Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
How Do Large Language Monkeys Get Their Power (Laws)?
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2502.17578.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T00:31:56.210081Z
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
0
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 ac143d01-b5cc-471d-87fb-70751c3cedd8 · inbound
Min-p, Max Exaggeration: A Critical Analysis of Min-p Sampling in Language Models cites this paper.
Min-p, Max Exaggeration: A Critical Analysis of Min-p Sampling in Language Models How Do Large Language Monkeys Get Their Power (Laws)?
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T00:31:56.210081Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c4d4a8e2-f39f-487d-8481-bd0bef63980b · inbound
Position: Machine Learning Conferences Should Establish a "Refutations and Critiques" Track cites this paper.
Position: Machine Learning Conferences Should Establish a "Refutations and Critiques" Track How Do Large Language Monkeys Get Their Power (Laws)?
Reference 84
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:13:20.708466Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c202173f-6b10-4037-a45a-dd386d86f6d4 · inbound
Don't Pass@k: A Bayesian Framework for Large Language Model Evaluation cites this paper.
Don't Pass@k: A Bayesian Framework for Large Language Model Evaluation How Do Large Language Monkeys Get Their Power (Laws)?
Reference 65
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T10:06:13.869059Z
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 b83a271b-ea82-4629-8ac5-c33dacdc6dc5 · inbound
Probabilistic Programs of Thought cites this paper.
Probabilistic Programs of Thought How Do Large Language Monkeys Get Their Power (Laws)?
Reference 53
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-10T05:51:10.208956Z
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 7a8edaae-f8a0-4769-a733-43c4fb1929bf · inbound
Characterizing Model-Native Skills cites this paper.
Characterizing Model-Native Skills How Do Large Language Monkeys Get Their Power (Laws)?
Reference 93
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-10T06:06:19.553634Z
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 5ade06ac-bc2f-4ecb-8261-8ce65db10d6a · inbound
Stories in Space: In-Context Learning Trajectories in Conceptual Belief Space cites this paper.
Stories in Space: In-Context Learning Trajectories in Conceptual Belief Space How Do Large Language Monkeys Get Their Power (Laws)?
Reference 97
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-13T05:22:18.875764Z
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 80680253-3a04-4778-a1eb-4c4cd29eeaa2 · inbound
An Asymptotic Theory of Chain-of-Thought in In-Context Learning cites this paper.
An Asymptotic Theory of Chain-of-Thought in In-Context Learning How Do Large Language Monkeys Get Their Power (Laws)?
Reference 18
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T05:16:38.973242Z
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 6951b9b8-b898-4975-9f78-d6382194754d · inbound
Item Response Scaling Laws: A Measurement Theory Approach for Efficient and Generalizable Neural Scaling Estimation cites this paper.
Item Response Scaling Laws: A Measurement Theory Approach for Efficient and Generalizable Neural Scaling Estimation How Do Large Language Monkeys Get Their Power (Laws)?
Reference 24
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-28T22:52:45.587470Z
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 ddd0f676-3ad8-4da3-b7e4-4c7f55b744cd · inbound
When More Sampling Hurts: The Modal Ceiling and Correlation Ceiling of Test-Time Scaling cites this paper.
When More Sampling Hurts: The Modal Ceiling and Correlation Ceiling of Test-Time Scaling How Do Large Language Monkeys Get Their Power (Laws)?
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T09:44:37.099865Z
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 55bf7bc1-b5d4-46bc-886b-1901e31a04e2 · inbound
Two AI Metrics Diverged: Will it Make All the Difference? cites this paper.
Two AI Metrics Diverged: Will it Make All the Difference? How Do Large Language Monkeys Get Their Power (Laws)?
Reference 6
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-02T12:36:56.112374Z
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.