Author

D. Gutfreund

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#artificial intelligence Preprint Oct 2026

Post-Training Large Language Models with LLMON

Large Language Models (LLMs) treat all context tokens uniformly, lacking explicit separation between executable instructions and passive data. This leads to prompt injection vulnerabilities, ambiguous instruction binding, and unreliable execution control. We present a post-training methodology using LLMON (LLM Object N...

Basel Shbita, Michael Hind, Chad DeLuca et al. · 0 citations

Preprint Aug 2026

The Sparsity Whisperer

A family of difference-informed pruning methods built upon this principle are introduced, suggesting that preserving output differences is a broadly useful and composable signal for post-training LLM sparsification.

Linghao Kong, Inimai Subramanian, Micah Adler et al. · 0 citations