Quantization-aware training (QAT) aims to recover reasoning capabilities in low-bit large language models (LLMs). Existing recipes typically supervise the quantized student with the original full-precision model on fixed response sequences. However, the resulting low-bit quantized models still struggle on long-horizon...
Bo-Yuan Zheng, Jing Xiong, Chen-Wei Wu et al.· 0 citations
Simulating economic responses at society scale requires agents whose decisions reflect household circumstances and the effects of earlier actions. We introduce S$^2$-MAS, a society-scale multi-agent simulation framework built on 500 million synthetic household personas grounded in real-survey data. Its Genese engine se...
Yang Li, Yi-Ke Wang, Diane Wan et al.· 0 citations
The hybrid system achieves 98--99% diagnostic tool recall with 92--96% precision, matches or exceeds cloud-only baselines on clinical accuracy, and maintains bandwidth-invariant latency (25--35,s) at 4--15x lower token cost.
Hei-Ting Chan, Chen-Wei Wu, Xue-Shen Liu et al.· 0 citations
TIER-MoE is introduced, a risk-guided subspace mixture-of-experts model that defines sample-specific modality reliability as the prediction loss its unimodal predictor is expected to incur, and demonstrates its superiority over state-of-the-art methods in predictive performance and probability calibration.
DIASENTINEL demonstrates a practical framework for reliable, auditable, and privacy-preserving LLM-based clinical decision support for type 2 diabetes mellitus risk screening and guideline-grounded report generation from electronic health records (EHRs).
A benchmark of multi-visit, multi-specialty patient records is introduced that evaluates long-context evidence retrieval, cross-time evidence aggregation, and cross-specialty clinical reasoning and proposes MedCache, a hybrid framework that constructs temporally valid patient memory and organizes evidence into overlapp...
Hei-Ting Chan, Chenwei Wu, Xue-Shen Liu et al.· 1 citation
A rigorous clinical stress test for multimodal model editing that evaluates whether an edit remains reliable, precise, and generalizable under the challenges of image and text variation, modality and protocol shifts, clinical knowledge composition, and temporal progression is introduced.
Guli Zhu, Chenwei Wu, Liyue Shen· 0 citations
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