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Qiankun Li

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#artificial intelligence Preprint Sep 2026

Still There, No Longer Seen: Exposing Compression-Induced Risk in Large Vision-Language Models

Visual token compression reduces the inference cost of Large Vision-Language Models (LVLMs). However, aggregate robustness measures do not reveal whether a particular adversarial failure is induced by compression or inherited from the underlying model. We define a compression-specific failure (CSF) as an adversarial in...

Qian-Kun Li, Yuechen Zhang, Bo-Wen Chen et al. · 0 citations

Synergistic Perception-Reasoning Governance: Grounding Medical MLLMs with Verifiable Anatomical Evidence

A holistic, training-free evidence-injection framework that systematically mitigates hallucinations through dual-side evidence injection and introduces a task-aware dynamic router to select modality-specific interventions based on task semantics, balancing perceptual grounding and linguistic fluency is proposed.

Rui Hao, Qiankun Li, Junyuan Mao et al. · 0 citations
Jul 2026

RareLens: Towards End-to-End Rare Disease Care via Aligning Divergent Large Language Model Reasoning

Autonomous RareLens and physicians assisted by RareLens both outperformed unaided physicians, while demonstrating that effective human-AI collaboration requires more than simply providing model outputs, and suggest a general strategy for building AI systems that operate reliably under high clinical uncertainty.

Xi Chen, Hong-Ru Zhou, Shi-Yu Feng et al. · 0 citations
Preprint Jul 2026

HIVE: Understanding Post-Hallucination Reasoning in Vision Language Models

This work studies Post Hallucination Reasoning (PHR), the stage in which hallucinated semantics enter the model's inference context and influence downstream predictions, and introduces HIVE, Hallucination Inference and Verification Engine, an evaluation infrastructure that enables controlled comparisons between faithfu...

Feng He, Zhenting Wang, Qifan Wang et al. · 1 citation

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