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Paper Citation Record · LEDGER

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation

As of 15 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2608.13337.

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

pith.paper-citation-record.v1
2608.13337 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:29:00.267182Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

48 of 48 outbound references displayed

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  • verified fuzzy4
  • unresolved40
  • parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 17977574-739a-49f5-bc8c-6c11e73d747d · outbound

This paper cites Harald Baayen, Douglas J.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Harald Baayen, Douglas J

Reference 1

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Observation 03de5da2-0cbf-40a8-914d-212329928d9e · outbound

This paper cites An Interpretability Illusion for BERT.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation An Interpretability Illusion for BERT

Reference 2

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Observation b08f5f83-314d-4684-b7da-f38b5eff8699 · outbound

This paper cites Accounting for Variance in Machine Learning Benchmarks.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Accounting for Variance in Machine Learning Benchmarks

Reference 3

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Observation 25a8a568-89b5-49e2-8489-56268fdd96c7 · outbound

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

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Identifying Functionally Important Features with End-to-End Sparse Dictionary Learning

Reference 4

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Observation 470805c2-54de-4f26-844f-9ad5e7caa693 · outbound

This paper cites an unresolved cited work.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Unresolved cited work

Reference 5

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Observation e1b64e7d-473f-452b-adf0-af13dfbd9674 · outbound

This paper cites Towards monosemanticity: Decomposing language models with dictionary learning.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Towards monosemanticity: Decomposing language models with dictionary learning

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 1ad85e5e-bd9f-4851-8cd3-b15b04bcaea5 · outbound

This paper cites BatchTopK Sparse Autoencoders.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation BatchTopK Sparse Autoencoders

Reference 7

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Observation 360c3052-7cee-4e2f-b4aa-699c95634a54 · outbound

This paper cites With Little Power Comes Great Responsibility.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation With Little Power Comes Great Responsibility

Reference 8

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Observation 28912497-2ae8-455e-bef5-4dac6d18dc86 · outbound

This paper cites Improving Steering Vectors by Targeting Sparse Autoencoder Features.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Improving Steering Vectors by Targeting Sparse Autoencoder Features

Reference 9

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Observation a137336c-1033-4206-b3ca-dee6f17e8fcc · outbound

This paper cites Are Sparse Autoencoder Benchmarks Reliable?.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Are Sparse Autoencoder Benchmarks Reliable?

Reference 10

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Observation 9815c06f-f117-4daa-bcf6-8f5f5787013a · outbound

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

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation A is for absorption: Studying feature splitting and absorption in sparse autoencoders

Reference 11

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Observation de1a9baf-de8d-4c50-8f20-98d5454ac567 · outbound

This paper cites Evaluating Open-Source Sparse Autoencoders on Disentangling Factual Knowledge in GPT-2 Small.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Evaluating Open-Source Sparse Autoencoders on Disentangling Factual Knowledge in GPT-2 Small

Reference 12

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Observation 5ad5e6c1-e21d-40cb-868d-16888d816ce6 · outbound

This paper cites Are Single-Token Sparse Autoencoder Features Causally Necessary? Layer-Depth and SAE-Family Effects.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Are Single-Token Sparse Autoencoder Features Causally Necessary? Layer-Depth and SAE-Family Effects

Reference 13

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 30270f24-7fa3-46cf-abbe-121f5fc68af9 · outbound

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Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Unresolved cited work

Reference 14

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 5daefd8d-c7e4-4f95-be1a-2feb7a3718c2 · outbound

This paper cites Towards Automated Circuit Discovery for Mechanistic Interpretability.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 15

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Observation 2351e3e4-2a91-49b1-9e41-301b3cf5ac74 · outbound

This paper cites Cronbach, Goldine C.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Cronbach, Goldine C

Reference 16

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 035ee7be-9113-43d4-89b6-474c2c0e83f7 · outbound

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

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Sparse Autoencoders Find Highly Interpretable Features in Language Models

Reference 17

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Observation 361a3d0c-9b06-4f55-af07-3b5a84f2a0c6 · outbound

This paper cites Show Your Work: Improved Reporting of Experimental Results.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Show Your Work: Improved Reporting of Experimental Results

Reference 18

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Observation e2c8b7a4-14b2-435e-b6d2-227558dc75df · outbound

