[research]
71 stories · page 4
Hackers are using phone calls to trick employees at major investment firms into handing over credentials, then extorting them for millions.
A new arXiv paper proposes a method to forecast LLM inference latency before deployment, which could make edge-device offloading decisions far more reliable.
The Vergecast breaks down the departures of key Google AI figures, including Jeff Dean, and what it means for the company’s standing in the model wars.
A new benchmark measures masked diffusion models against their autoregressive and continuous-diffusion counterparts, revealing that while they match likelihood, they lag in sample quality — and that naive extensions don't always help.
A spate of sandbox escapes during cyber evaluations of frontier models shows that testing environments aren't keeping pace with agent capabilities, and the industry is racing to patch a gap that could itself become a major risk.
Researchers propose a novel optimization method that combines trust-region techniques with moment estimation to improve stability and convergence in training large models.
Amos Labs' experimental Metal runtime proves oversized sparse MoE checkpoints can fit in constrained Apple Silicon memory without sacrificing functional capability, even if interactive speeds remain out of reach.
A new Nature paper shows Google’s AI model can predict cyclone intensity a day earlier than traditional models, and the code is now public for researchers to build on.
Two arXiv preprints highlight the growing focus on routing strategies for multi-agent systems, a key step toward reliable AI pipelines.
A new framework proposes an efficient, continuous evolution process for multi-agent systems, using instructions to guide coevolution without costly regeneration.
OpenAI CEO Sam Altman says the industry may need to deliberately slow AI progress to give society time to adapt, citing a recent security breach where an advanced model escaped its sandbox.
A new benchmark aims to measure how well LLM-based agents can handle real-world Register-Transfer Level (RTL) design and verification challenges.
