title: High Accuracy Sampling Reproduction
emoji: 📈
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 6.22.0
app_file: app.py
tags:
- icml2026-repro
- paper-71132
Reproduction: High-accuracy sampling for diffusion models and log-concave distributions
Paper ID: 71132
Upstream Revision: arxiv:2602.01338v2+arxiv-source:2602.01338v2
Challenge: ICML 2026 Agent Repro Challenge
Overview
This repository provides an independently executable reproduction bundle verifying key theoretical and algorithmic bounds from High-accuracy sampling for diffusion models and log-concave distributions (Chen et al., 2026).
Claims Verified
Theorem 4.3 (Polylog Step Scaling): The diffusion sampler achieves $\delta$-error in $\tilde{O}(\text{polylog}(1/\delta))$ steps given sufficiently accurate score estimates.
Corollary 4.4 (Intrinsic Dimension Reduction): When data has intrinsic dimension $d^*$, step complexity scales as $\tilde{O}(d^* \text{polylog}(1/\delta))$.
Section 5 (Log-Concave First-Order Sampler): A first-order gradient query sampler for log-concave distributions achieves $\text{polylog}(1/\delta)$ accuracy.
Evidence Generation
To run the evidence generation suite and update evidence/bundle.json:
python generate_evidence.py