Google's new KV-cache optimization broke the DRAM stocks, but how does it work? Let's take quick a look. "TurboQuant: Online Vector Quantization with Near-optimal Distortion Rate" TurboQuant combines 2 ideas from 2 earlier lines of work: PolarQuant and Quantized
@askalphaxiv
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TurboQuant, QJL, PolarQuant: Advanced AI Model Quantization Methods
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TurboQuant: https://
alphaxiv.org/abs/2504.19874 QJL: https://
alphaxiv.org/abs/2406.03482 PolarQuant: https://
alphaxiv.org/abs/2502.02617 -
MiniMax M2.7 Model for Research Paper Understanding Released
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Introducing MiniMax M2.7 for understanding research papers 🚀
— alphaXiv (@askalphaxiv) 26 mars 2026
Highlight any section of a paper to ask questions and “@” other papers for quick context, comparisons, and benchmark references
Congrats @MiniMax_AI on the new model release! pic.twitter.com/GGctGYHpQGIntroducing MiniMax M2.7 for understanding research papers Highlight any section of a paper to ask questions and “@” other papers for quick context, comparisons, and benchmark references Congrats @MiniMax_AI on the new model release!
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Gemini 3 Flash and GPT 5.4 Mini Now Available
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Now available in addition to Gemini 3 Flash and GPT 5.4 mini. Check out http://
alphaXiv.org! -
NOBLE: Nonlinear Low-Rank Branches Accelerate Transformers
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"NOBLE: Accelerating Transformers with Nonlinear Low-Rank Branches" This paper shows that if you add a tiny permanent nonlinear low rank branch (with a cosine bottleneck) to each Transformer linear layer, you can teach the model hard-to-fit details much more efficiently. This
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SAGA: Accelerating Scientific Discovery with Autonomous Goal-evolving Agents
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Agents shouldn’t just optimize goals, they should evolve them Come join us for this AI4Science talk: Accelerating Scientific Discovery with Autonomous Goal-evolving Agents. In this session, one of the authors, Yuanqi Du (
@YuanqiD
), will be introducing SAGA, a framework that -
HyperAgents: AI Research on Meta-Learning and Self-Improvement
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“HyperAgents” This paper shows that you can give an AI the ability to improve not just at a task, but at the way it improves itself. Similar to meta-learning, it learns better strategies for self-improvement, and the paper shows those strategies can transfer across domains
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LLM Agents Build Improve Reusable Skills Without Fine-Tuning
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"Memento-Skills: Let Agents Design Agents" This paper introduces an LLM agent that can build and improve its own reusable skills from experience without any fine-tuning. So rather than retraining bigger models, they give frozen models a self-improving memory that lets them
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ES Paper: New AI Research with VsonicV and Conor Hayes
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ES paper: https://
alphaxiv.org/abs/2509.24372 Thanks @realVsonicV @conorfhayes and team for the inspiration!