Sakana AI

Robert Lange, founding researcher at Sakana AI, joins Tim to discuss *Shinka Evolve* — a framework that combines LLMs with evolutionary algorithms to do open-ended program search. The core claim: systems like AlphaEvolve can optimize solutions to fixed problems, but real scientific progress requires co-evolving the problems themselves. GTC is coming, the premier AI conference, great opportunity to learn about AI. NVIDIA and partners will showcase breakthroughs in physical AI, AI factories, agentic AI, and inference, exploring the next wave of AI innovation for developers and researchers. Register for virtual GTC for free, using my link and win NVIDIA DGX Spark (https://nvda.ws/4qQ0LMg) • Why AlphaEvolve gets stuck — it needs a human to hand it the right problem. Shinka tries to invent new problems automatically, drawing on ideas from POET, PowerPlay, and MAP-Elites quality-diversity search. • The *architecture* of Shinka: an archive of programs organized as islands, LLMs used as mutation operators, and a UCB bandit that adaptively selects between frontier models (GPT-5, Sonnet 4.5, Gemini) mid-run. The credit-assignment problem across models turns out to be genuinely hard. • Concrete results — state-of-the-art circle packing with dramatically fewer evaluations, second place in an AtCoder competitive programming challenge, evolved load-balancing loss functions for mixture-of-experts models, and agent scaffolds for AIME math benchmarks. • Are these systems actually thinking outside the box, or are they parasitic on their starting conditions? When LLMs run autonomously, "nothing interesting happens." Robert pushes back with the stepping-stone argument — evolution doesn't need to extrapolate, just recombine usefully. • The AI Scientist question: can automated research pipelines produce real science, or just workshop-level slop that passes surface-level review? Robert is honest that the current version is more co-pilot than autonomous researcher. • Where this lands in 5-20 years — Robert's prediction that scientific research will be fundamentally transformed, and Tim's thought experiment about alien mathematical artifacts that no human could have conceived. Robert Lange: https://roberttlange.com/ --- TIMESTAMPS: 00:00:00 Introduction: Robert Lange, Sakana AI and Shinka Evolve 00:04:15 AlphaEvolve's Blind Spot: Co-Evolving Problems with Solutions 00:09:05 Unknown Unknowns, POET, and Auto-Curricula for AI Science 00:14:20 MAP-Elites and Quality-Diversity: Shinka's Evolutionary Architecture 00:28:00 UCB Bandits, Mutations and the Vibe Research Vision 00:40:00 Scaling Shinka: Meta-Evolution, Democratisation and the Three-Axis Model 00:47:10 Applications, ARC-AGI and the Future of Work 00:57:00 The AI Scientist and the Human Co-Pilot: Who Steers the Search? 01:06:00 AI Scientist v2, Slop Critique and the Future of Scientific Publishing --- REFERENCES: paper: [00:03:30] ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolution https://arxiv.org/abs/2509.19349 [00:04:15] AlphaEvolve: A Coding Agent for Scientific and Algorithmic Discovery https://arxiv.org/abs/2506.13131 [00:06:30] Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents https://arxiv.org/abs/2505.22954 [00:09:05] Paired Open-Ended Trailblazer (POET) https://arxiv.org/abs/1901.01753 [00:10:00] PowerPlay: Training an Increasingly General Problem Solver by Continually Searching for the Simplest Still Unsolvable Problem https://arxiv.org/abs/1112.5309 [00:10:40] Automated Capability Discovery via Foundation Model Self-Exploration https://arxiv.org/abs/2502.07577 [00:15:30] Illuminating Search Spaces by Mapping Elites (MAP-Elites) https://arxiv.org/abs/1504.04909 [00:47:10] Automated Design of Agentic Systems (ADAS) https://arxiv.org/abs/2408.08435 <trunc, see ReScript/YT> PDF : https://app.rescript.info/api/sessions/b8a9dcf60623657c/pdf/download Transcript: https://app.rescript.info/public/share/SDOD_3oXOcli3zTqcAtR8eibT5U3gam84oo4KRtI-Vk

