{"ID":5552420,"CreatedAt":"2026-07-02T01:54:51.863792489Z","UpdatedAt":"2026-07-03T07:34:59.203171219Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.00060","arxiv_id":"2607.00060","title":"Synergistic Perception-Reasoning Governance: Grounding Medical MLLMs with Verifiable Anatomical Evidence","abstract":"Multimodal large language models (MLLMs) show strong promise for clinical VQA and radiology report generation, yet inference-time hallucinations still undermine trustworthy use: models can produce fluent conclusions that conflict with imaging evidence. Existing mitigation strategies typically rely on additional training, external retrieval/knowledge bases, or multi-stage post-hoc verification, which increases cost and pipeline complexity and often generalizes poorly across models and tasks.To address this, we propose a holistic, training-free evidence-injection framework that systematically mitigates hallucinations through dual-side evidence injection. By leveraging ROI priors acquired using MedSAM in our implementation, we recalibrate the visual perception trajectory via ROI-guided activation modulation while anchoring the textual reasoning trajectory by mapping anatomical coordinates into discrete semantic tokens as verifiable external memory. Then we introduce a task-aware dynamic router to select modality-specific interventions based on task semantics, balancing perceptual grounding and linguistic fluency. We conduct systematic evaluations on 2 tasks and 5 datasets using \\texttt{LLaVA-1.5-7B}, \\texttt{LLaVA-Med-1.5-7B}, \\texttt{Qwen3-VL-8B/32B}, and \\texttt{InternVL-3.5-8B/38B}. Controlled ablations and visualizations further validate the framework, which consistently outperforms baselines across medical benchmarks, improving close-ended accuracy by up to $\\sim\\mathbf{6}\\%\\uparrow$ and reducing open-ended hallucinations by $\\sim\\mathbf{35}\\%\\downarrow$. The code has been made available on GitHub: \\href{https://github.com/Henry991115/SPRG}{\\textcolor{blue}{https://github.com/Henry991115/SPRG}}.","short_abstract":"Multimodal large language models (MLLMs) show strong promise for clinical VQA and radiology report generation, yet inference-time hallucinations still undermine trustworthy use: models can produce fluent conclusions that conflict with imaging evidence. Existing mitigation strategies typically rely on additional trainin...","url_abs":"https://arxiv.org/abs/2607.00060","url_pdf":"https://arxiv.org/pdf/2607.00060v1","authors":"[\"Rui Hao\",\"Qiankun Li\",\"Junyuan Mao\",\"Linghao Meng\",\"Dirui Xie\",\"Dayu Tan\",\"Zhigang Zeng\"]","published":"2026-06-30T08:07:25Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false,"code_links":[{"ID":613859,"CreatedAt":"2026-07-02T01:54:51.863792489Z","UpdatedAt":"2026-07-02T01:54:51.863792489Z","DeletedAt":null,"paper_id":5552420,"paper_url":"https://arxiv.org/abs/2607.00060","paper_title":"Synergistic Perception-Reasoning Governance: Grounding Medical MLLMs with Verifiable Anatomical Evidence","repo_url":"https://github.com/Henry991115/SPRG","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}