Paper Citation Record · LEDGER

Analyzing Hierarchical Structure in Vision Models with Sparse Autoencoders

As of 15 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2505.15970.

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

pith.paper-citation-record.v1
2505.15970 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:12:25.414652Z

measured 17 of 17 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

17 of 17 outbound references displayed

verified exact1
verified fuzzy7
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 24ed551c-59c3-4c94-94ec-92fab0ca9e1e · outbound

This paper cites Do convolutional neural networks learn class hierarchy? IEEE transactions on visualization and computer graphics, 24(1):152–162, 2017.

Analyzing Hierarchical Structure in Vision Models with Sparse Autoencoders Do convolutional neural networks learn class hierarchy? IEEE transactions on visualization and computer graphics, 24(1):152–162, 2017

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:12:27.299910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c47f8954-9b86-457c-8eb0-fe5e6f405b38 · outbound

This paper cites Generic attention- model explainability for interpreting bi-modal and encoder- decoder transformers.

Analyzing Hierarchical Structure in Vision Models with Sparse Autoencoders Generic attention- model explainability for interpreting bi-modal and encoder- decoder transformers

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:12:27.016575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fda0ab0c-2157-408e-84ce-9184b46e8532 · outbound

This paper cites Sparse Autoencoders Find Highly Interpretable Features in Language Models.

Analyzing Hierarchical Structure in Vision Models with Sparse Autoencoders Sparse Autoencoders Find Highly Interpretable Features in Language Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T15:12:24.225803Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f442003-4cf3-4819-b2ae-7eecc4976eae · outbound

This paper cites Daujotas.

Analyzing Hierarchical Structure in Vision Models with Sparse Autoencoders Daujotas

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:12:26.753098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2055bb90-3b85-4c04-a513-608145eb72a0 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Analyzing Hierarchical Structure in Vision Models with Sparse Autoencoders Imagenet: A large-scale hierarchical image database

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T15:12:24.410087Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc5e0f5c-7132-4fe3-8c6f-e1e57f15d118 · outbound

This paper cites Towards multimodal interpretability: Learn- ing sparse interpretable features in vision transform- ers.

Analyzing Hierarchical Structure in Vision Models with Sparse Autoencoders Towards multimodal interpretability: Learn- ing sparse interpretable features in vision transform- ers

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:12:26.552148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a875a42f-09d0-40dc-9bb1-c24fe8fa9de4 · outbound

This paper cites Scaling and evaluating sparse autoencoders.

Analyzing Hierarchical Structure in Vision Models with Sparse Autoencoders Scaling and evaluating sparse autoencoders

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T15:12:24.567773Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5894a23f-9255-42da-92b0-a209e060733c · outbound

This paper cites Sae- lens.

Analyzing Hierarchical Structure in Vision Models with Sparse Autoencoders Sae- lens

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:12:26.427018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 912370c2-dd42-4ea2-86b4-492e747a73e0 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Analyzing Hierarchical Structure in Vision Models with Sparse Autoencoders Adam: A Method for Stochastic Optimization

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T15:12:24.740434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb580ed5-bbb6-4827-919e-cd10a4e64ce4 · outbound

This paper cites The Geometry of Concepts: Sparse Autoencoder Feature Structure.

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 bc7c2bac-9b01-44a1-897e-6838ad8d0b7d · outbound

This paper cites Zoom in: An in- troduction to circuits.

Analyzing Hierarchical Structure in Vision Models with Sparse Autoencoders Zoom in: An in- troduction to circuits

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:12:26.259200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d671a085-8d74-4fa6-8bc1-860bc2e8a8c9 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Analyzing Hierarchical Structure in Vision Models with Sparse Autoencoders DINOv2: Learning Robust Visual Features without Supervision

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T15:12:25.002700Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9f846df-74a9-4af3-b1f0-4732b002dedb · outbound

This paper cites Category selectivity in human visual cortex: Beyond visual object recognition.

Analyzing Hierarchical Structure in Vision Models with Sparse Autoencoders Category selectivity in human visual cortex: Beyond visual object recognition

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:12:26.109732Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4191669c-0b5d-4741-8024-10122b03189b · outbound

This paper cites Improving Dictionary Learning with Gated Sparse Autoencoders.

Analyzing Hierarchical Structure in Vision Models with Sparse Autoencoders Improving Dictionary Learning with Gated Sparse Autoencoders

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T15:12:25.177430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e33d9ad-9726-4279-9b6b-5126395b2c64 · outbound

This paper cites Berg, and Li Fei-Fei.

Analyzing Hierarchical Structure in Vision Models with Sparse Autoencoders Berg, and Li Fei-Fei

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T15:12:25.232862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24370638-12a6-4538-ba2e-1d3f816f3ac9 · outbound

This paper cites Sparse autoencoders for scientifically rigorous interpre- tation of vision models.

Analyzing Hierarchical Structure in Vision Models with Sparse Autoencoders Sparse autoencoders for scientifically rigorous interpre- tation of vision models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T15:12:25.323050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0bac0343-9213-4f0c-898b-c7ae0f3ca397 · outbound

This paper cites HGCLIP: Exploring Vision-Language Models with Graph Representations for Hierarchical Understanding.

Analyzing Hierarchical Structure in Vision Models with Sparse Autoencoders HGCLIP: Exploring Vision-Language Models with Graph Representations for Hierarchical Understanding

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:12:25.643590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

No inbound Pith citation observations are available.