Paper Citation Record · LEDGER

Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 27 inbound Pith citation observations for arXiv:2404.05405.

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

pith.paper-citation-record.v1
2404.05405 v1

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measured 27 of 27 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

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Pith citing papers

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Paying Attention to Facts: Quantifying the Knowledge Capacity of Attention Layers cites this paper.

Paying Attention to Facts: Quantifying the Knowledge Capacity of Attention Layers Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

Reference 2

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Do we really have to filter out random noise in pre-training data for language models? cites this paper.

Do we really have to filter out random noise in pre-training data for language models? Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

Reference 67

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Enhancing LLMs via High-Knowledge Data Selection cites this paper.

Enhancing LLMs via High-Knowledge Data Selection Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

Reference 4

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Benchmarking and Rethinking Knowledge Editing for Large Language Models cites this paper.

Benchmarking and Rethinking Knowledge Editing for Large Language Models Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

Reference 41

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Who Reasons in the Large Language Models? cites this paper.

Who Reasons in the Large Language Models? Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

Reference 3

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How much do language models memorize? cites this paper.

How much do language models memorize? Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

Reference 1

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Is Random Attention Sufficient for Sequence Modeling? Disentangling Trainable Components in the Transformer cites this paper.

Is Random Attention Sufficient for Sequence Modeling? Disentangling Trainable Components in the Transformer Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

Reference 1

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Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research cites this paper.

Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

Reference 68

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LRM-1B: Towards Large Routing Model cites this paper.

LRM-1B: Towards Large Routing Model Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

Reference 1

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Beyond URLs: Metadata Diversity and Position for Efficient LLM Pretraining cites this paper.

Beyond URLs: Metadata Diversity and Position for Efficient LLM Pretraining Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

Reference 3

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Observation 2f4a7128-fe57-4182-bffd-7867cd67e99d · inbound

Physics of Language Models: Part 4.1, Architecture Design and the Magic of Canon Layers cites this paper.

Physics of Language Models: Part 4.1, Architecture Design and the Magic of Canon Layers Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

Reference 2025

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Unifying Learning Dynamics and Generalization in Transformers Scaling Law cites this paper.

Unifying Learning Dynamics and Generalization in Transformers Scaling Law Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

Reference 9

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Sharp Capacity Scaling of Spectral Optimizers in Learning Associative Memory cites this paper.

Sharp Capacity Scaling of Spectral Optimizers in Learning Associative Memory Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

Reference 1

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Cram Less to Fit More: Training Data Pruning Improves Memorization of Facts cites this paper.

Cram Less to Fit More: Training Data Pruning Improves Memorization of Facts Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

Reference 2

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Observation c8b17a7a-e22b-4e42-bde3-aab89807ec82 · inbound

Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval cites this paper.

Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

Reference 37

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The Statistical Cost of Adaptation in Multi-Source Transfer Learning cites this paper.

The Statistical Cost of Adaptation in Multi-Source Transfer Learning Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

Reference 149

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Observation 6c2c10a4-5353-46a6-9997-5f52857e7eb0 · inbound

Model Capacity Determines Grokking through Competing Memorisation and Generalisation Speeds cites this paper.

Model Capacity Determines Grokking through Competing Memorisation and Generalisation Speeds Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

Reference 22

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Synthetic Pre-Pre-Training Improves Language Model Robustness to Noisy Pre-Training Data cites this paper.

Synthetic Pre-Pre-Training Improves Language Model Robustness to Noisy Pre-Training Data Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

Reference 52

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Geometric Factual Recall in Transformers cites this paper.

Geometric Factual Recall in Transformers Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

Reference 18

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Predictable Confabulations: Factual Recall by LLMs Scales with Model Size and Topic Frequency cites this paper.

Predictable Confabulations: Factual Recall by LLMs Scales with Model Size and Topic Frequency Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

Reference 4

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Forgetting in Language Models: Capacity, Optimization, and Self-Generated Replay cites this paper.

Forgetting in Language Models: Capacity, Optimization, and Self-Generated Replay Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

Reference 19

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Revisiting Parameter-Based Knowledge Editing in Large Language Models: Theoretical Limits and Empirical Evidence cites this paper.

Revisiting Parameter-Based Knowledge Editing in Large Language Models: Theoretical Limits and Empirical Evidence Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

Reference 2

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Disentangling Visual and Factual Correctness in LVLMs' Visualization Literacy cites this paper.

Disentangling Visual and Factual Correctness in LVLMs' Visualization Literacy Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

Reference 60

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Why Muon Outperforms Adam: A Curvature Perspective cites this paper.

Why Muon Outperforms Adam: A Curvature Perspective Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

Reference 65

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User as Engram: Internalizing Per-User Memory as Local Parametric Edits cites this paper.

User as Engram: Internalizing Per-User Memory as Local Parametric Edits Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

Reference 81

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Local Redundancy: An Information-Theoretic Measure of Plasticity from Synthetic Memorization cites this paper.

Local Redundancy: An Information-Theoretic Measure of Plasticity from Synthetic Memorization Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

Reference 4

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Data Quality over Capacity: Internalizing Documents into LoRA Adapters for Closed-Book QA cites this paper.

Data Quality over Capacity: Internalizing Documents into LoRA Adapters for Closed-Book QA Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

Reference 2

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