SOURCE-LINKED INTELLIGENCE

ZGCM-1: A Fully Open and Extremely Efficient Foundation Model for Math and Agentic Search

arXiv · AI, language, vision and robotics · article · Sep 11, 2026 · UTC

In this work, we present ZGCM-1, a fully open 7B dense foundation model trained from scratch with extreme data, system, and algorithmic efficiency. ZGCM-1 is founded on a core premise: compact models cannot passively memorize the open web, but can overcome parametric capacity limits by coupling deliberate internal thinking with active external tool use. To support this paradigm across a 256K context, we develop an end-to-end, high-efficiency open training recipe: Architecture & System Co-design: interleaved gated sliding-window and full attention, and a stable FP8 Muon optimizer; Progressive C

Read original source ↗ Open in workspace

recordType

paper

region

Global