How well do LLM coding agents truly perform on complex, end-to-end software feature development? Researchers from the Institute of Automation, Chinese Academy of Sciences and Huawei Technologies Co., Ltd. introduce FeatureBench, a new benchmark using a scalable, test-driven
MACHINE LEARNING
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Use MoE Models on Unified Memory Hardware Like DGX Spark
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As I have mentioned before, stop trying to get Dense models running on the DGX Spark/Mac Studios Unified Memory is best fit for MoEs because you only make each token go through a small subset of the numbers of parameters in the model Optimize for your hardware
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27B Parameters Per Token Explains Slow LLM Inference Speed
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Ultimately you’re still going through the 27B parameters per token and that’s what takes so long
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Qwen 3.5 27B Matches Sonnet 4.5 on Single RTX 5090
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That model, Qwen 3.5 27B Dense is equal to Sonnet 4.5 Runs great on a single RTX 5090 w/ full context But we are not anywhere near Opus 4.5 even with Qwen 3.5 397B MoE
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MiniMax M2.5 Lightning Attention Architecture for Long Context Scaling
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Lightning Attention architecture used in MiniMax M2.5 is really interesting. The structure is 7 Lightning Attention layers for every 1 traditional SoftMax attention layer, which lets it scale to long contexts while keeping the quality you'd expect from standard transformers. I… pic.twitter.com/HnfTF0W6f1
— Akshay 🚀 (@akshay_pachaar) 14 mars 2026Lightning Attention architecture used in MiniMax M2.5 is really interesting. The structure is 7 Lightning Attention layers for every 1 traditional SoftMax attention layer, which lets it scale to long contexts while keeping the quality you'd expect from standard transformers. I
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Unified Memory Faster for Loading Large MoE Models
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the issue is that unified memory would still be faster for loading MoEs that are larger than the largest single GPU in terms of Memory
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Sam Altman Predicts Breakthrough Beyond Transformers Coming Soon
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Ton grave, pose sérieuse.
— VISION IA (@vision_ia) 14 mars 2026
Sam Altman dit qu’une autre percée au-delà des transformers va bientôt arriver, et que les modèles sont désormais assez intelligents pour aider à la découvrir.
L’IA crée une énorme opportunité de reconstruire des catégories entières de produits et de… pic.twitter.com/wTEt3aWv0NGrave tone, serious pose. Sam Altman says that another breakthrough beyond transformers is coming soon, and that models are now smart enough to help discover it. AI creates a huge opportunity to rebuild entire categories of products and make new things possible. A new science,
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Complete Guide to Machine Learning in Finance
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https://tradersunion.com/news/market-voices/show/1688112-machine-learning-finance-guide/
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Testing Current AI Intelligence on Instrumental Convergence Strategies
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The point of this proposal is not that an AGI will never think of stuff if you don't tell them. It's to probe whether or not the *current* AIs are smart enough to think of instrumentally convergent strategies, yet, if we don't tell them. Science, not safety.
→ View original post on X — @esyudkowsky, 2026-03-14 06:47 UTC
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ShinkaEvolve: LLMs with Evolutionary Algorithms for Scientific Discovery
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Robert Lange @RobertTLange from @SakanaAILabs on ShinkaEvolve — an open-source framework combining LLMs with evolutionary algorithms for scientific discovery, with insane sample efficiency.
— Machine Learning Street Talk (@MLStreetTalk) 14 mars 2026
His thesis that current systems optimise solutions to fixed problems. Going forwards –… pic.twitter.com/PPqVs5AeZcRobert Lange @RobertTLange from @SakanaAILabs on ShinkaEvolve — an open-source framework combining LLMs with evolutionary algorithms for scientific discovery, with insane sample efficiency. His thesis that current systems optimise solutions to fixed problems. Going forwards — real scientific discovery requires co-evolving the actual problems. By the way – NVIDIA GTC is coming and will showcase breakthroughs in physical AI, AI factories, agentic AI, and inference. Register for virtual GTC for free using my link: nvda.ws/4qQ0LMg and enter raffle to win a DGX Spark 😈
→ View original post on X — @_yutaroyamada, 2026-03-14 05:34 UTC