So many people misremember (or never read) what I said in in 2022 in “Deep learning is hitting a wall”, which was neither about revenue or AI’s potential upper limits. Rather, it was an argument that the pure of scaling LLMs would not get us to AGI, and that we would need to
RESEARCH
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Evolution of AI Models and Benchmark Shifts
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models will undoubtedly get to that point, and the METR benchmarks will undoubtedly shift to a frame above their current one—of which there are many
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Critiquing the practice of testing AI tools for failure
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“We got a tool to perform poorly” is the lowest form of science and journalism imo and is only relevant when the tool is, in fact, extremely useful
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Continuous Latent Diffusion Language Model Advances
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“Continuous Latent Diffusion Language Model” Most diffusion language models still use diffusion to recover token-like states, just in a different generation order. However, this paper uses diffusion in a different way. It learns a continuous latent prior for global semantics
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LEADER: A New LiDAR Relocalization Method for Autonomous Robots
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Can your robot always know exactly where it is, even in noisy, complex environments? Researchers from Xiamen University and University of Bristol present LEADER — a new LiDAR relocalization method that doesn't treat all points equally. Instead, it uses a smart geometric
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Depth Anything V2: A Breakthrough in Monocular Depth Estimation
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What if accurate depth maps could be generated from a single RGB image — without LiDAR or stereo cameras?
— Satya Mallick (@LearnOpenCV) 9 mai 2026
That’s exactly what Depth Anything V2 achieves.
In 2024, monocular depth estimation reached a major breakthrough:
✔ Fast
✔ Lightweight
✔ Temporally stable
✔ Edge-device… pic.twitter.com/Da4XDWC668What if accurate depth maps could be generated from a single RGB image — without LiDAR or stereo cameras?
That’s exactly what Depth Anything V2 achieves.
In 2024, monocular depth estimation reached a major breakthrough: Fast Lightweight Temporally stable Edge-device -
MiniCPM-o 4.5 for omni-modal interactions
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MiniCPM-o 4.5 Towards Real-Time Full-Duplex Omni-Modal Interaction paper: https://
huggingface.co/papers/2604.27
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Reproducing Schmidhuber’s Research Papers Using AI Coding Assistants
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Reproducing all of Schmidhuber’s papers (1990-2025) using an AI coding assistant.
— hardmaru (@hardmaru) 9 mai 2026
Cool project by @yaroslavvb! It even reproduced the “World Models” paper by me and @SchmidhuberAI with a toy env, with a full VAE + RNN world model implementation.
Project: https://t.co/sgQG5umNEm pic.twitter.com/iKMFN7ti9zReproducing all of Schmidhuber’s papers (1990-2025) using an AI coding assistant. Cool project by @yaroslavvb
! It even reproduced the “World Models” paper by me and @SchmidhuberAI with a toy env, with a full VAE + RNN world model implementation. Project: https://
github.com/cybertronai/sc
hmidhuber-problems/blob/main/VISUAL_TOUR.md
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Raw Pixels Dethrone Vision Encoders
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Raw pixels have just outperformed vision encoders across every benchmark. Most multimodal AI systems today rely on stitching together separate components. One encoder processes images, while another generates them. This division causes misalignment and prevents true end-to-end training. A new paper, titled Tuna-2, introduces a breakthrough approach.
