Artificial Intelligence and Computing Innovations
Artificial Intelligence and Computing Innovations (AICI) is an international, peer-reviewed, open-access quarterly journal, published twice a year in January and July, dedicated to advancing the frontiers of artificial intelligence and computing technologies. The journal provides a multidisciplinary platform for researchers, practitioners, and industry professionals to publish innovative findings that integrate AI theory, computational systems, and real-world applications. AICI focuses on promoting both theoretical and applied research in artificial intelligence, data science, and intelligent computing. The journal welcomes submissions that explore novel algorithms, architectures, and methodologies in areas such as deep learning, computer vision, natural language processing, robotics, cloud and edge computing, and intelligent decision-making. Contributions addressing emerging topics such as ethical AI, explainability, federated learning, and privacy-preserving computation are especially encouraged. The journal’s scope covers, but is not limited to, the following areas: Artificial Intelligence and Machine Learning: Deep neural networks, generative AI, reinforcement learning, and transfer learning. Data Science and Analytics: Big data frameworks, data mining, pattern recognition, and predictive modeling. Computing Systems and Architectures: Cloud-edge collaboration, distributed systems, quantum computing, and high-performance computation. Computer Vision and Multimedia Processing: Image analysis, object detection, multimodal learning, and video understanding. Natural Language Processing and Speech Intelligence: Large language models, dialogue systems, sentiment analysis, and multilingual communication. Robotics and Intelligent Control: Human–robot interaction, motion planning, and autonomous decision-making. AI Ethics, Security, and Sustainability: Fairness, interpretability, responsible innovation, and the societal impact of AI. Artificial Intelligence and Computing Innovations maintains a rigorous double-blind peer review process to ensure originality, technical quality, and relevance. The journal follows open-access principles, making all articles freely available to readers worldwide to promote rapid dissemination of scientific knowledge. With a distinguished international editorial board and a commitment to academic excellence, AICI strives to become a leading venue for cutting-edge research and cross-disciplinary dialogue in intelligent computing and AI-driven innovation.
Reinforcement Learning-Based Elastic Scaling Policy Optimization for Microservices
This paper focuses on the intelligent optimization of elastic scaling strategies in microservice architectures. It proposes an adaptive resource control method based on reinforcement learning. The method constructs a multi-dimensional state space that incorporates key performance indicators such as CPU utilization, response time, and queue length. A policy network is used to make dynamic decisions on scaling actions. To improve learning efficiency and system responsiveness, the method introduces an advantage function for policy optimization. It also integrates a temporal dependency modeling mechanism to enhance the model's awareness of historical workload patterns. A reward function is designed based on multiple system metrics. It jointly considers resource cost, performance assurance, and system stability, enabling fine-grained guidance of the policy behavior. In the experimental section, a sensitivity analysis is conducted with different discount factors, dynamic workload patterns, and environmental disturbances. The evaluation systematically examines the stability and robustness of the proposed method. Comparative results with mainstream baselines show significant improvements in accuracy, precision, and recall. The results demonstrate strong policy convergence and efficient scheduling, confirming the effectiveness of the proposed approach in microservice elastic control tasks.
Intelligent Backend Failure Detection with Uncertainty Quantification and Asymmetric Risk Optimization
Backend systems in cloud computing and microservice environments exhibit highly dynamic behavior, strong coupling, and substantial noise. These characteristics lead to challenges such as prediction instability and asymmetric risk in failure detection. To address the limitations of traditional approaches that rely on deterministic decisions, fail to represent predictive confidence, and ignore differences in error costs, this paper proposes a backend failure detection method that integrates uncertainty estimation with cost-sensitive learning. The method represents system states through probabilistic modeling. It outputs failure decisions while explicitly characterizing predictive uncertainty. Uncertainty information is further incorporated into a risk-weighted mechanism to suppress the influence of noisy samples and low-confidence predictions during model updates. In addition, a cost-sensitive learning objective is constructed so that the model explicitly accounts for the impact of different detection errors on system stability and service continuity during training. This design enables risk-aware decisions that better align with practical operational requirements. The proposed framework is evaluated under unified data and evaluation settings and compared with several representative methods. The results show consistent advantages in overall discriminative capability, decision stability, and risk control. The study demonstrates that joint modeling of predictive uncertainty and error cost improves the reliability and engineering applicability of backend failure detection in complex environments. It also provides an effective modeling approach for intelligent failure detection systems in real operational scenarios.
Enterprise Audit Risk Identification Through Temporal Context and Entity Relationship Anomaly Modeling
This paper addresses the challenges of concealed risk clues, scarce labels, and coupling of heterogeneous information from multiple sources in enterprise auditing scenarios. It proposes an audit risk identification framework based on anomaly detection, simultaneously characterizing anomalies at the attribute, temporal context, and relational structure levels from a unified modeling perspective. First, robust preprocessing of audit records weakens scaling differences and extreme value interference. Representation learning is then used to map records to a latent space, reconstructing the deviation measure to assess the strength of deviations from normal patterns. Subsequently, a time window mechanism is introduced to construct a local contextual baseline, calculating the contextual deviation of the latent representation relative to the baseline to identify unconventional behavioral fragments within the business context. Simultaneously, a subject relationship graph is constructed, and structural information is aggregated to form a subject structural representation, which uses structural deviations to characterize abnormal associations and suspicious links. Finally, multi-source deviation signals are fused into a unified risk score, and probability mapping is used to obtain a risk intensity expression that can be used for verification ranking and threshold decision-making, thus outputting a set of high-risk records and their associated clues. Comparative experimental results show that this method outperforms other methods in terms of accuracy, precision, recall, and AUROC, validating the effectiveness and stability of the multi-source anomaly characterization and fusion scoring mechanism in audit risk identification tasks.