Robert Lange

Founding Researcher

Sakana AITechnical University Berlin

This episode is sponsored by tastytrade. Trade stocks, options, futures, and crypto in one platform with low commissions and zero commission on stocks and crypto. Built for traders who think in probabilities, tastytrade offers advanced analytics, risk tools, and an AI-powered Search feature. Learn more at https://tastytrade.com/ Artificial intelligence is reaching a turning point. Instead of building bigger and bigger models, what if the real breakthrough comes from letting AI evolve? In this episode of Eye on AI, David Ha, Co-Founder and CEO of Sakana AI, explains why evolutionary strategies and collective intelligence could reshape the future of machine learning. We explore model merging, multi-agent systems, Monte Carlo tree search, and the AI Scientist framework designed to generate and evaluate new research ideas. The conversation dives into open-ended discovery, quality and diversity in AI systems, world models, and whether artificial intelligence can push beyond the boundaries of human knowledge. If you're interested in AGI, evolutionary AI, frontier models, AI research automation, or how AI could start discovering science on its own, this episode offers a clear look at where the field may be heading next. Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI (00:00) AI Should Evolve, Not Just Scale (03:54) David's Journey From Finance to Evolutionary AI (10:18) Why Gradient Descent Gets Stuck (18:12) Model Merging and Collective Intelligence (28:18) Combining Closed Frontier Models (32:56) Inside the AI Scientist Experiment (38:11) Parent Selection, Diversity and Innovation (49:25) Can AI Discover Truly New Knowledge? (53:05) Why Continual Learning Matter

David Ha

Research Scientist

GoogleSakana AI

Llion Jones cowrote "Attention Is All You Need," the seminal paper that introduced the transformer — the architecture that launched the generative AI revolution. Now he warns that the industry that grew out of this breakthrough is stifling the next one. Learn why the current corporate arms race is killing true innovation and how we can get back to bold exploration. Hosted on Acast. See acast.com/privacy for more information.

Llion Jones

Co-Founder and Researcher

Sakana AI

The Transformer architecture (which powers ChatGPT and nearly all modern AI) might be trapping the industry in a localized rut, preventing us from finding true intelligent reasoning, according to the person who co-invented it. Llion Jones and Luke Darlow, key figures at the research lab Sakana AI, join the show to make this provocative argument, and also introduce new research which might lead the way forwards. **SPONSOR MESSAGES START** — Build your ideas with AI Studio from Google - http://ai.studio/build — Tufa AI Labs is hiring ML Research Engineers https://tufalabs.ai/ — cyber•Fund https://cyber.fund/?utm_source=mlst is a founder-led investment firm accelerating the cybernetic economy Hiring a SF VC Principal: https://talent.cyber.fund/companies/cyber-fund-2/jobs/57674170-ai-investment-principal#content?utm_source=mlst Submit investment deck: https://cyber.fund/contact?utm_source=mlst — **END** The "Spiral" Problem – Llion uses a striking visual analogy to explain what current AI is missing. If you ask a standard neural network to understand a spiral shape, it solves it by drawing tiny straight lines that just happen to look like a spiral. It "fakes" the shape without understanding the concept of spiraling. Introducing the Continuous Thought Machine (CTM) Luke Darlow deep dives into their solution: a biology-inspired model that fundamentally changes how AI processes information. The Maze Analogy: Luke explains that standard AI tries to solve a maze by staring at the whole image and guessing the entire path instantly. Their new machine "walks" through the maze step-by-step. Thinking Time: This allows the AI to "ponder." If a problem is hard, the model can naturally spend more time thinking about it before answering, effectively allowing it to correct its own mistakes and backtrack—something current Language Models struggle to do genuinely. https://sakana.ai/ https://x.com/YesThisIsLion https://x.com/LearningLukeD TRANSCRIPT: https://app.rescript.info/public/share/crjzQ-Jo2FQsJc97xsBdfzfOIeMONpg0TFBuCgV2Fu8 TOC: 00:00:00 - Stepping Back from Transformers 00:00:43 - Introduction to Continuous Thought Machines (CTM) 00:01:09 - The Changing Atmosphere of AI Research 00:04:13 - Sakana’s Philosophy: Research Freedom 00:07:45 - The Local Minimum of Large Language Models 00:18:30 - Representation Problems: The Spiral Example 00:29:12 - Technical Deep Dive: CTM Architecture 00:36:00 - Adaptive Computation & Maze Solving 00:47:15 - Model Calibration & Uncertainty 01:00:43 - Sudoku Bench: Measuring True Reasoning REFS: Why Greatness Cannot be planned [Kenneth Stanley] https://www.amazon.co.uk/Why-Greatness-Cannot-Planned-Objective/dp/3319155237 https://www.youtube.com/watch?v=lhYGXYeMq_E The Hardware Lottery [Sara Hooker] https://arxiv.org/abs/2009.06489 https://www.youtube.com/watch?v=sQFxbQ7ade0 Continuous Thought Machines [Luke Darlow et al / Sakana] https://arxiv.org/abs/2505.05522 https://sakana.ai/ctm/ LSTM: The Comeback Story? [Prof. Sepp Hochreiter] https://www.youtube.com/watch?v=8u2pW2zZLCs Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis [Kumar/Stanley] https://arxiv.org/pdf/2505.11581 A Spline Theory of Deep Networks [Randall Balestriero] https://proceedings.mlr.press/v80/balestriero18b/balestriero18b.pdf https://www.youtube.com/watch?v=86ib0sfdFtw https://www.youtube.com/watch?v=l3O2J3LMxqI On the Biology of a Large Language Model [Anthropic, Jack Lindsey et al] https://transformer-circuits.pub/2025/attribution-graphs/biology.html The ARC Prize 2024 Winning Algorithm [Daniel Franzen and Jan Disselhoff] “The ARChitects” https://www.youtube.com/watch?v=mTX_sAq--zY Neural Turing Machine [Graves] https://arxiv.org/pdf/1410.5401 Adaptive Computation Time for Recurrent Neural Networks [Graves] https://arxiv.org/abs/1603.08983 Sudoko Bench [Sakana] https://pub.sakana.ai/sudoku/

