Paper Citation Record · LEDGER

On the transferability of Sparse Autoencoders for interpreting compressed models

As of 21 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2507.15977.

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

pith.paper-citation-record.v1
2507.15977 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:24:45.829437Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

50 of 50 outbound references displayed

verified exact1
verified fuzzy10
unresolved38
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 760a9c56-ce75-4a93-adb7-54fe81436325 · outbound

This paper cites Refusal in Language Models Is Mediated by a Single Direction.

On the transferability of Sparse Autoencoders for interpreting compressed models Refusal in Language Models Is Mediated by a Single Direction

Reference 1

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Observation a27462fd-9d0c-40ab-abda-158bbb4ee638 · outbound

This paper cites Batchtopk: A simple improvement for topk-saes, 2024a.

On the transferability of Sparse Autoencoders for interpreting compressed models Batchtopk: A simple improvement for topk-saes, 2024a

Reference 2

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Observation ad631d17-2349-45c2-aa50-c29b83211139 · outbound

This paper cites Towards efficient post-training quantization of pre-trained language models.

On the transferability of Sparse Autoencoders for interpreting compressed models Towards efficient post-training quantization of pre-trained language models

Reference 3

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Observation 7528f4df-94ec-40c7-bdd2-06122632f7fc · outbound

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

On the transferability of Sparse Autoencoders for interpreting compressed models A is for absorption: Studying feature splitting and absorption in sparse autoencoders, 2024

Reference 4

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Observation feb6303c-88b6-4118-9349-2903a1b408f9 · outbound

This paper cites Neuroplasticity and Corruption in Model Mechanisms: A Case Study Of Indirect Object Identification.

On the transferability of Sparse Autoencoders for interpreting compressed models Neuroplasticity and Corruption in Model Mechanisms: A Case Study Of Indirect Object Identification

Reference 5

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Observation 7031dfe9-5c38-4702-a192-33b10aadb435 · outbound

This paper cites Towards automated circuit discovery for mechanistic interpretability.

On the transferability of Sparse Autoencoders for interpreting compressed models Towards automated circuit discovery for mechanistic interpretability

Reference 6

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Source-reported events for the cited work

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Observation 4bb05150-e979-44bb-9967-c25e033bd83f · outbound

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

On the transferability of Sparse Autoencoders for interpreting compressed models Sparse Autoencoders Find Highly Interpretable Features in Language Models

Reference 7

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Observation df359496-e975-4035-b053-f8afe9e7b69a · outbound

This paper cites Sparsegpt: Massive language models can be accurately pruned in one-shot.

On the transferability of Sparse Autoencoders for interpreting compressed models Sparsegpt: Massive language models can be accurately pruned in one-shot

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:24:46.971245Z

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Observation 84963b34-f298-471a-b9dd-55da487127de · outbound

This paper cites SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot.

On the transferability of Sparse Autoencoders for interpreting compressed models SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot

Reference 9

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Observation cf74b71b-4c41-4189-8c0e-a78541647893 · outbound

This paper cites The Pile: An 800GB Dataset of Diverse Text for Language Modeling.

On the transferability of Sparse Autoencoders for interpreting compressed models The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 10

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Observation 21c0515c-4299-4cdc-887a-c30028ecf0d7 · outbound

This paper cites Scaling and evaluating sparse autoencoders.

On the transferability of Sparse Autoencoders for interpreting compressed models Scaling and evaluating sparse autoencoders

Reference 12

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Observation 6aa18751-5fea-4c60-97d7-3b37f0f98a44 · outbound

This paper cites Openwebtext corpus.

On the transferability of Sparse Autoencoders for interpreting compressed models Openwebtext corpus

Reference 13

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Observation 8d7856fb-37c0-42b9-9015-23e72fdc72bd · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

On the transferability of Sparse Autoencoders for interpreting compressed models DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 14

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Observation f25a916e-e558-4824-8691-95b94162b92c · outbound

This paper cites Finding Neurons in a Haystack: Case Studies with Sparse Probing.

On the transferability of Sparse Autoencoders for interpreting compressed models Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 15

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Observation 9fac792e-7112-4c1c-a65e-99e65578bf71 · outbound

This paper cites Learning both Weights and Connections for Efficient Neural Networks.

On the transferability of Sparse Autoencoders for interpreting compressed models Learning both Weights and Connections for Efficient Neural Networks

Reference 16

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Observation ba35d281-c19b-4dce-9d8a-243398d6c23c · outbound

This paper cites How does gpt-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model.

On the transferability of Sparse Autoencoders for interpreting compressed models How does gpt-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:24:46.834494Z

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Observation 3dab2165-8a15-405d-bcf5-f46909beadd2 · outbound

This paper cites Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression.

On the transferability of Sparse Autoencoders for interpreting compressed models Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression

Reference 18

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Source-reported events for the cited work

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Observation 0a599cdc-a07d-4dad-adfb-c16f25508174 · outbound

This paper cites Language model compression with weighted low-rank factorization.

On the transferability of Sparse Autoencoders for interpreting compressed models Language model compression with weighted low-rank factorization

Reference 19

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Observation 3d6b0d2d-e612-4aa8-9daa-30bbd357ac43 · outbound

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On the transferability of Sparse Autoencoders for interpreting compressed models Unresolved cited work

Reference 20

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Source-reported events for the cited work

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Observation 4112d7ac-bf41-414e-88b3-355f5663fc65 · outbound

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

On the transferability of Sparse Autoencoders for interpreting compressed models SAEBench: A Comprehensive Benchmark for Sparse Autoencoders in Language Model Interpretability

Reference 21

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Observation d1b6264e-c550-4059-84da-d981351810e3 · outbound

This paper cites Saebench: A comprehensive benchmark for sparse autoencoders in language model interpretability, 2025 b.

