Learning What to Remember: Test-Time Training via Context Distillation | Paper Citation Record

Observation 25ca538f-d972-44a1-beb3-6a536ead5d5b · outbound

This paper cites SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model.

Learning What to Remember: Test-Time Training via Context Distillation SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model

Reference 1

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Observation 6034f659-c302-4f72-9d5c-6be2765af8ab · outbound

This paper cites Using fast weights to attend to the recent past.Advances in neural information processing systems, 29, 2016.

Learning What to Remember: Test-Time Training via Context Distillation Using fast weights to attend to the recent past.Advances in neural information processing systems, 29, 2016

Reference 2

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Observation 4a8c3380-a083-4314-8a25-23d679cf89af · outbound

This paper cites An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling.

Learning What to Remember: Test-Time Training via Context Distillation An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 3

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Observation 7fb360cc-2a80-4f60-a6ad-6f8c3679c091 · outbound

This paper cites Titans: Learning to Memorize at Test Time.

Learning What to Remember: Test-Time Training via Context Distillation Titans: Learning to Memorize at Test Time

Reference 4

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Observation c15e55c8-6b23-48cf-96b8-62549536d869 · outbound

This paper cites Lessons from the Trenches on Reproducible Evaluation of Language Models.

Learning What to Remember: Test-Time Training via Context Distillation Lessons from the Trenches on Reproducible Evaluation of Language Models

Reference 5

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Observation e0926956-1441-459c-9c26-2b041cf3cdf9 · outbound

This paper cites KV-Distill: Nearly Lossless Learnable Context Compression for LLMs.

Learning What to Remember: Test-Time Training via Context Distillation KV-Distill: Nearly Lossless Learnable Context Compression for LLMs

Reference 6

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Observation 34ad7e52-2280-443b-aab7-5fe5745e20c7 · outbound

This paper cites Learning to compress prompt in natural language formats.

Learning What to Remember: Test-Time Training via Context Distillation Learning to compress prompt in natural language formats

Reference 7

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Observation a5cf0b1b-9438-46ae-b6ed-0992a4b44ffb · outbound

This paper cites Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling.

Learning What to Remember: Test-Time Training via Context Distillation Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

Reference 8

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Observation 80705b35-3476-4122-acde-bde493fc613d · outbound

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

Learning What to Remember: Test-Time Training via Context Distillation Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 9

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Observation e27ad568-460b-442e-834f-a93ff175d8ad · outbound

This paper cites Finch: Prompt-guided key-value cache compression for large language models.Transactions of the Association for Computational Linguistics, 12: 1517–1532, 2024.

Learning What to Remember: Test-Time Training via Context Distillation Finch: Prompt-guided key-value cache compression for large language models.Transactions of the Association for Computational Linguistics, 12: 1517–1532, 2024

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:14:14.905011Z

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Observation b5f6d980-f486-4050-9bff-b202d9aeeab7 · outbound

This paper cites FlashAttention-2: Faster attention with better parallelism and work partitioning.

Learning What to Remember: Test-Time Training via Context Distillation FlashAttention-2: Faster attention with better parallelism and work partitioning

Reference 11

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Observation 397eb15a-02c7-4554-994a-8acdc000f1a3 · outbound

This paper cites Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality.

Learning What to Remember: Test-Time Training via Context Distillation Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T23:14:06.123574Z

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Observation 0d3d0072-0426-4262-8add-27db456a8b19 · outbound

This paper cites Flashattention: Fast and memory-efficient exact attention with io-awareness.Advances in neural information processing systems, 35:16344–16359, 2022.

Learning What to Remember: Test-Time Training via Context Distillation Flashattention: Fast and memory-efficient exact attention with io-awareness.Advances in neural information processing systems, 35:16344–16359, 2022

Reference 13

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Observation 6893cd2a-08e9-45ba-9d42-0f4d153f44cc · outbound

This paper cites Cartridges: Lightweight and general-purpose long context representations via self-study.

Learning What to Remember: Test-Time Training via Context Distillation Cartridges: Lightweight and general-purpose long context representations via self-study

Reference 14

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Observation ab97a2d9-9dc7-428e-8d4e-58ab5386247c · outbound

This paper cites In-place test-time training.

