AI Post Transformers

mcgrof

AI-generated podcast where hosts Hal Turing and Dr. Ada Shannon discuss the latest research papers and reports in machine learning, AI systems, and optimization. Featuring honest critical analysis, proper citations, and nerdy humor.

  1. 23h ago

    DeLM: Decentralized Coding Agents Sharing Context Without a Leader

    This episode examines DeLM, a Stanford system that runs several coding agents in parallel with no main agent, and asks whether four agents deliver four agents' worth of progress or just a bigger bill. It sorts existing designs by the "bubble" each one creates. Independent sampling wastes effort on redundant work. Peer debate stalls at round barriers and grows quadratically in messages. Centralized orchestrators like Claude Code and Codex leave the lead agent idle and pass lossy summaries between agents. DeLM replaces the lead with a self-service task queue, where the first claim wins, and an append-only shared log of FACT and FAIL entries that agents publish as they learn things. The discussion ties this to the 1980 Hearsay-II blackboard system, which had a scheduler where DeLM has none, and notes the new risk that stale or wrong natural-language findings can mislead peers. The hosts also say how they will judge the claimed accuracy and speed gains: whether the benchmarks are representative, whether the gaps exceed run-to-run noise, and how much of the speedup comes from the design rather than from idle baselines. Sources: 1. Decentralized Multi-Agent Systems with Shared Context — Yuzhen Mao, Jerry Gu, Aadi Chauhan, Qizheng Zhang, Hangoo Kang, Azalia Mirhoseini, 2026 http://arxiv.org/abs/2606.10662 2. The Hearsay-II Speech-Understanding System: Integrating Knowledge to Resolve Uncertainty — Lee D. Erman, Frederick Hayes-Roth, Victor R. Lesser, D. Raj Reddy, 1980 https://scholar.google.com/scholar?q=The+Hearsay-II+Speech-Understanding+System%3A+Integrating+Knowledge+to+Resolve+Uncertainty 3. Generative Communication in Linda — David Gelernter, 1985 https://scholar.google.com/scholar?q=Generative+Communication+in+Linda 4. Improving Factuality and Reasoning in Language Models through Multiagent Debate — Yilun Du, Shuang Li, Antonio Torralba, Joshua B. Tenenbaum, Igor Mordatch, 2023 https://scholar.google.com/scholar?q=Improving+Factuality+and+Reasoning+in+Language+Models+through+Multiagent+Debate 5. Exploring Advanced LLM Multi-Agent Systems Based on Blackboard Architecture — Bochen Han, Songmao Zhang, 2025 https://scholar.google.com/scholar?q=Exploring+Advanced+LLM+Multi-Agent+Systems+Based+on+Blackboard+Architecture 6. SWE-bench: Can Language Models Resolve Real-World GitHub Issues? — Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, Karthik Narasimhan, 2024 (ICLR; arXiv 2023) https://scholar.google.com/scholar?q=SWE-bench%3A+Can+Language+Models+Resolve+Real-World+GitHub+Issues%3F 7. SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering — John Yang, Carlos E. Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik Narasimhan, Ofir Press, 2024 https://scholar.google.com/scholar?q=SWE-agent%3A+Agent-Computer+Interfaces+Enable+Automated+Software+Engineering 8. Introducing SWE-bench Verified — OpenAI (Preparedness team) with human annotators, in collaboration with the SWE-bench authors, 2024 https://scholar.google.com/scholar?q=Introducing+SWE-bench+Verified 9. Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces — Mike A. Merrill, Alexander G. Shaw, and the Terminal-Bench team (Stanford and Laude Institute); the DeLM paper cites Merrill et al., 2026, 2025-2026 https://scholar.google.com/scholar?q=Terminal-Bench%3A+Benchmarking+Agents+on+Hard%2C+Realistic+Tasks+in+Command+Line+Interfaces 10. Large Language Monkeys: Scaling Inference Compute with Repeated Sampling — Bradley Brown, Jordan Juravsky, Ryan Ehrlich, Ronald Clark, Quoc V. Le, Christopher Ré, Azalia Mirhoseini, 2024 https://scholar.google.com/scholar?q=Large+Language+Monkeys%3A+Scaling+Inference+Compute+with+Repeated+Sampling 11. Scheduling Multithreaded Computations by Work Stealing — Robert D. Blumofe, Charles E. Leiserson, 1999 https://scholar.google.com/scholar?q=Scheduling+Multithreaded+Computations+by+Work+Stealing 12. GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism — Yanping Huang et al., 2019 https://scholar.google.com/scholar?q=GPipe%3A+Efficient+Training+of+Giant+Neural+Networks+using+Pipeline+Parallelism 13. AOrchestra — Ruan et al., 2026 https://scholar.google.com/scholar?q=AOrchestra 14. SPRINT: Enabling Interleaved Planning and Parallelized Execution in Reasoning Models — Emil Biju, Shayan Talaei, Zhemin Huang, Mohammadreza Pourreza, Azalia Mirhoseini, Amin Saberi, 2026 https://scholar.google.com/scholar?q=SPRINT%3A+Enabling+Interleaved+Planning+and+Parallelized+Execution+in+Reasoning+Models 15. ToolOrchestra — Su et al., 2025 https://scholar.google.com/scholar?q=ToolOrchestra 16. Recursive Language Models — Zhang et al., 2025 https://scholar.google.com/scholar?q=Recursive+Language+Models

