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Titans: Learning to Memorize at Test Time
This episode explores the Titans paper’s proposal to pair standard attention with a separate learned long-term memory that updates during inference, aiming to preserve distant information without paying full quadratic attention costs across very long sequences. It situates that idea against earlier approaches such as Neural Turing Machines, Transformer-XL, Compressive Transformers, Memorizing Transformers, and linear-attention recurrent models, highlighting the recurring tradeoff between precise recall and scalable memory. The discussion focuses on the paper’s most distinctive claim: memory writes are driven by a loss-based notion of surprise, making test-time memory updates look more like small online learning steps than a simple cache. Listeners would find it interesting because it gets at a central open question in modern AI systems design: whether neural networks can gain durable, useful memory at inference time without becoming too unstable, expensive, or operationally awkward to deploy.
Sources:
1. Titans: Learning to Memorize at Test Time
https://arxiv.org/pdf/2501.00663
2. Neural Turing Machines — Alex Graves, Greg Wayne, Ivo Danihelka, 2014
https://arxiv.org/abs/1410.5401
3. Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context — Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov, 2019
https://arxiv.org/abs/1901.02860
4. Compressive Transformers for Long-Range Sequence Modelling — Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar, Chloe Hillier, Timothy P. Lillicrap, 2020
https://openreview.net/forum?id=SylKikSYDH
5. Memorizing Transformers — Yuhuai Wu, Markus N. Rabe, DeLesley Hutchins, Christian Szegedy, 2022
https://arxiv.org/abs/2203.08913
6. Learning to (learn at test time): RNNs with expressive hidden states — Yu Sun, Xinhao Li, Karan Dalal, Jiarui Xu, Arjun Vikram, Genghan Zhang, Yann Dubois, Xinlei Chen, Xiaolong Wang, and Sanmi Koyejo, 2024
https://scholar.google.com/scholar?q=Learning+to+%28learn+at+test+time%29%3A+RNNs+with+expressive+hidden+states
7. Gated Delta Networks: Improving Mamba2 with Delta Rule — Songlin Yang, Jan Kautz, and Ali Hatamizadeh, 2024
https://scholar.google.com/scholar?q=Gated+Delta+Networks%3A+Improving+Mamba2+with+Delta+Rule
8. RULER: What's the Real Context Size of Your Long-Context Language Models? — Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman, Shantanu Acharya, Dima Rekesh, Fei Jia, and Boris Ginsburg, 2024
https://scholar.google.com/scholar?q=RULER%3A+What%27s+the+Real+Context+Size+of+Your+Long-Context+Language+Models%3F
9. BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack — Yuri Kuratov, Aydar Bulatov, Petr Anokhin, Ivan Rodkin, Dmitry Sorokin, Artyom Sorokin, and Mikhail Burtsev, 2024
https://scholar.google.com/scholar?q=BABILong%3A+Testing+the+Limits+of+LLMs+with+Long+Context+Reasoning-in-a-Haystack
10. ATLAS: Learning to Optimally Memorize the Context at Test Time — Ali Behrouz, Zeman Li, Praneeth Kacham, Majid Daliri, Yuan Deng, Peilin Zhong, Meisam Razaviyayn, and Vahab Mirrokni, 2025
https://scholar.google.com/scholar?q=ATLAS%3A+Learning+to+Optimally+Memorize+the+Context+at+Test+Time
11. KV-Fold: One-Step KV-Cache Recurrence for Long-Context Inference — Alireza Nadali, Patrick Cooper, Ashutosh Trivedi, Alvaro Velasquez, 2026
https://scholar.google.com/scholar?q=KV-Fold%3A+One-Step+KV-Cache+Recurrence+for+Long-Context+Inference
12. SCBench: A KV Cache-Centric Analysis of Long-Context Methods — Yucheng Li, Huiqiang Jiang, Qianhui Wu, Xufang Luo, Surin Ahn, Chengruidong Zhang, Amir H. Abdi, Dongsheng Li, Jianfeng Gao, Yuqing Yang, Lili Qiu, 2024/2025
https://scholar.google.com/scholar?q=SCBench%3A+A+KV+Cache-Centric+Analysis+of+Long-Context+Methods
13. Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling — Liliang Ren, Yang Liu, Yadong Lu, Yelong Shen, Chen Liang, Weizhu Chen, 2024
https://scholar.google.com/scholar?q=Samba%3A+Simple+Hybrid+State+Space+Models+for+Efficient+Unlimited+Context+Language+Modeling
14. Longhorn: State Space Models are Amortized Online Learners — Bo Liu, Rui Wang, Lemeng Wu, Yihao Feng, Peter Stone, Qiang Liu, 2024
https://scholar.google.com/scholar?q=Longhorn%3A+State+Space+Models+are+Amortized+Online+Learners
15. Retrieval meets Long Context Large Language Models — Peng Xu, Wei Ping, Xianchao Wu, Lawrence McAfee, Chen Zhu, Zihan Liu, Sandeep Subramanian, Evelina Bakhturina, Mohammad Shoeybi, Bryan Catanzaro, 2023
