Paper Citation Record · LEDGER

Not All Language Model Features Are One-Dimensionally Linear

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 39 inbound Pith citation observations for arXiv:2405.14860.

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

pith.paper-citation-record.v1
2405.14860 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 39 of 39 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 39 of 39 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:52:24.255125Z

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

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

Observation bd548730-d06c-4725-b01c-4da06c7ccaeb · inbound

Harmonic Loss Trains Interpretable AI Models cites this paper.

Harmonic Loss Trains Interpretable AI Models Not All Language Model Features Are One-Dimensionally Linear

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-09T14:52:24.255125Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53139257-4cc2-4178-a89a-e7ad60874756 · inbound

Sparse Autoencoders Do Not Find Canonical Units of Analysis cites this paper.

Sparse Autoencoders Do Not Find Canonical Units of Analysis Not All Language Model Features Are One-Dimensionally Linear

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-08T21:12:13.824293Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da61c799-cd6e-46da-bef2-457e7d73ce2e · inbound

The Hidden Dimensions of LLM Alignment: A Multi-Dimensional Analysis of Orthogonal Safety Directions cites this paper.

The Hidden Dimensions of LLM Alignment: A Multi-Dimensional Analysis of Orthogonal Safety Directions Not All Language Model Features Are One-Dimensionally Linear

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T23:08:02.254996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44a87f73-8562-4a2b-98f8-8fb523dc1847 · inbound

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs cites this paper.

From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs Not All Language Model Features Are One-Dimensionally Linear

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T13:28:00.819179Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b3f89a17-2f54-4b91-b149-3593b7a074d4 · inbound

Sparsification and Reconstruction from the Perspective of Representation Geometry cites this paper.

Sparsification and Reconstruction from the Perspective of Representation Geometry Not All Language Model Features Are One-Dimensionally Linear

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T13:11:10.172162Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da87d8a2-5ba9-4978-81d9-3c72e69790c1 · inbound

Incorporating Hierarchical Semantics in Sparse Autoencoder Architectures cites this paper.

Incorporating Hierarchical Semantics in Sparse Autoencoder Architectures Not All Language Model Features Are One-Dimensionally Linear

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T11:56:15.166919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2ef8bf8-f3bb-49ea-bc85-52577a1bc164 · inbound

Internal Value Alignment in Large Language Models through Controlled Value Vector Activation cites this paper.

Internal Value Alignment in Large Language Models through Controlled Value Vector Activation Not All Language Model Features Are One-Dimensionally Linear

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T17:17:28.984932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4cad7f70-6925-454b-84c0-1e076421cc8e · inbound

How Do Transformers Learn to Associate Tokens: Gradient Leading Terms Bring Mechanistic Interpretability cites this paper.

How Do Transformers Learn to Associate Tokens: Gradient Leading Terms Bring Mechanistic Interpretability Not All Language Model Features Are One-Dimensionally Linear

Reference 7

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T11:20:52.634674Z

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 caee3fe2-910b-4c01-a668-51ceeffa284f · inbound

Logit Distance Bounds Representational Similarity cites this paper.

Logit Distance Bounds Representational Similarity Not All Language Model Features Are One-Dimensionally Linear

Reference 2006

Resolution: unresolved; no resolver link, observed 2026-08-02T22:57:36.025860Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b64c2577-70da-4c6d-aee8-65fc99871aa1 · inbound

The Lattice Representation Hypothesis of Large Language Models cites this paper.

The Lattice Representation Hypothesis of Large Language Models Not All Language Model Features Are One-Dimensionally Linear

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-21T12:04:09.560248Z

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 1401b968-426c-41ed-9981-730516cecf67 · inbound

The Lattice Representation Hypothesis of Large Language Models cites this paper.

The Lattice Representation Hypothesis of Large Language Models Not All Language Model Features Are One-Dimensionally Linear

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-02T19:43:25.400771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1971c98c-41e4-433a-b9eb-9d18adedf9b8 · inbound

Predicting Where Steering Vectors Succeed cites this paper.

Predicting Where Steering Vectors Succeed Not All Language Model Features Are One-Dimensionally Linear

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T11:00:03.823175Z

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 c6473e49-605c-40b6-8393-ef777b005572 · inbound

There Will Be a Scientific Theory of Deep Learning cites this paper.

There Will Be a Scientific Theory of Deep Learning Not All Language Model Features Are One-Dimensionally Linear

Reference 217

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T15:21:09.121872Z

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 fdb5656b-db9c-45e3-9f20-601071dc2f16 · inbound

H-Probes: Extracting Hierarchical Structures From Latent Representations of Language Models cites this paper.