Memory-Enhanced Transformer for Backend Microservice Anomaly Detection
With the widespread adoption of cloud-native architecture and service-oriented deployment, backend microservice systems, while enhancing business flexibility, also exhibit characteristics such as cross-service propagation, enhanced contextual coupling, and complex temporal evolution of abnormal behavior, posing higher demands on anomaly detection. Addressing the shortcomings of existing methods, such as insufficient utilization of historical states, inadequate characterization of long-distance dependencies, and limited ability to fuse multi-source runtime information, this paper proposes a backend microservice anomaly detection method based on memory-enhanced Transformers. This method, based on logs, monitoring metrics, and link observation data, first constructs continuous temporal input through a unified representation. Then, an external memory unit is introduced to store and retrieve key historical patterns, enabling the model to continuously incorporate anomaly-related contextual clues during current state discrimination. Subsequently, a gating fusion mechanism and Transformer encoding structure are combined to collaboratively model current observation features and historical memory information, thereby enhancing the ability to express complex anomaly semantics and long-term correlations. To achieve anomaly detection, this paper further utilizes reconstruction bias to construct an anomaly scoring method, enabling the model to characterize normal behavioral structures and anomaly deviations from the latent representation space. A comparative study was conducted on anomaly data of open-source microservices. The results show that the proposed method has good recognition ability and stable performance in backend microservice anomaly detection tasks. It can more effectively characterize the anomaly features in complex operating environments and provide a feasible methodological support for anomaly monitoring in intelligent operation and maintenance scenarios.
Intelligent Decision-Making for Advertising Recommendation via Data Mining and Graph Neural Networks
This paper addresses the challenges of data sparsity, complex interactions, difficulty in fully characterizing latent preferences, and lack of structural awareness in the decision-making process of advertising recommendation tasks. It proposes an intelligent decision-making method for advertising recommendation that combines hard data mining and graph neural networks. First, this method extracts multi-source information, including user, ad, and contextual information, from the original ad interaction logs. Then, it uses a hard data mining mechanism to identify high-value behavioral patterns and key relationship clues to reduce noise interference and enhance the ability to express effective information. Based on this, a heterogeneous interaction graph for advertising recommendation scenarios is constructed, unifying user interests, ad attributes, and environmental information into a graph structure modeling framework. Graph propagation learning is used to mine high-order dependencies between nodes, thereby more accurately representing potential associations in complex recommendation scenarios. Simultaneously, a graph reweighting and adaptive fusion mechanism is introduced to strengthen key structural information and improve the synergy between initial features and deep graph representations, making the resulting decision representation more consistent with the actual needs of advertising recommendation tasks. Furthermore, based on a unified decision modeling strategy, the matching relationship between users and ads is effectively estimated, thereby completing ad ranking and recommendation output. Comparative experimental results show that the proposed method achieves superior performance on multiple recommendation evaluation indicators, verifying its effectiveness and practicality in intelligent decision-making tasks for advertising recommendation.
Hierarchical Communication and Computation Scheduling for Efficient Federated Learning in Cloud-Edge Collaborative Intelligent Systems
As federated learning and cloud-edge collaborative training are increasingly applied in distributed intelligent computing, issues such as high communication overhead, strong heterogeneity of computing resources, and insufficient efficiency of multi-layer node collaboration have gradually become key factors restricting system performance improvement. To address these issues, this paper studies a joint scheduling optimization method for communication and computation, addressing the distributed training needs in cloud computing scenarios, and constructs a unified modeling framework suitable for collaborative training across terminals, edge nodes, and the cloud. In terms of method design, firstly, starting from the multi-layer collaborative structure, a systematic description of client access relationships, edge node activation states, and cloud coordination processes is provided, incorporating communication latency, local computation overhead, and regional forwarding costs during training into a unified latency model. Secondly, to improve the effectiveness of node participation and the rationality of resource allocation, a utility evaluation mechanism integrating data quality, training status, and resource consumption is introduced to achieve dynamic selection of participating nodes under constraints of limited bandwidth and computational budget. Subsequently, combining the characteristics of cloud-edge layered training, a joint optimization objective for resource-constrained environments is further constructed, considering latency control, training quality, and energy consumption costs collaboratively, and enhancing the organizational capacity and execution feasibility among multiple nodes through hierarchical scheduling constraints. Finally, an aggregation mechanism combining latency and data contribution is designed during the model update phase to mitigate the adverse effects of heterogeneous node differences on the global model evolution. Experimental results show that the proposed method performs well in terms of accuracy, overall performance, and system coordination capabilities, effectively improving resource utilization and overall training efficiency in federated learning and cloud-edge collaborative training scenarios.
