Author

Erik Arakelyan

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#artificial intelligence Preprint Sep 2026

Mo'Models, Mo'Problems: How to best select model pools when designing Multi-Agent Systems

Multi-agent Systems (MAS) combine multiple model outputs to solve complex reasoning tasks. However, despite rapid growth of available open-source models, there is limited research on how to select optimal model candidates out of this massive pool. We systematically evaluate 8 model selection strategies (including model...

Sara Vera Marjanovic, Jia-Chen Xu, A. Laptev et al. · 0 citations

#machine learning Preprint Sep 2026

From Zero to Hero: An Open LLM Ecosystem for Armenian

It is found that the largest public Armenian corpora overlap web-derived evaluation panels heavily, including a train/test self-overlap inside FineWeb-2.0, and that a small share of verified translated STEM data reverses the loss.

Erik Arakelyan, Khatun Avetisyan, M. Davtyan et al. · 0 citations

#natural language process... Jun 2026

SHERLOC: Structured Diagnostic Localization for Code Repair Agents

This work introduces SHERLOC (Structured Hypothesis-driven Exploration and Reasoning for Localization), a training-free framework pairing a reasoning LLM with compact repository tools and self-recovery, without fine-tuning or multi-agent orchestration.

Hovhannes Tamoyan, Sean Narenthiran, Erik Arakelyan et al. · arXiv.org · 3 citations