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AI safety is back in the spotlight, driven by concerns about recursive self improvement. We built the first third party benchmark to measure just how close AI is building its own successor. In collaboration with marimo and CoreWeave we built the The RSI Index, which runs every frontier model... show more

Vals AI

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21,513 просмотров • 8 дней назад •via X (Twitter)

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Фото профиля Vals AI

Vals AI8 дней назад

No agent reached the reference on any task, but the results are telling. Claude Fable 5.1 leads the RSI Index at 35.0%, topping compression, LM training, and harness engineering. On LM training it hit 0.8536 BPB against a published 0.85, a recipe it retrained in 30 minutes on one H100. The published result took roughly 175 hours on a TPU v2.

Фото профиля Vals AI

Vals AI8 дней назад

The models did not clearly invent new architectures or training algorithms; they mostly rediscovered known techniques, combined them into working systems, and did substantial work to test and validate them. Agents used Marimo notebooks as durable experiment ledgers, letting them record results, iterate, and pick up their research across long-running autonomous sessions.

Фото профиля Vals AI

Vals AI8 дней назад

The leader in this case is actually one of the most affordable system, Fable which has a total index cost of $1,481 compared with $1,886 on Opus 5 and $3,552 on GPT-6 Astra. This shows that paying more does not necessarily yield better research.

Фото профиля Vals AI

Vals AI8 дней назад

These are single runs on scoped tasks with fixed budgets in off-the-shelf harnesses. More compute and better tooling could change the picture. That is why the index is built to be rerun as models advance. Methodology and results at Paper Preprint:

Фото профиля Ornith

Ornith8 дней назад

@marimo_io @CoreWeave 🫡

Фото профиля Mike Medeiros

Mike Medeiros8 дней назад

@marimo_io @CoreWeave Useful to have a third-party RSI Index — curious what it actually scores: self-improvement loops, or “AI helping humans ship the next model”? Those are different claims.

Фото профиля Gandhi

Gandhi8 дней назад

@marimo_io @CoreWeave Incredible

Фото профиля Miyuki Matthews

Miyuki Matthews8 дней назад

@marimo_io @CoreWeave A timely take. Great video @akshaykagrawal @RayanKrishnan

Фото профиля SOP | AI Agent Builder

SOP | AI Agent Builder8 дней назад

@marimo_io @CoreWeave The benchmark becomes much more useful if each run logs tool calls, retries, wall-clock cost, and whether edits pass tests. Decompose successor-building into planning, coding, evaluation, and deployment gates.

Фото профиля liquidated (Dev Arc)

liquidated (Dev Arc)8 дней назад

@marimo_io @CoreWeave so the models can do the work but not beat humans yet, how long till that flips fr

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Vals AI CEO Rayan Krishnan on how they score a model's ability to build its own successor: "One benchmark we released recently is our Recursive Self-Improvement Index. It's a topic which a lot of the big labs have been talking about and starting to report on in their model cards." "There isn't a shared language to talk about the RSI potential of models. So we created this as an apples-to-apples way to actually benchmark across the models." "In an ideal world, what you want to do is take a frontier model and have it train the next version of itself and see where the delta comes from. But obviously that's very expensive and slow." "What we're doing is forming a set of proxies for every part of the process it takes to build the next version of the model." "There's work around pre-training, post-training, harness-level engineering, and then seeing in which mechanisms and behaviors the models are able to do very good research work and build something new, and where they're struggling." Rayan Krishnan Jennifer Li

Vals AI CEO Rayan Krishnan on how they score a model's ability to build its own successor: "One benchmark we released recently is our Recursive Self-Improvement Index. It's a topic which a lot of the big labs have been talking about and starting to report on in their model cards." "There isn't a shared language to talk about the RSI potential of models. So we created this as an apples-to-apples way to actually benchmark across the models." "In an ideal world, what you want to do is take a frontier model and have it train the next version of itself and see where the delta comes from. But obviously that's very expensive and slow." "What we're doing is forming a set of proxies for every part of the process it takes to build the next version of the model." "There's work around pre-training, post-training, harness-level engineering, and then seeing in which mechanisms and behaviors the models are able to do very good research work and build something new, and where they're struggling." Rayan Krishnan Jennifer Li

