AI for biology has several evidence levels. Keep them separate
Biological agents can turn existing measurements into useful leads. A map of the evidence shows where interpretation ends and validation begins.
Topic
AI research
AI writing, AI reviewing and the verification bottleneck
Citation matching, technical criticism, executable checks and formal proofs answer different questions. A map of what AI review can establish.
Apertus 1.5 broadens the public-model research stack
Apertus adds image and audio input, with public tooling that exposes the work between a text checkpoint and a multimodal assistant.
FlashProve and the limits of a cryptographic AI receipt
FlashProve makes model execution a checkable claim. Its research prototype also exposes the limits of what an AI receipt can guarantee.
Inside Google’s building blocks for agent-assisted wet labs
Google’s lab studies separate physical execution, molecular search and experimental validation. The distinctions explain what the agents achieved.
Model merging versus distillation: what the new evidence compares
Merging changes parameters; distillation trains on behavior. P²-MOPD explores a hybrid, with evidence bounded by its models, teachers and training budget.
The sleep framework puts forgetting at the centre of AI learning
Sleep consolidates temporary information into model parameters. Its experiments clarify what retention means and what the extra computation buys.