Stage 4: FORGE — CC-MotionGen V2 Architecture

**CC-MotionGen V2 = Flow Matching DiT + Two-Tier Deployment + Multi-Modal Conditioning + Physics-Grounded Learned Validation + Sensor Capture Flywheel** The system is built on 4 pillars: 1. **25D Motion Protocol** (unchanged, competitive moat) 2. **Flow Matching DiT** (replaces DDPM, 100x faster) 3. **Learned Quality System** (validation advantage evolved) 4. **iPhone Capture Studio** (data flywheel, first-mover) **Replaces**: `model/diffusion.py` (GaussianDiffusion) **File**: `model/flow_matching.py` (new) 1. **Never replace SanityChecker with learned physics** — Deterministic physics checks are trustworthy. Learned validation supplements, never replaces. 2. **Never train on SMPL and retarget to 25D** — Train on 25D natively. Retarget FROM SMPL for benchmarking only. 3. **Never unify Tiny and Full into one model** — Different deployment targets need different architectures. One-size-fits-all fails on-device. 4. **Never skip the Week 2 flow matching checkpoint** — If 25D flow matching doesn't converge, pivot to DDIM consistency distillation immediately. 5. **Never drop cc-anticipation (Rust) before learned prediction is validated** — Keep it as production fallback. 6. **Never collect motion data without user consent** — 25D is privacy-preserving but consent is mandatory. **1. Can this be built with available resources?** YES. Training: Mac4/Mac5 for prototyping, cloud GPU for production runs. iOS: Mac1 for builds. All tooling exists.