AI Post Transformers

This episode explores a systems paper on speeding up Transformer decoding by tightly fusing the SwiGLU MLP path, rather than focusing only on attention or long-context tricks. It explains why long output generation becomes memory-bandwidth bound, clarifying concepts like kernel fusion, HBM traffic, prefill versus autoregressive decode, and why repeated token-by-token inference exposes the MLP as a real bottleneck. The discussion walks through the paper’s main design choice: a disciplined fusion of the up-projection, gate projection, SiLU activation, and elementwise multiply into a single decode-stage kernel, while leaving the down projection separate to avoid worse scheduling and register-pressure tradeoffs. It also highlights the paper’s practical argument for profiler-driven runtime scheduling across row-major and column-major kernel variants, making the result interesting to listeners who care about how large-model serving performance is won through careful hardware-aware engineering rather than headline-grabbing algorithm changes.

Sources:

1. Deep Kernel Fusion for Transformer Decoding

https://arxiv.org/pdf/2602.11808

2. GLU Variants Improve Transformer — Noam Shazeer, 2020

https://scholar.google.com/scholar?q=GLU+Variants+Improve+Transformer

3. PaLM: Scaling Language Modeling with Pathways — Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Noam Shazeer and many others, 2022

https://scholar.google.com/scholar?q=PaLM%3A+Scaling+Language+Modeling+with+Pathways

4. DeepSpeed Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale — Reza Yazdani Aminabadi, Samyam Rajbhandari, Minjia Zhang, Ammar Ahmad Awan, Cheng Li and others, 2022

https://scholar.google.com/scholar?q=DeepSpeed+Inference%3A+Enabling+Efficient+Inference+of+Transformer+Models+at+Unprecedented+Scale

5. FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning — Tri Dao, 2023

https://scholar.google.com/scholar?q=FlashAttention-2%3A+Faster+Attention+with+Better+Parallelism+and+Work+Partitioning

6. SGLang: Efficient Execution of Structured Language Model Programs — Lianmin Zheng, Liangsheng Yin, Zhiqiang Xie, Chuyue Sun, Jeff Huang, Cody Hao Yu, Shiyi Cao, Christos Kozyrakis, Ion Stoica, Joseph E. Gonzalez, Clark Barrett, and Ying Sheng, 2024

https://scholar.google.com/scholar?q=SGLang%3A+Efficient+Execution+of+Structured+Language+Model+Programs

7. 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, and Ion Stoica, 2023

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

8. Welder: Scheduling Deep Learning Memory Access via Tile-Graph — Yining Shi, Zhi Yang, Jilong Xue, Lingxiao Ma, Yuqing Xia, Ziming Miao, Yuxiao Guo, Fan Yang, and Lidong Zhou, 2023

https://scholar.google.com/scholar?q=Welder%3A+Scheduling+Deep+Learning+Memory+Access+via+Tile-Graph

9. FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving — Zihao Ye, Lequn Chen, Ruihang Lai, Wuwei Lin, Yineng Zhang, Stephanie Wang, Tianqi Chen, Baris Kasikci, Vinod Grover, Arvind Krishnamurthy, and Luis Ceze, 2025

https://scholar.google.com/scholar?q=FlashInfer%3A+Efficient+and+Customizable+Attention+Engine+for+LLM+Inference+Serving

10. Masked Gated Linear Unit — unknown from snippet, likely 2024 or 2025

https://scholar.google.com/scholar?q=Masked+Gated+Linear+Unit

11. SCBench: A KV Cache-Centric Analysis of Long-Context Methods — unknown from snippet, likely 2025

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

12. Model Tells You Where to Merge: Adaptive KV Cache Merging for LLMs on Long-Context Tasks — unknown from snippet, likely 2025

https://scholar.google.com/scholar?q=Model+Tells+You+Where+to+Merge%3A+Adaptive+KV+Cache+Merging+for+LLMs+on+Long-Context+Tasks

13. MEDA: Dynamic KV Cache Allocation for Efficient Multimodal Long-Context Inference — unknown from snippet, likely 2025

https://scholar.google.com/scholar?q=MEDA%3A+Dynamic+KV+Cache+Allocation+for+Efficient+Multimodal+Long-Context+Inference

14. Efficient LLM Inference Using Dynamic Input Pruning and Cache-Aware Masking — unknown from snippet, likely 2025

https://scholar.google.com/scholar?q=Efficient+LLM+Inference+Using+Dynamic+Input+Pruning+and+Cache-Aware+Masking

15. Enhancing Transformer Performance and Portability Through Auto-Tuning Frameworks — P. Siwinska et al. (approx.), unknown, likely recent

https://scholar.google.com/scholar?q=Enhancing+Transformer+Performance+and+Portability+Through+Auto-Tuning+Frameworks

16. 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

17. AI Post Transformers: Why LLM Serving Needs Mathematical Optimization — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-05-05-why-llm-serving-needs-mathematical-optim-647fc6.mp3

18. AI Post Transformers: PackKV Lossy Compression for KV Caches — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-05-04-packkv-lossy-compression-for-kv-caches-b37bce.mp3

19. AI Post Transformers: SGLang for Faster Structured LLM Programs — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-05-06-sglang-for-faster-structured-llm-program-c59f1c.mp3

20. 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

21. AI Post Transformers: LAPS for Length-Aware LLM Serving — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-05-05-laps-for-length-aware-llm-serving-0c6149.mp3

22. AI Post Transformers: CacheFlow and 3D-Parallel KV Cache Restoration — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-05-01-cacheflow-and-3d-parallel-kv-cache-resto-8db883.mp3

23. 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

Interactive Visualization: Deep Kernel Fusion for Transformer Decoding

Episode title: Air Force One, Jensen Huang, and Anthropic's 2028 Memo

A close reading of Anthropic's policy post "2028: Two Scenarios for

Global AI Leadership" as what it actually is — a carefully timed

corporate advocacy document, not a peer-reviewed paper. The episode

unpacks the post's central "distillation attacks" framing,

distinguishes the four very different things that label gets used

for, and weighs Anthropic's policy recommendations against the

empirical literature on whether unauthorized knowledge distillation

is technically deterrable (citing Trace Rewriting, arXiv

2602.15143, and Watermark Robustness Against Distillation, arXiv

2502.11598). It situates the post in the news cycle of President

Trump's May 13–15 2026 state visit to Beijing, the inclusion of

Nvidia's Jensen Huang in the delegation, and the H200 clearance for

roughly ten Chinese firms — a policy direction that diverges from

what the post advocates. Mistral AI's Ministral 3 cascade-

distillation work serves as the empirical lens for what compact-

model distillation actually transfers in practice. The episode

acknowledges legitimate underlying concerns about frontier-

capability spread while declining to treat the post as research

evidence.

Sources (selected; the full citation list will be folded into the

script):

Anthropic — "2028: Two Scenarios for Global AI Leadership"

https://www.anthropic.com/research/2028-ai-leadership

Anthropic — "Detecting and preventing distillation attacks" (Feb 2026)

https://www.anthropic.com/news/detecting-and-preventing-distillation-attacks

Nathan Lambert — "The distillation panic"

https://www.interconnects.ai/p/the-distillation-panic

TIME — "How A.I. Was the Elephant in the Room at the Trump-Xi Summit"

https://time.com/article/2026/05/15/trump-xi-us-china-summit-ai-semiconductor-chips/

Bloomberg — "Nvidia's Huang Joins Trump's China Trip as Last-Minute Addition"

https://www.bloomberg.com/news/articles/2026-05-13/nvidia-s-huang-joins-trump-s-china-trip-as-last-minute-addition

CNBC — "Trump-Xi summit revives China tech rally hopes as U.S. clears Nvidia H200 sales"

https://www.cnbc.com/2026/05/14/trump-xi-meeting-china-stocks-ai-rally.html

CFR — "At the Trump-Xi Summit, China Will Have the Upper Hand"

https://www.cfr.org/articles/at-the-trump-xi-summit-china-will-have-the-upper-hand

CFR — "How Trump Should Approach AI Talks With China"

https://www.cfr.org/articles/how-trump-should-approach-ai-talks-with-china-targeted-dialogue-maximum-pressure

IAPS — "AI Distillation Attacks: The Case for Targeted Government Intervention"

https://www.iaps.ai/research/ai-distillation-attacks

Chatham House — "Anthropic's feud with the Pentagon reveals the limits of AI governance"

https://www.chathamhouse.org/2026/03/anthropics-feud-pentagon-reveals-limits-ai-governance

Small Wars Journal — "Selective Virtue: Anthropic, the Pentagon, and the Contradictions of AI Governance"

https://smallwarsjournal.com/2026/04/29/selective-virtue-anthropic-the-pentagon-ai-governance/

arXiv 2602.15143 — "Protecting Language Models Against Unauthorized Distillation through Trace Rewriting"

arXiv 2502.11598 — "Can LLM Watermarks Robustly Prevent Unauthorized Knowledge Distillation?"

