Paper Citation Record · LEDGER

Empirical Evaluation of Progressive Coding for Sparse Autoencoders

As of 18 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2505.00190.

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

pith.paper-citation-record.v1
2505.00190 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:56:22.844660Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

25 of 25 outbound references displayed

verified exact0
verified fuzzy4
unresolved19
parse uncertain0
malformed identifier1
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 647060f7-83c0-4851-8d7f-8bf9acd66a73 · outbound

This paper cites Matryoshka Multimodal Models.

Empirical Evaluation of Progressive Coding for Sparse Autoencoders Matryoshka Multimodal Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-16T04:56:22.737406Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f383d047-4075-425a-9d2b-476c3eb17128 · outbound

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

Empirical Evaluation of Progressive Coding for Sparse Autoencoders Sparse Autoencoders Find Highly Interpretable Features in Language Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T04:56:22.748324Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4bf5a4d6-e979-438a-ad95-1f11c53553f4 · outbound

This paper cites MatFormer: Nested Transformer for Elastic Inference.

Empirical Evaluation of Progressive Coding for Sparse Autoencoders MatFormer: Nested Transformer for Elastic Inference

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T04:56:22.753681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b21f2f88-3c67-4995-bb8b-05e0d7f3d4e1 · outbound

This paper cites pub/2022/toy_model/index.html.

Empirical Evaluation of Progressive Coding for Sparse Autoencoders pub/2022/toy_model/index.html

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:56:23.260581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6bb1595b-09d4-4d63-8b32-aba654d373c1 · outbound

This paper cites Scaling and evaluating sparse autoencoders.

Empirical Evaluation of Progressive Coding for Sparse Autoencoders Scaling and evaluating sparse autoencoders

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-16T04:56:22.763516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b715786-6965-4b69-8374-efd9cd54d6a2 · outbound

This paper cites The Llama 3 Herd of Models.

Empirical Evaluation of Progressive Coding for Sparse Autoencoders The Llama 3 Herd of Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T04:56:22.769439Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9df80d73-8f14-4a6a-bc3d-51fe4e7cb480 · outbound

This paper cites Matryoshka Diffusion Models.

Empirical Evaluation of Progressive Coding for Sparse Autoencoders Matryoshka Diffusion Models

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-16T04:56:22.773975Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 552833d1-1eb8-44e2-9172-98b8da21afcc · outbound

This paper cites Training Compute-Optimal Large Language Models.

Empirical Evaluation of Progressive Coding for Sparse Autoencoders Training Compute-Optimal Large Language Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-16T04:56:22.778653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f740e4d-df3c-4107-a606-3babc1f9cba1 · outbound

This paper cites an unresolved cited work.

Empirical Evaluation of Progressive Coding for Sparse Autoencoders Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-16T04:56:23.244721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b4be0705-de57-49cd-8a5a-d2c21d8e562c · outbound

This paper cites Similarity of Neural Network Models: A Survey of Functional and Representational Measures.

Empirical Evaluation of Progressive Coding for Sparse Autoencoders Similarity of Neural Network Models: A Survey of Functional and Representational Measures

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-16T04:56:22.789117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d792aa6-3587-4786-8ddd-63142382fed7 · outbound

This paper cites Matryoshka Representation Learning.

Empirical Evaluation of Progressive Coding for Sparse Autoencoders Matryoshka Representation Learning

Reference 15

Resolution: malformed identifier; no resolver link, observed 2026-08-16T04:56:22.793931Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07233ba7-60fb-444e-8879-6b26c3ec3d33 · outbound

This paper cites Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2.

Empirical Evaluation of Progressive Coding for Sparse Autoencoders Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-16T04:56:22.799628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a2716a7-1bc0-4217-a6c7-47a5ed1f212f · outbound

This paper cites Towards Principled Evaluations of Sparse Autoencoders for Interpretability and Control.

