DOVA: Deliberation-First Multi-Agent Orchestration for Autonomous Research Automation

TL;DR

DOVA introduces a multi-agent orchestration platform with explicit meta-reasoning, hybrid collaborative reasoning, and adaptive thinking, significantly improving complex research automation efficiency and reasoning capacity.

Contribution

The paper presents DOVA, a novel multi-agent system with a deliberation-first approach, hybrid reasoning pipeline, and adaptive token management, advancing research automation capabilities.

Findings

Reduces inference cost by 40-60% on simple tasks.

Improves answer confidence and source coverage.

Demonstrates effectiveness through architectural ablation studies.

Abstract

Large language model (LLM) agents have demonstrated remarkable capabilities in tool use, reasoning, and code generation, yet single-agent systems exhibit fundamental limitations when confronted with complex research tasks demanding multi-source synthesis, adversarial verification, and personalized delivery. We present DOVA (Deep Orchestrated Versatile Agent), a multi-agent platform introducing three key innovations: (1) deliberation-first orchestration, where explicit meta-reasoning precedes tool invocation, informed by a persistent user model and entity-aware conversation context; (2) hybrid collaborative reasoning, a composable three-phase pipeline unifying ensemble diversity, blackboard transparency, and iterative refinement; and (3) adaptive multi-tiered thinking, a six-level token-budget allocation scheme that reduces inference cost by 40-60% on simple tasks while preserving deep reasoning capacity. We formalize the core algorithms, present an architectural ablation study across seven system configurations, and analyze the contribution of each component to answer confidence, source coverage, and token efficiency.