Paper Citation Record · LEDGER

How predictable is language model benchmark performance?

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 inbound Pith citation observations for arXiv:2401.04757.

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

pith.paper-citation-record.v1
2401.04757 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 15 of 15 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 15 of 15 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:27:44.966255Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T12:36:56.159561Z

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 919639a0-3045-4e79-bedf-518059ad8d24 · inbound

Humanity's Last Exam cites this paper.

Humanity's Last Exam How predictable is language model benchmark performance?

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-10T18:40:50.245598Z

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 f4f514b0-5bae-4d7b-bb64-ab29ea276fb6 · inbound

Seed1.5-VL Technical Report cites this paper.

Seed1.5-VL Technical Report How predictable is language model benchmark performance?

Reference 102

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:26:06.167444Z

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 00a701f6-41b5-4a06-84e4-d6a0ca130aa0 · inbound

Inference Compute-Optimal Video Vision Language Models cites this paper.

Inference Compute-Optimal Video Vision Language Models How predictable is language model benchmark performance?

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T14:27:44.966255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 080a3031-0e61-44d9-a8f4-1d99a863ec85 · inbound

How Benchmark Prediction from Fewer Data Misses the Mark cites this paper.

How Benchmark Prediction from Fewer Data Misses the Mark How predictable is language model benchmark performance?

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T05:34:26.931350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40fd6055-0c1d-4a1c-bcc0-0685509ffc0b · inbound

Capability Salience Vector: Fine-grained Alignment of Loss and Capabilities for Downstream Task Scaling Law cites this paper.

Capability Salience Vector: Fine-grained Alignment of Loss and Capabilities for Downstream Task Scaling Law How predictable is language model benchmark performance?

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T00:42:01.341630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 341e967e-f53c-44d0-b95a-640968e74a98 · inbound

A Theory of Inference Compute Scaling: Reasoning through Directed Stochastic Skill Search cites this paper.

A Theory of Inference Compute Scaling: Reasoning through Directed Stochastic Skill Search How predictable is language model benchmark performance?

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T05:07:39.743953Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 478777c1-efa7-4e92-b886-d3e89bb84549 · inbound

Meek Models Shall Inherit the Earth cites this paper.

Meek Models Shall Inherit the Earth How predictable is language model benchmark performance?

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T18:33:50.399472Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75ebfafe-8d71-4a7a-90c1-c041c162b77d · inbound

The Art of Scaling Reinforcement Learning Compute for LLMs cites this paper.

The Art of Scaling Reinforcement Learning Compute for LLMs How predictable is language model benchmark performance?

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-16T16:29:14.017077Z

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 13129982-9ab4-4f8e-8c18-b0ab26e40f76 · inbound

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices cites this paper.

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices How predictable is language model benchmark performance?

Reference 143

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:36:19.960521Z

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 e49f9683-535a-48c1-a719-6f78dca6b220 · inbound

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices cites this paper.

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices How predictable is language model benchmark performance?

Reference 143

Resolution: verified exact; arxiv_id, observed 2026-05-13T07:32:30.280172Z

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 76e0903b-e60b-42c2-9ba4-cf33c163fb34 · inbound

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices cites this paper.

DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices How predictable is language model benchmark performance?

Reference 143

Resolution: verified exact; arxiv_id, observed 2026-05-21T07:59:50.184287Z

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 11b0c18c-a78e-4f3a-b1b2-c1af85369d78 · inbound

SuperValid: Capability-Aligned OOD Validation for Generalizable Downstream Scaling cites this paper.

SuperValid: Capability-Aligned OOD Validation for Generalizable Downstream Scaling How predictable is language model benchmark performance?

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T13:03:26.411671Z

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 1d678b81-fc30-4ae3-84d9-78050f0ac875 · inbound

Validity Threats for Foundation Model Research cites this paper.

Validity Threats for Foundation Model Research How predictable is language model benchmark performance?

Reference 73

Resolution: verified exact; arxiv_id, observed 2026-07-02T07:36:44.860071Z

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 334fce09-74a2-40df-8928-474a3ff94c37 · 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 predictable is language model benchmark performance?

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-07-02T12:36:56.161018Z

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 55cb6e29-b47e-46a7-911f-8a59565b5b1f · inbound

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments cites this paper.

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments How predictable is language model benchmark performance?

Reference 56

Resolution: unresolved; no resolver link, observed 2026-07-11T07:57:43.000834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.