arXiv subjects

Yuncong Liu

Publications and source records attributed to Yuncong Liu.

10 recordsLinked to original sources

arXiv2026

Sideband-Resolved 4H-SiC Optomechanical Resonators with Interference-Engineered Anchor-Loss Suppression

Sideband-resolved cavity optomechanical resonators provide a powerful platform for coherent photon--phonon interactions, enabling applications ranging from quantum state transduction and optomechanically induced transparency to precision sensing and microwave photonics. Achieving this regime in integrated microresonators, however, requires simultaneously realizing a narrow optical cavity linewidth, a high-frequency mechanical mode, and low mechanical dissipation. Here, we report the first sideband-resolved optomechanical resonators based on the 4H silicon carbide (4H-SiC) platform. By combining compact microdisk geometries with interference-engineered anchor-loss suppression, we simultaneously achieve intrinsic optical quality factors exceeding $1\times10^6$, room-temperature mechanical quality factors up to $1.51\times10^4$, and a sideband-resolution factor greater than seven. Systematic numerical and experimental studies reveal that a local minimum in anchor loss enables high mechanical quality factors without requiring aggressive undercutting, substantially improving fabrication yield and device robustness. We further demonstrate the first observation of optomechanically induced transparency in integrated 4H-SiC resonators, confirming coherent cavity optomechanical interactions in this material platform. These results establish 4H-SiC as a promising platform for integrated cavity optomechanics and provide a practical route toward scalable photon--phonon devices for classical and quantum photonic technologies.

physics.optics↗

arXiv2026

When Missing Becomes Structure: Intent-Preserving Policy Completion from Financial KOL Discourse

Key Opinion Leader (KOL) discourse on social media is widely consumed as investment guidance, yet turning it into executable trading strategies without injecting assumptions about unspecified execution decisions remains an open problem. We observe that the gaps in KOL statements are not random deficiencies but a structured separation: KOLs express directional intent (what to buy or sell and why) while leaving execution decisions (when, how much, how long) systematically unspecified. Building on this observation, we propose an intent-preserving policy completion framework that treats KOL discourse as a partial trading policy and uses offline reinforcement learning to complete the missing execution decisions around the KOL-expressed intent. Experiments on multimodal KOL discourse from YouTube and X (2022-2025) show that KICL achieves the best return and Sharpe ratio on both platforms while maintaining zero unsupported entries and zero directional reversals, and ablations confirm that the full framework yields an 18.9% return improvement over the KOL-aligned baseline.

cs.LG↗

arXiv2025

Chip-scale optically driven phononic frequency comb with 1-70 GHz span

A phononic frequency comb consists of equally spaced components in the mechanical frequency domain and holds promise for numerous applications. Yet, prior demonstrations have been limited in spectral range due to the inherently low mechanical frequencies. In this work, we report a phononic comb with a record span from 1 to 70 GHz. This result is achieved by harnessing the strong mechanical nonlinearity of a $2.5$-$\mu$m-radius silicon carbide microdisk, which supports a radial breathing mode at $1.655$ GHz with a mechanical quality factor of 13,500. With just 1 mW of dropped optical power, radiation pressure from a continuous-wave pump drives strong phonon lasing, generating 42 phase-locked harmonics with $1.655$ GHz spacing. The combination of such broad bandwidth, low phase noise (-132 dBc/Hz at 1 MHz offset frequency) and frequency stability ($<10^{-7}$ at 1 second of averaging time) positions this ultracompact phononic comb as a powerful platform for diverse applications.

physics.optics↗

arXiv2025

Ultracompact 4H-silicon carbide optomechanical resonator with $f_m\cdot Q_m$ exceeding $10^{13}$ Hz

Silicon carbide (SiC) has great potential for optomechanical applications due to its outstanding optical and mechanical properties. However, challenges associated with SiC nanofabrication have constrained its adoption in optomechanical devices, as embodied by the considerable optical loss or lack of integrated optical access in existing mechanical resonators. In this work, we overcome such challenges and demonstrate a low-loss, ultracompact optomechanical resonator in an integrated 4H-SiC-on-insulator (4H-SiCOI) photonic platform for the first time. Based on a suspended $4.3$-$\mu$m-radius microdisk, the SiC optomechanical resonator features low optical loss ($<1$ dB/cm), a high mechanical frequency $f_m$ of $0.95 \times 10^9$ Hz, a mechanical quality factor $Q_m$ of $1.92\times10^4$, and a footprint of $<1\times 10^{-5}$ mm$^2$. The corresponding $f_m\cdot Q_m$ product is estimated to be $1.82 \times 10^{13}$ Hz, which is among the highest reported values of optomechanical cavities tested in an ambient environment at room temperature. In addition, the strong optomechanical coupling in the SiC microdisk enables coherent regenerative optomechanical oscillations at a threshold optical dropped power of 14 $\mu$W, which also supports efficient harmonic generation at increased power levels. With such competitive performance, we envision a range of chip-scale optomechanical applications to be enabled by the low-loss 4H-SiCOI platform.

