{"data":{"kind":"file","path":"README.md","version_id":"lkdqwoph1k9mpxm3ti63lxtm","entry":{"name":"README.md","path":"README.md","is_directory":false,"size":866,"modified_at":"2026-04-29T10:50:44.659000","content_hash":"2a5aa2e94d1c73a4cf5d825c60590fbc857b4fc3fc7354af83a046b7ec95c8fc"},"entries":[],"content":"# sparse-fourier-recovery-tools\n\nTool-use sparse Fourier recovery — primitive composition (fft, ifft, threshold, compute_residual, sparsity_norm). No solver oracle.\n\nVerifiable Labs Scientific-RL environment. Published as a thin wrapper around the monorepo at https://github.com/stelioszach03/verifiable-labs-envs — the wrapper pulls the monorepo as a Git dependency so the full source of truth (rewards, forward operators, LLM adapter, conformal calibration) stays in one place.\n\n## Install\n\n```bash\nprime env install verifiable-labs/sparse-fourier-recovery-tools\n```\n\n## Use\n\n```python\nfrom verifiers import load_environment    # or Prime SDK equivalent\nenv = load_environment(\"sparse-fourier-recovery-tools\")\nout = env.run_baseline(seed=0)\nprint(out[\"reward\"])\n```\n\nSee the monorepo README + docs for the reward spec, contamination story, and benchmark data.\n","encoding":"utf-8","truncated":false,"total_bytes":866},"status":null}