Ministral 3 — discussed in the AI Post Transformers episode "Ministral 3: Cascade Distillation for Long-Context Multimodal Models"

AI Post Transformers — "Dario Amodei: Machines of Loving Grace"

https://podcast.do-not-panic.com/episodes/dario-amodei-machines-of-loving-grace/

AI Post Transformers — "Dario Amodei: The Adolescence of Technology"

https://podcast.do-not-panic.com/episodes/dario-amodei-the-adolescence-of-technology/

AI Post Transformers — "Trace Rewriting Against Unauthorized LLM Distillation"

(covers arXiv 2602.15143 / Xinhang Ma et al. WashU, with the

watermark-radioactivity literature as comparison)

Interactive Visualization: Air Force One, Jensen Huang, and Anthropic's 2028 Memo

This episode explores a paper that argues AI can help mathematics most by orchestrating the full research workflow rather than acting as a one-shot chatbot. It discusses why real mathematical work depends on durable memory, branching hypotheses, literature search, proof attempts, computation, and recorded failures, and contrasts that with both ordinary chat interfaces and formal theorem provers such as Lean or Coq. The conversation details the paper’s multi-agent design, where a coordinator delegates parallel tasks like literature review, coding, proof exploration, and claim checking into a living draft document with provenance and uncertainty markers. It also highlights reported results on 100 research-level problems, where the full system outperformed strong single-shot models by using tactics such as SAT reduction, theorem retrieval, and coordinated theory-computation pipelines, making the episode interesting for listeners curious about how AI might become a practical research collaborator instead of just a clever text generator.

Sources:

1. AI Co-Mathematician for Mathematical Research

https://arxiv.org/pdf/2605.06651

2. A Survey on Deep Learning for Theorem Proving — Zhaoyu Li, Jialiang Sun, Logan Murphy, Qidong Su, Zenan Li, Xian Zhang, Kaiyu Yang, Xujie Si, 2024

https://arxiv.org/abs/2404.09939

3. GPT-f: Generative Language Modeling for Automated Theorem Proving — Stanislas Polu, Ilya Sutskever, 2020

https://openai.com/index/generative-language-modeling-for-automated-theorem-proving//

4. Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs — Albert Q. Jiang, Sean Welleck, Jin Peng Zhou, Wenda Li, Jiacheng Liu, Mateja Jamnik, Timothee Lacroix, Yuhuai Wu, Guillaume Lample, 2023

https://arxiv.org/abs/2210.12283

5. Solving olympiad geometry without human demonstrations — Trieu H. Trinh, Yuhuai Wu, Quoc V. Le, He He, Thang Luong, 2024

https://www.nature.com/articles/s41586-023-06747-5

6. Exploration and Explanation in Computational Notebooks — Adam Rule, Aurelien Tabard, James D. Hollan, 2018

https://adamrule.com/files/papers/chi_2018_computational_notebooks_camera_ready.pdf

7. What’s Wrong with Computational Notebooks? Pain Points, Needs, and Design Opportunities — Souti Chattopadhyay, Ishita Prasad, Austin Z. Henley, Anita Sarma, Titus Barik, 2020

https://www.microsoft.com/en-us/research/publication/whats-wrong-with-computational-notebooks/

8. Principles for data analysis workflows — Sara Stoudt, Valeri N. Vasquez, Ciera C. Martinez, 2021

https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008770

9. Towards an AI co-scientist — Juraj Gottweis, Wei-Hung Weng, Alexander Daryin, Tao Tu, Anil Palepu, Petar Sirkovic and many others, 2025

https://arxiv.org/abs/2502.18864

10. Towards Autonomous Mathematics Research — T. Feng, T. H. Trinh, G. Bingham, D. Hwang, Y. Chervonyi, J. Jung, J. Lee, C. Pagano, S.-h. Kim, F. Pasqualotto, S. Gukov, J. N. Lee, J. Kim, K. Hou, G. Ghiasi, Y. Tay, Y. Li, C. Kuang, Y. Liu, H. Lin, E. Z. Liu, N. Nayakanti, X. Yang, H.-t. Cheng, D. Hassabis, K. Kavukcuoglu, Q. V. Le, and T. Luong, 2026

https://scholar.google.com/scholar?q=Towards+Autonomous+Mathematics+Research

11. Olympiad-level formal mathematical reasoning with reinforcement learning — T. Hubert, R. S. Mehta, L. Sartran, M. Z. Horvath, G. Zuzic, E. Wieser, A. Huang, J. Schrittwieser, Y. Schroecker, H. Masoom, O. Bertolli, T. Zahavy, A. Mandhane, J. Yung, I. Beloshapka, B. Ibarz, V. Veeriah, L. Yu, O. Nash, P. Lezeau, S. Mercuri, C. Sonne, B. Mehta, A. Davies, D. Zheng, F. Pedregosa, Y. Li, I. von Glehn, M. Rowland, S. Albanie, A. Velingker, S. Schmitt, E. Lockhart, E. Hughes, H. Michalewski, N. Sonnerat, D. Hassabis, P. Kohli, and D. Silver, 2025

https://scholar.google.com/scholar?q=Olympiad-level+formal+mathematical+reasoning+with+reinforcement+learning

12. FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI — E. Glazer, E. Erdil, T. Besiroglu, D. Chicharro, E. Chen, A. Gunning, C. F. Olsson, J.-S. Denain, A. Ho, E. de Oliveira Santos, O. Jarviniemi, M. Barnett, R. Sandler, M. Vrzala, J. Sevilla, Q. Ren, E. Pratt, L. Levine, G. Barkley, N. Stewart, B. Grechuk, T. Grechuk, S. V. Enugandla, and M. Wildon, 2024

https://scholar.google.com/scholar?q=FrontierMath%3A+A+Benchmark+for+Evaluating+Advanced+Mathematical+Reasoning+in+AI

13. Hard2Verify: A Step-Level Verification Benchmark for Open-Ended Frontier Math — S. Pandit, A. Xu, X.-P. Nguyen, Y. Ming, C. Xiong, and S. Joty, 2025

https://scholar.google.com/scholar?q=Hard2Verify%3A+A+Step-Level+Verification+Benchmark+for+Open-Ended+Frontier+Math

14. Longer Context, Deeper Thinking: Uncovering the Role of Long-Context Ability in Reasoning — Wang Yang et al., 2025

https://scholar.google.com/scholar?q=Longer+Context%2C+Deeper+Thinking%3A+Uncovering+the+Role+of+Long-Context+Ability+in+Reasoning

15. InftyThink: Breaking the Length Limits of Long-Context Reasoning in Large Language Models — Yuchen Yan et al., 2025

https://scholar.google.com/scholar?q=InftyThink%3A+Breaking+the+Length+Limits+of+Long-Context+Reasoning+in+Large+Language+Models

16. Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models — Andy Zhou et al., 2023

https://scholar.google.com/scholar?q=Language+Agent+Tree+Search+Unifies+Reasoning+Acting+and+Planning+in+Language+Models

17. Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training — Xidong Feng et al., 2023

https://scholar.google.com/scholar?q=Alphazero-like+Tree-Search+can+Guide+Large+Language+Model+Decoding+and+Training

18. ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving — Zhibin Gou et al., 2023

https://scholar.google.com/scholar?q=ToRA%3A+A+Tool-Integrated+Reasoning+Agent+for+Mathematical+Problem+Solving