Retrieval-Augmented Long-Text Reasoning with Semantic Conflict Detection and Cross-Paragraph Evidence Integration
This study addresses common challenges in long-text reasoning, including retrieval noise, dispersed evidence, semantic drift, and inconsistent generation, and proposes a retrieval-augmented generation framework for long-text understanding. The framework performs hierarchical semantic retrieval, cross-paragraph evidence graph modeling, semantic conflict detection and calibration, and evidence-guided generation to extract key segments from large-scale long texts, correct semantic relations, and produce reasoning outputs that align closely with factual content. In the retrieval stage, the model maps queries into a multi-scale semantic space and applies structured candidate filtering to improve cross-document evidence recall. In the evidence construction stage, graph structures represent semantic associations and potential conflicts across paragraphs, forming a unified semantic center that constrains subsequent generation. In the generation stage, evidence-aware attention and conditional control ensure that outputs rely strictly on real paragraph content, reducing hallucinations and enhancing the logical consistency of the reasoning chain. The experiments evaluate the model across answer accuracy, reasoning consistency, factual faithfulness, and cross-paragraph structural integration, and conduct sensitivity analyses on learning rate, context window length, retrieval candidate size, and training data scale. The results show that the framework achieves stable advantages across multiple metrics and generates high-quality reasoning outputs in complex long-text environments. Overall, this study provides a complete technical pathway that covers retrieval, integration, and generation, offering an effective solution for multi-source and multi-paragraph long-text tasks and establishing a methodological foundation for reliable long-text reasoning in knowledge-intensive application scenarios.
Dynamic User Interest Evolution Modeling for Personalized Recommendation Systems Based on Multi-Scale Temporal Awareness and Attention Mechanisms
This paper addresses the problem of dynamic user interest changes in recommendation systems by proposing a deep recommendation method based on time awareness and interest evolution modeling. The method introduces multi-scale time windows, time decay factors, and attention mechanisms to hierarchically model user behavior sequences. It extracts both short-term and long-term interest features and integrates them into a dynamic interest representation. The model considers not only the sequential nature of behaviors but also incorporates time intervals, behavior frequency, and the weighting of key interaction segments to improve the accuracy of interest modeling. In the experimental section, multiple evaluations are conducted on the MovieLens 1M dataset. The results show that the proposed method significantly outperforms existing mainstream models in terms of Precision@10, Recall@10, and NDCG@10. In addition, sensitivity experiments are conducted on learning rate, optimizer, and interest evolution speed. These experiments confirm the model's stability and robustness to parameter changes. Overall, this study builds a recommendation framework that performs well in dynamic environments. It effectively improves the accuracy and practicality of personalized recommendations.
Log Semantic Graph Construction and System Event Anomaly Detection via Convolutional Neural Networks
This paper proposes a system event anomaly detection method that integrates convolutional semantic encoding with structure-aware graph fusion. The method first introduces a convolution-based log semantic modeling module, which uses a multi-scale convolutional perception mechanism to extract contextual features and build embedding representations from raw log sequences. This significantly enhances the ability to capture local semantic dependencies and behavioral cues. Then, a structure-aware graph fusion module is designed to construct dynamic graphs based on event associations, modeling and reinforcing the potential interactions among logs. This captures multiple dependencies of log events across both temporal and structural dimensions. To improve the discriminability of the embedding space and the consistency of structural representations, a joint optimization objective is constructed. It combines contrastive loss and structural alignment constraints for collaborative training, enabling accurate semantic differentiation and anomaly detection at the event level. The proposed method demonstrates strong semantic parsing and structural modeling capabilities, achieving precise anomaly detection and pattern extraction in high-dimensional and sparse system logs. Modeling experiments on real log data verify the effectiveness and applicability of the approach in complex system scenarios.
Automatic Orchestration of Enterprise ETL Processes via Graph Neural Network and Large Language Model Collaborative Reasoning
As data source types, field semantics, quality rules, and task dependencies in enterprise data platforms become increasingly complex, traditional ETL process orchestration methods relying on manual configuration and static rules struggle to meet the demands for automation, consistency, and reliability in large-scale enterprise data processing. Addressing the limitations of traditional ETL process orchestration methods, which suffer from insufficient cross-source relationship identification, inadequate business semantic understanding, and the ever-increasing complexity of contract types, field semantics, quality rules, and task dependencies, this paper proposes an automatic orchestration method based on collaborative reasoning using graph neural networks and a large language model. This method first models data sources, fields, rules, and tasks as a unified ETL state graph and uses relation-aware graph representation learning to capture high-order relationships between field mappings, task dependencies, and rule constraints. Subsequently, it constructs a structural semantic joint context by combining business requirement text, data pattern information, and rule descriptions, enabling the large language model to complete task decomposition, process reasoning, and candidate ETL process generation under graph-structured evidence constraints. Furthermore, the method introduces constraint consistency verification and a multi-criteria scoring mechanism to screen candidate processes based on dependency rationality, rule coverage, and execution feasibility, thereby improving the stability and reliability of the automatic orchestration results. Comparative experiments based on publicly available enterprise data integration scenarios show that the proposed method has good overall performance in terms of process generation correctness, dependency consistency, rule coverage, and execution success rate, and can provide effective support for enterprise data warehouse construction, automated data governance, and intelligent data engineering.
