the diaries really don’t help there; maybe you missed the testimony today
RESEARCH
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Critique of AI Evaluation Metrics and Call for Trustworthy Measures
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they would think that is too implausible.
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AI Regulation Criteria Vague Without Better Non-Lab Benchmarks
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That doesn't mean that there should not be regulation and vetting, but it does suggest that is hard to write a criteria right now that is not somewhat vague. More R&D into non-lab benchmarks is urgently needed. We have remarkably few good ones that are unsaturated and clear.
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AI Regulation Hampered by Poor Benchmarks and Risk Metrics
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A challenge with AI regulation and vetting is how bad our benchmarks of AI model performance and risks are. There is no benchmark for risks and red-teaming requires experiments from dedicated specialist organizations & is not easy to put metrics around. No clear objective numbers
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Web2BigTable: Bi-Level Multi-Agent LLM System for Web-Scale Information Extraction
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Web2BigTable A Bi-Level Multi-Agent LLM System for Internet-Scale Information Search and Extraction paper: https://
huggingface.co/papers/2604.27
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UniVidX Unified Multimodal Framework for Versatile Video Generation
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UniVidX
— AK (@_akhaliq) 4 mai 2026
A Unified Multimodal Framework for Versatile Video Generation via Diffusion Priors
paper: https://t.co/N5jok7VRi2 pic.twitter.com/c4zVBGiLjFUniVidX A Unified Multimodal Framework for Versatile Generation via Diffusion Priors paper: https://
huggingface.co/papers/2605.00
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NVIDIA Megatron Core Adds Muon and Advanced Optimizers for LLM Training
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Training Kimi K2 and Qwen3 30B-scale models efficiently requires more than standard data-parallel tricks. NVIDIA Megatron Core now provides end-to-end support for emerging higher-order optimizers like Muon, alongside research optimizers such as MOP and REKLS, to push training
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Speculative Decoding Accelerates RL Post-Training Rollouts
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"Accelerating RL Post-Training Rollouts via System-Integrated Speculative Decoding" Speculative decoding for RL rollouts! This paper speeds up post-training without changing the target policy’s sampling distribution. So a draft model proposes multiple tokens, and the policy
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ASI’s potential for permanent advantage via chip innovation
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I agree those bottlenecks imply it won’t go foom overnight but an RSI-ing ASI may well invent better chip production process, better ways to use current chips, etc. in a way that adds up to permanent advantage.
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Jack Clark Predicts 60% Chance of Self-Recursive AI Improvement Within 3 Years
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Pay attention when @jackclarkSF says that there is a 60% chance of self-recursive improvement happening in less than 3 years time. That is the take off scenario – on a timeline that fits with all of Anthropic’s remarkably accurate predictions regarding AI capability development.