Paper Citation Record · LEDGER

Low-Rank Adapting Models for Sparse Autoencoders

As of 10 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 1 inbound Pith citation observation for arXiv:2501.19406.

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

pith.paper-citation-record.v1
2501.19406 v2

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T20:18:31.299154Z

measured 34 of 34 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:45:42.694548Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T04:45:43.037602Z

Reference resolution

33 of 33 outbound references displayed

verified exact1
verified fuzzy4
unresolved28
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4e0b2a2b-463b-4a9e-8e72-1fa207adeb67 · outbound

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

Low-Rank Adapting Models for Sparse Autoencoders Identifying Functionally Important Features with End-to-End Sparse Dictionary Learning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-09T20:18:30.554696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cca090fb-561f-4bbe-b732-fafcabf5575d · outbound

This paper cites Accessed: 2025-01-19.

Low-Rank Adapting Models for Sparse Autoencoders Accessed: 2025-01-19

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:18:32.025684Z

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 f4173e59-ba33-490e-b852-7c5a18de0ca5 · outbound

This paper cites BatchTopK Sparse Autoencoders.

Low-Rank Adapting Models for Sparse Autoencoders BatchTopK Sparse Autoencoders

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-09T20:18:30.572602Z

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Observation c61547e5-1ba5-4fbf-bd1a-94905fab97d8 · outbound

This paper cites an unresolved cited work.

Low-Rank Adapting Models for Sparse Autoencoders Unresolved cited work

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-09T20:18:30.645002Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1455651e-362a-4beb-a8c4-7d1c26ced448 · outbound

This paper cites Recurrent Neural Networks Learn to Store and Generate Sequences using Non-Linear Representations.

Low-Rank Adapting Models for Sparse Autoencoders Recurrent Neural Networks Learn to Store and Generate Sequences using Non-Linear Representations

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-09T20:18:30.679852Z

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Observation b72fe857-2979-418b-8121-137455196cf3 · outbound

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

Low-Rank Adapting Models for Sparse Autoencoders Sparse Autoencoders Find Highly Interpretable Features in Language Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-09T20:18:30.729131Z

Source-reported events for the cited work

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Observation f170a4ef-3914-4a89-b382-f96b4e769f17 · outbound

This paper cites Decomposing The Dark Matter of Sparse Autoencoders.

Low-Rank Adapting Models for Sparse Autoencoders Decomposing The Dark Matter of Sparse Autoencoders

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-09T20:18:30.772296Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81d2e431-52da-462e-96fc-682ebcc2919d · outbound

This paper cites Scaling and evaluating sparse autoencoders.

Low-Rank Adapting Models for Sparse Autoencoders Scaling and evaluating sparse autoencoders

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-09T20:18:30.824860Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3256ff99-703c-4c9c-b842-1501111ab52b · outbound

This paper cites Monotonic Representation of Numeric Properties in Language Models.

Low-Rank Adapting Models for Sparse Autoencoders Monotonic Representation of Numeric Properties in Language Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-09T20:18:30.913434Z

Source-reported events for the cited work

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Observation a56034c3-9933-47a2-910b-d6b8d55ba5df · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Low-Rank Adapting Models for Sparse Autoencoders Measuring Massive Multitask Language Understanding

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-09T20:18:30.935137Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02341f4d-7551-4631-a1ca-af433f7764ac · outbound

This paper cites The Remarkable Robustness of LLMs: Stages of Inference?.

Low-Rank Adapting Models for Sparse Autoencoders The Remarkable Robustness of LLMs: Stages of Inference?

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-09T20:18:30.963115Z

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Observation 8bcccf64-82e2-4530-bbbd-df27963c3d67 · outbound

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

Low-Rank Adapting Models for Sparse Autoencoders Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-09T20:18:30.973761Z

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Observation 7e36c8e7-7ee6-4e24-92cf-ef51cf84ab20 · outbound

This paper cites TruthfulQA: Measuring How Models Mimic Human Falsehoods.

Low-Rank Adapting Models for Sparse Autoencoders TruthfulQA: Measuring How Models Mimic Human Falsehoods

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-09T20:18:30.984011Z

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Observation 6469a91a-aa1b-4dbf-ad2d-5efe31b48369 · outbound

This paper cites Seeing is Believing: Brain-Inspired Modular Training for Mechanistic Interpretability.

Low-Rank Adapting Models for Sparse Autoencoders Seeing is Believing: Brain-Inspired Modular Training for Mechanistic Interpretability

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-09T20:18:30.988906Z

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Observation 88d564fa-a58c-4c07-a336-01f4bc66e732 · outbound

This paper cites KAN: Kolmogorov-Arnold Networks.

Low-Rank Adapting Models for Sparse Autoencoders KAN: Kolmogorov-Arnold Networks

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-09T20:18:30.994170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 496a8ff7-89c5-425d-bb04-8107220df803 · outbound

This paper cites Mudide, A., Engels, J., Michaud, E.

Low-Rank Adapting Models for Sparse Autoencoders Mudide, A., Engels, J., Michaud, E

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:18:32.011699Z

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 270e628b-ad2c-43a1-863e-ca0ac6f39967 · outbound

This paper cites Efficient Dictionary Learning with Switch Sparse Autoencoders.

