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Paper Citation Record · LEDGER

Scaling Inherently Interpretable Language Models

As of 12 August 2026, this Paper Citation Record lists 100 of 193 outbound references and 0 inbound Pith citation observations for arXiv:2608.07594.

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

pith.paper-citation-record.v1
2608.07594 v1

Coverage vector

measured 100 of 193 reference resolution

Typed states for the displayed outbound observations.

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measured 100 of 100 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

100 of 193 outbound references displayed

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Outbound references

Observation 899a6c02-aea0-4c89-abd3-6cb70d86a9b1 · outbound

This paper cites an unresolved cited work.

Scaling Inherently Interpretable Language Models Unresolved cited work

Reference 1

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Observation 52877553-34d2-4ca8-bf41-a058a7253f7b · outbound

This paper cites Second Conference on Language Modeling , year=.

Scaling Inherently Interpretable Language Models Second Conference on Language Modeling , year=

Reference 2

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Observation 085065e1-099c-4e17-8457-ded134ea55f8 · outbound

This paper cites The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=.

Scaling Inherently Interpretable Language Models The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=

Reference 3

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Observation 5ea2baa5-71d3-486a-99d1-f55881eaa259 · outbound

This paper cites Plug and Play Language Models: A Simple Approach to Controlled Text Generation.

Scaling Inherently Interpretable Language Models Plug and Play Language Models: A Simple Approach to Controlled Text Generation

Reference 4

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This paper cites Findings of the Association for Computational Linguistics: ACL 2022 , pages=.

Scaling Inherently Interpretable Language Models Findings of the Association for Computational Linguistics: ACL 2022 , pages=

Reference 5

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This paper cites Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=.

Scaling Inherently Interpretable Language Models Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

Reference 6

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This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Scaling Inherently Interpretable Language Models Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 7

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Observation d10e44d3-54fc-44f3-b888-2d4f4fb20e48 · outbound

This paper cites Proceedings of the AAAI conference on artificial intelligence , volume=.

Scaling Inherently Interpretable Language Models Proceedings of the AAAI conference on artificial intelligence , volume=

Reference 8

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Observation 2a4e0fcd-015e-4ec3-b572-ec2a278b8eb4 · outbound

This paper cites Communications of the ACM , volume=.

Scaling Inherently Interpretable Language Models Communications of the ACM , volume=

Reference 9

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This paper cites Training Compute-Optimal Large Language Models.

Scaling Inherently Interpretable Language Models Training Compute-Optimal Large Language Models

Reference 10

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This paper cites Scaling Law for Language Models Training Considering Batch Size.

Scaling Inherently Interpretable Language Models Scaling Law for Language Models Training Considering Batch Size

Reference 11

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This paper cites Findings of the Association for Computational Linguistics: EMNLP 2021 , pages=.

Scaling Inherently Interpretable Language Models Findings of the Association for Computational Linguistics: EMNLP 2021 , pages=

Reference 12

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This paper cites Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , pages=.

Scaling Inherently Interpretable Language Models Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , pages=

Reference 13

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Observation 75935f09-d0e3-4a4c-8caa-ce0798f25d62 · outbound

This paper cites MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies.

Scaling Inherently Interpretable Language Models MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies

Reference 14

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Scaling Inherently Interpretable Language Models arXiv preprint arXiv:2512.10858 , year=

Reference 15

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Observation 97d01b97-159d-4b1d-b016-25632a6c467c · outbound

This paper cites The Llama 3 Herd of Models.

Scaling Inherently Interpretable Language Models The Llama 3 Herd of Models

Reference 16

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Scaling Inherently Interpretable Language Models arXiv preprint arXiv:2510.03280 , year=

Reference 17

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Scaling Inherently Interpretable Language Models Scaling up Masked Diffusion Models on Text

Reference 18

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Scaling Inherently Interpretable Language Models arXiv preprint arXiv:2602.15014 , year=

Reference 19

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Observation 1df9f4ae-55b2-442f-a961-1f9db316aff8 · outbound

This paper cites DeepSeek LLM: Scaling Open-Source Language Models with Longtermism.