Deep Learning-Based Multi-View Behavioral Modeling and Trend Regression for Cloud Resource Prediction
This paper proposes a prediction framework for cloud computing environments that integrates a Behavior-Aware Modeling Module (BAMM) and a Multi-Scale Trend Regression Mechanism (MSTRM) to address the challenge of modeling complex access behaviors and their impact on bandwidth usage. BAMM employs multi-view embedding to jointly represent multi-dimensional features such as request type, access path, tenant information, and geographic location. It combines dilated temporal convolution and self-attention mechanisms for multi-dimensional temporal modeling and uses a service call graph for cross-service structural modeling. These three pathways are fused through residual connections and gated mechanisms to generate behavioral representations that capture both fine-grained local patterns and global structural dependencies. On this basis, MSTRM performs multi-scale regression modeling to simultaneously capture short-term fluctuations and long-term trends in bandwidth usage, enabling accurate prediction across multiple periods. The framework incorporates a task-specific loss function to balance prediction accuracy across different time scales while maintaining stable performance in dynamic multi-tenant scenarios. Experiments on a real-world cloud workload dataset demonstrate that the proposed method offers significant advantages in accurately characterizing bandwidth usage trends, reducing prediction errors, and improving robustness in complex environments, providing strong support for high-precision and fine-grained resource usage prediction.
Reinforcement Learning-Based Intelligent Decision Modeling for Large-Scale Distributed Systems
To address challenges in large-scale distributed systems, including high-dimensional state spaces, dynamic operating environments, and strongly coupled multi-node decisions, this study proposes a reinforcement learning based approach for intelligent decision modeling that provides a unified sequential formulation of the scheduling process. System states are abstracted into structured representations, while policy networks and value evaluation mechanisms jointly model the long-term impact of scheduling actions on system objectives. This enables integrated optimization of task completion efficiency, resource utilization, and load distribution. The method explicitly accounts for parallel operation across multiple nodes and the requirement of global objective consistency, allowing stable and effective scheduling behavior under complex dependencies. Comparative evaluations conducted in a public distributed system data environment demonstrate clear advantages across multiple system-level metrics, confirming the effectiveness and practical value of reinforcement learning driven intelligent decision modeling for improving the overall operational quality of large-scale distributed systems.
Learning-Driven Collaborative Scheduling for Multi-Tenant Distributed Inference Services
To address the scheduling complexity of multi-tenant distributed inference services under shared resources, model heterogeneity, and dynamic request conditions, this paper proposes a learning driven collaborative scheduling mechanism that models inference scheduling from a holistic system perspective. The approach abstracts system states and formulates scheduling decisions as a learnable mapping process. This enables the scheduling policy to dynamically adapt to workload variations and resource conditions during continuous operation. To capture intrinsic relationships in multi-tenant concurrent scenarios, collaborative utility modeling is incorporated into the scheduling objective. This guides scheduling behavior toward coordinated constraints among resource utilization efficiency, service stability, and tenant fairness. The proposed framework avoids strong dependence on specific execution details and provides a unified system-level treatment of request assignment and resource scheduling. It mitigates performance degradation caused by resource contention and workload fluctuation in multi-tenant parallel inference. System-level evaluation in multi-tenant distributed inference scenarios demonstrates that the method achieves strong overall performance in service response, system throughput, operational stability, and tenant fairness. These results confirm the effectiveness and practicality of learning driven collaborative scheduling in complex inference service environments.
Constraint-Aware Resource Matching for Virtual Machine Placement in Cloud Environments
With the continuous expansion of cloud platform scale and the increasing complexity of business workloads, resource matching and placement in virtual machine arrival flow scenarios have become a critical factor affecting the operational efficiency and service quality of cloud infrastructure. Addressing the shortcomings of existing methods, such as insufficient understanding of dynamic requests, inadequate characterization of node states, and limited ability to integrate constraints, this paper proposes a unified modeling method for placement recommendation centered on virtual machine resource allocation tasks. This method focuses on the adaptation relationship between virtual machine requests and candidate nodes, incorporating request priority information, node dynamic state information, and deployment constraint information into the resource placement decision process, constructing a recommendation framework consisting of request encoding, node encoding, feature interaction, constraint modeling, and ranking optimization. During data processing, continuous arrival flow samples are constructed based on publicly available cloud platform virtual machine trajectory data, and a supervision signal is generated by combining resource demand, node remaining capacity, and deployment feasibility relationships, enabling the model to learn effective matching patterns between requests and nodes. The results show that the proposed method can better improve the ranking quality of candidate nodes and the ability to identify target nodes, enhancing the model's ability to express dynamic resource relationships in complex cloud environments while ensuring placement feasibility. Overall, this method provides an effective solution for intelligent resource scheduling in virtual machine arrival flow scenarios, taking into account task importance, node carrying capacity, and actual constraints.
Semantic Energy-Guided Stable Fine-Tuning for Distribution-Controlled Generation in Large Language Models
This study proposes a stable fine-tuning method based on a semantic energy function to address common problems in large language model generation, including semantic drift, distribution instability, and insufficient long-range coherence. The method constructs a differentiable semantic energy field in the latent space and integrates an energy function, a smoothing term, and gradient constraints to guide the generation trajectory in a continuous and structured manner. This allows the model to evolve along low-energy paths during inference and maintain semantic consistency and distributional stability. The framework consists of semantic energy modeling, energy regularization, stable fine-tuning, and distribution guidance, and can be seamlessly integrated into existing pretrained language models without modifying the underlying architecture. The study further introduces evaluation metrics for semantic consistency, distribution shift, energy stability, and long-range coherence to assess the method from structural, distributional, and trajectory perspectives. Comparative experiments show that the method achieves superior performance across all metrics, with notable improvements in controllable distribution behavior and semantic trajectory smoothness. Sensitivity analyses on hyperparameters, environmental factors, and data properties confirm the robustness of the semantic energy framework and highlight its key role in semantic structure regulation and distribution management. Overall, the proposed semantic-energy-driven fine-tuning mechanism provides an interpretable, scalable, and generalizable approach for enhancing the stable generation capability of large language models.
