Paper Citation Record · LEDGER

CodecLM: Aligning Language Models with Tailored Synthetic Data

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2404.05875.

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

pith.paper-citation-record.v1
2404.05875 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 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 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:12:09.661664Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T09:24:32.394844Z

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 63aa6ea0-2f74-406b-9baf-08166ca78095 · inbound

Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing cites this paper.

Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing CodecLM: Aligning Language Models with Tailored Synthetic Data

Reference 151

Resolution: verified exact; arxiv_id, observed 2026-05-16T06:58:36.901957Z

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.

Observation b0e91473-4f47-4bc8-96ce-b0218d178772 · inbound

A Domain Adaptation Framework for Speech Recognition Systems with Only Synthetic data cites this paper.

A Domain Adaptation Framework for Speech Recognition Systems with Only Synthetic data CodecLM: Aligning Language Models with Tailored Synthetic Data

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T17:12:09.661664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e757d0cb-d62c-4142-8bc9-97b74cad6e9e · inbound

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning cites this paper.

What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning CodecLM: Aligning Language Models with Tailored Synthetic Data

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T23:12:19.031822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b125de1-3aa7-49cb-8222-c159da1a5b8f · inbound

Evaluating LLMs Without Oracle Feedback: Agentic Annotation Evaluation Through Unsupervised Consistency Signals cites this paper.

Evaluating LLMs Without Oracle Feedback: Agentic Annotation Evaluation Through Unsupervised Consistency Signals CodecLM: Aligning Language Models with Tailored Synthetic Data

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T20:11:41.660463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d42a66db-1ff9-4cc5-8190-6a67f302676e · inbound

Agents of Diffusion: Enhancing Diffusion Language Models with Multi-Agent Reinforcement Learning for Structured Data Generation (Extended Version) cites this paper.

Agents of Diffusion: Enhancing Diffusion Language Models with Multi-Agent Reinforcement Learning for Structured Data Generation (Extended Version) CodecLM: Aligning Language Models with Tailored Synthetic Data

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-03T11:14:51.774281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70dc97e8-cab6-492b-a517-440863db04ab · inbound

A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data cites this paper.

A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data CodecLM: Aligning Language Models with Tailored Synthetic Data

Reference 229

Resolution: unresolved; no resolver link, observed 2026-08-03T08:15:34.485285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 772f84ff-2f4d-4e10-94a4-c463c3d54dee · inbound

STELLAR-E: a Synthetic, Tailored, End-to-end LLM Application Rigorous Evaluator cites this paper.

STELLAR-E: a Synthetic, Tailored, End-to-end LLM Application Rigorous Evaluator CodecLM: Aligning Language Models with Tailored Synthetic Data

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-11T22:01:10.890992Z

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.

Observation 75db12aa-472a-4a8f-a74d-28d604286e60 · inbound

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation cites this paper.

TrafficAlign: Aligning Large Language Models for Traffic Scenario Generation CodecLM: Aligning Language Models with Tailored Synthetic Data

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-06-30T09:24:32.396708Z

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.