#Machine Learning

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.

How TTT-E2E achieves constant inference latency regardless of context length by treating long context as a learning problem rather than an architecture problem.

How the same transformer architecture powering GPT learned to predict molecular properties by treating chemistry as a language problem

How researchers adapted BERT for molecular property prediction, turning SMILES strings into drug discovery insights

How generative diffusion models can serve as fast surrogates for expensive biological simulations, achieving 22x speedup while preserving the stochastic diversity that makes these models scientifically useful.

Machine learning models now outperform FDA-approved biomarkers in predicting treatment response, but the best-performing models often resist explanation. Here's how precision oncology is navigating the trade-off between performance and interpretability.

Recent research suggests RL training optimizes search efficiency over existing capabilities rather than expanding reasoning capacity. Here's what the pass@k evidence actually shows.

A deep dive into Project Silicon's proposal to build differentiable CPU simulators, enabling gradient-based optimization of assembly code and opening a new frontier in neural algorithm synthesis.

Why computational biologists should stop building embeddings and start building simulators, with three tractable project ideas you can implement today using flow matching, Neural ODEs, and cell fate trajectory modeling.

Understanding when you're working with an environment versus a benchmark changes how you design experiments, interpret results, and communicate findings. This guide covers the practical differences every RL practitioner should know.

DeepMind's DiscoRL discovers reinforcement learning algorithms that outperform hand-designed methods like PPO and DQN. By treating algorithm design as a meta-learning problem, it found alternatives to value functions and bootstrapping through optimization alone.

Are large language models merely stochastic parrots, or do they develop genuine internal representations of the world? This investigation examines evidence from Othello-GPT, spatial encoding in LLMs, and the symbol grounding problem to explore what cognitive science reveals about AI understanding.

Mixtral uses 46.7B parameters but only activates 13B per token. This architectural trick called Mixture of Experts powers Gemini 1.5, DeepSeek V3, and more. Learn how MoE works, its hidden costs, and when to use it.

Pedro Domingos proposes that neural networks and symbolic AI are the same mathematical operation - a logical rule can be equivalently written as a tensor equation in Einstein summation notation. If true, we've been building separate tools for problems that share identical structure.

A machine trained on simple grid worlds beat every hand-designed RL algorithm on Atari. DeepMind's DiscoRL discovers algorithms through meta-learning that outperform DQN, PPO, and A3C - methods humans spent decades developing.

GPT-4's 128K context window? It only uses about 10% effectively. Google's TITANS architecture introduces test-time memory learning that outperforms GPT-4 on long-context tasks with 70x fewer parameters.

Why biological systems offer the ideal training ground for reinforcement learning: automated verification through physics, not human judgment. From protein design with AlphaFold to RNA folding with ViennaRNA, biology provides the verifiable inverse problems that RL needs at scale.

Machine learning is illuminating biology's hidden half: intrinsically disordered proteins and RNA structures that traditional methods could never capture. But can we trust what we're seeing?

World models enable AI agents to imagine futures and plan actions, achieving 10-100x better sample efficiency than traditional reinforcement learning. From DreamerV3 collecting diamonds in Minecraft to foundation models like Sora and Genie, world models represent AI's shift from pattern matching to simulating reality itself.

New research reveals a disturbing truth: reasoning models maintain high performance until they hit a complexity threshold, then collapse entirely. They don't degrade gracefully - they fall off a cliff.

An AI system at Google DeepMind discovered how bacteria share genes across species barriers using 7 days of computational reasoning. When tested, it matched unpublished experimental observations exactly.

Foundation models trained on biological data are transforming protein structure prediction, genomics, drug discovery, and pathology. Learn how machine learning benchmarks in 2024 are revealing biology's dark matter through RNA analysis and metagenomic discovery.

DeepAgent bridges the gap between research and production AI agents with autonomous memory folding and reinforcement learning that scales from tens to thousands of tools.

When you provide GPT-4 with examples, it adapts without training. This post reveals the mechanism: ghost fine-tuning through implicit weight updates.

Discover why monolithic embeddings fail for RAG systems and learn how chunking strategies can transform your retrieval performance.

How graph-based representations are transforming language models beyond sequential text to unlock deeper reasoning capabilities