Paper Citation Record · LEDGER

Enhancing Neural Network Interpretability with Feature-Aligned Sparse Autoencoders

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2411.01220.

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

pith.paper-citation-record.v1
2411.01220 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:03:01.307047Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T06:36:52.590054Z

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 4ccd8b59-f9fb-46b4-bddd-424074957b94 · inbound

Position: Mechanistic Interpretability Should Prioritize Feature Consistency in SAEs cites this paper.

Position: Mechanistic Interpretability Should Prioritize Feature Consistency in SAEs Enhancing Neural Network Interpretability with Feature-Aligned Sparse Autoencoders

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T14:03:01.307047Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c56b527-684e-4069-a7f4-e46d4a389ba9 · inbound

Interpreting CFD Surrogates through Sparse Autoencoders cites this paper.

Interpreting CFD Surrogates through Sparse Autoencoders Enhancing Neural Network Interpretability with Feature-Aligned Sparse Autoencoders

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T15:23:46.675097Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 588a5a40-fcbb-44db-97e7-6ee30c0ec064 · inbound

Query Circuits: Explaining How Language Models Answer User Prompts cites this paper.

Query Circuits: Explaining How Language Models Answer User Prompts Enhancing Neural Network Interpretability with Feature-Aligned Sparse Autoencoders

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-04T13:54:25.164744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c34d79f-05ac-482e-b3ce-bd742cc11030 · inbound

Stable and Steerable Sparse Autoencoders with Weight Regularization cites this paper.

Stable and Steerable Sparse Autoencoders with Weight Regularization Enhancing Neural Network Interpretability with Feature-Aligned Sparse Autoencoders

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-02T18:56:19.674755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a03be54e-713e-41cb-9a73-62bfbbdcb71f · inbound

Sparse Autoencoders as a Steering Basis for Phase Synchronization in Graph-Based CFD Surrogates cites this paper.

Sparse Autoencoders as a Steering Basis for Phase Synchronization in Graph-Based CFD Surrogates Enhancing Neural Network Interpretability with Feature-Aligned Sparse Autoencoders

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-14T22:19:31.795247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 030abb6d-ec68-492d-9713-50bfe45a9aef · inbound

Improving Sparse Autoencoder with Dynamic Attention cites this paper.

Improving Sparse Autoencoder with Dynamic Attention Enhancing Neural Network Interpretability with Feature-Aligned Sparse Autoencoders

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-10T11:10:09.002323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cf74728a-5246-4d31-8a1d-bf9f9a5f74ac · inbound

Aligned Training: A Parameter-Free Method to Improve Feature Quality and Stability of Sparse Autoencoders (SAE) cites this paper.

Aligned Training: A Parameter-Free Method to Improve Feature Quality and Stability of Sparse Autoencoders (SAE) Enhancing Neural Network Interpretability with Feature-Aligned Sparse Autoencoders

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:18:16.576638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4c9e1f7f-c3f1-4805-a22b-8d149b05a886 · inbound

Aligned Training: A Parameter-Free Method to Improve Feature Quality and Stability of Sparse Autoencoders (SAE) cites this paper.

Aligned Training: A Parameter-Free Method to Improve Feature Quality and Stability of Sparse Autoencoders (SAE) Enhancing Neural Network Interpretability with Feature-Aligned Sparse Autoencoders

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-07-01T14:55:48.795038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1b647ae7-7311-45c3-afbd-000200565e6a · inbound

Perplexity Can Miss SAE Feature Damage Under Quantization cites this paper.

Perplexity Can Miss SAE Feature Damage Under Quantization Enhancing Neural Network Interpretability with Feature-Aligned Sparse Autoencoders

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-07-02T01:36:25.772325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 34e933c1-8d2f-48ff-869b-08a277765eaf · inbound

A Unifying Framework for Concept-Based Representational Similarity cites this paper.

A Unifying Framework for Concept-Based Representational Similarity Enhancing Neural Network Interpretability with Feature-Aligned Sparse Autoencoders

Reference 72

Resolution: verified exact; arxiv_id, observed 2026-07-03T00:27:29.980256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5ee12ee0-3bc1-4963-9080-6c6577d84853 · inbound

Cross-seed explainability using Procrustes-conditioned Joint End-to-end Top-K Sparse Autoencoders cites this paper.

Cross-seed explainability using Procrustes-conditioned Joint End-to-end Top-K Sparse Autoencoders Enhancing Neural Network Interpretability with Feature-Aligned Sparse Autoencoders

Reference 8

Resolution: metadata mismatch; local_arxiv, observed 2026-07-10T06:36:52.591273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.