docs: Add Future Roadmap for agent-driven algorithm evolution to project details

docs/project-details.md

@@ -558,7 +558,12 @@ This section captures the latest validated state before pausing Global LMA work
 
 ### Next TODO (priority order)
 1. Performance pass: identify current bottlenecks and low-hanging optimizations.
-2. Batch replay of this quarter+global stage on previously processed data; classify failures and choose representative/challenging short test sequences.
+2. **Agent-Driven Algorithm Evolution (Future Roadmap):**
+   - **Adaptive Integration Windows:** Implement a competitive selection mechanism (e.g., 1x vs 4x integration) where the pipeline automatically selects the motion vector that yields the highest Contrast-to-Noise Ratio (CNR), rather than relying on hardcoded thresholds.
+   - **Fat Zero Auto-Scaling:** Automatically scale `cuas_rng_fz` (fat zero) down proportionally to the length of the temporal integration (longer integrations lower the stochastic noise floor, allowing closer-to-pure phase correlation).
+   - **Acceleration Compensation:** Refine the "Virtual Moving Camera" model to handle non-linear motion (like U-turns) that currently "squash" correlation peaks during long integrations.
+   - *Note: These deeper algorithmic optimizations are intentionally deferred. The strategy is to establish a working baseline first, expose the necessary low-bandwidth tile metrics via the MCP server, and then allow AI agents (Codex, Claude, Gemini) to autonomously sweep, analyze, and optimize these specific sub-problems.*
+3. Batch replay of this quarter+global stage on previously processed data; classify failures and choose representative/challenging short test sequences.
 3. Algorithm improvement for occlusion handling:
    - Predict likely-occluded tiles from depth/disparity behavior.
    - Conditionally zero tile weights even when correlation strength is high, if non-occluded data is sufficient.