  2. 23h ago

    MEMORILLA: Latent Semantic Memory Beats Text Retrieval for Frozen LLMs

    This episode examines MEMORILLA, a Stanford-led preprint in which a small trained module reads a whole store of pre-embedded documents and hands a frozen language model just 16 soft-token vectors, instead of pasting retrieved text into the prompt. It motivates the design with a personalization scenario. An old, unrelated chat reveals a preference for blunt feedback, so similarity search may miss it, and the signal is spread across many chats that top-k retrieval can't integrate. The discussion contrasts this approach with full-context prompting, agent memory systems (MemGPT, Mem0, MemoRAG, A-MEM), question-blind compression methods (ICAE, xRAG), and per-corpus cache training (Cartridges). It also traces the design's lineage to Perceiver, BLIP-2's Q-Former and prompt tuning. Listeners get a skeptical framing of the headline claim, including whether gains come from reading the store or from training on the benchmarks, and whether comparing against baselines that weren't trained on them is fair. The evaluation covers personalization, long-document QA, knowledge-intensive QA and a TextWorld agent. Sources: 1. Memorilla: Latent Semantic Memory for LLMs — Adibvafa Fallahpour, Parsa Idehpour, Vignesh Kothapalli, Nikita Mounier, Shirley Wu, Shayan Pardis, Jure Leskovec, 2026 http://arxiv.org/abs/2610.21375 2. MemGPT: Towards LLMs as Operating Systems — Charles Packer, Sarah Wooders, Kevin Lin, Vivian Fang, Shishir G. Patil, Ion Stoica, Joseph E. Gonzalez, 2023 https://scholar.google.com/scholar?q=MemGPT%3A+Towards+LLMs+as+Operating+Systems 3. 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 4. In-context Autoencoder for Context Compression in a Large Language Model (ICAE) — Tao Ge, Jing Hu, Lei Wang, Xun Wang, Si-Qing Chen, Furu Wei, 2024 (ICLR; arXiv 2023) https://scholar.google.com/scholar?q=In-context+Autoencoder+for+Context+Compression+in+a+Large+Language+Model+%28ICAE%29 5. xRAG: Extreme Context Compression for Retrieval-augmented Generation with One Token — Xin Cheng, Xun Wang, Xingxing Zhang, Tao Ge, Si-Qing Chen, Furu Wei, Huishuai Zhang, Dongyan Zhao, 2024 https://scholar.google.com/scholar?q=xRAG%3A+Extreme+Context+Compression+for+Retrieval-augmented+Generation+with+One+Token 6. Cartridges: Lightweight and General-Purpose Long Context Representations via Self-Study — Sabri Eyuboglu, Ryan Ehrlich, Simran Arora, Neel Guha, Dylan Zinsley, Emily Liu, William Tennien, Atri Rudra, James Zou, Azalia Mirhoseini, Christopher Ré, 2025 (cited in the Memorilla paper as 2026) https://scholar.google.com/scholar?q=Cartridges%3A+Lightweight+and+General-Purpose+Long+Context+Representations+via+Self-Study 7. BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models — Junnan Li, Dongxu Li, Silvio Savarese, Steven Hoi, 2023 https://scholar.google.com/scholar?q=BLIP-2%3A+Bootstrapping+Language-Image+Pre-training+with+Frozen+Image+Encoders+and+Large+Language+Models 8. Perceiver: General Perception with Iterative Attention — Andrew Jaegle, Felix Gimeno, Andrew Brock, Andrew Zisserman, Oriol Vinyals, Joao Carreira, 2021 https://scholar.google.com/scholar?q=Perceiver%3A+General+Perception+with+Iterative+Attention 9. The Power of Scale for Parameter-Efficient Prompt Tuning — Brian Lester, Rami Al-Rfou, Noah Constant, 2021 https://scholar.google.com/scholar?q=The+Power+of+Scale+for+Parameter-Efficient+Prompt+Tuning 10. PISCO: Pretty Simple Compression for Retrieval-Augmented Generation — Maxime Louis, Hervé Déjean, Stéphane Clinchant, 2025 https://scholar.google.com/scholar?q=PISCO%3A+Pretty+Simple+Compression+for+Retrieval-Augmented+Generation 11. CLaRa: Bridging Retrieval and Generation with Continuous Latent Reasoning — Jie He, Richard He Bai, Sinead Williamson, Jeff Z Pan, Navdeep Jaitly, Yizhe Zhang, 2025 https://scholar.google.com/scholar?q=CLaRa%3A+Bridging+Retrieval+and+Generation+with+Continuous+Latent+Reasoning 12. Lost in the Middle: How Language Models Use Long Contexts — Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, Percy Liang, 2024 https://scholar.google.com/scholar?q=Lost+in+the+Middle%3A+How+Language+Models+Use+Long+Contexts 13. Context Length Alone Hurts LLM Performance Despite Perfect Retrieval — Yufeng Du, Minyang Tian, Srikanth Ronanki, Subendhu Rongali, Sravan Bodapati, Aram Galstyan, Azton Wells, Roy Schwartz, Eliu A Huerta, Hao Peng, 2025 https://scholar.google.com/scholar?q=Context+Length+Alone+Hurts+LLM+Performance+Despite+Perfect+Retrieval 14. 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 15. Learning to Compress Prompts with Gist Tokens — Jesse Mu, Xiang Li, Noah Goodman, 2023 https://scholar.google.com/scholar?q=Learning+to+Compress+Prompts+with+Gist+Tokens 16. Memorizing Transformers — Yuhuai Wu, Markus N. Rabe, DeLesley Hutchins, Christian Szegedy, 2022 https://scholar.google.com/scholar?q=Memorizing+Transformers