TrendFormer: Two-Stage Trend Fusion for Financial Time Series Forecasting Using Transformers
This paper addresses key challenges in stock price sequence modeling, including trend entanglement, complex temporal dependencies, and limited training samples in financial markets. A two-stage Transformer prediction method is proposed to separate and model long-term and short-term trends. The method consists of a self-supervised pretraining stage and a supervised fine-tuning stage. In pretraining, two tasks are designed: trend reconstruction and temporal ordering. These tasks aim to capture the global evolution structure and local temporal relationships in price sequences. During fine-tuning, the model performs closing price regression based on the fused representations, reinforcing the structural consistency between trend encoding and target prediction. In terms of encoder architecture, the model adopts a dual-channel design for long-term and short-term modeling. It uses global and local Transformer modules to capture features at different temporal scales. A trend alignment and fusion mechanism is introduced to integrate multi-granularity representations. To validate the effectiveness of the method, a series of sensitivity and robustness experiments are conducted. The evaluations cover factors such as time window length, training data ratio, activation function selection, and noise injection. The results show that the proposed method outperforms existing sequence modeling approaches in accuracy, stability, and structural awareness. It is better suited for forecasting tasks under complex price environments.
MedOps-RiskAdapt: Risk-Adaptive Cloud-Native Orchestration for Multi-Agent Healthcare LLMs under Workload Drift
Multi-agent large language model (LLM) systems are increasingly considered for healthcare documentation, triage support, policy-aware reasoning, and operational coordination. Their deployment differs from general LLM serving because clinical workloads are non-stationary, patient-facing latency is safety relevant, and every inter-agent data exchange must satisfy privacy, audit, and interoperability constraints. This paper presents MedOps-RiskAdapt, a risk-adaptive cloud-native orchestration framework that combines workload-drift sensing, policy-gated agent routing, and adaptive cloud/edge placement for healthcare LLM agents. Unlike static Kubernetes or reactive autoscaling baselines, MedOps-RiskAdapt changes routing and escalation policy according to encounter risk, workload surge, and audit completeness signals. We evaluate the framework in a reproducible controlled benchmark using a Synthea-style synthetic patient cohort, three workload classes, two regulatory regions, three drift regimes, and 15 random seeds, producing 810 orchestration runs and 64,800 encounter-equivalents. Results show that MedOps-RiskAdapt reduces mean median latency from 6.42 s under static Kubernetes to 4.89 s, improves throughput from 166.7 to 213.1 encounters/min, and increases clinical task F1 from 80.7% to 84.4%. The p95 latency remains sensitive to surge conditions, rising to 12.55 s under MedOps-RiskAdapt, which is lower than both static Kubernetes and reactive HPA but not eliminated. Policy block rate improves from 98.2% to 99.3%, and audit completeness improves from 97.1% to 98.7%. The findings indicate that risk-adaptive orchestration can improve healthcare LLM infrastructure efficiency and governance, while residual tail latency and synthetic-data limitations remain important deployment constraints.
Dependency-Aware Hierarchical Reinforcement Learning for Resource Allocation in Hybrid Cloud Microservices
Hybrid cloud platforms allow enterprises to combine private infrastructure with elastic public-cloud capacity, but they make microservice resource allocation a coupled control problem across heterogeneous machines, service dependencies, workload bursts, and cost constraints. This paper presents HADR, a dependency-aware hierarchical reinforcement-learning method for resource allocation in hybrid cloud microservice systems. HADR models a deployed application as a dynamic service-resource graph whose vertices represent microservices, pods, virtual machines, queues, and storage components, and whose edges encode invocation, contention, placement, and scaling relations. A graph-temporal encoder predicts near-term demand and dependency pressure, while a two-level reinforcement-learning controller separates slow placement decisions from fast replica and resource-quota adjustments. The reward function jointly optimizes p95 latency, service-level objective violations, cloud cost, utilization balance, and migration overhead. A scenario-based evaluation over burst, diurnal, degradation, and dependency-shift workloads shows that HADR reduces operating cost by 36.2%, lowers p95 latency by 24.7%, and improves mean resource utilization to 0.82 compared with static provisioning, threshold autoscaling, and flat deep-Q baselines. The results demonstrate that dependency-aware hierarchical control provides a robust foundation for cost-efficient and performance-stable resource management in hybrid cloud microservices.
