e2b-b8d86362·2 events·first seen Aliases: E2B
RSIBench-Data is a new benchmark that isolates the data-centric research capabilities of LLM agents within a fixed post-training stack, testing whether agents can autonomously diagnose capability gaps and revise training-data strategies to improve a target model. Four frontier agents are evaluated across six benchmarks spanning software engineering, math, and scientific QA. Agents improve on their first valid attempt in 58% of settings, but 78% of runs that continue after a peak score end lower, indicating inconsistent self-improvement. The benchmark is open-sourced and provides a controlled testbed for measuring progress toward recursive self-improvement.
E2B is an open-source project providing secure, sandboxed execution environments designed for enterprise-grade AI agents with access to real-world tools. The repository has accumulated 12,290 GitHub stars with 31 new stars today, indicating steady community interest. It targets the agent-tool ecosystem by offering isolated runtime environments where agents can safely execute code and interact with external systems.