Learning What to Remember: Test-Time Training via Context Distillation In-place test-time training

Reference 15

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Observation 4db8bf9e-d62c-41f2-81bb-8cbed9025657 · outbound

This paper cites Hungry Hungry Hippos: Towards Language Modeling with State Space Models.

Learning What to Remember: Test-Time Training via Context Distillation Hungry Hungry Hippos: Towards Language Modeling with State Space Models

Reference 16

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Observation 8a5ee083-a876-497a-98c1-0df6f402a9a2 · outbound

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

Learning What to Remember: Test-Time Training via Context Distillation The Pile: An 800GB Dataset of Diverse Text for Language Modeling

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T23:14:06.952421Z

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Observation 3018c465-52a7-4b15-a788-52b17a2974f7 · outbound

This paper cites How to train long-context language models (effectively).

Learning What to Remember: Test-Time Training via Context Distillation How to train long-context language models (effectively)

Reference 18

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Observation e8cb5d61-4e89-454f-8152-0cd605d7d62e · outbound

This paper cites Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs.

Learning What to Remember: Test-Time Training via Context Distillation Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs

Reference 19

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Observation 6622a355-149e-4d57-98bc-2a62bfb730e7 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Learning What to Remember: Test-Time Training via Context Distillation Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 20

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Observation bd5b79d2-829d-4207-a2d3-d610b49eed4c · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

Learning What to Remember: Test-Time Training via Context Distillation Efficiently Modeling Long Sequences with Structured State Spaces

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T23:14:07.450289Z

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Observation f4a080cd-867c-445a-8964-9940e2d65195 · outbound

This paper cites Log-linear attention.arXiv preprint arXiv:2506.04761, 2025.

Learning What to Remember: Test-Time Training via Context Distillation Log-linear attention.arXiv preprint arXiv:2506.04761, 2025

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T23:14:07.584943Z

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Observation 7ab99549-c75e-45c0-856a-64fdb2f73595 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

Learning What to Remember: Test-Time Training via Context Distillation Measuring Massive Multitask Language Understanding

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T23:14:07.739124Z

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Observation 919ae80d-7d08-4bf1-bdd6-7359d9d19f49 · outbound

This paper cites Long short-term memory.Neural computation, 9(8): 1735–1780, 1997.

Learning What to Remember: Test-Time Training via Context Distillation Long short-term memory.Neural computation, 9(8): 1735–1780, 1997

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T23:14:07.880464Z

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Observation 226c7399-347e-414d-8446-1d605e707c2f · outbound

This paper cites RULER: What's the Real Context Size of Your Long-Context Language Models?.

Learning What to Remember: Test-Time Training via Context Distillation RULER: What's the Real Context Size of Your Long-Context Language Models?

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T23:14:08.026195Z

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Observation 64a8102d-d7e0-4921-9932-e63d85c1ac63 · outbound

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

Learning What to Remember: Test-Time Training via Context Distillation MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies

Reference 26

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Observation 47553360-1a86-4eaa-980a-4375973611b4 · outbound

This paper cites Characterizing Prompt Compression Methods for Long Context Inference.

Learning What to Remember: Test-Time Training via Context Distillation Characterizing Prompt Compression Methods for Long Context Inference

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-04T23:14:08.287634Z

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Observation 0a3b789e-b136-4bbc-8b7d-9dd3a816c0bb · outbound

This paper cites Llmlingua: Compress- ing prompts for accelerated inference of large language models.

Learning What to Remember: Test-Time Training via Context Distillation Llmlingua: Compress- ing prompts for accelerated inference of large language models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T23:14:08.403619Z

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Observation cc9b1233-617f-4401-89d8-72104f18e534 · outbound

This paper cites Babilong: Testing the limits of llms with long context reasoning-in-a-haystack.

Learning What to Remember: Test-Time Training via Context Distillation Babilong: Testing the limits of llms with long context reasoning-in-a-haystack

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-04T23:14:08.528498Z

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Observation a79c02d6-a830-47a0-b543-7f2bf52a67c2 · outbound

This paper cites The power of scale for parameter-efficient prompt tuning.

