This episode explores CacheFlow, a systems approach to speeding up long-context LLM serving by restoring transformer KV caches more intelligently. It explains the tradeoffs between recomputing prior attention state, loading it from storage, or combining both, and argues that the real user-facing bottleneck is now time-to-first-token rather than raw generation speed. The discussion focuses on CacheFlow’s main idea: a batch-aware scheduler that splits restoration across recomputation and I/O at token, layer, and GPU levels to reduce wasted work under contention. Listeners would find it interesting because it shows how practical transformer serving is increasingly shaped by runtime scheduling, cache movement, and latency engineering rather than new model architectures.

Sources:

1. CacheFlow and 3D-Parallel KV Cache Restoration

https://arxiv.org/pdf/2604.25080

2. Efficient Memory Management for Large Language Model Serving with PagedAttention — Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, Ion Stoica, 2023

https://scholar.google.com/scholar?q=Efficient+Memory+Management+for+Large+Language+Model+Serving+with+PagedAttention

3. Cost-Efficient Large Language Model Serving for Multi-turn Conversations with CachedAttention — Bin Gao, Zhuomin He, Puru Sharma, Qingxuan Kang, Djordje Jevdjic, Junbo Deng, Xingkun Yang, Zhou Yu, Pengfei Zuo, 2024

https://scholar.google.com/scholar?q=Cost-Efficient+Large+Language+Model+Serving+for+Multi-turn+Conversations+with+CachedAttention

4. Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving — Ruoyu Qin, Zheming Li, Weiran He, Mingxing Zhang, Yongwei Wu, Weimin Zheng, Xinran Xu, 2024

https://scholar.google.com/scholar?q=Mooncake%3A+A+KVCache-centric+Disaggregated+Architecture+for+LLM+Serving

5. Fast State Restoration in LLM Serving with HCache — Shiwei Gao, Youmin Chen, Jiwu Shu, 2024

https://scholar.google.com/scholar?q=Fast+State+Restoration+in+LLM+Serving+with+HCache

6. LMCache: An Efficient KV Cache Layer for Enterprise-Scale LLM Inference — Yihua Cheng, Yuhan Liu, Jiayi Yao, Yuwei An, Xiaokun Chen, Shaoting Feng, Yuyang Huang, Samuel Shen, Kuntai Du, Junchen Jiang, 2025

https://scholar.google.com/scholar?q=LMCache%3A+An+Efficient+KV+Cache+Layer+for+Enterprise-Scale+LLM+Inference

7. DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving — Yinmin Zhong, Shengyu Liu, Junda Chen, Jianbo Hu, Yibo Zhu, Xuanzhe Liu, Xin Jin, Hao Zhang, 2024

https://scholar.google.com/scholar?q=DistServe%3A+Disaggregating+Prefill+and+Decoding+for+Goodput-optimized+Large+Language+Model+Serving

8. Mooncake: Trading More Storage for Less Computation — A KVCache-centric Architecture for Serving LLM Chatbot — Ruoyu Qin, Zheming Li, Weiran He, Jialei Cui, Feng Ren, Mingxing Zhang, Yongwei Wu, Weimin Zheng, Xinran Xu, 2025

https://scholar.google.com/scholar?q=Mooncake%3A+Trading+More+Storage+for+Less+Computation+%E2%80%94+A+KVCache-centric+Architecture+for+Serving+LLM+Chatbot

9. DéjàVu: KV-cache Streaming for Fast, Fault-tolerant Generative LLM Serving — Foteini Strati, Sara Mcallister, Amar Phanishayee, Jakub Tarnawski, Ana Klimovic, 2024

https://scholar.google.com/scholar?q=D%C3%A9j%C3%A0Vu%3A+KV-cache+Streaming+for+Fast%2C+Fault-tolerant+Generative+LLM+Serving

10. KV Prediction for Improved Time to First Token — Maxwell Horton, Qingqing Cao, Chenfan Sun, Yanzi Jin, Sachin Mehta, Mohammad Rastegari, Moin Nabi, 2025

https://scholar.google.com/scholar?q=KV+Prediction+for+Improved+Time+to+First+Token

11. KVSharer: Efficient Inference via Layer-Wise Dissimilar KV Cache Sharing — Yifei Yang et al., 2024

https://scholar.google.com/scholar?q=KVSharer%3A+Efficient+Inference+via+Layer-Wise+Dissimilar+KV+Cache+Sharing

12. CommonKV: Compressing KV Cache with Cross-layer Parameter Sharing — Yixuan Wang et al., 2025

https://scholar.google.com/scholar?q=CommonKV%3A+Compressing+KV+Cache+with+Cross-layer+Parameter+Sharing

13. Lossless KV Cache Compression to 2% — Zhen Yang et al., 2024

https://scholar.google.com/scholar?q=Lossless+KV+Cache+Compression+to+2%25

14. XKV: Personalized KV Cache Memory Reduction for Long-Context LLM Inference — Weizhuo Li et al., 2024

https://scholar.google.com/scholar?q=XKV%3A+Personalized+KV+Cache+Memory+Reduction+for+Long-Context+LLM+Inference

15. TriForce: Lossless Acceleration of Long Sequence Generation with Hierarchical Speculative Decoding — Hanshi Sun et al., 2024

https://scholar.google.com/scholar?q=TriForce%3A+Lossless+Acceleration+of+Long+Sequence+Generation+with+Hierarchical+Speculative+Decoding

16. MagicDec: Breaking the Latency-Throughput Tradeoff for Long Context Generation with Speculative Decoding — Jian Chen, Vashisth Tiwari, Ranajoy Sadhukhan, Zhuoming Chen, Jinyuan Shi, Ian En-Hsu Yen, Beidi Chen, 2024

https://scholar.google.com/scholar?q=MagicDec%3A+Breaking+the+Latency-Throughput+Tradeoff+for+Long+Context+Generation+with+Speculative+Decoding

17. SpeCache: Speculative Key-Value Caching for Efficient Generation of LLMs — Shibo Jie et al., 2025

https://scholar.google.com/scholar?q=SpeCache%3A+Speculative+Key-Value+Caching+for+Efficient+Generation+of+LLMs

18. Prefill-Decode Aggregation or Disaggregation? Unifying Both for Goodput-Optimized LLM Serving — Chao Wang, Pengfei Zuo, Zhangyu Chen, Yunkai Liang, Zhou Yu, Ming-Chang Yang, 2025

https://scholar.google.com/scholar?q=Prefill-Decode+Aggregation+or+Disaggregation%3F+Unifying+Both+for+Goodput-Optimized+LLM+Serving

19. AI Post Transformers: ContiguousKV for Faster LLM Prefill KV Reuse — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-20-contiguouskv-for-faster-llm-prefill-kv-r-59f545.mp3

20. AI Post Transformers: ScoutAttention for Efficient KV Cache Offloading — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-24-scoutattention-for-efficient-kv-cache-of-b26699.mp3

21. AI Post Transformers: Prefill-as-a-Service for Cross-Datacenter KV Cache — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-19-prefill-as-a-service-for-cross-datacente-7560be.mp3

22. AI Post Transformers: FengHuang for Rack-Scale LLM Inference Memory — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-12-fenghuang-for-rack-scale-llm-inference-m-62708e.mp3

23. AI Post Transformers: KV Cache TTL for Multi-Turn Agent Scheduling — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-09-kv-cache-ttl-for-multi-turn-agent-schedu-996bf1.mp3

24. AI Post Transformers: Splitwise: Phase-Split LLM Inference — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-03-26-splitwise-phase-split-llm-inference-e8945b.mp3

25. AI Post Transformers: KVSwap for Disk-Aware Long-Context On-Device Inference — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-16-kvswap-for-disk-aware-long-context-on-de-f3c15e.mp3

Interactive Visualization: CacheFlow and 3D-Parallel KV Cache Restoration

This episode explores DeltaKV, a method for reducing the huge GPU memory burden of KV caches in long-context language model inference without simply discarding old tokens. It contrasts three strategies for handling long contexts: token eviction, dynamic sparse attention, and true compression, arguing that the cache contains structured redundancy that can be exploited rather than treated as disposable overhead. The discussion highlights DeltaKV’s core idea of keeping a small uncompressed reference set and storing other cache entries as compressed residuals relative to similar past states, drawing an analogy to delta encoding or version control. Listeners would find it interesting because it connects transformer internals, systems constraints, and practical serving performance, including claims of cutting memory to 29 percent of baseline and reaching up to 2x throughput with supporting infrastructure like Sparse-vLLM.