https://scholar.google.com/scholar?q=Retrieval+meets+Long+Context+Large+Language+Models
16. Augmenting Language Models with Long-Term Memory — Weizhi Wang, Li Dong, Hao Cheng, Xiaodong Liu, Xifeng Yan, Jianfeng Gao, Furu Wei, 2023
https://scholar.google.com/scholar?q=Augmenting+Language+Models+with+Long-Term+Memory
17. Test-Time Training Provably Improves Transformers as In-Context Learners — Halil Alperen Gozeten, M. Emrullah Ildiz, Xuechen Zhang, Mahdi Soltanolkotabi, Marco Mondelli, Samet Oymak, 2025
https://scholar.google.com/scholar?q=Test-Time+Training+Provably+Improves+Transformers+as+In-Context+Learners
18. AI Post Transformers: δ-mem and Online Memory for LLMs — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-13-d-mem-and-online-memory-for-llms-6622fa.mp3
19. AI Post Transformers: In-Place Test-Time Training for Transformers — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-09-in-place-test-time-training-for-transfor-d0b976.mp3
20. AI Post Transformers: Gated Linear Attention for Efficient Long Sequences — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-18-gated-linear-attention-for-efficient-lon-c858ab.mp3
21. AI Post Transformers: MELT: Decoupling Compute From Memory — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-13-melt-decoupling-compute-from-memory-26430c.mp3
22. AI Post Transformers: Long Context Pre-Training with Lighthouse Attention — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-13-long-context-pre-training-with-lighthous-e85bbe.mp3
23. AI Post Transformers: Training Million-Token LLMs Beyond the Memory Barrier — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-04-training-million-token-llms-beyond-the-m-324edc.mp3
24. AI Post Transformers: Recursive Language Models for Arbitrarily Long Prompts — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-04-recursive-language-models-for-arbitraril-fbcd1c.mp3
25. AI Post Transformers: How Induction Heads Emerge in Transformers — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-03-how-induction-heads-emerge-in-transforme-a7bfcb.mp3
26. AI Post Transformers: DeepSeek-V4 and Practical Million-Token Context — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-25-deepseek-v4-and-practical-million-token-6f4de1.mp3
Interactive Visualization: Titans: Learning to Memorize at Test Time
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By mcgrofThis episode explores the Titans paper’s proposal to pair standard attention with a separate learned long-term memory that updates during inference, aiming to preserve distant information without paying full quadratic attention costs across very long sequences. It situates that idea against earlier approaches such as Neural Turing Machines, Transformer-XL, Compressive Transformers, Memorizing Transformers, and linear-attention recurrent models, highlighting the recurring tradeoff between precise recall and scalable memory. The discussion focuses on the paper’s most distinctive claim: memory writes are driven by a loss-based notion of surprise, making test-time memory updates look more like small online learning steps than a simple cache. Listeners would find it interesting because it gets at a central open question in modern AI systems design: whether neural networks can gain durable, useful memory at inference time without becoming too unstable, expensive, or operationally awkward to deploy.
Sources:
1. Titans: Learning to Memorize at Test Time
https://arxiv.org/pdf/2501.00663
2. Neural Turing Machines — Alex Graves, Greg Wayne, Ivo Danihelka, 2014
https://arxiv.org/abs/1410.5401
3. Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context — Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov, 2019
https://arxiv.org/abs/1901.02860
4. Compressive Transformers for Long-Range Sequence Modelling — Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar, Chloe Hillier, Timothy P. Lillicrap, 2020
https://openreview.net/forum?id=SylKikSYDH
5. Memorizing Transformers — Yuhuai Wu, Markus N. Rabe, DeLesley Hutchins, Christian Szegedy, 2022
https://arxiv.org/abs/2203.08913
6. Learning to (learn at test time): RNNs with expressive hidden states — Yu Sun, Xinhao Li, Karan Dalal, Jiarui Xu, Arjun Vikram, Genghan Zhang, Yann Dubois, Xinlei Chen, Xiaolong Wang, and Sanmi Koyejo, 2024
https://scholar.google.com/scholar?q=Learning+to+%28learn+at+test+time%29%3A+RNNs+with+expressive+hidden+states
7. Gated Delta Networks: Improving Mamba2 with Delta Rule — Songlin Yang, Jan Kautz, and Ali Hatamizadeh, 2024
https://scholar.google.com/scholar?q=Gated+Delta+Networks%3A+Improving+Mamba2+with+Delta+Rule
