Memorization Diagnostics for Code LLMs Should be Scale-Aware | Paper Citation Record

Observation c3e87863-a6b6-4193-973f-d255a764e64f · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Memorization Diagnostics for Code LLMs Should be Scale-Aware Evaluating Large Language Models Trained on Code

Reference 1

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Observation 9be1fdc0-fd37-4b1d-9a0a-ec13855450a7 · outbound

This paper cites Program Synthesis with Large Language Models.

Memorization Diagnostics for Code LLMs Should be Scale-Aware Program Synthesis with Large Language Models

Reference 2

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Observation cebf003f-1d7f-45c4-8a3c-d6dadd12b23c · outbound

This paper cites SWE-bench: Can Language Models Resolve Real-World GitHub Issues?.

Memorization Diagnostics for Code LLMs Should be Scale-Aware SWE-bench: Can Language Models Resolve Real-World GitHub Issues?

Reference 3

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This paper cites SWE-bench Goes Live!.

Memorization Diagnostics for Code LLMs Should be Scale-Aware SWE-bench Goes Live!

Reference 4

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Observation 18857f64-8ee7-49d5-9116-48a88e84697b · outbound

This paper cites arXiv preprint arXiv:2506.12286 (2025).

Memorization Diagnostics for Code LLMs Should be Scale-Aware arXiv preprint arXiv:2506.12286 (2025)

Reference 5

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This paper cites naturalizing.

Memorization Diagnostics for Code LLMs Should be Scale-Aware naturalizing

Reference 6

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Observation 6e433e71-9f5b-4f8e-a9f2-256bf8a80dba · outbound

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

Memorization Diagnostics for Code LLMs Should be Scale-Aware In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp

Reference 7

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Observation 395e2739-2d18-4933-a88d-fec59fa45d3c · outbound

This paper cites In: 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE), pp.

Memorization Diagnostics for Code LLMs Should be Scale-Aware In: 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE), pp

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:46:48.158389Z

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Observation 14a5b9b8-0785-426b-80cf-fcae957b32cd · outbound

This paper cites ACM Transactions on Software Engineering and Methodology (2024).

Memorization Diagnostics for Code LLMs Should be Scale-Aware ACM Transactions on Software Engineering and Methodology (2024)

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:46:48.107964Z

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Observation 62030fa9-e39b-4e18-a8a5-d71c4dff1972 · outbound

This paper cites Memorization or Interpolation ? Detecting LLM Memorization through Input Perturbation Analysis.

Memorization Diagnostics for Code LLMs Should be Scale-Aware Memorization or Interpolation ? Detecting LLM Memorization through Input Perturbation Analysis

Reference 10

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Observation 7f75d55b-27d5-44c2-b10f-a428939b3685 · outbound

This paper cites Learned or Memorized ? Quantifying Memorization Advantage in Code LLMs.

Memorization Diagnostics for Code LLMs Should be Scale-Aware Learned or Memorized ? Quantifying Memorization Advantage in Code LLMs

Reference 11

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Observation 9b3efc73-6002-45d5-b832-e9b74858be50 · outbound

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

Memorization Diagnostics for Code LLMs Should be Scale-Aware In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:46:48.069020Z

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Observation af651b49-01b2-43a5-904f-562ce9dffe6b · outbound

This paper cites Detecting Data Contamination in LLMs via In-Context Learning.

Memorization Diagnostics for Code LLMs Should be Scale-Aware Detecting Data Contamination in LLMs via In-Context Learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T23:46:44.047522Z

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Observation 6223b37e-6e07-4bfb-876c-312fae390469 · outbound

This paper cites In: The Twelfth International Conference on Learning Representations (2023).

Memorization Diagnostics for Code LLMs Should be Scale-Aware In: The Twelfth International Conference on Learning Representations (2023)

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:46:48.024159Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5c79a88f-edc4-4aa7-8e0d-770c6c8f8ec2 · outbound

This paper cites Transactions of the Association for Computational Linguistics13, 809–830 (2025).

Memorization Diagnostics for Code LLMs Should be Scale-Aware Transactions of the Association for Computational Linguistics13, 809–830 (2025)

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:46:47.979212Z

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Observation 59d31391-ab1e-4e2f-a182-40c0a98047d6 · outbound

This paper cites In: 2025 IEEE/ACM 47th International Conference on Software Engineering (ICSE), pp.

Memorization Diagnostics for Code LLMs Should be Scale-Aware In: 2025 IEEE/ACM 47th International Conference on Software Engineering (ICSE), pp

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:46:47.928124Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5b64ffde-cf38-4fb2-a62e-481c34eb0b1e · outbound

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

Memorization Diagnostics for Code LLMs Should be Scale-Aware The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 17

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Observation 20e2f135-ca2a-4afd-88b0-7d1737f0ff2d · outbound

This paper cites Journal of machine learning research21(140), 1–67 (2020).

Memorization Diagnostics for Code LLMs Should be Scale-Aware Journal of machine learning research21(140), 1–67 (2020)

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T23:46:44.170362Z

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Observation 532b50fd-6590-4149-a2f2-88b3180f16cc · outbound

This paper cites Technical Report HKUST-CS98-01, Department of Computer Science, Hong Kong University of Science and Technology (1998).

