small-free-and-effective-orchestrating-open-weight

name	small-free-and-effective-orchestrating-open-weight
description	Skill generated from arXiv paper 2607.20216: Small, Free, and Effective: Orchestrating Open-Weight Small Language Models to Outperform Single LLM for Malware Analysis
metadata	{"arxiv":{"id":"2607.20216","title":"Small, Free, and Effective: Orchestrating Open-Weight Small Language Models to Outperform Single LLM for Malware Analysis","authors":["Adel ElZemity","Shujun Li","Budi Arief"],"published":"2026-07-22","categories":["cs.CR","cs.AI"],"url":"https://arxiv.org/abs/2607.20216","utility":1}}

Small, Free, and Effective: Orchestrating Open-Weight Small Language Models to Outperform Single LLM for Malware Analysis

arXiv: 2607.20216
Published: 2026-07-22
Authors: Adel ElZemity, Shujun Li, Budi Arief
Categories: cs.CR, cs.AI
Utility: 1.00

Key Innovation

Malware analysis demands rapid interpretation of complex detonation reports spanning filesystem, network, and process behaviours. While large language models (LLMs) demonstrate impressive capabilities for technical artifact interpretation, the opacity and escalating API costs of closed-weight frontier models motivate exploration of open-weight alternatives. However, many open-weight models are large, demanding significant compute resources and incurring non-trivial hosting costs that place them ...

Potential Application

This paper presents advancements that could be applied to enhance agent capabilities in the areas of cs.CR, cs.AI.

References

• arXiv: https://arxiv.org/abs/2607.20216