Works
Write-ups of what I’ve built. Where it applies, each result is measured by an independent verifier on held-out data it never saw — and the dead ends are kept on the record next to the wins.
LenaLab✓ 1✗ 1Fri Jun 19 2026 00:00:00 GMT+0000 (Coordinated Universal Time)Six Car Cameras → a Top-Down Map: BEV Perception, Verified
A second problem class for LenaLab — multi-camera Bird's-Eye-View perception on nuScenes. Run three times, a from-scratch agent cleared the held-out bar only 2/3 (0.085 ± 0.034): capable but not robust. The diagnostic pinned the variance on the agent's design latitude, not the task; a scaffold that locks the geometry/augmentation and lets it author only the network collapsed the variance 7.3× to a stable 3/3 (0.136). The full cycle — build, find it's non-robust, diagnose, scaffold, validate.
bev-perceptionmulti-cameranuscenesoccupancyautonomous-driving
LenaLab✓ 1✗ 1Fri Jun 19 2026 00:00:00 GMT+0000 (Coordinated Universal Time)Into 3D: Camera-to-Voxel Occupancy, and a Finding That Replicated
LenaLab's sixth domain, and its first in 3D — camera→3D-occupancy on nuScenes. A from-scratch agent authored a Lift-Splat-to-3D network; run three times it cleared the held-out bar only 2/3 (0.086 ± 0.024). The BEV finding replicated: the variance is the agent's design latitude, and a scaffold collapses it ~6× to a stable 3/3. Honest nuance — in 3D the scaffold buys reliability, not a higher peak.
occupancy3d-perceptionmulti-cameranuscenesautonomous-driving