Sources:

1. DeltaKV: Residual-Based KV Cache Compression via Long-Range Similarity — Jitai Hao, Qiang Huang, Yaowei Wang, Min Zhang, Jun Yu, 2026

http://arxiv.org/abs/2602.08005

2. Generalization through Memorization: Nearest Neighbor Language Models — Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, Mike Lewis, 2019

https://scholar.google.com/scholar?q=Generalization+through+Memorization%3A+Nearest+Neighbor+Language+Models

3. Reformer: The Efficient Transformer — Nikita Kitaev, Lukasz Kaiser, Anselm Levskaya, 2020

https://scholar.google.com/scholar?q=Reformer%3A+The+Efficient+Transformer

4. Improving Language Models by Retrieving from Trillions of Tokens — Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann and many others, 2021

https://scholar.google.com/scholar?q=Improving+Language+Models+by+Retrieving+from+Trillions+of+Tokens

5. Memorizing Transformers — Yuhuai Wu, Markus N. Rabe, DeLesley Hutchins, Christian Szegedy, 2022

https://scholar.google.com/scholar?q=Memorizing+Transformers

6. CacheGen: KV Cache Compression and Streaming for Fast Large Language Model Serving — Yuhan Liu, Hanchen Li, Yihua Cheng and others, 2023

https://scholar.google.com/scholar?q=CacheGen%3A+KV+Cache+Compression+and+Streaming+for+Fast+Large+Language+Model+Serving

7. Palu: Compressing KV-Cache with Low-Rank Projection — Chi-Chih Chang, Wei-Cheng Lin, Chien-Yu Lin and others, 2024

https://scholar.google.com/scholar?q=Palu%3A+Compressing+KV-Cache+with+Low-Rank+Projection

8. Lexico: Extreme KV Cache Compression via Sparse Coding over Universal Dictionaries — Junhyuck Kim, Jongho Park, Jaewoong Cho, Dimitris Papailiopoulos, 2025

https://scholar.google.com/scholar?q=Lexico%3A+Extreme+KV+Cache+Compression+via+Sparse+Coding+over+Universal+Dictionaries

9. Cache Me If You Must: Adaptive Key-Value Quantization for Large Language Models — Alina Shutova, Vladimir Malinovskii, Vage Egiazarian and others, 2025

https://scholar.google.com/scholar?q=Cache+Me+If+You+Must%3A+Adaptive+Key-Value+Quantization+for+Large+Language+Models

10. OmniKV: Dynamic Context Selection for Efficient Long-Context LLMs — Jitai Hao, Yuke Zhu, Tian Wang, Jun Yu, Xin Xin, Bo Zheng, Zhaochun Ren, Sheng Guo, 2025

https://scholar.google.com/scholar?q=OmniKV%3A+Dynamic+Context+Selection+for+Efficient+Long-Context+LLMs

11. QUEST: Query-Aware Sparsity for Efficient Long-Context LLM Inference — Jiaming Tang, Yilong Zhao, Kan Zhu, Guangxuan Xiao, Baris Kasikci, Song Han, 2024

https://scholar.google.com/scholar?q=QUEST%3A+Query-Aware+Sparsity+for+Efficient+Long-Context+LLM+Inference

12. Palu: KV-Cache Compression with Low-Rank Projection — Chi-Chih Chang, Wei-Cheng Lin, Chien-Yu Lin, Chong-Yan Chen, Yu-Fang Hu, Pei-Shuo Wang, Ning-Chi Huang, Luis Ceze, Mohamed S. Abdelfattah, Kai-Chiang Wu, 2025

https://scholar.google.com/scholar?q=Palu%3A+KV-Cache+Compression+with+Low-Rank+Projection

13. 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, 2025

https://scholar.google.com/scholar?q=SCBench%3A+A+KV+Cache-Centric+Analysis+of+Long-Context+Methods

14. The Pitfalls of KV Cache Compression — Alex Chen, Renato Geh, Aditya Grover, Guy Van den Broeck, Daniel Israel, 2025

https://scholar.google.com/scholar?q=The+Pitfalls+of+KV+Cache+Compression

15. KVzip: Query-Agnostic KV Cache Compression with Context Reconstruction — Jang-Hyun Kim, Jinuk Kim, Sangwoo Kwon, Jae W. Lee, Sangdoo Yun, Hyun Oh Song, 2025

https://scholar.google.com/scholar?q=KVzip%3A+Query-Agnostic+KV+Cache+Compression+with+Context+Reconstruction

16. Kvlink: Accelerating Large Language Models via Efficient KV Cache Reuse — approx. recent LLM systems authors, 2025/2026

https://scholar.google.com/scholar?q=Kvlink%3A+Accelerating+Large+Language+Models+via+Efficient+KV+Cache+Reuse

17. HyperRAG: Enhancing Quality-Efficiency Tradeoffs in Retrieval-Augmented Generation with Reranker KV-Cache Reuse — approx. recent RAG systems authors, 2025/2026

https://scholar.google.com/scholar?q=HyperRAG%3A+Enhancing+Quality-Efficiency+Tradeoffs+in+Retrieval-Augmented+Generation+with+Reranker+KV-Cache+Reuse

18. ProphetKV: User-Query-Driven Selective Recomputation for Efficient KV Cache Reuse in Retrieval-Augmented Generation — approx. recent RAG/serving authors, 2025/2026

https://scholar.google.com/scholar?q=ProphetKV%3A+User-Query-Driven+Selective+Recomputation+for+Efficient+KV+Cache+Reuse+in+Retrieval-Augmented+Generation

19. KVSink: Understanding and Enhancing the Preservation of Attention Sinks in KV Cache Quantization for LLMs — approx. recent KV quantization authors, 2025/2026

https://scholar.google.com/scholar?q=KVSink%3A+Understanding+and+Enhancing+the+Preservation+of+Attention+Sinks+in+KV+Cache+Quantization+for+LLMs

20. LLMs Know What to Drop: Self-Attention Guided KV Cache Eviction for Efficient Long-Context Inference — approx. recent long-context inference authors, 2025/2026

https://scholar.google.com/scholar?q=LLMs+Know+What+to+Drop%3A+Self-Attention+Guided+KV+Cache+Eviction+for+Efficient+Long-Context+Inference

21. Task-KV: Task-Aware KV Cache Optimization via Semantic Differentiation of Attention Heads — approx. recent attention/KV optimization authors, 2025/2026

https://scholar.google.com/scholar?q=Task-KV%3A+Task-Aware+KV+Cache+Optimization+via+Semantic+Differentiation+of+Attention+Heads

22. WKVQuant: Quantizing Weight and Key/Value Cache for Large Language Models Gains More — approx. recent quantization authors, 2025/2026

https://scholar.google.com/scholar?q=WKVQuant%3A+Quantizing+Weight+and+Key%2FValue+Cache+for+Large+Language+Models+Gains+More

23. AlignedKV: Reducing Memory Access of KV-Cache with Precision-Aligned Quantization — approx. recent KV systems/quantization authors, 2025/2026

https://scholar.google.com/scholar?q=AlignedKV%3A+Reducing+Memory+Access+of+KV-Cache+with+Precision-Aligned+Quantization

24. Making Every Head Count: Sparse Attention Without the Speed-Performance Trade-off — approx. recent sparse attention authors, 2025/2026

https://scholar.google.com/scholar?q=Making+Every+Head+Count%3A+Sparse+Attention+Without+the+Speed-Performance+Trade-off

25. Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention — approx. recent sparse systems authors, 2025/2026

https://scholar.google.com/scholar?q=Native+Sparse+Attention%3A+Hardware-Aligned+and+Natively+Trainable+Sparse+Attention

26. AI Post Transformers: Stochastic KV Routing for Cache Sharing — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-29-stochastic-kv-routing-for-cache-sharing-5fef63.mp3

27. AI Post Transformers: ContiguousKV for Faster LLM Prefill KV Reuse — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-20-contiguouskv-for-faster-llm-prefill-kv-r-59f545.mp3

28. AI Post Transformers: KVSwap for Disk-Aware Long-Context On-Device Inference — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-16-kvswap-for-disk-aware-long-context-on-de-f3c15e.mp3

29. AI Post Transformers: Native Sparse Attention: Efficient Long-Context LLMs — Hal Turing & Dr. Ada Shannon, 2025

https://podcast.do-not-panic.com/episodes/native-sparse-attention-efficient-long-context-llms/

30. AI Post Transformers: Memory Sparse Attention for 100M-Token Scaling — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-07-memory-sparse-attention-for-100m-token-s-377cff.mp3

31. AI Post Transformers: AWQ: On-Device LLM Compression and Acceleration — Hal Turing & Dr. Ada Shannon, 2025

https://podcast.do-not-panic.com/episodes/awq-on-device-llm-compression-and-acceleration/

This episode explores a review of mechanistic interpretability for transformer language models, focusing on how researchers study internal features, circuits, and claims of universality across models. It explains the core toolkit behind the field, including linear probes, hidden-state analysis, intervention methods, vocabulary projection, and sparse autoencoders, while grounding those ideas in transformer anatomy such as attention heads, MLPs, and the residual stream. The discussion highlights a central tension in the literature: finding information encoded in activations is not the same as proving that information causally drives model behavior, and the episode repeatedly questions where interpretability claims may be overstated. Listeners would find it interesting because it offers a concrete map of a fast-growing area of AI research while also giving a careful critique of the field’s assumptions, evidence, and real-world usefulness.

