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Language models and coding agents are great, but there is more to life, and more to AI, than just LLM agents.· AI 的广阔领域
<p>Language models and coding agents are great, but there is more to life, and more to AI,…
#tw-hardmaru · 20 条相关内容
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How do physical systems achieve collective intelligence and self-repair without a central brain? A new paper published today in Nature Communications by my Sakana AI colleague Sebastian Risi (@risi1979), along with co-authors from IT University of Copenhagen and Autodesk Research, presents a beautiful realization of biologically inspired robotics: Smart Cellular Bricks. The team built a system of physical 3D cubic units that can collectively infer their global shape and autonomously guide their own damage recovery using purely local interactions. Here is a deep dive into the paper’s key contributions: 1/ Neural Cellular Automata-based Architecture: Modular robots usually rely on central processors. This system flips that paradigm. Every block independently runs the exact same neural network on local microcontrollers. With no master plan or global coordinates, they communicate only with immediate neighbors. By passing continuous state vectors, hundreds of bricks achieve global consensus on their shape in under 3 minutes. 2/ Emergent Biological Morphogens: How does a block know it is part of a chair, not a table? The network’s internal memory automatically learns to establish continuous gradients across the structure. This beautifully mirrors how biological morphogens give positional info to developing cells. The bricks naturally form left-right, radial, and head-to-tail axes to align their identity. 3/ Performance and Generalization: Validated in large-scale simulations, the networks transferred seamlessly to nearly 200 physical hardware bricks, achieving a 100% convergence rate. Instead of rigid template-matching, the system infers broad categories. Even when tested on unseen variations, like an asymmetric table with five random legs, the collective correctly classified the structure. 4/ Fault Tolerance and Autonomous Damage Recovery: Hardware fails in the real world. This system easily tolerates up to 15% module failure without losing accuracy. By predicting spatial damage directions, the cells pinpointed missing components with 95% accuracy. They actively use these local signals to guide a self-repair process, regenerating back into the intended morphology. I believe this is a significant piece of research, bridging collective intelligence and Physical AI. This work represents the first successful physical realization of large-scale, decentralized 3D self-recognition and damage detection. By moving away from centralized control, this architecture paves the way for highly adaptive smart materials and resilient robotics that can survive and repair themselves. Read the full open-access paper: https://www.nature.com/articles/s41467-026-75166-7 Congratulations to the team on this achievement!· 物理系统如何实现集体智能和自我修复？
<p>How do physical systems achieve collective intelligence and self-repair without a centr…
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RT by @hardmaru: We are pleased to share our latest research, now published in Nature Communications: “Smart Cellular Bricks: Physical Modules That Recognize Their Own Shape and Repair Themselves.” Blog: https://sakana.ai/smart-cellular-bricks Paper: https://www.nature.com/articles/s41467-026-75166-7 A long-running theme in our work is collective intelligence: the idea that sophisticated, robust behavior can emerge from many simple parts following local rules, with no central controller, as it does in a colony, a tissue, or a brain. We had mostly studied this in software and simulation. So this time we asked a simple question. Do the same decentralized principles hold up in the physical world, where communication is noisy and modules fail? To find out, we built a collection of simple cubic bricks. Each brick runs the same small neural network and talks only to the bricks it is physically connected to. No brick is told its position, or which shape it is part of. Yet from these purely local exchanges, the collective converges on the correct global shape, locates where modules are missing or damaged, and can even guide its own repair, inspired by how living tissue self-organizes and regenerates after injury. For us, this is a first step in a broader direction: taking the principles of collective intelligence we have studied in software and letting them emerge, decentralized and robust, in the physical world. In the future, we imagine smart materials that let structures sense and report damage on their own, and LEGO-like systems that recognize their own configuration and adapt in real time, pointing toward environments that are more robust, adaptive, and regenerative. This work is a collaboration between Sakana AI, IT University of Copenhagen and Autodesk.· 智能细胞砖：可自我识别和修复的物理模块
<p>We are pleased to share our latest research, now published in Nature Communications: “S…
