AGP TurboQuant + ANE Benchmarks

This benchmark package makes the local `cog-rlm` TurboQuant and Apple Neural Engine research measurable inside the AGP research track. - `Desktop/cog-rlm/scripts/turboquant.py` - `Desktop/cog-rlm/scripts/ane_bridge.py` - `Desktop/cog-rlm/scripts/ane_whisper_spike.py` - `Desktop/cog-rlm/scripts/ane_mlx_train.py` - `Desktop/cog-rlm/scripts/ane_trainer.py` - `Desktop/Comp-Core/core/retrieval/cc-turboquant-index` The JSONL importer accepts raw vector arrays or object rows with `embedding`, `vector`, `values`, or `embedding_vector` fields. - TurboQuant embedding search quality and memory footprint. - Rust packed-code TurboQuant candidate generation over bit-packed corpus rows. - Rust packed snapshot persistence and metadata inspection. - JSONL ingestion for exported Orbit/RAG++ embedding slices. - TurboQuant activation packet compression for AGP-PTP transport. - Local ANE private MIL bridge availability. - Minimal ANE route-head compile/eval status through `ane_route_head_bench.py`. - It does not prove final retrieval latency; the current Python path pre-dequantizes rotated vectors to fp16. - The Rust sidecar v0 is packed-code scalar scan, not the final hand-tuned Apple Silicon SIMD kernel. - It does not claim ANE trains the full transformer. - It does not claim ANE route-head execution is currently stable; the current local bridge compiles but eval fails with Apple ANE status `0x2/statusType=0x9`. - It does not replace MLX/GPU as the stable training path.