hardmaru

@hardmaru.bsky.social

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Co-Founder & CEO, Sakana AI 🎏 → @sakanaai.bsky.social Visit → https://sakana.ai/

Schmidhuber: I support open-source models distilling what commercial companies distilled for free from the entire internet. I published distillation for free in 1991 in Europe - this was copied in the US and in China (https://x.com/SchmidhuberAI/status/1608870559609421831) Quoted: Director Michael Kratsios (US government worker) We have information that Moonshot AI distilled Anthropic’s Fable for the development of its K3 model. To do this they developed a sophisticated internal platform to conduct large scale distillation against U.S. models, allowing them to quickly switch between multiple methods of access to avoid detection. Moonshot AI has also acquired GB300-equipped servers and has accessed GB300s in Thailand, likely to train its AI models. The United States strongly supports the free and fair development of AI, including a thriving competitive ecosystem that spans frontier models, specialized systems, open-source frameworks, and open-weight models. Legitimate AI distillation used to create smaller, more efficient models plays a vital role in this open innovation ecosystem. However, large-scale, covert industrial distillation aimed at stealing proprietary U.S. technology and undermining American research is unacceptable.
Source: https://twitter.com/almmaasoglu/status/2050603144686473246
back arrowGo to ICLR 2026 Conference homepage EDINET-Bench: Evaluating LLMs on Complex Financial Tasks using Japanese Financial Statements Large Language Models (LLMs) have made remarkable progress, surpassing human performance on several benchmarks in domains such as mathematics and coding. A key driver of this progress has been the development of benchmark datasets. In contrast, the financial domain poses higher entry barriers due to its demand for specialized expertise, and benchmarks remain relatively scarce compared to those in mathematics or coding. We introduce EDINET-Bench, an open-source Japanese financial benchmark designed to evaluate LLMs on challenging tasks such as accounting fraud detection, earnings forecasting, and industry classification. EDINET-Bench is constructed from ten years of annual reports filed by Japanese companies. These tasks require models to process entire annual reports and integrate information across multiple tables and textual sections, demanding expert-level reasoning that is challenging even for human professionals. Our experiments show that even state-of-the-art LLMs struggle in this domain, performing only marginally better than logistic regression in binary classification tasks such as fraud detection and earnings forecasting. Our results show that simply providing reports to LLMs in a straightforward setting is not enough. This highlights the need for benchmark frameworks that better reflect the environments in which financial professionals operate, with richer scaffolding such as realistic simulations and task-specific reasoning support to enable more effective problem solving. We make our dataset and code publicly available to support future research.
“But perhaps this can be resolved by the realization that while cleverness and intelligence are somewhat correlated traits for humans, they are much more decoupled for AI tools (which are often optimized for cleverness), and viewing the current generation of such tools primarily as a stochastic generator of sometimes clever - and often useful - thoughts and outputs may be a more productive perspective when trying to use them to solve difficult problems.”
Why Scaling Is Not Enough I believe in scaling laws and I believe scaling will improve performance, and models like Gemini are clearly good models. The problem with scaling is this: for linear improvements, we previously had exponential growth as GPUs which canceled out the exponential resource requirements of scaling. This is no longer true. In other words, previously we invested roughly linear costs to get linear payoff, but now it has turned to exponential costs. Frontier AI Versus Economic Diffusion The US and China follow two different approaches to AI. The US follows the idea that there will be one winner who takes it all – the one that builds superintelligence wins. Even coming short of superintelligence of AGI, if you have the best model, almost all people will use your model and not the competition’s model. The idea is: develop the biggest, badest model and people will come. China’s philosophy is different. They believe model capabilities do not matter as much...
https://www.bloomberg.com/news/articles/2025-11-20/a-hedge-against-ai-crash-emerges-as-oracle-cds-market-explodes
In addition to our own works, the video features work by Jürgen Schmidhuber, Seth Bling, Igor Karpov, Jacob Schrum, Yulu G., Kenneth Stanley, Joel Lehman, Jeff Clune, Nick Cheney, Xingyou Song, Chelsea Finn, Julian Togelius, Sam Earle, Hod Lipson, and Jean-Baptiste Mouret.
