Real-world embodied tasks require agents to act under physical constraints while tracking evolving object and task states. Because tools mediate many everyday and professional activities, understanding these tasks requires reasoning about affordances, hand–tool–object geometry, procedural progress, and causal effects. Tool-centric embodied reasoning therefore calls for real-world egocentric training data and diagnostic benchmarks that connect these abilities. We introduce EgoTools, the first comprehensive suite for egocentric tool-use understanding. It combines EgoTools-Data, approximately 100 hours of tool-centric egocentric recordings with synchronized audio, dense captions, reasoning-heavy narrations, and supplementary 3D information, with EgoTools-Bench, a diagnostic benchmark of 1,000 QA pairs across four tracks spanning perception, geometry, procedure, and causal reasoning. EgoTools-Bench reveals a substantial gap between current models and human experts. Under strict source-video separation, supervised fine-tuning of Qwen3-VL-8B-Instruct on instruction data derived from EgoTools-Data improves accuracy from 50.0% to 60.9% on the full 1,000-question benchmark. These results establish EgoTools as a unified training and diagnostic evaluation resource for real-world egocentric tool-use understanding.

Finding the right north-star metrics is highly critical for advancing mathematical reasoning capabilities of foundation models, especially given that existing evaluations are either too easy or only focusing on getting correct short answers. To address these issues, we present IMO-Bench, a suite of advanced reasoning benchmarks that specifically targets the level of the International Mathematical Olympiad (IMO), the most prestigious venue for young mathematicians. IMOAnswerBench first tests models on 400 diverse Olympiad problems with verifiable short answers. IMO-ProofBench is the next-level evaluation for proof-writing capabilities, which includes both basic and advanced IMO problems as well as detailed grading guidelines to facilitate automatic grading. These benchmarks played a crucial role in our historic achievement of the gold-level performance at IMO 2025 with Gemini Deep Think (Luong and Lockhart, 2025). Our model achieved 80.0% on IMO-AnswerBench and 65.7% on the advanced IMO-ProofBench, surpassing the best non-Gemini models by large margins of 6.9% and 42.4% respectively. We also showed that autograders built with Gemini reasoning correlate well with human evaluations and construct IMO-GradingBench, with 1000 human gradings on proofs, to enable further progress in automatic evaluation of long-form answers. We hope that IMO-Bench will help the community towards advancing robust mathematical reasoning and release it at https://github.com/google-deepmind/superhuman/imobench.