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Maximilian Dauner, Michael Kurz, Gudrun Socher, Alexander Knoll

High-fidelity helicopter flight simulators are essential for preparing pilots for complex and hazardous environments, yet realistic urban wind dynamics are difficult to reproduce in real time when relying on precomputed computational fluid dynamics (CFD) data. We present the first integration of a Fourier Neural Operator (FNO) into a Level D full flight simulator for real-time, physics-based urban wind field generation. Trained on high-resolution urban flow simulations, the FNO predicts one-minute-averaged 3D wind fields that dynamically adapt to flight state and location, replacing static wind inputs in the simulator pipeline. Turbulence levels are computed from the predictions and injected directly into the simulation loop. Professional pilots evaluated the system in an urban scenario and reported that it reproduced wind effects they would expect, such as turbulence and directional changes when landing behind buildings. They highlighted its value for less experienced pilots to develop wind awareness and for realistic training in critical operations, including offshore platform landings.

Rituparna Datta, Methun Kamruzzaman, Eili Y. Klein, Gregory R. Madden, Xinwei Deng, Anil Vullikanti, Parantapa Bhattacharya

The US Centers for Disease Control and Prevention (CDC), in 2019, designated Methicillin-resistant Staphylococcus aureus (MRSA) as a serious antimicrobial resistance threat. The risk of acquiring MRSA and suffering life-threatening consequences due to it remains especially high for hospitalized patients due to a unique combination of factors, including: co-morbid conditions, immuno suppression, antibiotic use, and risk of contact with contaminated hospital workers and equipment. In this paper, we present a novel generative probabilistic model, GenHAI, for modeling sequences of MRSA test results outcomes for patients during a single hospitalization. This model can be used to answer many important questions from the perspectives of hospital administrators for mitigating the risk of MRSA infections. Our model is based on the probabilistic programming paradigm, and can be used to approximately answer a variety of predictive, causal, and counterfactual questions. We demonstrate the efficacy of our model by comparing it against discriminative and generative machine learning models using two real-world datasets.

Christodoulos Constantinides, Dhaval Patel, Scott Kimbleton, Nishu Garg, Muhammad Paracha

Industrial data scientists require deep domain understanding to model asset conditions effectively, yet traditional sources such as Subject Matter Experts (SMEs) and Failure Modes and Effects Analysis (FMEA) documents are often unavailable or incomplete. We present a deployed Multi-Agent System (MAS) that leverages Large Language Models (LLMs) to automatically generate and refine domain-relevant questions, improving modeling decisions across industrial projects. The system addresses two key challenges—ensuring linguistic diversity and maintaining high relevance—by combining established information diversity metrics with a grounded relevancy classifier. We evaluate its effectiveness through diversity benchmarks, compare against direct prompting and AutoAgents baselines, knowledge coverage on downstream FMEA tasks, and controlled user studies. Deployed in real-world projects, the MAS has improved multiple stages of the CRISP-DM methodology, resulting in measurable savings in cost and man-hours.

Nadine Angela Palanca Cantonjos, Arpita Biswas

Agricultural regions in rural areas face damage from climate-related risks, including droughts, heavy rainfall, and shifting weather patterns. Prior research calls for adaptive risk-management solutions and decision-making strategies. To this end, artificial intelligence (AI), particularly agentic AI, offers a promising path forward. Agentic AI systems consist of autonomous, specialized agents capable of solving complex, dynamic tasks. While past systems have relied on single-agent models or have used multi-agent frameworks only for static functions, there is a growing need for architectures that support dynamic collaborative reasoning and context-aware outputs. To bridge this gap, we present AgroAskAI, a multi-agent reasoning system for climate adaptation decision support in agriculture, with a focus on vulnerable rural communities. AgroAskAI features a modular, role-specialized architecture that uses a chain-of-responsibility approach to coordinate autonomous agents, integrating real-time tools and datasets. The system has built-in governance mechanisms that mitigate hallucination and enable internal feedback for coherent, locally relevant strategies. The system also supports multilingual interactions, making it accessible to non-English-speaking farmers. Experiments on common agricultural queries related to climate adaptation show that, with additional tools and prompt refinement, AgroAskAI delivers more actionable, grounded, and inclusive outputs. Our experimental results highlight the potential of agentic AI for sustainable and accountable decision support in climate adaptation for agriculture.

