Paper Citation Record · LEDGER

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders

As of 14 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 2 inbound Pith citation observations for arXiv:2412.12101.

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

pith.paper-citation-record.v1
2412.12101 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T21:17:17.200001Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T04:28:01.862787Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T20:18:55.813556Z

Reference resolution

30 of 30 outbound references displayed

verified exact0
verified fuzzy18
unresolved12
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8c072e6c-ee51-49ef-82ff-2f2908b7110f · outbound

This paper cites Evolutionary-scale prediction of atomic-level protein structure with a language model.

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders Evolutionary-scale prediction of atomic-level protein structure with a language model

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:17:17.465465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 42c62e05-fcd6-4989-a0b7-ba3aa80d5f49 · outbound

This paper cites Ruffolo, Eli N.

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders Ruffolo, Eli N

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:17:17.456021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8a82bdd0-3330-40d9-8ac5-1a98d0917d0e · outbound

This paper cites Wayment-Steele, Garyk Brixi, Haobo Wang, Dorothee Kern, and Sergey Ovchinnikov.

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders Wayment-Steele, Garyk Brixi, Haobo Wang, Dorothee Kern, and Sergey Ovchinnikov

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:17:17.447810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 19f37310-605d-4392-b585-2308f1082c11 · outbound

This paper cites The Claude 3 Model Family: Opus, Sonnet, Haiku, 2024.

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders The Claude 3 Model Family: Opus, Sonnet, Haiku, 2024

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:17:17.439257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 56fef9a8-f09f-42d1-8e10-b1a77e0d023c · outbound

This paper cites Language models for biological research: a primer.

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders Language models for biological research: a primer

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:17:17.430524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation fbba3c2c-3612-4cf6-aac1-269a460d364c · outbound

This paper cites Transformer protein language models are unsupervised structure learners.

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders Transformer protein language models are unsupervised structure learners

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:17:17.421804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7f474b1d-1804-4915-ada3-4612bda95a39 · outbound

This paper cites Varshney, Caiming Xiong, Richard Socher, and Nazneen Rajani.

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders Varshney, Caiming Xiong, Richard Socher, and Nazneen Rajani

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:17:17.413194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation cbfa89d7-ed4f-45fc-8c14-44e9ff94436d · outbound

This paper cites Marks, Lucy J.

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders Marks, Lucy J

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:17:17.404095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1a3b27e6-7ca8-4974-8bcd-3ff38f5add6a · outbound

This paper cites Ballard, Joshua Bambrick, Sebastian W.

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders Ballard, Joshua Bambrick, Sebastian W

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:17:17.394643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 20753915-9b9d-443d-adc4-27db4a1a1be8 · outbound

This paper cites Linear Algebraic Structure of Word Senses, with Applications to Polysemy.

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders Linear Algebraic Structure of Word Senses, with Applications to Polysemy

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T21:17:17.130412Z

Source-reported events for the cited work

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Observation daf5144a-5334-48d5-9d99-a7e6570f2df7 · outbound

This paper cites Zoom In: An Introduction to Circuits.

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders Zoom In: An Introduction to Circuits

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:17:17.385774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 98f3d81c-d442-4380-a139-967b60a08140 · outbound

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

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders Transformer visualization via dictionary learning: contextualized embedding as a linear superposition of transformer factors

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T21:17:17.139136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09396f4e-bf9d-42aa-91ca-9ab583d7293c · outbound

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

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders Sparse Autoencoders Find Highly Interpretable Features in Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T21:17:17.142810Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e75ee1f-06ab-4b50-ac1d-ff7932e9a910 · outbound

This paper cites Towards Monosemanticity: Decomposing Language Models With Dictionary Learning.

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders Towards Monosemanticity: Decomposing Language Models With Dictionary Learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:17:17.376761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d3375e00-6baf-41ab-8c33-3725d7233960 · outbound

This paper cites The Missing Curve Detectors of InceptionV1: Applying Sparse Autoencoders to InceptionV1 Early Vision.