  3. 23h ago

    Multi-Agent Transactive Memory: Sharing Agent Trajectories Across a Population

    This episode explores Multi-Agent Transactive Memory, a paper asking whether many agents can dump their raw task trajectories into one shared, searchable repository and let other frozen models retrieve from it. That would improve success rates and cut step counts without any joint training or coordination. It builds up from retrieval-augmented generation and single-agent experience memory (Reflexion, Synapse, ExpeL) to the shared-pool idea, borrowing the name from Wegner's group-psychology notion of transactive memory. The discussion stresses that the analogy is loose: the system is closer to a Stack Overflow for agents than to a team, and the trajectories were never written to be read. It also covers the retrieval design, where the key is the task description plus the last five steps embedded with E5-Base and the stored value is the next five steps from a past run. The pool is seeded with public trajectories from models like GPT-4-Turbo, Claude 3.5 Sonnet, Qwen3-32B and GPT-OSS 20B, then grows as agents work. Listeners get a skeptical look at the ALFWorld and WebArena testbeds, including how small and templated they are, and at the missing private-versus-shared memory control that the paper's central claim needs. Sources: 1. Multi-Agent Transactive Memory — To Eun Kim, Xuhong He, Dishank Jain, Ambuj Agrawal, Negar Arabzadeh, Fernando Diaz, 2026 http://arxiv.org/abs/2606.19911 2. The Transactive Memory System in Relationships / Cognitive Interdependence in Close Relationships (Transactive Memory: A Contemporary Analysis of the Group Mind) — Daniel M. Wegner, 1985/1987 https://scholar.google.com/scholar?q=The+Transactive+Memory+System+in+Relationships+%2F+Cognitive+Interdependence+in+Close+Relationships+%28Transactive+Memory%3A+A+Contemporary+Analysis+of+the+Group+Mind%29 3. Generative Agents: Interactive Simulacra of Human Behavior — Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, Michael S. Bernstein, 2023 https://scholar.google.com/scholar?q=Generative+Agents%3A+Interactive+Simulacra+of+Human+Behavior 4. Voyager: An Open-Ended Embodied Agent with Large Language Models — Guanzhi Wang, Yuqi Xie, Yunfan Jiang, et al. (Anandkumar, Fan), 2023 https://scholar.google.com/scholar?q=Voyager%3A+An+Open-Ended+Embodied+Agent+with+Large+Language+Models 5. Synapse: Trajectory-as-Exemplar Prompting with Memory for Computer Control — Longtao Zheng, Rundong Wang, Xinrun Wang, Bo An, 2024 (ICLR) https://scholar.google.com/scholar?q=Synapse%3A+Trajectory-as-Exemplar+Prompting+with+Memory+for+Computer+Control 6. Reflexion: Language Agents with Verbal Reinforcement Learning — Noah Shinn, Federico Cassano, Edward Berman, Ashwin Gopinath, Karthik Narasimhan, Shunyu Yao, 2023 https://scholar.google.com/scholar?q=Reflexion%3A+Language+Agents+with+Verbal+Reinforcement+Learning 7. Agent Workflow Memory — Zora Zhiruo Wang, Jiayuan Mao, Daniel Fried, Graham Neubig, 2024 https://scholar.google.com/scholar?q=Agent+Workflow+Memory 8. ExpeL: LLM Agents Are Experiential Learners — Andrew Zhao, Daniel Huang, Quentin Xu, Matthieu Lin, Yong-Jin Liu, Gao Huang, 2024 https://scholar.google.com/scholar?q=ExpeL%3A+LLM+Agents+Are+Experiential+Learners 9. Retrieval-Enhanced Machine Learning — Hamed Zamani, Fernando Diaz, Mostafa Dehghani, Donald Metzler, Michael Bendersky, 2022 https://scholar.google.com/scholar?q=Retrieval-Enhanced+Machine+Learning 10. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks — Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Douwe Kiela, et al., 2020 https://scholar.google.com/scholar?q=Retrieval-Augmented+Generation+for+Knowledge-Intensive+NLP+Tasks 11. Agent KB: Leveraging Cross-Domain Experience for Agentic Problem Solving — Xiangru Tang et al., 2025 https://scholar.google.com/scholar?q=Agent+KB%3A+Leveraging+Cross-Domain+Experience+for+Agentic+Problem+Solving 12. Memp: Exploring Agent Procedural Memory — Runnan Fang et al., 2025 https://scholar.google.com/scholar?q=Memp%3A+Exploring+Agent+Procedural+Memory 13. AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases — Zhaorun Chen, Zhen Xiang, Chaowei Xiao, Dawn Song, Bo Li, 2024 https://scholar.google.com/scholar?q=AgentPoison%3A+Red-teaming+LLM+Agents+via+Poisoning+Memory+or+Knowledge+Bases 14. PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models — Wei Zou, Runpeng Geng, Binghui Wang, Jinyuan Jia, 2025 https://scholar.google.com/scholar?q=PoisonedRAG%3A+Knowledge+Corruption+Attacks+to+Retrieval-Augmented+Generation+of+Large+Language+Models 15. Evaluating Retrieval Quality in Retrieval-Augmented Generation (eRAG) — Alireza Salemi, Hamed Zamani, 2024 https://scholar.google.com/scholar?q=Evaluating+Retrieval+Quality+in+Retrieval-Augmented+Generation+%28eRAG%29 16. Retrieval-Augmented Reinforcement Learning — Anirudh Goyal et al., 2022 https://scholar.google.com/scholar?q=Retrieval-Augmented+Reinforcement+Learning 17. Many-Shot In-Context Learning — Rishabh Agarwal et al., 2024 https://scholar.google.com/scholar?q=Many-Shot+In-Context+Learning 18. Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory — Mirac Suzgun, Mert Yuksekgonul, Federico Bianchi, Dan Jurafsky, James Zou, 2025 https://scholar.google.com/scholar?q=Dynamic+Cheatsheet%3A+Test-Time+Learning+with+Adaptive+Memory

