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Paper Citation Record · LEDGER

Scaling Inherently Interpretable Language Models

As of 11 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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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

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

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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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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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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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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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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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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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

This paper cites Qwen Technical Report.

Scaling Inherently Interpretable Language Models Qwen Technical Report

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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

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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=

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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=

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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

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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

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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=

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

This paper cites Transformer Circuits Thread , volume=.

Scaling Inherently Interpretable Language Models Transformer Circuits Thread , volume=

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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=

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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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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=sha256:715c00bf444b4e15507886cb90d73655f89c9b2a4f3fba3989a8098b3be06cff

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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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=

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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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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=

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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

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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

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source=arxiv_source observed=2026-08-11T00:36:58.650247Z digest=sha256:a1a25eaa2f1ecf82575b7df530bc488378553520c0e35c44fbd710cbab730286

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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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

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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=

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source=arxiv_source observed=2026-08-11T00:36:58.661301Z digest=sha256:1476d2ef4285da3d4f7c4da8ba832d1a3b5e3f3ac911ec13e4125404c27658a0

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

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

This paper cites Nature machine intelligence , volume=.

Scaling Inherently Interpretable Language Models Nature machine intelligence , volume=

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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

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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

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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=

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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

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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

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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=

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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=

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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=

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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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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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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:c13890de47fc4223f68cafa084a7bd27e5d6b6fa607209a6481a64945b574c79

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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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=

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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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source=arxiv_source observed=2026-08-11T00:36:58.737069Z digest=sha256:8dfe26ecd458d31870d92f28c0d07cd30c542ce31a6959e436122648aaa05761

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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no resolver link, observed 2026-08-11T00:36:58.744657Z

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source=arxiv_source observed=2026-08-11T00:36:58.744657Z digest=sha256:9c3d97d6d8a6bef025cc72e03411e5a1f9cab60702b259857bb0c219cd815990

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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no resolver link, observed 2026-08-11T00:36:58.748607Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-11T00:36:58.748607Z digest=sha256:e83684ab4f6524589f59bdf2b71623e9ca72dc9cd15b6425adac3640ee302b57

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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no resolver link, observed 2026-08-11T00:36:58.752584Z

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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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no resolver link, observed 2026-08-11T00:36:58.756401Z

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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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no resolver link, observed 2026-08-11T00:36:58.760110Z

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source=arxiv_source observed=2026-08-11T00:36:58.760110Z digest=sha256:ec58a4d49b49057d044f926a3f54c5a09d24ddf08612590099f71b2cbb16717b

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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no resolver link, observed 2026-08-11T00:36:58.763869Z

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source=arxiv_source observed=2026-08-11T00:36:58.763869Z digest=sha256:8442f517b47f04596803216c22079b600bcfda3c336fcf6dbed701ba2f6ad8e1

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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no resolver link, observed 2026-08-11T00:36:58.767568Z

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source=arxiv_source observed=2026-08-11T00:36:58.767568Z digest=sha256:b39635ae1679e5d92bc5b49fbf6fc1169d53371791c231cfb8a85a9b1b825025

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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no resolver link, observed 2026-08-11T00:36:58.771826Z

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source=arxiv_source observed=2026-08-11T00:36:58.771826Z digest=sha256:b4db054682de75f614572bb0549ee1a043eb622dddfd5ee010184c2853fcebb6

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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no resolver link, observed 2026-08-11T00:36:58.779385Z

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source=arxiv_source observed=2026-08-11T00:36:58.779385Z digest=sha256:0304ed2284c54de18e42c5b73ea37ed671ad0b0d4ba423804f817a51ce4a395f

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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no resolver link, observed 2026-08-11T00:36:58.783239Z

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source=arxiv_source observed=2026-08-11T00:36:58.783239Z digest=sha256:3089e78939974095407413c785eae675bbc2108157e2ea5728684cf7e0c3ef3f

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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no resolver link, observed 2026-08-11T00:36:58.786983Z

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source=arxiv_source observed=2026-08-11T00:36:58.786983Z digest=sha256:83353f05cd0a90f38e66fce904400f3c0d91764802b67c6d4a34c28a2d4866b2

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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no resolver link, observed 2026-08-11T00:36:58.790725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:36:58.790725Z digest=sha256:14d29d4c2b9e4e57f32cadfc58227beb0e60e3b698c09deb3952b9674e23fba5

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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no resolver link, observed 2026-08-11T00:36:58.794361Z

Source-reported events for the cited work

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source=arxiv_source observed=2026-08-11T00:36:58.794361Z digest=sha256:fd5f9e3da5a32a95b01d756b9eebbd8106287b684ca8c30905c00d2163828530

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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no resolver link, observed 2026-08-11T00:36:58.797964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T00:36:58.797964Z digest=sha256:bbcacfa012c4753208f3c227f0c7e76dfdacf6a7669994897baf59bca52deefc

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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no resolver link, observed 2026-08-11T00:36:58.802066Z

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source=arxiv_source observed=2026-08-11T00:36:58.802066Z digest=sha256:7f6001dd2e456b436d55bce2e661c8eca11a46673373aaa0d4b7d3eed39a2098

Pith citing papers

No inbound Pith citation observations are available.