mamba 3: Mamba with RoPE! "Improved Sequence Modeling using State Space Principles" They show that state space models can have both speed and performance! In this new iteration, Mamba now has better recurrence design, complex-valued state tracking, and a MIMO update. It
@askalphaxiv
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New ArXiv Preprint 2603.15617 Available for Reading
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read more: https://alphaxiv.org/abs/2603.15617
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HorizonMath: Measuring AI Progress in Mathematical Discovery
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can AI do real math research? "HorizonMath: Measuring AI Progress Toward Mathematical Discovery with Automatic Verification" This paper turns a vague debate into a measurable test by introducing a contamination-resistant benchmark of 101 mostly unsolved problems across 8
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Access to a Scientific Article on arXiv from March 17, 2026
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read more: https://alphaxiv.org/abs/2603.14312
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Autonomous Agents Enable Decentralized Scientific Discovery Through Coordination
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"Autonomous Agents Coordinating Distributed Discovery Through Emergent Artifact Exchange" This paper shows how to turn AI from a single helpful assistant into a decentralized scientific lab. With many autonomous agents independently run tools, exchange traceable research
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AlphaXIV Integration in Claude Code via MCP
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Add to Claude Code: claude mcp add –transport http alphaxiv https://api.alphaxiv.org/mcp/v1 (and then run /mcp in claude to authenticate) Docs:
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MCP for arXiv: Research Agents Access Millions Papers
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Introducing MCP for arXiv
— alphaXiv (@askalphaxiv) 17 mars 2026
Let your research agents stand on the shoulders of giants
Fast multi-turn retrieval, keyword search, and embedding search tools across millions of arXiv papers 🚀 pic.twitter.com/qFWwzWynGDIntroducing MCP for arXiv Let your research agents stand on the shoulders of giants Fast multi-turn retrieval, keyword search, and embedding search tools across millions of arXiv papers
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Scientific Article AlphaXIV Referenced 2603.15031
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read more: https://alphaxiv.org/abs/2603.15031
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Attention Residuals: Selective Computation in Deeper Transformers
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“Attention Residuals” is now available on AlphaXiv! In standard transformer, every layer just inherits an equal sum of all earlier layers, so as models get deeper, useful computations get diluted instead of being selectively reused. The research team at @Kimi_Moonshot proposes
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MaxRL: Rethinking Reinforcement Learning Optimization
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RL Isn’t Actually Optimizing What We Think, And That’s a Problem Come join us for this AI4Science talk: Maximum Likelihood Reinforcement Learning (MaxRL). In this session, the author of MaxRL @FahimTajwar10 will cover their paper that takes a step back and asks a fundamental