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Paper Citation Record · LEDGER

Evaluating SAE interpretability without explanations

As of 23 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 3 inbound Pith citation observations for arXiv:2507.08473.

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

pith.paper-citation-record.v1
2507.08473 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:24:52.521413Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T05:02:51.383123Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T14:23:30.730711Z

Reference resolution

31 of 31 outbound references displayed

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  • verified fuzzy8
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External citation measurements

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Outbound references

Observation 82bcfe99-8fda-4053-8bbf-26f566d20464 · outbound

This paper cites From mechanistic interpretability to mechanistic biology: Training, evaluating, and interpreting sparse autoencoders on protein language models.

Evaluating SAE interpretability without explanations From mechanistic interpretability to mechanistic biology: Training, evaluating, and interpreting sparse autoencoders on protein language models

Reference 1

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Observation 219379b0-a2d0-4faa-8c54-bb64b5abba40 · outbound

This paper cites SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model.

Evaluating SAE interpretability without explanations SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model

Reference 2

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Observation 47144a4c-fd38-4e8c-bbd6-d03a2966b4e6 · outbound

This paper cites Interpretability as Compression: Reconsidering SAE Explanations of Neural Activations with MDL-SAEs.

Evaluating SAE interpretability without explanations Interpretability as Compression: Reconsidering SAE Explanations of Neural Activations with MDL-SAEs

Reference 3

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Observation 75c812de-2076-4659-abb1-1d33c44a1092 · outbound

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

Evaluating SAE interpretability without explanations Language models can explain neurons in language models

Reference 4

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Source-reported events for the cited work

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Observation fe227b72-ccfb-4d8a-b1a8-632bbd80d096 · outbound

This paper cites Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization.

Evaluating SAE interpretability without explanations Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization

Reference 5

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Observation 2c147c9c-345d-4283-805f-31c9f9e3743e · outbound

This paper cites Reading tea leaves: How humans interpret topic models.

Evaluating SAE interpretability without explanations Reading tea leaves: How humans interpret topic models

Reference 6

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Observation 888b6e5f-4340-4154-8c1a-d9d24aa19d0d · outbound

This paper cites A is for absorption: Studying feature splitting and absorption in sparse autoencoders, 2024.

Evaluating SAE interpretability without explanations A is for absorption: Studying feature splitting and absorption in sparse autoencoders, 2024

Reference 7

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Observation 7895f8ae-cbde-4e41-95e7-80cf0be35e5e · outbound

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

Evaluating SAE interpretability without explanations Sparse Autoencoders Find Highly Interpretable Features in Language Models

Reference 8

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Observation ebbef96f-72b4-48ec-9e9e-65f41f72db67 · outbound

This paper cites Scaling and evaluating sparse autoencoders.

Evaluating SAE interpretability without explanations Scaling and evaluating sparse autoencoders

Reference 9

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Observation 9dc2bc81-ca36-432c-96f4-b889f65766a2 · outbound

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

Evaluating SAE interpretability without explanations The Missing Curve Detectors of InceptionV1: Applying Sparse Autoencoders to InceptionV1 Early Vision

Reference 10

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Observation 9bab99dc-d58a-4897-a207-f157f837be27 · outbound

This paper cites Enhancing Automated Interpretability with Output-Centric Feature Descriptions.

Evaluating SAE interpretability without explanations Enhancing Automated Interpretability with Output-Centric Feature Descriptions

Reference 11

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Observation 0a50bec3-c959-4b47-af7f-34a36a929314 · outbound

This paper cites SAEBench: A Comprehensive Benchmark for Sparse Autoencoders in Language Model Interpretability.

Evaluating SAE interpretability without explanations SAEBench: A Comprehensive Benchmark for Sparse Autoencoders in Language Model Interpretability

Reference 12

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Observation e5588cd8-eccd-402a-b791-69b3cb6c5229 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Evaluating SAE interpretability without explanations Adam: A Method for Stochastic Optimization

Reference 13

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Observation 68df6f77-0ab7-4466-a265-7855fc030ba9 · outbound

This paper cites From superposition to sparse codes: interpretable representations in neural networks.

Evaluating SAE interpretability without explanations From superposition to sparse codes: interpretable representations in neural networks

Reference 14

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Observation d09bee25-b238-425f-8bf4-50775d0c9729 · outbound

This paper cites Concept Bottleneck Models.