19. Efficient Tool Use with Chain-of-Abstraction Reasoning — Silin Gao et al., 2024

https://scholar.google.com/scholar?q=Efficient+Tool+Use+with+Chain-of-Abstraction+Reasoning

20. MuMath-Code: Combining Tool-Use Large Language Models with Multi-perspective Data Augmentation for Mathematical Reasoning — Shuo Yin et al., 2024

https://scholar.google.com/scholar?q=MuMath-Code%3A+Combining+Tool-Use+Large+Language+Models+with+Multi-perspective+Data+Augmentation+for+Mathematical+Reasoning

21. HybridProver: Augmenting Theorem Proving with LLM-Driven Proof Synthesis and Refinement — Jilin Hu et al., 2025

https://scholar.google.com/scholar?q=HybridProver%3A+Augmenting+Theorem+Proving+with+LLM-Driven+Proof+Synthesis+and+Refinement

22. A Minimal Agent for Automated Theorem Proving — Borja Requena et al., 2026

https://scholar.google.com/scholar?q=A+Minimal+Agent+for+Automated+Theorem+Proving

23. Tree Search for Language Model Agents — Jing Yu Koh et al., 2024

https://scholar.google.com/scholar?q=Tree+Search+for+Language+Model+Agents

24. AI Post Transformers: Test-time Scaling for Multi-Agent Collaborative Reasoning — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-22-test-time-scaling-for-multi-agent-collab-082570.mp3

25. AI Post Transformers: Agentic AI and the Next Intelligence Explosion — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-03-28-agentic-ai-and-the-next-intelligence-exp-d06561.mp3

26. AI Post Transformers: Kimi K2.5 and Visual Agent Swarms — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-24-kimi-k25-and-visual-agent-swarms-7d04d7.mp3

Interactive Visualization: AI Co-Mathematician for Mathematical Research

This episode explores TMAS, a framework for scaling test-time reasoning by coordinating multiple specialized agents instead of simply letting a single model think longer. It explains how the system combines proposal, verification, refinement, and shared hierarchical memory so that useful intermediate results and higher-level strategy guidance can be reused across parallel reasoning attempts. The discussion highlights the paper’s central argument that better orchestration, careful memory design, and reinforcement learning objectives for exploration and productive memory use can turn extra inference compute into genuine reasoning gains rather than redundant or noisy work. Listeners would find it interesting for its clear comparison to self-consistency, Tree of Thoughts, and newer coordinated-reasoning methods, along with its emphasis on compute-matched evaluation, reproducibility, and the practical challenge of making multi-agent “synergy” real instead of just expensive parallelism.

Sources:

1. TMAS: Scaling Test-Time Compute via Multi-Agent Synergy — George Wu, Nan Jing, Qing Yi, Chuan Hao, Ming Yang, Feng Chang, Yuan Wei, Jian Yang, Ran Tao, Bryan Dai, 2026

http://arxiv.org/abs/2605.10344

2. A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well? — Qiyuan Zhang, Fuyuan Lyu, Zexu Sun, Lei Wang, Weixu Zhang, Wenyue Hua, Haolun Wu, Zhihan Guo, Yufei Wang, Niklas Muennighoff, Irwin King, Xue Liu, Chen Ma, 2025

https://scholar.google.com/scholar?q=A+Survey+on+Test-Time+Scaling+in+Large+Language+Models%3A+What%2C+How%2C+Where%2C+and+How+Well%3F

3. Self-Consistency Improves Chain of Thought Reasoning in Language Models — Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, Denny Zhou, 2023

https://scholar.google.com/scholar?q=Self-Consistency+Improves+Chain+of+Thought+Reasoning+in+Language+Models

4. Tree of Thoughts: Deliberate Problem Solving with Large Language Models — Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, Karthik Narasimhan, 2023

https://scholar.google.com/scholar?q=Tree+of+Thoughts%3A+Deliberate+Problem+Solving+with+Large+Language+Models

5. PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoning — Jingcheng Hu, Yinmin Zhang, Shijie Shang, Xiaobo Yang, Yue Peng, Zhewei Huang, Hebin Zhou, Xin Wu, Jie Cheng, Fanqi Wan, Xiangwen Kong, Chengyuan Yao, Kaiwen Yan, Ailin Huang, Hongyu Zhou, Qi Han, Zheng Ge, Daxin Jiang, Xiangyu Zhang, Heung-Yeung Shum, 2026

https://scholar.google.com/scholar?q=PaCoRe%3A+Learning+to+Scale+Test-Time+Compute+with+Parallel+Coordinated+Reasoning

6. Do Not Waste Your Rollouts: Recycling Search Experience for Efficient Test-Time Scaling — Xinglin Wang, Jiayi Shi, Shaoxiong Feng, Peiwen Yuan, Yiwei Li, Yueqi Zhang, Chuyi Tan, Ji Zhang, Boyuan Pan, Yao Hu, Kan Li, 2026

https://scholar.google.com/scholar?q=Do+Not+Waste+Your+Rollouts%3A+Recycling+Search+Experience+for+Efficient+Test-Time+Scaling

7. Multi-Agent Verification: Scaling Test-Time Compute with Multiple Verifiers — Shalev Lifshitz, Sheila A. McIlraith, Yilun Du, 2025

https://scholar.google.com/scholar?q=Multi-Agent+Verification%3A+Scaling+Test-Time+Compute+with+Multiple+Verifiers

8. Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters — Charlie Snell, Jaehoon Lee, Kelvin Xu, Aviral Kumar, 2024

https://scholar.google.com/scholar?q=Scaling+LLM+Test-Time+Compute+Optimally+can+be+More+Effective+than+Scaling+Model+Parameters

9. Nemotron-Cascade 2: Post-Training LLMs with Cascade RL and Multi-Domain On-Policy Distillation — Zhuolin Yang, Zihan Liu, Yang Chen, Wenliang Dai, Boxin Wang, Sheng-Chieh Lin, Chankyu Lee, Yangyi Chen, Dongfu Jiang, Jiafan He, Renjie Pi, Grace Lam, Nayeon Lee, Alexander Bukharin, Mohammad Shoeybi, Bryan Catanzaro, Wei Ping, 2026

https://scholar.google.com/scholar?q=Nemotron-Cascade+2%3A+Post-Training+LLMs+with+Cascade+RL+and+Multi-Domain+On-Policy+Distillation

10. ArcMemo: Abstract Reasoning Composition with Lifelong LLM Memory — Matthew Ho et al., 2025

https://scholar.google.com/scholar?q=ArcMemo%3A+Abstract+Reasoning+Composition+with+Lifelong+LLM+Memory

11. Metacognitive Reuse: Turning Recurring LLM Reasoning Into Concise Behaviors — Aniket Didolkar, Nicolas Ballas, Sanjeev Arora, Anirudh Goyal, 2025

https://scholar.google.com/scholar?q=Metacognitive+Reuse%3A+Turning+Recurring+LLM+Reasoning+Into+Concise+Behaviors

12. Reuse, Don't Recompute: Efficient Large Reasoning Model Inference via Memory Orchestration — Daivik Patel, Shrenik Patel, 2025

https://scholar.google.com/scholar?q=Reuse%2C+Don%27t+Recompute%3A+Efficient+Large+Reasoning+Model+Inference+via+Memory+Orchestration

13. Diversity of Thought Improves Reasoning Abilities of Large Language Models — Ranjita Naik et al., 2023

https://scholar.google.com/scholar?q=Diversity+of+Thought+Improves+Reasoning+Abilities+of+Large+Language+Models

14. Diversity-Enhanced Reasoning for Subjective Questions — Yumeng Wang et al., 2025

https://scholar.google.com/scholar?q=Diversity-Enhanced+Reasoning+for+Subjective+Questions

15. Scaling Large Language Model-based Multi-Agent Collaboration — Chen Qian et al., 2024

https://scholar.google.com/scholar?q=Scaling+Large+Language+Model-based+Multi-Agent+Collaboration

16. Multi-Agent Sampling: Scaling Inference Compute for Data Synthesis with Tree Search-Based Agentic Collaboration — Hai Ye, Mingbao Lin, Hwee Tou Ng, Shuicheng Yan, 2024

https://scholar.google.com/scholar?q=Multi-Agent+Sampling%3A+Scaling+Inference+Compute+for+Data+Synthesis+with+Tree+Search-Based+Agentic+Collaboration

