Roadmap: Here’s what’s next: August: Coding-optimized model September: Multimodal agent October: AI video generation The pipeline is heating up fast.
AGENTS
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Grok 4 Heavy: Multi-Agent Collaboration with 10x Compute
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Multi-Agent Brainpower
— Futurepedia – Learn to Leverage AI (@futurepedia_io) 15 juillet 2025
Grok 4 Heavy spins up several agents in parallel, then they collaborate, "compare notes," and pick the best answer—like an elite study group for AI.
Musk refers to it as the “heavy” approach—10× more compute at test time! pic.twitter.com/ITHPguF76hMulti-Agent Brainpower
Grok 4 Heavy spins up several agents in parallel, then they collaborate, "compare notes," and pick the best answer—like an elite study group for AI. Musk refers to it as the “heavy” approach—10× more compute at test time! -
Grok 4 Heavy: Multi-Agent AI System with PhD-Level Reasoning
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Unveiled: Grok 4 Heavy – a multi-agent AI powerhouse with PhD-level smarts that uses multiple reasoning agents to crack the hardest problems. This isn’t just evolution; it’s a revolution! #AI #Grok4 #grok4Heavy
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Clipped multiagents section from Latent Space podcast
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clipped the multiagents section https://
x.com/latentspacepod
/status/1944846464120754529
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Framework for Recreating Grok Heavy Functionality Across Models
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Releasing this tomorrow: A framework for recreating Grok Heavy functionality.
— Pietro Schirano (@skirano) 14 juillet 2025
In this video, I'm using Claude 4 Heavy, but it works with any model (GPT, Kimi, Gemini). pic.twitter.com/v5zLxTltN3Releasing this tomorrow: A framework for recreating Grok Heavy functionality. In this video, I'm using Claude 4 Heavy, but it works with any model (GPT, Kimi, Gemini).
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LLMs can leverage external tools like chess engines for task performance
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Y en realidad sí podría porque si se acepta usar software como el que usaría la Atari para ganar consistentemente, nada impide al LLM buscar en internet, instalarse un módulo de ajedrez en su sistema y usarlo como herramienta. Es el problema de creerse los argumentos de Marcus.
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MIRIX: Multi-Agent Memory System for LLM-Based Agents
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MIRIX: Multi-Agent Memory System for LLM-Based Agents Yu Wang, Xi Chen: https://
arxiv.org/abs/2507.07957 #ArtificialIntelligence #DeepLearning #MachineLearning -
Reinforcement Learning Field of View Limitations and Policy Optimization
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Yep I think RL is misleading in that it restricts field of view. Eg like you mentioned you can imagine review/reflect doing a lot more – building tools for later use, or actively tuning the distribution for what to try next (instead of just sampling from policy independently as