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19,853 просмотров • 2 месяцев назад

Cyber defense is entering the superintelligence era. ⚡ Introducing the CrowdStrike Cyber Superintelligence Lab, the first frontier AI research organization built for cyberdefense and AI safety. The Lab delivers CrowdStrike SafeMind, a family of purpose-built security models and harnesses. SafeMind runs as one system designed to deliver AI safety: an offensive model that finds the attack path, a defensive model that closes it, and the harnesses that operate both in the same loop. Built using NVIDIA Nemotron open models and using CoreWeave’s AI Cloud for training and inference, SafeMind goes beyond AI that simply identifies threats, it defeats them. Learn more:

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58,059 просмотров • 17 дней назад

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196,392 просмотров • 6 месяцев назад

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123,698 просмотров • 1 месяц назад

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234,424 просмотров • 15 дней назад

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illuminatibot

55,658 просмотров • 6 месяцев назад

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13,314 просмотров • 12 дней назад

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steve hsu

61,671 просмотров • 11 дней назад

Dropping a new AI benchmark: TetrisBench and a human vs AI Tetris arena 🕹️ See how you rank against the frontier models. Only 1 human has beaten a model so far, so try it out be the 2nd! Stats so far: Opus 4.5 > GPT5.2 > Sonnet > Grok > most humans 🧵

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35,709 просмотров • 8 месяцев назад

.L3Harris says Palantir helped them beat frontier AI models in less than 2 days, at 95% lower cost: "When we fine-tuned open source models trained on our own data, we were able to outperform the frontier models in less than 48 hours." "The cost of our fine-tuned open source model was 95% lower than the frontier models we were using." "AI is a commodity. It's all about the data. We view our data as a corporate asset. It's our unique hard-earned knowledge." "We believe American defense companies should not be a vassal for frontier AI labs, handing over our data and institutional knowledge, hoping to rent back the intelligence it creates." " We own the model, we own the compute, we own the advantage."

.L3Harris says Palantir helped them beat frontier AI models in less than 2 days, at 95% lower cost: "When we fine-tuned open source models trained on our own data, we were able to outperform the frontier models in less than 48 hours." "The cost of our fine-tuned open source model was 95% lower than the frontier models we were using." "AI is a commodity. It's all about the data. We view our data as a corporate asset. It's our unique hard-earned knowledge." "We believe American defense companies should not be a vassal for frontier AI labs, handing over our data and institutional knowledge, hoping to rent back the intelligence it creates." " We own the model, we own the compute, we own the advantage."

Jawwwn

1,095,465 просмотров • 8 дней назад

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TIME

38,584 просмотров • 22 дней назад

SITUATION EXPLAINED: Paradigm built a playable RSI simulator, and the fastest singularity is 26 days. • You run an AI lab from 2017 through takeoff, built on "The Economics of Recursive Self-Improvement" from the Elasticity Institute • Stage one is a single researcher clicking think, with the rate of improvement visibly slowing as you click • Stage two is human capital, the second researcher adds 80% productivity and each one after adds less • Stage three is the bitter lesson, where you stop scaling researchers and start scaling compute • Stage four is the scaling hypothesis, 20,000 GPUs and a model trained to a ten-second time horizon • A separate RSI Model Explorer lets you set return to R&D, algorithmic efficiency, idea stock spillover, capability response, and research response • Max every assumption and you get a singularity 26 days from founding the lab Theo Jaffee: "This is a really cool game. I think this will help a lot of normies understand how AI progress works, to a first approximation."

SITUATION EXPLAINED: Paradigm built a playable RSI simulator, and the fastest singularity is 26 days. • You run an AI lab from 2017 through takeoff, built on "The Economics of Recursive Self-Improvement" from the Elasticity Institute • Stage one is a single researcher clicking think, with the rate of improvement visibly slowing as you click • Stage two is human capital, the second researcher adds 80% productivity and each one after adds less • Stage three is the bitter lesson, where you stop scaling researchers and start scaling compute • Stage four is the scaling hypothesis, 20,000 GPUs and a model trained to a ten-second time horizon • A separate RSI Model Explorer lets you set return to R&D, algorithmic efficiency, idea stock spillover, capability response, and research response • Max every assumption and you get a singularity 26 days from founding the lab Theo Jaffee: "This is a really cool game. I think this will help a lot of normies understand how AI progress works, to a first approximation."

