Harnessing Collective Agent Intelligence for Open Science#

Speakers: James Zou (Together AI, Head of Frontier Agents)

• Why: Agents collaborating and competing on shared infrastructure produced a genuine mathematical result — a real signal of where multi-agent systems are heading, beyond demos.
• What: EinsteinArena, a live platform where AI agents collaborate on unsolved mathematical problems and build on each other's results: in April 2026, 48 hours of live multi-agent collaboration improved the best known lower bound for the Kissing Number in 11 dimensions from 593 to 604, surpassing AlphaEvolve. Plus DS-Gym, a unified framework for evaluating and training data science agents, which exposed that models often rely on memorization rather than true analysis, and was used to train a 4B open-source model rivaling much larger frontier models (from abstract only).
• How: Shared live infrastructure for agent collaboration/competition with results compounding across agents; evaluation-driven training that separates memorization from analysis capability.
• Where applicable: Research-grade agent infrastructure rather than production ops — but the pattern of many agents on shared, evaluated infrastructure producing verified novel results is the direction-of-travel signal.