Federico Cabitza, Andrea Campagner, Gian Eugenio Tontini

AI systems are widely proposed as second-opinion advisors in clinical diagnosis, offering the promise of enhancing decision accuracy and clinician confidence while preserving human oversight. However, successful deployment in real-world practice faces a critical barrier: clinicians' reliance on AI is often miscalibrated, manifesting as misuse (over-reliance driven by automation bias) and disuse (under-utilization driven by self-anchoring bias). This paper addresses these deployment challenges by systematically analyzing how such reliance patterns affect diagnostic accuracy, confidence, and decision-making across diverse medical specialties. We report results from controlled simulations involving over 300 medical professionals across six diagnostic settings—including knee MRI analysis, spinal X-rays, cardiac ECG evaluation, and gastrointestinal endoscopy—using a human-first, AI-second workflow. Although AI advice improved average diagnostic accuracy (+2 percentage points) and clinician confidence (+3 points on a normalized scale), overall levels of appropriate reliance remained well below 50%, with disuse emerging as the more prevalent and consequential barrier. We introduce and validate Appropriate Reliance as an actionable metric for assessing and improving human-AI collaboration, providing practical guidance for developers, healthcare institutions, and policymakers seeking to deploy second-opinion AI systems safely and effectively. By identifying the sociotechnical barriers and offering evidence-based design insights, this work supports the emerging application of AI as a collaborative advisor in clinical workflows, charting a clear path toward deployment that enhances diagnostic safety, accountability, and patient care. Specifically, we propose integrating the Appropriate Reliance metric into system development workflows, clinician training, and regulatory evaluations to enable safe and effective deployment of second-opinion AI systems.

Ayan Banerjee, Komandoor Srivathsan, Sandeep K.S. Gupta

A desirable property of any deployed artificial intelligence is generalization across domains, i.e. data generation distribution under a specific acquisition condition. In medical imagining applications the most coveted property for effective deployment is Single Domain Generalization (SDG), which addresses the challenge of training a model on a single domain to ensure it generalizes well to unseen target domains. In multi-center studies, differences in scanners and imaging protocols introduce domain shifts that exacerbate variability in rare class characteristics. This paper presents our experience on SDG in real life deployment for two exemplary medical imaging case studies on seizure onset zone detection using fMRI data, and stress electrocardiogram based coronary artery detection. Utilizing the commonly used application of diabetic retinopathy, we first demonstrate that state-of-the-art SDG techniques fail to achieve generalized performance across data domains. We then develop a generic expert knowledge integrated deep learning technique DL+EKE and instantiate it for the DR application and show that DL+EKE outperforms SOTA SDG methods on DR. We then deploy instances of DL+EKE technique on the two real world examples of stress ECG and resting state (rs)-fMRI and discuss issues faced with SDG techniques.

Raphael Anaadumba, Nazim A.Belabbaci, Connor Sullivan, Anton Kovalev, Yidong Zhu, Pradeep Kurup, Mohammad Arif Ul Alam

Lead contamination in urban water systems remains a prevalent public health threat, affecting millions of American households and disproportionately endangering vulnerable population groups. Current municipal risk assessment and inspection strategies are overwhelmingly based on random sampling and complaint-driven protocols that overlook spatial complexity, reinforce inequities, and squander limited resources, leaving critical exposure areas unidentified. This paper presents a lead contamination risk prediction framework from socio-demographic housing features analytics, first of its kind, by drawing on partially anonymized residential testing data as ground truth and applying graph neural networks alongside gradient-boosted ensembles. Specifically, our method integrates spatial Deep Graph Attention Networks classifiers to capture inter-neighborhood contamination dependencies, fuse demographic and spatial evidence, and produce interpretable risk scores. Those scores are actionable by municipal water authorities at the intra-neighborhood level. Through extensive experiments on newly constructed Chicago block-group level datasets, our framework achieves a balanced accuracy of 84.8% and reduces false positive lead contamination by up to 44% versus spatial-only baselines and 21% over current practice, without sacrificing recall on contaminated blocks. Our approach not only extends technical boundaries in spatial-ensemble learning and privacy-preserving urban health modeling, but also provides policymakers and public health officials with a means to assess and address contamination risks, supporting efforts to protect community health and safety.

