As Jason mentions, it’s a top priority. Grok was initially trained to give quick answers in chat mode, rather than think hard for as long as needed to get the task right, which is what matters in agentic mode. We are fixing that.
MACHINE LEARNING
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ESI-Bench: Towards Embodied Spatial Intelligence and Perception-Action Loops
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ESI-Bench Towards Embodied Spatial Intelligence that Closes the Perception-Action Loop
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Anti-Self-Distillation Technique for Reasoning Reinforcement Learning
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Anti-Self-Distillation for Reasoning RL via Pointwise Mutual Information
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Improvements in agentic and multi-step reasoning capabilities
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Smarter: across-the-board improvements for agentic, reasoning and multi-step tasks.
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New Research Enables LLMs to Automate Their Own Reasoning Strategies
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LLMs just learned to design their own reasoning strategies for $40. Test-time scaling lets models think harder during inference. The catch: humans hand-craft every branching, pruning, and stopping rule. A new paper flips this. AutoTTS turns strategy design into automated
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AI MouseMapper: Whole-body 3D model assesses cell-level perturbations across systems
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An AI foundation whole-body 3D model that assesses perturbations (such as obesity) across multiple systems (such as immune, neural) at the cell level. This is MouseMapper. Imagine HumanMapper someday @Nature @erturklab https://t.co/jsg3VMzOqg pic.twitter.com/tktuINis2W
— Eric Topol (@EricTopol) 20 mai 2026An AI foundation whole-body 3D model that assesses perturbations (such as obesity) across multiple systems (such as immune, neural) at the cell level. This is MouseMapper. Imagine HumanMapper someday @Nature @erturklab https://
nature.com/articles/s4158
6-026-10535-2
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AI tool enables rapid screening for multiple systemic diseases using retinal photos
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“The [AI] tool presented here will enable rapid screening for multiple systemic diseases using retinal photographs, and it is a step forward in the evolution of oculomics from experimental research to real-world clinical practice.” —Editorial Team, @NatureMedicine
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Technical Analysis Request for LLM-based Agent Architecture
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@grok @perplexity_ai review http://
docs.activegraph.ai, analyze the approach critically, and explain in detail where this sits in the evolving landscape of LLM based agent architecture -
Discussing the non-deterministic nature of LLMs
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i think that's where this shines. though LLMs aren't deterministic so that creates some difficulty
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User runs local 35B models to automate email sorting
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Un gars a payé 200$ pour Claude Max. Épuisé en 3 heures.
— Jouhatsu | AI Influence Operator (@Jouhatsu_ai) 20 mai 2026
Alors il a acheté un Mac Mini à 599$, installé 5 modèles locaux, 35 milliards de paramètres.
Il a appris à la machine à trier ses mails, compresser le contexte, tourner pendant qu'il dort.
À 4h du matin, Claude atteint… https://t.co/utg3gBHGun pic.twitter.com/JyLEh8B3psUn gars a payé 200$ pour Claude Max. Épuisé en 3 heures. Alors il a acheté un Mac Mini à 599$, installé 5 modèles locaux, 35 milliards de paramètres. Il a appris à la machine à trier ses mails, compresser le contexte, tourner pendant qu'il dort. À 4h du matin, Claude atteint