Paper Citation Record · LEDGER

To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2502.12202.

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

pith.paper-citation-record.v1
2502.12202 v2

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 2026-08-16T06:30:59.297886+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:34:34.380935Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T21:12:08.271307Z

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 ab320333-5287-4bbb-8fec-fa8be5649af8 · inbound

ShadowCoT: Cognitive Hijacking for Stealthy Reasoning Backdoors in LLMs cites this paper.

ShadowCoT: Cognitive Hijacking for Stealthy Reasoning Backdoors in LLMs To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-22T21:12:08.275008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a9634968-3e05-4280-ad90-0c51b60ff403 · inbound

A Survey of Attacks on Large Language Models cites this paper.

A Survey of Attacks on Large Language Models To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T20:34:34.380935Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d75cb0af-a6c6-46a9-9942-7c510b871d61 · inbound

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey cites this paper.

Towards Concise and Adaptive Thinking in Large Reasoning Models: A Survey To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models

Reference 254

Resolution: unresolved; no resolver link, observed 2026-08-06T17:54:18.145133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4e22911-436d-4517-bfa7-23c32906200f · inbound

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit cites this paper.

BadReasoner: Planting Tunable Overthinking Backdoors into Large Reasoning Models for Fun or Profit To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-15T18:20:49.643167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc2e2508-da71-46fb-b67d-bf035531aceb · inbound

A Comprehensive Survey on Trustworthiness in Reasoning with Large Language Models cites this paper.

A Comprehensive Survey on Trustworthiness in Reasoning with Large Language Models To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models

Reference 164

Resolution: unresolved; no resolver link, observed 2026-08-05T10:39:07.061877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb903e6e-cd52-4538-aeb6-6bb27a9b862f · inbound

Reinforcement Learning for Scalable and Trustworthy Intelligent Systems cites this paper.

Reinforcement Learning for Scalable and Trustworthy Intelligent Systems To Think or Not to Think: Exploring the Unthinking Vulnerability in Large Reasoning Models

Reference 206

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:51:40.355125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.