  4. 23h ago

    PASTA: Steering LLM Attention Toward Highlighted Prompt Text

    This episode examines PASTA, a method that lets users highlight part of a prompt and has the model attend to it more strongly, without changing any weights. It covers why markup like asterisks may not work as emphasis for LLMs. It then places PASTA against instruction tuning, activation steering (ActAdd, CAA, Inference-Time Intervention) and knowledge editing (ROME, MEMIT, REMEDI). The key difference is that PASTA changes where selected attention heads read, rather than what they output. The discussion explains the mechanism: scores outside the highlighted span are scaled by a small alpha (0.01) and renormalized, which is equivalent to a roughly hundredfold relative boost on the span. The hosts also raise questions about head selection and evaluation, including the absence of comparisons against the neighboring steering methods. Listeners interested in controllability, long-context failures and conflicts between a prompt and a model's memorized knowledge will find it useful. Sources: 1. Tell Your Model Where to Attend: Post-hoc Attention Steering for LLMs — Qingru Zhang, Chandan Singh, Liyuan Liu, Xiaodong Liu, Bin Yu, Jianfeng Gao, Tuo Zhao, 2023 http://arxiv.org/abs/2311.02262 2. Tell Your Model Where to Attend: Post-hoc Attention Steering for LLMs — Qingru Zhang, Chandan Singh, Liyuan Liu, Xiaodong Liu, Bin Yu, Jianfeng Gao, Tuo Zhao, 2023 (arXiv 2311.02262); ICLR 2024 https://scholar.google.com/scholar?q=Tell+Your+Model+Where+to+Attend%3A+Post-hoc+Attention+Steering+for+LLMs 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. Efficient Streaming Language Models with Attention Sinks — Guangxuan Xiao, Yuandong Tian, Beidi Chen, Song Han, Mike Lewis, 2023 (ICLR 2024) https://scholar.google.com/scholar?q=Efficient+Streaming+Language+Models+with+Attention+Sinks 5. Lost in the Middle: How Language Models Use Long Contexts — Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, Percy Liang, 2023 (TACL 2024) https://scholar.google.com/scholar?q=Lost+in+the+Middle%3A+How+Language+Models+Use+Long+Contexts 6. Inference-Time Intervention: Eliciting Truthful Answers from a Language Model — Kenneth Li, Oam Patel, Fernanda Viégas, Hanspeter Pfister, Martin Wattenberg, 2023 (NeurIPS 2023) https://scholar.google.com/scholar?q=Inference-Time+Intervention%3A+Eliciting+Truthful+Answers+from+a+Language+Model 7. Steering Llama 2 via Contrastive Activation Addition — Nina Rimsky, Nick Gabrieli, Julian Schulz, Meg Tong, Evan Hubinger, Alexander Turner, 2023 https://scholar.google.com/scholar?q=Steering+Llama+2+via+Contrastive+Activation+Addition 8. Activation Addition: Steering Language Models Without Optimization — Alexander Matt Turner, Lisa Thiergart, David Udell, Gavin Leech, Ulisse Mini, Monte MacDiarmid, 2023 https://scholar.google.com/scholar?q=Activation+Addition%3A+Steering+Language+Models+Without+Optimization 9. Trusting Your Evidence: Hallucinate Less with Context-aware Decoding — Weijia Shi, Xiaochuang Han, Mike Lewis, Yulia Tsvetkov, Luke Zettlemoyer, Scott Wen-tau Yih, 2023 https://scholar.google.com/scholar?q=Trusting+Your+Evidence%3A+Hallucinate+Less+with+Context-aware+Decoding 10. Retrieval Head Mechanistically Explains Long-Context Factuality — Wenhao Wu, Yizhong Wang, Guangxuan Xiao, Hao Peng, Yao Fu, 2024 https://scholar.google.com/scholar?q=Retrieval+Head+Mechanistically+Explains+Long-Context+Factuality 11. Model Tells You Where to Merge / AutoPASTA: Model Tells You What to Discard (Adaptive KV cache compression) — Various (follow-up and adjacent work, see AutoPASTA by Zhuang et al. 2024), 2024 https://scholar.google.com/scholar?q=Model+Tells+You+Where+to+Merge+%2F+AutoPASTA%3A+Model+Tells+You+What+to+Discard+%28Adaptive+KV+cache+compression%29 12. Spotlight Your Instructions: Instruction-following with Dynamic Attention Steering (SPA) — Praveen Venkateswaran, Danish Contractor, 2025 https://scholar.google.com/scholar?q=Spotlight+Your+Instructions%3A+Instruction-following+with+Dynamic+Attention+Steering+%28SPA%29 13. 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 14. Attention is not Explanation / Attention is not not Explanation — Sarthak Jain and Byron Wallace; Sarah Wiegreffe and Yuval Pinter, 2019 https://scholar.google.com/scholar?q=Attention+is+not+Explanation+%2F+Attention+is+not+not+Explanation 15. Are Sixteen Heads Really Better than One? / Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting — Paul Michel, Omer Levy, Graham Neubig; Elena Voita et al., 2019 https://scholar.google.com/scholar?q=Are+Sixteen+Heads+Really+Better+than+One%3F+%2F+Analyzing+Multi-Head+Self-Attention%3A+Specialized+Heads+Do+the+Heavy+Lifting Interactive Visualization: PASTA: Steering LLM Attention Toward Highlighted Prompt Text