Llion Jones

Co-Founder and Researcher

Sakana AI

Luke Darlow

Researcher

Sakana AI

We speak with Sakana AI, who are building nature-inspired methods that could fundamentally transform how we develop AI systems. The guests include Chris Lu, a researcher who recently completed his DPhil at Oxford University under Prof. Jakob Foerster's supervision, where he focused on meta-learning and multi-agent systems. Chris is the first author of the DiscoPOP paper, which demonstrates how language models can discover and design better training algorithms. Also joining is Robert Tjarko Lange, a founding member of Sakana AI who specializes in evolutionary algorithms and large language models. Robert leads research at the intersection of evolutionary computation and foundation models, and is completing his PhD at TU Berlin on evolutionary meta-learning. The discussion also features Cong Lu, currently a Research Scientist at Google DeepMind's Open-Endedness team, who previously helped develop The AI Scientist and Intelligent Go-Explore. SPONSOR MESSAGES: *** CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments. Check out their super fast DeepSeek R1 hosting! https://centml.ai/pricing/ Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/ *** * DiscoPOP - A framework where language models discover their own optimization algorithms * EvoLLM - Using language models as evolution strategies for optimization The AI Scientist - A fully automated system that conducts scientific research end-to-end * Neural Attention Memory Models (NAMMs) - Evolved memory systems that make transformers both faster and more accurate TRANSCRIPT + REFS: https://www.dropbox.com/scl/fi/gflcyvnujp8cl7zlv3v9d/Sakana.pdf?rlkey=woaoo82943170jd4yyi2he71c&dl=0 Robert Tjarko Lange https://roberttlange.com/ Chris Lu https://chrislu.page/ Cong Lu https://www.conglu.co.uk/ Sakana https://sakana.ai/blog/ TOC: 1. LLMs for Algorithm Generation and Optimization [00:00:00] 1.1 LLMs generating algorithms for training other LLMs [00:04:00] 1.2 Evolutionary black-box optim using neural network loss parameterization [00:11:50] 1.3 DiscoPOP: Non-convex loss function for noisy data [00:20:45] 1.4 External entropy Injection for preventing Model collapse [00:26:25] 1.5 LLMs for black-box optimization using abstract numerical sequences 2. Model Learning and Generalization [00:31:05] 2.1 Fine-tuning on teacher algorithm trajectories [00:31:30] 2.2 Transformers learning gradient descent [00:33:00] 2.3 LLM tokenization biases towards specific numbers [00:34:50] 2.4 LLMs as evolution strategies for black box optimization [00:38:05] 2.5 DiscoPOP: LLMs discovering novel optimization algorithms 3. AI Agents and System Architectures [00:51:30] 3.1 ARC challenge: Induction vs. transformer approaches [00:54:35] 3.2 LangChain / modular agent components [00:57:50] 3.3 Debate improves LLM truthfulness [01:00:55] 3.4 Time limits controlling AI agent systems [01:03:00] 3.5 Gemini: Million-token context enables flatter hierarchies [01:04:05] 3.6 Agents follow own interest gradients [01:09:50] 3.7 Go-Explore algorithm: archive-based exploration [01:11:05] 3.8 Foundation models for interesting state discovery [01:13:00] 3.9 LLMs leverage prior game knowledge 4. AI for Scientific Discovery and Human Alignment [01:17:45] 4.1 Encoding Alignment & Aesthetics via Reward Functions [01:20:00] 4.2 AI Scientist: Automated Open-Ended Scientific Discovery [01:24:15] 4.3 DiscoPOP: LLM for Preference Optimization Algorithms [01:28:30] 4.4 Balancing AI Knowledge with Human Understanding [01:33:55] 4.5 AI-Driven Conferences and Paper Review