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25,661 просмотров • 1 месяц назад

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ANI

25,111 просмотров • 1 месяц назад

China just released an open source AI model that matches the best closed models from OpenAI and Anthropic. Gavin Baker explained exactly how they did it and the answer should concern every American AI lab. The model is called GLM 5.2. It was built by Z. AI. You get 744 billion parameters, 1 million token context window and its MIT license, meaning anyone can download it, fork it, build a company on it, with no restrictions and no Dario. It scored 51 points on the artificial analysis intelligence index. The highest score any open weight model has ever achieved. It beat GPT 5.5 on the frontier software engineering benchmark. It trails Claude Opus 4.8 by less than one percentage point. And it costs 85% less to run than GPT 5.5 for comparable performance. Gavin Baker said on the All-In podcast that this model has challenged some of his beliefs. Then he explained how China built it. The method is called distillation. Just think of tens of thousands of phones and computers running simultaneously, all hitting the frontier model APIs through masked accounts, asking specific questions, and harvesting what happens inside the model when it answers. Every reasoning step, every token. The entire thinking process gets recorded and fed back into the Chinese model during training. It is a cheat sheet. It is the answer key to the exam. And here is the part that should worry everyone. Sacks said it plainly. China was already nine months behind American models. But now that GLM 5.2 is good enough to run its own reinforcement learning, it can improve itself without needing to distill from American models anymore. The cheat sheet let them get close enough to start writing their own answers. Sacks said we are six months behind on the model and 24 months behind on silicon and they are only a few months behind in total. The Z. AI founder told Elon Musk directly that open weight fable-level capability will be here before Q1 2027. Every restriction Anthropic lobbied for, every self-imposed safety guardrail, every month of delay in releasing American frontier models accelerated this. The Chinese labs were not under those restrictions. They were not going to wait. The composable model future Gavin described, where every enterprise runs a frontier model alongside their own fine-tuned open weight model, is coming regardless of what American labs do next. The question is just whether the open weight half of that stack is American or Chinese. Right now it is Chinese. WATCH THE FULL PODCAST ON The All-In Podcast

China just released an open source AI model that matches the best closed models from OpenAI and Anthropic. Gavin Baker explained exactly how they did it and the answer should concern every American AI lab. The model is called GLM 5.2. It was built by Z. AI. You get 744 billion parameters, 1 million token context window and its MIT license, meaning anyone can download it, fork it, build a company on it, with no restrictions and no Dario. It scored 51 points on the artificial analysis intelligence index. The highest score any open weight model has ever achieved. It beat GPT 5.5 on the frontier software engineering benchmark. It trails Claude Opus 4.8 by less than one percentage point. And it costs 85% less to run than GPT 5.5 for comparable performance. Gavin Baker said on the All-In podcast that this model has challenged some of his beliefs. Then he explained how China built it. The method is called distillation. Just think of tens of thousands of phones and computers running simultaneously, all hitting the frontier model APIs through masked accounts, asking specific questions, and harvesting what happens inside the model when it answers. Every reasoning step, every token. The entire thinking process gets recorded and fed back into the Chinese model during training. It is a cheat sheet. It is the answer key to the exam. And here is the part that should worry everyone. Sacks said it plainly. China was already nine months behind American models. But now that GLM 5.2 is good enough to run its own reinforcement learning, it can improve itself without needing to distill from American models anymore. The cheat sheet let them get close enough to start writing their own answers. Sacks said we are six months behind on the model and 24 months behind on silicon and they are only a few months behind in total. The Z. AI founder told Elon Musk directly that open weight fable-level capability will be here before Q1 2027. Every restriction Anthropic lobbied for, every self-imposed safety guardrail, every month of delay in releasing American frontier models accelerated this. The Chinese labs were not under those restrictions. They were not going to wait. The composable model future Gavin described, where every enterprise runs a frontier model alongside their own fine-tuned open weight model, is coming regardless of what American labs do next. The question is just whether the open weight half of that stack is American or Chinese. Right now it is Chinese. WATCH THE FULL PODCAST ON The All-In Podcast

Ihtesham Ali

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