Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
How Random is Random? Evaluating the Randomness and Humaness of LLMs' Coin Flips
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2406.00092.
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 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T18:23:35.969722Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T12:56:57.224899Z
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 54a2aba9-3d64-470f-a181-b82dd3cd0961 · inbound
Can Hallucinations Help? Boosting LLMs for Drug Discovery cites this paper.
Can Hallucinations Help? Boosting LLMs for Drug Discovery How Random is Random? Evaluating the Randomness and Humaness of LLMs' Coin Flips
Reference 46
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T15:39:26.435868Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6d49536c-a504-44c0-8f7b-e9cde13cdc64 · inbound
Evaluating Binary Decision Biases in Large Language Models: Implications for Fair Agent-Based Financial Simulations cites this paper.
Evaluating Binary Decision Biases in Large Language Models: Implications for Fair Agent-Based Financial Simulations How Random is Random? Evaluating the Randomness and Humaness of LLMs' Coin Flips
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T18:23:35.969722Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bfe699db-0139-4b2d-8495-78866bd4dc75 · inbound
CapBencher: Give Your LLM Benchmark a Built-in Alarm for Test-Set Overfitting cites this paper.
CapBencher: Give Your LLM Benchmark a Built-in Alarm for Test-Set Overfitting How Random is Random? Evaluating the Randomness and Humaness of LLMs' Coin Flips
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:44:34.321674Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ae67e2b6-d109-4b0c-9785-b8766395eea3 · 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 How Random is Random? Evaluating the Randomness and Humaness of LLMs' Coin Flips
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:33:48.892989Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d620471b-6b3e-4f56-8c79-5cc6f03caf19 · inbound
Flipping Against All Odds: Reducing LLM Coin Flip Bias via Verbalized Rejection Sampling cites this paper.
Flipping Against All Odds: Reducing LLM Coin Flip Bias via Verbalized Rejection Sampling How Random is Random? Evaluating the Randomness and Humaness of LLMs' Coin Flips
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T09:27:14.828197Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation d269a2e2-3670-42ea-baac-e9fb0a5e34c5 · inbound
In Search of the Ingredients of Open-Endedness: Replicating Picbreeder with Large Vision-Language Models cites this paper.
In Search of the Ingredients of Open-Endedness: Replicating Picbreeder with Large Vision-Language Models How Random is Random? Evaluating the Randomness and Humaness of LLMs' Coin Flips
Reference 37
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T15:09:20.426018Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f626a6c7-156d-43ee-b5d8-613f86a32f9a · inbound
FLIPS: Instance-Fingerprinting for LLMs via Pseudo-random Sequences cites this paper.
FLIPS: Instance-Fingerprinting for LLMs via Pseudo-random Sequences How Random is Random? Evaluating the Randomness and Humaness of LLMs' Coin Flips
Reference 37
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-02T01:56:27.928520Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 2d40b2f6-998b-4f4f-9d17-3a6b723eeaf2 · inbound
UnpredictaBench: A Benchmark for Evaluating Distributional Randomness in LLMs cites this paper.
UnpredictaBench: A Benchmark for Evaluating Distributional Randomness in LLMs How Random is Random? Evaluating the Randomness and Humaness of LLMs' Coin Flips
Reference 13
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T12:56:57.226416Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.
Observation 7e160766-a204-4e92-b88a-d49ea938f7e5 · inbound
One Token Is Enough: Fingerprinting and Verifying Large Language Models from Single-Token Output Distributions cites this paper.
One Token Is Enough: Fingerprinting and Verifying Large Language Models from Single-Token Output Distributions How Random is Random? Evaluating the Randomness and Humaness of LLMs' Coin Flips
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-07-14T13:10:47.045131Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.