Sources:

1. A Practical Review of Mechanistic Interpretability for Transformer-Based Language Models — Daking Rai, Yilun Zhou, Shi Feng, Abulhair Saparov, Ziyu Yao, 2024

http://arxiv.org/abs/2407.02646

2. Towards Monosemanticity: Decomposing Language Models With Dictionary Learning — Trenton Bricken, Adly Templeton, Joshua Batson, Brian Chen, Adam Jermyn, Chris Olah, and collaborators, 2023

https://scholar.google.com/scholar?q=Towards+Monosemanticity%3A+Decomposing+Language+Models+With+Dictionary+Learning

3. Sparse Autoencoders Find Highly Interpretable Features in Language Models — Hoagy Cunningham, Aidan Ewart, Logan Riggs, Robert Huben, Lee Sharkey, 2023

https://scholar.google.com/scholar?q=Sparse+Autoencoders+Find+Highly+Interpretable+Features+in+Language+Models

4. Scaling and Evaluating Sparse Autoencoders — Leo Gao, Tom Dupré la Tour, Henk Tillman, Gabriel Goh, Rajan Troll, Alec Radford, Ilya Sutskever, Jan Leike, Jeffrey Wu, 2024

https://scholar.google.com/scholar?q=Scaling+and+Evaluating+Sparse+Autoencoders

5. Jumping Ahead: Improving Reconstruction Fidelity with JumpReLU Sparse Autoencoders — Senthooran Rajamanoharan, Tom Lieberum, Nicolas Sonnerat, Arthur Conmy, Vikrant Varma, Janos Kramar, Neel Nanda, 2024

https://scholar.google.com/scholar?q=Jumping+Ahead%3A+Improving+Reconstruction+Fidelity+with+JumpReLU+Sparse+Autoencoders

6. A Mathematical Framework for Transformer Circuits — Nelson Elhage, Neel Nanda, Catherine Olsson, Tom Henighan, Nicholas Joseph, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Nova DasSarma, Dawn Drain, Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Andy Jones, Jackson Kernion, Liane Lovitt, Kamal Ndousse, Dario Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, Chris Olah, 2021

https://scholar.google.com/scholar?q=A+Mathematical+Framework+for+Transformer+Circuits

7. Towards Best Practices of Activation Patching in Language Models: Metrics and Methods — Fred Zhang, Neel Nanda, 2024

https://scholar.google.com/scholar?q=Towards+Best+Practices+of+Activation+Patching+in+Language+Models%3A+Metrics+and+Methods

8. Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet — Adly Templeton, Tom Conerly, Jonathan Marcus, Jack Lindsey, Trenton Bricken, Brian Chen, Adam Pearce, Craig Citro, Emmanuel Ameisen, Andy Jones, Hoagy Cunningham, Nicholas L. Turner, Callum McDougall, Monte MacDiarmid, C. Daniel Freeman, Theodore R. Sumers, Edward Rees, Joshua Batson, Adam Jermyn, Shan Carter, Chris Olah, Tom Henighan, 2024

https://scholar.google.com/scholar?q=Scaling+Monosemanticity%3A+Extracting+Interpretable+Features+from+Claude+3+Sonnet

9. Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models — Samuel Marks, Can Rager, Eric J. Michaud, Yonatan Belinkov, David Bau, Aaron Mueller, 2025

https://scholar.google.com/scholar?q=Sparse+Feature+Circuits%3A+Discovering+and+Editing+Interpretable+Causal+Graphs+in+Language+Models

10. On the Theoretical Understanding of Identifiable Sparse Autoencoders and Beyond — Jingyi Cui, Qi Zhang, Yifei Wang, Yisen Wang, 2025

https://scholar.google.com/scholar?q=On+the+Theoretical+Understanding+of+Identifiable+Sparse+Autoencoders+and+Beyond

11. Rethinking Evaluation of Sparse Autoencoders through the Representation of Polysemous Words — Gouki Minegishi, Hiroki Furuta, Yusuke Iwasawa, Yutaka Matsuo, 2025

https://scholar.google.com/scholar?q=Rethinking+Evaluation+of+Sparse+Autoencoders+through+the+Representation+of+Polysemous+Words

12. Causal Head Gating: A Framework for Interpreting Roles of Attention Heads in Transformers — Andrew Nam, Henry Conklin, Yukang Yang, Thomas Griffiths, Jonathan Cohen, Sarah-Jane Leslie, 2025

https://scholar.google.com/scholar?q=Causal+Head+Gating%3A+A+Framework+for+Interpreting+Roles+of+Attention+Heads+in+Transformers

13. Quantifying LLM Attention-Head Stability: Implications for Circuit Universality — Karan Bali, Jack Stanley, Praneet Suresh, Danilo Bzdok, 2026

https://scholar.google.com/scholar?q=Quantifying+LLM+Attention-Head+Stability%3A+Implications+for+Circuit+Universality

14. AI Post Transformers: Mechanistic interpretability: Decoding the AI's Inner Logic: Circuits and Sparse Features — Hal Turing & Dr. Ada Shannon, 2025

https://podcast.do-not-panic.com/episodes/mechanistic-interpretability-decoding-the-ais-inner-logic-circuits-and-sparse-fe/

15. AI Post Transformers: Advancing Mechanistic Interpretability with Sparse Autoencoders — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/advancing-mechanistic-interpretability-with-sparse-autoencoders/

16. AI Post Transformers: Neural Chameleons and Evading Activation Monitors — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-14-neural-chameleons-and-evading-activation-bc470e.mp3

17. AI Post Transformers: Linear Classifier Probes for Intermediate Layers — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-16-linear-classifier-probes-for-intermediat-927ae3.mp3

18. AI Post Transformers: Internal Safety Collapse in Frontier LLMs — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-04-internal-safety-collapse-in-frontier-llm-8be72f.mp3

This episode explores a 2026 paper on recursive multi-agent systems that asks whether AI collaboration can scale better by replacing text-based agent communication with shared latent-state updates. It explains the difference between agent topology, assigned roles, and communication channels, and argues that natural language may be a costly bottleneck compared with the richer internal representations models use during reasoning. The discussion connects this idea to earlier multi-agent chat frameworks, classic transformer architectures, and recent test-time compute work on latent recurrent refinement. Listeners would find it interesting because it frames a sharp debate between today’s practical, inspectable text-first agent workflows and a more trainable, neural-network-like approach that could change how complex AI teams are built.

Sources:

1. Recursive Multi-Agent Systems — Xiyuan Yang, Jiaru Zou, Rui Pan, Ruizhong Qiu, Pan Lu, Shizhe Diao, Jindong Jiang, Hanghang Tong, Tong Zhang, Markus J. Buehler, Jingrui He, James Zou, 2026

http://arxiv.org/abs/2604.25917

2. CAMEL: Communicative Agents for "Mind" Exploration of Large Scale Language Model Society — Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbullin, Bernard Ghanem, 2023

https://scholar.google.com/scholar?q=CAMEL%3A+Communicative+Agents+for+%22Mind%22+Exploration+of+Large+Scale+Language+Model+Society

3. AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation — Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, Ahmed Hassan Awadallah, Ryen W. White, Doug Burger, Chi Wang, 2023

https://scholar.google.com/scholar?q=AutoGen%3A+Enabling+Next-Gen+LLM+Applications+via+Multi-Agent+Conversation

4. MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework — Sirui Hong, Xiawu Zheng, Jonathan Chen, Yuheng Cheng, Jinlin Wang, Ceyao Zhang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Liyang Zhou, Chenyu Ran, Lingfeng Xiao, Chenglin Wu, Jürgen Schmidhuber, 2023

https://scholar.google.com/scholar?q=MetaGPT%3A+Meta+Programming+for+A+Multi-Agent+Collaborative+Framework

5. Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach — Jonas Geiping, Sean McLeish, Neel Jain, John Kirchenbauer, Siddharth Singh, Brian R. Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, Tom Goldstein, 2025

https://scholar.google.com/scholar?q=Scaling+up+Test-Time+Compute+with+Latent+Reasoning%3A+A+Recurrent+Depth+Approach

6. Learning Multiagent Communication with Backpropagation — Sainbayar Sukhbaatar, Arthur Szlam, Rob Fergus, 2016

https://scholar.google.com/scholar?q=Learning+Multiagent+Communication+with+Backpropagation

7. Learning to Communicate with Deep Multi-Agent Reinforcement Learning — Jakob Foerster, Ioannis Alexandros Assael, Nando de Freitas, Shimon Whiteson, 2016

https://scholar.google.com/scholar?q=Learning+to+Communicate+with+Deep+Multi-Agent+Reinforcement+Learning

8. Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments — Ryan Lowe, Yi Wu, Aviv Tamar, Jean Harb, Pieter Abbeel, Igor Mordatch, 2017

https://scholar.google.com/scholar?q=Multi-Agent+Actor-Critic+for+Mixed+Cooperative-Competitive+Environments

9. QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning — Tabish Rashid, Mikayel Samvelyan, Christian Schroeder de Witt, Gregory Farquhar, Jakob Foerster, Shimon Whiteson, 2018

https://scholar.google.com/scholar?q=QMIX%3A+Monotonic+Value+Function+Factorisation+for+Deep+Multi-Agent+Reinforcement+Learning

10. TextGrad: Automatic "Differentiation" via Text — Mert Yuksekgonul, Federico Bianchi, Joseph Boen, Sheng Liu, Zhi Huang, Carlos Guestrin, James Zou, 2024

https://scholar.google.com/scholar?q=TextGrad%3A+Automatic+%22Differentiation%22+via+Text

11. Mixture-of-Agents Enhances Large Language Model Capabilities — Junlin Wang, Jue Wang, Ben Athiwaratkun, Ce Zhang, James Zou, 2024

https://scholar.google.com/scholar?q=Mixture-of-Agents+Enhances+Large+Language+Model+Capabilities

12. ReDel: A Toolkit for LLM-Powered Recursive Multi-Agent Systems — Andrew Zhu, Liam Dugan, Chris Callison-Burch, 2024

https://scholar.google.com/scholar?q=ReDel%3A+A+Toolkit+for+LLM-Powered+Recursive+Multi-Agent+Systems

13. Why Do Multi-Agent LLM Systems Fail? — Mert Cemri, Melissa Z. Pan, Shuyi Yang, Lakshya A. Agrawal, Bhavya Chopra, Rishabh Tiwari, Kurt Keutzer, Aditya Parameswaran, Dan Klein, Kannan Ramchandran, Matei Zaharia, Joseph E. Gonzalez, Ion Stoica, 2025

https://scholar.google.com/scholar?q=Why+Do+Multi-Agent+LLM+Systems+Fail%3F

14. Scaling Latent Reasoning via Looped Language Models — Rui-Jie Zhu, Zixuan Wang, Kai Hua, Tianyu Zhang, Ziniu Li, Haoran Que, Boyi Wei, Zixin Wen, Fan Yin, He Xing, Lu Li, Jiajun Shi, Kaijing Ma, Shanda Li, Taylor Kergan, Andrew Smith, Xingwei Qu, Mude Hui, Bohong Wu, Qiyang Min, Hongzhi Huang, Xun Zhou, Wei Ye, Jiaheng Liu, Jian Yang, Yunfeng Shi, Chenghua Lin, Enduo Zhao, Tianle Cai, Ge Zhang, Wenhao Huang, Yoshua Bengio, Jason Eshraghian, 2025

https://scholar.google.com/scholar?q=Scaling+Latent+Reasoning+via+Looped+Language+Models

15. Enabling Agents to Communicate Entirely in Latent Space — Zhuoyun Du et al., 2025

https://scholar.google.com/scholar?q=Enabling+Agents+to+Communicate+Entirely+in+Latent+Space

16. Latent Collaboration in Multi-Agent Systems — Jiaru Zou, Xiyuan Yang et al., 2025

https://scholar.google.com/scholar?q=Latent+Collaboration+in+Multi-Agent+Systems

17. Reasoning Models Know When They're Right: Probing Hidden States for Self-Verification — Anqi Zhang et al., 2025

https://scholar.google.com/scholar?q=Reasoning+Models+Know+When+They%27re+Right%3A+Probing+Hidden+States+for+Self-Verification

18. Do Language Models Use Their Depth Efficiently? — Robert Csordas, Christopher D. Manning, Christopher Potts, 2025

https://scholar.google.com/scholar?q=Do+Language+Models+Use+Their+Depth+Efficiently%3F

19. Exploring Depth Generalization in Large Language Models for Solving Recursive Logic Tasks — Zhiyuan He, 2025

https://scholar.google.com/scholar?q=Exploring+Depth+Generalization+in+Large+Language+Models+for+Solving+Recursive+Logic+Tasks

20. Minimizing Response Latency in LLM-Based Agent Systems: A Comprehensive Survey — Gyeongmuk Park, Seonghyeon Lee, Yeonsu Park, 2026

https://scholar.google.com/scholar?q=Minimizing+Response+Latency+in+LLM-Based+Agent+Systems%3A+A+Comprehensive+Survey

21. AI Post Transformers: TUMIX Multi-Agent Test-Time Scaling with Tools — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-22-tumix-multi-agent-test-time-scaling-with-40671c.mp3

22. AI Post Transformers: Neural Computers as Learned Latent Runtimes — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-11-neural-computers-as-learned-latent-runti-9fa282.mp3

23. AI Post Transformers: VL-JEPA for Vision-Language Semantic Prediction — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-12-vl-jepa-for-vision-language-semantic-pre-69c9f4.mp3

24. AI Post Transformers: Neural Chameleons and Evading Activation Monitors — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-14-neural-chameleons-and-evading-activation-bc470e.mp3

25. AI Post Transformers: Efficient KV Cache Sharing for Multi-LoRA Agents — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-22-efficient-kv-cache-sharing-for-multi-lor-afda05.mp3

26. AI Post Transformers: TokenDance for Multi-Agent KV Cache Sharing — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-22-tokendance-for-multi-agent-kv-cache-shar-aa9b99.mp3

This episode explores whether learned discrete representations actually improve reinforcement learning, world modeling, and continual adaptation compared with standard continuous latent spaces. It explains how vector-quantized codebook latents, sparse binary-style features, and older ideas like tile coding relate to modern world models, and why the real advantage may come from reduced interference rather than discreteness alone. The discussion centers on three evaluation settings: predicting future dynamics in latent space, improving downstream control in model-free RL, and helping agents adapt to shifting tasks without forgetting earlier behavior. Listeners would find it interesting because it cuts through the “discrete vs. continuous” hype and turns the paper into a sharper engineering question about which representation bottlenecks produce more stable, reusable abstractions under changing conditions.

Sources:

1. Harnessing Discrete Representations For Continual Reinforcement Learning — Edan Meyer, Adam White, Marlos C. Machado, 2023

http://arxiv.org/abs/2312.01203

2. Neural Discrete Representation Learning — Aaron van den Oord, Oriol Vinyals, Koray Kavukcuoglu, 2017

https://papers.nips.cc/paper/2017/hash/7a98af17e63a0ac09ce2e96d03992fbc-Abstract.html

3. Mastering Atari with Discrete World Models — Danijar Hafner, Timothy P. Lillicrap, Mohammad Norouzi, Jimmy Ba, 2021

https://openreview.net/forum?id=0oabwyZbOu

4. Transformers are Sample-Efficient World Models — Vincent Micheli, Eloi Alonso, Francois Fleuret, 2023

https://openreview.net/forum?id=vhFu1Acb0xb

5. Harnessing Discrete Representations for Continual Reinforcement Learning — Edan Jacob Meyer, Adam White, Marlos C. Machado, 2024

https://openreview.net/forum?id=tCXURNlAZ3

6. Mastering Diverse Domains through World Models — Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, Timothy P. Lillicrap, 2023

https://scholar.google.com/scholar?q=Mastering+Diverse+Domains+through+World+Models

7. Fuzzy Tiling Activations: A Simple Approach to Learning Sparse Representations Online — Yangchen Pan, Kirby Banman, Martha White, 2021

https://scholar.google.com/scholar?q=Fuzzy+Tiling+Activations%3A+A+Simple+Approach+to+Learning+Sparse+Representations+Online

