Samuel J Paech — accepted AI-conference papers

2026

Antislop: A Comprehensive Framework for Identifying and Eliminating Repetitive Patterns in Language Models

Samuel J Paech, Allen G Roush, Judah Goldfeder, Ravid Shwartz-Ziv

ICLR 2026poster

Repetitive lexical patterns in LLM output, termed "slop," degrade writing quality through over-use and make AI-generated text immediately recognizable. We present Antislop, a comprehensive framework providing tools to both detect and eliminate these overused patterns. Our approach combines three inn…

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2026

Democratizing Diplomacy: A Harness for Evaluating Any Large Language Model on Full-Press Diplomacy

Alexander Duffy, Samuel J Paech, Ishana Shastri, Elizabeth Karpinski, Baptiste Alloui-Cros, Tyler Marques +1

AAAI 2026technical

We present the first evaluation harness that enables any out-of-the-box, local, Large Language Models (LLMs) to play full-press Diplomacy without fine-tuning or specialized training. Previous work required frontier LLMs, or fine-tuning, due to the high complexity and information density of Diplomacy

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