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R to @hardmaru: Here is a visualization of the AI Picbreeder engine in action. (From our blog: https://pub.sakana.ai/picbreeder-vlm/) To recreate a collaborative human ecosystem, we run 10 VLM “breeder” agents in parallel. These agents constantly sample from a shared archive, interactively evolve new candidates through mutation and crossover, and publish their favorites. Meanwhile, VLM “critic” agents step in to evaluate the growing phylogenetic tree of art, forming an endless loop of digital cultural production.· AI Picbreeder 引擎的可视化
<p>Here is a visualization of the AI Picbreeder engine in action. (From our blog: <a href=…
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One of my first journeys in neural networks started over a decade ago with implementing CPPN-NEAT! Back then, I built a clone of ‘Picbreeder’ not only to study the mechanics of neural nets, but to explore the human creativity process itself, and generate some cool abstract art. Neurogram: https://otoro.net/neurogram/ Gallery: https://otoro.net/gallery/ Today, things have come full circle. We are now trying to use modern VLMs and frontier LLM agents within open-ended exploration algorithms. We want to see if we can finally computationally derive the underlying mechanics of human creativity: serendipity, memory, exploration versus exploitation, and novelty search. Can modern AI actually replicate the magic of human open-endedness? Dive into our new AI Picbreeder Experiment here: https://pub.sakana.ai/picbreeder-vlm/· 现代AI与人类创造力实验
<p>One of my first journeys in neural networks started over a decade ago with implementing…
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RT by @hardmaru: Yann LeCun claimed on social media [7] that my foundational 1990 paper on Neural World Models [1] "was never accepted through peer review." This is simply false. The core concepts from the tech report [1] were peer-reviewed and published at international conferences right away: ★ Planning with recurrent world models: peer-reviewed and published at IJCNN'90 (San Diego, June 1990) [2]. ★ Artificial curiosity through generative & adversarial nets (GANs): peer-reviewed and published at SAB'91 [3][4]. BTW, the 1990 paper [1] was the first of its kind to use the term "World Model" for a predictor neural network learning to predict the consequences of the actions of a controller neural network. Instead of acknowledging that "modern" architectures like "JEPA" (2022) are essentially identical to our 1992 Predictability Maximization (PMAX) [6][7] - a fact recently validated by others [8] - LeCun resorts to false claims about peer review. For the full timeline of the 1990-2015 publications that his 2022 AMI paper rehashes without citation, read the complete, updated receipts here [6]: https://people.idsia.ch/~juergen/lecun-rehash-1990-2022.html Other claims about LeCun are debunked in [8][9]. REFERENCES (easy to find on the web): [1] J. Schmidhuber (JS). Making the world differentiable: On using fully recurrent self-supervised neural networks for dynamic reinforcement learning and planning in non-stationary environments. Technical Report FKI-126-90, TUM, Feb 1990, revised Nov 1990. The first paper on planning with reinforcement learning recurrent neural networks (NNs) and recurrent world models (more), and on generative adversarial networks where a generator NN is fighting a predictor NN in a minimax game (more). Apparently, it was also the first paper of this kind to use the term "world model" for the predictor NN (although the basic concept of a world model is much older than that). [2] JS. An on-line algorithm for dynamic reinforcement learning and planning in reactive environments. Proc. IEEE/INNS International Joint Conference on Neural Networks, San Diego, volume 2, pages 253-258, June 17-21, 1990. Based on [1]. [3] JS. A possibility for implementing curiosity and boredom in model-building neural controllers. In J. A. Meyer and S. W. Wilson, editors, Proc. of the International Conference on Simulation of Adaptive Behavior: From Animals to Animats, pages 222-227. MIT Press/Bradford Books, 1991. Based on [1]. [4] JS. Generative Adversarial Networks are Special Cases of Artificial Curiosity (1990) and also Closely Related to Predictability Minimization (1991). Neural Networks, Volume 127, p 58-66, 2020. Preprint arXiv/1906.04493 [5] JS. The Neural World Model Boom. Technical Note IDSIA-2-26, April 2026. [6] JS (2022, updated 2026). LeCun's 2022 paper on autonomous machine intelligence rehashes but does not cite essential work of 1990-2015. Years ago, Schmidhuber's team published most of what LeCun calls his "main original contributions:" neural nets that learn multiple time scales and levels of abstraction, generate subgoals, use intrinsic motivation to improve world models, and plan (1990); controllers that learn informative predictable representations (1997), etc. This was also discussed on Hacker News, reddit, and in the media. [7] JS. Who invented JEPA? With a reply to LeCun's response. Technical Note IDSIA-3-22, IDSIA, Switzerland, April 2026. [8] JS. How 3 Turing awardees republished key methods and ideas whose creators they failed to credit. Technical Report IDSIA-23-23, Switzerland, 2023 (updated 2026). [9] JS. Who invented convolutional neural networks? Hint: LeCun didn't. CNN basics: Fukushima (1979-86). Backpropagation for CNNs: Zhang et al. (1988-), others. Technical Note IDSIA-17-25, Switzerland, 2025.
