Research & Development
AI agent research, benchmarks, frameworks, and datasets. We track and explain significant developments shaping the future of AI agents.
Featured Research
Stanford AgentBench Framework
AgentBench is a comprehensive benchmarking framework developed by Stanford researchers to evaluate LLM-based agents on real-world interactive tasks. It tests agents across operating systems, databases, knowledge graphs, and web environments, providing standardized metrics for comparing agent capabilities.
MIT Agent-Based Drug Discovery System
MIT researchers have developed a multi-agent AI system capable of autonomously screening molecular compounds, predicting binding affinities, and designing synthesis pathways for potential drug candidates. The system demonstrates how coordinated AI agents can accelerate pharmaceutical research.
Anthropic Model Specification Research
Anthropic's Model Spec research explores methods for precisely specifying how AI models should behave in various scenarios. This work is crucial for building reliable AI agents that follow intended guidelines while remaining helpful, providing a foundation for safer agent deployment.
GAIA: General AI Assistants Benchmark
GAIA (General AI Assistants) is a benchmark designed to test AI assistants on real-world tasks that humans can easily solve but remain challenging for AI. It includes 466 carefully curated questions across three difficulty levels, focusing on web browsing, multi-modal reasoning, and tool use.