Visual Para-Thinker++: A Single-Policy Multi-Agent Framework for Visual Reasoning
Summary
Visual Para-Thinker++ proposes a single-policy multi-agent framework for visual reasoning that uses role-conditioned agents (Main, Worker, Summary) and dedicated training methods to reduce hallucinations and improve efficiency, outperforming baselines on hallucination-sensitive benchmarks.
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Paper page - Visual Para-Thinker++: A Single-Policy Multi-Agent Framework for Visual Reasoning
Source: https://huggingface.co/papers/2606.09290
Abstract
A multi-agent framework with shared MLLM policy and role-specific training methods improves visual reasoning by reducing hallucinations and enabling efficient parallel processing.
Visual reasoningrequires integrating evidence distributed across regions, attributes, and relations, making single-chain reasoning prone to early perceptual commitment andhallucination. We propose Visual Para-Thinker++, a single-policymulti-agent frameworkin which one sharedMLLM policyis instantiated as role-conditioned Main, Worker, andSummary Agents. TheMain Agentdecomposes the task with fixed allocation patterns;Worker Agentsreason in parallel under context isolation; and theSummary Agentreconciles full Worker reasoning traces rather than majority-voting on final labels. The shared policy is trained byMulti-Agent Capability InjectionandRole-Decoupled Multi-Agent Optimization, which assign role-specific rewards and advantages to corresponding token segments to reducegradient conflictamong collaborative roles. A native inference engine enables efficient multi-agent rollout through sharedvisual prefixandKV cache reuse. Across V*, CountBench, the RefCOCO family, and HallusionBench, Visual Para-Thinker++ consistently outperforms single-trajectory and inference-time parallel baselines, with especially strong gains onhallucination-sensitivevisual reasoning.
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