arXiv subjects

Tiancheng Luo

Publications and source records attributed to Tiancheng Luo.

4 recordsLinked to original sources

arXiv2026

Cladding Layer Enhanced GHz Bulk Acoustic Wave Resonance in Sodium Niobate Thin Films on Silicon

Bulk acoustic wave resonators (BAWR) and bandpass filters operating at GHz frequency are the workhorse of (Vo-)LTE telecommunication and broadband internet. In line with the Singapore Green Plan 2030 for innovating environmentally friendly products, we fabricated lead-free BAWR with sodium niobate (NaNbO3) piezoelectric on silicon with a high electromechanical coupling factor up to 31.3% operating at ~4 GHz. We disclose our crucial strategy where the NaNbO3 layer is cladded between two thin layers of high band gap insulators, which satisfies two primary objectives, i.e. leakage current mitigation and crack avoidance. In addition, we also verified the efficacy of reducing lattice parameters of the cladding layers in promoting vertically distorted tetragonal phase NaNbO3 and producing stronger BAWR signals.

cond-mat.mtrl-sci↗

arXiv2026

From Recognition to Reasoning: Benchmarking and Enhancing MLLMs on Real-World Receipt Document Understanding

Extracting structured information from visual documents (Visual Information Extraction, VIE) is a cornerstone of business automation. While recent Multimodal Large Language Models (MLLMs) have shown promising capabilities, existing benchmarks suffer from critical limitations in scale and realism, lack semantic granularity, and fail to cover diverse document types. To bridge this gap, we introduce ReceiptBench, a large-scale, human-annotated benchmark consisting of 10k diverse receipts, organizing information extraction into four hierarchical sub-tasks: (1) Basic Perception for raw text spotting, (2) Format Normalization for strictly following standardization instructions, (3) Semantic Reasoning for inferring implicit attributes from context, and (4) Structure Parsing for handling nested line items. Furthermore, we propose a two-stage training framework incorporating Metric-Aware Group Relative Policy Optimization (GRPO), which translates rigorous evaluation constraints into reinforcement learning signals to enhance structural consistency. Extensive experiments demonstrate that our method yields state-of-the-art performance, surpassing leading proprietary models on complex reasoning tasks. We release our datasets and code at https://github.com/wwwT0ri/ReceiptBench.

cs.CV↗

arXiv2026

Unseen-Codebases-Domain Data Synthesis and Training Based on Code Graphs

In the context of newly release software frameworks, large language models (LLMs) often exhibit poor performance and a high rate of hallucination, as they are not exposed to such environments during training. Although inference-time augmentation techniques such as retrieval-augmented generation (RAG) can partially mitigate hallucinations, knowledge injection through prompting alone is insufficient to enable models to fully understand the intrinsic relationships among different components of a codebase, or to reason about the correct compositions and apply. Although explicit knowledge injection can be achieved through post-training, compared with public code domains, unseen codebases typically provide only source code and lack large volumes of high-quality, usage-oriented code that can be directly leveraged as training data. Consequently, existing data synthesis approaches are insufficient to adequately capture unseen codebases usage scenarios when restricted to source code alone. To address these challenges, we propose UCD-Training, a two-stage training framework for reasoning-aware data synthesis grounded in a code graph constructed from unseen codebases. UCD-Training first parses the source code to build a code graph, then conducts dependency-preserving continued pretraining (CPT) using file-level dependency data, followed by graph-grounded supervised fine-tuning (SFT) on three types of synthesized data augmented with explicit reasoning traces: (1) single-hop relation reasoning data, (2) compositional API reasoning data, and (3) codebase utilization data. We further introduce a new benchmark, UnseenCodeBench, for code generation on unseen codebases and conduct comprehensive experiments across multiple codebases.

cs.SE↗

arXiv2020

Time-Stretched Femtosecond Lidar Using Microwave Photonic Signal Processing

A real-time ranging lidar with 0.1 Mega Hertz update rate and few-micrometer resolution incorporating dispersive Fourier transformation and instantaneous microwave frequency measurement is proposed and demonstrated. As time-stretched femtosecond laser pulse passing through an all-fiber Mach-Zehnder Interferometer, where the detection light beam is inserted into the optical path of one arm, the displacement is encoded to the frequency variation of the temporal interferogram. To deal with the challenges in storage and real-time processing of the microwave pulse generated on a photodetector, we turn to all-optical signal processing. A carrier wave is modulated by the time-domain interferogram using an intensity modulator. After that, the frequency variation of the microwave pulse is uploaded to the first order sidebands. Finally, the frequency shift of the sidebands is turned into transmission change through a symmetric-locked frequency discriminator. In experiment, A real-time ranging system with adjustable dynamic range and detection sensitivity is realized by incorporating a programmable optical filter. Standard deviation of 7.64 μm, overall mean error of 19.10 μm over 15 mm detection range and standard deviation of 37.73 μm, overall mean error of 36.63 μm over 45 mm detection range are obtained respectively.

eess.SP↗