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

Key points

• Problem: For research queries that need academic database search, code repository analysis, cross-checking of model registries, and synthesis with citations, the authors see four limits of single-agent systems. They process information linearly and miss cross-domain links, accept initial findings without adversarial challenge, call tools on keyword patterns, and spend the same reasoning depth on trivial and complex queries, wasting tokens on the former and starving the latter.
• Input and output: This is a system paper proposing the architecture and algorithms of DOVA (Deep Orchestrated Versatile Agent), a multi-agent platform that answers research queries. It takes a user query, a persistent user model, and conversation context, and returns an answer with source attribution drawn from ArXiv, GitHub, HuggingFace, and the Web, plus a confidence score. The same orchestration backend is exposed through a REST (Representational State Transfer) API, a CLI (command-line interface), a browser UI (user interface), and an MCP (Model Context Protocol) server, which the authors say offers five tools to Claude Code.
• Method: Before any tool call, the orchestrator weighs the user's expertise, mentioned entities, the six most recent turns, and the available tools to decide whether external information is needed, and temporal words such as latest or recent, or years from 2025 on, always force tool use. Collaborative reasoning chains an ensemble in which agents solve the problem independently, a blackboard where they post evidence and votes, and iterative refinement through rounds of critique. Thinking budgets have six levels from OFF to XHIGH (65,536 tokens), chosen by task type, a complexity hint, and query length.
• Implementation: The agent pool has five agents: ResearchAgent for MCP search, ProfilingAgent for the user model, ValidationAgent for code analysis and sandboxed execution, SynthesisAgent for writing with source attribution, and DebateAgent, where Bull and Bear agents argue for 2 rounds by default. The experiments use Claude Haiku 4.5 (Basic), Sonnet 4.6 (Standard), and Opus 4.6 (Advanced) by task tier. The official repository alfredcs/dova holds src, tests, frontend, infra, and dova-claude-plugin folders plus Docker configuration files, and its README describes the system as built on the AWS Strands Agents SDK and Amazon Bedrock AgentCore and lists biomedical sources such as PubMed, ClinicalTrials.gov, and PubChem in addition to the four sources in the paper.
• Results: The authors state that the values in their ablation table (Table 5), which compares seven configurations, are expected relative performance based on architectural analysis. In that table the full DOVA has confidence 0.82 and source coverage 0.90, removing collaboration gives 0.68 and 0.65, and the single-LLM baseline gives 0.52 and 0.00. For token efficiency, based on the thinking budgets, classification drops from 16K to 1K tokens (94%) and summarization by 75% versus a fixed MEDIUM budget, and the authors put aggregate savings at 40 to 60% for workloads where 40 to 60% of queries are simple.
• Limits: The authors state that the ablation rests on architectural analysis and that evaluation on standard benchmarks such as HotpotQA and MMLU is future work. Confidence is scored by the same LLM that produced the answer, and all agents share one LLM backbone. In-memory MMR (Maximal Marginal Relevance) search cost grows with the number of stored items and results, so very large memory stores need indexing.

Paper links

External research summaries. These are not HDATF publications or measured product results.