Paper Citation Record · LEDGER

Observational Scaling Laws and the Predictability of Language Model Performance

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 27 inbound Pith citation observations for arXiv:2405.10938.

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

pith.paper-citation-record.v1
2405.10938 v3

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 2026-08-13T06:32:02.005865+00:00

measured 27 of 27 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:41:46.173180Z

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

2
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 fc45107e-6534-48a5-8971-9e534aa10c14 · inbound

Predicting Emergent Capabilities by Finetuning cites this paper.

Predicting Emergent Capabilities by Finetuning Observational Scaling Laws and the Predictability of Language Model Performance

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-12T13:41:46.173180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2969d4bf-fb2b-480c-aab9-9f3cccf6ecd0 · inbound

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models cites this paper.

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models Observational Scaling Laws and the Predictability of Language Model Performance

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-11T23:15:28.695929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41312c47-03c6-4569-b53a-064d0ea16056 · inbound

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models cites this paper.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Observational Scaling Laws and the Predictability of Language Model Performance

Reference 165

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.953121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c5e1e07-084b-49f6-b0ee-7d213c6fd937 · inbound

Optimizing Pretraining Data Mixtures with LLM-Estimated Utility cites this paper.

Optimizing Pretraining Data Mixtures with LLM-Estimated Utility Observational Scaling Laws and the Predictability of Language Model Performance

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-10T18:00:05.044733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c3cca9e-2f10-4978-9f50-28b1c2445d49 · inbound

Improving LLM Leaderboards with Psychometrical Methodology cites this paper.

Improving LLM Leaderboards with Psychometrical Methodology Observational Scaling Laws and the Predictability of Language Model Performance

Reference 306

Resolution: unresolved; no resolver link, observed 2026-08-10T12:49:36.935178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72478f8f-797d-4a77-8054-4166b6a683fe · inbound

Scaling Inference-Efficient Language Models cites this paper.

Scaling Inference-Efficient Language Models Observational Scaling Laws and the Predictability of Language Model Performance

Reference 2011

Resolution: unresolved; no resolver link, observed 2026-08-10T00:43:29.116548Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4b300bb-5e98-47f1-a0a1-b36ddc1f8eaf · inbound

Mordal: Automated Pretrained Model Selection for Vision Language Models cites this paper.

Mordal: Automated Pretrained Model Selection for Vision Language Models Observational Scaling Laws and the Predictability of Language Model Performance

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-09T19:46:19.942383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a73e08cc-4e91-46ae-b558-2ddcd579cfa3 · inbound

A Frontier AI Risk Management Framework: Bridging the Gap Between Current AI Practices and Established Risk Management cites this paper.

A Frontier AI Risk Management Framework: Bridging the Gap Between Current AI Practices and Established Risk Management Observational Scaling Laws and the Predictability of Language Model Performance

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-08T14:48:44.790274Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02581296-63ea-4e73-b75e-a5660839a9fe · inbound

MixLLM: Dynamic Routing in Mixed Large Language Models cites this paper.

MixLLM: Dynamic Routing in Mixed Large Language Models Observational Scaling Laws and the Predictability of Language Model Performance

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-08T18:11:31.750552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7e8a351-e14b-424d-a026-b4ecd801d074 · inbound

NegVQA: Can Vision Language Models Understand Negation? cites this paper.

NegVQA: Can Vision Language Models Understand Negation? Observational Scaling Laws and the Predictability of Language Model Performance

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T13:01:13.794242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 038e1472-3bcc-4b24-8e14-2ca2d85834fe · inbound

Cross-Task Experiential Learning on LLM-based Multi-Agent Collaboration cites this paper.

Cross-Task Experiential Learning on LLM-based Multi-Agent Collaboration Observational Scaling Laws and the Predictability of Language Model Performance

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T12:59:57.701494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8cb38d0a-01a8-4a6d-b405-c3a0a16d7c4d · inbound

Benchmarking Misuse Mitigation Against Covert Adversaries cites this paper.

