Paper Citation Record · LEDGER

Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop Experiment

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 17 inbound Pith citation observations for arXiv:2311.08596.

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

pith.paper-citation-record.v1
2311.08596 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T04:43:27.307708Z

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

1
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 fc3d6c4b-c7a7-4956-9545-80b57ffb5c06 · inbound

Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistencies cites this paper.

Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistencies Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop Experiment

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-08T04:43:27.307708Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f1d13927-1dc7-4c22-9040-94661f54fb49 · inbound

LLMs Get Lost In Multi-Turn Conversation cites this paper.

LLMs Get Lost In Multi-Turn Conversation Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop Experiment

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-14T01:11:09.232010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation bc8217e7-49bb-4a50-85c4-b5747c275eff · inbound

B-score: Detecting biases in large language models using response history cites this paper.

B-score: Detecting biases in large language models using response history Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop Experiment

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T14:33:49.174083Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4df5e8a-0f8c-4991-89c9-85255de74185 · inbound

Herd Behavior: Investigating Peer Influence in LLM-based Multi-Agent Systems cites this paper.

Herd Behavior: Investigating Peer Influence in LLM-based Multi-Agent Systems Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop Experiment

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T13:45:25.333973Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e87feb2-7423-4e88-8b1b-29f2c3fdc83d · inbound

BASIL: Bayesian Assessment of Sycophancy in LLMs cites this paper.

BASIL: Bayesian Assessment of Sycophancy in LLMs Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop Experiment

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T21:56:52.000793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5c16d096-67af-4732-89ab-b2a341a18c89 · inbound

User Detection and Response Patterns of Sycophantic Behavior in Conversational AI cites this paper.

User Detection and Response Patterns of Sycophantic Behavior in Conversational AI Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop Experiment

Reference 36

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T14:07:58.764297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5558a29a-fe74-4a63-9907-b2d985e4f019 · inbound

SycoEval-EM: Sycophancy Evaluation of Large Language Models in Simulated Clinical Encounters for Emergency Care cites this paper.

SycoEval-EM: Sycophancy Evaluation of Large Language Models in Simulated Clinical Encounters for Emergency Care Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop Experiment

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T08:39:30.412601Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c482b472-01bd-4493-ab47-f2ca510ea8c5 · inbound

Too Polite to Disagree: Understanding Sycophancy Propagation in Multi-Agent Systems cites this paper.

Too Polite to Disagree: Understanding Sycophancy Propagation in Multi-Agent Systems Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop Experiment

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T20:13:13.995294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 18e89a7e-ef12-4a18-9363-609cf5f14d5c · inbound

Too Polite to Disagree: Understanding Sycophancy Propagation in Multi-Agent Systems cites this paper.

Too Polite to Disagree: Understanding Sycophancy Propagation in Multi-Agent Systems Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop Experiment

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-02T16:57:39.838460Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ae3a3d9-84c2-4bd2-a46d-96905dd72ccb · inbound

Compared to What? Baselines and Metrics for Counterfactual Prompting cites this paper.

Compared to What? Baselines and Metrics for Counterfactual Prompting Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop Experiment

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-05-09T19:05:10.414046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ef697d0d-7b1c-4878-80d9-aa4b018aad85 · inbound

Towards Emotion Consistency Analysis of Large Language Models in Emotional Conversational Contexts cites this paper.

Towards Emotion Consistency Analysis of Large Language Models in Emotional Conversational Contexts Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop Experiment

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T20:06:12.899263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 535b8f9a-eaf0-4bfe-971f-a1c5c592ab12 · inbound

How LLMs Are Persuaded: A Few Attention Heads, Rerouted cites this paper.

How LLMs Are Persuaded: A Few Attention Heads, Rerouted Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop Experiment

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T06:26:24.301302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1c1c0c47-da5b-4c5b-a3e6-a4f793afc945 · inbound

What Counts as AI Sycophancy? A Taxonomy and Expert Survey of a Fragmented Construct cites this paper.

What Counts as AI Sycophancy? A Taxonomy and Expert Survey of a Fragmented Construct Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop Experiment

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-22T08:51:18.405646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 902fba5c-3174-42de-b380-d99934bb858c · inbound

PseudoBench: Measuring How Agentic Auto-Research Fuels Pseudoscience cites this paper.

PseudoBench: Measuring How Agentic Auto-Research Fuels Pseudoscience Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop Experiment

Reference 125

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:18:56.449174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4179136a-eaea-4438-9913-19f8746a1566 · inbound

Same Evidence, Different Answer: Auditing Order Sensitivity in Multimodal Large Language Models cites this paper.

Same Evidence, Different Answer: Auditing Order Sensitivity in Multimodal Large Language Models Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop Experiment

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T20:40:07.569477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2a291b7c-4e4b-4143-85b8-bc784d7045f3 · inbound

Most LLM Conformity Needs No Speaker: Measuring the Speaker-Free Floor in Peer-Pressure Benchmarks cites this paper.

Most LLM Conformity Needs No Speaker: Measuring the Speaker-Free Floor in Peer-Pressure Benchmarks Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop Experiment

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-11T06:02:15.946864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11a93628-9b4a-4d3f-ad51-483a63abe15b · inbound

MedPRESS: A Multi-turn Benchmark for Patient-Pressure-Induced Medical Sycophancy in LLMs cites this paper.

MedPRESS: A Multi-turn Benchmark for Patient-Pressure-Induced Medical Sycophancy in LLMs Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop Experiment

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-04T05:39:51.084130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.