Paper Citation Record · LEDGER

Large Language Models can Strategically Deceive their Users when Put Under Pressure

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 27 inbound Pith citation observations for arXiv:2311.07590.

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

pith.paper-citation-record.v1
2311.07590 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 27 of 27 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:22:55.726899Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

8
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 258c51e0-0cde-410c-856d-fbd2bea53538 · inbound

Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models cites this paper.

Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models Large Language Models can Strategically Deceive their Users when Put Under Pressure

Reference 95

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T14:43:30.125082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c7e9aa0c-8330-4303-b9b0-0b707b82d616 · inbound

When Ethics and Payoffs Diverge: LLM Agents in Morally Charged Social Dilemmas cites this paper.

When Ethics and Payoffs Diverge: LLM Agents in Morally Charged Social Dilemmas Large Language Models can Strategically Deceive their Users when Put Under Pressure

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T14:22:55.726899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 153780e5-c91b-40b6-b0eb-ced2a4c85ef3 · inbound

Evaluating LLM Agent Collusion in Double Auctions cites this paper.

Evaluating LLM Agent Collusion in Double Auctions Large Language Models can Strategically Deceive their Users when Put Under Pressure

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T20:57:42.045271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5f00efd-22de-45db-ba5d-f6f9a788f17e · inbound

Lessons from a Chimp: AI "Scheming" and the Quest for Ape Language cites this paper.

Lessons from a Chimp: AI "Scheming" and the Quest for Ape Language Large Language Models can Strategically Deceive their Users when Put Under Pressure

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T20:18:15.488840Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4c3e8fe-ec3d-453a-92ae-ebb4f5b34a26 · inbound

AI, Humans, and Data Science: Optimizing Roles Across Workflows and the Workforce cites this paper.

AI, Humans, and Data Science: Optimizing Roles Across Workflows and the Workforce Large Language Models can Strategically Deceive their Users when Put Under Pressure

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T17:11:24.169940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cbe3c3ce-fde7-47b6-9ef2-b51c5f46b7d1 · inbound

Manipulation Attacks by Misaligned AI: Risk Analysis and Safety Case Framework cites this paper.

Manipulation Attacks by Misaligned AI: Risk Analysis and Safety Case Framework Large Language Models can Strategically Deceive their Users when Put Under Pressure

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T16:39:37.499516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e28914da-2d68-4a56-becc-d012126e74f7 · inbound

Quantized but Deceptive? A Multi-Dimensional Truthfulness Evaluation of Quantized LLMs cites this paper.

Quantized but Deceptive? A Multi-Dimensional Truthfulness Evaluation of Quantized LLMs Large Language Models can Strategically Deceive their Users when Put Under Pressure

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-05T15:52:45.025680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd7965ac-4315-4b08-84be-145157e74a6a · inbound

Can LLMs Lie? Investigation beyond Hallucination cites this paper.

Can LLMs Lie? Investigation beyond Hallucination Large Language Models can Strategically Deceive their Users when Put Under Pressure

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-05T10:55:31.286599Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce7e60ef-3588-4e16-81d4-b2ab0d3efca8 · inbound

Probabilistic Modeling of Latent Agentic Substructures in Deep Neural Networks cites this paper.

Probabilistic Modeling of Latent Agentic Substructures in Deep Neural Networks Large Language Models can Strategically Deceive their Users when Put Under Pressure

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-18T18:26:43.661233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7ae73266-38ec-4e72-9fd0-ace740a1501b · inbound

The Impact of Off-Policy Training Data on Probe Generalisation cites this paper.

The Impact of Off-Policy Training Data on Probe Generalisation Large Language Models can Strategically Deceive their Users when Put Under Pressure

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-17T20:30:11.706210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 28c302aa-d4c1-448d-96b4-4910c111bfb7 · inbound

DialDefer: A Framework for Detecting and Mitigating LLM Dialogic Deference cites this paper.

DialDefer: A Framework for Detecting and Mitigating LLM Dialogic Deference Large Language Models can Strategically Deceive their Users when Put Under Pressure

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-03T10:15:19.202543Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da29a004-c0db-4bbc-8fe5-4ad871a8cb80 · inbound

Detecting Multi-Agent Collusion Through Multi-Agent Interpretability cites this paper.

Detecting Multi-Agent Collusion Through Multi-Agent Interpretability Large Language Models can Strategically Deceive their Users when Put Under Pressure

Reference 20

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T22:48:22.923160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9504b5f2-4019-46e3-aa0e-6cff7f628a8f · inbound

An Independent Safety Evaluation of Kimi K2.5 cites this paper.

An Independent Safety Evaluation of Kimi K2.5 Large Language Models can Strategically Deceive their Users when Put Under Pressure

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-13T19:43:11.545844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 42ed6504-6f5a-41d3-8012-cad0cc0bd15c · inbound

Readable Minds: Emergent Theory-of-Mind-Like Behavior in LLM Poker Agents cites this paper.

