Haeun Kwon | Searcharxiv

arXiv2026

MedICL: Modality-Anatomy-Aware Visual In-Context Learning for Medical Visual Question Answering

Training-free adaptation of medical vision-language models (LVLMs) is a deployment requirement, yet visual in-context learning (ICL) injects modality- and style-mismatched demonstrations into medical contexts, teaching a frozen model the wrong answering style. We present MedICL, which curates demonstrations instead of retraining the model. MADS selects modality-anatomy aligned demonstrations that cover complementary interpretation dimensions; W2C curates them with weak retrieval signals and a strong verification vote that checks demonstrations rather than the model's own outputs; ECC answers with W anchored demonstration sets, aggregates by abnormality-aware signatures, and re-answers once on conflict. On VQA-RAD, SLAKE, and PathVQA under the LLaVA-Med zero-shot protocol, MedICL improves the strongest zero-shot baseline by +11 - +14 points on open-ended and +7 - +14 on closed questions, recovers 71% of fine-tuned open-ended performance on VQA-RAD at zero training cost, and stays robust to shifted or corrupted demonstration banks.

cs.CV↗

arXiv2025

ContextualLVLM-Agent: A Holistic Framework for Multi-Turn Visually-Grounded Dialogue and Complex Instruction Following

Despite significant advancements in Large Language Models (LLMs) and Large Vision-Language Models (LVLMs), current models still face substantial challenges in handling complex, multi-turn, and visually-grounded tasks that demand deep reasoning, sustained contextual understanding, entity tracking, and multi-step instruction following. Existing benchmarks often fall short in capturing the dynamism and intricacies of real-world multi-modal interactions, leading to issues such as context loss and visual hallucinations. To address these limitations, we introduce MMDR-Bench (Multi-Modal Dialogue Reasoning Benchmark), a novel dataset comprising 300 meticulously designed complex multi-turn dialogue scenarios, each averaging 5-7 turns and evaluated across six core dimensions including visual entity tracking and reasoning depth. Furthermore, we propose CoLVLM Agent (Contextual LVLM Agent), a holistic framework that enhances existing LVLMs with advanced reasoning and instruction following capabilities through an iterative "memory-perception-planning-execution" cycle, requiring no extensive re-training of the underlying models. Our extensive experiments on MMDR-Bench demonstrate that CoLVLM Agent consistently achieves superior performance, attaining an average human evaluation score of 4.03, notably surpassing state-of-the-art commercial models like GPT-4o (3.92) and Gemini 1.5 Pro (3.85). The framework exhibits significant advantages in reasoning depth, instruction adherence, and error suppression, and maintains robust performance over extended dialogue turns, validating the effectiveness of its modular design and iterative approach for complex multi-modal interactions.

cs.CL↗