17. Reasoning Relay: Evaluating Stability and Interchangeability of Large Language Models in Mathematical Reasoning — Leo Lu et al., 2025

https://scholar.google.com/scholar?q=Reasoning+Relay%3A+Evaluating+Stability+and+Interchangeability+of+Large+Language+Models+in+Mathematical+Reasoning

18. AI Post Transformers: Test-time Scaling for Multi-Agent Collaborative Reasoning — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-22-test-time-scaling-for-multi-agent-collab-082570.mp3

19. 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

20. AI Post Transformers: DeepVerifier: Self-Evolving Research Agents via Rubric-Guided Verification — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/deepverifier-self-evolving-research-agents-via-rubric-guided-verification/

21. AI Post Transformers: MEMSEARCHER: Reinforcement Learning for LLM Memory Management — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-04-memsearcher-reinforcement-learning-for-l-e9ad84.mp3

22. 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

23. AI Post Transformers: Breaking the Prefix Barrier with Shared KV Cache — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-24-breaking-the-prefix-barrier-with-shared-a5e5a6.mp3

Interactive Visualization: TMAS: Scaling Test-Time Compute with Multi-Agent Synergy

This episode explores MELT, a looped language model architecture that aims to preserve latent reasoning benefits while preventing KV-cache memory from growing with every reasoning pass. It explains how the paper reframes the problem as a systems and architecture challenge, replacing per-loop cached attention state with a single shared, gated cache per layer inspired by recurrent models like LSTMs and Universal Transformers. The discussion weighs whether this is a genuine shift in reasoning architecture or a narrower engineering improvement, ultimately arguing that the paper’s real contribution is efficient cache management rather than a wholly new paradigm. Listeners would find it interesting for its clear breakdown of inference-time compute scaling, latent reasoning, and why memory bottlenecks could shape the future of practical reasoning models.

Sources:

1. MELT: Decoupling Compute From Memory

https://arxiv.org/pdf/2605.07721

2. Scaling Latent Reasoning via Looped Language Models — Rui-Jie Zhu et al., 2025

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

3. Universal Transformers — Mostafa Dehghani, Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, Lukasz Kaiser, 2018

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

4. Reducing Transformer Key-Value Cache Size with Cross-Layer Attention — William Brandon, Mayank Mishra, Aniruddha Nrusimha, Rameswar Panda, Jonathan Ragan Kelly, 2024

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

5. GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints — Joshua Ainslie, James Lee-Thorp, Michiel de Jong, Yury Zemlyanskiy, Federico Lebron, Sumit Sanghai, 2023

https://scholar.google.com/scholar?q=GQA%3A+Training+Generalized+Multi-Query+Transformer+Models+from+Multi-Head+Checkpoints

6. Fast Transformer Decoding: One Write-Head is All You Need — Noam Shazeer, 2019

https://scholar.google.com/scholar?q=Fast+Transformer+Decoding%3A+One+Write-Head+is+All+You+Need

7. Beyond Speedup -- Utilizing KV Cache for Sampling and Reasoning — Zeyu Xing, Xing Li, Hui-Ling Zhen, Mingxuan Yuan, Sinno Jialin Pan, 2026

https://scholar.google.com/scholar?q=Beyond+Speedup+--+Utilizing+KV+Cache+for+Sampling+and+Reasoning

8. MemShare: Memory Efficient Inference for Large Reasoning Models through KV Cache Reuse — Kaiwen Chen, Xin Tan, Minchen Yu, Hong Xu, 2025

https://scholar.google.com/scholar?q=MemShare%3A+Memory+Efficient+Inference+for+Large+Reasoning+Models+through+KV+Cache+Reuse

9. Beyond Attention: Breaking the Limits of Transformer Context Length with Recurrent Memory — Aydar Bulatov, Yurii Kuratov, Yermek Kapushev, Mikhail Burtsev, 2024

https://scholar.google.com/scholar?q=Beyond+Attention%3A+Breaking+the+Limits+of+Transformer+Context+Length+with+Recurrent+Memory

10. Towards Understanding Distilled Reasoning Models: A Representational Approach — David D. Baek, Max Tegmark, 2025

https://scholar.google.com/scholar?q=Towards+Understanding+Distilled+Reasoning+Models%3A+A+Representational+Approach

11. 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

12. AI Post Transformers: PackKV Lossy Compression for KV Caches — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-05-04-packkv-lossy-compression-for-kv-caches-b37bce.mp3

13. AI Post Transformers: RetrievalAttention for Long-Context LLM Inference — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-17-retrievalattention-for-long-context-llm-ddf566.mp3

14. AI Post Transformers: Parallelizing DeltaNet Linear Transformers over Sequence Length — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-18-parallelizing-deltanet-linear-transforme-2d0377.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: Jet-Nemotron and PostNAS for Faster Long Context — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-03-24-jet-nemotron-and-postnas-for-faster-long-436381.mp3

17. 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

18. 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

Interactive Visualization: MELT: Decoupling Compute From Memory

This episode explores the Qwen-Image-2.0 technical report and its central claim that a single unified multimodal diffusion model can handle high-quality image generation, precise image editing, multilingual text rendering, ultra-long text, and complex instruction following in one system. It traces the technical background from vision transformers and latent diffusion through newer editing and text-rendering methods, explaining why image editing is fundamentally harder than generation because users expect strict preservation of identity, layout, and other details. The discussion emphasizes that text in images remains a stubborn problem because models must treat letters as exact symbols rather than visual texture, especially for posters, ads, slides, comics, and UI mockups. Listeners would find it interesting because it connects benchmark claims to real product failures and asks whether this model finally reduces the messy patchwork of specialized tools into a single backbone that can actually obey, spell, preserve, and compose well.

Sources:

1. Qwen-Image-2.0 Technical Report — Bing Zhao, Chenfei Wu, Deqing Li, Hao Meng, Jiahao Li, Jie Zhang, Jingren Zhou, Junyang Lin, Kaiyuan Gao, Kuan Cao, Kun Yan, Liang Peng, Lihan Jiang, Niantong Li, Ningyuan Tang, Shengming Yin, Tianhe Wu, Xiao Xu, Xiaoyue Chen, Xihua Wang, Yan Shu, Yanran Zhang, Yi Wang, Yilei Chen, Ying Ba, Yixian Xu, Yujia Wu, Yuxiang Chen, Zecheng Tang, Zekai Zhang, Zhendong Wang, Zihao Liu, Zikai Zhou, An Yang, Chen Cheng, Chenxu Lv, Dayiheng Liu, Fan Zhou, Hantian Xiong, Hongzhu Shi, Hu Wei, Huihong Zhao, Ivy Liu, Jianwei Zhang, Jiawei Zhang, Kai Chen, Kang He, Levon Xue, Lin Qu, Linhan Tang, Luwen Feng, Minggang Wu, Minmin Sun, Na Ni, Rui Men, Shuai Bai, Sishou Zheng, Tao Lan, Tianqi Zhang, Tingkun Wen, Wei Wang, Weixu Qiao, Weiyi Lu, Wenmeng Zhou, Xiaodong Deng, Xiaoxiao Xu, Xinlei Fang, Xionghui Chen, Yanan Wang, Yang Fan, Yichang Zhang, Yixuan Xu, Yu Wu, Zhiyuan Ma, Zhizhi Cai, 2026

http://arxiv.org/abs/2605.10730

2. SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations — Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, Stefano Ermon, 2021

https://scholar.google.com/scholar?q=SDEdit%3A+Guided+Image+Synthesis+and+Editing+with+Stochastic+Differential+Equations

3. Prompt-to-Prompt Image Editing with Cross-Attention Control — Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, Daniel Cohen-Or, 2022

https://scholar.google.com/scholar?q=Prompt-to-Prompt+Image+Editing+with+Cross-Attention+Control

4. InstructPix2Pix: Learning to Follow Image Editing Instructions — Tim Brooks, Aleksander Holynski, Alexei A. Efros, 2022

https://scholar.google.com/scholar?q=InstructPix2Pix%3A+Learning+to+Follow+Image+Editing+Instructions

