Paper Citation Record · LEDGER

Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions

As of 23 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 4 inbound Pith citation observations for arXiv:2502.00620.

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

pith.paper-citation-record.v1
2502.00620 v4

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T18:24:40.315345Z

measured 59 of 59 standing notices

One-hop event checks from named stored sources.

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measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:17:48.315561Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T06:40:24.869852Z

Reference resolution

55 of 55 outbound references displayed

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External citation measurements

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Outbound references

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This paper cites write newline.

Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions write newline

Reference 1

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This paper cites Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning.

Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning

Reference 2

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This paper cites Linear algebraic structure of word senses, with applications to polysemy.

Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Linear algebraic structure of word senses, with applications to polysemy

Reference 3

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This paper cites Qwen Technical Report.

Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Qwen Technical Report

Reference 4

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This paper cites L., Long, P.

Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions L., Long, P

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions H., and Vaucher, A

Reference 6

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This paper cites Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision.

Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision

Reference 7

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This paper cites Quantifying the Gain in Weak-to-Strong Generalization.

Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Quantifying the Gain in Weak-to-Strong Generalization

Reference 8

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Chembench: The molecule benchmarks and molmapnet datasets, September 2020

Reference 9

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions The componentwise distance to the nearest singular matrix

Reference 10

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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This paper cites Inversion error, condition number, and approximate inverses of uncertain matrices.

Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Inversion error, condition number, and approximate inverses of uncertain matrices

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This paper cites Molecular representation learning with language models and domain-relevant auxiliary tasks.

Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Molecular representation learning with language models and domain-relevant auxiliary tasks

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Sparse coding in the primate cortex

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions S., and Bartlett, P

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This paper cites Aligning AI With Shared Human Values.

Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Aligning AI With Shared Human Values

Reference 16

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This paper cites Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning.

Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning

Reference 17

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This paper cites The Low-Rank Simplicity Bias in Deep Networks.

Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions The Low-Rank Simplicity Bias in Deep Networks

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Neural tangent kernel: Convergence and generalization in neural networks

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Implicit regularization of random feature models

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions The power of contrast for feature learning: A theoretical analysis

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Sgd on neural networks learns functions of increasing complexity

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Adam: A Method for Stochastic Optimization

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Theoretical Analysis of Weak-to-Strong Generalization

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Sparse modeling for image and vision processing

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions A kernel-based view of language model fine-tuning

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Benign, tempered, or catastrophic: Toward a refined taxonomy of overfitting

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions MTEB: Massive Text Embedding Benchmark

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Classification vs regression in overparameterized regimes: Does the loss function matter? Journal of Machine Learning Research, 22 0 (222): 0 1--69, 2021

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions I., Deng, Z., Ji, W., Zou, J., and Zhang, L

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Emergent Linear Representations in World Models of Self-Supervised Sequence Models

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Convolutional neural networks analyzed via convolutional sparse coding

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Multi-scale feature learning dynamics: Insights for double descent

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Data augmentation as feature manipulation

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Weak-to-Strong Generalization Through the Data-Centric Lens

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions A transfer learning framework for weak-to-strong generalization

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions High-dimensional probability: An introduction with applications in data science, volume 47

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Benign overfitting in multiclass classification: All roads lead to interpolation

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Crowdsourcing Multiple Choice Science Questions

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions and Li, Y

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Provable Weak-to-Strong Generalization via Benign Overfitting

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions N., Gomes, J., Geniesse, C., Pappu, A

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Which features are learnt by contrastive learning? on the role of simplicity bias in class collapse and feature suppression

Reference 50

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Linear spatial pyramid matching using sparse coding for image classification

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Understanding deep learning (still) requires rethinking generalization

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Character-level convolutional networks for text classification

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Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Representation Engineering: A Top-Down Approach to AI Transparency

Reference 54

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This paper cites Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization.

Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions Understanding the Generalization of Adam in Learning Neural Networks with Proper Regularization

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-09T18:24:40.315345Z

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Pith citing papers

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On Weak-to-Strong Generalization and f-Divergence cites this paper.

On Weak-to-Strong Generalization and f-Divergence Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions

Reference 58

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On the Blessing of Pre-training in Weak-to-Strong Generalization cites this paper.

On the Blessing of Pre-training in Weak-to-Strong Generalization Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions

Reference 122

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:36:08.656531Z

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Feature Learning in Linear-Width Two-Layer Networks: Two vs. One Step of Gradient Descent cites this paper.

Feature Learning in Linear-Width Two-Layer Networks: Two vs. One Step of Gradient Descent Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions

Reference 251

Resolution: verified exact; arxiv_id, observed 2026-05-20T01:32:56.037707Z

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Feature Learning in Linear-Width Two-Layer Networks: Two vs. One Step of Gradient Descent cites this paper.

Feature Learning in Linear-Width Two-Layer Networks: Two vs. One Step of Gradient Descent Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions

Reference 251

Resolution: verified exact; arxiv_id, observed 2026-05-25T06:40:24.872274Z

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