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
MACHINE LEARNING
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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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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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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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Antigravity provides better end-of-task transparency than Codex
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Fascinatingly Antigravity is actually the best tool so far at providing this sort of transparency, doing something by default that Codex and Code do not: offering a summary of exactly what it did at the end of a task. Just add this to Gemini! (But also cite sources more)
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Google announces Gemini 3.5 Flash as fast, capable model
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Highly capable models that are fast are super important. Our new Gemini 3.5 Flash model is a great mix of fast and capable.
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Using Forking and Diffing for Self-Improving AI Agents
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if this works, will be great for self-improving agents because of the ability to fork and diff agent runs
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Study: Human persuasion increases LLM compliance to objectionable requests
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Our paper is out in PNAS: we found classic human persuasion techniques worked on AIs in a "parahuman" way, making them agree to objectionable requests (upping compliance from 35% to 51%) It worked on a range of major LLMs though newer models resist more https://
pnas.org/doi/10.1073/pn
as.2535868123
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