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders The Missing Curve Detectors of InceptionV1: Applying Sparse Autoencoders to InceptionV1 Early Vision

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T21:17:17.149995Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bbe54719-0883-48e5-ba81-d3f49fbcfb57 · outbound

This paper cites In-context Learning and Induction Heads.

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders In-context Learning and Induction Heads

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:17:17.367917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e77f782d-76f1-434f-82ea-5efd42aca9db · outbound

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

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T21:17:17.157603Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a841016-9293-4382-8c4d-8a0632aa6126 · outbound

This paper cites SAE Visualizer, 2024.

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders SAE Visualizer, 2024

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:17:17.357256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation bcb2b009-78df-4d37-b008-1160699c12f7 · outbound

This paper cites Language models can explain neurons in language models, 2023.

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders Language models can explain neurons in language models, 2023

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T21:17:17.164146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31962ad9-b3bf-4a8b-873a-364bbc733c3d · outbound

This paper cites Daniel Freeman, Theodore R.

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders Daniel Freeman, Theodore R

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:17:17.343664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a6d37708-16e0-4eaf-b85f-d88bc19d7689 · outbound

This paper cites Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game Models.

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T21:17:17.170614Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e1c4470-ca29-4886-8244-3aa5434a3d8b · outbound

This paper cites UMAP: Uniform Manifold Approximation and Projection.

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders UMAP: Uniform Manifold Approximation and Projection

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:17:17.334529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c924483c-5e5c-41f5-b510-cb0f1bda544d · outbound

This paper cites On expert curation and scalability: UniProtKB/Swiss-Prot as a case study.

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders On expert curation and scalability: UniProtKB/Swiss-Prot as a case study

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:17:17.325954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b5d5b6d8-64b9-4399-a68c-639c511b6c46 · outbound

This paper cites InterPro in 2022.

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders InterPro in 2022

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:17:17.316627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ea07f05d-6c57-4c37-afac-fc9948387059 · outbound

This paper cites an unresolved cited work.

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-12T21:17:17.307917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 14c1bb1a-c0df-4826-b881-fc932f61df5a · outbound

This paper cites NNsight and NDIF: Democratizing Access to Open-Weight Foundation Model Internals.

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders NNsight and NDIF: Democratizing Access to Open-Weight Foundation Model Internals

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-12T21:17:17.186855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation faa3deaa-9212-47e5-88b5-14a32f5e2306 · outbound

This paper cites Scaling and evaluating sparse autoencoders.

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders Scaling and evaluating sparse autoencoders

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T21:17:17.190224Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 921cda4f-2f9a-4b0a-8733-1a59cef5f7e2 · outbound

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

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders Jumping Ahead: Improving Reconstruction Fidelity with JumpReLU Sparse Autoencoders, July

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:17:17.298666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0c22a108-5382-4e22-88ba-fb89956978d7 · outbound

This paper cites Transcoders Find Interpretable LLM Feature Circuits.

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders Transcoders Find Interpretable LLM Feature Circuits

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-12T21:17:17.200001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e9c8aeb-89a5-4997-9078-d0c9ec1fbbbf · outbound

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

InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders Jumping Ahead: Improving Reconstruction Fidelity with JumpReLU Sparse Autoencoders

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-12T21:17:17.196773Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 9ed1b193-f7c3-4e15-88a6-21b3f9ca35c3 · inbound

AudioSAE: Towards Understanding of Audio-Processing Models with Sparse AutoEncoders cites this paper.

AudioSAE: Towards Understanding of Audio-Processing Models with Sparse AutoEncoders InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-03T04:28:01.862787Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 777cf1a1-8c3f-495e-a660-e02d917b1775 · inbound

Mechanistic Interpretability and Causal Feature Steering of Neural Quantum States via Sparse Autoencoders cites this paper.

Mechanistic Interpretability and Causal Feature Steering of Neural Quantum States via Sparse Autoencoders InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:18:55.814927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.