4/6 ReAct-style agents encode the entire plan in text, and only get evaluated at the very end. In long-horizon tasks, that turns execution into a high-variance gamble.
LLMS
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Maestro: Optimizing Compute Through Agentic Framework Structure
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2/6 SWE-bench is not the story. Maestro is not a dedicated SWE agent, it’s an agentic framework that optimizes based on structure and learned priors. ex: knowing that 4 runs of GPT-5 mini are cheaper and better than one GPT-5 run, leads to better use of the same compute –
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Test-Time Compute: Optimizing Where and When to Spend Resources
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3/6 Once you treat execution as a test-time compute problem, the question changes. It’s no longer “which model should I use?” It’s “where should I spend compute, and when should I stop?”. That’s why multiple cheaper runs can win:
you’re buying optional paths, not just more -
Orchestrated Test-Time Compute Scaling for Long-Horizon Agentic Tasks
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1/6 Long-horizon agentic tasks are breaking our mental models. More tokens, bigger models, and best-of-N only go so far. Orchestrated approach to test-time-compute scaling is what long-horizon tasks need. Here’s what we learned using SWE-bench as a test case. Read the blog for
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Generative AI Tech Stack: Six Layers Powering Autonomous Agents
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The Generative AI ecosystem is evolving into a full tech stack — powering autonomous AI agents.
From infrastructure and LLMs to RAG pipelines, agent behaviors and orchestration layers, this framework shows the 6 layers driving next-gen AI systems. Credit: @goyalshalini #AI -
Hinton: AI Can Tackle Math Autonomously as a Closed System
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Le père de l'IA, Geoffrey Hinton, affirme que les mathématiques sont un système fermé, ce qui permet aux IA de les aborder comme un jeu.
— VISION IA (@vision_ia) 7 janvier 2026
Elles peuvent se poser leurs propres problèmes, tester des démonstrations et apprendre de ce qui fonctionne, sans dépendre d’exemples humains.… pic.twitter.com/TCciauPV22Le père de l'IA, Geoffrey Hinton, affirme que les mathématiques sont un système fermé, ce qui permet aux IA de les aborder comme un jeu. Elles peuvent se poser leurs propres problèmes, tester des démonstrations et apprendre de ce qui fonctionne, sans dépendre d’exemples humains.
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Cursor reduces tokens by 46.9% with dynamic context
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Cursor release a dynamic context discovery for all models which reduced total tokens by 46.9%
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Native Parallel Reasoner: LLMs Learn Multi-Path Problem Solving
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What if an AI could truly think in parallel, not just fake it? Researchers at BIGAI introduce Native Parallel Reasoner (NPR), a new framework that teaches LLMs to break down and solve problems across multiple "thought" paths simultaneously, learning by trial and error. The
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Gemini hits 20% AI chatbot traffic share
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Gemini surpassed 20% traffic share threshold among the overall traffic for AI chatbots. Grok is above 3% now as well