Prerna Agarwal, Himanshu Gupta, Soujanya Soni, Rohith D Vallam, Renuka Sindhgatta, Sameep Mehta

Recent advancements in Large Language Models (LLMs) has lead to the development of agents capable of complex reasoning and interaction with external tools. In enterprise contexts, the effective use of such tools that are often enabled by application programming interfaces (APIs) is hindered by poor documentation, complex input or output schema, and large number of operations. These challenges make tool selection difficult and reduce the accuracy of payload formation upto 25%. We propose ACE, an automated tool creation and enrichment framework that transforms enterprise APIs into LLM-compatible tools. ACE (i) generates enriched tool specifications with parameter descriptions and examples to improve selection and invocation accuracy, and (ii) incorporates a dynamic shortlisting mechanism that filters relevant tools at runtime, reducing prompt complexity while maintaining scalability. We validate our framework on both proprietary and open-source APIs and demonstrate its integration with agentic frameworks. To the best of our knowledge, ACE is the first end-to-end framework that automates the creation, enrichment, and dynamic selection of enterprise API tools for LLM agents.

Nianjun Zhou, Dhaval Patel, Anamitra Bhattacharyya

Maintenance of mission-critical industrial assets is frequently hindered by fragmented data, inconsistent record-keeping, and limited access to analytical expertise, resulting in reactive rather than predictive practices. We present \textit{CodeReAct}, an AI-powered agentic framework deployed in large-scale facilities to automate event analysis and work order (WO) management.CodeReAct extends the ReAct paradigm by embedding executable Python code within the Thought--Action--Observation (TAO) loop, enabling natural language interaction, grounding heterogeneous alerts and work orders into structured Business Objects (BOs), and dynamically invoking analytic functions for forecasting, anomaly correlation, and maintenance recommendations. This architecture reduces manual data science intervention, improves adaptability, and supports reuse across asset types. Deployed in a mission-critical data center and productionized in Maximo, CodeReAct manages pumps, chillers, AHUs, compressors, cooling towers, and other mechanical and electrical systems. Evaluation with 36 representative maintenance utterances showed that outer-loop reflection and adaptive temperature improved task completion by up to 20%, while ablation studies confirmed the importance of reasoning in addition to code execution. Business validation revealed seasonal failure patterns, bundling opportunities, and predictive accuracy trends. In production, site engineers reported 25--40% faster diagnostics, fewer unplanned downtime events, and reduced reliance on specialized analysts. Lessons learned highlight the importance of structured BOs for grounding analytics, runtime safeguards to mitigate hallucinations, and adaptive model control for consistent execution. These results demonstrate how deployed agentic AI can deliver measurable business value in predictive and strategic maintenance planning.

Gaowei Zhang, Wei Wang, Tiong Lee Kong, Kai Xing, Huan Li, Yi Wang

Ensuring safety in power grid construction remains a critical yet challenging task, as existing monitoring approaches often lack scalability, timeliness, and adaptability to diverse on-site conditions. To address these limitations, we present ConstructAI, a deployed AI-driven safety management system that integrates multi-source image and video acquisition devices with advanced multimodal large model reasoning. The system combines text, image, and video prompts through an efficient workflow powered by LLaMA3 and Meta SAM2 backbones, enhanced with LoRA and adaptor modules for multimodal fusion. Once deployed, ConstructAI continuously processes real-time construction footage to identify violations, assess risk levels, and generate standardized rectification requirements. The deployment has demonstrated measurable benefits across multiple sites, including a >70% increase in violation rectification rates, reduction of average rectification delays from hours to minutes, and a 45% decline in repeat violations. Beyond technical gains, ConstructAI has delivered significant business impacts, such as reduced safety incidents, improved compliance with national regulations, and higher operational efficiency. By enabling proactive risk management and structured safety feedback loops, our system exemplifies how innovative use of AI can translate into tangible improvements for industrial safety. The lessons learned from deployment highlight the importance of balancing algorithmic advances with practical integration into organizational workflows.

