Beyond Monoliths: Expert Orchestration for More Capable, Democratic, and Safe Language Models

ACL ARR 2026 January Submission6539 Authors

Keywords: Router, LLMs, Judge, transparency, control, alignment, performance, safety

Abstract:

This position paper argues that the prevailing trajectory toward ever larger, more expensive generalist foundation models controlled by a handful of companies limits innovation and constrains progress.
We challenge this approach by advocating for an "Expert Orchestration" (EO) framework as a superior alternative that democratizes LLM advancement.
Our proposed framework intelligently selects from many existing models based on query requirements and decomposition, focusing on identifying what models do well rather than how they work internally.
Independent "judge" models assess various models' capabilities across dimensions that matter to users, while "router" systems direct queries to the most appropriate specialists within an approved set.
This approach delivers superior performance by leveraging targeted expertise rather than forcing costly generalist models to address all user requirements.
EO enhances transparency, control, alignment, performance, safety and democratic participation through intelligent model selection.

Paper Type: Long

Research Area: Safety and Alignment in LLMs

Research Area Keywords: Ethics, Bias, Fairness, Safety, Alignment, Democratic, Judge, Router

Contribution Types: Position papers

Languages Studied: N/A

Submission Number: 6539