Benchmarking Misuse Mitigation Against Covert Adversaries Observational Scaling Laws and the Predictability of Language Model Performance

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-19T10:32:14.430814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b515bf99-dcf7-4650-b11c-3290558b5a87 · inbound

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

How Benchmark Prediction from Fewer Data Misses the Mark Observational Scaling Laws and the Predictability of Language Model Performance

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T05:34:26.960631Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59ef818c-4067-446c-9518-4fc5eda21d87 · inbound

Predictable Scale: Part II, Farseer: A Refined Scaling Law in Large Language Models cites this paper.

Predictable Scale: Part II, Farseer: A Refined Scaling Law in Large Language Models Observational Scaling Laws and the Predictability of Language Model Performance

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T04:21:29.389103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c005dc66-f098-46ca-9697-9dbc9d6651ed · 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 Observational Scaling Laws and the Predictability of Language Model Performance

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T00:42:01.938683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55d90686-d264-4dcc-acd3-a88ee28203c9 · 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 Observational Scaling Laws and the Predictability of Language Model Performance

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T05:07:39.748185Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f1e7fc3-938a-4fa4-b1ee-72bbcba4edff · inbound

Position: Stop Evaluating AI with Human Tests, Develop Principled, AI-specific Tests instead cites this paper.

Position: Stop Evaluating AI with Human Tests, Develop Principled, AI-specific Tests instead Observational Scaling Laws and the Predictability of Language Model Performance

Reference 63

Resolution: verified exact; arxiv_id, observed 2026-05-19T02:12:55.711088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b9720cbe-1dcf-4070-8683-8df9cd1a62cd · inbound

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

The Art of Scaling Reinforcement Learning Compute for LLMs Observational Scaling Laws and the Predictability of Language Model Performance

Reference 17

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T16:29:14.024807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f272d83c-a6b2-4c07-b2f3-06e2045b63b0 · inbound

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs cites this paper.

Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs Observational Scaling Laws and the Predictability of Language Model Performance

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-18T05:30:55.131188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0502d724-7def-45db-9761-47b555c9a5c4 · inbound

Rethinking Expert Trajectory Utilization in LLM Post-training for Mathematical Reasoning cites this paper.

Rethinking Expert Trajectory Utilization in LLM Post-training for Mathematical Reasoning Observational Scaling Laws and the Predictability of Language Model Performance

Reference 33

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T22:43:37.882617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation aa3f7bda-5649-45d8-b746-93a03b0e1ca8 · inbound

Query-efficient model evaluation using cached responses cites this paper.

Query-efficient model evaluation using cached responses Observational Scaling Laws and the Predictability of Language Model Performance

Reference 135

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:56:00.183166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d72e7980-5f28-434e-b71f-1ae3d651c4f3 · inbound

When Mean CE Fails: Median CE Can Better Track Language Model Quality cites this paper.

When Mean CE Fails: Median CE Can Better Track Language Model Quality Observational Scaling Laws and the Predictability of Language Model Performance

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-06-30T13:24:39.964653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 73923a6f-b987-4aaa-a8fa-634b708733c1 · inbound

Comprehensive AI governance requires addressing non-model gains cites this paper.

Comprehensive AI governance requires addressing non-model gains Observational Scaling Laws and the Predictability of Language Model Performance

Reference 80

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T08:15:31.697210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5e99705e-e139-42fe-8ec7-b016308adb5e · inbound

Predicting Inference-Time Scaling Gains from Labeled Validation-Set Output Statistics cites this paper.

Predicting Inference-Time Scaling Gains from Labeled Validation-Set Output Statistics Observational Scaling Laws and the Predictability of Language Model Performance

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T02:16:26.628718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f3bbd12a-ec91-4382-abca-3dd5dae085c6 · 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 Observational Scaling Laws and the Predictability of Language Model Performance

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T22:52:45.597116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 3e49b294-79ca-4791-82c8-e05c21dc5edc · inbound

Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale cites this paper.

Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale Observational Scaling Laws and the Predictability of Language Model Performance

Reference 67

Resolution: verified exact; arxiv_id, observed 2026-07-02T22:17:25.638413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c7c6e6ae-4d1e-41ba-9124-1a301b67e354 · inbound

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

EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments Observational Scaling Laws and the Predictability of Language Model Performance

Reference 61

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.