Framework for evaluating and improving agents
Measuring and evolving with the frontier of agent work
Terminal-Bench-Science: Evaluating AI agents on research workflows across scientific domains
Long Horizon Terminal Benchmark with Dense Reward Grading
Taking the pi coding agent to Claude Code-level performance: 0.539 β 0.773 pass@1 on Terminal-Bench 2.1 with the same self-hosted 27B model, by re-engineering the agent.
Fusion-MoA v0.9.4.8: single-writer collective inference, proposal review, and reproducible Terminal-Bench 2.1 records.
BoundaryBench: benchmark coding-agent harnesses (Claude Code, Codex, Terminus 2, Grok) on Terminal-Bench tasks under hardened NIST-derived sandbox policies. Inspect AI + Harbor compatible.