H-Probes: Extracting Hierarchical Structures From Latent Representations of Language Models Not All Language Model Features Are One-Dimensionally Linear

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T14:10:28.842529Z

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 332ab266-1b4f-47a3-93f8-997d92b30daf · inbound

Manifold Steering Reveals the Shared Geometry of Neural Network Representation and Behavior cites this paper.

Manifold Steering Reveals the Shared Geometry of Neural Network Representation and Behavior Not All Language Model Features Are One-Dimensionally Linear

Reference 205

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T17:16:06.969598Z

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 d7a541ed-ae90-49c3-b09f-74f8fa3620d5 · inbound

Tree SAE: Learning Hierarchical Feature Structures in Sparse Autoencoders cites this paper.

Tree SAE: Learning Hierarchical Feature Structures in Sparse Autoencoders Not All Language Model Features Are One-Dimensionally Linear

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T03:15:54.387341Z

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 a57d3192-6627-4e4c-8f6b-b0834607e294 · inbound

Tree SAE: Learning Hierarchical Feature Structures in Sparse Autoencoders cites this paper.

Tree SAE: Learning Hierarchical Feature Structures in Sparse Autoencoders Not All Language Model Features Are One-Dimensionally Linear

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T07:16:25.371063Z

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 f78a01aa-39b5-4d40-ab9e-17cd95f9cd34 · inbound

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

Tool Calling is Linearly Readable and Steerable in Language Models Not All Language Model Features Are One-Dimensionally Linear

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:10:52.721023Z

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 629d436a-31e6-4e78-a889-54c1723729ec · inbound

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

Tensor Product Representation Probes Reveal Shared Structure Across Linear Directions Not All Language Model Features Are One-Dimensionally Linear

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T07:16:30.298763Z

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 9bc12940-3a01-4c8c-8e36-5dcc4ce30711 · inbound

WriteSAE: Sparse Autoencoders for Recurrent State cites this paper.

WriteSAE: Sparse Autoencoders for Recurrent State Not All Language Model Features Are One-Dimensionally Linear

Reference 39

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T20:59:28.564457Z

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 0e9c5996-8459-4dac-9d93-5d79ffec6cdf · inbound

WriteSAE: Sparse Autoencoders for Recurrent State cites this paper.

WriteSAE: Sparse Autoencoders for Recurrent State Not All Language Model Features Are One-Dimensionally Linear

Reference 39

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T04:59:45.200561Z

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 6bc4200b-a4f3-4338-9320-f39f80371e64 · inbound

WriteSAE: Sparse Autoencoders for Recurrent State cites this paper.

WriteSAE: Sparse Autoencoders for Recurrent State Not All Language Model Features Are One-Dimensionally Linear

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T07:49:50.100762Z

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 383a1443-61e9-4889-b6b8-cbd31759d547 · inbound

Rethinking Layer Relevance in Large Language Models Beyond Cosine Similarity cites this paper.

Rethinking Layer Relevance in Large Language Models Beyond Cosine Similarity Not All Language Model Features Are One-Dimensionally Linear

Reference 28

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T05:09:46.256471Z

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 7cf0129d-1f53-4d86-85b0-50bef9abe05b · inbound

Geometry of Human Perceptual Domains Emerges Transiently in LLM Representations cites this paper.

Geometry of Human Perceptual Domains Emerges Transiently in LLM Representations Not All Language Model Features Are One-Dimensionally Linear

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-06-29T12:33:24.317059Z

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 58141d5e-4aea-4be7-8f3c-c61e8ef479d1 · inbound

Sign-Aware Gated Sparse Autoencoders: Modeling Anticorrelated Features with Bi-Jump-ReLU Activations cites this paper.

Sign-Aware Gated Sparse Autoencoders: Modeling Anticorrelated Features with Bi-Jump-ReLU Activations Not All Language Model Features Are One-Dimensionally Linear

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-06-29T14:23:30.789091Z

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 34173d6d-fdf3-4433-848d-c5ddf4e0862f · inbound

Sign-Aware Gated Sparse Autoencoders: Modeling Anticorrelated Features with Bi-Jump-ReLU Activations cites this paper.

Sign-Aware Gated Sparse Autoencoders: Modeling Anticorrelated Features with Bi-Jump-ReLU Activations Not All Language Model Features Are One-Dimensionally Linear

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T05:02:51.288089Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e59b3cf3-c9b0-459a-8df8-a775b5f23dc1 · inbound

Temporal Preference Concepts and their Functions in a Large Language Model cites this paper.

