Thibaut Forest
Claude Opus 5 (1M context)
Campaign 2: agent_reports_bgkf README + FRONTIER board (cross-lane composition hub)
a96ad81 | # bgkf-CV improvement campaign — live agent reports (2026-08-22 night) | |
| Second full campaign, on the settled CV: building-GroupKFold, | |
| deployment-faithful (cv.py v5, folds summed per scenario). | |
| **THE SCORE**: `venv/bin/python -m batteryswap_example.cv bgkf` | |
| Champion (plain logbag) baseline: **1760.2** | |
| (warmup 2616.1 / panel 1773.4 / nearend 544.0; swaps 9.5/scenario; | |
| all-building out-of-fold totals, mean over 48 scenarios). | |
| Baseline cells: /tmp/cv_bgkf.csv (fold x origin). Candidates pair on the | |
| 48 per-scenario all-building sums. Survivor bar: delta < 0 beyond ~2*SE | |
| with swaps ~flat per scenario. Quarantine (public-proven, phase 56-58): | |
| count-coupled and label-process wins are report-only; split-file leak | |
| forbidden. This instrument measures CROSS-FLEET transfer (unseen | |
| buildings) - the deployment path both public and private splits take. | |
| | file | theme | | |
| |---|---| | |
| | ft_findings.md | feature engineering (relative/rank forms favored here) | | |
| | md_findings.md | model families & hyperparameters | | |
| | pl_findings.md | planner: timing, routing (count-coupled = report-only) | | |
| | bl_findings.md | blends, ensembles, calibration | | |
| | tr_findings.md | training composition: weights, labels, sampling | | |
| | rk_findings.md | learning-to-rank objectives | | |
| | wd_findings.md | RADICAL: drop p42 entirely + registry sweep + wild cards | | |
| Runs are ~40-120s each - no triage subsets needed, full runs always. | |