Scaling Inherently Interpretable Language Models DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

Reference 20

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Scaling Inherently Interpretable Language Models International conference on machine learning , pages=

Reference 21

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Scaling Inherently Interpretable Language Models The Thirteenth International Conference on Learning Representations , year=

Reference 22

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Scaling Inherently Interpretable Language Models Proceedings of the 57th annual meeting of the association for computational linguistics , pages=

Reference 23

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Observation c3d7b839-14d2-4cce-857d-831b2531fea3 · outbound

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Scaling Inherently Interpretable Language Models Promises and Pitfalls of Black-Box Concept Learning Models

Reference 24

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Observation b8f3e0c2-2b8b-40eb-a641-d1e059477cf1 · outbound

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Scaling Inherently Interpretable Language Models Towards Unifying Interpretability and Control: Evaluation via Intervention

Reference 25

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This paper cites AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse Autoencoders.

Scaling Inherently Interpretable Language Models AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse Autoencoders

Reference 26

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Scaling Inherently Interpretable Language Models International Conference on Machine Learning , pages=

Reference 27

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Scaling Inherently Interpretable Language Models Proceedings of the 26th annual international conference on machine learning , pages=

Reference 28

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Scaling Inherently Interpretable Language Models International conference on machine learning , pages=

Reference 29

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Scaling Inherently Interpretable Language Models The annals of mathematical statistics , volume=

Reference 30

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Scaling Inherently Interpretable Language Models Journal of the Royal statistical society: series B (Methodological) , volume=

Reference 31

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Scaling Inherently Interpretable Language Models Journal of Pharmacokinetics and Biopharmaceutics , volume=

Reference 32

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Scaling Inherently Interpretable Language Models Social psychological and personality science , volume=

Reference 33

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Scaling Inherently Interpretable Language Models GPT-4 Technical Report

Reference 34

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Scaling Inherently Interpretable Language Models 2024 , url =

Reference 35

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Scaling Inherently Interpretable Language Models Gemma: Open Models Based on Gemini Research and Technology

Reference 36

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Scaling Inherently Interpretable Language Models DeepSeek-V3 Technical Report

Reference 37

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Scaling Inherently Interpretable Language Models Unresolved cited work

Reference 38

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Scaling Inherently Interpretable Language Models Gemini: A Family of Highly Capable Multimodal Models

Reference 39

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Scaling Inherently Interpretable Language Models Qwen Technical Report

Reference 40

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Observation 590d1a1f-897b-4705-a3ec-7be135aa7f54 · outbound

This paper cites Olmo 3.

Scaling Inherently Interpretable Language Models Olmo 3

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Observation 80e3cae1-ffc1-49b5-8936-ea230abf84a3 · outbound

This paper cites Advances in neural information processing systems , volume=.

Scaling Inherently Interpretable Language Models Advances in neural information processing systems , volume=

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Observation 8ca70d5b-8257-46e5-ad48-a9b7925a1994 · outbound

This paper cites Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data.

Scaling Inherently Interpretable Language Models Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data

Reference 43

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Observation baa9b3b8-9dd5-4b0a-80d2-e0ae2e766926 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Scaling Inherently Interpretable Language Models Advances in Neural Information Processing Systems , volume=

Reference 44

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Observation 84f1f7f0-4b8b-44bc-b0a9-bc11cd23d88d · outbound

This paper cites Advances in neural information processing systems , volume=.

Scaling Inherently Interpretable Language Models Advances in neural information processing systems , volume=

Reference 45

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source=arxiv_source observed=2026-08-11T00:36:58.592837Z digest=sha256:ec93aca26065cae53b754bfc03519156ee528783580e13850d28b9649650c872

Observation b6dccb48-b7da-4555-938c-55fcb7b673f2 · outbound

This paper cites Mercury: Ultra-Fast Language Models Based on Diffusion.

