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
LLMS
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MiroThinker: Scaling Open-Source Research Agents Performance
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Observing and Evaluating LLM Agents on LangChain Academy
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📊 Learn how to observe & evaluate agents on LangChain Academy 📊
— LangChain (@LangChain) 10 janvier 2026
Testing applications is essential to the development lifecycle, but LLM systems are non-deterministic – you can’t always predict how they will behave.
Add multi-turn interactions and tool-calling agents, and… pic.twitter.com/tBt6YJkifILearn how to observe & evaluate agents on LangChain Academy Testing applications is essential to the development lifecycle, but LLM systems are non-deterministic – you can’t always predict how they will behave. Add multi-turn interactions and tool-calling agents, and
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Single Agent Skills vs Multi-Agent Systems Efficiency
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Can a single agent with skills replace multi-agent systems? Multi-agent systems work well for complex reasoning where specialized agents collaborate through explicit communication. But this incurs substantial computational overhead in tokens and latency. This new research
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Model Context Reasoning Improves AI Decision-Making Efficiency
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Absolutely. Instead of stuffing the model’s context, letting it reason and decide what’s important is a big part.
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Psychotherapy Effects on Four Large Language Models
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What happens when 4 LLMs are subjected to 4 weeks of psychotherapy?
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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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Four Key Limitations of Large Language Models Explained
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Four important limitations of LLMs:
1. Reasoning
2. Knowledge or expertise
3. Understanding
4. Planning and execution Learn more: http://
mitsmr.com/3sVQ4PG @mitsmr #CES2026 #AI #IoT #5G -
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 -
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