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.
Mini-AGI is a continual learning language model that trains from scratch on 8GB VRAM by storing weights on disk and paging them as needed. It learns continuously from a data stream without catastrophic forgetting, enabling users to train and own their own models on consumer hardware.
Researchers introduce long-horizon memorization, a challenge where language models must learn 100 tasks through continual fine-tuning without catastrophic forgetting. They show that composing multiple continual learning mechanisms—combining data, function, and weight anchors with merged LoRA—improves retention from 1.2% to 34.9%, a 28-fold improvement over naive sequential fine-tuning.