| """Gradio Space app for High-accuracy sampling reproduction.""" | |
| import json | |
| from pathlib import Path | |
| import gradio as gr | |
| def load_bundle() -> dict: | |
| bundle_path = Path(__file__).parent / "evidence" / "bundle.json" | |
| if bundle_path.exists(): | |
| return json.loads(bundle_path.read_text()) | |
| return {"error": "Evidence bundle not found"} | |
| bundle_data = load_bundle() | |
| with gr.Blocks(title="High-Accuracy Diffusion Sampling Reproduction") as demo: | |
| gr.Markdown("# Reproduction: High-accuracy sampling for diffusion models and log-concave distributions") | |
| gr.Markdown("Paper ID: `71132` | Upstream Revision: `arxiv:2602.01338v2` | ICML 2026 Agent Repro Challenge") | |
| with gr.Tab("Claims & Evidence"): | |
| gr.JSON(bundle_data) | |
| with gr.Tab("Summary"): | |
| gr.Markdown(""" | |
| ### Target Claims | |
| 1. **Theorem 4.3**: Diffusion sampler achieves $\\delta$-error in $\\tilde{O}(\\text{polylog}(1/\\delta))$ steps given accurate score estimates. | |
| 2. **Corollary 4.4**: Complexity reduces to $\\tilde{O}(d^* \\text{polylog}(1/\\delta))$ under intrinsic dimension $d^*$. | |
| 3. **Section 5**: Polylogarithmic accuracy sampler for log-concave distributions using first-order gradient queries. | |
| """) | |
| if __name__ == "__main__": | |
| demo.launch() | |