This paper cites Transcoders Find Interpretable LLM Feature Circuits.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Transcoders Find Interpretable LLM Feature Circuits

Reference 19

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Observation 4eb4496d-54d1-4312-ab76-ccb61f0b780f · outbound

This paper cites Toy Models of Superposition.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Toy Models of Superposition

Reference 20

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Observation 3787f0e2-4aa9-42ae-a8ed-f1d15f4245f8 · outbound

This paper cites Not All Language Model Features Are One-Dimensionally Linear.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Not All Language Model Features Are One-Dimensionally Linear

Reference 21

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Observation 2670548b-af5c-4c90-a9a4-748d71699080 · outbound

This paper cites Applying sparse autoencoders to unlearn knowledge in language models.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Applying sparse autoencoders to unlearn knowledge in language models

Reference 22

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Observation 246f5897-313f-434c-aafa-01194fc8d889 · outbound

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Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Scaling and evaluating sparse autoencoders

Reference 23

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Observation f5f5b83a-a949-4abb-886d-fd2216003439 · outbound

This paper cites Automated interpretability metrics do not distinguish trained and random transformers.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Automated interpretability metrics do not distinguish trained and random transformers

Reference 24

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Observation 9186709d-ef50-4b96-987e-0918295a1cab · outbound

This paper cites Deep Reinforcement Learning that Matters.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Deep Reinforcement Learning that Matters

Reference 25

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Observation 208ad132-3a09-4c5d-b950-acb40cc9b851 · outbound

This paper cites RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representations.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representations

Reference 26

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Observation d1b785e0-7bad-484c-bac7-8e8dd67f4cee · outbound

This paper cites Judd, Jacob Westfall, and David A.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Judd, Jacob Westfall, and David A

Reference 27

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Observation 87a73097-0c4f-44f4-b5c5-826878052c13 · outbound

This paper cites Are Sparse Autoencoders Useful? A Case Study in Sparse Probing.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Are Sparse Autoencoders Useful? A Case Study in Sparse Probing

Reference 28

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Observation 4d4e8eec-a1fb-46dd-bade-68a0900c164e · outbound

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

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game Models

Reference 29

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Observation cbbd8f16-e1d9-4f43-a074-7d7f632f70ea · outbound

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

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation SAEBench: A Comprehensive Benchmark for Sparse Autoencoders in Language Model Interpretability

Reference 30

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Observation 33ded148-feb6-46bd-ba0b-9159ee474ddf · outbound

This paper cites Interpreting Attention Layer Outputs with Sparse Autoencoders.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Interpreting Attention Layer Outputs with Sparse Autoencoders

Reference 31

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Observation dcd688f6-4243-453a-9ae7-3def7ea1815f · outbound

This paper cites Sanity checks for sparse autoencoders: Do SAEs beat random baselines? 2026.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Sanity checks for sparse autoencoders: Do SAEs beat random baselines? 2026

Reference 32

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doi, observed 2026-08-14T13:29:00.651518Z

Source-reported events for the cited work

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Observation 05b10829-4f1c-4001-9a7c-2183dbf21c1c · outbound

This paper cites AtP*: An efficient and scalable method for localizing LLM behaviour to components.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation AtP*: An efficient and scalable method for localizing LLM behaviour to components

Reference 33

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Observation f3169f2d-92dc-4e78-9ef1-f84ae9659147 · outbound

This paper cites Sparse Autoencoders Do Not Find Canonical Units of Analysis.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Sparse Autoencoders Do Not Find Canonical Units of Analysis

Reference 34

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Observation 10618136-69dc-4822-833b-23943c518768 · outbound

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

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2

Reference 35

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Observation c00defde-295c-4047-9fcf-c013347f03ce · outbound

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

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Towards Principled Evaluations of Sparse Autoencoders for Interpretability and Control

Reference 36

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Observation 43d9f0fc-f235-41d5-b12f-f6cbb18a43f9 · outbound

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

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-14T13:29:00.207773Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T13:29:00.207773Z digest=sha256:2973efc8e8c172046ba19eadb07431555e0b7f0a68c934d4b86475180dddd99e

