Yue Cui

Yue Cui, Liuyi Yao, Shuchang Tao et al.

Large language models (LLMs) have significantly advanced natural language processing, particularly through the integration of external tools and APIs. However, their effectiveness is frequently hampered by parameter mis-filling during tool calling. In this paper, we propose the Hierarchical Tool Error Checklist (HiTEC) framework to systematically diagnose and mitigate tool-calling errors without relying on extensive real-world interactions. HiTEC introduces a two-tiered approach: a global error checklist that identifies common, cross-tool issues, and a local error checklist that targets tool-specific and contextual failures. Building on this structure, we propose two deployments: HiTEC-In Context Learning (HiTEC-ICL) and HiTEC-Kahneman-Tversky Optimization (HiTEC-KTO). HiTEC-ICL embeds the global checklist in the initial prompts and leverages a two-round conversational interaction to dynamically refine parameter handling, while HiTEC-KTO generates high-quality negative examples to drive fine-tuning via preference-based optimization. Extensive experiments across five public datasets demonstrate that our framework significantly improves parameter-filling accuracy and tool-calling success rates compared to baseline methods.

Degang Sun, Yue Cui, Siye Wang et al.

RFID as an important component technology of IoT faces important security risks while being rapidly applied, among which the discovery of unauthorized readers in space is crucial. There are some researches proposed the unauthorized reader detection algorithm based on commercial off the shell(COTS) devices, but these detection algorithms are often easily affected by moving objects blocking interference in space, causing false alarms. We propose a new method of eliminating moving object interference, which can reduce the system false alarm rate to less than 7.9% by experimental testing

Kewei Song, Yue Cui, Tiannan Tao et al.

Constructing precise micro-nano metal patterns on complex three-dimensional (3D) plastic parts allows the fabrication of functional devices for advanced applications. However, this patterning is currently expensive and requires complex processes with long manufacturing lead time. The present work demonstrates a process for the fabrication of micro-nano 3D metal-plastic composite structures with arbitrarily complex shapes. In this approach, a light-cured resin is modified to prepare an active precursor capable of allowing subsequent electroless plating (ELP). A multi-material digital light processing 3D printer was newly developed to enable the fabrication of parts containing regions made of either standard resin or active precursor resin nested within each other. Selective 3D ELP processing of such parts provided various metal-plastic composite parts having complicated hollow micro-nano structures with specific topological relationships on a size scale as small as 40 um. Using this technique, 3D metal topologies that cannot be manufactured by traditional methods are possible, and metal patterns can be produced inside plastic parts as a means of further miniaturizing electronic devices. The proposed method can also generate metal coatings exhibiting improved adhesion of metal to plastic substrate. Based on this technique, several sensors composed of different functional nonmetallic materials and specific metal patterns were designed and fabricated. The present results demonstrate the viability of the proposed method and suggest potential applications in the fields of smart 3D micro-nano electronics, 3D wearable devices, micro/nano-sensors, and health care.