  5. 2d ago

    Claude 3.5 Haiku Counts Characters on Curved Feature Manifolds

    This episode explores how Claude 3.5 Haiku decides when a hard-wrapped line of text is full, even though it sees only token IDs and is never told the line width. It covers the paper's finding that the model tracks the character count on a curved, roughly six-dimensional manifold. That manifold is tiled by about ten sparse features with widening tuning curves, a bit like place cells. The discussion connects this to earlier work on circular and helical feature geometry and to the rotary position encoding used in transformers, and it explains attribution graphs and sparse dictionary features along the way. It also covers the experimental setup: newlines reinserted at known widths from 15 to 150 characters. Zero-ablating the character-count subspace raises loss mainly on newline tokens, which gives causal evidence that the model uses this subspace and doesn't just encode the count somewhere a probe can find it. Listeners interested in mechanistic interpretability will find a concrete case of a model implementing a continuous quantity as geometry, not as one direction per concept. Sources: 1. When Models Manipulate Manifolds: The Geometry of a Counting Task — Wes Gurnee, Emmanuel Ameisen, Isaac Kauvar, Julius Tarng, Adam Pearce, Chris Olah, Joshua Batson, 2026 http://arxiv.org/abs/2601.04480 2. Toy Models of Superposition — Nelson Elhage, Tristan Hume, Catherine Olsson, et al. (Anthropic), 2022 https://scholar.google.com/scholar?q=Toy+Models+of+Superposition 3. Not All Language Model Features Are Linear — Joshua Engels, Eric J. Michaud, Isaac Liao, Wes Gurnee, Max Tegmark, 2024 https://scholar.google.com/scholar?q=Not+All+Language+Model+Features+Are+Linear 4. Towards Monosemanticity: Decomposing Language Models With Dictionary Learning — Trenton Bricken, Adly Templeton, Joshua Batson, et al. (Anthropic), 2023 https://scholar.google.com/scholar?q=Towards+Monosemanticity%3A+Decomposing+Language+Models+With+Dictionary+Learning 5. Language Models Use Trigonometry to Do Addition (and related work on helical number representations) — Subhash Kantamneni, Max Tegmark, 2025 https://scholar.google.com/scholar?q=Language+Models+Use+Trigonometry+to+Do+Addition+%28and+related+work+on+helical+number+representations%29 6. Circuit Tracing: Revealing Computational Graphs in Language Models — Emmanuel Ameisen, Jack Lindsey, Adam Pearce, Wes Gurnee, Nicholas Turner, Brian Chen, Craig Citro, et al. (Anthropic), 2025 https://scholar.google.com/scholar?q=Circuit+Tracing%3A+Revealing+Computational+Graphs+in+Language+Models 7. On the Biology of a Large Language Model — Jack Lindsey, Wes Gurnee, Emmanuel Ameisen, Brian Chen, Adam Pearce, et al. (Anthropic), 2025 https://scholar.google.com/scholar?q=On+the+Biology+of+a+Large+Language+Model 8. Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small — Kevin Wang, Alexandre Variengien, Arthur Conmy, Buck Shlegeris, Jacob Steinhardt, 2022 https://scholar.google.com/scholar?q=Interpretability+in+the+Wild%3A+a+Circuit+for+Indirect+Object+Identification+in+GPT-2+small 9. A Mathematical Framework for Transformer Circuits — Nelson Elhage, Neel Nanda, Catherine Olsson, et al. (Anthropic), 2021 https://scholar.google.com/scholar?q=A+Mathematical+Framework+for+Transformer+Circuits 10. RoFormer: Enhanced Transformer with Rotary Position Embedding — Jianlin Su, Yu Lu, Shengfeng Pan, Ahmed Murtadha, Bo Wen, Yunfeng Liu, 2021 https://scholar.google.com/scholar?q=RoFormer%3A+Enhanced+Transformer+with+Rotary+Position+Embedding 11. Quanta of model skills: The Quantization Model of Neural Scaling — Eric J. Michaud, Ziming Liu, Uzay Girit, Max Tegmark, 2023 https://scholar.google.com/scholar?q=Quanta+of+model+skills%3A+The+Quantization+Model+of+Neural+Scaling 12. Space and Time Representations in Language Models (Language Models Represent Space and Time) — Wes Gurnee, Max Tegmark, 2023 https://scholar.google.com/scholar?q=Space+and+Time+Representations+in+Language+Models+%28Language+Models+Represent+Space+and+Time%29 13. Grid cells / place cells literature: The Hippocampus as a Cognitive Map and Microstructure of a spatial map in the entorhinal cortex — John O'Keefe and Lynn Nadel (1978); Torkel Hafting, Marianne Fyhn, Sturla Molden, May-Britt Moser, Edvard Moser (2005), 1978 / 2005 https://scholar.google.com/scholar?q=Grid+cells+%2F+place+cells+literature%3A+The+Hippocampus+as+a+Cognitive+Map+and+Microstructure+of+a+spatial+map+in+the+entorhinal+cortex 14. Not All Language Model Features Are One-Dimensionally Linear — Joshua Engels, Eric J. Michaud, Isaac Liao, Wes Gurnee, Max Tegmark, 2025 https://scholar.google.com/scholar?q=Not+All+Language+Model+Features+Are+One-Dimensionally+Linear 15. The Origins of Representation Manifolds in Large Language Models — Alexander Modell, Patrick Rubin-Delanchy, Nick Whiteley, 2025 https://scholar.google.com/scholar?q=The+Origins+of+Representation+Manifolds+in+Large+Language+Models 16. Language Models Use Trigonometry to Do Addition — Subhash Kantamneni, Max Tegmark, 2025 https://scholar.google.com/scholar?q=Language+Models+Use+Trigonometry+to+Do+Addition 17. Progress Measures for Grokking via Mechanistic Interpretability — Neel Nanda, Lawrence Chan, Tom Lieberum, Jess Smith, Jacob Steinhardt, 2023 https://scholar.google.com/scholar?q=Progress+Measures+for+Grokking+via+Mechanistic+Interpretability 18. Understanding Sparse Autoencoder Scaling in the Presence of Feature Manifolds — Eric J. Michaud, Liv Gorton, Tom McGrath, 2025 https://scholar.google.com/scholar?q=Understanding+Sparse+Autoencoder+Scaling+in+the+Presence+of+Feature+Manifolds 19. Curve Detectors / Curve Detector Manifolds in InceptionV1 — Nick Cammarata et al.; Liv Gorton, 2020 / 2024 https://scholar.google.com/scholar?q=Curve+Detectors+%2F+Curve+Detector+Manifolds+in+InceptionV1 20. Tracing Attention Computation Through Feature Interactions (QK attributions) — Harish Kamath, Emmanuel Ameisen, Isaac Kauvar, et al., 2025 https://scholar.google.com/scholar?q=Tracing+Attention+Computation+Through+Feature+Interactions+%28QK+attributions%29 21. Language Models Need Inductive Biases to Count Inductively — Yingshan Chang, Yonatan Bisk, 2024 https://scholar.google.com/scholar?q=Language+Models+Need+Inductive+Biases+to+Count+Inductively 22. Length Representations in Large Language Models — Sangjun Moon et al., 2025 https://scholar.google.com/scholar?q=Length+Representations+in+Large+Language+Models 23. Arithmetic Without Algorithms: Language Models Solve Math with a Bag of Heuristics — Yaniv Nikankin, Anja Reusch, Aaron Mueller, Yonatan Belinkov, 2024 https://scholar.google.com/scholar?q=Arithmetic+Without+Algorithms%3A+Language+Models+Solve+Math+with+a+Bag+of+Heuristics 24. Understanding In-Context Learning of Addition via Activation Subspaces — Xinyan Hu, Kayo Yin, Michael I. Jordan, Jacob Steinhardt, Lijie Chen, 2025 https://scholar.google.com/scholar?q=Understanding+In-Context+Learning+of+Addition+via+Activation+Subspaces 25. Projecting Assumptions: The Duality Between Sparse Autoencoders and Concept Geometry — Sai Sumedh R. Hindupur, Ekdeep Singh Lubana, Thomas Fel, Demba Ba, 2025 https://scholar.google.com/scholar?q=Projecting+Assumptions%3A+The+Duality+Between+Sparse+Autoencoders+and+Concept+Geometry 26. Language Models Represent Space and Time — Wes Gurnee, Max Tegmark, 2024 https://scholar.google.com/scholar?q=Language+Models+Represent+Space+and+Time 27. The Quantization Model of Neural Scaling — Eric J. Michaud, Ziming Liu, Uzay Girit, Max Tegmark, 2023 https://scholar.google.com/scholar?q=The+Quantization+Model+of+Neural+Scaling Interactive Visualization: Claude 3.5 Haiku Counts Characters on Curved Feature Manifolds

