Beyond Post-Hoc Temperature Scaling: Bilevel Optimization for LLM Calibration Learn Your Reference Model for Real Good Alignment

Reference 154

Resolution
unresolved
no resolver link, observed 2026-08-15T14:33:57.483448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T14:33:57.483448Z digest=sha256:87df9d552258364c45bd45a6cb8a94b7b44248711339140e4d01c81dbd443435