Paper Citation Record · LEDGER

Understanding Layer Significance in LLM Alignment

As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2410.17875.

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

pith.paper-citation-record.v1
2410.17875 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-03T21:31:31.831806Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T21:38:58.237859Z

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 42477f70-a7fe-4537-b12c-935ef190d518 · inbound

PERSA: Reinforcement Learning for Professor-Style Personalized Feedback with LLMs cites this paper.

PERSA: Reinforcement Learning for Professor-Style Personalized Feedback with LLMs Understanding Layer Significance in LLM Alignment

Reference 65

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T15:51:42.621868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f8c588db-7d64-432b-a236-c9996ec4d065 · inbound

Gradient-Based LoRA Rank Allocation Under GRPO: An Empirical Study cites this paper.

Gradient-Based LoRA Rank Allocation Under GRPO: An Empirical Study Understanding Layer Significance in LLM Alignment

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:45:58.125788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1ed17bfc-ae55-49df-aed9-03ada23a917a · inbound

Rotation-Preserving Supervised Fine-Tuning cites this paper.

Rotation-Preserving Supervised Fine-Tuning Understanding Layer Significance in LLM Alignment

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-13T06:27:24.410789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1d2fe589-611b-4cd2-8316-41282cd600e6 · inbound

EASE-TTT: Evidence-Aligned Selective Test-Time Training for Long-Context Question Answering cites this paper.

EASE-TTT: Evidence-Aligned Selective Test-Time Training for Long-Context Question Answering Understanding Layer Significance in LLM Alignment

Reference 20

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T17:17:15.078201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 28625c0c-f488-4a38-8947-6aa9ae5d6a56 · inbound

Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training cites this paper.

Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training Understanding Layer Significance in LLM Alignment

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T14:57:03.898375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8e9ae075-ee0c-4bb8-b5e5-a9566785ca00 · inbound

Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training cites this paper.

Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training Understanding Layer Significance in LLM Alignment

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T21:38:58.239487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.