---
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

1. **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.

2. **Corollary 4.4 (Intrinsic Dimension Reduction):**
   When data has intrinsic dimension $d^*$, step complexity scales as $\tilde{O}(d^* \text{polylog}(1/\delta))$.

3. **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`:

```bash
python generate_evidence.py
```