Paper Citation Record · LEDGER

Legend: Leveraging Representation Engineering to Annotate Safety Margin for Preference Datasets

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2406.08124.

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

pith.paper-citation-record.v1
2406.08124 v2

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 2026-08-18T06:34:40.430872+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:26:28.135081Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-17T22:45:24.472610Z

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 815b95ed-0252-4225-bb7e-561ec69a019e · inbound

Cross-model Transferability among Large Language Models on the Platonic Representations of Concepts cites this paper.

Cross-model Transferability among Large Language Models on the Platonic Representations of Concepts Legend: Leveraging Representation Engineering to Annotate Safety Margin for Preference Datasets

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T22:38:16.279479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 234a356a-2c90-4361-9b72-3c79d61db402 · inbound

On Almost Surely Safe Alignment of Large Language Models at Inference-Time cites this paper.

On Almost Surely Safe Alignment of Large Language Models at Inference-Time Legend: Leveraging Representation Engineering to Annotate Safety Margin for Preference Datasets

Reference 107

Resolution: unresolved; no resolver link, observed 2026-08-09T16:18:40.998837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9e25239-6e6b-4202-b0ba-8344602acddb · inbound

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation cites this paper.

Icon$^{2}$: Aligning Large Language Models Using Self-Synthetic Preference Data via Inherent Regulation Legend: Leveraging Representation Engineering to Annotate Safety Margin for Preference Datasets

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T16:26:28.135081Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a435af8f-5ea8-40e6-8954-c962ee1b4913 · inbound

Abstract 3D Perception for Spatial Intelligence in Vision-Language Models cites this paper.

Abstract 3D Perception for Spatial Intelligence in Vision-Language Models Legend: Leveraging Representation Engineering to Annotate Safety Margin for Preference Datasets

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-17T22:45:24.475580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.