Usman Salis
⬤ A breakthrough in scientific AI emerged this week with the release of SciAgent, a unified multi-agent system built to tackle Olympiad-level challenges in mathematics, physics, and chemistry. The system achieves human gold-medal performance on some of the world's toughest academic tests. It works through a Coordinator Agent that analyzes each problem, determines the subject and complexity, then routes it to specialized Worker Systems that generate and verify complete solutions.
⬤ SciAgent delivers impressive results across major international competitions: 36/42 on IMO 2025, a perfect 100/100 on IMC 2025, 83.3 on IPhO 2025, and strong scores on chemistry Olympiad tasks. These numbers put it squarely in gold-medalist territory, matching or beating average scores from top human competitors across different fields.
⬤ The system adapts its approach based on the problem type. Math solutions use a generate-review-improve cycle to build rigorous proofs. Physics problems trigger a think-act-observe workflow combining equations, diagrams, and code simulations. Chemistry tasks rely on molecular structure analysis and SMILES-string validation to ensure reaction accuracy. Performance charts show SciAgent meeting or exceeding gold-medalist benchmarks across all tested competitions.
⬤ SciAgent represents a major leap in advanced scientific reasoning AI. As these systems reach expert-level performance in complex academic domains, they're reshaping expectations for automated research, scientific problem-solving, and technical workflows—with ripple effects across both academic research and industrial innovation.
Usman Salis