Temporal Preference Concepts and their Functions in a Large Language Model Not All Language Model Features Are One-Dimensionally Linear

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-07-01T14:05:47.187058Z

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 1db4c80b-7165-4016-96cd-421e242dd481 · inbound

Temporal Preference Concepts and their Functions in a Large Language Model cites this paper.

Temporal Preference Concepts and their Functions in a Large Language Model Not All Language Model Features Are One-Dimensionally Linear

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-12T17:03:44.315006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05e3641c-e8cd-4b4f-a8cd-6ecc0c91b122 · inbound

Subspace-Aware Sparse Autoencoders for Effective Mechanistic Interpretability cites this paper.

Subspace-Aware Sparse Autoencoders for Effective Mechanistic Interpretability Not All Language Model Features Are One-Dimensionally Linear

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T02:11:29.085215Z

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 4252f86c-9887-4d15-8bd0-7ade27ff6397 · inbound

Closure-Validated Circuit Discovery in Attention Heads: Co-activation Proposes, Ablation Disposes cites this paper.

Closure-Validated Circuit Discovery in Attention Heads: Co-activation Proposes, Ablation Disposes Not All Language Model Features Are One-Dimensionally Linear

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-07-03T00:17:29.333025Z

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 548f1b33-918c-4b0f-90f1-a7202210d2ca · inbound

Muon Learns More Robust and Transferable Features than Adam cites this paper.

Muon Learns More Robust and Transferable Features than Adam Not All Language Model Features Are One-Dimensionally Linear

Reference 120

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T00:27:30.214165Z

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 c31441fa-cd1c-408f-b70e-46770a6a16db · inbound

Density Ridge Selective Prediction for LLM and VLM Hallucination Detection under Calibration Label Scarcity cites this paper.

Density Ridge Selective Prediction for LLM and VLM Hallucination Detection under Calibration Label Scarcity Not All Language Model Features Are One-Dimensionally Linear

Reference 14

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T00:27:29.503504Z

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 bdb436bc-7722-4224-897d-5d9c96f4e129 · inbound

Size Doesn't Matter: Cosine-Scored Sparse Autoencoders cites this paper.

Size Doesn't Matter: Cosine-Scored Sparse Autoencoders Not All Language Model Features Are One-Dimensionally Linear

Reference 32

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T07:15:29.669716Z

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 abdc302e-9d48-4cd8-99e0-f773d1af635c · inbound

Structuring Sparsity: Block-Sparse Featurizers Capture Visual Concept Manifolds cites this paper.

Structuring Sparsity: Block-Sparse Featurizers Capture Visual Concept Manifolds Not All Language Model Features Are One-Dimensionally Linear

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-07-04T17:09:58.969579Z

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 055b7f6e-8c13-43d7-a417-8df7f05eb430 · inbound

At the Edge of Understanding: Sparse Autoencoders Trace The Limits of Transformer Generalization cites this paper.

At the Edge of Understanding: Sparse Autoencoders Trace The Limits of Transformer Generalization Not All Language Model Features Are One-Dimensionally Linear

Reference 41

Resolution: metadata mismatch; arxiv_id, observed 2026-06-26T01:28:50.560096Z

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 b9b88ed0-c644-481f-9383-ead7b3cdb1a3 · inbound

Do Models Read What They Write? Causal Registers in Scratchpad Reasoning cites this paper.

Do Models Read What They Write? Causal Registers in Scratchpad Reasoning Not All Language Model Features Are One-Dimensionally Linear

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-06-30T07:34:21.854001Z

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 6fa62143-c4a6-4e01-b16f-2f525b11630a · inbound

Training, Reading, and Editing Legible Transformers cites this paper.

Training, Reading, and Editing Legible Transformers Not All Language Model Features Are One-Dimensionally Linear

Reference 55

Resolution: unresolved; no resolver link, observed 2026-07-13T05:35:58.568346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8f70879-0299-4241-8ecb-01922e686687 · inbound

Are Single-Token Sparse Autoencoder Features Causally Necessary? Layer-Depth and SAE-Family Effects cites this paper.

Are Single-Token Sparse Autoencoder Features Causally Necessary? Layer-Depth and SAE-Family Effects Not All Language Model Features Are One-Dimensionally Linear

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T10:03:54.939594Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5e874e5-8fb3-4fe0-9415-af59f4f32906 · inbound

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 Not All Language Model Features Are One-Dimensionally Linear

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-31T15:17:59.776360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.