Geometry, Not Training, Causes Few‑Step Text Generation Collapse
Deterministic few‑step decoders work for image latents but fail on text because a smooth map can’t commit to sharp categorical choices. The paper proves the failure stems from geometric constraints on readout sharpness, not model size or data. It also offers diagnostics (DABI, CCI) and shows how autoregressive or stochastic tricks bypass the limit.