README.md

---
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