ORCH: many analyses, one merge-a deterministic multi-agent orchestrator for discrete-choice reasoning with EMA-guided routing
Hanlin Zhou, Huah Yong Chan

TL;DR
ORCH is a deterministic multi-agent framework for discrete-choice reasoning with LLMs, improving accuracy, interpretability, and reproducibility by orchestrating heterogeneous models and optionally using an EMA-guided router for agent selection.
Contribution
The paper introduces ORCH, a deterministic multi-agent orchestrator that enhances discrete-choice reasoning with fixed rules and optional EMA-guided routing, improving performance and interpretability.
Findings
ORCH outperforms single-model baselines and majority-vote ensembles on multiple benchmarks.
On MMLU-Pro, ORCH improves accuracy by over 10 points.
On GSM8K, ORCH achieves over 50 points gain.
Abstract
Recent advances in large-scale language models (LLMs) have made multi-agent architectures attractive for challenging reasoning tasks. However, many existing systems rely on stochastic routing or ad-hoc heuristics, making their behavior difficult to reproduce and their decision process hard to interpret. We propose ORCH, a deterministic coordination framework for discrete-choice reasoning that orchestrates heterogeneous LLMs. ORCH follows a ``many analyses, one decision'' paradigm: multiple base models independently produce structured analyses, and a dedicated merge agent outputs the final choice. The framework uses fixed rules for task decomposition and answer aggregation, keeping the pipeline predictable, reproducible, and training-free. Determinism here refers to fixed routing and aggregation rules under a fixed evaluation protocol, rather than strict bit-level reproducibility across…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Speech and dialogue systems