Empirical Evaluation of Progressive Coding for Sparse Autoencoders Towards Principled Evaluations of Sparse Autoencoders for Interpretability and Control

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T04:56:22.804430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10e4efdb-65fb-478b-a707-1fe5392d2f8a · outbound

This paper cites Efficient Dictionary Learning with Switch Sparse Autoencoders.

Empirical Evaluation of Progressive Coding for Sparse Autoencoders Efficient Dictionary Learning with Switch Sparse Autoencoders

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T04:56:22.809692Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dbb2a7ff-867e-41b1-a309-064054805740 · outbound

This paper cites Automatically Interpreting Millions of Features in Large Language Models.

Empirical Evaluation of Progressive Coding for Sparse Autoencoders Automatically Interpreting Millions of Features in Large Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T04:56:22.814603Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fdc0869f-545e-4a37-aa19-f28582528ac6 · outbound

This paper cites Jumping Ahead: Improving Reconstruction Fidelity with JumpReLU Sparse Autoencoders.

Empirical Evaluation of Progressive Coding for Sparse Autoencoders Jumping Ahead: Improving Reconstruction Fidelity with JumpReLU Sparse Autoencoders

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T04:56:22.819382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ab8742b-9738-4217-a35e-ca63de188bf0 · outbound

This paper cites The scatter plots show pairwise relationships with linear regression fits, displaying both Pearson correlation coefficients (r) and coefficients of determination (R²).

Empirical Evaluation of Progressive Coding for Sparse Autoencoders The scatter plots show pairwise relationships with linear regression fits, displaying both Pearson correlation coefficients (r) and coefficients of determination (R²)

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:56:23.211247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 567fbd88-2906-4290-929d-7513c1918c53 · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

Empirical Evaluation of Progressive Coding for Sparse Autoencoders Gemma 2: Improving Open Language Models at a Practical Size

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T04:56:22.829655Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6557d3f-2a06-4c0c-a246-eb0783ecd5b9 · outbound

This paper cites Yun, Z., Chen, Y ., Olshausen, B.

Empirical Evaluation of Progressive Coding for Sparse Autoencoders Yun, Z., Chen, Y ., Olshausen, B

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:56:23.228347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 17ccfba0-5239-4bfa-9e85-f3d8937d2c02 · outbound

This paper cites Transformer visualization via dictionary learning: contextualized embedding as a linear superposition of transformer factors.

Empirical Evaluation of Progressive Coding for Sparse Autoencoders Transformer visualization via dictionary learning: contextualized embedding as a linear superposition of transformer factors

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T04:56:22.839774Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb0230eb-7c09-4fa3-9e78-0529f5e5390e · outbound

This paper cites Learning Ordered Representations with Nested Dropout.

Empirical Evaluation of Progressive Coding for Sparse Autoencoders Learning Ordered Representations with Nested Dropout

Reference 2014

Resolution: metadata mismatch; local_arxiv, observed 2026-08-16T04:56:22.919649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d72e1a01-85a7-4bcc-8b44-04e5dc1fdc2d · outbound

This paper cites Language Models are Few-Shot Learners.

Empirical Evaluation of Progressive Coding for Sparse Autoencoders Language Models are Few-Shot Learners

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-16T04:56:22.732000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13e342e3-d916-4b5f-9875-b17d47a936ae · outbound

This paper cites PaLM: Scaling Language Modeling with Pathways.

Empirical Evaluation of Progressive Coding for Sparse Autoencoders PaLM: Scaling Language Modeling with Pathways

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-16T04:56:22.743107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92b3d317-0273-49f5-8bc0-fcb8fccb1c18 · outbound

This paper cites Brown, T.

Empirical Evaluation of Progressive Coding for Sparse Autoencoders Brown, T

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:56:23.277222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 158a5504-5a04-4716-b9be-f9209f25b561 · outbound

This paper cites Identifying Functionally Important Features with End-to-End Sparse Dictionary Learning.

Empirical Evaluation of Progressive Coding for Sparse Autoencoders Identifying Functionally Important Features with End-to-End Sparse Dictionary Learning

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-16T04:56:22.721171Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.