arXiv subjects

Yulu Huang

Publications and source records attributed to Yulu Huang.

7 recordsLinked to original sources

arXiv2026

Frontiers in FinTech: Multimodal Foundation Models for Financial Reporting and Decision Science

Financial information no longer arrives in a single format. Research reports come as PDFs, financial statements live in spreadsheets, market trends are captured in images, and policy documents reach analysts as scans, each carrying part of the picture the others cannot supply. Accounting information systems built around single-modality extraction pipelines and rule-based tools therefore struggle to assemble the full picture, slowing financial statement analysis, complicating audit evidence corroboration, and limiting investment decision support. This study presents FinVision, a multimodal large language model that unites vision-language models with domain-specific financial reasoning. Instead of processing documents in isolation, FinVision reads text, tables, and images together, converts them into consistent structured data, and verifies cross-modal agreement, in the same spirit as auditors corroborating evidence from independent sources. The model is trained in two stages, pre-trained on large-scale public financial corpora and fine-tuned on institution-specific investment data, so it can apply established valuation methodologies and audit risk assessment frameworks while outperforming zero-shot and single-stage baselines. A natural-language decision pipeline lets users describe what they need and turns those descriptions into executable workflows, supporting portfolio optimization, real-time risk monitoring, and refinement through multi-turn dialogue. Across 200 listed companies, FinVision reduced valuation error by 19 percent relative to the strongest baseline; a user study with 48 accounting and investment professionals reported a 51 percent reduction in task completion time. These results carry implications for audit automation, financial reporting quality, and more inclusive access to expert-level financial analysis.

cs.CE↗

arXiv2026

AccountAgent: AI Accounting Assistant System

The AI Accounting Assistant System is an innovative tool that improves the accuracy and efficiency of financial management and is becoming a core support for enterprise accounting. It relies on machine learning, natural language processing, and data visualization to automate the full accounting agent including bookkeeping, report generation, and data analysis, substantially reducing manual operations and minimizing human error. Designed to resolve the pain points of low efficiency, cumbersome workflows, and data lag in traditional accounting, the system shifts financial work from repetitive labor toward high-value decision support. It deeply mines historical financial data, precisely identifies operating trends, and provides real-time, targeted insight for strategic planning, risk prevention, and operating decisions. By reconstructing the accounting agent, the system realizes automated bookkeeping, intelligent analysis, and efficient compliance, driving the accounting profession from a bookkeeping orientation toward a management orientation. This document presents the architecture, methodology, key algorithms, and functional modules of the platform.

cs.CE↗

arXiv2026

DeepResearch Agent System

The DeepResearch Agent System is a large language model system engineered for deep information retrieval, multi-step reasoning, and autonomous research tasks. Built upon a sparse activation architecture with 30 billion total parameters of which only 3 billion are activated per token, the system achieves state-of-the-art performance on multiple agent search benchmarks while delivering 3.2 times faster inference compared to dense counterparts of equivalent scale. The system supports a 128K-token context window with hierarchical attention mechanisms that yield 18.7% accuracy and 23.4% recall improvements over standard long-context approaches. A dual-mode reasoning engine provides both a ReAct paradigm for basic multi-step problem solving and an IterResearch mode for high-performance iterative research with up to 20 reasoning steps, collectively delivering a 31.2% accuracy improvement over single-pass baselines. Multi-tool coordination integrates retrieval, computation, web search, and file parsing modules to achieve 92.1% tool-use accuracy. A reinforcement learning optimization framework based on the GRPO algorithm provides token-level policy gradients that improve training stability by 35% and accelerate convergence by 42%. An automated data synthesis pipeline with seed-based expansion achieves a 92.5% usability rate. Benchmark results include 87.3% on Humanity's Last Exam, 85.3% on BrowserComp Chinese, and 91.2% on WebWalkerQA. The system is fully open-sourced, including data synthesis, training, and inference code, and supports applications in academic research, business analysis, R&D support, and education.

cs.AI↗

arXiv2025

SugarTextNet: A Transformer-Based Framework for Detecting Sugar Dating-Related Content on Social Media with Context-Aware Focal Loss

