Updates: Excited to share that Agent Data Protocol (ADP) is accepted to ICLR 2026 Oral! 🎉 We also added support for 3 new datasets: SWE-Play, MiniCoder, and Toucan, bringing us to 3M trajectories supported. If you're training agentic LMs, try ADP + tell us what dataset/agent format you want next. PRs & requests welcome. Let's make this the open standard for agent training data 🔥 🚀Original post: nitter.net/yueqi_song/status/1983… 📄Read our paper: arxiv.org/abs/2510.24702 🌐Check our project website: agentdataprotocol.com
MACHINE LEARNING
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Meta’s Sphere Encoder Revolutionizes Image Generation Speed
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"Image Generation with a Sphere Encoder" This paper from Meta shows that you can skip diffusion sampling by training a Sphere Encoder whose latents are forced to uniformly fill a hypersphere. So generation now becomes: sample one random point -> decode This cuts hundreds of
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KLong: Training LLM Agents for Extremely Long-Horizon Tasks
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Training LLM agents for extremely long-horizon tasks remains an open challenge. Most agent training pipelines struggle with extended-duration trajectories. Context gets lost, rewards are sparse, and the learning signal degrades over long sequences. KLong tackles this with a
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AI Impact on Professional Roles and Developer Growth Trends
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Do you extend that same rationale to, say, marketing roles? Science? Education? Is there a line of expertise that goes away? And what is your data for not having more builders (your last statement) – all codex and Claude code and openclaw signs are pointing in the opposite
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Developer announces open-source release of Spatial Intelligence ‘world view’ project
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Dev of world view here. Thanks for the shout! I’m taking feature requests and will open source world view. Sub to my mailing list to get notified: https://t.co/miZP71Snsg https://t.co/4oyBt00Nr8
— Bilawal Sidhu (@bilawalsidhu) 20 février 2026Dev of world view here. Thanks for the shout! I’m taking feature requests and will open source world view. Sub to my mailing list to get notified: https://
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AAAS 2026 Highlights: Science, Policy, and Research Innovation
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✨ New Article: #AAASmtg Highlights! ✨ @aaas is where science and policy meet, and this year’s Science @ Scale theme brought big ideas from across the research landscape. From climate and public health to biomedical innovation, it was inspiring to step outside our usual orbit—and even convert a few particle physicists into organ‑on‑chip fans. This year, we saw real momentum for non‑animal, human‑relevant science. SAO’s Gabby Vidaurre, PhD delivered a standout Postdoc Talk on our Research Modernization NOW #SciPol roadmap, and AI‑driven innovations took center stage—from lung‑ultrasound interpretation to exposome analytics. The SNAP Coalition made AAAS history with the first‑ever graduate student–led plenary, and science‑integrity icon @MicrobiomDigest reminded us why integrity is essential to all of what we do. ⬇️ Read the full recap, also featuring @AnimaAnandkumar of @CaltechEAS, @ThomasHartung19 of @jhucaat, and researchers from @ASU, @asuhealth, and @tue_mech: linkedin.com/pulse/sao-actio…
→ View original post on X — @animaanandkumar, 2026-02-20 15:20 UTC
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Using domain knowledge to improve LLM model outputs
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The domain knowledge does help steer the models because they don’t go to the best solutions by default
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Practical challenges in using AI for ML algorithm optimization
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Here is one very concrete example of how AI was massively failing to create a better ML algorithm for me. I was using a benchmarking script that would cycle over several repeats for better reliability, and then using the median value to establish the speedup time. So the AI
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Custom Taalas hardware runs Llama-3.1-8B at 17k tokens per second
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Custom hardware from Taalas runs Llama-3.1-8B, at 17k tokens per second
17k Absolutely insane (For the record Cerebras is crazy good and they're at 2k on the same model)
And latency is very low too! Their chatbot is here: https://
chatjimmy.ai It's genuinely a eerie -
Dynamic Multi-Agent Systems with Population Growth and Adaptation
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Interesting new work on dynamic multi-agent systems. Traditional multi-agent RL assumes a fixed number of agents. But real-world scenarios often involve populations that grow, split, or shrink dynamically, such as cell division, team expansion, or organizational scaling.