Paper Citation Record · LEDGER

Rethinking Conventional Wisdom in Machine Learning: From Generalization to Scaling

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2409.15156.

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

pith.paper-citation-record.v1
2409.15156 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T23:37:30.217739Z

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 757969a4-aaae-4192-9c69-763805ecd373 · inbound

Optimizers for Stabilizing Likelihood-free Inference cites this paper.

Optimizers for Stabilizing Likelihood-free Inference Rethinking Conventional Wisdom in Machine Learning: From Generalization to Scaling

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-09T23:37:30.217739Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63c08d1b-7f65-464c-9cc5-dde6561344ff · inbound

The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training cites this paper.

The Surprising Agreement Between Convex Optimization Theory and Learning-Rate Scheduling for Large Model Training Rethinking Conventional Wisdom in Machine Learning: From Generalization to Scaling

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-09T21:56:50.797601Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation daf49d49-f471-46bf-8352-787c0b0f9991 · inbound

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks cites this paper.

Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks Rethinking Conventional Wisdom in Machine Learning: From Generalization to Scaling

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-06T20:48:55.089882Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24be7fbe-5afa-4084-82cf-c93fe5e8a55a · inbound

Proximal Supervised Fine-Tuning cites this paper.

Proximal Supervised Fine-Tuning Rethinking Conventional Wisdom in Machine Learning: From Generalization to Scaling

Reference 24

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T20:42:50.938439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 527004a7-5077-456c-b76c-e82b561cccf9 · inbound

Hybrid Policy Distillation for LLMs cites this paper.

Hybrid Policy Distillation for LLMs Rethinking Conventional Wisdom in Machine Learning: From Generalization to Scaling

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-10T00:54:49.094867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ce67e99d-b37c-4c07-87fd-1c75f94b1a39 · inbound

Optimistic Dual Averaging Unifies Modern Optimizers cites this paper.

Optimistic Dual Averaging Unifies Modern Optimizers Rethinking Conventional Wisdom in Machine Learning: From Generalization to Scaling

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T05:52:21.820665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b377593c-9c5e-46a3-b080-5196e00c8f74 · inbound

Scale-Invariant Neural Network Optimization: Norm Geometry and Heavy-Tailed Noise cites this paper.

Scale-Invariant Neural Network Optimization: Norm Geometry and Heavy-Tailed Noise Rethinking Conventional Wisdom in Machine Learning: From Generalization to Scaling

Reference 133

Resolution: verified exact; arxiv_id, observed 2026-05-20T08:53:24.521254Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 010fdcdb-907d-4be6-867d-88901900660e · inbound

Scale-Invariant Neural Network Optimization: Norm Geometry and Heavy-Tailed Noise cites this paper.

Scale-Invariant Neural Network Optimization: Norm Geometry and Heavy-Tailed Noise Rethinking Conventional Wisdom in Machine Learning: From Generalization to Scaling

Reference 124

Resolution: verified exact; arxiv_id, observed 2026-07-01T14:55:48.592221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4db49b23-7097-4ab0-bdba-ff97cab57064 · inbound

Entropy-KL Divergence-based Token Masking: A Novel Approach for Selective Fine-tuning of Large Language Models cites this paper.

Entropy-KL Divergence-based Token Masking: A Novel Approach for Selective Fine-tuning of Large Language Models Rethinking Conventional Wisdom in Machine Learning: From Generalization to Scaling

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-06-29T08:03:13.498564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.