Paper Citation Record · LEDGER

Emergent Linear Representations in World Models of Self-Supervised Sequence Models

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 43 inbound Pith citation observations for arXiv:2309.00941.

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

pith.paper-citation-record.v1
2309.00941 v2

Coverage vector

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measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 43 of 43 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T15:55:23.286662Z

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

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External citation measurements

3
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

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Towards Best Practices of Activation Patching in Language Models: Metrics and Methods cites this paper.

Towards Best Practices of Activation Patching in Language Models: Metrics and Methods Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 105

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Refusal in Language Models Is Mediated by a Single Direction cites this paper.

Refusal in Language Models Is Mediated by a Single Direction Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 164

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Observation bef047a8-055d-43d0-8c47-4e2a9750ed40 · inbound

Too Big to Fool: Resisting Deception in Language Models cites this paper.

Too Big to Fool: Resisting Deception in Language Models Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 36

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LLaVA Steering: Visual Instruction Tuning with 500x Fewer Parameters through Modality Linear Representation-Steering cites this paper.

LLaVA Steering: Visual Instruction Tuning with 500x Fewer Parameters through Modality Linear Representation-Steering Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 43

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Concept Boundary Vectors cites this paper.

Concept Boundary Vectors Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 18

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Towards scientific discovery with dictionary learning: Extracting biological concepts from microscopy foundation models cites this paper.

Towards scientific discovery with dictionary learning: Extracting biological concepts from microscopy foundation models Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 2015

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Observation 694b52da-2449-4a49-85d1-611c2155d860 · inbound

ICLR: In-Context Learning of Representations cites this paper.

ICLR: In-Context Learning of Representations Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 49

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Observation 7387344f-2b53-4931-b2cc-64a8922bc2cf · inbound

Spot Risks Before Speaking! Unraveling Safety Attention Heads in Large Vision-Language Models cites this paper.

Spot Risks Before Speaking! Unraveling Safety Attention Heads in Large Vision-Language Models Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 27

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How Do Artificial Intelligences Think? The Three Mathematico-Cognitive Factors of Categorical Segmentation Operated by Synthetic Neurons cites this paper.

How Do Artificial Intelligences Think? The Three Mathematico-Cognitive Factors of Categorical Segmentation Operated by Synthetic Neurons Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 53

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Low-Rank Adapting Models for Sparse Autoencoders cites this paper.

Low-Rank Adapting Models for Sparse Autoencoders Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 23

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Observation c2d7b254-970a-4ac9-94af-cb0d55c4bae7 · inbound

Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions cites this paper.

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

Reference 34

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Observation 7b55d802-9926-4402-8e5f-7d467a5a5e89 · inbound

When Do Neural Networks Learn World Models? cites this paper.

When Do Neural Networks Learn World Models? Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 63

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The Process of Categorical Clipping at the Core of the Genesis of Concepts in Synthetic Neural Cognition cites this paper.

The Process of Categorical Clipping at the Core of the Genesis of Concepts in Synthetic Neural Cognition Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 91

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The Origins of Representation Manifolds in Large Language Models cites this paper.

The Origins of Representation Manifolds in Large Language Models Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 34

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Observation cc192d4b-c79b-4d19-b930-07014592f85c · inbound

Understanding the learned look-ahead behavior of chess neural networks cites this paper.

Understanding the learned look-ahead behavior of chess neural networks Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 7

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Observation 2e442602-811d-4164-be37-aae9d9a4c077 · inbound

Linear Spatial World Models Emerge in Large Language Models cites this paper.

Linear Spatial World Models Emerge in Large Language Models Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 36

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Observation 740cb4f6-0837-459f-8fa0-a3c2cf3d6896 · inbound

A Statistical Physics of Language Model Reasoning cites this paper.

A Statistical Physics of Language Model Reasoning Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 26

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Observation a388cbc4-65e3-4248-a348-4ebebf3db623 · inbound

Behavioural vs. Representational Systematicity in End-to-End Models: An Opinionated Survey cites this paper.

Behavioural vs. Representational Systematicity in End-to-End Models: An Opinionated Survey Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 65

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Large Language Models and Emergence: A Complex Systems Perspective cites this paper.

Large Language Models and Emergence: A Complex Systems Perspective Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 46

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Model Organisms for Emergent Misalignment cites this paper.

Model Organisms for Emergent Misalignment Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 21

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Convergent Linear Representations of Emergent Misalignment cites this paper.

