MiroThinker: Pushing the Performance Boundaries of Open-Source Research Agents via Model, Context, and Interactive Scaling https://
buff.ly/jXip8ln
#AI #MachineLearning #DeepLearning #LLMs #DataScience
MACHINE LEARNING
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MiroThinker: Scaling Open-Source Research Agents Performance
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Epiplexity: Measuring Learnable Structure for Bounded Intelligence
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Now Trending on alphaXiv From Entropy to Epiplexity: Rethinking Information for Computationally Bounded Intelligence This paper introduces epiplexity, a way to measure how much learnable & reusable structure a computationally limited model can extract from data This
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Jacobi Forcing: Generate Multiple Tokens in Parallel Without Quality Loss
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What if you could generate 4-5 tokens at once without losing quality? Researchers from UC San Diego, SJTU, and Snowflake introduce "Jacobi Forcing." It smoothly converts standard autoregressive models into parallel decoders by training them on their own predicted token
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9 Essential AI Skills for 2026: From Prompting to System Design
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9 AI skills that will matter in 2026 Prompting • Agents • RAG • Automation • Tool stacking • Multimodal • Fine-tuning • Evaluation Using AI is table stakes.
Designing AI systems is the edge. Credit: @rathanuday #AI #GenAI #FutureOfWork #AIAgents -
UniCardio AI Model Cleans Noisy Heart Signal Data
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This AI could fix messy heart data and even generate missing signals. UniCardio is a unified model that uses the relationships between heart signals to clean up noisy wearables, fill in gaps, and translate between signal types. It beats specialized models in denoising &
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Digital Red Queen: AIs Evolve Through Competitive Self-Play
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Sakana makes AIs "hunt" each other, and they begin to converge in evolution
— 机器之心 JIQIZHIXIN (@jiqizhixin) 10 janvier 2026
Researchers from MIT & Sakana AI present "Digital Red Queen."
They use LLMs in a self-play loop to evolve "warrior" programs that compete in Core War, a classic programming game. Each new warrior must… https://t.co/n9Los852dUSakana makes AIs "hunt" each other, and they begin to converge in evolution Researchers from MIT & Sakana AI present "Digital Red Queen." They use LLMs in a self-play loop to evolve "warrior" programs that compete in Core War, a classic programming game. Each new warrior must
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Alpamayo: First Reasoning Autonomous Vehicle AI at CES2026
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Alpamayo: The first reasoning autonomous vehicle AI announced at #CES2026.
— Lucian Fogoros (@fogoros) 9 janvier 2026
It processes sensors to control driving, reasons about actions, and learns from human drivers for natural behavior. #AutonomousVehicles #PhysicalAI pic.twitter.com/gGbfiPuAmiAlpamayo: The first reasoning autonomous vehicle AI announced at #CES2026.
It processes sensors to control driving, reasons about actions, and learns from human drivers for natural behavior. #AutonomousVehicles #PhysicalAI -
AI Transforms People Insights Beyond Traditional Dashboards
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Free Your Data: How AI Is Taking People Insights Beyond The Dashboard
#AI #AIio #AIInnovation #ML #DataScience #Futureofwork @fabiomoioli @pascal_bornet @alliekmiller @mattshumer_ @OfficialLoganK @jeremyphoward @GaryMarcus -
Cosmos Foundation Models Enable Physical AI for Robotics and Autonomous Systems
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"The “ChatGPT moment” for physical AI is nearly here. 👏
— NVIDIA AI (@NVIDIAAI) 9 janvier 2026
Cosmos open world foundation models understand reality to create and reason about rich synthetic worlds—so self-driving cars and robots can learn from endlessly varied, physics-aware scenarios.
From a single image, 3D… pic.twitter.com/PzICiMNL8o"The “ChatGPT moment” for physical AI is nearly here. Cosmos open world foundation models understand reality to create and reason about rich synthetic worlds—so self-driving cars and robots can learn from endlessly varied, physics-aware scenarios. From a single image, 3D
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AI21 Labs Shares Research on Scaling Agentic SWE-bench Evaluation
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Our Research team just dropped a few behind-the-scenes blogs on scaling agentic SWE-bench evaluation, including the failure modes we hit and what finally worked. I'm curious to hear your thoughts about our work