Paper Citation Record · LEDGER

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit

As of 18 August 2026, this Paper Citation Record lists 100 of 172 outbound references and 5 inbound Pith citation observations for arXiv:2512.10092.

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

pith.paper-citation-record.v1
2512.10092 v2

Coverage vector

measured 100 of 172 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T17:19:40.443666Z

measured 105 of 105 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T00:24:40.152374Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T10:29:45.387096Z

Reference resolution

100 of 172 outbound references displayed

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External citation measurements

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Outbound references

Observation a7af40d6-6f8a-480d-8117-891a824b348f · outbound

This paper cites Miranda, Alisa Liu, Nouha Dziri, Shane Lyu, Yuling Gu, Saumya Malik, Victoria Graf, Jena D.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Miranda, Alisa Liu, Nouha Dziri, Shane Lyu, Yuling Gu, Saumya Malik, Victoria Graf, Jena D

Reference 1

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Observation 3292f9b4-899b-47ac-a14d-bdc56ca3327f · outbound

This paper cites Introducing docent.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Introducing docent

Reference 2

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Observation 46925ca9-0dfb-44ca-8158-c2d4f3d6e712 · outbound

This paper cites Semantic operators: A declarative model for rich, ai-based data processing, 2025.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Semantic operators: A declarative model for rich, ai-based data processing, 2025

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T17:19:30.233824Z

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Observation a85fcf5b-c35b-4121-b0f1-bac227d07260 · outbound

This paper cites Parameswaran, and Eugene Wu.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Parameswaran, and Eugene Wu

Reference 4

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Observation b7f64f93-a936-428f-a9b3-5c301cef0fe5 · outbound

This paper cites an unresolved cited work.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Unresolved cited work

Reference 5

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Observation f9b78d4a-602d-4247-b4a5-732bd4445216 · outbound

This paper cites The order effect: Investigating prompt sensitivity to input order in llms, 2025.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit The order effect: Investigating prompt sensitivity to input order in llms, 2025

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T17:19:30.541002Z

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Observation d5825de2-20bb-4612-ac98-f5e4eb32f5d6 · outbound

This paper cites Sentence-bert: Sentence embeddings using siamese bert- networks, 2019.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Sentence-bert: Sentence embeddings using siamese bert- networks, 2019

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T17:19:30.636042Z

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Observation 38436c79-014e-4997-b28c-69b5ef653830 · outbound

This paper cites Sparse autoencoders find highly interpretable features in language models, 2023.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Sparse autoencoders find highly interpretable features in language models, 2023

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T17:19:30.822864Z

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Observation 8ed9098e-5393-4135-89d6-6e5275fc5f81 · outbound

This paper cites Towards monosemanticity: Decomposing language models with dictionary learning.Transformer Circuits Thread, 2023.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Towards monosemanticity: Decomposing language models with dictionary learning.Transformer Circuits Thread, 2023

Reference 9

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Observation 730f7be2-6d1f-45af-8648-a8f44063b960 · outbound

This paper cites Daniel Freeman, Theodore R.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Daniel Freeman, Theodore R

Reference 10

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Observation 66bf4353-70a4-4cdc-b2fa-b8d849ec2a33 · outbound

This paper cites A vector space model for automatic indexing.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit A vector space model for automatic indexing

Reference 11

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Observation 6e8f8b75-b89a-4e5a-a266-af62de89ab92 · outbound

This paper cites Term weighting approaches in automatic text retrieval.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Term weighting approaches in automatic text retrieval

Reference 12

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Observation 3e5dc2b2-b6dd-4762-9c32-b91c4c2466b9 · outbound

This paper cites Semaxis: A lightweight framework to charac- terize domain-specific word semantics beyond sentiment, 2018.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Semaxis: A lightweight framework to charac- terize domain-specific word semantics beyond sentiment, 2018

Reference 13

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Observation 964dcdb5-c31b-4a89-a01d-1954aafe1a23 · outbound

This paper cites The polar framework: Polar opposites enable interpretability of pre-trained word embeddings, 2020.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit The polar framework: Polar opposites enable interpretability of pre-trained word embeddings, 2020