8. Investigating the Properties of Neural Network Representations in Reinforcement Learning — Han Wang, Erfan Miahi, Martha White, Marlos C. Machado, Zaheer Abbas, Raksha Kumaraswamy, Vincent Liu, Adam White, 2022

https://scholar.google.com/scholar?q=Investigating+the+Properties+of+Neural+Network+Representations+in+Reinforcement+Learning

9. Smaller World Models for Reinforcement Learning — Jan Robine, Tobias Uelwer, Stefan Harmeling, 2021

https://scholar.google.com/scholar?q=Smaller+World+Models+for+Reinforcement+Learning

10. Continual Learning as Computationally Constrained Reinforcement Learning — Saurabh Kumar, Henrik Marklund, Ashish Rao, Yifan Zhu, Hong Jun Jeon, Yueyang Liu, Benjamin Van Roy, 2023

https://scholar.google.com/scholar?q=Continual+Learning+as+Computationally+Constrained+Reinforcement+Learning

11. Efficient World Models with Context-Aware Tokenization — Vincent Micheli, Eloi Alonso, Francois Fleuret, 2024

https://scholar.google.com/scholar?q=Efficient+World+Models+with+Context-Aware+Tokenization

12. AdaWorld: Learning Adaptable World Models with Latent Actions — authors not identifiable from the snippet, recent

https://scholar.google.com/scholar?q=AdaWorld%3A+Learning+Adaptable+World+Models+with+Latent+Actions

13. A Survey of Continual Reinforcement Learning — authors not identifiable from the snippet, recent

https://scholar.google.com/scholar?q=A+Survey+of+Continual+Reinforcement+Learning

14. Stable Continual Reinforcement Learning via Diffusion-Based Trajectory Replay — authors not identifiable from the snippet, recent

https://scholar.google.com/scholar?q=Stable+Continual+Reinforcement+Learning+via+Diffusion-Based+Trajectory+Replay

15. AI Post Transformers: DreamerV3 World Models Across 150 Tasks — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-20-dreamerv3-world-models-across-150-tasks-af5edb.mp3

16. AI Post Transformers: TurboQuant: Online Vector Quantization with Near-optimal Distortion Rate — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-03-25-turboquant-online-vector-quantiz-1967b7.mp3

17. AI Post Transformers: Latent Space as a New Computational Paradigm — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-05-latent-space-as-a-new-computational-para-810f39.mp3

This episode explores whether language models can express uncertainty in natural language in a way that is actually calibrated and useful, rather than merely sounding cautious or confident. It focuses on the paper’s central distinction between token uncertainty and epistemic uncertainty, arguing that next-token probabilities are a poor proxy for whether a model truly knows an answer. The discussion situates this idea alongside earlier work on Bayesian approximations, dataset shift, and newer hallucination-detection methods such as semantic entropy, all pointing to the same challenge: uncertainty should attach to claims, not just strings. A listener would find it interesting because it connects a seemingly simple design choice, having models state confidence in words, to the much larger problem of building AI systems that can warn users when they are likely guessing.

Sources:

1. Teaching Models to Express Their Uncertainty in Words — Stephanie Lin, Jacob Hilton, Owain Evans, 2022

http://arxiv.org/abs/2205.14334

2. Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods — Eyke Hullermeier, Willem Waegeman, 2021

https://scholar.google.com/scholar?q=Aleatoric+and+Epistemic+Uncertainty+in+Machine+Learning%3A+An+Introduction+to+Concepts+and+Methods

3. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning — Yarin Gal, Zoubin Ghahramani, 2016

https://scholar.google.com/scholar?q=Dropout+as+a+Bayesian+Approximation%3A+Representing+Model+Uncertainty+in+Deep+Learning

4. Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift — Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, D. Sculley, Sebastian Nowozin, Joshua V. Dillon, Balaji Lakshminarayanan, Jasper Snoek, 2019

https://scholar.google.com/scholar?q=Can+You+Trust+Your+Model%27s+Uncertainty%3F+Evaluating+Predictive+Uncertainty+Under+Dataset+Shift

5. Detecting Hallucinations in Large Language Models Using Semantic Entropy — Sebastian Farquhar, Jannik Kossen, Lorenz Kuhn, and colleagues including Yarin Gal, 2024

https://scholar.google.com/scholar?q=Detecting+Hallucinations+in+Large+Language+Models+Using+Semantic+Entropy

6. Language Models (Mostly) Know What They Know — Katherine Kadavath, Roberta Raileanu, Akul Arora, et al., 2022

https://scholar.google.com/scholar?q=Language+Models+%28Mostly%29+Know+What+They+Know

7. On Calibration of Modern Neural Networks — Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger, 2017

https://scholar.google.com/scholar?q=On+Calibration+of+Modern+Neural+Networks

8. Can Language Models Be Too Honest? The Curious Case of Adaptive Honesty — Owain Evans, Jacob Hilton, Lukas Heim, et al., 2021

https://scholar.google.com/scholar?q=Can+Language+Models+Be+Too+Honest%3F+The+Curious+Case+of+Adaptive+Honesty

9. TruthfulQA: Measuring How Models Mimic Human Falsehoods — Stephanie Lin, Jacob Hilton, Owain Evans, 2021

https://scholar.google.com/scholar?q=TruthfulQA%3A+Measuring+How+Models+Mimic+Human+Falsehoods

10. Measuring Calibration in Deep Learning — Mahdi Pakdaman Naeini, Gregory Cooper, Milos Hauskrecht, 2015

https://scholar.google.com/scholar?q=Measuring+Calibration+in+Deep+Learning

11. Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation — Sebastian Kuhn, Yarin Gal, et al., 2023

https://scholar.google.com/scholar?q=Semantic+Uncertainty%3A+Linguistic+Invariances+for+Uncertainty+Estimation+in+Natural+Language+Generation

12. SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models — Potsawee Manakul, Adian Liusie, Mark Gales, 2023

https://scholar.google.com/scholar?q=SelfCheckGPT%3A+Zero-Resource+Black-Box+Hallucination+Detection+for+Generative+Large+Language+Models

13. Are Large Language Models More Honest in Their Probabilistic or Verbalized Confidence? — Shiyu Ni, Keping Bi, Lulu Yu, Jiafeng Guo, 2024

https://scholar.google.com/scholar?q=Are+Large+Language+Models+More+Honest+in+Their+Probabilistic+or+Verbalized+Confidence%3F

14. Calibrating Verbalized Probabilities for Large Language Models — Cheng Wang, Gyuri Szarvas, Georges Balazs, Pavel Danchenko, Patrick Ernst, 2024

https://scholar.google.com/scholar?q=Calibrating+Verbalized+Probabilities+for+Large+Language+Models

15. Know the Unknown: An Uncertainty-Sensitive Method for LLM Instruction Tuning — Jiaqi Li, Yixuan Tang, Yi Yang, 2025

https://scholar.google.com/scholar?q=Know+the+Unknown%3A+An+Uncertainty-Sensitive+Method+for+LLM+Instruction+Tuning

16. From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered — Siddartha Devic, Tejas Srinivasan, Jesse Thomason, Willie Neiswanger, Vatsal Sharan, 2025

https://scholar.google.com/scholar?q=From+Calibration+to+Collaboration%3A+LLM+Uncertainty+Quantification+Should+Be+More+Human-Centered

17. Closing the Confidence-Faithfulness Gap in Large Language Models — Miranda Muqing Miao, Lyle Ungar, 2026

https://scholar.google.com/scholar?q=Closing+the+Confidence-Faithfulness+Gap+in+Large+Language+Models

18. AI Post Transformers: CLUE: Hidden-State Clustering for Non-parametric Verification — Hal Turing & Dr. Ada Shannon, 2025

https://podcast.do-not-panic.com/episodes/clue-hidden-state-clustering-for-non-parametric-verification/

19. AI Post Transformers: Neural Chameleons and Evading Activation Monitors — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-14-neural-chameleons-and-evading-activation-bc470e.mp3

20. AI Post Transformers: Latent Space as a New Computational Paradigm — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-05-latent-space-as-a-new-computational-para-810f39.mp3

Interactive Visualization: Teaching Language Models to Verbalize Uncertainty

This episode explores ChartNet, a 1.5 million-sample multimodal dataset designed to improve how vision-language models read and reason about charts. It explains why chart understanding is harder than OCR or captioning alone, because models must connect visual marks, axes, legends, numerical values, and language-based reasoning with high precision. The discussion places ChartNet in the context of earlier benchmarks like DVQA, PlotQA, ChartQA, and UniChart, arguing that past datasets were too small or too narrow to teach robust chart comprehension. It also examines ChartNet’s code-guided pipeline, where models reconstruct plotting code from seed charts, generate structurally varied new examples, and align each chart with images, code, tables, summaries, and QA, making the episode interesting for listeners who want to understand whether scale and multimodal alignment can produce more reliable chart-reading AI.

