Learning Situated Awareness in the Real World
RESEARCH
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Frontier Safety Roadmap and Risk Report Documentation Released
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And you can find links to all relevant RSP documents, including the initial Frontier Safety Roadmap and the initial Risk Report, here:
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Frontier AI Safety Roadmaps and Risk Quantification Commitments
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We’re now separating the safety commitments we’ll make unilaterally and our recommendations for the industry. We’re also committing to publish new Frontier Safety Roadmaps with detailed safety goals, and Risk Reports that quantify risk across all our deployed models.
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LLM Performance Degradation Over Extended Context Windows
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Performance degrades over the course of 100k tokens even, let alone the whole currently supported window… After a few turns of coding Python, it just can't reliably use its tools anymore. Requires constant jumping back and/or offloading.
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Midtraining in ML Pipelines: When It Helps and When It Fails
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Midtraining is a new part of many training pipelines, but when does it help and can it backfire? 🤔 In our new preprint, we use controlled experiments to pin this down. TL;DR; midtraining helps the most when it “bridges” pretraining and posttraining, and mitigates forgetting after posttraining. Timing is also very important. 🧵
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Introducing the LFM2-24B-A2B Model on Hugging Face
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huggingface.co/LiquidAI/LFM2… [Translated from EN to English]
→ View original post on X — @maximelabonne, 2026-02-24 17:48 UTC
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Unfortunate timing: 24B-A2B release coincides with Qwen3.5 launch
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Releasing a 24B-A2B model on the same day as Qwen3.5-35B-A3B is NOT great timing 🥲 Qwen (@Alibaba_Qwen) 🚀 Introducing the Qwen 3.5 Medium Model Series Qwen3.5-Flash · Qwen3.5-35B-A3B · Qwen3.5-122B-A10B · Qwen3.5-27B ✨ More intelligence, less compute. • Qwen3.5-35B-A3B now surpasses Qwen3-235B-A22B-2507 and Qwen3-VL-235B-A22B — a reminder that better architecture, data quality, and RL can move intelligence forward, not just bigger parameter counts. • Qwen3.5-122B-A10B and 27B continue narrowing the gap between medium-sized and frontier models — especially in more complex agent scenarios. • Qwen3.5-Flash is the hosted production version aligned with 35B-A3B, featuring: – 1M context length by default – Official built-in tools 🔗 Hugging Face: huggingface.co/collections/Q… 🔗 ModelScope: modelscope.cn/collections/Qw… 🔗 Qwen3.5-Flash API: modelstudio.console.alibabac… Try in Qwen Chat 👇 Flash: chat.qwen.ai/?models=qwen3.5… 27B: chat.qwen.ai/?models=qwen3.5… 35B-A3B: chat.qwen.ai/?models=qwen3.5… 122B-A10B: chat.qwen.ai/?models=qwen3.5… Would love to hear what you build with it. — https://nitter.net/Alibaba_Qwen/status/2026339351530188939#m
→ View original post on X — @maximelabonne, 2026-02-24 17:01 UTC
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Learning from Experience: Definition of Machine Learning
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Learn from Experience! — the key to success in life and in Machine Learning. "Machine learning is the study of computer algorithms that automatically improve with experience." — From the first page of Tom Mitchell's book [1997]: https://
cs.cmu.edu/afs/cs.cmu.edu
/user/mitchell/ftp/mlbook.html
… My paraphrase: "ML is the -
Liquid AI Releases Largest LFM2-24B-A2B Model with Fast Inference
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Early checkpoint of our biggest LFM2 model to date 🎉 It shows good scaling and extremely fast inference vs. gpt-oss-20b and Qwen3-30B-A3B We'll release an LFM2.5 version with more pre-training and RL in a few months Liquid AI (@liquidai) Today, we release our largest LFM2 model: LFM2-24B-A2B 🐘 > 24B total parameters > 2.3B active per token > Built on our hybrid, hardware-aware LFM2 architecture It combines LFM2’s fast, memory-efficient design with a Mixture of Experts setup, so only 2.3B parameters activate each run. The result: best-in-class efficiency, fast edge inference, and predictable log-linear scaling all in a 32GB, 2B-active MoE footprint. 🧵 — https://nitter.net/liquidai/status/2026301771539202269#m
→ View original post on X — @maximelabonne, 2026-02-24 14:31 UTC
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ArXiv eBook: Linear Models in Machine Learning and Statistical Learning
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[Download 433-page PDF eBook] Linear Models in #MachineLearning and Statistical Learning: https://
arxiv.org/abs/2401.00649 ——————
#Mathematics #DataScience #LinearAlgebra #Statistics #DataScientist #ML