Researchers are using AI to decipher, transcribe and cross-reference documents that long remained out of reach. Beyond saving time, the technology is opening new avenues for studying the past.
A preprint shows that an agent can trigger a tool before the model has even voiced a refusal, revealing a gap between what the model says and what the orchestrator actually executes. The study also lists 253 verified bugs across 118 MCP servers, arguing that actions must be checked independently of model output.
CERN and Mistral AI want to teach an AI model to read particle collisions from the Large Hadron Collider. Billed as a world first, the project could change how physicists explore the subatomic world.
Internal notes from Microsoft and OpenAI acknowledge that by diverting traffic away from websites, AI is drying up the human-written content it depends on. That shortfall could in turn threaten the training of future models.
Latent Space's newsletter highlights major claims from Nick Cammarata that agents have disproved polysemanticity and substantially advanced interpretability and alignment. The headline frames these as claims rather than established results.
Researchers found that brain organoids, small clusters of neurons grown from stem cells, could recognize the Braille alphabet with high accuracy. The result feeds into work on computing with living neural tissue.