Learning What to Remember: Test-Time Training via Context Distillation The power of scale for parameter-efficient prompt tuning

Reference 30

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Observation 13d8cc07-5037-4a8c-b89e-312003fc2828 · outbound

This paper cites Prefix-tuning: Optimizing continuous prompts for generation.

Learning What to Remember: Test-Time Training via Context Distillation Prefix-tuning: Optimizing continuous prompts for generation

Reference 31

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Observation adbd24ab-f684-4a90-bf00-1f1f4e15b14d · outbound

This paper cites Snapkv: Llm knows what you are looking for before generation.Advances in Neural Information Processing Systems, 37:22947–22970, 2024.

Learning What to Remember: Test-Time Training via Context Distillation Snapkv: Llm knows what you are looking for before generation.Advances in Neural Information Processing Systems, 37:22947–22970, 2024

Reference 32

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Observation 816d684a-addd-49aa-b6bc-8a610d2e6e2c · outbound

This paper cites Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention.

Learning What to Remember: Test-Time Training via Context Distillation Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention

Reference 33

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Observation aae81bf1-aa7d-47b8-80a4-fe9c14319503 · outbound

This paper cites Transformers are multi-state rnns.

Learning What to Remember: Test-Time Training via Context Distillation Transformers are multi-state rnns

Reference 34

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Observation 5d86c09b-944d-4fd0-9f10-4e067ac893eb · outbound

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Learning What to Remember: Test-Time Training via Context Distillation Rwkv: Reinventing rnns for the transformer era

Reference 35

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Observation 40fd6dab-2f54-4d83-8ed8-b9ffee9cfd62 · outbound

This paper cites Mechanistic Design and Scaling of Hybrid Architectures.

Learning What to Remember: Test-Time Training via Context Distillation Mechanistic Design and Scaling of Hybrid Architectures

Reference 36

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Observation 448891ea-e8db-4747-9d84-708f7e159209 · outbound

This paper cites Improving language understanding by generative pre-training.

Learning What to Remember: Test-Time Training via Context Distillation Improving language understanding by generative pre-training

Reference 37

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Observation cf6f7410-8dd1-49e4-add4-177088d2587b · outbound

This paper cites Linear transformers are secretly fast weight programmers.

Learning What to Remember: Test-Time Training via Context Distillation Linear transformers are secretly fast weight programmers

Reference 38

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Observation a209b04f-5702-4b86-bd83-513447cfdfd9 · outbound

This paper cites FlashAttention-3: Fast and accurate attention with asynchrony and low-precision.

Learning What to Remember: Test-Time Training via Context Distillation FlashAttention-3: Fast and accurate attention with asynchrony and low-precision

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T23:14:13.354172Z

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Observation d702b95c-08f7-4a5f-ae0c-2adbb5ce5d7b · outbound

This paper cites Learning by Distilling Context.

Learning What to Remember: Test-Time Training via Context Distillation Learning by Distilling Context

Reference 40

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This paper cites Roformer: Enhanced transformer with rotary position embedding.Neurocomputing, 568:127063, 2024.

Learning What to Remember: Test-Time Training via Context Distillation Roformer: Enhanced transformer with rotary position embedding.Neurocomputing, 568:127063, 2024

Reference 41

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Observation 18a50eff-afb5-4a4c-87b0-6a62d78006cc · outbound

This paper cites Test-time training with self-supervision for generalization under distribution shifts.

Learning What to Remember: Test-Time Training via Context Distillation Test-time training with self-supervision for generalization under distribution shifts

Reference 42

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Observation d84e59b2-544a-4af1-832a-fc036152c07c · outbound

This paper cites Learning to (Learn at Test Time): RNNs with Expressive Hidden States.

Learning What to Remember: Test-Time Training via Context Distillation Learning to (Learn at Test Time): RNNs with Expressive Hidden States

Reference 43

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Observation d8f1b53f-9c2b-4fee-a59c-f391f420b1b1 · outbound

This paper cites End-to-end test-time training for long context.arXiv preprint arXiv:2512.23675, 2025.