On the transferability of Sparse Autoencoders for interpreting compressed models Saebench: A comprehensive benchmark for sparse autoencoders in language model interpretability, 2025 b

Reference 22

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Observation b13a9366-5b20-4c26-9cd0-b59e55016998 · outbound

This paper cites Quantifying Feature Space Universality Across Large Language Models via Sparse Autoencoders.

On the transferability of Sparse Autoencoders for interpreting compressed models Quantifying Feature Space Universality Across Large Language Models via Sparse Autoencoders

Reference 23

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Observation 24dd1b20-a7a7-4d39-8967-9b1121bcd397 · outbound

This paper cites Taking features out of superposition with sparse autoencoders, 2022.

On the transferability of Sparse Autoencoders for interpreting compressed models Taking features out of superposition with sparse autoencoders, 2022

Reference 24

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Observation ba8ef8f1-9b1d-48bd-96f6-4a8756cb9402 · outbound

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

On the transferability of Sparse Autoencoders for interpreting compressed models Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2

Reference 25

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Observation c53f52fc-e83d-4806-b8a5-1fdd6bd37801 · outbound

This paper cites Awq: Activation-aware weight quantization for on-device llm compression and acceleration.

On the transferability of Sparse Autoencoders for interpreting compressed models Awq: Activation-aware weight quantization for on-device llm compression and acceleration

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:24:46.568765Z

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Observation f2aa06c7-a29f-4bde-869b-0710e68d086c · outbound

This paper cites Llm-pruner: On the structural pruning of large language models.

On the transferability of Sparse Autoencoders for interpreting compressed models Llm-pruner: On the structural pruning of large language models

Reference 27

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Observation f3cb0488-bec5-465d-b1a9-f4b58168d4f2 · outbound

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

On the transferability of Sparse Autoencoders for interpreting compressed models Towards Principled Evaluations of Sparse Autoencoders for Interpretability and Control

Reference 28

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Observation edb2ad18-8efb-47ad-947d-b95b94839753 · outbound

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

On the transferability of Sparse Autoencoders for interpreting compressed models Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models

Reference 29

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Observation c1ad57ed-b05f-4e7c-9dbf-c3bf23fb9dc1 · outbound

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On the transferability of Sparse Autoencoders for interpreting compressed models Transformerlens

Reference 30

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Observation 737c8818-a4d5-49f2-adf8-d23dba927c0c · outbound

This paper cites Sparse Autoencoders Trained on the Same Data Learn Different Features.

On the transferability of Sparse Autoencoders for interpreting compressed models Sparse Autoencoders Trained on the Same Data Learn Different Features

Reference 31

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This paper cites Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity Tracking.

On the transferability of Sparse Autoencoders for interpreting compressed models Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity Tracking

Reference 32

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On the transferability of Sparse Autoencoders for interpreting compressed models Language models are unsupervised multitask learners

Reference 33

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On the transferability of Sparse Autoencoders for interpreting compressed models Jumping Ahead: Improving Reconstruction Fidelity with JumpReLU Sparse Autoencoders

Reference 34

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This paper cites Compressing Large Language Models using Low Rank and Low Precision Decomposition.

On the transferability of Sparse Autoencoders for interpreting compressed models Compressing Large Language Models using Low Rank and Low Precision Decomposition

Reference 35

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On the transferability of Sparse Autoencoders for interpreting compressed models OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models

Reference 36

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On the transferability of Sparse Autoencoders for interpreting compressed models A Simple and Effective Pruning Approach for Large Language Models

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This paper cites Jermyn A., Turner N., Anil C., Denison C., Lasenby R.

On the transferability of Sparse Autoencoders for interpreting compressed models Jermyn A., Turner N., Anil C., Denison C., Lasenby R

Reference 38

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On the transferability of Sparse Autoencoders for interpreting compressed models Unresolved cited work

Reference 39

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On the transferability of Sparse Autoencoders for interpreting compressed models Gemma 2: Improving Open Language Models at a Practical Size

Reference 40

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On the transferability of Sparse Autoencoders for interpreting compressed models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 41

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On the transferability of Sparse Autoencoders for interpreting compressed models Attention is all you need

Reference 42

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This paper cites Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small.

On the transferability of Sparse Autoencoders for interpreting compressed models Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small

Reference 43

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This paper cites High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications.

On the transferability of Sparse Autoencoders for interpreting compressed models High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications

Reference 44

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On the transferability of Sparse Autoencoders for interpreting compressed models Smoothquant: Accurate and efficient post-training quantization for large language models

Reference 45

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Observation 13ecddf7-a8ca-4638-9b6c-345b174940c9 · outbound

This paper cites Beyond Perplexity: Multi-dimensional Safety Evaluation of LLM Compression.

On the transferability of Sparse Autoencoders for interpreting compressed models Beyond Perplexity: Multi-dimensional Safety Evaluation of LLM Compression

Reference 46

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On the transferability of Sparse Autoencoders for interpreting compressed models A Survey on Efficient Inference for Large Language Models

Reference 47

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On the transferability of Sparse Autoencoders for interpreting compressed models write newline

Reference 48

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On the transferability of Sparse Autoencoders for interpreting compressed models @esa (Ref

Reference 49

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On the transferability of Sparse Autoencoders for interpreting compressed models Unresolved cited work

Reference 50

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On the transferability of Sparse Autoencoders for interpreting compressed models dzaL :3,cu' UW

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Pith citing papers

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