Sugar dating-related content has rapidly proliferated on mainstream social media platforms, giving rise to serious societal and regulatory concerns, including commercialization of intimate relationships and the normalization of transactional relationships.~Detecting such content is highly challenging due to the prevalence of subtle euphemisms, ambiguous linguistic cues, and extreme class imbalance in real-world data.~In this work, we present SugarTextNet, a novel transformer-based framework specifically designed to identify sugar dating-related posts on social media.~SugarTextNet integrates a pretrained transformer encoder, an attention-based cue extractor, and a contextual phrase encoder to capture both salient and nuanced features in user-generated text.~To address class imbalance and enhance minority-class detection, we introduce Context-Aware Focal Loss, a tailored loss function that combines focal loss scaling with contextual weighting.~We evaluate SugarTextNet on a newly curated, manually annotated dataset of 3,067 Chinese social media posts from Sina Weibo, demonstrating that our approach substantially outperforms traditional machine learning models, deep learning baselines, and large language models across multiple metrics.~Comprehensive ablation studies confirm the indispensable role of each component.~Our findings highlight the importance of domain-specific, context-aware modeling for sensitive content detection, and provide a robust solution for content moderation in complex, real-world scenarios.

cs.CL↗

arXiv2024

Elasticity-Controlled Jamming Criticality in Soft Composite Solids

Soft composite solids are made of inclusions dispersed within soft matrices. They are ubiquitous in nature and form the basis of many biological tissues. In the field of materials science, synthetic soft composites are promising candidates for building various engineering devices due to their highly programmable features. However, when the volume fraction of the inclusions increases, predicting the mechanical properties of these materials poses a significant challenge for the classical theories of composite mechanics. The difficulty arises from the inherently disordered, multi-scale interactions between the inclusions and the matrix. To address this challenge, we systematically investigated the mechanics of densely filled soft elastomers containing stiff microspheres. We experimentally demonstrated how the strain-stiffening response of the soft composites is governed by the critical scalings in the vicinity of a shear-jamming transition of the included particles. The proposed criticality framework quantitatively connects the overall mechanics of a soft composite with the elasticity of the matrix and the particles, and captures the diverse mechanical responses observed across a wide range of material parameters. The findings uncover a novel design paradigm of composite mechanics that relies on engineering the jamming properties of the embedded inclusions.

cond-mat.soft↗

arXiv2022

Autonomous waves and global motion modes in living active solids

Elastic active matter or active solid consists of self-propelled units embedded in an elastic matrix. Active solid resists deformation; the shape-preserving property and the intrinsic non-equilibrium nature make active solids a superior component for self-driven devices. Nonetheless, the mechanical properties and emergent behavior of active solids are poorly understood. Using a biofilm-based bacterial active solid, here we discovered self-sustained elastic waves with unique wave properties not seen in passive solids, such as power-law scaling of wave speed with activity. Under isotropic confinement, the active solid develops two topologically distinct global motion modes that can be selectively excited, with a surprising step-like frequency jump at mode transition. Our findings reveal novel spatiotemporal order in elastic active matter and may guide the development of solid-state adaptive or living materials.

cond-mat.soft↗

arXiv2013

Mechanical design and analysis for a low beta squeezed half-wave resonator

A superconducting half-wave resonator (HWR) of frequency=162.5 MHz and β=0.09 has been developed at Institute of Modern Physics. Mechanical stability of the low beta HWR cavity is a big challenge in cavity design and optimization. The mechanical deformations of a radio frequency superconducting cavity could be a source of instability, both in continues wave(CW) operation or in pulsed mode. Generally, the lower beta cavities have stronger Lorentz force detuning than that of the higher beta cavities. In this paper, a basic design consideration in the stiffening structure for the detuning effect caused by helium pressure and Lorentz force has been presented. The mechanical modal analysis has been investigated with finite element method(FEM). Based on these considerations, a new stiffening structure has been promoted for the HWR cavity. The computation results concerning the frequency shift show that the low beta HWR cavity with new stiffening structure has low frequency sensitivity coefficient, Lorentz force detuning coefficient KL and stable mechanical property.

physics.acc-ph↗