5. Emu Edit: Precise Image Editing via Recognition and Generation Tasks — Shelly Sheynin, Adam Polyak, Uriel Singer, Yuval Kirstain, Amit Zohar, Oron Ashual, Devi Parikh, Yaniv Taigman, 2023

https://scholar.google.com/scholar?q=Emu+Edit%3A+Precise+Image+Editing+via+Recognition+and+Generation+Tasks

6. GlyphDraw: Seamlessly Rendering Text with Intricate Spatial Structures in Text-to-Image Generation — Jian Ma, Mingjun Zhao, Chen Chen, Ruichen Wang, Di Niu, Haonan Lu, Xiaodong Lin, 2023

https://scholar.google.com/scholar?q=GlyphDraw%3A+Seamlessly+Rendering+Text+with+Intricate+Spatial+Structures+in+Text-to-Image+Generation

7. TextDiffuser: Diffusion Models as Text Painters — Jingye Chen, Yupan Huang, Tengchao Lv, Lei Cui, Qifeng Chen, Furu Wei, 2023

https://scholar.google.com/scholar?q=TextDiffuser%3A+Diffusion+Models+as+Text+Painters

8. AnyText: Multilingual Visual Text Generation and Editing — Yuxiang Tuo, Wangmeng Xiang, Jun-Yan He, Yifeng Geng, Xuansong Xie, 2023

https://scholar.google.com/scholar?q=AnyText%3A+Multilingual+Visual+Text+Generation+and+Editing

9. EasyText: Controllable Diffusion Transformer for Multilingual Text Rendering — Runnan Lu, Yuxuan Zhang, Jailing Liu, Haifa Wang, Yiren Song, 2025

https://scholar.google.com/scholar?q=EasyText%3A+Controllable+Diffusion+Transformer+for+Multilingual+Text+Rendering

10. Improving Image Generation with Better Captions — James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang, Joyce Lee, Yufei Guo, Wesam Manassra, Prafulla Dhariwal, Casey Chu, Yunxin Jiao, Aditya Ramesh, 2023

https://scholar.google.com/scholar?q=Improving+Image+Generation+with+Better+Captions

11. T2I-CompBench: A Comprehensive Benchmark for Open-world Compositional Text-to-image Generation — Kaiyi Huang, Kaiyue Sun, Enze Xie, Zhenguo Li, Xihui Liu, 2023

https://scholar.google.com/scholar?q=T2I-CompBench%3A+A+Comprehensive+Benchmark+for+Open-world+Compositional+Text-to-image+Generation

12. MagicBrush: A Manually Annotated Dataset for Instruction-Guided Image Editing — Kai Zhang, Lingbo Mo, Wenhu Chen, Huan Sun, Yu Su, 2023

https://scholar.google.com/scholar?q=MagicBrush%3A+A+Manually+Annotated+Dataset+for+Instruction-Guided+Image+Editing

13. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale — Alexey Dosovitskiy et al., 2020

https://scholar.google.com/scholar?q=An+Image+is+Worth+16x16+Words%3A+Transformers+for+Image+Recognition+at+Scale

14. High-Resolution Image Synthesis with Latent Diffusion Models — Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Bjorn Ommer, 2022

https://scholar.google.com/scholar?q=High-Resolution+Image+Synthesis+with+Latent+Diffusion+Models

15. Scalable Diffusion Models with Transformers — William Peebles, Saining Xie, 2023

https://scholar.google.com/scholar?q=Scalable+Diffusion+Models+with+Transformers

16. PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis — Junsong Chen et al., 2024

https://scholar.google.com/scholar?q=PixArt-%CE%B1%3A+Fast+Training+of+Diffusion+Transformer+for+Photorealistic+Text-to-Image+Synthesis

17. FLUX — Black Forest Labs, 2024

https://scholar.google.com/scholar?q=FLUX

18. Qwen3-VL — Qwen Team, 2025

https://scholar.google.com/scholar?q=Qwen3-VL

19. OpenAI Image Generation System Card / product report — OpenAI, 2025

https://scholar.google.com/scholar?q=OpenAI+Image+Generation+System+Card+%2F+product+report

20. Google image generation product/report cited in the introduction — Google, 2025

https://scholar.google.com/scholar?q=Google+image+generation+product%2Freport+cited+in+the+introduction

21. Long-Text-to-Image Generation via Compositional Prompt Decomposition — Jen-Yuan Huang, Tong Lin, Yilun Du, 2026

https://scholar.google.com/scholar?q=Long-Text-to-Image+Generation+via+Compositional+Prompt+Decomposition

22. TIT-Score: Evaluating Long-Prompt Based Text-to-Image Alignment via Text-to-Image-to-Text Consistency — Juntong Wang et al., 2025

https://scholar.google.com/scholar?q=TIT-Score%3A+Evaluating+Long-Prompt+Based+Text-to-Image+Alignment+via+Text-to-Image-to-Text+Consistency

23. ImgEdit: A Unified Image Editing Dataset and Benchmark — Yang Ye, Xianyi He, Zongjian Li, Bin Lin, Shenghai Yuan, Zhiyuan Yan, Bohan Hou, Li Yuan, 2025

https://scholar.google.com/scholar?q=ImgEdit%3A+A+Unified+Image+Editing+Dataset+and+Benchmark

24. UniVG: A Generalist Diffusion Model for Unified Image Generation and Editing — Tsu-Jui Fu, Yusu Qian, Chen Chen, Wenze Hu, Zhe Gan, Yinfei Yang, 2025

https://scholar.google.com/scholar?q=UniVG%3A+A+Generalist+Diffusion+Model+for+Unified+Image+Generation+and+Editing

25. DreamOmni: Unified Image Generation and Editing — Bin Xia, Yuechen Zhang, Jingyao Li, Chengyao Wang, Yitong Wang, Xinglong Wu, Bei Yu, Jiaya Jia, 2024

https://scholar.google.com/scholar?q=DreamOmni%3A+Unified+Image+Generation+and+Editing

26. Taming Rectified Flow for Inversion and Editing — Jiangshan Wang, Junfu Pu, Zhongang Qi, Jiayi Guo, et al., 2024

https://scholar.google.com/scholar?q=Taming+Rectified+Flow+for+Inversion+and+Editing

27. InstantEdit: Text-Guided Few-Step Image Editing with Piecewise Rectified Flow — Yiming Gong, Zhen Zhu, Minjia Zhang, 2025

https://scholar.google.com/scholar?q=InstantEdit%3A+Text-Guided+Few-Step+Image+Editing+with+Piecewise+Rectified+Flow

28. DS-VLM: Diffusion Supervision Vision Language Model — Zhen Sun, Yunhang Shen, Jie Li, Xing Sun, Pingyang Dai, Liujuan Cao, Rongrong Ji, 2025

https://scholar.google.com/scholar?q=DS-VLM%3A+Diffusion+Supervision+Vision+Language+Model

29. 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

Interactive Visualization: Qwen-Image-2.0 for Unified Generation and Editing

This episode explores the paper δ-mem, which argues that long context windows are not the same as true memory and proposes a compact online memory module for frozen language models. It explains how the method uses a tiny mutable state matrix, updated with a delta rule, to store residual errors over time and feed that state back into generation as a low-rank attention correction rather than replaying full conversation history. The discussion also examines why benchmarks like LoCoMo and MemoryAgentBench matter more than generic reasoning tests for evaluating memory, because they probe persistence, conflict resolution, and incremental updating across turns. Listeners would find it interesting because the episode connects an unusual architectural idea to concrete empirical gains, including stronger results on memory-heavy tasks despite using an extremely small memory state.

