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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.

Source: paper_references, paper_reference_links, observed 2026-08-11T00:36:58.802066Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

100 of 193 outbound references displayed

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External citation measurements

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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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Observation 6b5f2e08-efe2-4754-a9b0-b66917c11fbe · outbound

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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Observation db515cd9-c51c-40c5-86a0-e3928f769054 · outbound

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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Observation 0b549f49-adc4-4097-bc72-cd580e1db3db · outbound

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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Observation 478ed296-a58f-4141-93c3-4df42f4a7c39 · outbound

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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Observation 6dad12ec-1d7e-45fe-b822-5e1cd2738eb5 · outbound

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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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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Observation 079b6332-98d0-408c-82f6-72a41eabf690 · outbound

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

Reference 17

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Observation af9f02b3-3956-49f3-be39-db620d1fa309 · outbound

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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

This paper cites Towards Unifying Interpretability and Control: Evaluation via Intervention.

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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Observation c0d58aef-ce99-4354-8ba5-4406157b8cdb · outbound

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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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Observation 525d935d-1d4d-477d-8839-17c1c0a9d8ce · outbound

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

Reference 39

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Observation a0819fbd-5904-4107-bf61-459d1e1d01f7 · outbound

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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

Reference 41

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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=

Reference 42

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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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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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source=arxiv_source observed=2026-08-11T00:36:58.596715Z digest=sha256:f666c1079291611f6e003aec6e52f3d5fa60b45678ecdd093f1eda4e43500f54

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=sha256:17aa44374192431142d527c9453a504344b819e7f5ab86a6e1faa5d35444c190

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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source=arxiv_source observed=2026-08-11T00:36:58.604980Z digest=sha256:079fd011f3732208c82997c322c0c29e55d22f8093dfff9f9e1c28cf9767c7d6

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=sha256:980b5f5e7d8b3e892845d4d63350df59c34ee5a0709007a4d0d816f838d7cdc8

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=sha256:488abf405beda3bed68f0e65df238e1c100f8cb540d8570317d0f31659f8ffeb

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=sha256:8f06715cb04b2ca8c75f9ca813dcfc1d315f4fd567894238afb7a69ec0461ce1

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=sha256:6bda7d2095886d7d015b888c3d214c49586459652a94badcc6ceef8f8be9961e

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:f1f200a8cf5e6dd2b09e4962236d2a0b0eb886c8278d60128153751da3d52c40

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=sha256:4e7434eb9aeb5a7973f0fd53df1330cfe24ffbec92340176a7a13cf5b312238d

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:e41f8f0f772f907b62100fddecc06b09abf78887db1066deef0b2728ac8c8148

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=sha256:02c4bb9208d7d251973f130d8d97d78f01941943335451d38138b0910e61b21b

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=sha256:028235d579684da74dfaea2d9c7de45b91cd0161c6c4fb2f3fd70b6217b371bc

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=sha256:8a0c9c0a205221978354e16102e5478c0f19da69d47bef8e3b1a59a6307e6491

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=sha256:23645ab9df9750cbce5f005ce496ba506fee18603ddf1a30252496068248fa56

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:e88b8bed5b40340c3f4f2ac14ba246f19c283680c8ffcde312b7e48d848515d2

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=sha256:1d41bbf6c07043c55045af42cd1de5c47da1b17095c61dd7d0bc53ff5901e426

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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source=arxiv_source observed=2026-08-11T00:36:58.657462Z digest=sha256:a90ed3656cc8e15fb86c4b302878e346bebbfc2e63ad8fcd2d48416bd3894933

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:d206368a80458b99e997de2f7061792f4202b2388d5f269509a96a221c20fa6c

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=sha256:97ed66e98530bea619c90c65e6af83960da4f1a1a3dc9fa80c2b59e551303697

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=sha256:557956819665fc175e21f5d99f4f1a4a4357cb61164e17896d6bbddfc6eff0d5

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=sha256:278ffced94ab761d8c70b658809e61fc04507698f2be51e12e17afd9baf6fc6d

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:ae690c6735e20e56b32a039843275dc3839096ace36bb55d7dad43819baec05c

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:eb3e8af9d56a1d1fa0c477a20370088165666f4ab40c31aa73e17c9f3bcbbd7d

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:f6ae3f2121c55a3492a2d58309831c3f410d0caa07bfc79b264b7978284ac4df

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=sha256:10ead5e3ec635ef64b1f6fab83ea5dad22fe9baa5f1b761e27391c328d9d1a31

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:d59dc380fee4614d22751f98bd09aca3883bd9ac5d0651c651d883f626e4bdf7

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:d61f35b6df9fa7e04b2bf0736ac3696b903b3be3428b27cddeab8f8640f2d915

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=sha256:4d00ac87ded5d31cc90e408f95f1d1d4da4b41e9760cb646620a081851fbcda5

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=sha256:70bdee9610d0ee3e53bd0600f47f3008ce1e8189ca99be8aaa45cce810e8b774

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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source=arxiv_source observed=2026-08-11T00:36:58.706599Z digest=sha256:c99ddd824b7a309ee2655d7bf0d0d04d9df68b1d4f63039a418c3b3598fe7bf3

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:e5e821340ceb02bdfce4ec704f67bee29f884533fba9c6f6e8029f2648155d4e

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:b70f3aee0a43a8097518ac73f52546897caa6a39c807ebd1e0ec3139f9c13124

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:a526f7590560997206f00409f55486d9f98fcc0bfc41721bd16e6c07f4bc6afa

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:d69164225d3606b359c89be9e440cb96ac56162b98217bbf3cdde938c15c3cd7

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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source=arxiv_source observed=2026-08-11T00:36:58.725538Z digest=sha256:69b79e8cf0bbcc719369b9f554da64e8da51d2a8992b7dba7c237d7e4d7d6057

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=sha256:9b00cd4237298243b400194b468e483c8599797ec3031e3e4e0693bd304856e0

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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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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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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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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source=arxiv_source observed=2026-08-11T00:36:58.767568Z digest=sha256:2812e8a7efc13828918171c58d058be5fb417ff7a78044709febab2c08102240

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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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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source=arxiv_source observed=2026-08-11T00:36:58.779385Z digest=sha256:8f83d333457df13cd23739cfe1eaf176e453ed822da827ea5b29a647cfd38a3b

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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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:bbb10eabd864932c350813d2b674973219ab7684eb882c0ddda65e029b24e125

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:d6b5d2af0f5d8259d2831d610a8e1a39e8c4a114fcb40e6c1c5eb18706ae0164

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:c9a270852e864f45ddd53c97097604d60b8d6b105d76893dcbc38b462b2eb3b1

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=sha256:6b12f5c436eb87c1e3d3dfe5af99c33427af9f69890b2645640c97b346be7534

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.