# LILT **We build the multilingual layer for English-first AI.** Custom evals, benchmarks, and RL environments across 200+ languages. Most agent and coding benchmarks ship in English. We build the audited non-English counterparts — and the multilingual environments models train on — so labs and enterprises can measure and improve what their models actually do in the languages their users speak. ### Why we publish here Open releases make it easier for the community to stress-test our work, reproduce our scores, and extend our benchmarks to new languages. Every artifact is paired with a paper, a scoring script, and explicit limitations. ### What you'll find here - **Benchmarks & datasets** — multilingual evaluations across coding, agents, tool use, long context, instruction following, and domain QA. Audited splits across our priority languages, scalable to 200+. - **RL environments** — multilingual training environments for agentic and tool-using models, with reproducible scoring. - **Leaderboards & scoring** — Gradio Spaces with reproducible submission flows. - **Baselines** — frontier-model scores published with exact prompts, decoding params, and dated snapshots. - **Papers** — methodology, audit workflow, and findings. ### Currently featured 📌 **GAIA-v2-LILT** — multilingual agent benchmark across AR / DE / HI / KO / PT-BR. +20.7pp average gain post human-audit on frontier agents. Dataset, paper, and leaderboard linked in the pinned collection. 🛠️ **LILTBench Hackathon (Jun 15–21, 2026)** — one-week community challenge to crowdsource non-English coding tasks that break Claude Opus 4.6 in Terminal-Bench. Co-hosted with The AI Collective. [Sign up](https://luma.com/55v3wgi9). ### Links - Website: - Multilingual benchmarks: - AI for Frontier Labs: - GitHub: - Contact (data services): ### Citation If you use one of our datasets or benchmarks, please cite the corresponding paper linked on each dataset card.