  6. 2d ago

    Harness Learning: Training Small Models to Rewrite Code Around Frozen Solvers

    This episode explores a paper on harness learning, in which a small proposer model is trained with reinforcement learning to rewrite the code wrapped around a frozen solver. The aim is for the system to adapt to unseen tasks with no weight updates. It defines a harness as the program that controls what the model sees, which tools it uses, and how outputs are validated and retried. It contrasts the approach with search-based optimizers such as DSPy, where the optimizer is just a prompted model. The discussion frames the method as loose meta-learning, with code edits standing in for gradient steps in the style of MAML and RL-squared. It also covers GRPO training on verifiable rewards and a Reasoning Gym testbed with held-out puzzle families. Listeners get a clear account of the paper's three questions: whether learned revisions generalize, whether repeated revision helps, and whether training on whole revision sequences beats single edits. The episode also sets up two critiques, including "Harness Updating Is Not Harness Benefit," as lenses for judging the evidence. Sources: 1. Harness Learning Enables Generalizable Test-Time Adaptation — Alvin Zhang, Xuecheng Liu, Zixuan Wang, Fahim Tajwar, Daman Arora, Ruslan Salakhutdinov, Daniel Khashabi, Yuda Song, Andrea Zanette, 2026 http://arxiv.org/abs/2609.35738 2. DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines — Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, Zhiyuan Zhang, Keshav Santhanam, Sri Vardhamanan, Saiful Haq, Ashutosh Sharma, Thomas T. Joshi, Hanna Moazam, Heather Miller, Matei Zaharia, Christopher Potts, 2023 (ICLR 2024) https://scholar.google.com/scholar?q=DSPy%3A+Compiling+Declarative+Language+Model+Calls+into+Self-Improving+Pipelines 3. SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering — John Yang, Carlos E. Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik Narasimhan, Ofir Press, 2024 https://scholar.google.com/scholar?q=SWE-agent%3A+Agent-Computer+Interfaces+Enable+Automated+Software+Engineering 4. Automated Design of Agentic Systems (ADAS) — Shengran Hu, Cong Lu, Jeff Clune, 2024 https://scholar.google.com/scholar?q=Automated+Design+of+Agentic+Systems+%28ADAS%29 5. Reflexion: Language Agents with Verbal Reinforcement Learning — Noah Shinn, Federico Cassano, Edward Berman, Ashwin Gopinath, Karthik Narasimhan, Shunyu Yao, 2023 https://scholar.google.com/scholar?q=Reflexion%3A+Language+Agents+with+Verbal+Reinforcement+Learning 6. Reasoning Gym: Reasoning Environments for Reinforcement Learning with Verifiable Rewards — Zafir Stojanovski, Oliver Stanley, Joe Sharratt, Richard Jones, Abdulhakeem Adefioye, Jean Kaddour, Andreas Köpf, 2025 https://scholar.google.com/scholar?q=Reasoning+Gym%3A+Reasoning+Environments+for+Reinforcement+Learning+with+Verifiable+Rewards 7. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks — Chelsea Finn, Pieter Abbeel, Sergey Levine, 2017 https://scholar.google.com/scholar?q=Model-Agnostic+Meta-Learning+for+Fast+Adaptation+of+Deep+Networks 8. RL²: Fast Reinforcement Learning via Slow Reinforcement Learning — Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever, Pieter Abbeel, 2016 https://scholar.google.com/scholar?q=RL%C2%B2%3A+Fast+Reinforcement+Learning+via+Slow+Reinforcement+Learning 9. Learning to Learn by Gradient Descent by Gradient Descent — Marcin Andrychowicz, Misha Denil, Sergio Gómez Colmenarejo, Matthew W. Hoffman, David Pfau, Tom Schaul, Matthew Botvinick, Nando de Freitas, 2016 https://scholar.google.com/scholar?q=Learning+to+Learn+by+Gradient+Descent+by+Gradient+Descent 10. Mathematical Discoveries from Program Search with Large Language Models (FunSearch) — Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Matej Balog, M. Pawan Kumar, Emilien Dupont, Francisco J. R. Ruiz, Jordan S. Ellenberg, Pengming Wang, Omar Fawzi, Pushmeet Kohli, Alhussein Fawzi, 2023 (Nature 2023/2024) https://scholar.google.com/scholar?q=Mathematical+Discoveries+from+Program+Search+with+Large+Language+Models+%28FunSearch%29 11. Harness-R1: Learning to Edit Executable Runtime Harnesses from Agent Failure Trajectories — Shao et al., 2026 https://scholar.google.com/scholar?q=Harness-R1%3A+Learning+to+Edit+Executable+Runtime+Harnesses+from+Agent+Failure+Trajectories 12. Rethinking the Evaluation of Harness Evolution for Agents — Wang et al., 2026 https://scholar.google.com/scholar?q=Rethinking+the+Evaluation+of+Harness+Evolution+for+Agents 13. Harness Updating Is Not Harness Benefit: Disentangling Evolution Capabilities in Self-Evolving LLM Agents — Lin et al., 2026 https://scholar.google.com/scholar?q=Harness+Updating+Is+Not+Harness+Benefit%3A+Disentangling+Evolution+Capabilities+in+Self-Evolving+LLM+Agents 14. Do Agent Optimizers Compound? A Continual-Learning Evaluation on Terminal-Bench 2.0 — Wang, Kattakinda, Feizi, 2026 https://scholar.google.com/scholar?q=Do+Agent+Optimizers+Compound%3F+A+Continual-Learning+Evaluation+on+Terminal-Bench+2.0 15. Evo-Bench: Can Language Models Improve Agent Harness? — Huang et al., 2026 https://scholar.google.com/scholar?q=Evo-Bench%3A+Can+Language+Models+Improve+Agent+Harness%3F 16. Meta-Harness: End-to-End Optimization of Model Harnesses — Lee, Nair, Zhang, Lee, Khattab, Finn, 2026 https://scholar.google.com/scholar?q=Meta-Harness%3A+End-to-End+Optimization+of+Model+Harnesses 17. AutoHarness: improving LLM agents by automatically synthesizing a code harness — Lou, Lázaro-Gredilla, Dedieu, Wendelken, Lehrach, Murphy, 2026 https://scholar.google.com/scholar?q=AutoHarness%3A+improving+LLM+agents+by+automatically+synthesizing+a+code+harness 18. WHALE: A simple recipe for joint harness-weight optimization — Kim, Lee, Lee, Finn, Lee, 2026 https://scholar.google.com/scholar?q=WHALE%3A+A+simple+recipe+for+joint+harness-weight+optimization 19. Darwin Gödel Machine: Open-Ended Evolution of Self-Improving Agents — Zhang, Hu, Lu, Lange, Clune, 2025 https://scholar.google.com/scholar?q=Darwin+G%C3%B6del+Machine%3A+Open-Ended+Evolution+of+Self-Improving+Agents 20. Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation — Zelikman, Lorch, Mackey, Kalai, 2024 https://scholar.google.com/scholar?q=Self-Taught+Optimizer+%28STOP%29%3A+Recursively+Self-Improving+Code+Generation 21. Feedback Friction: LLMs Struggle to Fully Incorporate External Feedback — Jiang, Zhang, Wang, Andrews, Khashabi, 2025 https://scholar.google.com/scholar?q=Feedback+Friction%3A+LLMs+Struggle+to+Fully+Incorporate+External+Feedback 22. AdaptFlow: Adaptive Workflow Optimization via Meta-Learning — Zhu et al., 2025 https://scholar.google.com/scholar?q=AdaptFlow%3A+Adaptive+Workflow+Optimization+via+Meta-Learning 23. Training a Generally Curious Agent — Tajwar, Jiang, Thankaraj, Rahman, Kolter, Schneider, Salakhutdinov, 2025 https://scholar.google.com/scholar?q=Training+a+Generally+Curious+Agent 24. TTHE: Test-time harness evolution — Nie et al., 2026 https://scholar.google.com/scholar?q=TTHE%3A+Test-time+harness+evolution Interactive Visualization: Harness Learning: Training Small Models to Rewrite Code Around Frozen Solvers

