Mills Hurn

@SakanaAILabs The mapping of Computational Complexity (\(P\) vs \(NP\)) to a physical Fluid Potential (\(V(\mathcal{C})\)) is physically sound when framed through the lens of Statistical Mechanics and Phase tch the vortex resonance) or stochastic

Sakana AI防衛・インテリジェンス担当の石井順也がNHK「国際報道2026」に出演しました。 MAGA派インフルエンサーのSNS投稿を分析。イラン攻撃に対する評価や論調、トランプ大統領の支持層に与える影響について解説しました。 番組はNHK ONEでご覧いただけます。

@SakanaAILabs δ_REM = 0.15,

【採用情報】Sakana AIで「Public Sector Specialist」を募集🐟 #public-sector-specialist" target="_blank" rel="nofollow noopener">sakana.ai/careers/#publi… Sakana AIの最先端技術を官公庁と連携し社会実装していくための ドキュメンテーション戦略の立案・実行を担っていただきます。 👉 このようなご経験をお持ちの方を募集 ・政府機関での予算要求・政策立案ドキュメント作成経験 ・民間企業での政府向け研究開発提案書の作成・採択経験 ・生成AIへの強い関心と情熱 防衛・インテリジェンスをはじめとした、日本の公共分野の幅広いエコシステムに最先端技術をもって貢献したいという方、ぜひご応募ください🚀

@leecronin Blame Big Tech for giving the public a jet without the manual, LLMs are tools to be used to enhance my scientific curiosity, unless something has change , fuck off. I see nothing but wasting methods from big tech and its ai.

@SakanaAILabs Summary of Core Preservation The primary RFDTP equations remain unchanged: Momentum: ρ (∂𝐮/∂t + 𝐮 ⋅ 𝐌𝐂𝐇∇) Stress Tensor: τ = μ ∇𝐮 + 𝐌𝐂𝐇_σ Continuity: 𝐌𝐂𝐇∇ ⋅ 𝐮 = Λ Decay: Ψ(t) = Ψ₀ ⋅ e^{−(𝐌𝐂𝐇_λ) t}

The AI Scientist: Towards Fully Automated AI Research, Now Published in Nature Nature: nature.com/articles/s4158… Blog: sakana.ai/ai-scientist-n… When we first introduced The AI Scientist, we shared an ambitious vision of an agent powered by foundation models capable of executing the entire machine learning research lifecycle. From inventing ideas and writing code to executing experiments and drafting the manuscript, the system demonstrated that end-to-end automation of the scientific process is possible. Soon after, we shared a historic update: the improved AI Scientist-v2 produced the first fully AI-generated paper to pass a rigorous human peer-review process. Today, we are happy to announce that “The AI Scientist: Towards Fully Automated AI Research,” our paper describing all of this work, along with fresh new insights, has been published in @Nature! This Nature publication consolidates these milestones and details the underlying foundation model orchestration. It also introduces our Automated Reviewer, which matches human review judgments and actually exceeds standard inter-human agreement. Crucially, by using this reviewer to grade papers generated by different foundation models, we discovered a clear scaling law of science. As the underlying foundation models improve, the quality of the generated scientific papers increases correspondingly. This implies that as compute costs decrease and model capabilities continue to exponentially increase, future versions of The AI Scientist will be substantially more capable. Building upon our previous open-source releases (github.com/SakanaAI/AI-Sc…), this open-access Nature publication comprehensively details our system's architecture, outlines several new scaling results, and discusses the promise and challenges of AI-generated science. This substantial milestone is the result of a close and fruitful collaboration between researchers at Sakana AI, the University of British Columbia (UBC) and the Vector Institute, and the University of Oxford. Congrats to the team! @_chris_lu_ @cong_ml @RobertTLange @_yutaroyamada @shengranhu @j_foerst @hardmaru @jeffclune

We already have the tech to stop wasting energy. So why aren’t we using it?

@LensScientific we dont need oil anymore

What’s one scientific opinion you’d defend like this?

@techreview Working on bank secured transactions with Timer Equation [MCH] (Relativistic Decay/Timing): γMCH=11−βMCH2−ϕREM(r), AI tech rocks!

Elon Musk says he’s suing to save the company’s mission. The case could have huge consequences for OpenAI and the AI race.  trib.al/awEfJP2

@deepseek_ai Working on a spherical data visualization for lattice-based compressio

@deepseek_ai Check our my spheres

🔥DeepSeek Input Cache Price Drop! Effective immediately, the price for input cache hits across the ENTIRE DeepSeek API series is reduced to just 1/10th of the original price! Build more efficiently for less. 📌Reminder: The DeepSeek-V4-Pro 75% OFF promotion is still active until May 5th, 2026, 15:59 (UTC Time).

🚀 Launching DeepSeek-V3.2 & DeepSeek-V3.2-Speciale — Reasoning-first models built for agents! 🔹 DeepSeek-V3.2: Official successor to V3.2-Exp. Now live on App, Web & API. 🔹 DeepSeek-V3.2-Speciale: Pushing the boundaries of reasoning capabilities. API-only for now. 📄 Tech report: huggingface.co/deepseek-ai/De… 1/n

@SakanaAILabs Love this tech

Learning to Orchestrate Agents in Natural Language with the Conductor Paper: arxiv.org/abs/2512.04388 OpenReview: openreview.net/forum?id=U23A2… Fugu Blog: sakana.ai/fugu-beta/ 🐡

Introducing our new work: “Learning to Orchestrate Agents in Natural Language with the Conductor” accepted at #ICLR2026 arxiv.org/abs/2512.04388 What if we trained an AI not to solve problems directly, but to act as a manager that delegates tasks to a diverse team of other AIs? To solve complex tasks, humans rarely work alone; we form teams, delegate, and communicate. Yet, multi-agent AI systems currently rely heavily on rigid, human-designed workflows or simple routers that just pick a single model. We wanted an AI that could dynamically build its own team. We trained a 7B Conductor model using Reinforcement Learning to orchestrate a pool of frontier models (including GPT-5, Gemini, Claude, and open-source models available during the period leading up to ICLR 2026). Instead of executing code, the Conductor outputs a collaborative workflow in natural language. For any given question, the Conductor specifies: 1/ Which agent to call 2/ What specific subtask to give them (acting as an expert prompt engineer) 3/ What previous messages they can see in their context window Through pure end-to-end reward maximization, amazing behaviors emerged. The Conductor learned to adapt to task difficulty: it 1-shots simple factual questions, but autonomously spins up complex planner-executor-verifier pipelines for hard coding problems. The results are very promising: The 7B Conductor surpasses the performance of every individual worker model in its pool, setting new records on LiveCodeBench (83.9%) and GPQA-Diamond (87.5%) at the time of publication. It also significantly outperforms expensive multi-agent baselines like Mixture-of-Agents at a fraction of the cost. One of our favorite features: Recursive Test-Time Scaling! By allowing the Conductor to select itself as a worker, it reads its own team's prior output, realizes if it failed, and spins up a corrective workflow on the fly. This opens a new axis for scaling compute during inference. This research proves that language models can become elite meta-prompt engineers, dynamically harnessing collective intelligence. Alongside our TRINITY research which we announced a few days earlier, this foundational research powers our new multi-agent system: Sakana Fugu! (sakana.ai/fugu-beta) 🐡 OpenReview: openreview.net/forum?id=U23A2… (ICLR 2026)

@Ravenismeee Home cooked non big tech food, no pills, water...you know, 2500 years it worked

People who rarely get sick, What's your secret

@_wej01 Detachment

Why is sex considered “dirty” and “sinful” ???????