8. RULER: What's the Real Context Size of Your Long-Context Language Models? — Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman, Shantanu Acharya, Dima Rekesh, Fei Jia, and Boris Ginsburg, 2024
https://scholar.google.com/scholar?q=RULER%3A+What%27s+the+Real+Context+Size+of+Your+Long-Context+Language+Models%3F
9. BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack — Yuri Kuratov, Aydar Bulatov, Petr Anokhin, Ivan Rodkin, Dmitry Sorokin, Artyom Sorokin, and Mikhail Burtsev, 2024
https://scholar.google.com/scholar?q=BABILong%3A+Testing+the+Limits+of+LLMs+with+Long+Context+Reasoning-in-a-Haystack
10. ATLAS: Learning to Optimally Memorize the Context at Test Time — Ali Behrouz, Zeman Li, Praneeth Kacham, Majid Daliri, Yuan Deng, Peilin Zhong, Meisam Razaviyayn, and Vahab Mirrokni, 2025
https://scholar.google.com/scholar?q=ATLAS%3A+Learning+to+Optimally+Memorize+the+Context+at+Test+Time
11. KV-Fold: One-Step KV-Cache Recurrence for Long-Context Inference — Alireza Nadali, Patrick Cooper, Ashutosh Trivedi, Alvaro Velasquez, 2026
https://scholar.google.com/scholar?q=KV-Fold%3A+One-Step+KV-Cache+Recurrence+for+Long-Context+Inference
12. SCBench: A KV Cache-Centric Analysis of Long-Context Methods — Yucheng Li, Huiqiang Jiang, Qianhui Wu, Xufang Luo, Surin Ahn, Chengruidong Zhang, Amir H. Abdi, Dongsheng Li, Jianfeng Gao, Yuqing Yang, Lili Qiu, 2024/2025
https://scholar.google.com/scholar?q=SCBench%3A+A+KV+Cache-Centric+Analysis+of+Long-Context+Methods
13. Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling — Liliang Ren, Yang Liu, Yadong Lu, Yelong Shen, Chen Liang, Weizhu Chen, 2024
https://scholar.google.com/scholar?q=Samba%3A+Simple+Hybrid+State+Space+Models+for+Efficient+Unlimited+Context+Language+Modeling
14. Longhorn: State Space Models are Amortized Online Learners — Bo Liu, Rui Wang, Lemeng Wu, Yihao Feng, Peter Stone, Qiang Liu, 2024
https://scholar.google.com/scholar?q=Longhorn%3A+State+Space+Models+are+Amortized+Online+Learners
15. Retrieval meets Long Context Large Language Models — Peng Xu, Wei Ping, Xianchao Wu, Lawrence McAfee, Chen Zhu, Zihan Liu, Sandeep Subramanian, Evelina Bakhturina, Mohammad Shoeybi, Bryan Catanzaro, 2023
https://scholar.google.com/scholar?q=Retrieval+meets+Long+Context+Large+Language+Models
16. Augmenting Language Models with Long-Term Memory — Weizhi Wang, Li Dong, Hao Cheng, Xiaodong Liu, Xifeng Yan, Jianfeng Gao, Furu Wei, 2023
https://scholar.google.com/scholar?q=Augmenting+Language+Models+with+Long-Term+Memory
17. Test-Time Training Provably Improves Transformers as In-Context Learners — Halil Alperen Gozeten, M. Emrullah Ildiz, Xuechen Zhang, Mahdi Soltanolkotabi, Marco Mondelli, Samet Oymak, 2025
https://scholar.google.com/scholar?q=Test-Time+Training+Provably+Improves+Transformers+as+In-Context+Learners
18. AI Post Transformers: δ-mem and Online Memory for LLMs — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-13-d-mem-and-online-memory-for-llms-6622fa.mp3
19. AI Post Transformers: In-Place Test-Time Training for Transformers — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-09-in-place-test-time-training-for-transfor-d0b976.mp3
20. AI Post Transformers: Gated Linear Attention for Efficient Long Sequences — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-18-gated-linear-attention-for-efficient-lon-c858ab.mp3
21. AI Post Transformers: MELT: Decoupling Compute From Memory — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-13-melt-decoupling-compute-from-memory-26430c.mp3
22. AI Post Transformers: Long Context Pre-Training with Lighthouse Attention — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-13-long-context-pre-training-with-lighthous-e85bbe.mp3
23. AI Post Transformers: Training Million-Token LLMs Beyond the Memory Barrier — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-04-training-million-token-llms-beyond-the-m-324edc.mp3
24. AI Post Transformers: Recursive Language Models for Arbitrarily Long Prompts — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-04-recursive-language-models-for-arbitraril-fbcd1c.mp3
25. AI Post Transformers: How Induction Heads Emerge in Transformers — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-05-03-how-induction-heads-emerge-in-transforme-a7bfcb.mp3
26. AI Post Transformers: DeepSeek-V4 and Practical Million-Token Context — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-25-deepseek-v4-and-practical-million-token-6f4de1.mp3
Interactive Visualization: Titans: Learning to Memorize at Test Time