@@ -563,8 +563,12 @@ This section captures the latest validated state before pausing Global LMA work
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@@ -563,8 +563,12 @@ This section captures the latest validated state before pausing Global LMA work
-**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).
-**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.
-**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.*
-*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.**FPGA / Hardware Teaming Roadmap (U of U Collaboration):**
3. Algorithm improvement for occlusion handling:
-**MCP for GTKWave:** Develop a Model Context Protocol (MCP) bridge to allow LLMs to natively analyze `.vcd` files. This will enable natural language querying of simulation waveform data (e.g., "Find the memory arbiter hang").
-**Cocotb Integration:** Revive the Python-based simulation-to-hardware workflow. The goal is to ensure that testbenches used in Icarus Verilog remain perfectly valid through physical hardware testing and eventual C-code kernel driver development.
-**Agent-Assisted Onboarding:** Leverage agents to bridge the gap for "occasional" users (like graduate students) by guiding them through the specialized hardware/Verilog knowledge base.
4. Batch replay of this quarter+global stage on previously processed data; classify failures and choose representative/challenging short test sequences.
5. Algorithm improvement for occlusion handling:
- Predict likely-occluded tiles from depth/disparity behavior.
- Predict likely-occluded tiles from depth/disparity behavior.
- Conditionally zero tile weights even when correlation strength is high, if non-occluded data is sufficient.
- Conditionally zero tile weights even when correlation strength is high, if non-occluded data is sufficient.
- Keep enough constraints for small-overlap scenes where joint XYZATR fitting can become underconstrained.
- Keep enough constraints for small-overlap scenes where joint XYZATR fitting can become underconstrained.