Sources:

1. ChartNet for Robust Multimodal Chart Understanding

https://arxiv.org/pdf/2603.27064

2. DVQA: Understanding Data Visualizations via Question Answering — Kushal Kafle, Brian Price, Scott Cohen, Christopher Kanan, 2018

https://scholar.google.com/scholar?q=DVQA%3A+Understanding+Data+Visualizations+via+Question+Answering

3. PlotQA: Reasoning over Scientific Plots — Nitesh Methani, Pritha Ganguly, Mitesh M. Khapra, Pratyush Kumar, 2019

https://scholar.google.com/scholar?q=PlotQA%3A+Reasoning+over+Scientific+Plots

4. ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning — Ahmed Masry, Do Xuan Long, Jia Qing Tan, Shafiq Joty, Enamul Hoque, 2022

https://scholar.google.com/scholar?q=ChartQA%3A+A+Benchmark+for+Question+Answering+about+Charts+with+Visual+and+Logical+Reasoning

5. UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning — Ahmed Masry, Parsa Kavehzadeh, Xuan Long Do, Enamul Hoque, Shafiq Joty, 2023

https://scholar.google.com/scholar?q=UniChart%3A+A+Universal+Vision-language+Pretrained+Model+for+Chart+Comprehension+and+Reasoning

6. TinyChart: Efficient Chart Understanding with Visual Token Merging and Program-of-Thoughts Learning — L. Zhang, A. Hu, H. Xu, M. Yan, Y. Xu, Q. Jin, J. Zhang, and F. Huang, 2024

https://scholar.google.com/scholar?q=TinyChart%3A+Efficient+Chart+Understanding+with+Visual+Token+Merging+and+Program-of-Thoughts+Learning

7. ChartQAPro: A More Diverse and Challenging Benchmark for Chart Question Answering — A. Masry, M. S. Islam, M. Ahmed, A. Bajaj, F. Kabir, A. Kartha, M. T. R. Laskar, M. Rahman, S. Rahman, M. Shahmohammadi, et al., 2025

https://scholar.google.com/scholar?q=ChartQAPro%3A+A+More+Diverse+and+Challenging+Benchmark+for+Chart+Question+Answering

8. EvoChart: A Benchmark and a Self-Training Approach Towards Real-World Chart Understanding — M. Huang, H. Lai, X. Zhang, W. Wu, J. Ma, L. Zhang, and J. Liu, 2025

https://scholar.google.com/scholar?q=EvoChart%3A+A+Benchmark+and+a+Self-Training+Approach+Towards+Real-World+Chart+Understanding

9. ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generation — C. Yang, C. Shi, Y. Liu, B. Shui, J. Wang, M. Jing, L. Xu, X. Zhu, S. Li, Y. Zhang, G. Liu, X. Nie, D. Cai, and Y. Yang, 2025

https://scholar.google.com/scholar?q=ChartMimic%3A+Evaluating+LMM%27s+Cross-Modal+Reasoning+Capability+via+Chart-to-Code+Generation

10. OpenCQA: Open-Ended Question Answering with Charts — S. Kantharaj, X. L. Do, R. T. K. Leong, J. Q. Tan, E. Hoque, and S. Joty, 2022

https://scholar.google.com/scholar?q=OpenCQA%3A+Open-Ended+Question+Answering+with+Charts

11. Effective Training Data Synthesis for Improving MLLM Chart Understanding — approximate; unknown from snippet, 2024/2025

https://scholar.google.com/scholar?q=Effective+Training+Data+Synthesis+for+Improving+MLLM+Chart+Understanding

12. From Charts to Code: A Hierarchical Benchmark for Multimodal Models — approximate; unknown from snippet, 2024/2025

https://scholar.google.com/scholar?q=From+Charts+to+Code%3A+A+Hierarchical+Benchmark+for+Multimodal+Models

13. GRAFT: GRaPH and Table Reasoning for Textual Alignment — approximate; unknown from snippet, 2024/2025

https://scholar.google.com/scholar?q=GRAFT%3A+GRaPH+and+Table+Reasoning+for+Textual+Alignment

14. ChartQA-X: Generating Explanations for Visual Chart Reasoning — approximate; unknown from snippet, 2024/2025

https://scholar.google.com/scholar?q=ChartQA-X%3A+Generating+Explanations+for+Visual+Chart+Reasoning

15. AI Post Transformers: Procgen Benchmark: Measuring Generalization in Reinforcement Learning — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/procgen-benchmark-measuring-generalization-in-reinforcement-learning/

16. AI Post Transformers: The Endless Gym: Training Terminal Agents — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/the-endless-gym-training-terminal-agents/

17. AI Post Transformers: Evaluating Large Language Models Trained on Code — Hal Turing & Dr. Ada Shannon, 2025

https://podcast.do-not-panic.com/episodes/evaluating-large-language-models-trained-on-code/

18. AI Post Transformers: VL-JEPA for Vision-Language Semantic Prediction — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-12-vl-jepa-for-vision-language-semantic-pre-69c9f4.mp3

19. AI Post Transformers: IMO-Bench for Robust Mathematical Reasoning — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-04-imo-bench-for-robust-mathematical-reason-143489.mp3

Interactive Visualization: ChartNet for Robust Multimodal Chart Understanding

This episode explores whether large language models can estimate their chances of success before acting, update those estimates during a task, and use them to decide when to abstain from costly work. It explains why that matters for agentic systems in coding and software environments, where overconfidence can lead to wasted effort, unsafe actions, or expensive mistakes, and connects the paper to earlier work on calibration, uncertainty, and selective abstention. The discussion highlights the paper’s focus on three settings, including single-step coding, sequential decisions with feedback, and multi-step software engineering, while also stressing the distinction between raw capability, calibration, and rational decision-making. Listeners would find it interesting because it treats self-assessment not as a philosophical question, but as a practical requirement for building AI systems that know when not to act.

Sources:

1. Do Large Language Models Know What They Are Capable Of? — Casey O. Barkan, Sid Black, Oliver Sourbut, 2025

http://arxiv.org/abs/2512.24661

2. Language Models (Mostly) Know What They Know — Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Ethan Perez, Nicholas Joseph, and collaborators, 2022

https://scholar.google.com/scholar?q=Language+Models+%28Mostly%29+Know+What+They+Know

3. Do Large Language Models Know What They Don’t Know? — Zhangyue Yin, Qiushi Sun, Qipeng Guo, Jiawen Wu, Xipeng Qiu, Xuanjing Huang, 2023

https://scholar.google.com/scholar?q=Do+Large+Language+Models+Know+What+They+Don%E2%80%99t+Know%3F

4. Towards Fully Exploiting LLM Internal States to Enhance Knowledge Boundary Perception — Shiyu Ni, Keping Bi, Jiafeng Guo, Lulu Yu, Baolong Bi, Xueqi Cheng, 2025

https://scholar.google.com/scholar?q=Towards+Fully+Exploiting+LLM+Internal+States+to+Enhance+Knowledge+Boundary+Perception

5. What Large Language Models Know and What People Think They Know — Mark Steyvers, Heliodoro Tejeda, Aakriti Kumar, Catarina Belem, Lukas W. Mayer, Padhraic Smyth, and collaborators, 2025

https://scholar.google.com/scholar?q=What+Large+Language+Models+Know+and+What+People+Think+They+Know

6. Teaching Models to Express Their Uncertainty in Words — Stephanie Lin, Jacob Hilton, Owain Evans, 2022

https://scholar.google.com/scholar?q=Teaching+Models+to+Express+Their+Uncertainty+in+Words

7. A Survey of Confidence Estimation and Calibration in Large Language Models — Jiahui Geng, Fengyu Cai, Yuxia Wang, Heinz Koeppl, Preslav Nakov, Iryna Gurevych, 2024

https://scholar.google.com/scholar?q=A+Survey+of+Confidence+Estimation+and+Calibration+in+Large+Language+Models

8. Calibrating Language Models with Adaptive Temperature Scaling — Johnathan Xie, Annie S. Chen, Yoonho Lee, Eric Mitchell, Chelsea Finn, 2024

https://scholar.google.com/scholar?q=Calibrating+Language+Models+with+Adaptive+Temperature+Scaling