Chris Lu

Researcher

Sakana AIOpenAI

Robert Tjarko Lange

Founding Member / Research Lead

Sakana AI

Cong Lu

Researcher

Sakana AI

Prof. Jakob Foerster, a leading AI researcher at Oxford University and Meta, and Chris Lu, a researcher at OpenAI -- they explain how AI is moving beyond just mimicking human behaviour to creating truly intelligent agents that can learn and solve problems on their own. Foerster champions open-source AI for responsible, decentralised development. He addresses AI scaling, goal misalignment (Goodhart's Law), and the need for holistic alignment, offering a quick look at the future of AI and how to guide it. SPONSOR MESSAGES: *** CentML offers competitive pricing for GenAI model deployment, with flexible options to suit a wide range of models, from small to large-scale deployments. Check out their super fast DeepSeek R1 hosting! https://centml.ai/pricing/ Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/ *** TRANSCRIPT/REFS: https://www.dropbox.com/scl/fi/yqjszhntfr00bhjh6t565/JAKOB.pdf?rlkey=scvny4bnwj8th42fjv8zsfu2y&dl=0 Prof. Jakob Foerster https://x.com/j_foerst https://www.jakobfoerster.com/ University of Oxford Profile: https://eng.ox.ac.uk/people/jakob-foerster/ Chris Lu: https://chrislu.page/ TOC 1. GPU Acceleration and Training Infrastructure [00:00:00] 1.1 ARC Challenge Criticism and FLAIR Lab Overview [00:01:25] 1.2 GPU Acceleration and Hardware Lottery in RL [00:05:50] 1.3 Data Wall Challenges and Simulation-Based Solutions [00:08:40] 1.4 JAX Implementation and Technical Acceleration 2. Learning Frameworks and Policy Optimization [00:14:18] 2.1 Evolution of RL Algorithms and Mirror Learning Framework [00:15:25] 2.2 Meta-Learning and Policy Optimization Algorithms [00:21:47] 2.3 Language Models and Benchmark Challenges [00:28:15] 2.4 Creativity and Meta-Learning in AI Systems 3. Multi-Agent Systems and Decentralization [00:31:24] 3.1 Multi-Agent Systems and Emergent Intelligence [00:38:35] 3.2 Swarm Intelligence vs Monolithic AGI Systems [00:42:44] 3.3 Democratic Control and Decentralization of AI Development [00:46:14] 3.4 Open Source AI and Alignment Challenges [00:49:31] 3.5 Collaborative Models for AI Development REFS [[00:00:05] ARC Benchmark, Chollet https://github.com/fchollet/ARC-AGI [00:03:05] DRL Doesn't Work, Irpan https://www.alexirpan.com/2018/02/14/rl-hard.html [00:05:55] AI Training Data, Data Provenance Initiative https://www.nytimes.com/2024/07/19/technology/ai-data-restrictions.html [00:06:10] JaxMARL, Foerster et al. https://arxiv.org/html/2311.10090v5 [00:08:50] M-FOS, Lu et al. https://arxiv.org/abs/2205.01447 [00:09:45] JAX Library, Google Research https://github.com/jax-ml/jax [00:12:10] Kinetix, Mike and Michael https://arxiv.org/abs/2410.23208 [00:12:45] Genie 2, DeepMind https://deepmind.google/discover/blog/genie-2-a-large-scale-foundation-world-model/ [00:14:42] Mirror Learning, Grudzien, Kuba et al. https://arxiv.org/abs/2208.01682 [00:16:30] Discovered Policy Optimisation, Lu et al. https://arxiv.org/abs/2210.05639 [00:24:10] Goodhart's Law, Goodhart https://en.wikipedia.org/wiki/Goodhart%27s_law [00:25:15] LLM ARChitect, Franzen et al. https://github.com/da-fr/arc-prize-2024/blob/main/the_architects.pdf [00:28:55] AlphaGo, Silver et al. https://arxiv.org/pdf/1712.01815.pdf [00:30:10] Meta-learning, Lu, Towers, Foerster https://direct.mit.edu/isal/proceedings-pdf/isal2023/35/67/2354943/isal_a_00674.pdf [00:31:30] Emergence of Pragmatics, Yuan et al. https://arxiv.org/abs/2001.07752 [00:34:30] AI Safety, Amodei et al. https://arxiv.org/abs/1606.06565 [00:35:45] Intentional Stance, Dennett https://plato.stanford.edu/entries/ethics-ai/ [00:39:25] Multi-Agent RL, Zhou et al. https://arxiv.org/pdf/2305.10091 [00:41:00] Open Source Generative AI, Foerster et al. https://arxiv.org/abs/2405.08597 <trunc, see PDF/YT>

