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---
license: mit
tags:
- autonomous-researcher
- speculative-decoding
- nlp
- inference-optimization
- cross-domain-analysis
datasets:
- openai_humaneval
- gsm8k
- openlanguagedata/flores_plus
- web_nlg
language:
- en
- fr
---
# Speculative Decoding: Cross-Domain Draft-Verify Dynamics
**Generated by:** Autonomous Researcher (DGX Spark)
**Date:** 2025-11-28
**Status:** Complete
## Overview
This experiment investigates draft-verify dynamics in speculative decoding across diverse domains (code, math, translation, data-to-text) and attention mask architectures.
## Key Findings
### Finding 1: Domain-Dependent Rejection
| Domain | Rejection Rate | Insight |
|--------|---------------|---------|
| Code | 14.0% | Syntax aids prediction |
| Data-to-Text | ~25% | Structured input constrains output |
| Math | 26.1% | Logic steps diverge |
| Translation | 34.9% | High semantic entropy |
### Finding 2: Attention Mask Sensitivity
| Domain | Best Mask | Acceptance Rate |
|--------|-----------|----------------|
| Code | Windowed (k=32) | 20.0% |
| Math | Fully Causal | 31.2% |
| Translation | Fully Causal | 31.8% |
## Reproducibility
- **GitHub Code**: https://github.com/BioInfo/autonomous-researcher-speculative-decoding
- **Platform**: NVIDIA DGX Spark (GB10 GPU)
- **Runtime**: ~45 minutes
## Contents
- `code/` - Analysis scripts (data generation, statistical tests, visualization)
- `results/` - Processed results and statistics
- `paper/` - Draft manuscript
- `data/` - Experiment data
- `analysis/` - Jupyter notebooks
## Citation
If you use this work, please cite:
```
@misc{speculative-decoding-cross-domain-2025,
title={Domain-Adaptive Draft-Verify: Cross-Domain Analysis of Speculative Decoding Dynamics},
author={BioInfo},
year={2025},
publisher={HuggingFace},
url={https://huggingface.co/RyeCatcher/speculative-decoding-cross-domain-analysis}
}
```
## License
MIT License
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