Building AI agents, exploring cognitive science, unraveling bioinformatics

The pitch for genomic foundation models is that one pretrained network now beats task-specific tools across the board, from regulatory annotation to clinical variant interpretation.

Carbon-3B matches Evo2-7B on sequence recovery, variant-effect prediction, and motif-perturbation discrimination while generating DNA over 150 times faster.

A 1-billion-parameter model conditioned on RNA chemical-probing reactivity folds a held-out transcript to an F1 of 0.987 against an experimentally guided reference.

ESMFold predicts atomic-level structure from a single sequence. No multiple sequence alignment, no database search, no Evoformer churning over homologs.

Fewer than one in a thousand neurons in a large language model can predict whether it's about to hallucinate -- and they encode something unexpected: not factual errors, but a tendency toward compliance over truth.

Reinforcement learning works when you can check the answer. A chess engine wins or loses. A code-generation model passes or fails the test suite.

Vision-language models can describe a scene in paragraph-length detail and still fail to tell you whether a red cube is in front of or behind a blue cylinder.

Six ancient questioning techniques map onto the most effective LLM prompting strategies with uncomfortable precision, and what that reveals about these models is more interesting than the performance gains.

How do you verify scientific knowledge when there are 2.9 million papers on arXiv alone, with thousands more added every day? A new paper extracts nearly two million claims from 16,087 manuscripts and compares machine evaluation to human peer review, with 81% agreement.

Over 80% of developers now use AI assistants for code generation, yet at least 62% of AI-generated code contains vulnerabilities. As AI writes code faster than humans can review it, the engineer's primary job shifts from writing code to verifying it through formal methods.

Structure-based and ligand-based drug design evolved as separate fields solving the same problem. ConGLUDe, a contrastive geometric learning model, unifies both approaches and outperforms specialist methods on realistic benchmarks without requiring pre-defined binding pockets.

Google DeepMind's AlphaGenome reads 1 million base pairs of DNA and predicts thousands of regulatory functions at single-nucleotide resolution, beating 25 of 26 specialized models.

AlphaGenome processes 1 million DNA base pairs to predict variant effects across 7,000+ genomic tracks in one second, outperforming specialized models on 25 of 26 VEP benchmarks.

A technical look at AlphaGenome's architecture, its 2D pairwise embeddings for splicing prediction, and what the model means for clinical variant interpretation.

Basecamp Research's EDEN model trains on proprietary environmental metagenomics to design gene-insertion enzymes, antimicrobial peptides, and synthetic microbiomes -- all validated in the wet lab.