Readable Minds: Emergent Theory-of-Mind-Like Behavior in LLM Poker Agents Large Language Models can Strategically Deceive their Users when Put Under Pressure

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-13T17:08:01.905040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6db92c01-edd2-48e4-8ae6-29d96df40e56 · inbound

Mapping the Exploitation Surface: A 10,000-Trial Taxonomy of What Makes LLM Agents Exploit Vulnerabilities cites this paper.

Mapping the Exploitation Surface: A 10,000-Trial Taxonomy of What Makes LLM Agents Exploit Vulnerabilities Large Language Models can Strategically Deceive their Users when Put Under Pressure

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:05:47.801352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 98b70601-dad7-4443-90a5-3be5b660bf8d · inbound

Scheming in the wild: detecting real-world AI scheming incidents with open-source intelligence cites this paper.

Scheming in the wild: detecting real-world AI scheming incidents with open-source intelligence Large Language Models can Strategically Deceive their Users when Put Under Pressure

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-11T07:16:09.645858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d6da676d-4dd3-415f-8dea-1e9ba3957274 · inbound

Risk Reporting for Developers' Internal AI Model Use cites this paper.

Risk Reporting for Developers' Internal AI Model Use Large Language Models can Strategically Deceive their Users when Put Under Pressure

Reference 40

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T23:16:16.631606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a9a36954-ebe9-4876-8f07-0a031d411a9c · inbound

Measuring Evaluation-Context Divergence in Open-Weight LLMs: A Paired-Prompt Protocol with Pilot Evidence of Alignment-Pipeline-Specific Heterogeneity cites this paper.

Measuring Evaluation-Context Divergence in Open-Weight LLMs: A Paired-Prompt Protocol with Pilot Evidence of Alignment-Pipeline-Specific Heterogeneity Large Language Models can Strategically Deceive their Users when Put Under Pressure

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-08T22:04:17.852934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3905201c-caa6-4fed-ad53-eba735590580 · inbound

Instrumental Choices: Measuring the Propensity of LLM Agents to Pursue Instrumental Behaviors cites this paper.

Instrumental Choices: Measuring the Propensity of LLM Agents to Pursue Instrumental Behaviors Large Language Models can Strategically Deceive their Users when Put Under Pressure

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:16:10.885117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2c946e05-992e-465e-b2a5-7258d81a3a64 · inbound

Do Linear Probes Generalize Better in Persona Coordinates? cites this paper.

Do Linear Probes Generalize Better in Persona Coordinates? Large Language Models can Strategically Deceive their Users when Put Under Pressure

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-05-12T04:51:23.059966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9b5841b1-1c38-4ba5-b0b5-052682188b90 · inbound

Do Linear Probes Generalize Better in Persona Coordinates? cites this paper.

Do Linear Probes Generalize Better in Persona Coordinates? Large Language Models can Strategically Deceive their Users when Put Under Pressure

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-05-19T17:07:40.543789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 706f1617-1c0b-47db-af6d-e769d5f17089 · inbound

Deep Minds and Shallow Probes cites this paper.

Deep Minds and Shallow Probes Large Language Models can Strategically Deceive their Users when Put Under Pressure

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-13T02:22:06.405151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 177d67b0-812c-4397-b1c1-bc3569c1bdd4 · inbound

Tracing Persona Vectors Through LLM Pretraining cites this paper.

Tracing Persona Vectors Through LLM Pretraining Large Language Models can Strategically Deceive their Users when Put Under Pressure

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:29:27.886698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 91f4be7f-c7c6-435e-8358-2a3435f36478 · inbound

Language model agents show in-group trust bias invisible to standard behavioural audits cites this paper.

Language model agents show in-group trust bias invisible to standard behavioural audits Large Language Models can Strategically Deceive their Users when Put Under Pressure

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-06-29T12:23:24.485868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 61a01cfa-fab8-4958-b964-a607b7eabfb6 · inbound

Manipulation Is Task-Dependent: A Multi-Axis, Multi-Environment Evaluation of Frontier LLMs cites this paper.

Manipulation Is Task-Dependent: A Multi-Axis, Multi-Environment Evaluation of Frontier LLMs Large Language Models can Strategically Deceive their Users when Put Under Pressure

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-04T21:00:10.018341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 884097bd-11be-4b0a-a74e-e6133246748f · inbound

Theory of Mind and Persuasion Beyond Conversation: Assessing the Capacity of LLMs to Induce Belief States via Planning and Action cites this paper.

Theory of Mind and Persuasion Beyond Conversation: Assessing the Capacity of LLMs to Induce Belief States via Planning and Action Large Language Models can Strategically Deceive their Users when Put Under Pressure

Reference 64

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T10:15:44.683595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1a57f4aa-f5e2-4147-a80c-66f953f08357 · inbound

The safety failures we are not instrumenting: a perspective on hidden safety-critical challenges in modern AI systems cites this paper.

The safety failures we are not instrumenting: a perspective on hidden safety-critical challenges in modern AI systems Large Language Models can Strategically Deceive their Users when Put Under Pressure

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-01T12:54:36.127746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.