Paper Citation Record · LEDGER

Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2205.10770.

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

pith.paper-citation-record.v1
2205.10770 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 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 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:46:00.286306Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T21:10:09.085378Z

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 31c38f91-e3c3-4c57-b65a-c55fb0f06e40 · inbound

Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling cites this paper.

Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models

Reference 86

Resolution: verified exact; arxiv_id, observed 2026-05-15T17:45:17.841955Z

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 64850f52-a768-4d0b-a176-271db1db500e · inbound

Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection cites this paper.

Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models

Reference 67

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T14:15:11.146587Z

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 b2d255f2-c235-4029-bef4-26e22dbaf3b7 · inbound

DUET: Optimizing Training Data Mixtures via Feedback from Unseen Evaluation Tasks cites this paper.

DUET: Optimizing Training Data Mixtures via Feedback from Unseen Evaluation Tasks Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-23T04:02:30.434055Z

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 204a5eed-85f0-45d8-bd61-07358d2fd01f · inbound

What is the role of memorization in Continual Learning? cites this paper.

What is the role of memorization in Continual Learning? Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-07T14:46:00.286306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d414a4b0-7877-4fb0-bf7d-2931d92c6792 · inbound

Too Big to Think: Capacity, Memorization, and Generalization in Pre-Trained Transformers cites this paper.

Too Big to Think: Capacity, Memorization, and Generalization in Pre-Trained Transformers Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T05:04:55.951254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f97a63e-318d-4b68-9e8e-374f30348d9f · inbound

Thinking About Thinking: SAGE-nano's Inverse Reasoning for Self-Aware Language Models cites this paper.

Thinking About Thinking: SAGE-nano's Inverse Reasoning for Self-Aware Language Models Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-06T21:38:17.730528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 488762f6-2ac2-4154-b732-c89feb21cd82 · inbound

Asking Back: Interaction-Layer Antidistillation Watermarks cites this paper.

Asking Back: Interaction-Layer Antidistillation Watermarks Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T18:13:37.509908Z

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 c676786b-ef17-461c-bc6b-d5e8b33765b8 · inbound

NumLeak: Public Numeric Benchmarks as Latent Labels in Foundation Models cites this paper.

NumLeak: Public Numeric Benchmarks as Latent Labels in Foundation Models Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models

Reference 4

Resolution: malformed identifier; arxiv_id, observed 2026-06-29T09:03:15.626624Z

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 5716ce69-64f9-4f27-b5aa-ab11fd722b2c · inbound

Weibull Weight-Scale Parameter Evolution under AdamW Training Dynamics cites this paper.

Weibull Weight-Scale Parameter Evolution under AdamW Training Dynamics Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-07-03T13:48:20.867982Z

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 c40e93b4-583d-4095-8584-93ab37c43f26 · inbound

Natural Ungrokking: Asymmetric Control of Which Rules Survive Pretraining cites this paper.

Natural Ungrokking: Asymmetric Control of Which Rules Survive Pretraining Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-07-04T21:10:09.086907Z

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.