Reference 14

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Observation 89aaf0bd-c221-4e11-8bf7-ab2c72fd9069 · outbound

This paper cites Frameaxis: characterizing mi- croframe bias and intensity with word embedding.PeerJ Computer Science, 7:e644, July 2021.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Frameaxis: characterizing mi- croframe bias and intensity with word embedding.PeerJ Computer Science, 7:e644, July 2021

Reference 15

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Observation 409a20d0-5522-4f41-925d-f0de6551fb81 · outbound

This paper cites Learning interpretable word embeddings via bidirectional alignment of dimensions with semantic concepts.Information Processing & Management, 59(3):102925, 2022.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Learning interpretable word embeddings via bidirectional alignment of dimensions with semantic concepts.Information Processing & Management, 59(3):102925, 2022

Reference 16

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Observation 20ecbc5d-e807-4e02-a7df-3e73fbb04d5b · outbound

This paper cites Sensepolar: Word sense aware interpretability for pre-trained contextual word embeddings, 2023.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Sensepolar: Word sense aware interpretability for pre-trained contextual word embeddings, 2023

Reference 17

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Observation 0ce40590-88c9-4756-9d59-d551757e0bb7 · outbound

This paper cites Morris, Richard Antonello, Ion Stoica, Alexander G.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Morris, Richard Antonello, Ion Stoica, Alexander G

Reference 18

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Observation c1260263-24c1-4d97-bdf0-bf3a68d26453 · outbound

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Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Unresolved cited work

Reference 19

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Observation fefc0b8d-94e2-4ebe-868d-c5b44d03f927 · outbound

This paper cites Interpret and control dense retrieval with sparse latent features, 2025.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Interpret and control dense retrieval with sparse latent features, 2025

Reference 20

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Observation 806671c4-e6bd-42b4-8458-b94559a59bce · outbound

This paper cites Sparse autoencoders for hypothesis generation, 2025.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Sparse autoencoders for hypothesis generation, 2025

Reference 21

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Observation f09d5e46-d0ae-4287-8cd0-2d644ff8eb46 · outbound

This paper cites Vibecheck: Discover and quantify qualitative differences in large language models, 2025.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Vibecheck: Discover and quantify qualitative differences in large language models, 2025

Reference 22

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Observation fc21127c-0b18-46be-9f2b-30c4c0ca64a2 · outbound

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Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Unresolved cited work

Reference 23

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Observation 8f6d8bab-8207-4ca6-8171-59c9edffcb79 · outbound

This paper cites Zico Kolter, and Zhuang Liu.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Zico Kolter, and Zhuang Liu

Reference 24

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Observation 572a5b1c-b39b-4c49-a650-6626735708dc · outbound

This paper cites Llm comparator: Visual analytics for side-by-side evaluation of large language models, 2024.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Llm comparator: Visual analytics for side-by-side evaluation of large language models, 2024

Reference 25

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Observation cb73cbf1-efb4-4132-96e0-1eacf54e502e · outbound

This paper cites Automatically interpreting millions of features in large language models, 2024.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Automatically interpreting millions of features in large language models, 2024

Reference 26

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Observation 45fdb733-4727-4909-bc0c-d10d192371ac · outbound

This paper cites Llama 3.3 model card.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Llama 3.3 model card

Reference 27

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Observation e07ca0f2-9535-401c-b2c4-566bfd7e7de5 · outbound

This paper cites P Xing, Joseph E.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit P Xing, Joseph E

Reference 28

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This paper cites Understanding and steering llama 3 with sparse autoencoders, 2024.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Understanding and steering llama 3 with sparse autoencoders, 2024

Reference 29

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Observation f81f25fc-dfb5-49eb-952f-b6ccf4cf49d1 · outbound

This paper cites Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities, 2025.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities, 2025

Reference 30

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Observation d1813a0f-4006-43fc-b98d-4e3d949e10c2 · outbound

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Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Openai embeddings

Reference 31

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Observation edca56af-7617-415c-910b-7686a39366c1 · outbound

This paper cites Gonzalez, and Serena Yeung-Levy.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Gonzalez, and Serena Yeung-Levy