<p>Yann LeCun claimed on social media [7] that my foundational 1990 paper on Neural World …
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We just launched Sakana Translate! https://sakana.ai/translate-release/#English I personally rely on this tool every day. Huge congratulations to the team for shipping this! Standard translation tools often miss the deep nuance of Japanese business honorifics, cultural concepts, and internet slang. We built a tool that actually translates the context and tone.· Sakana Translate 发布！
<p>We just launched Sakana Translate!<br> <a href="https://sakana.ai/translate-release/#En…
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RT by @hardmaru: Sakana AI is heading to #ICML2026 in Seoul (July 6–11)! 🐟🇰🇷 Our team will present 11 papers spanning multi-agent coordination, sparse and efficient LLMs, test-time scaling, long-term memory, and agent benchmarks. A thread of everything we're presenting:· Sakana AI 团队将参加 ICML2026 大会
<p>Sakana AI is heading to <a href="https://rss.xcancel.com/search?f=tweets&q=%23ICML2026"…
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R to @hardmaru: Program Manager (RSI Lab) https://sakana.ai/careers/program-manager-rsi-lab/· RSI实验室项目经理招聘
<p>Program Manager (RSI Lab)<br> <a href="https://sakana.ai/careers/program-manager-rsi-la…
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I’m looking to hire a Program Manager to help manage Sakana AI’s fast growing Recursive Self-Improvement (RSI) Lab 🚀 RSI Lab (English): https://sakana.ai/rsi-lab/ RSI Lab (日本語): https://sakana.ai/rsi-lab-jp/ Job Description: https://sakana.ai/careers/program-manager-rsi-lab/· 招聘 Program Manager 管理 RSI 实验室
<p>I’m looking to hire a Program Manager to help manage Sakana AI’s fast growing Recursive…
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RT by @hardmaru: Fugu is now available on OpenCode! ✨ When our team was developing Fugu’s multi-agent orchestration, OpenCode was our tool of choice to verify our models. We share a core philosophy with the OpenCode team: the future of coding agents should be an open, collective ecosystem.· Fugu现已上线OpenCode！
<p>Fugu is now available on OpenCode! ✨<br> <br> When our team was developing Fugu’s multi…
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R to @hardmaru: To really move the needle in terms of innovation, like the era in Japan when you had companies like Sony or Panasonic come out, there are a few factors at play. One is that the nation needs to have a collective sense of hope. A nation needs to feel hopeful. It could be poor. Everyone can be dirt poor, but they have hope. And hope leads to things being done. So, what creates hope in Japan? Is it more money? Is it less taxes? Or is it more benefits? Probably not. I think that it is stories and narratives that can create hope. If I have a magic wand, if the population in Japan is more optimistic about the future of this country, suddenly they’re persuaded, to be more optimistic. People should be optimistic. With optimism comes change. And change leads to more optimism. 🎏 https://www.disruptingjapan.com/the-future-of-ai-looks-very-different-in-japan/· 日本的AI未来大不相同
<p>To really move the needle in terms of innovation, like the era in Japan when you had co…
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皆が集団として希望を感じていることが必要だと思います。その希望こそが、物事を動かしていく。日本に希望をもたらすのは何でしょうか。お金でしょうか。減税でしょうか。社会保障の充実でしょうか。おそらく、そのどれでもないはずです。もし魔法の杖を一本持てるとしたら、日本の人々が未来をもっと楽観的に捉えられるようにしたい。希望によって変化が起こり、その変化がさらなる希望をもたらすのではないでしょうか。
<p>皆が集団として希望を感じていることが必要だと思います。その希望こそが、物事を動かしていく。日本に希望をもたらすのは何でしょうか。お金でしょうか。減税でしょうか。社会保障の充実…
行业资讯精选 85
RT by @hardmaru: Sakana Fugu Technical Report Instead of training one larger model, Sakana AI trains an orchestrator that reads each query and dynamically routes or composes GPT-5.5, Gemini-3.1-Pro, Claude Opus 4.8 and other agents into query-specific workflows. With Fugu being the fast router, and Fugu-Ultra being the deep multi-agent conductor, trained with SFT, evolutionary strategies and GRPO to build adaptive scaffolds. The idea is to have the model pick GPT for math, Gemini for science and recall, Opus for debugging, then synthesize them when no single agent is best. This router is able to get SoTA results across SWE-Bench Pro, Terminal Bench, LiveCodeBench, GPQA-Diamond, CharXiv and more, demonstrating the potential of orchestration being a practical alternative beyond training.· Sakana Fugu 技术报告
<p>Sakana Fugu Technical Report<br> <br> Instead of training one larger model, Sakana AI t…
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RT by @hardmaru: apart from the technical content of the paper (which is insanely cool), the writing style is so direct and to the point unlike papers these days with needless complexity, i aspire to write papers and architectures as well as @SchmidhuberAI and @hardmaru !
