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

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