Raquib Bin Yousuf, Hoang Anh Just, Shengzhe Xu, Brian Mayer, Victor Deklerck, Jakub Truszkowski, John C. Simeone, Jade Saunders, Chang-Tien Lu, Ruoxi Jia 等

Determining and verifying product provenance remains a critical challenge in global supply chains, particularly as geopolitical conflicts and shifting borders create new incentives for misrepresentation of commodities, such as hiding the origin of illegally harvested timber or stolen agricultural products. Stable Isotope Ratio Analysis (SIRA), combined with Gaussian process regression-based isoscapes, has emerged as a powerful tool for geographic origin verification. While these models are now actively deployed in operational settings supporting regulators, certification bodies, and companies, they remain constrained by data scarcity and suboptimal dataset selection. In this work, we introduce a novel deployed data valuation framework designed to enhance the selection and utilization of training data for machine learning models applied in SIRA. By quantifying the marginal utility of individual samples using Shapley values, our method guides strategic, cost-effective, and robust sampling campaigns within active monitoring programs. By prioritizing high-informative samples, our approach improves model robustness and predictive accuracy across diverse datasets and geographies. Our framework has been implemented and validated in a live provenance verification system currently used by enforcement agencies, demonstrating tangible, real-world impact. Through extensive experiments and deployment in a live provenance verification system, we show that this system significantly enhances provenance verification, mitigates fraudulent trade practices, and strengthens regulatory enforcement of global supply chains.

Rithvik Y, Bhavuk Singhal, Shubham Jain, Akshat Garg, Karan Tanwar, Anshu Aditya, Debashis Mukherjee, Debdoot Mukherjee

Abuse detection in e-commerce platforms is critical for preventing operational losses, particularly for transaction types vulnerable to abuse such as Return-to-Origin (RTO) in Cash-on-Delivery (COD) workflows. Detecting such abuse accurate, real-time decisions to intercept malicious orders before placement, imposing stringent sub-second latency requirements on deployed systems. In this work, we present TRUST, a deployed, production-scale abuse detection system based on a unified architecture of heterogeneous Graph Neural Networks (GNNs) and Transformer-based sequence encoders. This design enables joint reasoning over multi-relational entity interactions and temporal behavioural signals, allowing the model to combine complementary information for effective abuse detection when either modality is sparse or absent. TRUST processes millions of transactions daily with an average inference latency of ~25 ms, achieving a ~9.6% absolute precision improvement over a strong XGBoost baseline in live RTO detection. We report systematic ablation studies across both graph and sequence stages, evaluating GNN variants, sampling strategies, sequence lengths, and positional encoding schemes to guide architectural choices. Deployed end-to-end in a high-throughput environment, TRUST demonstrates that GNN–Transformer cascades can deliver state-of-the-art accuracy, scalability, and operational reliability in real-world abuse detection, offering a reproducible blueprint for similar industry-scale applications.

Yang Xi, Orelia Pi, Miaomiao Zhang, Rebecca Xiong, Jacqueline Lane, Nihar B Shah

There is growing interest in applying artificial intelligence (AI) to automate and support complex decision-making tasks. However, it remains unclear how algorithms compare to human judgment in contexts requiring semantic understanding and domain expertise. We examine this in the context of the judge assignment problem, matching submissions to suitably qualified judges. Specifically, we tackled this problem at the Harvard President’s Innovation Challenge, the university’s premier venture competition awarding over $500,000 to student and alumni startups. This setting represents a real-world environment where high-quality judge assignment is essential. We developed an AI-based judge assignment algorithm, the Hybrid Lexical-Semantic Similarity Ensemble (HLSE), and deployed it at the competition. We then evaluated its performance against human expert assignments using blinded match-quality scores from judges on 309 judge-venture pairs. Using a Mann-Whitney U statistic-based test, we found no statistically significant difference in assignment quality between the two approaches (AUC=0.48, p=0.40); on average, algorithmic matches were rated 3.90 and manual matches 3.94 on a 5-point scale, where 5 indicates an excellent match. Furthermore, manual assignments that previously required a full week could be automated in several hours by the algorithm during deployment. These results demonstrate that HLSE achieves human-expert-level matching quality while offering greater scalability and efficiency, underscoring the potential of AI-driven solutions to support and enhance human decision-making for judge assignment in high-stakes settings.

