docs/TRAIN_GEN_MISMATCH.md

Train/Gen Path Mismatch — Root Cause

Finding

Training uses → :

• Multi-level causal linear attention over the full chunk
• Kuramoto RK4 integrate
• MoE over all positions

Default generation used → :

• Single-step attention (level-0 style path)
• Kuramoto one Euler step (theta + 0.1 * derivative)
• MoE on one token

So the model was trained on one dynamics and decoded with another.

Fixes

1. Code: Kuramoto now uses (same as train).
2. Decode: Prefer / sliding-window chunk gen for train-aligned logits.
3. Decode surgery: still useful for attractor lock; does not replace path alignment.

Expected

Aligning dynamics removes a major source of collapse/incoherence. Coherent language still depends on further digestion under stage2 dense CE.