At present, we are seeing a record amount of capital pouring into AI compute at a rate which may not be sustainable, funding AI businesses without a clear path to profitability, and consuming unprecedented levels of energy, to develop AI models under the assumption of near limitless resources. But is this really the future that we want to build? As an AI R&D company in Tokyo, we also question whether this is the right approach for Japan, a nation with limited resources, to build its Sovereign AI. We believe that intelligent life has arisen not from an abundance of resources but rather from the lack of it. Nature ultimately selects systems that are able to do more with less. It is debatable whether such constraints are a requirement for intelligence to emerge, but it is undeniable that our own intelligence is a result of resource constraints. Sakana AI has always done things differently. Since the beginning, our research has focused on developing efficient AI technology sustainably. For instance, rather than reinventing the wheel by training yet another large foundation model from scratch, we instead pioneered ways of using evolution to combine existing open-source models, using tree search to combine closed models, getting models to self-improve, to push the frontier of AI capabilities. Last year, we also pioneered the use of LLM agents to automate AI science, enabling more efficient algorithms to be discovered by AI itself. We developed energy efficient language models that work on edge devices, and we are also working on entirely new AI architectures which may carry us into a vastly more efficient AI paradigm. We also created our business model with sustainability and profitability in mind. This year, we built a healthy and growing enterprise AI business, where we have partnered with some of the largest enterprises in Japan to deploy AI-enabled business applications that can truly bring return on investment to our clients with our technology.
Why Greatness Cannot Be Planned book covers, for both Japanese and English editions, as well as a Sakana AI bookmark.
Imagine going to sleep in 1875 in New York City and waking up thirty years later. As you shut your eyes, there is no electric lighting, Coca-Cola, basketball, or aspirin. There are no cars or “sneakers.” The tallest building in Manhattan is a church. When you wake up in 1905, the city has been remade with towering steel-skeleton buildings called “skyscrapers.” The streets are filled with novelty: automobiles powered by new internal combustion engines, people riding bicycles in rubber-soled shoes—all recent innovations. The Sears catalog, the cardboard box, and aspirin are new arrivals. People have enjoyed their first sip of Coca-Cola and their first bite of what we now call an American hamburger. The Wright brothers have flown the first airplane. When you passed into slumber, nobody had taken a picture with a Kodak camera or used a machine that made motion pictures, or bought a device to play recorded music. By 1905, we have the first commercial versions of all three—the simple box camera, the cinematograph, and the phonograph.
“Because many US companies have taken a secretive approach to developing foundation models—a reasonable business strategy—the leading companies spend huge…to recruit key team members from each other who might know the ‘secret sauce’ that enabled a competitor to develop certain capabilities. So knowledge does circulate, but at high cost and slowly. In contrast, in China’s open AI ecosystem, many advanced foundation model companies undercut each other on pricing and poach each others’ employees and customers. This Darwinian life-or-death struggle will lead to the demise of many of the existing players, but the intense competition breeds strong companies.”
Statement about subversive hidden LLM prompts Submitting a paper with a "hidden" prompt is scientific misconduct if that prompt is intended to obtain a favorable review from an LLM. The inclusion of such a prompt is an attempt to subvert the peer-review process. Although ICML 2025 reviewers are forbidden from using LLMs to produce their reviews of paper submissions, this fact does not excuse the attempted subversion. (For an analogous example, consider that an author who tries to bribe a reviewer for a favorable review is engaging in misconduct even though the reviewer is not supposed to accept bribes.) Note that this use of hidden prompts is distinct from those intended to detect if LLMs are being used by reviewers; the latter is an acceptable use of hidden prompts.
Credit: Dragon Ball Z, Toei Animation （東映アニメーション） Source: https://pinterest.com/pin/943293084426270223/
“One, he believes that AI is so scary that only they should do it,” Huang said of Amodei at a press briefing in Paris. “Two, [he believes] that AI is so expensive, nobody else should do it…And three, AI is so incredibly powerful that everyone will lose their jobs, which explains why they should be the only company building it.” “I think AI is a very important technology; we should build it and advance it safely and responsibly,” Huang said. “If you want things to be done safely and responsibly, you do it in the open…Don’t do it in a dark room and tell me it’s safe.”
Essay: https://paulgraham.com/aord.html