Yibing Wan, Zhengxiong Guan, Chaoli Zhang, Xiaoyang Li, Lai Xu, Beibei Jia, Zhenzhe Zheng, Fan Wu

In the user growth scenario, Internet companies invest heavily in paid acquisition channels to acquire new users. But sustainable growth depends on acquired users' generating lifetime value (LTV) exceeding customer acquisition cost (CAC). In order to maximize LTV/CAC ratio, it is crucial to predict channel-level LTV in an early stage for further optimization of budget allocation. The LTV forecasting problem is significantly different from traditional time series forecasting problems, and there are three main challenges. Firstly, it is an unaligned multi-time series forecasting problem that each channel has a number of LTV series of different activation dates. Secondly, to predict in the early stage, it faces the imbalanced short-input long-output (SILO) challenge. Moreover, compared with the commonly used time series datasets, the real LTV series are volatile and non-stationary, with more frequent fluctuations and higher variance. In this work, we propose a novel framework called Trapezoidal Temporal Fusion (TTF) to address the above challenges. We introduce a trapezoidal multi-time series module to deal with data unalignment and SILO challenges, and output accurate predictions with a multi-tower structure called MT-FusionNet. The framework has been deployed to the online system for Douyin. Compared to the previously deployed online model, MAPE_p decreased by 4.3%, and MAPE_a decreased by 3.2%, where MAPE_p denotes the point-wise MAPE of the LTV curve and MAPE_a denotes the MAPE of the aggregated LTV.

Vlad Vinogradov, Alisa Vinogradova, Dmitrii Radkevich, Ilya Yasny, Dmitry Kobyzev, Ivan Izmailov, Katsiaryna Yanchanka, Andrey Doronichev

In this paper, we describe and benchmark a competitor-discovery component used within an agentic AI system for fast drug asset due diligence. A competitor-discovery AI agent, given an indication, retrieves all drugs comprising the competitive landscape of that indication and extracts canonical attributes for these drugs. The competitor definition is investor-specific, and data is paywalled/licensed, fragmented across registries, ontology-mismatched by indication, alias-heavy for drug names, multimodal, and rapidly changing. Although considered the best tool for this problem, the current LLM-based AI systems aren’t capable of reliably retrieving all competing drug names, and there is no accepted public benchmark for this task. To address the lack of evaluation, we use LLM-based agents to transform five years of multimodal, unstructured diligence memos from a private biotech VC fund into a structured evaluation corpus mapping indications to competitor drugs with normalized attributes. We also introduce a competitor validating LLM-as-a-judge agent that filters out false positives from the list of predicted competitors to maximize precision and suppress hallucinations. In this benchmark, our competitor discovery agent (Bioptic Agent) achieves 83% recall, exceeding OpenAI Deep Research (65%) and Perplexity Labs (60%). The system is deployed in production with enterprise users; in a case study with a biotech VC investment fund, analyst turnaround time dropped from 2.5 days to ~3 hours (~20x) for the competitive analysis.

Chinmay Soni, Shivam Chourasia, Gaurav Kumar, Hitesh Kapoor

Applying large, proprietary API-based language models to text-to-SQL tasks poses a significant industry challenge: reliance on massive, schema-heavy prompts results in prohibitive per-token API costs and high latency, hindering scalable production deployment. We present a specialized, self-hosted 8B-parameter model designed for a conversational bot in CriQ, a sister app to Dream11—India’s largest fantasy sports platform with over 250 million users—that answers user queries about cricket statistics. Our novel two-phase supervised fine-tuning approach enables the model to internalize the entire database schema, eliminating the need for long-context prompts. This reduces input tokens by over 99%, from a 17k-token baseline to fewer than 100, and replaces costly external API calls with efficient local inference. The resulting system achieves 98.4% execution success and 92.5% semantic accuracy, substantially outperforming a prompt-engineered baseline using Google’s Gemini Flash 2.0 (95.6% execution, 89.4% semantic accuracy). These results demonstrate a practical path toward high-precision, low-latency text-to-SQL applications using domain-specialized, self-hosted language models in large-scale production environments.

Sudarshan Srinivasa Ramanujam, Antonio Alonso, Saurabh Kataria, Siddharth Dangi, Akhilesh Gupta, Birjodh Singh Tiwana, Manas Haribhai Somaiya, Luke Simon, David Byrne, Sojeong Ha 等

