paperJune 2026Unreviewed

SGTO-MAS: Secure Gorilla Troops Optimization for Multi-Agent LLM Systems

Saeid Jamshidi

Abstract

Multi-agent large language model (LLM) systems offer strong capabilities for complex reasoning and decision-making, yet coordination across agents introduces error propagation, security risks, and inefficient use of resources. Existing methods often rely on heuristic, static strategies and lack a principled mechanism for balancing performance, security, and computational cost. This paper formulates multi-agent LLM coordination as a constrained optimization problem and proposes a security-aware m

Cite

@misc{jamshidi2026sgtomas,
  title = {{SGTO-MAS: Secure Gorilla Troops Optimization for Multi-Agent LLM Systems}},
  author = {Saeid Jamshidi},
  year = {2026},
  month = jun,
  eprint = {2606.07940},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2606.07940}
}