My takeaway: AI inference is no longer just a model problem. It's a system-level time problem. For enterprise leaders, AI performance and AI economics are becoming inseparable. Learn more about Tau Scaling and what it means for the post-Moore era: https://
chinaxiv.org/abs/202605.002
24?locale=en
… What
RESEARCH
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Tau Scaling: AI inference as system-level time problem in post-Moore era
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Huawei’s Tau Scaling Law redefines AI inference bottleneck
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Most AI teams are optimizing the model.
— Ronald van Loon (@Ronald_vanLoon) 8 juin 2026
But the real bottleneck in inference is underneath it.
Huawei's Tau Scaling Law (Her's Law) was just introduced at IEEE ISCAS in Shanghai.
It reframes how we think about AI performance entirely.
Here's the breakdown…#HuaweiPartner… pic.twitter.com/MvwfNu7ZisMost AI teams are optimizing the model. But the real bottleneck in inference is underneath it. Huawei's Tau Scaling Law (Her's Law) was just introduced at IEEE ISCAS in Shanghai. It reframes how we think about AI performance entirely. Here's the breakdown… #HuaweiPartner
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Anthropic reports 8x code, 52x optimization, and 64% better decisions
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Recursive self-improvement may no longer be just a theory.
Anthropic reports: 8x more code per engineer 76% success on open-ended coding tasks 52x training optimization Better research decisions than humans 64% of the time
The feedback loop is getting tighter.
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800K model aces impossible Sudoku in 15 minutes using Lattice Deduction Transformer
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An 800K model just aced impossible Sudoku in 15 minutes. Most reasoning models scale up to get smarter. This one goes the other way. A new paper introduces the Lattice Deduction Transformer. It is a tiny looped model that reasons like a SAT solver. Instead of guessing
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High-level notes and comparisons on PivotRL paper
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Anyways, that was a cool paper. I took some high-level notes and comparisons here. PivotRL all the things! https://
maximelabonne.substack.com/p/nemotron-3-u
ltra-what-distillation
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A free AI model trained for $7,800 beats a model 400x larger
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Breaking: A free AI model trained for $7,800 has just outperformed a model 400 times larger in mathematics competitions. It is compact enough to run on a laptop. Weibo's AI lab published the results and put all the
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Low 20T token budget and bug halt DSV4 pre-training
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Could it be linked to the (very) low 20T token pre-training budget? DSV4 was trained on 33T tokens. More room for knowledge in the case of HLE. Looks like they had to stop it early due to a bug they never root-caused. (Not a great demo for NVFP4 pre-training to be honest.)
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Nemotron 3 Ultra unable to recover HLE and code performance via OPD
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Nemotron 3 Ultra can't recover perf on HLE, code, etc. via OPD The teacher was trained on DeepSeek-V4-Pro traces (DSV4 Max achieves 37.7% on HLE!). Looks like the MOPD warmup failed to properly init the student? No good trajectory → No improvement via OPD
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TextPro-SLM Approach Bridges Speech and Text AI Gap
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Why do speech AI models still lag behind text AI? Researchers at CUHK and Huawei propose TextPro-SLM — an approach that shrinks the gap by making spoken input look more like text input. Instead of tweaking the output, they redesign the input side with a unified speech
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New directions in AI based on continual interaction and causality
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In this blog, we explore new potential directions for the field of AI based on continual interaction and causality: https://love4all.ai/blog/continual-interactive-causal-agents/ … We have been working on this for years. Pedro Ortega pointed out the problem much earlier, when I