ZGCM-1

ZGCM-1 (ZGCM Team — Zhongguancun Academy & Zhongguancun Institute of Artificial Intelligence, 2026) is a fully open, 7.39B-parameter dense foundation model trained from scratch for mathematical reasoning and agentic search, and it is built to make a point about who gets to do frontier research. Two barriers keep the academic community out of this space: a scale barrier — deep reasoning is assumed to require hundreds of billions of parameters — and an opacity barrier — the models that do exist are released as opaque weights, with their filtering recipes, mid-training curricula, long-context schedules, and agent data kept private.

ZGCM-1 attacks both. Its core premise is that a compact model cannot passively memorize the open web, but it can overcome that parametric ceiling by coupling deliberate internal thinking with active external tool use — thinking harder and looking things up instead of trying to store everything. To make that trainable on academic compute across a 256K context, the team builds an end-to-end high-efficiency recipe: a hybrid gated sliding-window / global attention backbone, a stable FP8 Muon training stack, a progressive context curriculum with MDP-style supervision, and execution-grounded think/no-think alignment.

The efficiency numbers are the headline. Hybrid attention cuts the KV cache 6.4x and lifts throughput 3.94x at 256K; the FP8+Muon+TWEO co-design multiplies out to a ~4.2x pre-training time-to-loss speedup over a BF16/AdamW baseline. The capability numbers say the thesis holds: ZGCM-1-7B ranks first on average across 14 reasoning benchmarks at the 7B-8B scale (97.1% MATH-500, 75.0% AIME 2026) and stays competitive with frontier models such as Qwen3-235B-A22B and GLM-5.1 on agentic search.

And it is fully open: weights from every stage, intermediate checkpoints, training code, per-stage data and recipes, and W&B logs — plus eight distilled empirical findings and an honest account of an AI-native, agent-swarm development workflow.