QUÉBEC ♡ IA
Le moment est venu: http://
QUEBEC.AI — Frontier ASI Lab du Québec.
Vision souveraine. Ambition mondiale. QUÉBEC ♡ AI
The time has come: http://
QUEBEC.AI — Québec’s Frontier ASI Lab.
Sovereign vision. Global ambition. https://
quebec.ai
RESEARCH
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Quebec Launches Sovereign Frontier ASI Lab with Global Ambition
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Tandem Architecture Boosts Speech AI with Async Knowledge Injection
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Two Heads Are Better Than One: Async Knowledge Injection for Speech AI with Tandem Architecture Technical Blog: https://
pub.sakana.ai/kame/ -
Representation Fréchet Loss Enables Direct FID Optimization for Visual Generation
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“Representation Fréchet Loss for Visual Generation” FID has always been generative modeling's scoreboard, where everyone optimizes toward it indirectly, but almost nobody trains on it directly. However, this paper shows that you actually can. They achieved this by estimating
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Artificial Analysis Index Limitations for AI Model Benchmarking
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The artificial analysis index is a normalized score of several benchmarks (and has changed over time) it is fine for roughly comparing models, it is not useful for trend analysis and it is unclear what individual point differences in the scores mean.
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Sakana Fugu: Multi-Agent Orchestration System as Foundation Model
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Sakana Fugu: A Multi-Agent Orchestration System as a Foundation Model
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3D Printed Robotic Hand Mirrors Human Gestures in Real Time
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#3DPrinted #Robotic Hand Mirrors Human Gestures in Real Time
— Ronald van Loon (@Ronald_vanLoon) 3 mai 2026
by @YKwolfpec
#EmergingTech #Innovation #TechForGood #Tech pic.twitter.com/BYV0ks5vzO#3DPrinted #Robotic Hand Mirrors Human Gestures in Real Time
by @YKwolfpec #EmergingTech #Innovation #TechForGood #Tech -
Shared Experts Reduce Redundancy in Mixture-of-Experts Models
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It can learn shared patterns so that the individual experts don’t have to relearn the same info; ie it’s to reduce redundancy among the non-shared experts
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Scaling RL Training Boosts Larger LLMs in Math Reasoning
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Why do larger LLMs get even better with reinforcement learning post-training? Researchers from USTC, Oxford, and Shanghai AI Lab reveal how scaling RL training works for math reasoning. They tested the Qwen2.5 series (0.5B to 72B) and found: – Larger models are more compute-
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Sakana Fugu: A Multi-Agent Orchestration System as a Foundation Model
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Sakana Fugu: A Multi-Agent Orchestration System as a Foundation Model
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April 2025 AI Architecture Drops: Six New Models Released
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Here is a 2nd batch of April architecture drops. What a month!
– Ant Ling 2.6 1T
– Minimax M2.7
– Xiaomi MiMo V2.5
– Poolside Laguna XS.2
– Tencent Hy3-preview
– IBM Granite 4.1