physics.optics↗

arXiv2025

Enhancing LLM Reliability via Explicit Knowledge Boundary Modeling

Large language models (LLMs) are prone to hallucination stemming from misaligned self-awareness, particularly when processing queries exceeding their knowledge boundaries. While existing mitigation strategies employ uncertainty estimation or query rejection mechanisms, they suffer from computational efficiency and sacrificed helpfulness. To address these issues, we propose the Explicit Knowledge Boundary Modeling (EKBM) framework, integrating fast and slow reasoning systems to harmonize reliability and usability. The framework first employs a fast-thinking model to generate confidence-labeled responses, enabling immediate utilization of high-confidence outputs, whereas uncertain predictions trigger a slow refinement model for accuracy improvement. To align model behavior with our proposed object, we propose a hybrid training pipeline, enhancing self-awareness without degrading task performance. Evaluations on dialogue state tracking tasks demonstrate that EKBM achieves superior model reliability over uncertainty-based baselines. Further analysis reveals that refinement substantially boosts accuracy while maintaining low computational overhead. The framework establishes a scalable paradigm for deploying reliable LLMs in error-sensitive applications, effectively balancing accuracy and practical utility.

cs.CL↗

arXiv2024

FinGPT: Enhancing Sentiment-Based Stock Movement Prediction with Dissemination-Aware and Context-Enriched LLMs

Financial sentiment analysis is crucial for understanding the influence of news on stock prices. Recently, large language models (LLMs) have been widely adopted for this purpose due to their advanced text analysis capabilities. However, these models often only consider the news content itself, ignoring its dissemination, which hampers accurate prediction of short-term stock movements. Additionally, current methods often lack sufficient contextual data and explicit instructions in their prompts, limiting LLMs' ability to interpret news. In this paper, we propose a data-driven approach that enhances LLM-powered sentiment-based stock movement predictions by incorporating news dissemination breadth, contextual data, and explicit instructions. We cluster recent company-related news to assess its reach and influence, enriching prompts with more specific data and precise instructions. This data is used to construct an instruction tuning dataset to fine-tune an LLM for predicting short-term stock price movements. Our experimental results show that our approach improves prediction accuracy by 8\% compared to existing methods.

cs.CL↗

arXiv2022

DFM: Dialogue Foundation Model for Universal Large-Scale Dialogue-Oriented Task Learning

Building a universal conversational agent has been a long-standing goal of the dialogue research community. Most previous works only focus on a small set of dialogue tasks. In this work, we aim to build a unified dialogue foundation model (DFM) which can be used to solve massive diverse dialogue tasks. To achieve this goal, a large-scale well-annotated dialogue dataset with rich task diversity (DialogZoo) is collected. We introduce a framework to unify all dialogue tasks and propose novel auxiliary self-supervised tasks to achieve stable training of DFM on the highly diverse large scale DialogZoo corpus. Experiments show that, compared with models of the same size, DFM can achieve state-of-the-art or competitive performance on very rich cross-domain downstream dialogue tasks. This demonstrates that DFM largely extends the ability of unified dialogue pre-trained model.

cs.CL↗

arXiv2022

OPAL: Ontology-Aware Pretrained Language Model for End-to-End Task-Oriented Dialogue

This paper presents an ontology-aware pretrained language model (OPAL) for end-to-end task-oriented dialogue (TOD). Unlike chit-chat dialogue models, task-oriented dialogue models fulfill at least two task-specific modules: dialogue state tracker (DST) and response generator (RG). The dialogue state consists of the domain-slot-value triples, which are regarded as the user's constraints to search the domain-related databases. The large-scale task-oriented dialogue data with the annotated structured dialogue state usually are inaccessible. It prevents the development of the pretrained language model for the task-oriented dialogue. We propose a simple yet effective pretraining method to alleviate this problem, which consists of two pretraining phases. The first phase is to pretrain on large-scale contextual text data, where the structured information of the text is extracted by the information extracting tool. To bridge the gap between the pretraining method and downstream tasks, we design two pretraining tasks: ontology-like triple recovery and next-text generation, which simulates the DST and RG, respectively. The second phase is to fine-tune the pretrained model on the TOD data. The experimental results show that our proposed method achieves an exciting boost and get competitive performance even without any TOD data on CamRest676 and MultiWOZ benchmarks.

cs.CL↗

arXiv2022

UniDU: Towards A Unified Generative Dialogue Understanding Framework

With the development of pre-trained language models, remarkable success has been witnessed in dialogue understanding (DU). However, current DU approaches usually employ independent models for each distinct DU task without considering shared knowledge across different DU tasks. In this paper, we propose a unified generative dialogue understanding framework, named {\em UniDU}, to achieve effective information exchange across diverse DU tasks. Here, we reformulate all DU tasks into a unified prompt-based generative model paradigm. More importantly, a novel model-agnostic multi-task training strategy (MATS) is introduced to dynamically adapt the weights of diverse tasks for best knowledge sharing during training, based on the nature and available data of each task. Experiments on ten DU datasets covering five fundamental DU tasks show that the proposed UniDU framework largely outperforms task-specific well-designed methods on all tasks. MATS also reveals the knowledge-sharing structure of these tasks. Finally, UniDU obtains promising performance in the unseen dialogue domain, showing the great potential for generalization.

cs.CL↗

arXiv2020

An Investigation on Different Underlying Quantization Schemes for Pre-trained Language Models

Recently, pre-trained language models like BERT have shown promising performance on multiple natural language processing tasks. However, the application of these models has been limited due to their huge size. To reduce its size, a popular and efficient way is quantization. Nevertheless, most of the works focusing on BERT quantization adapted primary linear clustering as the quantization scheme, and few works try to upgrade it. That limits the performance of quantization significantly. In this paper, we implement k-means quantization and compare its performance on the fix-precision quantization of BERT with linear quantization. Through the comparison, we verify that the effect of the underlying quantization scheme upgrading is underestimated and there is a huge development potential of k-means quantization. Besides, we also compare the two quantization schemes on ALBERT models to explore the robustness differences between different pre-trained models.

cs.CL↗