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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.

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

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.

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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Observation 2d3f982f-bc1b-419e-8e24-e9ce26595ab7 · outbound

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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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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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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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

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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source=arxiv_source observed=2026-08-11T00:36:58.592837Z digest=sha256:4ac317fe46c917237a44b3b3562383db022c1ee3281fffe0f4ce8a5c933a42e3

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:c1b58f764187f8b8ddd0e69b551135bee753d158f0e477ef6ea1b95eaab608cb

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:bdbc633497f5f5d87ab4e7916953347ea49f81ccb9f3f93571658c997e5a363d

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:f2e4a8f72753c45ae337bb31c81677441c1cdbd4f9bf6bcfd468c190d4e87e76

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:f136fb3f2f90296ba9ad77c6a4288c078922ae596873c076ef5e6f7749c896c8

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=sha256:233c3f73eabdd9b3f09a35251725f0dfb173aebba9fe50870fb645e949c688a4

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=sha256:2883a1f93cc219b9dbac39f379b9f0d6faafbea8c19cd69d496e517abe24d271

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=sha256:5276953996ca582ef2bf431c034f99c8bce88c3ad2951de9174bae36ca325ea5

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:ee3108b5e9dbfaf32debd2353a06dda33a5907d9ad53cf02b7bc700219a28a48

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=sha256:4a454deed8d23750e66ded66defc1ea8c9e37f7d774b51145e82e2f00d2935cc

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=sha256:6bf75daeebd4bd15e2f7390a75e1f70e0c45a6ec81250047173b7a27a9e4e81f

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=sha256:4bebe0fdc6d096040ea8d70aa2bae34f4dec9886daf030745cf2883fcd7688f1

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=sha256:7852a7e6ba473066484223b4a679ef349443f2cd4775777e374ee022799731af

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=sha256:4163e28c2f5c69a8609ee811f747e8f787e89d3f712c832df0001dc53a2c82ec

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=sha256:7aa65dcac3f92077afebf2a20a3ba19e23cd524e0fd0af9e6066d2a68682090a

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=sha256:8c92e9057b2fb539d8bf48b96333bf2ab74f7358a7f52cb8f43b061a77b0e7fb

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:f448b754f1f6ab9a78ec5947ab8b4c81cafbaa8950f5cb3ebf96820f0a8e7447

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=sha256:0eeac88f8ea9bbb51ec8283fe021424e7503c0d6b2e322ac7911f22426b42506

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=sha256:7165eb628572bca3ed3de96f087182fe4be82bf9fb7e2c96813ecf62dcc98428

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=sha256:720ae365a3b9cd813c03bee7b5e1db7fe05b08ed12de503e34e8f6f11291448c

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=sha256:85f6a55077d40cc42da03f8753618991a0fc89f6c652dea3a5ccec4a5ebaad38

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:ef53abc964ab0cfaa18b8fa5e9c444aff3ced65f1d974ae064fedf7a0aa8ded9

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=sha256:15c4268ff1080042d4e169765faaaa1ef011093658fc5ac432db0b19c6036e0c

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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source=arxiv_source observed=2026-08-11T00:36:58.687809Z digest=sha256:c1bb3e4f468c4c3b7ff3e186c8019b6e11b3768113d63239c7e41b674b106b19

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=sha256:393ffa6ecb032222453b43fcf3e17a3fc02282441604bac972c3ad9ca8d0de7c

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=sha256:73f64497fb94ff3695f6c3e28354c55f874d96c3c5b1a82538381c9c60ad5e8c

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:e4eafe78b9c9ec68476b0fe5d8c6cca988ae6ed23c38c5ac37b49e2710ed6f78

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:c30a12c9c9fef4cc05193ac8996183cb9dbe5ffc2005af43c1f6599ddbf1d24c

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:f505c12295ec9ecc39ed3c4bf1dbe6d68a9cb709b4cec2f9bbf84f7082e62aaf

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:d90b76ce19ed7c812123f74a1a5d6bf0fb27b71adb1f6ced24ef75c8fdd5a1af

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=sha256:31cd2448cdf66b4e60b45084983298ffaf411994118b70ea6ed081ab61852760

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:c3ffc22d76ee6ac3c4f4f61fa91a13182b69f07b6f4621c1661cfaaaa2cd529e

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=sha256:3e6b476c6b9f81327f7cfdada5806d6f26f7746ae3174b5da4d9696873b286a6

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=sha256:5124cf99bec543c513a6d723d5faf78ae05c84b00d20d9f0bee4c32f7822128d

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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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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source=arxiv_source observed=2026-08-11T00:36:58.767568Z digest=sha256:617f38b50ca271c4f2142ef4fe470846f155dfcbefad6c0eb3c18af1dd87cb48

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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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=sha256:2733d70b526bdf98c4f05a3c3ad11e89fac710fb96b2ae8a40df5a2021a77e66

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=sha256:959dfd227165de2baf2e5d09ee0f5e4778c7ce464cba4924fd0e12d3f2e9520c

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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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:ef5c18e11a4344ae94d8ebb6f6c0630e498101214f1ead3869678ae9e1df83de

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=sha256:8c56f3a77ef8d47b05684f6540b30b9d2c3d9198d1abf57d98670ee170466a27

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.