Quartermaster is a privacy-focused portfolio allocation tool that aggregates investments across multiple accounts to show true asset allocation, identify tax inefficiencies, and model financial goals without connecting to financial accounts or uploading data.
Thomson-1.0-Small is an open-weight frontier foundation model developed by Thomson Reuters using continual learning on the Qwen3.6-35B base model. It achieves high performance across legal, tax, and journalism domains through constitutional value alignment, data-centric training on 19T+ tokens, and agentic deep research capabilities, demonstrating that frontier model performance is achievable by institutions beyond heavily funded players.
Thomson-1.0-Small is a 35.1 billion parameter language model developed by Thomson Reuters with partners, specialized for legal, tax, and journalism applications. Built on Qwen architecture with a 262K token context window, it achieves 74.6% average benchmark performance through continual learning and proprietary data curation, designed for high-stakes professional work requiring factual accuracy.