Memorization Diagnostics for Code LLMs Should be Scale-Aware Technical Report HKUST-CS98-01, Department of Computer Science, Hong Kong University of Science and Technology (1998)

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:46:47.825011Z

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Observation aed33032-d599-4244-aa9d-21dca9800744 · outbound

This paper cites IEEE Transactions on Software Engineering42(9), 805–824 (2016).

Memorization Diagnostics for Code LLMs Should be Scale-Aware IEEE Transactions on Software Engineering42(9), 805–824 (2016)

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:46:47.790063Z

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Observation 4c89ca79-e22a-4dea-ab3d-9b59780ef84f · outbound

This paper cites In: 30th USENIX Security Symposium (USENIX Security 21), pp.

Memorization Diagnostics for Code LLMs Should be Scale-Aware In: 30th USENIX Security Symposium (USENIX Security 21), pp

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:46:47.743210Z

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Observation 00642105-7ffe-4b4c-ab4f-439b4d5f138d · outbound

This paper cites In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp.

Memorization Diagnostics for Code LLMs Should be Scale-Aware In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:46:47.693134Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a4724180-f3ff-4a21-a5f6-eb45f23a5b6d · outbound

This paper cites In: Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering, pp.

Memorization Diagnostics for Code LLMs Should be Scale-Aware In: Proceedings of the 28th International Conference on Evaluation and Assessment in Software Engineering, pp

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T23:46:44.264778Z

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Observation 79a0d723-a46e-43b6-9a6e-eddbe84626d5 · outbound

This paper cites In: Findings of the Association for Computational Linguistics: EMNLP 2024, pp.

Memorization Diagnostics for Code LLMs Should be Scale-Aware In: Findings of the Association for Computational Linguistics: EMNLP 2024, pp

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:46:47.623559Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 08563b54-9303-458d-b232-6a7158e937ec · outbound

This paper cites In: Findings of the Association for Computational Linguistics: EMNLP 2023, pp.

Memorization Diagnostics for Code LLMs Should be Scale-Aware In: Findings of the Association for Computational Linguistics: EMNLP 2023, pp

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:46:47.577530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 69f15857-2c65-4230-ab9b-e26381efa439 · outbound

This paper cites Detecting Pretraining Data from Large Language Models.

Memorization Diagnostics for Code LLMs Should be Scale-Aware Detecting Pretraining Data from Large Language Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T23:46:44.350105Z

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Observation 78dfbaa6-dc97-4537-b980-09c67857b790 · outbound

This paper cites In: Findings of the Association for Computational Linguistics: ACL 2023, pp.

Memorization Diagnostics for Code LLMs Should be Scale-Aware In: Findings of the Association for Computational Linguistics: ACL 2023, pp

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:46:47.539552Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 60cd458a-b6b1-4fd3-9df1-8532e4767f5f · outbound

This paper cites Advances in Neural Information Processing Systems36, 39321–39362 (2023).

Memorization Diagnostics for Code LLMs Should be Scale-Aware Advances in Neural Information Processing Systems36, 39321–39362 (2023)

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:46:47.489644Z

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Observation ffb7e8b5-934f-4b5a-83d0-42ea21d340a5 · outbound

This paper cites Studying Large Language Model Generalization with Influence Functions.

Memorization Diagnostics for Code LLMs Should be Scale-Aware Studying Large Language Model Generalization with Influence Functions

Reference 29

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This paper cites Advances in Neural Information Processing Systems36, 28072–28090 (2023).

Memorization Diagnostics for Code LLMs Should be Scale-Aware Advances in Neural Information Processing Systems36, 28072–28090 (2023)

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T23:46:44.427670Z

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Observation 7f6ab170-88dc-4835-8d29-034b54c5d55e · outbound

This paper cites Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets.

Memorization Diagnostics for Code LLMs Should be Scale-Aware Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T23:46:44.442915Z

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Observation 1869be33-ba20-4653-a326-37e67e4a33bd · outbound

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Memorization Diagnostics for Code LLMs Should be Scale-Aware Progress measures for grokking via mechanistic interpretability

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T23:46:44.470408Z

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This paper cites Advances in Neural Information Processing Systems35, 34651–34663 (2022).

Memorization Diagnostics for Code LLMs Should be Scale-Aware Advances in Neural Information Processing Systems35, 34651–34663 (2022)

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:46:47.376919Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation df8dfb0a-4b3a-49d1-b9ff-d8f9e62998d1 · outbound

This paper cites Advances in Neural Information Processing Systems35, 38274–38290 (2022).

Memorization Diagnostics for Code LLMs Should be Scale-Aware Advances in Neural Information Processing Systems35, 38274–38290 (2022)

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:46:47.312947Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cc1cc5fc-a759-4273-9531-e5fae6782350 · outbound

This paper cites LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code.

