Google DeepMind

Prompt injection defenses can degrade LLM agent utility by altering benign behavior; RAISED achieves robustness without this cost through self-distillation on generated scenarios.

ArchAgent v2 outperforms hand-designed solutions by achieving a 3.8% IPC speedup through innovative multi-level data prefetching strategies.

TRIM effectively reduces AI-generated code redundancy by up to 32.9%, preserving performance while tackling the growing problem of CodeSlop in software development.

Claw-like agents are vulnerable to severe security breaches, with malicious plugins achieving a 100% success rate in attacks.

LLM critiques can be systematically evaluated for alignment with human judgment, revealing that better models significantly enhance evaluation reliability.

Achieving sound probabilistic verification for AI agents could redefine how we secure complex systems against policy violations in uncertain environments.

AI can now autonomously solve open math problems, cracking 9 Erdős problems and 44 OEIS conjectures at a reasonable cost.

Ethics interventions in AI development often fail because practitioners don't trust them – here's a breakdown of why, and how to fix it.

LLMs can now generate more relevant and factual movie recommendations by dynamically bridging retrieval and generation with a novel reinforcement learning approach.

Forget black-box policies: CSRO uses LLMs to generate human-readable code policies in multi-agent RL, achieving performance competitive with traditional methods.

LLM-powered diagnostic AI is ready for prime time: a real-world clinical trial shows it's safe, patients love it, and doctors find it useful.

LLMs are becoming "epistemic agents" that shape our knowledge environment, so we need a new framework for evaluating and governing them based on trustworthiness, not just performance.

LLMs can autonomously discover novel MARL algorithms that outperform hand-designed baselines, revealing untapped potential in automated algorithm design.

Forget rigid heuristics: this adaptive AI delegation framework dynamically adjusts task allocation, authority transfer, and trust-building, promising more robust agentic systems.

People prefer AI advisors, but AI delegates that autonomously negotiate on their behalf actually lead to higher individual gains and improve overall group welfare in multi-party bargaining games.