Open Source→Official→Allen Institute for AI
The Allen Institute for AI has released MolmoAct 2, a fully open robotics foundation model designed to improve 3D action reasoning for real-world robot tasks. The release also includes a new bimanual manipulation dataset to support research and reproducibility.
Why it matters: This open-source model and dataset could accelerate robotics research by enabling reproducible study of bimanual manipulation.
Jul 11, 2026
Companies Funding→Official→Allen Institute for AI
In a Q&A, Ai2's Interim CEO Peter Clark shares his thoughts on the institute's current moment and its ongoing commitment to open science. He outlines where the organization is headed next and reflects on the institute's vision for the future.
Why it matters: This provides direct insight into the leadership and strategic direction of a major AI research institute.
Jul 11, 2026
Policy Safety→Official→Allen Institute for AI
AstaBench's latest update introduces new results for frontier models, including GPT-5.5, and notes increasing adoption by organizations such as the UK AISI, General Reasoning, Elicit, SciSpace, Distyl AI, and EvoScientist.
Why it matters: AstaBench's growing adoption by industry and evaluators suggests its rising importance as a benchmark for AI reasoning.
Jul 11, 2026
Models→Official→Allen Institute for AI
The Allen Institute for AI has introduced MolmoPoint and MolmoWeb, expanding the Molmo family from visual understanding to visual action. These open tools allow models to point, navigate, and interact with the world they see.
Why it matters: This advancement provides researchers with open tools for models that can perform visual actions, enabling active interaction rather than just passive understanding.
Jul 11, 2026
Models→Official→Allen Institute for AI
The Allen Institute for AI has announced that OlmoEarth Studio now allows users to export custom embeddings from its OlmoEarth foundation models. These embeddings can be used for downstream tasks such as similarity search, few-shot mapping, change detection, and unsupervised exploration.
Why it matters: This capability enables researchers and practitioners to leverage powerful Earth-observation embeddings for a wide range of geospatial analysis tasks without needing to train models from scratch.
Jul 11, 2026