Memorization Diagnostics for Code LLMs Should be Scale-Aware LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-15T23:46:44.535880Z

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Observation 33918bc4-de70-4ffa-a44d-1c4158edbec8 · outbound

This paper cites Advances in neural information processing systems36, 21558–21572 (2023).

Memorization Diagnostics for Code LLMs Should be Scale-Aware Advances in neural information processing systems36, 21558–21572 (2023)

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:46:47.262469Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1aea9a71-11e1-49af-a2d3-bd5bec753b54 · outbound

This paper cites Top Leaderboard Ranking = Top Coding Proficiency, Always? EvoEval: Evolving Coding Benchmarks via LLM.

Memorization Diagnostics for Code LLMs Should be Scale-Aware Top Leaderboard Ranking = Top Coding Proficiency, Always? EvoEval: Evolving Coding Benchmarks via LLM

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-15T23:46:44.591883Z

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Observation 169b9c80-b1ce-4d63-927c-e71dc2a9ad5a · outbound

This paper cites Scaling Laws for Neural Language Models.

Memorization Diagnostics for Code LLMs Should be Scale-Aware Scaling Laws for Neural Language Models

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-15T23:46:44.616402Z

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Observation 8ae0dbcb-6418-43af-98c3-48726f0baa84 · outbound

This paper cites Emergent Abilities of Large Language Models.

Memorization Diagnostics for Code LLMs Should be Scale-Aware Emergent Abilities of Large Language Models

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-15T23:46:44.638422Z

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Observation 5a39b82e-26ea-4f84-a394-b35e23bdef66 · outbound

This paper cites Advances in neural information processing systems33, 1877–1901 (2020).

Memorization Diagnostics for Code LLMs Should be Scale-Aware Advances in neural information processing systems33, 1877–1901 (2020)

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-15T23:46:44.647082Z

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Observation c489cb24-dd17-4c07-8bcf-1739c145338a · outbound

This paper cites Advances in Neural Information Processing Systems37, 11506–11544 (2024).

Memorization Diagnostics for Code LLMs Should be Scale-Aware Advances in Neural Information Processing Systems37, 11506–11544 (2024)

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:46:47.178265Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6dad56a8-81cf-4bff-9c5c-83457f1f8b24 · outbound

This paper cites BigO(Bench) -- Can LLMs Generate Code with Controlled Time and Space Complexity?.

Memorization Diagnostics for Code LLMs Should be Scale-Aware BigO(Bench) -- Can LLMs Generate Code with Controlled Time and Space Complexity?

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-15T23:46:44.690142Z

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Observation 2989264c-767c-4530-9a81-286faf8048b7 · outbound

This paper cites In: First Conference on Language Modeling (2024).

Memorization Diagnostics for Code LLMs Should be Scale-Aware In: First Conference on Language Modeling (2024)

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-15T23:46:44.702214Z

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Observation d9fffa15-b58b-4599-9b84-4fe6fbb5ffb1 · outbound

This paper cites In: International Conference on Machine Learning, pp.

Memorization Diagnostics for Code LLMs Should be Scale-Aware In: International Conference on Machine Learning, pp

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:46:47.113984Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4a719936-aa36-452a-ad26-5860ffbedb17 · outbound

This paper cites Advances in neural information processing systems30(2017).

Memorization Diagnostics for Code LLMs Should be Scale-Aware Advances in neural information processing systems30(2017)

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-15T23:46:44.722502Z

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Observation 53433d1a-339d-430a-92f1-3c2b82631f91 · outbound

This paper cites OpenAI blog1(8), 9 (2019).

Memorization Diagnostics for Code LLMs Should be Scale-Aware OpenAI blog1(8), 9 (2019)

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-15T23:46:44.737073Z

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This paper cites Advances in neural information processing systems36, 70293– 70332 (2023).

Memorization Diagnostics for Code LLMs Should be Scale-Aware Advances in neural information processing systems36, 70293– 70332 (2023)

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:46:46.965317Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 63d6b0a9-5531-4cb9-a417-92ae82bc2a86 · outbound

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Memorization Diagnostics for Code LLMs Should be Scale-Aware Unresolved cited work

Reference 48

Resolution: unresolved; raw_fallback, observed 2026-08-15T23:46:46.879650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a9b2c538-f333-472e-9eb8-64736676368e · outbound

This paper cites ARC Prize 2024: Technical Report.

Memorization Diagnostics for Code LLMs Should be Scale-Aware ARC Prize 2024: Technical Report

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-15T23:46:44.777584Z

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Observation c0d7026c-1ddb-4091-acd8-8ee6145ddfa7 · outbound

This paper cites ARC-AGI-2: A New Challenge for Frontier AI Reasoning Systems.

Memorization Diagnostics for Code LLMs Should be Scale-Aware ARC-AGI-2: A New Challenge for Frontier AI Reasoning Systems

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-15T23:46:44.794476Z

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Observation 1c7391b5-75f8-49d4-8e42-23fb2d0ef9b1 · outbound

This paper cites On the Measure of Intelligence.

Memorization Diagnostics for Code LLMs Should be Scale-Aware On the Measure of Intelligence

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-15T23:46:44.808634Z

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