README.md

metadata

title: 'DiffRes: traceable 100-ID Gaussian-mixture study'
emoji: 🔬
colorFrom: blue
colorTo: indigo
sdk: static
pinned: false
tags:
  - icml2026-repro
  - paper-M0e5XORjAW
  - reproducibility
  - diffusion

DiffRes: traceable 100-ID Gaussian-mixture study

This is a static, local publication package for Diffusion differentiable resampling by Jennifer Rosina Andersson and Zheng Zhao (arXiv:2512.10401, OpenReview:M0e5XORjAW).

Evidence statement. A local study wrapper using released implementation, stored numerical random keys and pinned source config recorded a traceable 100-ID Gaussian-mixture result; a separate validator recomputed raw-record aggregates, paired intervals, and source hashes.

The files here are historic stored outputs, transferred without rerunning the numerical evaluation. This static site has no execution, upload, or model hosting component.

The judge-readable, claim-level record is in the evidence logbook. It restates the concrete conditions, numbers, and limits below in Markdown because the challenge judge reads those pages rather than the static landing page or linked JSON files.

What the evidence covers

• Released implementation: zgbkdlm/diffres at commit 767effe3e755067eb8a04422597fbf37eb8ab754 (MPL-2.0).
• One paper-selected Gaussian-mixture configuration, with stored Monte Carlo IDs 0–99, 10,000 particles, 1,000 sliced-Wasserstein projections, probability-flow ODE T=3, 128 steps, and the Jentzen--Kloeden integrator.
• A paired comparison between diffusion resampling and the multinomial baseline only.
• A post-hoc, standard-Python validator that checks strict JSON, the 100 raw records, reported aggregates, paired intervals, the recorded source pin, and hashes of the released source files.

Historical stored aggregate (lower is better)	Diffusion resampling	Multinomial baseline
Sliced Wasserstein L1, mean ± population SD	0.0807686 ± 0.0212899	0.0824334 ± 0.0250323
Squared posterior-mean residual L2, mean ± population SD	0.0373908 ± 0.0299358	0.0378330 ± 0.0442877

The stored paired 95% t intervals cross zero for both metrics. They are not evidence of a statistically significant improvement.

Evidence files

• Raw 100-ID result — preserved per-ID values, source provenance, configuration, and recorded aggregates.
• Post-hoc validation record — the historic validator output.
• Validator source — copied byte-for-byte as an audit artefact; it does not rerun the JAX evaluation.
• Self-contained stored-evidence verifier — a standard-library-only check of the JSON structure and recorded arithmetic; run it with python3 evidence/verify_stored_gms_evidence.py.
• Evidence guide — lineage, portability, and limits.
• Source and artefact manifest and package SHA-256 manifest.

Limits

This is not an exact reproduction, an independent replication, a complete Table 1 or Table 2 reproduction, a publication score, or a broad outperformance claim. It does not evaluate the OT, Gumbel-Softmax, or soft-resampling baselines, and it does not rerun the numerical study in this package. The included validator recomputes historic raw-record statistics and checks the recorded source identity; it is not a second numerical run. The self-contained verifier recomputes the stored record's configuration, ordered IDs, aggregates, and lower-counts without a source checkout or SciPy. It does not validate source hashes, execution provenance, paper-level results, or any comparative claim.

Safety and selection note

The selected material is a benign numerical-methods study package: static HTML/Markdown, two JSON evidence records, a non-networked validator, and licensing/provenance files. The transfer excludes the upstream source tree, virtual environments, caches, runners, model artefacts, partial studies, and all LGSSM material. A pre-transfer content scan found no private local paths, credential-shaped values, upload clients, or source files from excluded study areas. The validator's only generic token-pattern match is a JSON-parser parameter, not credential handling.

Licence and attribution

The package-authored wrapper material is under the MIT Licence. The upstream DiffRes implementation is not redistributed here; it remains available under MPL-2.0, with attribution in NOTICE and a copy of the upstream licence at UPSTREAM-MPL-2.0.txt.