Sources:

1. $δ$-mem: Efficient Online Memory for Large Language Models — Jingdi Lei, Di Zhang, Junxian Li, Weida Wang, Kaixuan Fan, Xiang Liu, Qihan Liu, Xiaoteng Ma, Baian Chen, Soujanya Poria, 2026

http://arxiv.org/abs/2605.12357

2. Adaptive Switching Circuits — Bernard Widrow, Marcian E. Hoff, 1960

https://isl.stanford.edu/~widrow/papers/c1960adaptiveswitching.pdf

3. Online Learning and Online Convex Optimization — Shai Shalev-Shwartz, 2012

https://cir.nii.ac.jp/crid/1363388845866612864

4. Test-Time Training with Self-Supervision for Generalization under Distribution Shifts — Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei A. Efros, Moritz Hardt, 2020

https://proceedings.mlr.press/v119/sun20b.html

5. TiC-CLIP: Continual Training of CLIP Models — Saurabh Garg, Mehrdad Farajtabar, Hadi Pouransari, Sachin Mehta, Raviteja Vemulapalli, Oncel Tuzel, Vaishaal Shankar, Fartash Faghri, 2024

https://machinelearning.apple.com/research/tic-clip-v2

6. Evaluating Very Long-Term Conversational Memory of LLM Agents — Adyasha Maharana, Dong-Ho Lee, Sergey Tulyakov, Mohit Bansal, Francesco Barbieri, Yuwei Fang, 2024

https://scholar.google.com/scholar?q=Evaluating+Very+Long-Term+Conversational+Memory+of+LLM+Agents

7. Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions — Yuanzhe Hu, Yu Wang, Julian McAuley, 2025

https://scholar.google.com/scholar?q=Evaluating+Memory+in+LLM+Agents+via+Incremental+Multi-Turn+Interactions

8. Titans: Learning to Memorize at Test Time — Ali Behrouz, Peilin Zhong, Vahab Mirrokni, 2025

https://scholar.google.com/scholar?q=Titans%3A+Learning+to+Memorize+at+Test+Time

9. Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory — Prateek Chhikara, Dev Khant, Saket Aryan, Taranjeet Singh, Deshraj Yadav, 2025

https://scholar.google.com/scholar?q=Mem0%3A+Building+Production-Ready+AI+Agents+with+Scalable+Long-Term+Memory

10. E-mem: Multi-agent based Episodic Context Reconstruction for LLM Agent Memory — Kaixiang Wang, Yidan Lin, Jiong Lou, Zhaojiacheng Zhou, Bunyod Suvonov, Jie Li, 2026

https://scholar.google.com/scholar?q=E-mem%3A+Multi-agent+based+Episodic+Context+Reconstruction+for+LLM+Agent+Memory

11. HiMem: Hierarchical Long-Term Memory for LLM Long-Horizon Agents — Ningning Zhang, Xingxing Yang, Zhizhong Tan, Weiping Deng, Wenyong Wang, 2026

https://scholar.google.com/scholar?q=HiMem%3A+Hierarchical+Long-Term+Memory+for+LLM+Long-Horizon+Agents

12. Continuum Memory Architectures for Long-Horizon LLM Agents — Joe Logan, 2026

https://scholar.google.com/scholar?q=Continuum+Memory+Architectures+for+Long-Horizon+LLM+Agents

13. Gated Delta Networks: Improving Mamba2 with Delta Rule — Songlin Yang, Jan Kautz, Ali Hatamizadeh, 2024/2025

https://scholar.google.com/scholar?q=Gated+Delta+Networks%3A+Improving+Mamba2+with+Delta+Rule

14. Parallelizing Linear Transformers with the Delta Rule over Sequence Length — Songlin Yang, Bailin Wang, Yu Zhang, Yikang Shen, Yoon Kim, 2024

https://scholar.google.com/scholar?q=Parallelizing+Linear+Transformers+with+the+Delta+Rule+over+Sequence+Length

15. Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs — Jonas Hübotter, Sascha Bongni, Ido Hakimi, Andreas Krause, 2024

https://scholar.google.com/scholar?q=Efficiently+Learning+at+Test-Time%3A+Active+Fine-Tuning+of+LLMs

16. Test-Time Learning for Large Language Models — Jinwu Hu, Zhitian Zhang, Guohao Chen, Xutao Wen, Chao Shuai, Wei Luo, Bin Xiao, Yuanqing Li, Mingkui Tan, 2025

https://scholar.google.com/scholar?q=Test-Time+Learning+for+Large+Language+Models

17. Efficient Low Rank Attention for Long-Context Inference in Large Language Models — Tenghui Li, Guoxu Zhou, Xuyang Zhao, Yuning Qiu, Qibin Zhao, 2025

https://scholar.google.com/scholar?q=Efficient+Low+Rank+Attention+for+Long-Context+Inference+in+Large+Language+Models

18. AI Post Transformers: Explicit Information Transmission for Context Compression — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-05-05-explicit-information-transmission-for-co-24e3c2.mp3

19. AI Post Transformers: Parallelizing DeltaNet Linear Transformers over Sequence Length — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-18-parallelizing-deltanet-linear-transforme-2d0377.mp3

20. 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

21. 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

22. AI Post Transformers: PackKV Lossy Compression for KV Caches — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-05-04-packkv-lossy-compression-for-kv-caches-b37bce.mp3

Interactive Visualization: δ-mem and Online Memory for LLMs

This episode explores a May 7, 2026 arXiv paper on Lighthouse Attention and asks whether long-context language models can be pretrained cheaply with hierarchical sparse attention, then switched back to standard dense attention late in training without losing dense-model quality. It explains why long-context training is so expensive even with FlashAttention, contrasting dense quadratic attention with sparse and hierarchical schemes that try to narrow which tokens interact. The discussion walks through the paper’s core design: building multi-level pooled query/key/value pyramids, using a gradient-free top-K selector to choose relevant causal subsequences, running ordinary FlashAttention on that smaller set, and scattering the results back. Listeners would find it interesting because it frames the method as a practical systems bet with potentially major implications for 128K- to million-token pretraining, while also stressing that the evidence is still preliminary and far from proving it works at frontier scale.

Sources:

1. Long Context Pre-Training with Lighthouse Attention

https://arxiv.org/pdf/2605.06554

2. H-Transformer-1D: Fast One-Dimensional Hierarchical Attention for Sequences — Zhenhai Zhu, Radu Soricut, 2021

https://scholar.google.com/scholar?q=H-Transformer-1D%3A+Fast+One-Dimensional+Hierarchical+Attention+for+Sequences

3. LongT5: Efficient Text-To-Text Transformer for Long Sequences — Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang, 2021

https://scholar.google.com/scholar?q=LongT5%3A+Efficient+Text-To-Text+Transformer+for+Long+Sequences

4. HISA: Efficient Hierarchical Indexing for Fine-Grained Sparse Attention — Yufei Xu, Fanxu Meng, Fan Jiang, Yuxuan Wang, Ruijie Zhou, Jiexi Wu, Zhixin Pan, Zhaohui Wang, Xiaojuan Tang, Wenjie Pei, Tongxuan Liu, Di Yin, Xing Sun, Muhan Zhang, 2026

https://scholar.google.com/scholar?q=HISA%3A+Efficient+Hierarchical+Indexing+for+Fine-Grained+Sparse+Attention

5. Long Context Pre-Training with Lighthouse Attention — Bowen Peng, Subho Ghosh, Jeffrey Quesnelle, 2026

https://scholar.google.com/scholar?q=Long+Context+Pre-Training+with+Lighthouse+Attention

6. Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention — Jingyang Yuan, Huazuo Gao, Damai Dai, Junyu Luo, Liang Zhao, Zhengyan Zhang, Zhenda Xie, Y. X. Wei, Lean Wang, Zhiping Xiao, Yuqing Wang, Chong Ruan, Ming Zhang, Wenfeng Liang, Wangding Zeng, 2025

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

7. MoBA: Mixture of Block Attention for Long-Context LLMs — Enzhe Lu, Zhejun Jiang, Jingyuan Liu, Yulun Du, Tao Jiang, Chao Hong, Shaowei Liu, Weiran He, Enming Yuan, Yuzhi Wang, Zhiqi Huang, Huan Yuan, Suting Xu, Xinran Xu, Guokun Lai, Yanru Chen, Huabin Zheng, Junjie Yan, Jianlin Su, Yuxin Wu, Neo Y. Zhang, Zhilin Yang, Xinyu Zhou, Mingxing Zhang, Jiezhong Qiu, 2025

https://scholar.google.com/scholar?q=MoBA%3A+Mixture+of+Block+Attention+for+Long-Context+LLMs

8. FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness — Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, Christopher Re, 2022

https://scholar.google.com/scholar?q=FlashAttention%3A+Fast+and+Memory-Efficient+Exact+Attention+with+IO-Awareness