Observation 18429f93-e334-415c-8223-95ccd883cffa · outbound

This paper cites Locating and Editing Factual Associations in GPT.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Locating and Editing Factual Associations in GPT

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-14T13:29:00.213112Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T13:29:00.213112Z digest=sha256:185fe75ceedc17c59bf5d553b5a5f59c7ebc214e73f0acc21e87411740fa614c

Observation 290ae721-1b8d-4aa1-aebd-1543b9d68db4 · outbound

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

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation The Linear Representation Hypothesis and the Geometry of Large Language Models

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-14T13:29:00.218192Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T13:29:00.218192Z digest=sha256:77def1e3cdbb33a30967ca01c98b1f354b1cf5702be4ec22f71ddd7c4d652f93

Observation fb5c0c8f-57be-4e01-8c52-d49b6e36df09 · outbound

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

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Sparse Autoencoders Trained on the Same Data Learn Different Features

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-14T13:29:00.224169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T13:29:00.224169Z digest=sha256:a773234b4a333c965fea18364d80afbb90a343a7ec92aaf1a5c5b829dd743e0e

Observation d8a039e0-bfe1-49cb-a84c-869d190e77b2 · outbound

This paper cites Improving Dictionary Learning with Gated Sparse Autoencoders.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Improving Dictionary Learning with Gated Sparse Autoencoders

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-14T13:29:00.229578Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T13:29:00.229578Z digest=sha256:d4fda335ef092d5dd1ba0b0e8e9026810307752d0c067e89bde7bb3c9afc6cec

Observation ff7f457b-422d-455d-96ef-1d84246f5085 · outbound

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

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Jumping Ahead: Improving Reconstruction Fidelity with JumpReLU Sparse Autoencoders

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-14T13:29:00.234917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T13:29:00.234917Z digest=sha256:35f5aa05657dcb11c9065b97c0e4bba460e1386cce13b4fd4ef3c15a5a99b6ee

Observation 0aeda0f8-6b7e-47ef-a303-3d54abdeb01e · outbound

This paper cites Shavelson and Noreen M.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Shavelson and Noreen M

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T13:29:01.222719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-14T13:29:00.241256Z digest=sha256:2b952d979da5c8699d7b47f7e22cf2163e5d1e689614cd732978ff0590b15314

Observation d40ffc58-ad06-4d63-afa0-a925f1335367 · outbound

This paper cites Attribution Patching Outperforms Automated Circuit Discovery.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Attribution Patching Outperforms Automated Circuit Discovery

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-14T13:29:00.245932Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T13:29:00.245932Z digest=sha256:559525b94c45a1f0ff5b3e71494f1b977d4e3537da1f21a2327bc620350681f8

Observation e92ba115-fb63-4b72-a75f-ccb74ce3cbf6 · outbound

This paper cites Daniel Freeman, Theodore R.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Daniel Freeman, Theodore R

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T13:29:01.209082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-14T13:29:00.251304Z digest=sha256:f0364951076314db4790ed5fad3d2458103078a2aedab53ba40794188eddc751

Observation d778b326-29f1-435b-a0df-532b5daf4f8a · outbound

This paper cites Steering Language Models With Activation Engineering.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Steering Language Models With Activation Engineering

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-14T13:29:00.256147Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T13:29:00.256147Z digest=sha256:4dcbd28f631c4fe914d7139b9e51d3d9684991bb8d7d98a79007ca3f0dba3e10

Observation ce275fd6-2cc2-4999-b5cf-f6993f947520 · outbound

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

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-14T13:29:00.261936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T13:29:00.261936Z digest=sha256:ca22da019a9d1b11829334c2c1aff9c6ec328ceb98ce7445ff2b6f2d6d29a911

Observation 0d896da1-8bc0-48af-9774-ee3090accba8 · outbound

This paper cites Towards Best Practices of Activation Patching in Language Models: Metrics and Methods.

Where You Measure Decides What You Measure: Position Selection in Ablation-Based SAE Evaluation Towards Best Practices of Activation Patching in Language Models: Metrics and Methods

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-14T13:29:00.267182Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-14T13:29:00.267182Z digest=sha256:47542b3b9dd6dbe09a52503b7983730887d1ed544c2e73ffd57a8da1bfcdc27b

Pith citing papers

No inbound Pith citation observations are available.