<p>apart from the technical content of the paper (which is insanely cool), the writing sty…
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R to @OpenRouter: openrouter.ai/sakana/fugu-ul…· R to @OpenRouter: 分享链接
<p><a href="https://openrouter.ai/sakana/fugu-ultra">openrouter.ai/sakana/fugu-ul…</a></p>…
行业资讯精选 75
Excited to partner with @OpenRouter ⚡ Products like OpenRouter Fusion and Sakana Fugu have sparked a serious conversation about dependency and resilience in AI. I believe this is just the start of a great architectural shift to come in AI development.· 与 OpenRouter 合作引发的思考
<p>Excited to partner with <a href="https://rss.xcancel.com/OpenRouter" title="OpenRouter"…
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R to @hardmaru: Sakana Fugu Technical Report https://github.com/SakanaAI/fugu/blob/main/Fugu_technical_report.pdf Release Notes: https://sakana.ai/fugu-release/· Fugu技术报告及发布说明
<p>Sakana Fugu Technical Report<br> <a href="https://github.com/SakanaAI/fugu/blob/main/Fu…
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Pinned: Human intelligence is fundamentally a collective intelligence. We solve complex problems by participating in a vast cultural network that builds upon ideas across generations. I believe the strongest AI systems will become a collective intelligence, too. Since we started Sakana AI, our core conviction has been that the most powerful AI systems will be collaborative ecosystems, not isolated monoliths. Evolution innovates under constraints, and the future belongs to systems that explicitly learn how to coordinate collective intelligence. Today, we are taking a major step toward that future with the launch of Sakana Fugu. Fugu dynamically orchestrates the world’s best models to tackle complex tasks. We are proving that a well-orchestrated pool of swappable agents can match restricted frontier models like Fable and Mythos. But Fugu is about more than just performance. I believe that Orchestration Models are the next frontier, beyond bigger models. Relying on a single company’s model for national infrastructure is a massive risk. As recent export controls have shown, access to top models can disappear overnight. Collective intelligence is the practical hedge against this concentration of power. Fugu simply routes around vendor restrictions by relying on an entirely swappable agent pool. I am incredibly proud of our Tokyo team for shipping this. By orchestrating the world’s models, we are delivering the resilient blueprint required for AI sovereignty. Read our full vision and results here: https://sakana.ai/fugu-release 🐡· 集体智能是AI的未来
<p>Human intelligence is fundamentally a collective intelligence. We solve complex problem…
行业资讯精选 85
RT by @hardmaru: AI that builds AI - 3 early steps of Recursive Self-Improvement (RSI) ▪️@AnthropicAI: 80% of the code merged into their codebase was authored by Claude ▪️@SakanaAILabs - RSI is their mission. With research like The AI Scientist and Darwin Gödel Machine, they already have one of the strongests foundation for RSI ▪️ @Recursive_SI is automating the research loop itself with the Recursive system, generating and testing improvements to models, training recipes, and GPU kernels. Here is a full guild to what is RSI exactly, how it works in these 3 cases and how they transform research loops today: https://www.turingpost.com/p/what-is-recursive-self-improvement· AI 自建 AI - 递归自我改进的三个早期步骤
<p>AI that builds AI - 3 early steps of Recursive Self-Improvement (RSI)<br> <br> ▪️<a hre…