Reference 32

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Observation 67a521da-2083-4f55-9fe9-cd92cfcdddee · outbound

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Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Maas, Raymond E

Reference 33

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This paper cites P Xing, Hao Zhang, Joseph E.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit P Xing, Hao Zhang, Joseph E

Reference 34

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Observation 04db1457-1b58-429c-b5b3-f2fd98f87b9b · outbound

This paper cites Llava-next: Improved reasoning, ocr, and world knowledge, January 2024.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Llava-next: Improved reasoning, ocr, and world knowledge, January 2024

Reference 35

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Observation 84d09e38-6549-4bdd-872b-338c6a28d832 · outbound

This paper cites Measuring coding challenge competence with apps.NeurIPS, 2021.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Measuring coding challenge competence with apps.NeurIPS, 2021

Reference 36

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Observation 31c115d1-75e3-4595-92bb-fd4b281e8a43 · outbound

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Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Unresolved cited work

Reference 37

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This paper cites Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification

Reference 38

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This paper cites Smith, Luke Zettlemoyer, and Tao Yu.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Smith, Luke Zettlemoyer, and Tao Yu

Reference 39

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Observation 2ed8f8d1-1d55-45cd-b071-b8398356eb98 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Training Verifiers to Solve Math Word Problems

Reference 40

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This paper cites Ms marco: A human generated machine reading comprehension dataset.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Ms marco: A human generated machine reading comprehension dataset

Reference 41

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Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit MTEB: Massive Text Embedding Benchmark

Reference 42

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Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Mmteb: Massive multilingual text embedding benchmark

Reference 43

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This paper cites Description-based text similarity, 2024.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Description-based text similarity, 2024

Reference 44

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Observation eaff7793-0300-475b-aba4-2a736c24ba4c · outbound

This paper cites Llama-nemotron: Efficient reasoning models, 2025.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Llama-nemotron: Efficient reasoning models, 2025

Reference 45

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This paper cites The pile: An 800gb dataset of diverse text for language modeling, 2020.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit The pile: An 800gb dataset of diverse text for language modeling, 2020

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Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Clement, Matthew Bierbaum, Kevin P

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Observation 319f2056-d3fa-4a62-ad37-9b96af12bda3 · outbound

This paper cites Hierarchical neural story generation, 2018.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Hierarchical neural story generation, 2018

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Observation 5b77418a-6363-4f40-8865-b7f88a798616 · outbound

This paper cites Verbosity bias in preference labeling by large language models, 2023.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Verbosity bias in preference labeling by large language models, 2023

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Observation 408bd0cd-0a9b-40e4-b41e-74aebd0b4680 · outbound

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

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit A is for absorption: Studying feature splitting and absorption in sparse autoencoders, 2024

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Observation 220c464c-d3a7-49eb-b597-8a29ece7b2e2 · outbound

This paper cites Describing differences between text distributions with natural language, 2022.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Describing differences between text distributions with natural language, 2022

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Observation a5e64ecb-b723-4e02-94e8-869e6e4a1f9e · outbound

This paper cites Goal driven discovery of distributional differences via language descriptions, 2023.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Goal driven discovery of distributional differences via language descriptions, 2023

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Observation dc746728-2405-4657-8e65-2c931af93553 · outbound

This paper cites Are sparse autoencoders useful? a case study in sparse probing, 2025.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Are sparse autoencoders useful? a case study in sparse probing, 2025

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Observation 4605348b-42e2-4ef4-8864-71ec795e09c7 · outbound

This paper cites Survey of word co-occurrence measures for collocation detection.Computa- cion y Sistemas, 20:327–344, 09 2016.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Survey of word co-occurrence measures for collocation detection.Computa- cion y Sistemas, 20:327–344, 09 2016

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Observation 37c64b85-873d-42e0-9220-e1a238833325 · outbound

This paper cites Word association norms, mutual information, and lexicography.Computational Linguistics, 16(1):22–29, 1990.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Word association norms, mutual information, and lexicography.Computational Linguistics, 16(1):22–29, 1990