Reinforcement Learning–Based Dynamic Decision Framework for Server Backend Resource Management
With the continuous development of cloud computing platforms, microservice architectures, and online service systems, server backend resource management faces challenges such as frequent load fluctuations, significant resource contention, complex service relationships, and diverse control objectives. Traditional management methods relying on static rules or local optimization are no longer adequate for the real-time control needs of highly dynamic environments. To address this issue, this paper studies reinforcement learning modeling and dynamic decision-making methods for server backend resource management, constructing a unified framework covering state representation, context awareness, policy learning, value assessment, and multi-objective reward constraints. At the state modeling level, operational information such as resource occupancy, task queuing, service latency, and dependencies is jointly represented to enhance the ability to perceive complex backend environments. At the decision modeling level, a context-aware mechanism and policy network are introduced to adaptively control resource scheduling behavior during continuous operation. At the optimization level, the stability of policy updates is improved by combining value functions and advantage estimation, and a comprehensive reward design is used to coordinate the relationship between throughput, latency, resource utilization efficiency, and operating costs. An experimental environment was built based on open-source cluster trajectory data, and compared with other methods in the same direction. The results show that the proposed method can achieve good overall performance in complex backend resource management scenarios, demonstrating strong decision-making effectiveness and system adaptability. The research results indicate that reinforcement learning can provide a more intelligent, systematic, and long-term optimization-capable modeling path for server backend resource allocation and dynamic scheduling.
Learning Collaborative and Robust Scheduling Policies for Microservice Backends under Uncertainty
With the continuous development of cloud computing, containerized deployment, and service-oriented architecture, microservice server backends face higher demands in handling high-concurrency requests, coordinating complex service dependencies, and managing dynamic resources. Traditional scheduling methods relying on static rules or local optimization are no longer suitable for operating environments with multiple coupled services, rapid state changes, and abnormal disturbances. To address this issue, this paper proposes a reinforcement learning-based collaborative scheduling and robust optimization method for microservice server backends. Starting from the overall system operation process, it unifies information such as queue state, resource consumption, dependency strength, and latency fluctuations into a scheduling state representation. Based on this, a joint decision-making mechanism is constructed, incorporating request allocation, instance scaling, and resource reallocation into a unified action space to improve overall coordination capabilities under complex service chain conditions. Furthermore, to address uncertainties in the backend environment, such as traffic surges, node degradation, and resource jitter, this paper introduces robust optimization concepts into the policy learning process. Through disturbance modeling and constraint control, it enhances the stability and adaptability of the scheduling strategy in complex operating scenarios. To ensure the engineering usability of the method, this paper simultaneously considers service quality constraints, resource capacity limitations, and system operating costs, enabling the proposed model to balance deployment rationality and control feasibility while pursuing improved throughput and response efficiency. Related research results show that the proposed method can achieve good overall scheduling performance in microservice server backend scenarios, demonstrating strong advantages in service processing capacity, resource utilization, and operational stability. This research provides a modeling approach for intelligent scheduling in microservice environments that considers global collaboration, dynamic decision-making, and robust control, and also offers methodological references for research on cloud platform backend resource management and service governance.
Causal Inference-Guided Bias Correction and Hallucination Suppression for Trustworthy Text Summarization
We propose a bias correction and illusion suppression method that integrates causal inference. This method treats the summary generation process as a conditional generation decision influenced by potential confounding factors. By constructing a target distribution for causal correction, it weakens the systematic influence of corpus distribution and narrative style on information selection. Furthermore, it introduces a reweighting strategy based on propensity scoring to calibrate bias-related conditions during the learning phase, improving the consistency and robustness of the generation mechanism across different contexts. To reduce the hallucination risk caused by unfounded completion and over-certainty, the method explicitly establishes the alignment relationship between the summary statement and the input evidence fragments during generation and incorporates consistency constraints to make the output more inclined towards facts and entity information supported by the original text. Simultaneously, the method constructs semantically equivalent input variants through counterfactual perturbations and uses distribution consistency constraints to suppress the model's sensitivity to non-causal surface factors, thereby reducing generation bias caused by style differences and rewriting. This paper compares and analyzes the proposed method with related methods under a unified evaluation index system. The results show that the proposed method performs more balancedly in terms of semantic similarity, factual consistency, and entity alignment, and can effectively control the risks associated with hallucination. This verifies the role of causal correction and evidence consistency constraints in improving the credibility and interpretability of the abstract.
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Collaborative Decision Optimization for Timely Order Fulfillment and Service Quality Enhancement in E-Commerce Supply Chains
To address the decision-making complexity arising from the large order volume, long fulfillment chain, and high service requirements in e-commerce, this paper proposes a supply chain collaborative decision optimization method for e-commerce fulfillment scenarios. This method constructs a unified collaborative modeling framework based on multi-dimensional business information such as order processing, seller response, product attributes, customer region, and fulfillment execution. It integrates heterogeneous features through a state fusion mechanism, uses collaborative correlation analysis to characterize the intrinsic relationship between orders and fulfillment conditions, and combines seller embedding representations to enhance the supporting role of supply-side features in decision generation, ultimately forming a decision output that balances timeliness and service performance. Compared with traditional methods that only focus on local links or single rule allocation, the proposed method can more systematically describe the organizational relationships under the combined influence of multiple subjects, factors, and constraints in the order fulfillment process, thereby improving the integrity and coordination of the supply chain decision-making process. Comparative analysis of related methods shows that the proposed method has better overall performance in terms of on-time order delivery, fulfillment stability, service quality, and collaborative execution capabilities, indicating that it can effectively support order fulfillment organization and service management in e-commerce scenarios. The research results show that building a collaborative decision-making optimization framework around the fulfillment process can help improve the operational quality of the e-commerce supply chain and provide a methodological basis for intelligent decision-making modeling in relevant business scenarios.
