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RESEARCH
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UK AI Institute: Mythos and GPT-5.5 show rapid cyber growth
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The UK’s state AI Security iIstitute findings:
1) Mythos is a big gain in cyber capabilities. But so is GPT-5.5
2) It is hard to establish an upper bound on Mythos/GPT-5.5, which appear to be limited by tokens used, rather than ability.
3) Capability doubling time is 4.5 months -
Stanford HAI Launches Lab to Study AI’s Impact on Organizations and Work
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AI is being deployed across organizations at unprecedented speed. We have limited empirical understanding of its actual effects on how people work together. @StanfordHAI just launched the AI and Organizations Lab to change that: https://
hai.stanford.edu/news/stanford-
hai-launches-ai-and-organizations-lab-to-study-science-of-ai-in-the-workplace
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OpenAI teases ultrafast mode and image model update for Thursday
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what the heck, openai is cooking – ultrafast mode incoming probably this thursday – + an update to the new image model thats already freaking good openai has such a run lately, love it
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RL is not sufficient for AI alignment
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agreed. RL is not (at least by itself) the way to alignment https://t.co/CIH9mXaC0H
— Gary Marcus (@GaryMarcus) 13 mai 2026agreed. RL is not (at least by itself) the way to alignment
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Tolaria: A New Mac App for Markdown Knowledge Bases Inspired by Karpathy
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Karpathy's LLM wiki idea just became a real Mac app! Tolaria is a Mac app for managing markdown knowledge bases. Your notes are plain markdown files stored in a git repository. No cloud, no subscriptions, completely offline. The core idea: knowledge bases work better when
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Collaboration to Develop Infrastructure for Large-Scale RL Agents
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We're working with @IneffableLabs to co-design the infrastructure for large-scale, reinforcement-learning agents and accelerate discovery across science and industry.
Our engineers have teamed up to explore how to create the training pipeline that will allow agents to discover -
New research challenges the assumption that multi-agent systems improve LLM reasoning
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Do multi-agent systems make LLM reasoning better? Most AI devs assume that it should. But this new paper shows that this is often not the case. It ran 22,500 deterministic trajectories across GAIA, SWE-bench, and Multi-Challenge with three frontier models. Agents frequently
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Meta-RL with Rubric-guided Policy Decomposition Research
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RubricEM Meta-RL with Rubric-guided Policy Decomposition beyond Verifiable Rewards
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Low-commitment attention modification for model training
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Interesting paper. What I like about this is that it is a relatively low-commitment attention modification. I.e., one can use it during most of training, switch back to vanilla attention near the end, and recover roughly the same modeling performance as if full attention had