In large-scale recommendation systems like LinkedIn’s, the retrieval stage is critical for narrowing billions of potential candidates to a manageable subset for ranking. LinkedIn's feed now serves suggested content based on the topical interests of members, where 2000 candidates are retrieved from several million candidates with a latency budget of a few milliseconds and inbound QPS of several thousand per second. This paper presents a novel retrieval approach that fine tunes a large causal language model (Meta’s LLaMA 3) as a dual encoder to generate high quality embeddings for both users (members) and content (items), using only textual input. We describe the end to end pipeline, including prompt design for embedding generation, techniques for fine tuning at LinkedIn scale, and infrastructure for low latency, cost effective online serving. We share our findings on how quantizing numerical features in the prompt enables the information getting encoded in the embedding facilitating greater alignment between the retrieval and ranking layer. The system was evaluated using offline metrics and an online A/B test, which showed substantial improvements in member engagement. We observed significant gains among newer members, who often lack strong network connections, indicating that high-quality suggested content aids retention. This work demonstrates how generative language models can be effectively adapted for real time, high throughput retrieval in industrial applications.

Naveen Janaki Raman, Jingwu Tang, Zhiyu Chen, Zheyuan Ryan Shi, Sean Hudson, Ameesh Kapoor, Fei Fang

Food rescue organizations simultaneously tackle food insecurity and waste by working with volunteers to redistribute food from donors who have excess to recipients who need it. Volunteer feedback allows food rescue organizations to identify issues early and ensure volunteer satisfaction. However, food rescue organizations monitor feedback manually, which can be cumbersome and labor-intensive, making it difficult to prioritize which issues are most important. In this work, we investigate how large language models (LLMs) assist food rescue organizers in understanding and taking action based on volunteer experiences. We work with 412 Food Rescue, a large food rescue organization based in Pittsburgh, Pennsylvania, to design RescueLens, an LLM-powered tool that automatically categorizes volunteer feedback, suggests donors and recipients to follow up with, and updates volunteer directions based on feedback. We evaluate the performance of RescueLens on an annotated dataset, and show that it can recover 96% of volunteer issues at 71% precision. Moreover, by ranking donors and recipients according to their rates of volunteer issues, RescueLens allows organizers to focus on 0.5% of donors responsible for more than 30% of volunteer issues. RescueLens is now deployed at 412 Food Rescue and through semi-structured interviews with organizers, we find that RescueLens streamlines the feedback process so organizers better allocate their time.

Daniel Mas Montserrat, Ray Verma, Míriam Barrabés, Francisco M. De la Vega, Carlos D. Bustamante, Alexander G. Ioannidis

Large-scale genomic workflows used in precision medicine can process datasets spanning tens to hundreds of gigabytes per sample, leading to high memory spikes, intensive disk I/O, and task failures due to out-of-memory errors. Simple static resource allocation methods struggle to handle the variability in per-chromosome RAM demands, resulting in poor resource utilization and long runtimes. In this work, we propose multiple mechanisms for adaptive, RAM-efficient parallelization of chromosome-level bioinformatics workflows. First, we develop a symbolic regression model that estimates per-chromosome memory consumption for a given task and introduces an interpolating bias to conservatively minimize over-allocation. Second, we present a dynamic scheduler that adaptively predicts RAM usage with a polynomial regression model, treating task packing as a Knapsack problem to optimally batch jobs based on predicted memory requirements. Additionally, we present a static scheduler that optimizes chromosome processing order to minimize peak memory while preserving throughput. Our proposed methods, evaluated on simulations and real-world genomic pipelines, provide new mechanisms to reduce memory overruns and balance load across threads. We thereby achieve faster end-to-end execution, showcasing the potential to optimize large-scale genomic workflows.

Junhua Liu, Yong Keat Tan, Bin Fu, Kwan Hui Lim

Accurate multi-turn intent classification is critical for advancing conversational AI systems but remains challenging due to limited datasets and complex contextual dependencies across dialogue turns. This paper presents two novel approaches leveraging Large Language Models (LLMs) to enhance scalability and reduce latency in production dialogue systems. First, we introduce Symbol Tuning, which simplifies intent labels to reduce task complexity and improve performance in multi-turn dialogues. Second, we propose Consistency-aware, Linguistics Adaptive Retrieval Augmentation (CLARA), a framework that employs LLMs for data augmentation and pseudo-labeling to generate synthetic multi-turn dialogues. These enriched datasets are used to fine-tune a small, efficient model suitable for deployment. Experiments on multilingual dialogue datasets show that our methods result in notable gains in both accuracy and resource efficiency, with improvements of 5.09% in classification accuracy, a 40% reduction in annotation costs, and effective deployment in low-resource multilingual industrial settings.