Scaling Inherently Interpretable Language Models Mercury: Ultra-Fast Language Models Based on Diffusion

Reference 46

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Observation 2c886252-65aa-4f79-a719-3884e4b85aab · outbound

This paper cites Seed Diffusion: A Large-Scale Diffusion Language Model with High-Speed Inference.

Scaling Inherently Interpretable Language Models Seed Diffusion: A Large-Scale Diffusion Language Model with High-Speed Inference

Reference 47

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source=arxiv_source observed=2026-08-11T00:36:58.600729Z digest=sha256:63d98b04c33b9084fd457b1c33adf3ac7e88d1492410a9928080a2801edd5cd4

Observation 2d99f286-db26-465a-9713-1c2293dd4d98 · outbound

This paper cites Findings of the Association for Computational Linguistics: EACL 2026 , pages=.

Scaling Inherently Interpretable Language Models Findings of the Association for Computational Linguistics: EACL 2026 , pages=

Reference 48

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Observation 1101566a-be80-4cfa-a8fc-c67c253531bd · outbound

This paper cites Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models.

Scaling Inherently Interpretable Language Models Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models

Reference 49

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source=arxiv_source observed=2026-08-11T00:36:58.608734Z digest=sha256:85e1f4c964ef1f24e19439de49ae0e273d04a8e80a9db6c87b6200dc785198df

Observation 454e4a56-d4f8-48d8-8f80-d553c9061d31 · outbound

This paper cites Toy Models of Superposition.

Scaling Inherently Interpretable Language Models Toy Models of Superposition

Reference 50

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source=arxiv_source observed=2026-08-11T00:36:58.612743Z digest=sha256:ddf40aa09c6f68bcf1373d8e8c57e2b977711215c6b75e07377c11031ca63005

Observation 8e404870-a582-4641-84ca-9b11d60a4c3f · outbound

This paper cites Transformer Circuits Thread , volume=.

Scaling Inherently Interpretable Language Models Transformer Circuits Thread , volume=

Reference 51

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source=arxiv_source observed=2026-08-11T00:36:58.616782Z digest=sha256:15dcf84da2054165901315c748bc2b83d861eab716a2cb4b2c6e6ce119c8500d

Observation 0daabdb8-c5c6-4ad5-934a-5ebd1869ad48 · outbound

This paper cites Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=.

Scaling Inherently Interpretable Language Models Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

Reference 52

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source=arxiv_source observed=2026-08-11T00:36:58.620930Z digest=sha256:02d734b84060724632032893936bbb539233afc308a76eac58267f8f6cb4c785

Observation b158920a-768d-4ec0-b7e4-91ad927e0bc6 · outbound

This paper cites 2025 , journal=.

Scaling Inherently Interpretable Language Models 2025 , journal=

Reference 53

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source=arxiv_source observed=2026-08-11T00:36:58.624686Z digest=sha256:58049e49d097408afece96417bd4a284002233a8e1e8fe44ae9fb0cc50abcad9

Observation e6b3f84b-5b01-4faa-8264-60592c32fc26 · outbound

This paper cites an unresolved cited work.

Scaling Inherently Interpretable Language Models Unresolved cited work

Reference 54

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source=arxiv_source observed=2026-08-11T00:36:58.628285Z digest=sha256:3155d91dd4c52169e5d711ce35add408f367904fb2fd11bfe58c4c1f49c6c733

Observation dbbb0662-8aa5-4d39-a37e-301007a0e567 · outbound

This paper cites International Conference on Learning Representations , volume=.

Scaling Inherently Interpretable Language Models International Conference on Learning Representations , volume=

Reference 55

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source=arxiv_source observed=2026-08-11T00:36:58.632085Z digest=sha256:9b1f2db4019845dadc7dddea32e017b994ca4607c1af177b9c7d60197491a836

Observation 38340329-2017-4565-998b-47227341bec3 · outbound

This paper cites International conference on machine learning , pages=.