9. Every Token Counts: Generalizing 16M Ultra-Long Context in Large Language Models — Xiang Hu, Zhanchao Zhou, Ruiqi Liang, Zehuan Li, Wei Wu, Jianguo Li, 2025

https://scholar.google.com/scholar?q=Every+Token+Counts%3A+Generalizing+16M+Ultra-Long+Context+in+Large+Language+Models

10. Hyperattention: Long-context attention in near-linear time — not verified from snippet, recent (not verified from snippet)

https://scholar.google.com/scholar?q=Hyperattention%3A+Long-context+attention+in+near-linear+time

11. When Does Content-Based Routing Work? Representation Requirements for Selective Attention in Hybrid Sequence Models — not verified from snippet, recent (not verified from snippet)

https://scholar.google.com/scholar?q=When+Does+Content-Based+Routing+Work%3F+Representation+Requirements+for+Selective+Attention+in+Hybrid+Sequence+Models

12. Delta attention: Fast and accurate sparse attention inference by delta correction — not verified from snippet, recent (not verified from snippet)

https://scholar.google.com/scholar?q=Delta+attention%3A+Fast+and+accurate+sparse+attention+inference+by+delta+correction

13. Spargeattention: Accurate and training-free sparse attention accelerating any model inference — not verified from snippet, recent (not verified from snippet)

https://scholar.google.com/scholar?q=Spargeattention%3A+Accurate+and+training-free+sparse+attention+accelerating+any+model+inference

14. Post-training sparse attention with double sparsity — not verified from snippet, recent (not verified from snippet)

https://scholar.google.com/scholar?q=Post-training+sparse+attention+with+double+sparsity

15. 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

16. AI Post Transformers: RetrievalAttention for Long-Context LLM Inference — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-17-retrievalattention-for-long-context-llm-ddf566.mp3

17. 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

18. 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

19. AI Post Transformers: Explicit Information Transmission for Context Compression — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-05-05-explicit-information-transmission-for-co-24e3c2.mp3

20. 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

Interactive Visualization: Long Context Pre-Training with Lighthouse Attention

This episode explores MiA-Signature, a long-context reasoning method that argues models should approximate a broad, query-driven activation pattern over memory rather than rely on narrow top-k retrieval. It explains how the paper builds a two-stage pipeline: first retrieving a wide pool of potentially relevant context, then compressing that pool into a compact signature of high-level concepts chosen with submodular optimization to maximize coverage and reduce redundancy. The discussion digs into the paper’s central claim that this behaves more like a planning or memory-compression layer than classic RAG, while also questioning whether the real gains come from the signature itself or from the broader retrieval and refinement machinery around it. Listeners would find it interesting because it connects long-context failures, agent memory design, and distributed evidence tracking into a concrete systems debate about what better memory access in LLMs should look like.

Sources:

1. MiA-Signature: Approximating Global Activation for Long-Context Understanding — Yuqing Li, Jiangnan Li, Mo Yu, Zheng Lin, Weiping Wang, Jie Zhou, 2026

http://arxiv.org/abs/2605.06416

2. An Analysis of Approximations for Maximizing Submodular Set Functions—I — George L. Nemhauser, Laurence A. Wolsey, Marshall L. Fisher, 1978

https://scholar.google.com/scholar?q=An+Analysis+of+Approximations+for+Maximizing+Submodular+Set+Functions%E2%80%94I

3. Submodular Function Maximization — Andreas Krause, Daniel Golovin, 2014

https://scholar.google.com/scholar?q=Submodular+Function+Maximization

4. Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies — Andreas Krause, Ajit Singh, Carlos Guestrin, 2008

https://scholar.google.com/scholar?q=Near-Optimal+Sensor+Placements+in+Gaussian+Processes%3A+Theory%2C+Efficient+Algorithms+and+Empirical+Studies

5. A Class of Submodular Functions for Document Summarization — Hui Lin, Jeff Bilmes, 2011

https://scholar.google.com/scholar?q=A+Class+of+Submodular+Functions+for+Document+Summarization

6. Working Memory — Alan D. Baddeley, Graham Hitch, 1974

https://scholar.google.com/scholar?q=Working+Memory

7. The Episodic Buffer: A New Component of Working Memory? — Alan Baddeley, 2000

https://scholar.google.com/scholar?q=The+Episodic+Buffer%3A+A+New+Component+of+Working+Memory%3F

8. Hybrid Computing Using a Neural Network with Dynamic External Memory — Alex Graves, Greg Wayne, Malcolm Reynolds and colleagues, 2016

https://scholar.google.com/scholar?q=Hybrid+Computing+Using+a+Neural+Network+with+Dynamic+External+Memory

9. Transformer-XL: Attentive Language Models beyond a Fixed-Length Context — Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc Le, Ruslan Salakhutdinov, 2019

https://scholar.google.com/scholar?q=Transformer-XL%3A+Attentive+Language+Models+beyond+a+Fixed-Length+Context

10. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks — Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Kuttler, Mike Lewis, Wen-tau Yih, Tim Rocktaschel, Sebastian Riedel, Douwe Kiela, 2020

https://scholar.google.com/scholar?q=Retrieval-Augmented+Generation+for+Knowledge-Intensive+NLP+Tasks

11. What is consciousness, and could machines have it? — Stanislas Dehaene, Hakwan Lau, Sid Kouider, 2017

https://scholar.google.com/scholar?q=What+is+consciousness%2C+and+could+machines+have+it%3F

12. DetectiveQA: Evaluating Long-Context Reasoning on Detective Novels — Zhe Xu, Jiasheng Ye, Xiangyang Liu, Tianxiang Sun, Xiaoran Liu, Qipeng Guo, Linlin Li, Qun Liu, Xuanjing Huang, Xipeng Qiu, 2024

https://scholar.google.com/scholar?q=DetectiveQA%3A+Evaluating+Long-Context+Reasoning+on+Detective+Novels

13. One Thousand and One Pairs: A "novel" challenge for long-context language models — Marzena Karpinska, Katherine Thai, Kyle Lo, Tanya Goyal, Mohit Iyyer, 2024

https://scholar.google.com/scholar?q=One+Thousand+and+One+Pairs%3A+A+%22novel%22+challenge+for+long-context+language+models

14. Stateful Evidence-Driven Retrieval-Augmented Generation with Iterative Reasoning — Qi Dong, Ziheng Lin, Ning Ding, 2026

https://scholar.google.com/scholar?q=Stateful+Evidence-Driven+Retrieval-Augmented+Generation+with+Iterative+Reasoning

15. LongRAG: Enhancing Retrieval-Augmented Generation with Long-Context LLMs — approx. Wang et al., 2024/2025

https://scholar.google.com/scholar?q=LongRAG%3A+Enhancing+Retrieval-Augmented+Generation+with+Long-Context+LLMs

16. Retrieval Augmented Generation or Long-Context LLMs? A Study and Hybrid Approach — approx. Li et al., 2024/2025

https://scholar.google.com/scholar?q=Retrieval+Augmented+Generation+or+Long-Context+LLMs%3F+A+Study+and+Hybrid+Approach

17. Long Context Compression with Activation Beacon — approx. Liu et al., 2024

https://scholar.google.com/scholar?q=Long+Context+Compression+with+Activation+Beacon

18. UniGist: Towards General and Hardware-Aligned Sequence-Level Long Context Compression — approx. authors unclear from snippet, 2024/2025

https://scholar.google.com/scholar?q=UniGist%3A+Towards+General+and+Hardware-Aligned+Sequence-Level+Long+Context+Compression

19. Retaining Key Information under High Compression Ratios: Query-Guided Compressor for LLMs — approx. authors unclear from snippet, 2024/2025

https://scholar.google.com/scholar?q=Retaining+Key+Information+under+High+Compression+Ratios%3A+Query-Guided+Compressor+for+LLMs

20. QEC-LLM: Training Query-Focused Extractive Compression Model with LLM-Guiding for Open Domain QA — approx. authors unclear from snippet, 2024/2025

https://scholar.google.com/scholar?q=QEC-LLM%3A+Training+Query-Focused+Extractive+Compression+Model+with+LLM-Guiding+for+Open+Domain+QA