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Resolution: unresolved; no resolver link, observed 2026-08-03T17:19:36.543960Z

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Observation 1b2dbddb-2f54-44ae-b909-d4dc35f85fbe · outbound

This paper cites The probabilistic relevance framework: Bm25 and beyond.Foundations and Trends in Information Retrieval, 3:333–389, 01 2009.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit The probabilistic relevance framework: Bm25 and beyond.Foundations and Trends in Information Retrieval, 3:333–389, 01 2009

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Observation 21edfa56-0966-495b-90c7-d0ea0a946d03 · outbound

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Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Unresolved cited work

Reference 57

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Observation 16db68e1-46a5-4c9e-b6b0-6e8593d5e099 · outbound

This paper cites A tutorial on spectral clustering, 2007.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit A tutorial on spectral clustering, 2007

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Observation cfaa9464-7a0f-4f52-be03-d3cb8f8834af · outbound

This paper cites hdbscan: Hierarchical density based clustering.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit hdbscan: Hierarchical density based clustering

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Observation 0dd98c62-98f2-4473-bcf6-a8fe58716c9c · outbound

This paper cites Constrained k-means clustering with background knowledge.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Constrained k-means clustering with background knowledge

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Observation 5ab44429-4755-4f77-a228-0c1235046eb5 · outbound

This paper cites Distance metric learning with application to clustering with side-information.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Distance metric learning with application to clustering with side-information

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Observation b4de6597-3eb2-4178-9759-393a83785c95 · outbound

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Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Unresolved cited work

Reference 62

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Observation 0b90a111-eba9-4bc4-823a-80c98e667054 · outbound

This paper cites an unresolved cited work.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Unresolved cited work

Reference 63

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Observation ead0d79e-87c7-4a8f-8caf-df6395ef92c6 · outbound

This paper cites Dasgupta and V.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Dasgupta and V

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Observation db58db1d-4767-437e-97f1-e9d8a1f9eac3 · outbound

This paper cites Local algorithms for interactive clustering.Journal of Machine Learning Research, 18(3):1–35, 2017.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Local algorithms for interactive clustering.Journal of Machine Learning Research, 18(3):1–35, 2017

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Observation 9093079a-f286-4fc9-aa0e-426260c7e429 · outbound

This paper cites Interactive topic modeling.Mach.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Interactive topic modeling.Mach

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Resolution: unresolved; no resolver link, observed 2026-08-03T17:19:37.528575Z

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Observation 1c2a54a0-9f37-4a79-ae31-375a8b16f554 · outbound

This paper cites Lita: An efficient llm-assisted iterative topic augmentation framework, 2025.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Lita: An efficient llm-assisted iterative topic augmentation framework, 2025

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Observation c0720a15-4589-409d-bee6-cfbda14f524a · outbound

This paper cites Large language models enable few-shot clustering, 2023.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Large language models enable few-shot clustering, 2023

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Observation ffc08bea-1e27-4538-bacc-a834454da969 · outbound

This paper cites Interpretable company similarity with sparse autoencoders, 2025.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Interpretable company similarity with sparse autoencoders, 2025

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Resolution: unresolved; no resolver link, observed 2026-08-03T17:19:37.885316Z

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Observation d04ef121-7631-42bb-be9a-5ec070f1f7c2 · outbound

This paper cites Sgpt: Gpt sentence embeddings for semantic search, 2022.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Sgpt: Gpt sentence embeddings for semantic search, 2022

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Resolution: unresolved; no resolver link, observed 2026-08-03T17:19:37.962018Z

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Observation e0874b7b-8875-4ddb-9bb2-2b90516f57e6 · outbound

This paper cites Improving text embeddings with large language models, 2024.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Improving text embeddings with large language models, 2024

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Resolution: unresolved; no resolver link, observed 2026-08-03T17:19:38.021982Z

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Observation 4f3543bd-63fc-4cb0-99da-6c3b25dfb1b3 · outbound

This paper cites Nv-embed: Improved techniques for training llms as generalist embedding models, 2025.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Nv-embed: Improved techniques for training llms as generalist embedding models, 2025

