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RESEARCH
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Artificial intelligence and consciousness: do they advance at the same pace?
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La inteligencia artificial está transformando el mundo a una velocidad extraordinaria. La pregunta es si nuestra conciencia avanzará al mismo ritmo. pic.twitter.com/nPbeCbEMD1
— Juan Merodio (@juanmerodio) 15 juin 2026Artificial intelligence is transforming the world at an extraordinary speed. The question is whether our consciousness will advance at the same pace.
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Mythos: model class above Opus, Preview and Fable versions
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Mythos is not a model, it's a class of models (cf this for example: https://anthropic.com/glasswing) above Opus, of which Mythos-Preview was the first commercial version, and Fable is the first public version. In Babinet's assertions that I note (1-usage
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Homogeneity in LLM training: same evals, data, distillation
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When everyone uses the same evals, data, distillation and vendors to train LLMs. Courtesy of: https://
arxiv.org/abs/2512.15567 -
HarnessX: A Composable, Adaptive, and Evolvable Agent Harness Foundry
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HarnessX: A Composable, Adaptive, and Evolvable Agent Harness Foundry Paper: https://
arxiv.org/abs/2606.14249 -
Xiaomi’s HarnessX lets AI agents redesign their own runtime
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What if AI agents could redesign their own runtime on the fly? Darwin Agent Team From Xiaomi introduces HarnessX, a foundry that lets agent harnesses—prompts, tools, memory, and control flow—compose, adapt, and evolve automatically. Instead of hand-crafting scaffolding for
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Deep Learning with Co Architecture for Designing Supercomputer Utilization
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Deep Learning with Co Architecture for Designing Supercomputer Utilization! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming
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New book: A Practical Guide to Reinforcement Learning from Human Feedback
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New release from @PacktDataML at http://
amzn.to/3PMn1ZL "A Practical Guide to Reinforcement Learning from Human Feedback (RLHF)" 𝗔𝗺𝗮𝘇𝗼𝗻 𝘀𝘂𝗺𝗺𝗮𝗿𝘆: RLHF is a powerful approach to AI alignment and human-centered machine learning. By combining reinforcement learning -
Self-Evolving Multi-Agent Systems via Decentralized Memory
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Multi-agent systems typically share a single memory pool, but this causes agents to converge towards the same behavior and leads to a loss of useful specialization. This article attributes to
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Dense Supervision, Sparse Updates in On-Policy Distillation
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"Dense Supervision, Sparse Updates: On the Sparsity and Geometry of On-Policy Distillation" OPD uses dense teacher feedback on student-generated rollouts, so we might expect dense parameter rewriting. However, this paper shows that is not the case. They found that OPD updates