| title: Learning Randomized Reductions Evidence | |
| emoji: 🧪 | |
| colorFrom: blue | |
| colorTo: indigo | |
| sdk: gradio | |
| sdk_version: 6.20.0 | |
| app_file: app.py | |
| pinned: false | |
| tags: | |
| - paper-hCAEcqig2C | |
| - icml2026-repro | |
| # Learning Randomized Reductions Artifact Reproduction | |
| This repository contains the CPU-only reproduction audit for ICML 2026 paper `hCAEcqig2C` (**Learning Randomized Reductions**, arXiv:2412.18134v5) under attempt `eb10c79b-fc26-47c4-88c1-6f45cb592833`. | |
| ## Local Reproduction Instructions | |
| To run the complete audit locally: | |
| ```bash | |
| # 1. Install dependencies | |
| uv sync --frozen | |
| # 2. Acquire and verify upstream artifacts (network required only for acquire) | |
| uv run lrr-repro acquire --manifest evidence/inputs/upstream_manifest.json --cache-dir .cache/upstream | |
| # 3. Execute offline reproduction audit | |
| uv run lrr-repro audit --project-root . --cache-dir .cache/upstream --schema schema/evidence-v1.schema.json --output evidence/results.json | |
| # 4. Validate evidence JSON bundle against schema | |
| uv run lrr-repro validate evidence/results.json --schema schema/evidence-v1.schema.json --validation-output evidence/validation.json | |
| # 5. Run full pytest suite | |
| uv run pytest -q | |
| ``` | |
| ## Reviewer Interface | |
| Launch the read-only Gradio viewer locally: | |
| ```bash | |
| uv run python app.py | |
| ``` | |
| Note: Remote LLM inference (Claude-Opus-4.1), GPU training, paid API calls, and Gurobi solver reruns were not executed as part of this audit. All results are derived deterministically from released primary raw artifacts and formal algebraic/finite-model checks. | |