A 7B model that treats tool use as its missing memory
ZGCM-1 takes a contrarian route to compact-model performance: instead of trying to memorize the open web, its 7B model is trained to think deliberately and search externally across a 256K context. The team reports competitive results on math and agentic-search benchmarks against models many times larger, plus a roughly 4.2x improvement in 16K pretraining time-to-loss. The unusual part is the release: weights, checkpoints, code, data recipes, and training logs are all open.