Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
The Geometry of Concepts: Sparse Autoencoder Feature Structure
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2410.19750.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T05:41:19.107700Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T13:39:50.842822Z
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 e84e1afb-5a51-423f-962d-6d65c12a9ae2 · inbound
Rethinking Evaluation of Sparse Autoencoders through the Representation of Polysemous Words cites this paper.
Rethinking Evaluation of Sparse Autoencoders through the Representation of Polysemous Words The Geometry of Concepts: Sparse Autoencoder Feature Structure
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T21:28:15.932223Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 25b3b621-fc1d-42a2-a252-82566c9b5a5e · inbound
Harmonic Loss Trains Interpretable AI Models cites this paper.
Harmonic Loss Trains Interpretable AI Models The Geometry of Concepts: Sparse Autoencoder Feature Structure
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T14:52:24.243940Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4831fb51-53de-43c8-a86b-bf1b91560f49 · inbound
Representation Learning on a Random Lattice cites this paper.
Representation Learning on a Random Lattice The Geometry of Concepts: Sparse Autoencoder Feature Structure
Reference 1998
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T05:41:19.107700Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation eb580ed5-bbb6-4827-919e-cd10a4e64ce4 · inbound
Analyzing Hierarchical Structure in Vision Models with Sparse Autoencoders cites this paper.
Analyzing Hierarchical Structure in Vision Models with Sparse Autoencoders The Geometry of Concepts: Sparse Autoencoder Feature Structure
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:12:24.830380Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f8b66d2d-829b-40d4-972f-5710a5b59856 · inbound
Sparsification and Reconstruction from the Perspective of Representation Geometry cites this paper.
Sparsification and Reconstruction from the Perspective of Representation Geometry The Geometry of Concepts: Sparse Autoencoder Feature Structure
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:11:38.527027Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 17c0ee9a-ad58-4570-ab50-e8b399288b88 · inbound
Towards Understanding the Robustness of Sparse Autoencoders cites this paper.
Towards Understanding the Robustness of Sparse Autoencoders The Geometry of Concepts: Sparse Autoencoder Feature Structure
Reference 6
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T05:46:10.226383Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 51cf4368-8dec-4ea5-a6a2-f1f726a6503c · inbound
From Token Lists to Graph Motifs: Weisfeiler-Lehman Analysis of Sparse Autoencoder Features cites this paper.
From Token Lists to Graph Motifs: Weisfeiler-Lehman Analysis of Sparse Autoencoder Features The Geometry of Concepts: Sparse Autoencoder Feature Structure
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T20:16:11.043247Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation bc94049d-f41d-41a1-b8be-8ce4927199d8 · inbound
The Geometric Wall: Manifold Structure Predicts Layerwise Sparse Autoencoder Scaling Laws cites this paper.
The Geometric Wall: Manifold Structure Predicts Layerwise Sparse Autoencoder Scaling Laws The Geometry of Concepts: Sparse Autoencoder Feature Structure
Reference 9
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T05:56:25.293813Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 67871e06-5b80-48b3-aab9-1db72209b352 · inbound
Where Do Models Find Happiness? Emotion Vectors in Open-Source LLMs cites this paper.
Where Do Models Find Happiness? Emotion Vectors in Open-Source LLMs The Geometry of Concepts: Sparse Autoencoder Feature Structure
Reference 1
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-04T13:39:50.844439Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation d7b8a835-33c6-40bc-9294-e5ec18e9df17 · 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 The Geometry of Concepts: Sparse Autoencoder Feature Structure
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T10:03:56.646522Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.