Author

Rachel Lawrence

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#machine learning Preprint Oct 2026

Activation Denoising: A Robustness View on Parallel vs Sequential LLM Quantization

Post-training quantization is a powerful tool for compressing large language models. The most scalable methods quantize every layer in parallel, but quantization errors then compound through the residual stream, as no layer corrects for the errors of the layers before it. Sequential quantization accounts for this error...

Yan Scholten, Rachel Lawrence, James Hensman et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Subagents vs Agent Skills: Executing Reusable Knowledge for Long-Horizon Agentic Tasks

This work investigates an alternative approach in which skill packages are instead invoked as subagents, and shows that subagent execution outperforms agent-skill execution when skill packages expose clear input-output contracts and their instructions encode the procedural knowledge needed to fulfill those contracts.

Wasu Top Piriyakulkij, Rachel Lawrence, Alicia Curth et al. · 0 citations