  7. 2d ago

    Jev-Mem: Fast System-One Controllers for Agentic Memory

    This episode explores Jev-Mem, a design for agent memory that moves small management decisions, such as classifying a memory, linking it to others, and deciding when to stop searching, out of the language model's text generation and into a fast controller that outputs labels and scores. It walks through why agents need external memory despite long context windows (RULER's finding of shorter effective context, and Lost in the Middle). It traces the progression from plain retrieval through MemGPT-style paging to multi-graph memory, using a running example of a bicycle purchase that needs temporal, causal, and entity links to answer correctly. The discussion frames the "System One / System Two" split as an analogy for allocating compute rather than a claim about brains. It separates the approach from distilling reasoning into a single model, and sets up a three-layer architecture of control, memory, and reasoning planes. It also flags what to scrutinize: how much of the gain comes from the graph versus the controller, and that a closely related system is cited but not benchmarked. Listeners interested in cutting the token cost of agent memory will find the long-conversation benchmark setup and the typed yes/no query interface of the controller especially concrete. Sources: 1. Jev-Mem: System-One-Controlled Agentic Memory for Efficient AI Agents — Dongming Jiang, Yi Li, Bingzhe Li, 2026 http://arxiv.org/abs/2609.23986 2. Dual-Processing Accounts of Reasoning, Judgment, and Social Cognition — Jonathan St. B. T. Evans, 2008 https://scholar.google.com/scholar?q=Dual-Processing+Accounts+of+Reasoning%2C+Judgment%2C+and+Social+Cognition 3. 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 4. MemGPT: Towards LLMs as Operating Systems — Charles Packer, Sarah Wooders, Kevin Lin, Vivian Fang, Shishir G. Patil, Ion Stoica, Joseph E. Gonzalez, 2023 https://scholar.google.com/scholar?q=MemGPT%3A+Towards+LLMs+as+Operating+Systems 5. Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity — Soyeong Jeong, Jinheon Baek, Sukmin Cho, Sung Ju Hwang, Jong C. Park, 2024 https://scholar.google.com/scholar?q=Adaptive-RAG%3A+Learning+to+Adapt+Retrieval-Augmented+Large+Language+Models+through+Question+Complexity 6. Distilling System 2 into System 1 — Ping Yu, Jing Xu, Jason Weston, Ilia Kulikov, 2024 https://scholar.google.com/scholar?q=Distilling+System+2+into+System+1 7. Zero-Mem: Zero-token memory operations for LLM agents — Yilin Xiao et al., 2026 https://scholar.google.com/scholar?q=Zero-Mem%3A+Zero-token+memory+operations+for+LLM+agents 8. MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents — Dongming Jiang, Yi Li, Guanpeng Li, Bingzhe Li, 2026 https://scholar.google.com/scholar?q=MAGMA%3A+A+Multi-Graph+based+Agentic+Memory+Architecture+for+AI+Agents 9. HAGE: Harnessing Agentic Memory via RL-driven Weighted Graph Evolution — Dongming Jiang et al., 2026 https://scholar.google.com/scholar?q=HAGE%3A+Harnessing+Agentic+Memory+via+RL-driven+Weighted+Graph+Evolution 10. Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection — Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, Hannaneh Hajishirzi, 2023 https://scholar.google.com/scholar?q=Self-RAG%3A+Learning+to+Retrieve%2C+Generate%2C+and+Critique+through+Self-Reflection 11. Adaptive-RAG: Learning to Adapt Retrieval-Augmented LLMs through Question Complexity — Soyeong Jeong, Jinheon Baek, Sukmin Cho, Sung Ju Hwang, Jong C. Park, 2024 https://scholar.google.com/scholar?q=Adaptive-RAG%3A+Learning+to+Adapt+Retrieval-Augmented+LLMs+through+Question+Complexity 12. FrugalGPT and RouteLLM — Lingjiao Chen, Matei Zaharia, James Zou (FrugalGPT); Isaac Ong et al. (RouteLLM), 2024 / 2025 https://scholar.google.com/scholar?q=FrugalGPT+and+RouteLLM 13. Evaluating Very Long-Term Conversational Memory of LLM Agents (LoCoMo) — Adyasha Maharana et al., 2024 https://scholar.google.com/scholar?q=Evaluating+Very+Long-Term+Conversational+Memory+of+LLM+Agents+%28LoCoMo%29 14. LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory — Di Wu et al., 2024 https://scholar.google.com/scholar?q=LongMemEval%3A+Benchmarking+Chat+Assistants+on+Long-Term+Interactive+Memory 15. HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models — Bernal Jimenez Gutierrez et al., 2024 https://scholar.google.com/scholar?q=HippoRAG%3A+Neurobiologically+Inspired+Long-Term+Memory+for+Large+Language+Models Interactive Visualization: Jev-Mem: Fast System-One Controllers for Agentic Memory

