ORCA: Orchestrated Reasoning with Collaborative Agents for Document Visual Question Answering

• URL: http://arxiv.org/abs/2603.02438v1
• Date: Mon, 02 Mar 2026 22:21:25 GMT
• Title: ORCA: Orchestrated Reasoning with Collaborative Agents for Document Visual Question Answering
• Authors: Aymen Lassoued, Mohamed Ali Souibgui, Yousri Kessentini,
• Abstract summary: We present ORCA: Orchestrated Reasoning with Collaborative Agents for Document Visual Question Answering.<n>Our framework leverages a set of specialized AI agents, each dedicated to a distinct modality, enabling fine-language understanding and collaborative reasoning across diverse document components.<n>Our approach achieves significant improvements over state-of-the-art methods, establishing a new paradigm for collaborative agent systems in vision-grained reasoning.
• Score: 8.852709980681626
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Document Visual Question Answering (DocVQA) remains challenging for existing Vision-Language Models (VLMs), especially under complex reasoning and multi-step workflows. Current approaches struggle to decompose intricate questions into manageable sub-tasks and often fail to leverage specialized processing paths for different document elements. We present ORCA: Orchestrated Reasoning with Collaborative Agents for Document Visual Question Answering, a novel multi-agent framework that addresses these limitations through strategic agent coordination and iterative refinement. ORCA begins with a reasoning agent that decomposes queries into logical steps, followed by a routing mechanism that activates task-specific agents from a specialized agent dock. Our framework leverages a set of specialized AI agents, each dedicated to a distinct modality, enabling fine-grained understanding and collaborative reasoning across diverse document components. To ensure answer reliability, ORCA employs a debate mechanism with stress-testing, and when necessary, a thesis-antithesis adjudication process. This is followed by a sanity checker to ensure format consistency. Extensive experiments on three benchmarks demonstrate that our approach achieves significant improvements over state-of-the-art methods, establishing a new paradigm for collaborative agent systems in vision-language reasoning.

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