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Panyu Chen

Publications and source records attributed to Panyu Chen.

6 recordsLinked to original sources
arXiv2026

MFlowAudio: Efficient Text-to-Audio Synthesis via Mamba-based Stateful Flow Matching

Recent advancements in audio generation have been largely driven by Transformer-based diffusion models. However, these models suffer from quadratic complexity of self-attention, severely bottlenecking the long-form audio synthesis. To overcome this limitation, we propose MFlowAudio, a novel latent audio generation framework that synergizes the continuous-time dynamics of Flow Matching with a custom-designed TCMamba backbone. TCMamba uses an innovative dual-scan mechanism: a TimeMamba module to capture long-range causal dependencies with linear complexity, and a ChannelMamba module to model cross-channel correlations such as harmonic structures. Exploiting this structural foundation, we formulate the Stateful Flow Matching (SFM) paradigm. This framework inherently enables chunk-wise training and streaming generation, maintaining an $\mathcal{O}(1)$ caching complexity without incurring extra computational overhead. To enable fine-grained controllable synthesis, we devise a novel guidance mechanism that neutralizes vector field collisions precipitated by on-the-fly semantic transitions of prompts. Comprehensive empirical evaluations confirm that MFlowAudio yields generative fidelity on par with state-of-the-art baselines while establishing significant computational efficiency, achieving above $14.5$% acceleration in generation and facilitating perceptually seamless streaming synthesis with a constant latency of $1.8$ seconds. Demo: https://huggingface.co/spaces/mflowaudio/MFlowAudio

cs.SD↗
arXiv2026

Toward Automated Detection of Documentation Inconsistencies in Electronic Health Records

Objective:To characterize the kinds of internal documentation inconsistencies a general-domain large language model (LLM) can surface from real-world discharge summaries, and to identify recurring failure modes that limit reliability at scale. Materials and Methods: We applied a two-stage LLM pipeline---open-ended candidate identification (Gemini 2.5 Pro) followed by context-grounded verification (Gemini 2.5 Flash)---to 3000 MIMIC-IV-Note discharge summaries drawn as three contiguous 1000-note batches from distinct positions in the corpus. Two clinicians independently reviewed a domain-stratified sample of 268 retained candidates; a third adjudicated disagreements. Results: Stage~1 proposed 11333 candidates; 3461 were retained after Stage~2 verification---machine-generated candidates---with at least one in 69.7% of admissions. Medication (43.2%) and diagnosis or clinical-finding (25.3%) candidates predominated. In the reviewed sample, reviewers agreed on 91.0\% of candidates, and 253 of 268 (94.4\%) were judged valid after adjudication. Representative examples from all seven domains carried direct implications for clinical reasoning or patient safety. Review also revealed recurring failure modes involving temporal reasoning, evolving diagnoses, and outpatient-prescribing conventions. Discussion: Detection is highly context-dependent: judging a flagged pair requires locating each statement and assessing whether the conflict reflects a true contradiction or missing context. We propose a graded ontology spanning strict contradiction, ambiguity, and gap, with a schema characterizing each flagged case by type, section, scope, domain, attribute, and inconsistency axis. Conclusion: This formative study establishes a methodological foundation and conceptual framework to guide subsequent validated, large-scale EHR-inconsistency analysis.

cs.CL↗
arXiv2026

Reliable Curation of EHR Dataset via Large Language Models under Environmental Constraints

Electronic health records (EHRs) are central to modern healthcare delivery and research; yet, many researchers lack the database expertise necessary to write complex SQL queries or generate effective visualizations, limiting efficient data use and scientific discovery. To address this barrier, we introduce CELEC, a large language model (LLM)-powered framework for automated EHR data extraction and analytics. CELEC translates natural language queries into SQL using a prompting strategy that integrates schema information, few-shot demonstrations, and chain-of-thought reasoning, which together improve accuracy and robustness. CELEC also adheres to strict privacy protocols: the LLM accesses only database metadata (e.g., table and column names), while all query execution occurs securely within the institutional environment, ensuring that no patient-level data is ever transmitted to or shared with the LLM. On a subset of the EHRSQL benchmark, CELEC achieves execution accuracy comparable to prior systems while maintaining low latency, cost efficiency, and strict privacy by exposing only database metadata to the LLM. Ablation studies confirm that each component of the SQL generation pipeline, particularly the few-shot demonstrations, plays a critical role in performance. By lowering technical barriers and enabling medical researchers to query EHR databases directly, CELEC streamlines research workflows and accelerates biomedical discovery.

cs.DB↗
arXiv2025

Models That Are Interpretable But Not Transparent

Faithful explanations are essential for machine learning models in high-stakes applications. Inherently interpretable models are well-suited for these applications because they naturally provide faithful explanations by revealing their decision logic. However, model designers often need to keep these models proprietary to maintain their value. This creates a tension: we need models that are interpretable--allowing human decision-makers to understand and justify predictions, but not transparent, so that the model's decision boundary is not easily replicated by attackers. Shielding the model's decision boundary is particularly challenging alongside the requirement of completely faithful explanations, since such explanations reveal the true logic of the model for an entire subspace around each query point. This work provides an approach, FaithfulDefense, that creates model explanations for logical models that are completely faithful, yet reveal as little as possible about the decision boundary. FaithfulDefense is based on a maximum set cover formulation, and we provide multiple formulations for it, taking advantage of submodularity.

cs.LG↗
arXiv2024

Kuaiji: the First Chinese Accounting Large Language Model

Large Language Models (LLMs) like ChatGPT and GPT-4 have demonstrated impressive proficiency in comprehending and generating natural language. However, they encounter difficulties when tasked with adapting to specialized domains such as accounting. To address this challenge, we introduce Kuaiji, a tailored Accounting Large Language Model. Kuaiji is meticulously fine-tuned using the Baichuan framework, which encompasses continuous pre-training and supervised fine-tuning processes. Supported by CAtAcctQA, a dataset containing large genuine accountant-client dialogues, Kuaiji exhibits exceptional accuracy and response speed. Our contributions encompass the creation of the first Chinese accounting dataset, the establishment of Kuaiji as a leading open-source Chinese accounting LLM, and the validation of its efficacy through real-world accounting scenarios.

cs.CL↗