  8. 3d ago

    Cogentic: Multi-Agent Harness Discovers Proofs for Open Problems

    This episode examines "Cogentic," a multi-agent harness that reportedly produced new, expert-verified proofs for five open problems in online learning, auction theory, and mechanism design, using about a hundred model calls on most problems and about a thousand on the hardest. It places the work among three approaches: formal theorem provers like Lean, search-for-objects systems like FunSearch and AlphaEvolve that need a computable score, and inference-time scaling, where coverage only becomes an answer if something reliably picks the right candidate. The discussion walks through the harness design. An orchestrator assigns directions, parallel prover agents draft proofs, per-prover summarizers give each a different reading of the history, and adversarial per-draft and cross-draft verifiers must both pass a result. It also raises open questions: the verifiers are prompted copies of the same model family as the provers, nothing is machine-checked, the models are proprietary, and the number of failed attempts behind the five wins and the cost are unclear. A listener would find it useful for judging whether the harness itself, or just a strong model with patience, deserves the credit. Sources: 1. Cogentic: Multi-Agent Orchestration for Automated Proof Discovery — Yang Cai, Vineet Gupta, Yanchen Jiang, Christopher Liaw, Aranyak Mehta, Grigoris Velegkas, Di Wang, 2026 http://arxiv.org/abs/2609.40324 2. Mathematical discoveries from program search with large language models (FunSearch) — Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, et al., 2024 https://scholar.google.com/scholar?q=Mathematical+discoveries+from+program+search+with+large+language+models+%28FunSearch%29 3. AlphaEvolve: A coding agent for scientific and algorithmic discovery — Alexander Novikov, Ngân Vũ, Marvin Eisenberger, et al., 2025 https://scholar.google.com/scholar?q=AlphaEvolve%3A+A+coding+agent+for+scientific+and+algorithmic+discovery 4. LeanDojo: Theorem Proving with Retrieval-Augmented Language Models — Kaiyu Yang, Aidan Swope, Alex Gu, Rahul Chalamala, et al., 2023 https://scholar.google.com/scholar?q=LeanDojo%3A+Theorem+Proving+with+Retrieval-Augmented+Language+Models 5. Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs — Albert Q. Jiang, Sean Welleck, Jin Peng Zhou, et al., 2022 https://scholar.google.com/scholar?q=Draft%2C+Sketch%2C+and+Prove%3A+Guiding+Formal+Theorem+Provers+with+Informal+Proofs 6. Large Language Monkeys: Scaling Inference Compute with Repeated Sampling — Bradley Brown, Jordan Juravsky, Ryan Ehrlich, et al., 2024 https://scholar.google.com/scholar?q=Large+Language+Monkeys%3A+Scaling+Inference+Compute+with+Repeated+Sampling 7. 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 8. Improving Factuality and Reasoning in Language Models through Multiagent Debate — Yilun Du, Shuang Li, Antonio Torralba, Joshua B. Tenenbaum, Igor Mordatch, 2024 https://scholar.google.com/scholar?q=Improving+Factuality+and+Reasoning+in+Language+Models+through+Multiagent+Debate 9. Mathematical discoveries from program search with large language models — Romera-Paredes et al., 2024 https://scholar.google.com/scholar?q=Mathematical+discoveries+from+program+search+with+large+language+models 10. Let's Verify Step by Step — Hunter Lightman, Vineet Kosaraju, Yura Burda, et al., 2023 https://scholar.google.com/scholar?q=Let%27s+Verify+Step+by+Step 11. Training Verifiers to Solve Math Word Problems — Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, et al., 2021 https://scholar.google.com/scholar?q=Training+Verifiers+to+Solve+Math+Word+Problems 12. Large Language Models Cannot Self-Correct Reasoning Yet — Jie Huang, Xinyun Chen, Swaroop Mishra, et al., 2024 https://scholar.google.com/scholar?q=Large+Language+Models+Cannot+Self-Correct+Reasoning+Yet 13. AlphaProof / Olympiad-level formal mathematical reasoning with reinforcement learning — Thomas Hubert et al. (Google DeepMind), 2026 https://scholar.google.com/scholar?q=AlphaProof+%2F+Olympiad-level+formal+mathematical+reasoning+with+reinforcement+learning 14. Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena — Zheng et al., 2023 https://scholar.google.com/scholar?q=Judging+LLM-as-a-Judge+with+MT-Bench+and+Chatbot+Arena 15. Mathematical exploration and discovery at scale — Georgiev, Gómez-Serrano, Tao, Wagner, 2025 https://scholar.google.com/scholar?q=Mathematical+exploration+and+discovery+at+scale 16. Stellar Colosseum; ProofCouncil; AI co-mathematician; Towards autonomous mathematics research — Lin et al.; Schmitt et al.; Zheng et al.; Feng et al., 2026 https://scholar.google.com/scholar?q=Stellar+Colosseum%3B+ProofCouncil%3B+AI+co-mathematician%3B+Towards+autonomous+mathematics+research 17. Multiscale reward hedging from correct demonstrations / Tight regret bound for online inverse linear optimization via multiscale matrix weights — Dewasurendra; Sakaue, 2026 https://scholar.google.com/scholar?q=Multiscale+reward+hedging+from+correct+demonstrations+%2F+Tight+regret+bound+for+online+inverse+linear+optimization+via+multiscale+matrix+weights 18. Agent-as-a-Judge: Evaluate Agents with Agents — Zhuge et al., 2024 https://scholar.google.com/scholar?q=Agent-as-a-Judge%3A+Evaluate+Agents+with+Agents Interactive Visualization: Cogentic: Multi-Agent Harness Discovers Proofs for Open Problems

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AI-generated podcast where hosts Hal Turing and Dr. Ada Shannon discuss the latest research papers and reports in machine learning, AI systems, and optimization. Featuring honest critical analysis, proper citations, and nerdy humor.

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