Low-Rank Adapting Models for Sparse Autoencoders Efficient Dictionary Learning with Switch Sparse Autoencoders

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-09T20:18:31.008627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6086e946-671f-4eda-b162-269ac43f1836 · outbound

This paper cites Emergent Linear Representations in World Models of Self-Supervised Sequence Models.

Low-Rank Adapting Models for Sparse Autoencoders Emergent Linear Representations in World Models of Self-Supervised Sequence Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-09T20:18:31.013489Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f42e21ef-db1d-4442-9554-c88208d619f0 · outbound

This paper cites https://distill.pub/2020/circuits/zoom-in.

Low-Rank Adapting Models for Sparse Autoencoders https://distill.pub/2020/circuits/zoom-in

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-09T20:18:31.018176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aee6e5eb-cb38-4ad8-a0ca-b5238e4a994b · outbound

This paper cites Features that Make a Difference: Leveraging Gradients for Improved Dictionary Learning.

Low-Rank Adapting Models for Sparse Autoencoders Features that Make a Difference: Leveraging Gradients for Improved Dictionary Learning

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-09T20:18:31.468811Z

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 5f5ab4ad-a849-4c7a-a88a-002319a7cecb · outbound

This paper cites The Linear Representation Hypothesis and the Geometry of Large Language Models.

Low-Rank Adapting Models for Sparse Autoencoders The Linear Representation Hypothesis and the Geometry of Large Language Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-09T20:18:31.027409Z

Source-reported events for the cited work

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Observation f382c86c-9efd-49c8-933f-815abee9b925 · outbound

This paper cites an unresolved cited work.

Low-Rank Adapting Models for Sparse Autoencoders Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-08-09T20:18:31.996765Z

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 4173d6d1-ef88-4caf-86cc-42ffe994ac4a · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Low-Rank Adapting Models for Sparse Autoencoders Gemini: A Family of Highly Capable Multimodal Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-09T20:18:31.156568Z

Source-reported events for the cited work

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Observation 68fd28ed-bc4b-467c-a545-0ac3cfd1a36f · outbound

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

Low-Rank Adapting Models for Sparse Autoencoders Gemma 2: Improving Open Language Models at a Practical Size

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-09T20:18:31.188156Z

Source-reported events for the cited work

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Observation 1265c0b6-402c-440d-b3d6-57772734ea33 · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

Low-Rank Adapting Models for Sparse Autoencoders HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-09T20:18:31.251686Z

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Observation 9bac061e-4f6b-424b-8ffd-6a170b5d797d · outbound

This paper cites A higher score reflects better feature representation (Karvonen et al., 2024).

Low-Rank Adapting Models for Sparse Autoencoders A higher score reflects better feature representation (Karvonen et al., 2024)

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:18:31.977855Z

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 100eae61-91e1-4b54-a531-ccd91d663c70 · outbound

This paper cites Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models.

Low-Rank Adapting Models for Sparse Autoencoders Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models

Reference 2013

Resolution: unresolved; no resolver link, observed 2026-08-09T20:18:30.999141Z

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Unavailable: canonical work link unavailable.

Observation 80bf5b95-9807-453f-bb49-e95241f8e46a · outbound

This paper cites Language Models are Few-Shot Learners.

Low-Rank Adapting Models for Sparse Autoencoders Language Models are Few-Shot Learners

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-09T20:18:30.567936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cdd5d2b5-3c19-429e-9fd1-3485635a209b · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Low-Rank Adapting Models for Sparse Autoencoders LoRA: Low-Rank Adaptation of Large Language Models

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-09T20:18:30.949006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27a0c046-55ac-4352-a6fa-33d9948f044a · outbound

This paper cites Bricken, T., Marcus, J., Rivoire, K., and Henighan, T.

Low-Rank Adapting Models for Sparse Autoencoders Bricken, T., Marcus, J., Rivoire, K., and Henighan, T

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T20:18:32.041715Z

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 01720643-bb5e-448f-b4ae-e4a7ed2649cf · outbound

This paper cites Mechanistic Interpretability for AI Safety -- A Review.

Low-Rank Adapting Models for Sparse Autoencoders Mechanistic Interpretability for AI Safety -- A Review

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-09T20:18:30.549374Z

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Observation 75c4e53e-9a54-450a-a2fb-d33a00ac8c4b · outbound

This paper cites Open Problems in Mechanistic Interpretability.

Low-Rank Adapting Models for Sparse Autoencoders Open Problems in Mechanistic Interpretability

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-09T20:18:31.107491Z

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Unavailable: canonical work link unavailable.

Observation 7fee4390-6299-41e1-a199-52b7c4b26e79 · outbound

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

Low-Rank Adapting Models for Sparse Autoencoders Jumping Ahead: Improving Reconstruction Fidelity with JumpReLU Sparse Autoencoders

Reference 6014

Resolution: unresolved; no resolver link, observed 2026-08-09T20:18:31.032548Z

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Pith citing papers

Observation ae5cf393-4ed4-46e6-9c7a-82c4691bf7ab · inbound

Resa: Transparent Reasoning Models via SAEs cites this paper.

Resa: Transparent Reasoning Models via SAEs Low-Rank Adapting Models for Sparse Autoencoders

Reference 5

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T04:45:43.042912Z

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.