Scaling Inherently Interpretable Language Models International conference on machine learning , pages=

Reference 56

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source=arxiv_source observed=2026-08-11T00:36:58.635768Z digest=sha256:0f67132f5b22caf51f6d9b9458382c58fc57a78d43422386a58a304d82759034

Observation 6006fbfe-cbd7-4153-ae18-f9d77946bfb6 · outbound

This paper cites International Conference on Learning Representations , volume=.

Scaling Inherently Interpretable Language Models International Conference on Learning Representations , volume=

Reference 57

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source=arxiv_source observed=2026-08-11T00:36:58.639387Z digest=sha256:2f2a852c2a0175544cf62ba0ce9c1203fb02fed7ec30c6c09c6db9090bed3c1b

Observation fa78121d-a5ff-4e02-8473-8fb17d9a5239 · outbound

This paper cites arXiv preprint arXiv:2506.10892 , year=.

Scaling Inherently Interpretable Language Models arXiv preprint arXiv:2506.10892 , year=

Reference 58

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source=arxiv_source observed=2026-08-11T00:36:58.642761Z digest=sha256:7e1564e05fd117c44f3e039536969b1590bee9aa02791329bc36cbb362582f4a

Observation 1ac576a8-8784-445f-baac-83747506b7cb · outbound

This paper cites Esoteric Language Models: A Family of Any-Order Diffusion LLMs.

Scaling Inherently Interpretable Language Models Esoteric Language Models: A Family of Any-Order Diffusion LLMs

Reference 59

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source=arxiv_source observed=2026-08-11T00:36:58.646253Z digest=sha256:76b16cebd5d552738514243873afe8454f2316dee8971a1f36e3a60d223c2d7a

Observation eb76b229-a91c-4a0e-8cdf-e1f5beafa000 · outbound

This paper cites Steering Language Models With Activation Engineering.

Scaling Inherently Interpretable Language Models Steering Language Models With Activation Engineering

Reference 60

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source=arxiv_source observed=2026-08-11T00:36:58.650247Z digest=sha256:1b5572812bbb5c2da40368cabbd7e9d2b946f5ba7600d8add46eff9f399f9560

Observation 02e3e126-6dfb-4780-ba3b-b0d09865e792 · outbound

This paper cites Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=.

Scaling Inherently Interpretable Language Models Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

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source=arxiv_source observed=2026-08-11T00:36:58.653751Z digest=sha256:2cf5a52bcafb9e8eb1607e09f7d8af34c32e82c074803a0991c5cc476151f67f

Observation 6199a7c6-f3f9-43f1-87b3-5993f0aed4b4 · outbound

This paper cites Understanding intermediate layers using linear classifier probes.

Scaling Inherently Interpretable Language Models Understanding intermediate layers using linear classifier probes

Reference 62

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Observation d165b11b-e838-4e6a-a0f4-13e512cdfc3a · outbound

This paper cites International conference on machine learning , pages=.

Scaling Inherently Interpretable Language Models International conference on machine learning , pages=

Reference 63

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source=arxiv_source observed=2026-08-11T00:36:58.661301Z digest=sha256:992202d4376e8c9f8563af5c50de080b14484037465027977e2bd0cecec22936

Observation aac6f879-afe1-4e8e-9a30-c19fa1aef878 · outbound

This paper cites Representation Engineering: A Top-Down Approach to AI Transparency.

Scaling Inherently Interpretable Language Models Representation Engineering: A Top-Down Approach to AI Transparency

Reference 64

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source=arxiv_source observed=2026-08-11T00:36:58.664963Z digest=sha256:4ef5e0dc806417ba1421a7dee3557d1e68445af7bc4953f9a4d13d52bd77a9aa

Observation df021274-a922-4a5a-abaa-55f1f31c599f · outbound

This paper cites 2024 , publisher=.

Scaling Inherently Interpretable Language Models 2024 , publisher=

Reference 65

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source=arxiv_source observed=2026-08-11T00:36:58.668737Z digest=sha256:b5b20b774bedcf2a25b4e64afd52eb0b91016a0aab598fe2fc103be9316a454b

Observation d6efa557-2340-44d5-b633-cb5717f058e8 · outbound

This paper cites Nature machine intelligence , volume=.

