Efficient #Robot navigation inspired by honeybee learning flights
by Dequan Ou Jesse J. Hagenaars @Nature Learn more: https://
bit.ly/4tEdhPB #Robotics #ArtificialIntelligence #Innovation #Technology
RESEARCH
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AI-Inspired Honeybee Navigation for Robots
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Analysis of RoPE Limitations in Long-Context LLMs
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“RoPE Distinguishes Neither Positions Nor Tokens in Long Contexts” RoPE is the default way LLMs track position, but this paper proves it breaks down as context gets long. Attention becomes close to random at telling near from far, and can even assign the same score to different
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Using Gemini 3.5 and Antigravity to Auto-Train AlphaZero for Go
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Pretty wild to be able to automatically train AlphaZero and serve a playable Go demo with just 2 prompts using Gemini 3.5 Flash and Antigravity 2.0 … https://t.co/rsz3jQKqtG
— Thang Luong (@lmthang) 20 mai 2026Pretty wild to be able to automatically train AlphaZero and serve a playable Go demo with just 2 prompts using Gemini 3.5 Flash and Antigravity 2.0 …
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Two real complexity classes: tractable and intractable in AI
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There are only two real complexity classes in computer science: tractable (computing a function's output from its inputs) and intractable (computing a function's inputs from its output). AI deals with the latter. The rest is details.
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UniVidX: a unified model for multiple video-generation tasks
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What if one AI could handle multiple video generation tasks without needing separate models for each? Researchers from HKUST, Stanford, Tsinghua, and other top labs present UniVidX. It uses three simple tricks: random condition masking to let the model learn any input-output
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Technical evaluation of recurrent language models and evolution strategies
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It actually holds up on real reasoning tasks like GSM8K and Countdown (not toy stuff) and matches GRPO pretty well. They even did full pretraining of a recurrent LM from scratch in pure int8. Sample efficiency is lower than backprop (normal for evolution strategies), but you get
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Google releasing AI tools to accelerate science
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Got to play with a little of this before launch as well. My experience as a social scientist was that it was more bioscience focused right now, but I think Google has been the leading lab in releasing serious AI tools to accelerate science & expect to see them improve fast.
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Release of Nemotron-Labs-Diffusion parallel generation language models
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Most language models only generate one token at a time.
— NVIDIA AI (@NVIDIAAI) 19 mai 2026
We just released Nemotron-Labs-Diffusion, a family of diffusion language models that take a different approach, generating multiple tokens in parallel within a single model. Rather than committing to each token permanently,… pic.twitter.com/fTOBmQ8KaMMost language models only generate one token at a time. We just released Nemotron-Labs-Diffusion, a family of diffusion language models that take a different approach, generating multiple tokens in parallel within a single model. Rather than committing to each token permanently,
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Gemini 3.5 Flash performance in BullshitBench
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BullshitBench update: Gemini 3.5 Flash did pretty badly – below a bit Gemma 4 even (31b high is 80th)
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Antigravity reveals model thinking traces
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The crazy thing is that Antigravity does this quite well! So it isn't like Google is hiding these thinking traces because of distillation or because they are full of insane mutterings. I guess you need to either do the work in Antigravity or not at all?