Paper Citation Record · LEDGER

Language Model Unalignment: Parametric Red-Teaming to Expose Hidden Harms and Biases

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2310.14303.

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

pith.paper-citation-record.v1
2310.14303 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 2026-08-10T06:31:04.303077+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:24:09.939717Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T23:11:19.202942Z

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 0251516f-c6ac-4eb0-a3e5-3b0d96163af3 · inbound

Open Problems in Machine Unlearning for AI Safety cites this paper.

Open Problems in Machine Unlearning for AI Safety Language Model Unalignment: Parametric Red-Teaming to Expose Hidden Harms and Biases

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T21:24:09.939717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6fcfcd8-651e-4314-9848-00fa399ccd0d · inbound

Model Tampering Attacks Enable More Rigorous Evaluations of LLM Capabilities cites this paper.

Model Tampering Attacks Enable More Rigorous Evaluations of LLM Capabilities Language Model Unalignment: Parametric Red-Teaming to Expose Hidden Harms and Biases

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-09T14:47:15.034861Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88135b24-5ebd-47bb-8d64-bb2b0a9d578d · inbound

Safety Drift After Fine-Tuning: Evidence from High-Stakes Domains cites this paper.

Safety Drift After Fine-Tuning: Evidence from High-Stakes Domains Language Model Unalignment: Parametric Red-Teaming to Expose Hidden Harms and Biases

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:11:19.208310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d84647d7-2c0c-4410-bd72-802a3e858c4b · inbound

On-Policy Distillation for LLM Safety: A Routing Approach to Template-Robust Realignment cites this paper.

On-Policy Distillation for LLM Safety: A Routing Approach to Template-Robust Realignment Language Model Unalignment: Parametric Red-Teaming to Expose Hidden Harms and Biases

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-30T11:44:36.344752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.