Scaling Inherently Interpretable Language Models Nature machine intelligence , volume=

Reference 66

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source=arxiv_source observed=2026-08-11T00:36:58.672355Z digest=sha256:58c6027e81d4300f3818e34f69acb9d9d270a1fad0daf422e20a600f789201eb

Observation 848feaa0-1bc1-42f5-a9a1-c9f46a41d426 · outbound

This paper cites Towards A Rigorous Science of Interpretable Machine Learning.

Scaling Inherently Interpretable Language Models Towards A Rigorous Science of Interpretable Machine Learning

Reference 67

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source=arxiv_source observed=2026-08-11T00:36:58.676160Z digest=sha256:ed2a65983b71e0aad0c292018f395bbed1624c44f3239d6e1525a848fbcedafe

Observation b6c6fd73-ce04-4543-a310-a108c9418fbe · outbound

This paper cites Emergent Abilities of Large Language Models.

Scaling Inherently Interpretable Language Models Emergent Abilities of Large Language Models

Reference 68

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source=arxiv_source observed=2026-08-11T00:36:58.680147Z digest=sha256:4460a5563f22cc5d11b7d6c741fd0c5f6fb6cffb1d38ecd5737454b697b42389

Observation d30eadff-ce37-48aa-ba9a-3432e53c5ccb · outbound

This paper cites International Conference on Learning Representations , volume=.

Scaling Inherently Interpretable Language Models International Conference on Learning Representations , volume=

Reference 69

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source=arxiv_source observed=2026-08-11T00:36:58.683999Z digest=sha256:c30607f7f2ca0351803675a799712a40a569d6c99aa7ce23ea9090dd68dc9d34

Observation ae64a33b-a5aa-44fa-90e2-d1043db9d9a6 · outbound

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

Scaling Inherently Interpretable Language Models The Linear Representation Hypothesis and the Geometry of Large Language Models

Reference 70

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source=arxiv_source observed=2026-08-11T00:36:58.687809Z digest=sha256:f34eefaa5c64f3037c894c2eefb4f4bd2008874a1786f5704e466c5c80a3e5af

Observation d8b5769f-1a06-4a77-8d56-3dc529d796da · outbound

This paper cites A is B” fail to learn “B is A.

Scaling Inherently Interpretable Language Models A is B” fail to learn “B is A

Reference 71

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source=arxiv_source observed=2026-08-11T00:36:58.691731Z digest=sha256:14bf89440d15ee768d048336d031520bc1420b4a3a10c88b1cdd2fb346df301d

Observation 01d05d4a-930a-41e6-9f52-2c9dfeb29d2a · outbound

This paper cites International Conference on Learning Representations , volume=.

Scaling Inherently Interpretable Language Models International Conference on Learning Representations , volume=

Reference 72

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source=arxiv_source observed=2026-08-11T00:36:58.695269Z digest=sha256:50396a33ca06979da4c94946dd0f97205e3ea133ce3eb4030ce7fc2cd54458f6

Observation d9447e02-9eff-4efd-8330-ce004eff61cb · outbound

This paper cites International conference on machine learning , pages=.

Scaling Inherently Interpretable Language Models International conference on machine learning , pages=

Reference 73

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source=arxiv_source observed=2026-08-11T00:36:58.698865Z digest=sha256:48d90da27d53e2c3ff421bb0b91f3c4145d13b10d5c9ac4c3ca0d3c0e3855b60

Observation 1a704b34-0cf7-40ba-aa20-9515d7d4b4df · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Scaling Inherently Interpretable Language Models Advances in Neural Information Processing Systems , volume=

Reference 74

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source=arxiv_source observed=2026-08-11T00:36:58.702709Z digest=sha256:162359a41d0a148c9d8f98979baf40a2db1560e377802e845bedda4cb3cd37c1

Observation fcd2e860-2383-4fae-9f76-26b36fdfd1f0 · outbound

This paper cites 2023 , howpublished =.