Dynamic Low-Rank Routing for Efficient Multi-Task Instruction Fine-Tuning of Large Language Models
This paper proposes a multi-task instruction fine-tuning method that integrates low-rank adaptation with dynamic routing to address the challenges of insufficient adaptation efficiency and severe task conflicts in large-scale language models. The method first applies low-rank decomposition to restrict parameter updates to a low-dimensional subspace, which reduces redundant computation and storage costs while preserving semantic representation capacity. On this basis, a dynamic routing mechanism is introduced to adaptively select optimal paths and task-specific modules according to input instruction features, enabling differentiated modeling for different tasks within a shared representation space. This mechanism effectively alleviates performance interference and gradient conflicts commonly encountered in multi-task training, ensuring stable coordination across tasks. To achieve unified optimization, a joint objective function is constructed by combining task loss, low-rank update regularization, and routing distribution constraints, thereby improving stability and controllability during training while maintaining performance. The experimental design covers multi-dimensional analyses of hyperparameter sensitivity, environmental sensitivity, and data sensitivity, verifying the superiority of the proposed method in key metrics such as instruction accuracy, average multi-task performance, parameter efficiency, and task conflict. The results demonstrate that the method achieves efficient fine-tuning and robust performance in complex task environments, providing a solution that balances efficiency and stability for multi-task instruction modeling.
Policy-Aware Enterprise Risk Assessment through LLM-Based Cross-Source Representation and Reasoning
Against the backdrop of frequent policy adjustments and continuous changes in the business environment, traditional enterprise risk identification methods generally suffer from insufficient perception of external institutional constraints, limited ability to integrate multi-source heterogeneous information, and weak semantic interpretability in the risk judgment process. These shortcomings make it difficult to meet the practical needs of dynamic identification and comprehensive assessment of enterprise risks in complex governance scenarios. To address these issues, this paper proposes an LLM Agent-based enterprise risk judgment method that integrates policy text analysis and enterprise operational status modeling. This method incorporates regulatory requirements, constraint logic, and objects of action from policy texts with the enterprise's financial performance, operational status, and abnormal signals into a unified analytical framework, enabling cross-source semantic modeling and intelligent judgment of enterprise risks. Methodologically, the policy text is first structurally analyzed to extract institutional constraint information and regulatory semantic clues related to enterprise risks. Then, the enterprise's operational status is dynamically represented, constructing a multi-dimensional state representation that reflects solvency, profitability, cash flow pressure, and operational stability. Based on this, a cross-source alignment mechanism is used to map policy semantics and enterprise state information to a unified representation space, and the LLM Agent's memory organization and reasoning decision-making capabilities are combined to complete the risk judgment. This method not only enhances the modeling ability of the correlation between policy information and enterprise operational information but also improves the integrity, consistency, and semantic expressiveness of the risk assessment process. Comparative experimental results show that the proposed method exhibits good overall performance in enterprise risk identification tasks, indicating that the constructed policy text parsing and enterprise operational status joint modeling framework can provide effective support for enterprise risk assessment in complex scenarios.
Cross-Domain Optimization of Large Language Models via Data-Centric Representation Alignment with Self-Supervised Learning
Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse domains, including natural language understanding, generation, and reasoning. However, their performance is often constrained by challenges such as data heterogeneity, distribution shifts, and inefficient utilization of large-scale corpora, particularly in real-world scenarios where data sources are diverse and continuously evolving. These limitations can lead to unstable training dynamics and reduced generalization when models are transferred to unseen domains. To address these challenges, this paper proposes a data-centric optimization framework that integrates adaptive representation alignment and self-supervised auxiliary tasks to enhance generalization and stability in LLM training. The framework focuses on improving the quality and consistency of learned representations by aligning feature distributions across domains while simultaneously leveraging unlabeled data through self-supervised learning objectives. This dual strategy enables the model to capture both domain-invariant features and task-relevant semantics, thereby improving robustness under distributional variations. The proposed method jointly optimizes task-specific objectives and distribution alignment constraints within a unified multi-objective learning paradigm, allowing for effective coordination between performance optimization and cross-domain consistency. In addition, an adaptive weighting mechanism is introduced to dynamically balance multiple learning objectives during training, improving convergence efficiency and mitigating optimization conflicts. Extensive experiments conducted on multiple benchmark datasets demonstrate that the proposed approach achieves improved accuracy, reduced divergence across data distributions, and enhanced training efficiency compared to conventional fine-tuning strategies. These results highlight the effectiveness of the framework in enabling scalable, robust, and generalizable LLM training in heterogeneous data environments.
AI-Driven Multi-Omics Integration for Biomarker Discovery and Disease Risk Prediction
The rapid growth of high-throughput biological data has created new opportunities for understanding disease mechanisms, identifying biomarkers, and improving precision medicine. However, biological datasets such as genomics, transcriptomics, proteomics, and metabolomics are often high-dimensional, heterogeneous, noisy, and difficult to integrate using traditional statistical methods. This paper proposes an AI-driven multi-omics integration framework for biomarker discovery and disease risk prediction. The proposed method combines deep representation learning, attention-based feature selection, and graph neural networks to capture complex relationships among genes, proteins, metabolites, and clinical phenotypes. By constructing a biological interaction graph and learning cross-omics representations, the model can identify disease-associated molecular patterns and improve predictive performance. Experimental analysis on public multi-omics datasets demonstrates that the proposed framework achieves higher accuracy, F1-score, and area under the receiver operating characteristic curve compared with conventional machine learning baselines. The results suggest that AI-based multi-omics integration can provide a powerful computational tool for discovering reliable biomarkers, supporting early diagnosis, and advancing personalized biomedical research.