Evaluating SAE interpretability without explanations Concept Bottleneck Models

Reference 15

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Observation ef608de2-630d-491a-9053-69b01248b126 · outbound

This paper cites Learning biologically relevant features in a pathology foundation model using sparse autoencoders.

Evaluating SAE interpretability without explanations Learning biologically relevant features in a pathology foundation model using sparse autoencoders

Reference 16

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Observation e207ae10-aca5-4ae7-bb5b-b35637abd0ca · outbound

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

Evaluating SAE interpretability without explanations Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2

Reference 17

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Observation 5b231f97-8fd4-4e64-9058-4e18fd2cdbb3 · outbound

This paper cites Compositional Explanations of Neurons.

Evaluating SAE interpretability without explanations Compositional Explanations of Neurons

Reference 18

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Observation 8ed14f3c-06e1-4796-bc8f-70f81dda4c00 · outbound

This paper cites Partially Rewriting a Transformer in Natural Language.

Evaluating SAE interpretability without explanations Partially Rewriting a Transformer in Natural Language

Reference 19

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Source-reported events for the cited work

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Observation 6e6b6711-37ba-499f-b663-95b831dca041 · outbound

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

Evaluating SAE interpretability without explanations Automatically Interpreting Millions of Features in Large Language Models

Reference 20

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Observation 3589bb0f-3603-42e3-84bd-8cb9977fac8b · outbound

This paper cites Spine: Sparse interpretable neural embeddings.

Evaluating SAE interpretability without explanations Spine: Sparse interpretable neural embeddings

Reference 21

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Observation 84a75aa4-6d5a-488b-bd85-be1070abb129 · outbound

This paper cites Unpacking sdxl turbo: Interpreting text-to-image models with sparse autoencoders, 2024.

Evaluating SAE interpretability without explanations Unpacking sdxl turbo: Interpreting text-to-image models with sparse autoencoders, 2024

Reference 22

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Observation 8d2964b7-504a-4f00-9221-c49c86348ed2 · outbound

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Evaluating SAE interpretability without explanations The Llama 3 Herd of Models

Reference 23

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Observation ff64be5b-f0c7-45cc-b993-9effba4be3ac · outbound

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Evaluating SAE interpretability without explanations Daniel Freeman, Theodore R

Reference 24

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Observation 3f58d450-f26b-4f00-aee0-e64a6954daaa · outbound

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Evaluating SAE interpretability without explanations A sample survey study of poly-semantic neurons in deep cnns

Reference 25

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Evaluating SAE interpretability without explanations Zimmermann, Thomas Klein, and Wieland Brendel

Reference 26

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Evaluating SAE interpretability without explanations Zimmermann, David A

Reference 27

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Evaluating SAE interpretability without explanations write newline

Reference 28

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Evaluating SAE interpretability without explanations @esa (Ref

Reference 29

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Evaluating SAE interpretability without explanations Unresolved cited work

Reference 30

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Evaluating SAE interpretability without explanations f.[RQ^ UWM9 =qNղl 8sj W s sn|x` Wy i G2 q N

Reference 31

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Pith citing papers

Observation 383acfd1-95fc-479c-940c-8c7bdf830e13 · inbound

Descriptive Collision in Sparse Autoencoder Auto-Interpretability: When One Explanation Describes Many Features cites this paper.

Descriptive Collision in Sparse Autoencoder Auto-Interpretability: When One Explanation Describes Many Features Evaluating SAE interpretability without explanations

Reference 22

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Observation 6352d0cd-9034-4684-9b59-2d1dda2092a1 · inbound

Sign-Aware Gated Sparse Autoencoders: Modeling Anticorrelated Features with Bi-Jump-ReLU Activations cites this paper.

Sign-Aware Gated Sparse Autoencoders: Modeling Anticorrelated Features with Bi-Jump-ReLU Activations Evaluating SAE interpretability without explanations

Reference 42

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Observation a101d157-3758-431b-9819-edb9f7677b77 · inbound

Sign-Aware Gated Sparse Autoencoders: Modeling Anticorrelated Features with Bi-Jump-ReLU Activations cites this paper.

Sign-Aware Gated Sparse Autoencoders: Modeling Anticorrelated Features with Bi-Jump-ReLU Activations Evaluating SAE interpretability without explanations

Reference 41

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