Scaling Inherently Interpretable Language Models 2023 , howpublished =

Reference 75

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Observation 28d33b06-5612-4c3f-a3bf-0a1ae97f534a · outbound

This paper cites GitHub repository , howpublished =.

Scaling Inherently Interpretable Language Models GitHub repository , howpublished =

Reference 76

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source=arxiv_source observed=2026-08-11T00:36:58.710591Z digest=sha256:eb714f7455417773e396dc3512ea29bd24559ed24a8c9d472f1891591b5f74b5

Observation d379ed9d-40e9-4ed0-b05d-a3727abf864b · outbound

This paper cites Cryptographers’ track at the RSA conference , pages=.

Scaling Inherently Interpretable Language Models Cryptographers’ track at the RSA conference , pages=

Reference 77

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source=arxiv_source observed=2026-08-11T00:36:58.714240Z digest=sha256:e6e0b05a9de224b642cd0853da8928e4760efe71653c2f992b4a4b094b4b1acf

Observation a052f412-de7e-46d2-a404-c2b007547174 · outbound

This paper cites Scientific american , volume=.

Scaling Inherently Interpretable Language Models Scientific american , volume=

Reference 78

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source=arxiv_source observed=2026-08-11T00:36:58.717873Z digest=sha256:9170ddcec77dbf83beb651fb5580c6064642d6614058b6ed8861cd982817e569

Observation 9f1db654-54c4-4e63-8de9-36b011961aef · outbound

This paper cites StarCoder: may the source be with you!.

Scaling Inherently Interpretable Language Models StarCoder: may the source be with you!

Reference 79

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source=arxiv_source observed=2026-08-11T00:36:58.721501Z digest=sha256:b180d755c81cd12e76f1650857c6998fc4ecde7bc51986b2eeaec4742c9f7b51

Observation 2736c428-14f6-4fad-93ff-0b23159f7c34 · outbound

This paper cites an unresolved cited work.

Scaling Inherently Interpretable Language Models Unresolved cited work

Reference 80

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Observation 5ef5289f-404e-4e4c-a7de-f61b7a4ade9b · outbound

This paper cites International Conference on Learning Representations , volume=.

Scaling Inherently Interpretable Language Models International Conference on Learning Representations , volume=

Reference 81

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source=arxiv_source observed=2026-08-11T00:36:58.729330Z digest=sha256:919c57fa115447f17d90a348f677ea2d46257b863409326b17cfca3b7362765c

Observation 131490c0-125d-4de1-aaa4-374c5020ee55 · outbound

This paper cites Llemma: An Open Language Model For Mathematics.

Scaling Inherently Interpretable Language Models Llemma: An Open Language Model For Mathematics

Reference 82

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no resolver link, observed 2026-08-11T00:36:58.733264Z

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Observation 73076905-61b8-4ae0-a923-7b33142f40e3 · outbound

This paper cites 2026 , month =.

Scaling Inherently Interpretable Language Models 2026 , month =

Reference 83

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Observation 6444b19e-7ad6-4ba1-98f8-179447161d04 · outbound

This paper cites doi:10.5281/zenodo.12608602 , url =.

Scaling Inherently Interpretable Language Models doi:10.5281/zenodo.12608602 , url =

Reference 84

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Observation a8ef7112-9410-47b2-8161-794f5d024bfd · outbound

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

Scaling Inherently Interpretable Language Models The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 85

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Observation 70cbfe3c-6d8d-4dae-a25b-b96e87c86df5 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Scaling Inherently Interpretable Language Models Measuring Massive Multitask Language Understanding

Reference 86

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Observation 56c72ea9-33df-4243-b9b3-8eedf88c2933 · outbound

This paper cites Nelson and Ku.

Scaling Inherently Interpretable Language Models Nelson and Ku

Reference 87

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Observation 39cee7d0-d90f-4592-9302-958ec7223ddc · outbound

This paper cites International Conference on Learning Representations , volume=.

