Correspondence-scaling timing benchmark + group timing scripts under scripts/timing/

[efahnestock]

Stacked on #632.

What

1. Introduces experimental/overhead_matching/swag/scripts/timing/ as the home for pipeline timing benchmarks, so they don't accumulate in the scripts/ catch-all. Moves measure_pipeline_latency.py (from Add a script to measure latency #632) into it.
2. Adds the local-LLM correspondence timing benchmark (from the now-superseded Local-LLM correspondence timing benchmark (Gemma 3 27B) #633): measures how long a local LLM would take to replace the learned correspondence model, by matching a panorama's landmarks directly against a city's full OSM landmark set.

Files (in scripts/timing/)

• measure_pipeline_latency.py — moved from scripts/ (unchanged).
• correspondence_scaling_common.py — loads the Chicago OSM stockpile (28,582 unique tag bundles) + panorama query landmarks, builds the match prompt, validates JSON output, extrapolates per-call timings to per-landmark / per-panorama / full-city cost.
• benchmark_correspondence_scaling_ollama.py — gemma3:27b sweep via ollama (candidate-fill × query-batch × thinking on/off).
• correspondence_scaling_README.md — setup + reproduction.
• BUILD — the three targets.

scripts/BUILD: drops the measure_pipeline_latency target (moved) and widens evaluate_histogram_on_paths visibility so the relocated target can depend on it.

Result (backs the paper)

	per panorama
Gemma 3 27B, no thinking	~5.4 min
Gemma 3 27B, chain-of-thought	~7.6 min
learned correspondence step	3.7 s

Notes

• Requires a local GPU + ollama (gemma3:27b pulled on first run) and VIGOR/pano_v2 data under /data/overhead_matching/datasets/ — not CI-reproducible; commands in the README.
• Reference sweep results live at /data/overhead_matching/evaluation/timing/.
• Supersedes Local-LLM correspondence timing benchmark (Gemma 3 27B) #633 (same benchmark, but stacked on Add a script to measure latency #632 and relocated into scripts/timing/).