Pipeline Baseline: Status of Stages and Variants

A single entry point summarizing the status of every pipeline stage and algorithmic variant evaluated so far, cross-linking the detailed writeups rather than duplicating them. Two datasets appear only on this page: the attribution of failures to pipeline stage (PCA vs. CCA), and a benchmark of densify_method="voronoi".

Stage and feature status

Stage / feature	Status	Finding	Details
PCA subclustering (baseline)	production default	essentially never the terminal failure cause (see below)	this page
CCA vanilla Fibonacci sticking	production default	reference baseline	experiments.md
CCA backtracking pairing	production default	5% → 100% single-shot success in the hard regime; boundary moved outward across the grid	backtracking_pairing.md, boundary_sweep_v2.md
CCA pairing, greedy first-fit	superseded default	diagnosed bottleneck — 97.4% of hard-regime failures had a rescuable alternative pairing	pairing_frustration.md
CCA pairing, exact matching / leaf-weighted matching	opt-in	no measurable improvement (+0.2pp over 4200 trials); the cheap feasibility graph it optimizes over cannot predict sticking success	matching_pairing.md
CCA overlap-failure census (cca_overlap_census_enabled)	opt-in, diagnostic	hard-regime failures involve a median 9/24 particles in the failing pair (~37.5%), not a small handful	overlap_failure_census.md
CCA drop-rescue (cca_drop_rescue_enabled)	opt-in, recommended off	default budget inert by design; permissive budgets trade an inconsistent success effect for a systematic accuracy cost	drop_rescue.md
CCA retry rotation modes (alternate, dual_jitter, coarse_grid, coarse_to_fine)	opt-in, archived	no measurable benefit	experiments.md
CCA candidate ordering (leaf_soft, leaf_score, leaf_hybrid)	opt-in, archived	no measurable benefit	experiments.md
CCA pair pre-filters (bounding-volume, SSA)	opt-in, archived	no measurable benefit	experiments.md
CCA Γ-expansion	opt-in, archived	no measurable benefit	experiments.md
CCA FFT rigid-body docking	opt-in, archived	no measurable benefit	experiments.md
CCA soft potential relaxation	opt-in, archived	no measurable benefit	experiments.md
Densification, radial	opt-in	earlier favorable conclusion withdrawn: matches Rg but not the target Df (f(r) measures ~0.5 low), and pre-fix versions emitted invalid geometry	correlation_validation.md
Densification, voronoi	opt-in	inferior to radial on both axes: ~11.6x slower and materially less accurate	this page, below
JAX/GPU kernel port	not pursued	numba faster by 1–4 orders of magnitude at every tested size	gpu_acceleration.md
Structured event log (event_log_path)	opt-in, diagnostic	per-merge/per-run failure statistics; quantifies non-locality and non-nearness of failures	event_logging.md
Feasibility warning	production default (advisory)	logistic model of the measured boundary; 97.7% agreement with the ≥50% feasibility call	feasibility_criterion.md
Df/kf/σ/N stability boundary	characterized	grids before and after backtracking	hard_regime_boundary_sweep.md, boundary_sweep_v2.md, full_stability_sweep.md

Failure attribution by pipeline stage (PCA vs. CCA)

Motivation

Earlier sweeps report end-to-end success/failure only. BenchmarkResult has carried a failure_stage field since sticking_benchmark.py was written, but it was never populated: run_simulation() had no way to report which stage failed, so both the sequential path (StickingBenchmark.run_single_trial) and the Dask path (stability_sweep.py) hardcoded "UNKNOWN". An optional diagnostics dict parameter on run_simulation(), populated at each retry-loop exit point with one of PCA, CCA, TIMEOUT, PARAMS, or RADII_GEN, now supplies it to both paths.

The attribution records the last attempt made before a trial gives up (retry exhaustion or wall-clock timeout) or succeeds — not a breakdown of every one of the up to 20 internal retries. A trial where PCA fails on attempts 1–5 and CCA fails on attempt 6 (the timeout point) is attributed to CCA.

Method

configs/pipeline_stage_census.toml: Df ∈ {1.4, 1.8, 2.0, 2.2, 2.5}, kf ∈ {0.6, 0.9, 1.0, 1.2, 1.4}, σ ∈ {1.0, 1.5, 1.9}, N ∈ {128, 512, 1024}, 4 seeds per combination — 225 combinations, 900 trials, run via benchmarks/stability_sweep.py (Dask, local cluster). The grid spans from comfortably safe to well past the known hard regime, so that both PCA- and CCA-dominated failure territory would appear in the same run if either exists.

Results

515/900 trials succeeded (57.2%). Of the 385 failures:

Failure stage	Count	Share
CCA	311	80.8%
TIMEOUT	74	19.2%
PCA	0	0.0%

PCA subclustering did not terminally fail once in this grid; every failure that was not a wall-clock timeout was attributed to CCA. This quantifies, across a spanning grid rather than one hard-regime point, what pairing_frustration.md established qualitatively: the CCA pairing/sticking stage is where the pipeline fails.

The TIMEOUT share grows with N, since larger aggregates cost more per attempt, making the 90 s budget more likely to expire before all 20 retries complete:

N	CCA failures	TIMEOUT failures	Success rate
128	117	0	61.0% (183/300)
512	115	15	56.7% (170/300)
1024	79	59	54.0% (162/300)

Raw output: benchmark_results/pipeline_stage_census/.

Densify method comparison (radial vs. voronoi)

Method

benchmarks/densify_method_comparison.py, same hard-regime target/source parameters as the densify table in experiments.md (Df=2.25 target, source Df=2.0/kf=1.0, N=128, σ=1.9), 30 seeds per method, with pyfracval.fractal.validate_fractal_structure supplying the accuracy columns.

Results

Method	Success rate	Avg |Rg error|	Rg within 5%	Avg wall time
radial	30/30 (100%)	+0.4%	30/30	2.3s
voronoi	30/30 (100%)	+8.5%	8/30	26.7s

Both methods report 100% “success” in the sense that run_simulation returns a usable aggregate either way; the cost of voronoi is hidden inside that figure. Its iterative migration frequently fails to converge within max_densify_iters/max_push_iters and falls back to best-result (logged as Densification did not fully converge; using best result), producing aggregates whose radius of gyration misses the target by an average of 8.5% (worst observed case: +7.8% with an estimated empirical Df of 1.448 against a target of 2.25). radial converges cleanly and is roughly 11.6× faster on average.

Raw output: benchmark_results/densify_method_comparison/densify_method_comparison.json.

Discussion

Two conclusions follow from the data on this page: (1) search-strategy work belongs on the CCA side of the pipeline — PCA subclustering is not a terminal cause of lost trials; and (2) densify_method="voronoi" should not be preferred over radial at these settings — it is slower and less accurate, and its 100% success rate masks an accuracy problem visible only in validate_fractal_structure’s Rg/Df error columns rather than the binary success flag. The broader caution about densification as a whole — that Rg agreement alone is an insufficient validation target — is established in correlation_validation.md.

Limitations

The PCA/CCA attribution captures only the last attempt’s outcome per trial, not a per-attempt census across all 20 retries; a trial that fails PCA repeatedly before timing out on CCA shows no PCA attribution. The voronoi comparison covers one regime and one source Df and has not been swept across the broader Df/kf/σ/N grid the way the radial path has.