code/report/README.md

Report

Comprehensive write-up of the LeWM × PushT control experiment: the horizon-reset procrastination pathology, the arrival-and-hold fix, and the ablation study.

Contents

path	what
report.tex	the document — abstract, full formula inventory, ablations, statistics
figures/	eight figures, each as .pdf (for LaTeX) and .png (for viewing)
tables/	five LaTeX tables, \input by the document
make_figures.py	regenerates figures/ from the raw result files
make_tables.py	regenerates tables/; also recomputes and persists the paired statistics
recover_profiles.py	recovers the original controller's per-offset validation profile (its checkpoint predates that logging)
check_tex.py	static check of the .tex — dangling refs, missing inputs/figures, brace and math balance
build.sh	regenerate everything, then compile

Building

bash report/build.sh

Or step by step:

python report/make_figures.py     # -> report/figures/
python report/make_tables.py      # -> report/tables/
latexmk -pdf report/report.tex    # -> report/report.pdf

No LaTeX engine is installed on the machine this was written on, so the PDF has not been compiled here — report.tex passes check_tex.py but has not been through a real typesetter. To build it, install one of:

• Windows — winget install MiKTeX.MiKTeX
• macOS — brew install --cask mactex-no-gui
• Debian/Ubuntu — sudo apt install texlive-latex-recommended texlive-fonts-recommended
• Anywhere — cargo install tectonic (single binary, fetches packages on demand)

Or upload this directory to Overleaf; it is self-contained and uses only stock packages (amsmath, booktabs, graphicx, caption, subcaption, xcolor, microtype, hyperref, geometry).

Provenance

Every number in the document is generated, not transcribed. The scripts read only:

• data/runs/eval/results.jsonl — closed-loop evaluations
• data/runs/diagnostics/ — contraction fits, refinement traces, support statistics
• data/runs/*/controller.pt — checkpoints, for hyperparameters and validation profiles

so the prose cannot silently drift from the results. The paired statistics are recomputed by make_tables.py using the same functions as scripts/paired_stats.py and written back to data/runs/eval/paired_stats.jsonl.

One figure input is not a result file: fig_training_signal parses raw training logs, two of which live under the job temp directory. If those are gone, that figure will render with fewer series; everything else is unaffected.