Paper Citation Record · LEDGER

Intuitive Fine-Tuning: Towards Simplifying Alignment into a Single Process

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2405.11870.

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

pith.paper-citation-record.v1
2405.11870 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T22:06:54.138286Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T04:54:16.270835Z

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 9c552e04-9553-4e22-91b6-8151cb91e19a · inbound

Towards Data Governance of Frontier AI Models cites this paper.

Towards Data Governance of Frontier AI Models Intuitive Fine-Tuning: Towards Simplifying Alignment into a Single Process

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-11T22:06:54.138286Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71467c5b-ca64-498d-ad56-06f457e4537a · inbound

Align-Pro: A Principled Approach to Prompt Optimization for LLM Alignment cites this paper.

Align-Pro: A Principled Approach to Prompt Optimization for LLM Alignment Intuitive Fine-Tuning: Towards Simplifying Alignment into a Single Process

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T21:59:10.051147Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 932a3072-0ac5-4b0c-9781-d99920a86c0e · inbound

A Survey of Reinforcement Learning for Large Reasoning Models cites this paper.

A Survey of Reinforcement Learning for Large Reasoning Models Intuitive Fine-Tuning: Towards Simplifying Alignment into a Single Process

Reference 207

Resolution: verified exact; arxiv_id, observed 2026-05-18T00:05:31.619472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6cefbc49-b4a0-4cc6-be2d-19a370e5af6a · inbound

ARMOR: Adaptive Retriever Optimization for Low-Resource Telecom Question Answering cites this paper.

ARMOR: Adaptive Retriever Optimization for Low-Resource Telecom Question Answering Intuitive Fine-Tuning: Towards Simplifying Alignment into a Single Process

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-06-30T04:54:16.272285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.