21. HMT: Hierarchical Memory Transformer for Efficient Long Context Language Processing — approx. authors unclear from snippet, 2024/2025

https://scholar.google.com/scholar?q=HMT%3A+Hierarchical+Memory+Transformer+for+Efficient+Long+Context+Language+Processing

22. UniMem: Towards a Unified View of Long-Context Large Language Models — approx. authors unclear from snippet, 2025

https://scholar.google.com/scholar?q=UniMem%3A+Towards+a+Unified+View+of+Long-Context+Large+Language+Models

23. AI Post Transformers: Explicit Information Transmission for Context Compression — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-05-05-explicit-information-transmission-for-co-24e3c2.mp3

24. AI Post Transformers: MEMSEARCHER: Reinforcement Learning for LLM Memory Management — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-04-memsearcher-reinforcement-learning-for-l-e9ad84.mp3

25. AI Post Transformers: Memory Intelligence Agents for Deep Research — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-04-09-memory-intelligence-agents-for-deep-rese-cd39e3.mp3

Interactive Visualization: MiA-Signature and Global Activation for Long Context

This episode explores ForkKV, a systems paper on serving multiple LoRA-based agents from one base language model without duplicating massive KV caches for shared context. It explains why ordinary prefix caching breaks once different LoRA adapters change the activations, then walks through the paper’s core idea: split cache state into a large shared base and a small adapter-specific residual, using an operating-system-style copy-on-write model for agent branches. The discussion connects that design to prior work on LoRA, prefix caching, PagedAttention, and disaggregated memory, making the argument that the real win is practical GPU memory efficiency for coding assistants and tool-using agent workflows. Listeners would find it interesting because it frames transformer serving as a memory-management problem and shows how borrowing ideas from Unix process forking could make multi-agent LLM systems far more scalable.

Sources:

1. ForkKV: Scaling Multi-LoRA Agent Serving via Copy-on-Write Disaggregated KV Cache — Shao Wang, Rui Ren, Lin Gui, 2026

http://arxiv.org/abs/2604.06370

2. The UNIX Time-Sharing System — Dennis M. Ritchie and Ken Thompson, 1974

https://scholar.google.com/scholar?q=The+UNIX+Time-Sharing+System

3. 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, and Ion Stoica, 2023

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

4. vAttention: Dynamic Memory Management for Serving LLMs without PagedAttention — Ramya Prabhu, Ajay Nayak, Jayashree Mohan, Ramachandran Ramjee, and Ashish Panwar, 2024

https://scholar.google.com/scholar?q=vAttention%3A+Dynamic+Memory+Management+for+Serving+LLMs+without+PagedAttention

5. ForkKV: Scaling Multi-LoRA Agent Serving via Copy-on-Write Disaggregated KV Cache — Shao Wang, Rui Ren, and Lin Gui, 2026

https://scholar.google.com/scholar?q=ForkKV%3A+Scaling+Multi-LoRA+Agent+Serving+via+Copy-on-Write+Disaggregated+KV+Cache

6. Efficiently Programming Large Language Models using SGLang — Lianmin Zheng, Liangsheng Yin, Zhiqiang Xie, Jeff Huang, Chuyue Sun, Cody Hao Yu, Shiyi Cao, Christos Kozyrakis, Ion Stoica, Joseph E. Gonzalez, Clark Barrett, and Ying Sheng, 2023

https://scholar.google.com/scholar?q=Efficiently+Programming+Large+Language+Models+using+SGLang

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, and Hao Zhang, 2024

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

8. MemServe: Context Caching for Disaggregated LLM Serving with Elastic Memory Pool — Cunchen Hu, Heyang Huang, Junhao Hu, Jiang Xu, Xusheng Chen, Tao Xie, Chenxi Wang, Sa Wang, Yungang Bao, Ninghui Sun, and Yizhou Shan, 2024

https://scholar.google.com/scholar?q=MemServe%3A+Context+Caching+for+Disaggregated+LLM+Serving+with+Elastic+Memory+Pool

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

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

10. LRAgent: efficient kv cache sharing for multi-lora llm agents — H. Jeon, H. Ha, and J. Kim, 2026

https://scholar.google.com/scholar?q=LRAgent%3A+efficient+kv+cache+sharing+for+multi-lora+llm+agents

11. Punica: multi-tenant lora serving — L. Chen, Z. Ye, Y. Wu, D. Zhuo, L. Ceze, and A. Krishnamurthy, 2024

https://scholar.google.com/scholar?q=Punica%3A+multi-tenant+lora+serving

12. S-LoRA: serving thousands of concurrent lora adapters — Y. Sheng, S. Cao, D. Li, C. Hooper, N. Lee, S. Yang, C. Chou, B. Zhu, L. Zheng, K. Keutzer, J. E. Gonzalez, and I. Stoica, 2023

https://scholar.google.com/scholar?q=S-LoRA%3A+serving+thousands+of+concurrent+lora+adapters

13. DLoRA: dynamically orchestrating requests and adapters for LoRA LLM serving — B. Wu, R. Zhu, Z. Zhang, P. Sun, X. Liu, and X. Jin, 2024

https://scholar.google.com/scholar?q=DLoRA%3A+dynamically+orchestrating+requests+and+adapters+for+LoRA+LLM+serving

14. Tokencake: A KV-Cache-centric Serving Framework for LLM-based Multi-Agent Applications — Z. Bian, F. Wu, T. Ma, and Y. Zhuo, 2025

https://scholar.google.com/scholar?q=Tokencake%3A+A+KV-Cache-centric+Serving+Framework+for+LLM-based+Multi-Agent+Applications

15. KVFlow: Efficient Prefix Caching for Accelerating LLM-Based Multi-Agent Workflows — Z. Pan, A. Patel, Z. Hu, Y. Shen, Y. Guan, W. Li, L. Qin, Y. Wang, and Y. Ding, 2025

https://scholar.google.com/scholar?q=KVFlow%3A+Efficient+Prefix+Caching+for+Accelerating+LLM-Based+Multi-Agent+Workflows

16. MobiLoRA: Accelerating LoRA-based LLM Inference on Mobile Devices via Context-aware KV Cache Optimization — Borui Li et al., 2025

https://scholar.google.com/scholar?q=MobiLoRA%3A+Accelerating+LoRA-based+LLM+Inference+on+Mobile+Devices+via+Context-aware+KV+Cache+Optimization

17. Efficient Multi-Adapter LLM Serving via Cross-Model KV-Cache Reuse with Activated LoRA — Allison Li, Kristjan Greenewald, Thomas Parnell, Navid Azizan, 2025

https://scholar.google.com/scholar?q=Efficient+Multi-Adapter+LLM+Serving+via+Cross-Model+KV-Cache+Reuse+with+Activated+LoRA

18. KVLink: Accelerating Large Language Models via Efficient KV Cache Reuse — Jingbo Yang et al., 2025

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

19. AdaFuse: Accelerating Dynamic Adapter Inference via Token-Level Pre-Gating and Fused Kernel Optimization — Qiyang Li et al., 2026

https://scholar.google.com/scholar?q=AdaFuse%3A+Accelerating+Dynamic+Adapter+Inference+via+Token-Level+Pre-Gating+and+Fused+Kernel+Optimization

20. ChunkKV: Semantic-Preserving KV Cache Compression for Efficient Long-Context LLM Inference — Xiang Liu et al., 2025

https://scholar.google.com/scholar?q=ChunkKV%3A+Semantic-Preserving+KV+Cache+Compression+for+Efficient+Long-Context+LLM+Inference

21. SCBench: A KV Cache-Centric Analysis of Long-Context Methods — Yucheng Li et al., 2025

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

22. ELORA: Efficient LoRA and KV Cache Management for Multi-LoRA LLM Serving — Jiuchen Shi et al., 2026

https://scholar.google.com/scholar?q=ELORA%3A+Efficient+LoRA+and+KV+Cache+Management+for+Multi-LoRA+LLM+Serving

23. 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

24. 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

25. 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

26. 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

27. AI Post Transformers: Why LLM Serving Needs Mathematical Optimization — Hal Turing & Dr. Ada Shannon, 2026

https://podcast.do-not-panic.com/episodes/2026-05-05-why-llm-serving-needs-mathematical-optim-647fc6.mp3

Interactive Visualization: ForkKV for Multi-LoRA Agent Serving