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
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
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
LLMVisor is presented, a roofline-guided latency attribution model that captures the memory-bound and compute-bound phases via a concise piecewise-linear form over features proportional to FLOPs and memory I/O traffic and runs efficiently at microsecond scale.
Shuowei Jin, Xue-Shen Liu, Jiaxin Shan et al.· 3 citations
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