Paper Citation Record · LEDGER

Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive?

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2406.04391.

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

pith.paper-citation-record.v1
2406.04391 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:21:06.804001Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T14:28:31.177053Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
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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 18fc604e-ec9c-4f6e-a60b-1648ce180adf · inbound

Loss-to-Loss Prediction: Scaling Laws for All Datasets cites this paper.

Loss-to-Loss Prediction: Scaling Laws for All Datasets Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive?

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-12T17:07:23.305990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9180e72-1339-4a17-a896-3633447eb38d · inbound

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

Optimizing Pretraining Data Mixtures with LLM-Estimated Utility Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive?

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-10T18:00:05.058416Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7068dbf6-0708-4ce8-8de9-7d697bdceed7 · inbound

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws cites this paper.

LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive?

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:52:27.070448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a993a0dd-8e0d-479b-b909-ab556aaddb16 · 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 Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive?

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-06T23:13:20.792740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc66ec79-ad39-4e2d-b482-8806421cb46a · inbound

Language Models Improve When Pretraining Data Matches Target Tasks cites this paper.

Language Models Improve When Pretraining Data Matches Target Tasks Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive?

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-06T16:53:13.357733Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39f6ecb1-ebb6-4953-90e6-af124b5c14eb · inbound

Signal and Noise: A Framework for Reducing Uncertainty in Language Model Evaluation cites this paper.

Signal and Noise: A Framework for Reducing Uncertainty in Language Model Evaluation Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive?

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-15T17:21:06.804001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6390ebab-ed4f-473e-a292-3860adc6f767 · inbound

Prescriptive Scaling Reveals the Evolution of Language Model Capabilities cites this paper.

Prescriptive Scaling Reveals the Evolution of Language Model Capabilities Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive?

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-02T22:58:14.258071Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ce6be99-3848-4895-87bf-4f2bbdf9e5eb · 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 Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive?

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-06-28T22:52:45.584532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3a26300e-8d91-46ae-82cd-11f15465d745 · inbound

Will Scaling Improve Social Simulation with LLMs? cites this paper.

Will Scaling Improve Social Simulation with LLMs? Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive?

Reference 31

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T14:28:31.178456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fedbdcaf-6e03-433a-8d28-53dd00d9ed21 · inbound

Will Scaling Improve Social Simulation with LLMs? cites this paper.

Will Scaling Improve Social Simulation with LLMs? Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive?

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-02T09:03:29.939712Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39d24ac8-dbef-4fc4-b44b-d4e1109039b5 · inbound

Toward a Theory of Value in AI Alignment cites this paper.

Toward a Theory of Value in AI Alignment Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive?

Reference 232

Resolution: unresolved; no resolver link, observed 2026-08-14T04:13:40.501469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.