Scaling Inherently Interpretable Language Models International Conference on Learning Representations , volume=

Reference 88

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Observation 8adc7414-1744-476a-a688-0649d09575a5 · outbound

This paper cites Proceedings of the IEEE conference on computer vision and pattern recognition , pages=.

Scaling Inherently Interpretable Language Models Proceedings of the IEEE conference on computer vision and pattern recognition , pages=

Reference 89

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Observation 946be5cf-e39c-48e3-b277-0129d85fc076 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Scaling Inherently Interpretable Language Models Advances in Neural Information Processing Systems , volume=

Reference 90

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Observation bcb0d6f2-3973-47b5-a8d3-b1df41fcc32b · outbound

This paper cites Label-Free Concept Bottleneck Models.

Scaling Inherently Interpretable Language Models Label-Free Concept Bottleneck Models

Reference 91

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Observation 1d68cabd-83ea-418d-8302-009403e77ced · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Scaling Inherently Interpretable Language Models Advances in Neural Information Processing Systems , volume=

Reference 92

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Observation 06b14b64-3bb0-472d-94cd-163d1c10f23e · outbound

This paper cites Zyda-2: a 5 Trillion Token High-Quality Dataset.

Scaling Inherently Interpretable Language Models Zyda-2: a 5 Trillion Token High-Quality Dataset

Reference 93

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no resolver link, observed 2026-08-11T00:36:58.775332Z

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Observation 26626bd1-c9bc-4d3d-878a-039a6c37f7cf · outbound

This paper cites Proceedings of the 58th annual meeting of the association for computational linguistics , pages=.

Scaling Inherently Interpretable Language Models Proceedings of the 58th annual meeting of the association for computational linguistics , pages=

Reference 94

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Observation ff63b0ee-f88b-4dab-b8cd-3cf1bd09aec0 · outbound

This paper cites Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=.

Scaling Inherently Interpretable Language Models Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

Reference 95

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source=arxiv_source observed=2026-08-11T00:36:58.783239Z digest=sha256:dbc12da4e9dc320b997a9db06daa0c4a18a0927cdb248beb3bfa99a7603b4d0f

Observation 8174b139-d584-466b-984c-ccb5f03f7023 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Scaling Inherently Interpretable Language Models Training Verifiers to Solve Math Word Problems

Reference 96

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source=arxiv_source observed=2026-08-11T00:36:58.786983Z digest=sha256:c3c8c179d4c7a8df12c791cba97374a17839fb1874ea3307ce3e1211023b8496

Observation d0daf3df-f185-45f5-a352-bb138a965296 · outbound

This paper cites an unresolved cited work.

Scaling Inherently Interpretable Language Models Unresolved cited work

Reference 97

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source=arxiv_source observed=2026-08-11T00:36:58.790725Z digest=sha256:67ac2b8da5a38cdbc42ffa53a2d8291f92089e04942764d7f35a0477c514f055

Observation 79663dc9-0359-4c43-b18d-07a0d03956ff · outbound

This paper cites International conference on machine learning , pages=.

Scaling Inherently Interpretable Language Models International conference on machine learning , pages=

Reference 98

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source=arxiv_source observed=2026-08-11T00:36:58.794361Z digest=sha256:c00bf628dc901f1102759081ae8aebfde33a33895c941dc818afd7709d7508d4

Observation f7791534-b14a-417f-893b-bd4c6fc5e543 · outbound

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

Scaling Inherently Interpretable Language Models Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models

Reference 99

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source=arxiv_source observed=2026-08-11T00:36:58.797964Z digest=sha256:38dab335497ab17875e9fe710618159506961aab56a9bb4b3004c68fc2727968

Observation cfb6723d-d4a7-4d02-aff4-b2fe88f136a9 · outbound

This paper cites IEEE transactions on big data , volume=.

Scaling Inherently Interpretable Language Models IEEE transactions on big data , volume=

Reference 100

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Pith citing papers

No inbound Pith citation observations are available.