Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Refusal Tokens: A Simple Way to Calibrate Refusals in Large Language Models
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2412.06748.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T19:21:19.137233Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-18T23:31:54.505691Z
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 97659b8a-d5bd-486d-83fd-10fb7c270555 · inbound
Forbidden Science: Dual-Use AI Challenge Benchmark and Scientific Refusal Tests cites this paper.
Forbidden Science: Dual-Use AI Challenge Benchmark and Scientific Refusal Tests Refusal Tokens: A Simple Way to Calibrate Refusals in Large Language Models
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T19:21:19.137233Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c01ad35f-d372-4f9f-b395-75534335cdf1 · inbound
Linearly Decoding Refused Knowledge in Aligned Language Models cites this paper.
Linearly Decoding Refused Knowledge in Aligned Language Models Refusal Tokens: A Simple Way to Calibrate Refusals in Large Language Models
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T21:27:34.924646Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e7fea9ee-863f-40a8-a950-93d49e6e37b4 · inbound
ORFuzz: Fuzzing the "Other Side" of LLM Safety -- Testing Over-Refusal cites this paper.
ORFuzz: Fuzzing the "Other Side" of LLM Safety -- Testing Over-Refusal Refusal Tokens: A Simple Way to Calibrate Refusals in Large Language Models
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T23:31:54.511134Z
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 a161f0dc-f6f2-4fcf-8c5e-44d44cfe8f82 · inbound
Turning the Spell Around: Lightweight Alignment Amplification via Rank-One Safety Injection cites this paper.
Turning the Spell Around: Lightweight Alignment Amplification via Rank-One Safety Injection Refusal Tokens: A Simple Way to Calibrate Refusals in Large Language Models
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T14:57:12.836202Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6415b973-faae-41ad-87e8-f5a3dc7d50e5 · inbound
BAS: A Decision-Theoretic Approach to Evaluating Large Language Model Confidence cites this paper.
BAS: A Decision-Theoretic Approach to Evaluating Large Language Model Confidence Refusal Tokens: A Simple Way to Calibrate Refusals in Large Language Models
Reference 24
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T19:53:11.931957Z
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.