Multi-Scale Attention-Enhanced Vision Transformer for Automated Tumor Segmentation in Medical Imaging
Accurate tumor segmentation is a critical step in computer-aided diagnosis, treatment planning, and disease progression monitoring. However, medical images often contain tumors with irregular shapes, heterogeneous textures, and blurred boundaries, making precise segmentation challenging for conventional convolutional neural networks. This paper proposes a multi-scale attention-enhanced Vision Transformer framework for automated tumor segmentation in medical imaging. The proposed model integrates local convolutional feature extraction with global self-attention modeling to capture both fine-grained anatomical details and long-range contextual dependencies. A multi-scale feature fusion module is designed to improve the representation of tumors with varying sizes, while an attention-guided boundary refinement mechanism is introduced to enhance segmentation accuracy around ambiguous lesion margins. Experimental evaluation on public medical imaging datasets demonstrates that the proposed method improves Dice coefficient, Intersection over Union, and boundary accuracy compared with baseline CNN-based and transformer-based models. The results suggest that combining multi-scale representation learning with attention-based global modeling can provide a more reliable and clinically useful approach for tumor segmentation in complex medical imaging scenarios.
Task Graph Modeling and Constraint-Aware Scheduling for Reliable Large Language Model Agent Execution
This paper addresses the instability of large language model agents under real-world constraints, proposing an integrated approach of robust task decomposition and constraint-aware scheduling to improve the controllability, compliance, and stability of long-link interactive tasks. The method first structures high-level instructions into a task graph, explicitly modeling subtask dependencies, resource sharing, and executable conditions. Verifiable pre-checks, alternative paths, and fallback actions are configured for key steps to reduce error accumulation caused by incomplete information and environmental changes. Subsequently, during the scheduling phase, latency and risk are incorporated into a unified priority decision, dynamically selecting the execution order and allocating resources based on critical path identification and failure cost assessment. When tools become unavailable, constraints are triggered, or execution deviations occur, rapid recovery is achieved through local reordering and path switching, avoiding the high overhead of global rework. Comparative experimental results show that this method exhibits superior overall performance in key agent metrics such as task completion, tool invocation reliability, constraint violation control, interaction efficiency, and recovery capability. It can maintain a more stable end-to-end execution process under a limited budget and uncertain environments, providing a feasible agent execution paradigm for real-world business process automation and multi-tool collaboration scenarios.
Research on Hierarchical Multi-Agent Reinforcement Learning Resource Orchestration for Large-Scale Heterogeneous Distributed Clusters with Dynamic Load Awareness
To address the challenges of complex resource states, random task arrivals, significant differences in node capabilities, and multiple couplings of scheduling objectives in large-scale heterogeneous distributed clusters, a hierarchical multi-agent reinforcement learning resource orchestration method integrating dynamic load awareness is constructed. This method first extracts time-varying load information from machine resource occupancy, task queuing status, service deployment density, and node capacity constraints, and constructs a state representation with global context awareness capabilities by combining cluster topology relationships to enhance the method's ability to characterize complex operating environments. Based on this, a hierarchical collaborative decision-making mechanism is adopted, dividing the resource orchestration process into global coordination by the upper-level manager and local control by the lower-level executors, achieving effective connection between global planning and node-level execution through sub-objective propagation. Furthermore, to address the matching problem between heterogeneous nodes and task requirements, a compatibility-aware scoring mechanism is introduced to improve the rationality of resource allocation, execution stability, and orchestration accuracy. This method can balance resource utilization, task waiting control, load balancing, and scheduling success capability within a unified framework, thereby improving the overall resource organization quality in complex cluster environments. Comparative experimental results show that the proposed method exhibits good overall performance across multiple key evaluation metrics, indicating that the method can effectively adapt to large-scale heterogeneous distributed cluster resource orchestration scenarios driven by dynamic loads, and has strong method effectiveness and application value.
Modeling Financial Market Dynamics with Temporal and Relational Learning: An LSTM-GNN Approach
This study proposes a stock market prediction method based on long short-term memory network (LSTM) and graph neural network (GNN) to simultaneously capture the time series characteristics of individual stocks and the market network relationship. Traditional stock prediction methods mainly rely on time series models such as LSTM and GRU, but these methods often ignore the complex interactive relationships between stocks. To solve this problem, this study constructs a graph structure of the stock market and uses GNN for relationship modeling, so that the prediction model can comprehensively consider the historical trading data of individual stocks and the overall market dynamics. The experiment is verified based on the trading data of the S&P 500 index in 2018, and MLP, 1D-CNN, GRU and Transformer are used as comparison models. The experimental results show that LSTM+GNN achieves the best performance in terms of mean square error (MSE), root mean square error (RMSE) and coefficient of determination (R²), verifying the effectiveness of integrating time series and market structure information. In addition, ablation experiments and optimizer sensitivity experiments further illustrate the contribution of GNN structure and AdamW optimizer in improving prediction accuracy. Future research can combine multimodal data and reinforcement learning to further optimize the model and explore more efficient financial market prediction methods to enhance the decision-making ability of intelligent trading systems.