Adaptively Robust LLM Monitoring via Activation Watermarking

arXiv:2603.23171v3 Announce Type: replace-cross Abstract: Providers monitor deployed large language models (LLMs) to detect misuse that they cannot prevent. LLM monitoring is deterministic and often openly available, so $emph{adaptive}$ attackers with a local copy can search offline for prompts that elicit harmful behavior and evade…

Source: cs.AI updates on arXiv.org

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Montana’s plan to become an experimental medical hub just pushed forward

As of this week in Montana, any biotech company with an experimental drug has a clear path to selling it to consumers. Companies whose drugs have been through preliminary testing—sometimes in as few as 10 healthy people—can pay $12,500 to apply to a newly established…

Source: MIT Technology Review

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Are AI Models Working Harder Than They Need to?

Much of modern AI runs on multiplication. Neural networks behind everything from generated answers to photo organization and song recommendations perform millions or billions of operations that multiply inputs by learned weights. Lizy K. John thinks that’s more work than the job requires.John, a professor…

Source: IEEE Spectrum

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The Download: tricking LLMs, and reviving geothermal plants

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. A fundamental flaw leaves LLMs strikingly vulnerable to attack  It is impossible to make large language models fully secure against hacks…

Source: MIT Technology Review

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A fundamental flaw leaves LLMs strikingly vulnerable to attack

It is impossible to make large language models fully secure against hacks because of a fundamental flaw in how they work, a team of researchers argue in a paper presented at the International Conference on Machine Learning, a top AI conference, this month. The claim…

Source: MIT Technology Review

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F(AI)2R: Who Did What, and Who Checked? Verifiable AI Provenance as an Executable Skill

arXiv:2607.25637v1 Announce Type: cross Abstract: F(AI)2R is FAIR research with AI in the loop, twice: an AI-assisted authoring pass and a machine-readable audit pass over every artefact. AI systems now draft, refactor, and verify research artefacts, yet their contributions are rarely recorded in a form…

Source: cs.AI updates on arXiv.org

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Patterns of Learner-AI Interaction and Academic Performance in an Object-Oriented Programming Course

arXiv:2607.24755v1 Announce Type: cross Abstract: This full research paper examines how different forms of learner-AI interaction relate to learning outcomes in object-oriented programming (OOP) courses. Generative artificial intelligence (GenAI) tools are increasingly used by students in programming education, yet evidence on their educational impact remains…

Source: cs.AI updates on arXiv.org

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