Jakob Foerster

Professor

University of Oxford

Chris Lu

Researcher

Sakana AIOpenAI

Today we’re joined by David Ha, a research scientist at Google.  In nature, there are many examples of “bottlenecks”, or constraints, that have shaped our development as a species. Building upon this idea, David posits that these same evolutionary bottlenecks could work when training neural network models as well. In our conversation with David, we cover a TON of ground, including the aforementioned biological inspiration for his work, then digging deeper into the different types of constraints he’s applied to ML systems. We explore abstract generative models and how advanced training agents inside of generative models has become, and quite a few papers including Neuroevolution of self-interpretable agents, World Models and Attention for Reinforcement Learning, and The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning. This interview is Nerd Alert certified, so get your notes ready!  PS. David is one of our favorite follows on Twitter (@hardmaru), so check him out and share your thoughts on this interview and his work! The complete show notes for this episode can be found at twimlai.com/go/535

David Ha

Research Scientist

GoogleSakana AI

We speak with Robert Lange! Robert is a PhD student at the Technical University Berlin. His research combines Deep Multi-Agent Reinforcement Learning and Cognitive Science to study the learning dynamics of large collectives. He has a brilliant blog where he distils and explains cutting edge ML research. We spoke about his story, economics, multi-agent RL, intelligence and AGI, and his recent article summarising the state of the art in neural network pruning. Robert's article on pruning in NNs https://roberttlange.github.io/posts/2020/06/lottery-ticket-hypothesis/ 00:00:00 Intro 00:04:17 Show start and intro to Robert 00:11:39 Economics background 00:27:20 Intrinsic motivation 00:33:22 Intelligence/consciousness 00:48:16 Lottery ticket/pruning article discussion 01:43:21 Robert's advice for younger self and state of deep learning Robert's LinkedIn: https://www.linkedin.com/in/robert-tjarko-lange-19539a12a/ @RobertTLange #machinelearning #deeplearning

Robert Lange

Founding Researcher

Sakana AITechnical University Berlin