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Resolution: unresolved; no resolver link, observed 2026-08-03T17:19:38.124435Z

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Observation a1d8c650-ff3f-4856-81fc-bf6c3b9d6e42 · outbound

This paper cites Scaling sentence embeddings with large language models.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Scaling sentence embeddings with large language models

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Observation 06d09b19-f302-41eb-b1b1-5a5ac6ac8b60 · outbound

This paper cites Gonzalez, and Ion Stoica.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Gonzalez, and Ion Stoica

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Resolution: unresolved; no resolver link, observed 2026-08-03T17:19:38.353770Z

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Observation c905c81c-b813-4c5e-96f5-b292a5b764b5 · outbound

This paper cites Correlated topic models.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Correlated topic models

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-03T17:19:38.459577Z

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Observation 07d0d211-8606-445b-8f39-e14326e7b642 · outbound

This paper cites bab2min/tomotopy: 0.12.3, July 2022.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit bab2min/tomotopy: 0.12.3, July 2022

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Resolution: unresolved; no resolver link, observed 2026-08-03T17:19:38.541290Z

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Observation 72c843b0-3401-4516-82cd-e02229801a0d · outbound

This paper cites an unresolved cited work.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Unresolved cited work

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-03T17:19:38.618693Z

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Observation e5e815b9-b150-4d48-b07c-71fad58c84ab · outbound

This paper cites Semeval-2017 task 4: Sentiment analysis in twitter.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Semeval-2017 task 4: Sentiment analysis in twitter

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-03T17:19:38.740168Z

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Observation ea773cc5-13d4-4dd1-8378-eb2eeb900024 · outbound

This paper cites CARER: Contextualized affect representations for emotion recognition.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit CARER: Contextualized affect representations for emotion recognition

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Resolution: unresolved; no resolver link, observed 2026-08-03T17:19:38.846793Z

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Observation 6cc92349-6860-488e-ad67-a6c6280078d9 · outbound

This paper cites Gemini embedding: Generalizable embeddings from gemini, 2025.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Gemini embedding: Generalizable embeddings from gemini, 2025

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Resolution: unresolved; no resolver link, observed 2026-08-03T17:19:38.983987Z

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Observation 2c88db1d-ce9a-4567-9e29-bdf6bde7fff3 · outbound

This paper cites Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-03T17:19:39.079534Z

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Observation bea08c0a-d11c-474f-8634-39e3b1128828 · outbound

This paper cites Bm25s: Orders of magnitude faster lexical search via eager sparse scoring, 2024.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Bm25s: Orders of magnitude faster lexical search via eager sparse scoring, 2024

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Resolution: unresolved; no resolver link, observed 2026-08-03T17:19:39.142743Z

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Observation 223d4777-887a-4aeb-a53e-e37b3b12dc75 · outbound

This paper cites Cormack, Charles L A Clarke, and Stefan Buettcher.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Cormack, Charles L A Clarke, and Stefan Buettcher

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Resolution: unresolved; no resolver link, observed 2026-08-03T17:19:39.222430Z

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Observation d97e962d-8e48-4311-9d97-97206a5c7df7 · outbound

This paper cites A similarity measure for indefinite rankings.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit A similarity measure for indefinite rankings

Reference 84

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Observation 363a1b07-2063-474e-99fb-62585e4cc05c · outbound

This paper cites Measuring sparse autoencoder feature sensitivity.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Measuring sparse autoencoder feature sensitivity

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Resolution: unresolved; no resolver link, observed 2026-08-03T17:19:39.381023Z

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Observation d997c5ef-ceb7-43d1-a145-921d6550cbce · outbound

This paper cites Assistant states it lacks information.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Assistant states it lacks information

Reference 86

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Observation 0a6bc61e-4e21-4375-ac87-5eff9edb3289 · outbound

This paper cites Look at the context BEFORE the marked tokens as well - the preceding tokens often provide crucial information about what the feature is detecting.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Look at the context BEFORE the marked tokens as well - the preceding tokens often provide crucial information about what the feature is detecting

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-03T17:19:39.507125Z

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Observation e6480cc7-697a-4d61-98ed-4e286c7835bb · outbound

