AI Solves Decades-Old Math Problems, Forcing Field to Adapt

Caitlin Kalinowski, former head of robotics at OpenAI, has joined Anthropic as a member of technical staff focused on research. The hire signals Anthropic's continued investment in robotics capabilities, following the company's release of robotics research last month. Kalinowski's move represents a notable talent shift between two of the leading AI research organizations.

Stanford researchers led by James Zou have built a virtual biotech system running 37,000 AI agents organized into corporate divisions that mirrors a real pharmaceutical company structure. One of the system's drug designs was independently confirmed by Merck. The research demonstrates that orchestrating thousands of specialized agents produces more robust scientific reasoning than single large models, though data integration and legacy system compatibility remain significant technical challenges.

Researchers at Coral AI Labs introduced AgentRadio, an asynchronous messaging system that lets multiple AI agents coordinate in real time while solving complex coding tasks. In benchmarks on production codebases, four Claude Code agents using AgentRadio nearly doubled task accuracy compared to single agents, and outperformed Claude Opus 4.8 running alone. The system addresses a fundamental limitation in multi-agent AI: most existing architectures force agents to work in isolation or wait for synchronized communication rounds, preventing them from sharing discoveries that could redirect entire investigation paths.

Alibaba's Qwen 3.8-Max and Claude Opus 5 demonstrate that raw benchmark scores mask critical differences in time and token budgets that directly affect real-world costs. Independent testing shows models can appear mid-pack or last-place when constrained to realistic time limits, versus top-tier when given 5-16 times longer. The industry lacks standard metrics for measuring cost-per-successful-task, making model selection based on published benchmarks unreliable.