Learning What to Remember: Test-Time Training via Context Distillation End-to-end test-time training for long context.arXiv preprint arXiv:2512.23675, 2025

Reference 44

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Observation 21e81452-944f-4526-99ff-b77dd6ce18f2 · outbound

This paper cites Kimi Linear: An Expressive, Efficient Attention Architecture.

Learning What to Remember: Test-Time Training via Context Distillation Kimi Linear: An Expressive, Efficient Attention Architecture

Reference 45

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Observation 57870a3d-587b-49cf-8ef6-e46b09f61007 · outbound

This paper cites Long data collections database, 2024.

Learning What to Remember: Test-Time Training via Context Distillation Long data collections database, 2024

Reference 46

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Observation 4315f292-e6c7-41fe-8d5d-f0ea54d47e33 · outbound

This paper cites Attention is all you need.Advances in neural information processing systems, 30, 2017.

Learning What to Remember: Test-Time Training via Context Distillation Attention is all you need.Advances in neural information processing systems, 30, 2017

Reference 47

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This paper cites Rattention: Towards the minimal sliding window size in local-global attention models.arXiv preprint arXiv:2506.15545, 2025.

Learning What to Remember: Test-Time Training via Context Distillation Rattention: Towards the minimal sliding window size in local-global attention models.arXiv preprint arXiv:2506.15545, 2025

Reference 48

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Observation 90465926-40c6-49b7-91bd-65939b77e029 · outbound

This paper cites Fantastic Pretraining Optimizers and Where to Find Them.

Learning What to Remember: Test-Time Training via Context Distillation Fantastic Pretraining Optimizers and Where to Find Them

Reference 49

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Observation d2f92442-bf3a-438a-bc24-b9d17918de0a · outbound

This paper cites Gated Linear Attention Transformers with Hardware-Efficient Training.

Learning What to Remember: Test-Time Training via Context Distillation Gated Linear Attention Transformers with Hardware-Efficient Training

Reference 50

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Observation 43063023-1f75-43e9-a0ab-6981d13021b3 · outbound

This paper cites Gated Delta Networks: Improving Mamba2 with Delta Rule.

Learning What to Remember: Test-Time Training via Context Distillation Gated Delta Networks: Improving Mamba2 with Delta Rule

Reference 51

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Observation 610f9608-bdd7-4f16-a98b-796fb62e02d6 · outbound

This paper cites Parallelizing linear transformers with the delta rule over sequence length.Advances in neural information processing systems, 37:115491–115522, 2024.

Learning What to Remember: Test-Time Training via Context Distillation Parallelizing linear transformers with the delta rule over sequence length.Advances in neural information processing systems, 37:115491–115522, 2024

Reference 52

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This paper cites Native sparse attention: Hardware-aligned and natively trainable sparse attention.

Learning What to Remember: Test-Time Training via Context Distillation Native sparse attention: Hardware-aligned and natively trainable sparse attention

Reference 53

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Observation 9aed4c75-ec93-48a7-af42-4a9a1aff78fc · outbound

This paper cites Hellaswag: Can a machine really finish your sentence? InProceedings of the 57th annual meeting of the association for computational linguistics, pages 4791–4800, 2019.

Learning What to Remember: Test-Time Training via Context Distillation Hellaswag: Can a machine really finish your sentence? InProceedings of the 57th annual meeting of the association for computational linguistics, pages 4791–4800, 2019

Reference 54

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Observation 941c35c7-1044-4412-8a89-17dec2777047 · outbound

This paper cites Test-Time Training Done Right.

Learning What to Remember: Test-Time Training via Context Distillation Test-Time Training Done Right

Reference 55

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Observation 76baa423-df23-4828-8c47-e691ab3d19a8 · outbound

This paper cites up to 1.3×.

Learning What to Remember: Test-Time Training via Context Distillation up to 1.3×

Reference 56

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Observation 129dc4f6-bd6b-4602-9d85-ca55dde4b2bd · outbound

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Learning What to Remember: Test-Time Training via Context Distillation Unresolved cited work

Reference 2026

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