This paper cites an unresolved cited work.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Unresolved cited work

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-03T17:19:39.555905Z

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Observation 0654b223-e0ff-4d07-94d2-a839008290f1 · outbound

This paper cites label":.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit label":

Reference 89

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Observation c060bee2-21b1-42b4-bb32-4b8a92497992 · outbound

This paper cites category.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit category

Reference 90

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Observation 0721499e-d50d-4990-9b3f-86a4d438c72e · outbound

This paper cites This response.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit This response

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-03T17:19:39.823660Z

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Observation ec7e9809-916a-4623-8543-4ab2f2cad955 · outbound

This paper cites an unresolved cited work.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Unresolved cited work

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-03T17:19:39.906633Z

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Observation 598a4f7c-bc9f-40b2-a492-e3574ce7b840 · outbound

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Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Unresolved cited work

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-03T17:19:39.966783Z

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Observation 3dc8e1ef-05fc-46f2-b44d-faa1117a02fd · outbound

This paper cites If the property is more frequent in Model A, the percentage difference should be positive.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit If the property is more frequent in Model A, the percentage difference should be positive

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-03T17:19:40.035218Z

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Observation 7fa7a212-1796-408d-a71b-f51d554fbabd · outbound

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Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit differences

Reference 95

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Observation b364c748-0eb9-427a-93cc-021ff8e3281c · outbound

This paper cites This response.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit This response

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-03T17:19:40.199666Z

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Observation bf15bb94-47e0-43fd-bbae-fbd5e88c0ac7 · outbound

This paper cites The context BEFORE the marked tokens often provides crucial information about what the feature is detecting.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit The context BEFORE the marked tokens often provides crucial information about what the feature is detecting

Reference 97

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Observation aa68357c-96b6-4c63-928a-896e52283082 · outbound

This paper cites an unresolved cited work.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit Unresolved cited work

Reference 98

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Observation 8bddb77f-9871-488a-a7bb-bccfb5823ac3 · outbound

This paper cites If you see <<eot_id>> in the samples, ignore it as it’s just a technical marker for the end of text, not a meaningful activation.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit If you see <<eot_id>> in the samples, ignore it as it’s just a technical marker for the end of text, not a meaningful activation

Reference 99

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Observation e79971ff-fcc8-405d-bf5d-a018d0c0d70f · outbound

This paper cites If the feature description does not accurately describe the tokens marked with << >>, you should disregard the feature.

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit If the feature description does not accurately describe the tokens marked with << >>, you should disregard the feature

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-03T17:19:40.443666Z

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Pith citing papers

Observation 7abfb311-932e-4538-844c-aed88212f061 · inbound

In your own words: computationally identifying interpretable themes in free-text survey data cites this paper.

In your own words: computationally identifying interpretable themes in free-text survey data Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit

Reference 14

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Observation 717b4859-b48d-4d4e-8e5f-a58d9e185b12 · inbound

Three Years of r/ChatGPT: Societal Impact Evaluations from Social Media Data cites this paper.

Three Years of r/ChatGPT: Societal Impact Evaluations from Social Media Data Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit

Reference 6

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Observation 292e0a6c-1dd2-4b28-b7d3-a61cfd3c6e3a · inbound

Anatomy of Post-Training: Using Interpretability to Characterize Data and Shape the Learning Signal cites this paper.

Anatomy of Post-Training: Using Interpretability to Characterize Data and Shape the Learning Signal Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit

Reference 46

Resolution: metadata mismatch; arxiv_id, observed 2026-07-24T01:23:02.615718Z

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Observation 4da9c3a9-8633-4acb-848d-834225595fd2 · inbound

Do Sparse Autoencoders Learn Meaningful Concept Hierarchies? cites this paper.

Do Sparse Autoencoders Learn Meaningful Concept Hierarchies? Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-07-24T01:23:02.615718Z

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Observation 09f3bbf2-d274-47e1-aa5b-6ec9a82f2f36 · inbound

Subliminal Learning is Non-Semantic Distillation cites this paper.

Subliminal Learning is Non-Semantic Distillation Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit

Reference 13

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