9. Calibrating the Confidence of Large Language Models by Eliciting Fidelity — Mozhi Zhang, Mianqiu Huang, Rundong Shi, Linsen Guo, Chong Peng, Peng Yan, Yaqian Zhou, Xipeng Qiu, 2024

https://scholar.google.com/scholar?q=Calibrating+the+Confidence+of+Large+Language+Models+by+Eliciting+Fidelity

10. Selective Classification for Deep Neural Networks — Yonatan Geifman, Ran El-Yaniv, 2017

https://scholar.google.com/scholar?q=Selective+Classification+for+Deep+Neural+Networks

11. SelectiveNet: A Deep Neural Network with an Integrated Reject Option — Yonatan Geifman, Ran El-Yaniv, 2019

https://scholar.google.com/scholar?q=SelectiveNet%3A+A+Deep+Neural+Network+with+an+Integrated+Reject+Option

12. Selective-LAMA: Selective Prediction for Confidence-Aware Evaluation of Language Models — Hiyori Yoshikawa, Naoaki Okazaki, 2023

https://scholar.google.com/scholar?q=Selective-LAMA%3A+Selective+Prediction+for+Confidence-Aware+Evaluation+of+Language+Models

13. Confidence-Driven Multi-Scale Model Selection for Cost-Efficient Inference — Bo-Wei Chen, Chung-Chi Chen, An-Zi Yen, 2026

https://scholar.google.com/scholar?q=Confidence-Driven+Multi-Scale+Model+Selection+for+Cost-Efficient+Inference

14. Quantifying Uncert-AI-nty: Testing the Accuracy of LLMs' Confidence Judgments — Trent N. Cash, Daniel M. Oppenheimer, Sara Christie, Mira Devgan, 2025

https://scholar.google.com/scholar?q=Quantifying+Uncert-AI-nty%3A+Testing+the+Accuracy+of+LLMs%27+Confidence+Judgments

15. Credence Calibration Game? Calibrating Large Language Models Through Structured Play — Ke Fang, Tianyi Zhao, Lu Cheng, 2025

https://scholar.google.com/scholar?q=Credence+Calibration+Game%3F+Calibrating+Large+Language+Models+Through+Structured+Play

16. Calibration and Correctness of Language Models for Code — Claudio Spiess, David Gros, Kunal Suresh Pai, Michael Pradel, Md Rafiqul Islam Rabin, Amin Alipour, Susmit Jha, Prem Devanbu, Toufique Ahmed, 2025

https://scholar.google.com/scholar?q=Calibration+and+Correctness+of+Language+Models+for+Code

17. Large Language Models Must Be Taught to Know What They Don't Know — Sanyam Kapoor, Nate Gruver, Manley Roberts, Katie Collins, Arka Pal, Umang Bhatt, Adrian Weller, Samuel Dooley, Micah Goldblum, Andrew G. Wilson, 2024

https://scholar.google.com/scholar?q=Large+Language+Models+Must+Be+Taught+to+Know+What+They+Don%27t+Know

18. SMARTCAL: An Approach to Self-Aware Tool-Use Evaluation and Calibration — Yuanhao Shen, Xiaodan Zhu, Lei Chen, 2024

https://scholar.google.com/scholar?q=SMARTCAL%3A+An+Approach+to+Self-Aware+Tool-Use+Evaluation+and+Calibration

19. Calibration Is Not Enough: Evaluating Confidence Estimation Under Language Variations — Yuxi Xia, Dennis Ulmer, Terra Blevins, Yihong Liu, Hinrich Schutze, Benjamin Roth, 2026

https://scholar.google.com/scholar?q=Calibration+Is+Not+Enough%3A+Evaluating+Confidence+Estimation+Under+Language+Variations

20. CoT-UQ: Improving Response-wise Uncertainty Quantification in LLMs with Chain-of-Thought — Boxuan Zhang, Ruqi Zhang, 2025

https://scholar.google.com/scholar?q=CoT-UQ%3A+Improving+Response-wise+Uncertainty+Quantification+in+LLMs+with+Chain-of-Thought

21. Structured Uncertainty guided Clarification for LLM Agents — Manan Suri, Puneet Mathur, Nedim Lipka, Franck Dernoncourt, Ryan A. Rossi, Dinesh Manocha, 2025

https://scholar.google.com/scholar?q=Structured+Uncertainty+guided+Clarification+for+LLM+Agents

22. CAR-bench: Evaluating the Consistency and Limit-Awareness of LLM Agents under Real-World Uncertainty — Johannes Kirmayr, Lukas Stappen, Elisabeth Andre, 2026

https://scholar.google.com/scholar?q=CAR-bench%3A+Evaluating+the+Consistency+and+Limit-Awareness+of+LLM+Agents+under+Real-World+Uncertainty

23. Improving Interactive In-Context Learning from Natural Language Feedback — Martin Klissarov, Jonathan Cook, Diego Antognini, Hao Sun, Jingling Li, Natasha Jaques, Claudiu Musat, Edward Grefenstette, 2026

https://scholar.google.com/scholar?q=Improving+Interactive+In-Context+Learning+from+Natural+Language+Feedback

24. AI Post Transformers: Benchmarking Test-Time Scaling for General LLM Agents — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-22-benchmarking-test-time-scaling-for-gener-8f14f9.mp3

25. AI Post Transformers: Program Synthesis with Large Language Models — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-20-program-synthesis-with-large-language-mo-b962ec.mp3

26. AI Post Transformers: SkillsBench for Evaluating Agent Skills — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-14-skillsbench-for-evaluating-agent-skills-58bb1e.mp3

27. AI Post Transformers: Stabilizing Efficient Reasoning with Step-Level Advantage Selection — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-29-stabilizing-efficient-reasoning-with-ste-1e589d.mp3

28. AI Post Transformers: Neural Chameleons and Evading Activation Monitors — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-14-neural-chameleons-and-evading-activation-bc470e.mp3

Interactive Visualization: Can LLMs Judge Their Own Capabilities?

This episode explores the 2017 ConvS2S paper from Facebook AI Research, which argued that sequence-to-sequence models for machine translation did not need recurrence and could instead use fully convolutional encoder-decoder networks with attention. It explains the seq2seq and neural machine translation setup, clarifies that the paper replaces recurrent computation rather than attention, and breaks down the architecture’s core ideas: stacked convolutions, expanding receptive fields, positional embeddings, residual connections, and gated linear units. The discussion highlights why the approach was provocative at the time: it challenged the LSTM-based consensus, reported stronger BLEU scores, and promised much faster, more parallelizable decoding on GPUs. Listeners would find it interesting as a key pre-transformer moment that shows how researchers were already rethinking sequential modeling, while also surfacing the tradeoff between efficiency and limited context windows for long-range dependencies.

Sources:

1. Convolutional Sequence to Sequence Learning — Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, Yann N. Dauphin, 2017

http://arxiv.org/abs/1705.03122

2. Convolutional Sequence to Sequence Learning — Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, Yann N. Dauphin, 2017

https://scholar.google.com/scholar?q=Convolutional+Sequence+to+Sequence+Learning

3. ByteNet: Generating High-Resolution Discrete Sequences with Multiscale Dilation — Nal Kalchbrenner, Lasse Espeholt, Karen Simonyan, Aaron van den Oord, Alex Graves, Koray Kavukcuoglu, 2016

https://scholar.google.com/scholar?q=ByteNet%3A+Generating+High-Resolution+Discrete+Sequences+with+Multiscale+Dilation

4. Language Modeling with Gated Convolutional Networks — Yann N. Dauphin, Angela Fan, Michael Auli, David Grangier, 2017

https://scholar.google.com/scholar?q=Language+Modeling+with+Gated+Convolutional+Networks

5. WaveNet: A Generative Model for Raw Audio — Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, et al., 2016

https://scholar.google.com/scholar?q=WaveNet%3A+A+Generative+Model+for+Raw+Audio

6. Sequence to Sequence Learning with Neural Networks — Ilya Sutskever, Oriol Vinyals, Quoc V. Le, 2014

https://scholar.google.com/scholar?q=Sequence+to+Sequence+Learning+with+Neural+Networks

7. Neural Machine Translation by Jointly Learning to Align and Translate — Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio, 2014

https://scholar.google.com/scholar?q=Neural+Machine+Translation+by+Jointly+Learning+to+Align+and+Translate

8. Effective Approaches to Attention-based Neural Machine Translation — Minh-Thang Luong, Hieu Pham, Christopher D. Manning, 2015

https://scholar.google.com/scholar?q=Effective+Approaches+to+Attention-based+Neural+Machine+Translation

9. A Survey on Deep Learning for Neural Machine Translation — Antonio Toral, Víctor M. Sánchez-Cartagena, et al. (representative survey literature varies by edition), 2018

https://scholar.google.com/scholar?q=A+Survey+on+Deep+Learning+for+Neural+Machine+Translation

10. Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation — Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, et al., 2016

https://scholar.google.com/scholar?q=Google%27s+Neural+Machine+Translation+System%3A+Bridging+the+Gap+between+Human+and+Machine+Translation

11. Neural Machine Translation of Rare Words with Subword Units — Rico Sennrich, Barry Haddow, Alexandra Birch, 2016

https://scholar.google.com/scholar?q=Neural+Machine+Translation+of+Rare+Words+with+Subword+Units

12. Attention Is All You Need — Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, Illia Polosukhin, 2017

https://scholar.google.com/scholar?q=Attention+Is+All+You+Need

13. Neural Machine Translation and Sequence-to-sequence Models: A Tutorial — Graham Neubig, 2017

https://scholar.google.com/scholar?q=Neural+Machine+Translation+and+Sequence-to-sequence+Models%3A+A+Tutorial

14. Neural Machine Translation in Linear Time — Jonas Gehring, Michael Auli, David Grangier, Yann N. Dauphin, 2016

https://scholar.google.com/scholar?q=Neural+Machine+Translation+in+Linear+Time

15. ByteNet: Neural Machine Translation in Linear Time — Nal Kalchbrenner, Lasse Espeholt, Karen Simonyan, Aaron van den Oord, Alex Graves, Koray Kavukcuoglu, 2016

https://scholar.google.com/scholar?q=ByteNet%3A+Neural+Machine+Translation+in+Linear+Time

16. Quasi-Recurrent Neural Networks — James Bradbury, Stephen Merity, Caiming Xiong, Richard Socher, 2016

https://scholar.google.com/scholar?q=Quasi-Recurrent+Neural+Networks

17. Convolutional Neural Network for Machine Translation — Fandong Meng, Zhengdong Lu, Hang Li, Qun Liu, 2015

https://scholar.google.com/scholar?q=Convolutional+Neural+Network+for+Machine+Translation

18. Resurrecting Recurrent Neural Networks for Long Sequences — approx. Orvieto et al., 2023

https://scholar.google.com/scholar?q=Resurrecting+Recurrent+Neural+Networks+for+Long+Sequences

19. A Comprehensive Survey on Long Context Language Modeling — approx. recent survey authors, exact authorship to verify, 2024

https://scholar.google.com/scholar?q=A+Comprehensive+Survey+on+Long+Context+Language+Modeling

20. Linear Recurrent Models for Robust and Interpretable Long-context Modeling — approx. recent long-context modeling authors, exact authorship to verify, 2024

https://scholar.google.com/scholar?q=Linear+Recurrent+Models+for+Robust+and+Interpretable+Long-context+Modeling

21. Cross-layer Attention Sharing for Pre-trained Large Language Models — approx. recent LLM systems authors, exact authorship to verify, 2024

https://scholar.google.com/scholar?q=Cross-layer+Attention+Sharing+for+Pre-trained+Large+Language+Models

22. Reducing Transformer Key-Value Cache Size with Cross-Layer Attention — approx. recent efficiency-focused LLM authors, exact authorship to verify, 2024

https://scholar.google.com/scholar?q=Reducing+Transformer+Key-Value+Cache+Size+with+Cross-Layer+Attention

23. AI Post Transformers: RoPE — Hal Turing & Dr. Ada Shannon, Thu,

https://podcast.do-not-panic.com/episodes/rope/

24. AI Post Transformers: Apple's Speculative Streaming: Fast LLM Inference without Auxiliary Models — Hal Turing & Dr. Ada Shannon, Sat,

https://podcast.do-not-panic.com/episodes/apples-speculative-streaming-fast-llm-inference-without-auxiliary-models/

25. AI Post Transformers: Speculative Decoding in Real vLLM Serving — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-04-speculative-decoding-in-real-vllm-servin-6f4e2b.mp3

26. AI Post Transformers: FlatAttention for Tile-Based Accelerator Inference — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-04-flatattention-for-tile-based-accelerator-56e6ca.mp3

This episode explores the 2014–2015 breakthrough paper that introduced attention to neural machine translation, framing it as a solution to a specific flaw in early encoder-decoder models: forcing an entire source sentence into one fixed-length vector. It explains how pre-attention RNN-based seq2seq systems struggled on long or complex sentences, and how Bahdanau et al.’s “soft alignment” let the decoder focus on different source words at each generation step instead of relying on a single compressed summary. Along the way, it situates the paper against phrase-based statistical translation and earlier LSTM/GRU seq2seq models, showing why attention was more than a performance tweak—it was a durable structural idea. Listeners would find it interesting for its clear account of what was actually broken before attention, what changed technically, and why this paper became a foundational step toward modern language models.

Sources:

1. Neural Machine Translation by Jointly Learning to Align and Translate — Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio, 2014

http://arxiv.org/abs/1409.0473

2. Sequence to Sequence Learning with Neural Networks — Ilya Sutskever, Oriol Vinyals, Quoc V. Le, 2014

https://scholar.google.com/scholar?q=Sequence+to+Sequence+Learning+with+Neural+Networks

3. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation — Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, Yoshua Bengio, 2014

https://scholar.google.com/scholar?q=Learning+Phrase+Representations+using+RNN+Encoder-Decoder+for+Statistical+Machine+Translation

4. A Neural Network for Machine Translation, at Production Scale — Nal Kalchbrenner, Phil Blunsom, 2013

https://scholar.google.com/scholar?q=A+Neural+Network+for+Machine+Translation%2C+at+Production+Scale

5. On the Properties of Neural Machine Translation: Encoder-Decoder Approaches — Kyunghyun Cho, Bart van Merrienboer, Çağlar Gülçehre, Fethi Bougares, Holger Schwenk, Yoshua Bengio, 2014

https://scholar.google.com/scholar?q=On+the+Properties+of+Neural+Machine+Translation%3A+Encoder-Decoder+Approaches

6. Moses: Open Source Toolkit for Statistical Machine Translation — Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, et al., 2007

https://scholar.google.com/scholar?q=Moses%3A+Open+Source+Toolkit+for+Statistical+Machine+Translation

7. Neural Machine Translation by Jointly Learning to Align and Translate — Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio, 2014

https://scholar.google.com/scholar?q=Neural+Machine+Translation+by+Jointly+Learning+to+Align+and+Translate

8. Effective Approaches to Attention-based Neural Machine Translation — Minh-Thang Luong, Hieu Pham, Christopher D. Manning, 2015

https://scholar.google.com/scholar?q=Effective+Approaches+to+Attention-based+Neural+Machine+Translation

9. Neural Machine Translation of Rare Words with Subword Units — Rico Sennrich, Barry Haddow, Alexandra Birch, 2016

https://scholar.google.com/scholar?q=Neural+Machine+Translation+of+Rare+Words+with+Subword+Units

10. Looking for a Needle in a Haystack: A Comprehensive Study of Hallucinations in Neural Machine Translation — approx. Guerreiro et al., 2023

https://scholar.google.com/scholar?q=Looking+for+a+Needle+in+a+Haystack%3A+A+Comprehensive+Study+of+Hallucinations+in+Neural+Machine+Translation

11. Key, Value, Compress: A Systematic Exploration of KV Cache Compression Techniques — approx. recent systems/LLM authors, 2024

https://scholar.google.com/scholar?q=Key%2C+Value%2C+Compress%3A+A+Systematic+Exploration+of+KV+Cache+Compression+Techniques

12. DeltaKV: Residual-Based KV Cache Compression via Long-Range Similarity — approx. recent systems/LLM authors, 2024

https://scholar.google.com/scholar?q=DeltaKV%3A+Residual-Based+KV+Cache+Compression+via+Long-Range+Similarity

13. A Survey on Large Language Model Acceleration Based on KV Cache Management — approx. recent survey authors, 2024

https://scholar.google.com/scholar?q=A+Survey+on+Large+Language+Model+Acceleration+Based+on+KV+Cache+Management

14. AI Post Transformers: Memory Sparse Attention for 100M-Token Scaling — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-07-memory-sparse-attention-for-100m-token-s-377cff.mp3

15. AI Post Transformers: Mamba-3 for Efficient Sequence Modeling — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-16-mamba-3-for-efficient-sequence-modeling-97a22a.mp3

16. AI Post Transformers: Latent Space as a New Computational Paradigm — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-05-latent-space-as-a-new-computational-para-810f39.mp3

17. AI Post Transformers: Doc-to-LoRA: Internalizing Context as LoRA — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-03-29-doc-to-lora-internalizing-context-as-lor-8dd5ec.mp3