PROMPT 7 – El asesino de suposiciones "Enumere todos los supuestos que la MAYORÍA de estos artículos comparten
pero nunca lo pruebe ni lo justifique explícitamente. Para cada supuesto:
-Dilo claramente
– Nombra 1 o 2 artículos que más se basan en él.
– Explique qué pasaría con
MACHINE LEARNING
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Prompt Engineering Framework for Analyzing AI Research Assumptions
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Gemini Excels in Benchmarks, Becoming Top AI for Code
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The ACM Turing Award will be announced tomorrow. Any guesses on who won it?
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Composer Uses RL Self-Summarization to Cut Compaction Error 50%
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We trained Composer to self-summarize through RL instead of a prompt. This reduces the error from compaction by 50% and allows Composer to succeed on challenging coding tasks requiring hundreds of actions.
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Preserving Figures in AI Chat: A Solution for Scientific Papers
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When you ask ChatGPT, Claude, Gemini about a paper, they strip out the figures and give you prose. Scientists choose to include plots and diagrams for a reason — they are dense and precise. I've cooked a way to preserve them in our chat agent, with architecture diagrams, tables, plots, linked back to the source PDF. How was this not already a thing? tiptreesystems.com/blog/comp…
→ View original post on X — @hugo_larochelle, 2026-03-17 17:50 UTC
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KARL: Knowledge Agents via Reinforcement Learning Research
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"You can think of this as RAG — but like RAG plus plus plus plus plus plus, all the way up to 200 vector database calls." — @jefrankle
, Chief AI Scientist at Databricks. We published new research on KARL — Knowledge Agents via Reinforcement Learning — a RAG agent trained -
Axelera AI Partners with DeGirum for Edge AI Solutions
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We're excited to announce our partnership with DeGirum, bringing their PySDK and face recognition platform to Axelera AI's Metis® edge AI accelerators for a powerful, production-ready edge AI solution. What our customers get with DeGirum: * Industry-leading inference performance on Metis hardware * An easy-to-use platform for building and deploying AI solutions * An accessible platform that removes barriers to edge AI deployment * Axelera customers receive one year of premium technical support from DeGirum at no cost The best edge AI outcomes come from pairing the right software with the right hardware. That's exactly what this partnership delivers. Read the full announcement: eu1.hubs.ly/H0sKxpT0 #EdgeAI #ComputerVision #AIAcceleration
→ View original post on X — @axeleraai, 2026-03-17 16:22 UTC
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AI-Science Feedback Loop: Models and Scientists Advancing Together
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The feedback loop between domain experts and AI engineers is exactly what makes AI for science so exciting. The model gets better from the scientist, the scientist gets to explore more because of the model.
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Replit Agent 4: Faster Planning, Design, and Development
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Move faster with Agent 4
— Replit ⠕ (@Replit) 17 mars 2026
• Plan, design, and build at the same time
• Build multiple features in parallel
Try now at: https://t.co/aQCQXj8cbH pic.twitter.com/eN4G4aqdCCMove faster with Agent 4 • Plan, design, and build at the same time
• Build multiple features in parallel Try now at: http://
replit.com/agent4 -
AI Secret: Scale versus Proprietary Techniques
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Is there a "secret sauce" driving the development of AI models?
— MIT CSAIL (@MIT_CSAIL) 17 mars 2026
FutureTech researchers at MIT CSAIL found that at the frontier, sheer scale drives LLM performance, but proprietary techniques & algorithmic advances matter more away from it: https://t.co/zi6jCN7twT pic.twitter.com/UpDdLG7c6YIs there a "secret sauce" driving the development of AI models? FutureTech researchers at MIT CSAIL found that at the frontier, sheer scale drives LLM performance, but proprietary techniques & algorithmic advances matter more away from it: bit.ly/4l76v2g [Translated from EN to English]
→ View original post on X — @mit_csail, 2026-03-17 16:00 UTC