Convergent Linear Representations of Emergent Misalignment Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 19

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Distinct Computations Emerge From Compositional Curricula in In-Context Learning cites this paper.

Distinct Computations Emerge From Compositional Curricula in In-Context Learning Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 26

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The Geometry of Harmfulness in LLMs through Subconcept Probing cites this paper.

The Geometry of Harmfulness in LLMs through Subconcept Probing Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 18

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A Single Direction of Truth: An Observer Model's Linear Residual Probe Exposes and Steers Contextual Hallucinations cites this paper.

A Single Direction of Truth: An Observer Model's Linear Residual Probe Exposes and Steers Contextual Hallucinations Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 24

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Towards Atoms of Large Language Models cites this paper.

Towards Atoms of Large Language Models Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 23

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Higher Embedding Dimension Creates a Stronger World Model for a Simple Sorting Task cites this paper.

Higher Embedding Dimension Creates a Stronger World Model for a Simple Sorting Task Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 18

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Predicting Where Steering Vectors Succeed cites this paper.

Predicting Where Steering Vectors Succeed Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 8

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Decodable but Not Corrected by Fixed Residual-Stream Linear Steering: Evidence from Medical LLM Failure Regimes cites this paper.

Decodable but Not Corrected by Fixed Residual-Stream Linear Steering: Evidence from Medical LLM Failure Regimes Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 50

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Tool Calling is Linearly Readable and Steerable in Language Models cites this paper.

Tool Calling is Linearly Readable and Steerable in Language Models Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 62

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Tensor Product Representation Probes Reveal Shared Structure Across Linear Directions cites this paper.

Tensor Product Representation Probes Reveal Shared Structure Across Linear Directions Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 18

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Stories in Space: In-Context Learning Trajectories in Conceptual Belief Space cites this paper.

Stories in Space: In-Context Learning Trajectories in Conceptual Belief Space Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 21

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Observation 93d65bcf-d254-4999-8e07-1370d471365f · inbound

Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces cites this paper.

Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 246

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Multi-Turn Neural Transparency: Surfacing Neural Activations Improves User Calibration to LLM Behavioral Drift cites this paper.

Multi-Turn Neural Transparency: Surfacing Neural Activations Improves User Calibration to LLM Behavioral Drift Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 31

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Observation 7525c5af-6a0e-4bc3-9d34-bea755b8333f · inbound

GeoMathCode: Understanding Interleaved Math-Code Reasoning for Geometry Problem Solving cites this paper.

GeoMathCode: Understanding Interleaved Math-Code Reasoning for Geometry Problem Solving Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 32

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Observation 4ccf0d14-90fa-4316-b1d9-7486f15f7dc6 · inbound

A Close Look At World Model Recovery In Supervised Fine-Tuned LLM Planners cites this paper.

A Close Look At World Model Recovery In Supervised Fine-Tuned LLM Planners Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 18

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The New Associationism: Lessons from Deep Learning cites this paper.

The New Associationism: Lessons from Deep Learning Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 89

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Observation 3eeb4439-99ef-4f66-86f2-60f638a08696 · inbound

Investigating The Security of Modern AI and Cloud Infrastructure cites this paper.

Investigating The Security of Modern AI and Cloud Infrastructure Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 102

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Observation fd4f8eb0-375e-4397-aab1-6c786969f6ab · inbound

How are linear representations learned? Exact solutions to the dynamics of abstraction cites this paper.

How are linear representations learned? Exact solutions to the dynamics of abstraction Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 2

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Exposure is not manifestation: measurement target and output resolution jointly determine which behavioural-faithfulness evaluator wins cites this paper.

Exposure is not manifestation: measurement target and output resolution jointly determine which behavioural-faithfulness evaluator wins Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 43

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Inside the Unfair Judge: A Mechanistic Interpretability Account of LLM-as-Judge Bias cites this paper.

Inside the Unfair Judge: A Mechanistic Interpretability Account of LLM-as-Judge Bias Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 55

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When Does Reward Teach State? A Hidden-Automaton Instrument and a Group-Language Warning Signal cites this paper.

When Does Reward Teach State? A Hidden-Automaton Instrument and a Group-Language Warning Signal Emergent Linear Representations in World Models of Self-Supervised Sequence Models

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Geometry-Guided Constraint Learning for LLM Safety Classification cites this paper.

Geometry-Guided Constraint Learning for LLM Safety Classification Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 14

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Context Is King: How In-Context Specification Shapes the Geometry of Concepts cites this paper.

Context Is King: How In-Context Specification Shapes the Geometry of Concepts Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 14

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