arXiv subjects

Sike Xiang

Publications and source records attributed to Sike Xiang.

3 recordsLinked to original sources

arXiv2026

Sparse Multi-Stage Expert-Agent Routing for Complex Clinical Reasoning

Complex clinical reasoning requires models to update diagnostic hypotheses as new evidence emerges and to coordinate different medical specialities under limited consultation resources. Existing LLM-based clinical reasoning systems typically perform single-pass prediction or rely on fixed multi-agent workflows, making expert participation either static or unnecessarily exhaustive. We propose Sparse Multi-Stage Expert-Agent Routing, a language-based clinical reasoning framework that models diagnosis as a stage-wise routing process. Given progressively available clinical evidence derived from multiple modalities, the framework maintains an evolving case state and adaptively activates a sparse set of medical expert agents, supported by expert-specific memory across stages. To evaluate free-text diagnostic conclusions beyond surface similarity, we further introduce ClinFEScore, a fact-aware semantic evaluation protocol for clinical reasoning outputs. On reconstructed multi-stage cases from MAC and AgentClinic-NEJM, our framework reduces the average number of activated experts from 17.0 to 3.0 whilst maintaining strong fact-level diagnostic quality. On 200 real-world hospital MDT cases, ClinFEScore correlates strongly with clinician judgements (Spearman's $\rho=0.81$; Pearson's $r=0.87$), whilst our method achieves 91.5\% clinician-verified diagnostic accuracy with approximately five expert-agent/LLM calls per case. These results support sparse stage-wise coordination as an efficient and clinically relevant approach to LLM-based clinical reasoning.

cs.CV↗

arXiv2026

Checkup2Action: A Multimodal Clinical Check-up Report Dataset for Patient-Oriented Action Card Generation

Routine clinical check-up reports combine laboratory measurements, physiological assessments, imaging findings and visually structured information, but rarely tell patients what to do next. Translating them into follow-up actions requires models to connect evidence across pages, tables and modalities, identify clinically relevant issues and communicate next steps without unsupported diagnostic or treatment claims. Yet this report-to-action capability remains poorly benchmarked. We introduce C2A, a dataset and benchmark for generating structured \textit{Action Cards} from multimodal check-up reports, together with Checkup2Action, a constrained workflow for the task. C2A contains 2,000 de-identified real-world reports covering physical examinations, laboratory tests, cardiovascular assessments and imaging evidence. Each card specifies one issue, its priority, recommended department, follow-up window, patient-facing explanation and questions for clinicians. Our evaluation measures issue coverage and precision, priority consistency, department and timing accuracy, action complexity, usefulness, readability and safety. Experiments across general-purpose and medical large language models show that no model performs best on every dimension. Clinical experts judged 84% of evaluated outputs fully reasonable and effective. Removing safety constraints increased problem recall from 0.527 to 0.825, but produced more diagnostic overstatement. C2A therefore supports systematic study of the balance between coverage, actionable guidance and safe patient communication.

cs.CL↗

arXiv2025

BcQLM: Efficient Vision-Language Understanding with Distilled Q-Gated Cross-Modal Fusion

As multimodal large language models (MLLMs) advance, their large-scale architectures pose challenges for deployment in resource-constrained environments. In the age of large models, where energy efficiency, computational scalability and environmental sustainability are paramount, the development of lightweight and high-performance models is critical for real-world applications. As such, we propose a lightweight MLLM framework for end-to-end visual question answering. Our proposed approach centres on BreezeCLIP, a compact yet powerful vision-language encoder optimised for efficient multimodal understanding. With only 1.2 billion parameters overall, our model significantly reduces computational cost while achieving performance comparable to standard-size MLLMs. Experiments conducted on multiple datasets further validate its effectiveness in balancing accuracy and efficiency. The modular and extensible design enables generalisation to broader multimodal tasks. The proposed lightweight vision-language framework is denoted as BcQLM (BreezeCLIP-enhanced Q-Gated Multimodal Language Model). It offers a promising path toward deployable MLLMs under practical hardware constraints. The source code is available at https://github.com/thico0224/BcQLM.

cs.CV↗