Papers
Event:
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2510.0024ViewLECTOR: LLM-Enhanced Concept-based Test-Oriented RepetitionSpaced repetition systems are fundamental to efficient learning and memory retention, but existing algorithms often struggle with semantic interference and personalized adaptation. We present LECTOR (\textbf{L}LM-\textbf{E}nhanced \textbf{C}oncept-based \textbf{T}est-\textbf{O}riented \textbf{R}epetition), a novel adaptive scheduling algorithm specifically designed for test-oriented learning scenarios, particularly language examinations where success rate is paramount. LECTOR leverages large language models for semantic analysis while incorporating personalized learning profiles, addressing the critical challenge of semantic confusion in vocabulary learning by utilizing LLM-powered semantic similarity assessment and integrating it with established spaced repetition principles. Our comprehensive evaluation against six baseline algorithms (SSP-MMC, SM2, HLR, FSRS, ANKI, THRESHOLD) across 100 simulated learners over 100 days demonstrates significant improvements: LECTOR achieves a 90.2\% success rate compared to 88.4\% for the best baseline (SSP-MMC), representing a 2.0\% relative improvement. The algorithm shows particular strength in handling semantically similar concepts, reducing confusion-induced errors while maintaining computational efficiency. Our results establish LECTOR as a promising direction for intelligent tutoring systems and adaptive learning platforms.
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2510.0023ViewRobust Zero-Shot NER for Crises via Iterative Knowledge Distillation and Confidence-Gated InductionThis research presents a comprehensive diagnostic study of confidence-gated iterative induction for zero-shot Named Entity Recognition (NER) in crisis scenarios. While existing approaches struggle to adapt to novel disaster lexicons without manually curated resources, we investigate whether iterative knowledge distillation can overcome these limitations. Our framework leverages a pretrained language model to extract high-recall entity candidates, then iteratively distills domain knowledge through a self-correcting loop that uses high-confidence seeds to induce micro-gazetteers and syntactic rules. Comprehensive evaluations on synthetic crisis data reveal that the framework maintains a constant zero-shot F1-score of approximately 0.295 across all experimental configurations, demonstrating that the iterative mechanism provides no measurable improvement over baseline approaches. This negative result offers valuable diagnostic insights into the fundamental challenges of adaptive NER in dynamic crisis domains, including confidence threshold calibration difficulties, clustering algorithm limitations, and error propagation risks. The findings provide a cautionary tale for researchers working on adaptive NER systems and establish a foundation for future research on more robust zero-shot approaches in crisis scenarios.
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2510.0022ViewAdaptive Log Anomaly Detection through Data--Centric Drift Characterization and Policy-Driven Lifelong LearningLog-based anomaly detectors degrade over time due to concept drift arising from software updates or workload changes. Existing systems typically react by retraining entire models, leading to catastrophic forgetting and inefficiencies. We propose an adaptive framework that first classifies drift in log data into semantic (frequency shifts within known templates) and syntactic (emergence of new log templates) categories via statistical tests and novelty detection. Based on the identified drift type, a policy-driven lifelong learning manager applies targeted updates---experience replay to mitigate forgetting under semantic drift and dynamic model expansion to accommodate syntactic drift. This approach is validated on semi-synthetic logs and real-world longitudinal datasets (HDFS, Apache, and BGL), maintaining high F1-scores, reducing computational overhead, and preserving historical knowledge compared to monolithic retraining.
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2510.0021ViewConFIT: A Robust Knowledge-Guided Contrastive Framework for Financial ExtractionFinancial text extraction faces serious challenges in multi-entity sentiment attribution and numerical sensitivity, often leading to pitfalls in real-world deployment. In this work, we propose ConFIT (Contrastive Financial Information Tuning), a knowledge-guided contrastive learning framework that employs a Semantic-Preserving Perturbation (SPP) engine to generate high-quality, programmatically synthesized hard negatives. By integrating domain knowledge sources such as the Loughran-McDonald lexicon and Wikidata, and applying rigorous perplexity and Natural Language Inference (NLI) filtering, ConFIT trains language models to differentiate subtle perturbations in financial statements. Evaluations on FiQA and SENTiVENT using FinBERT and Llama-3 8B show both promise improvements and unexpected pitfalls, highlighting challenges that warrant further research.
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2510.0020ViewHierarchical Change Signature Analysis: A Framework for Online Discrimination of Incipient Faults and Benign Drifts in Industrial Time SeriesIndustrial fault detection systems often struggle to distinguish benign operational drifts (e.g., tool wear, recipe changes) from incipient faults, frequently adapting to faults as new ``normal'' states and risking catastrophic failures. This work proposes a hierarchical framework that decouples change detection from change characterization. When a drift is detected, the system generates a Multi-Scale Change Signature (MSCS) that quantifies geometric and statistical transformations in the primary detectorβs latent space. An unsupervised Drift Characterization Module (DCM), trained on an Online Normality Baseline (ONB), classifies each signature as benign or potentially faulty. Benign drifts are ignored, while potential faults are flagged for review; confirmed benign drifts are incorporated into the ONB for future adaptation. The framework is model-agnostic, computationally efficient, and scalable through a tiered human-in-the-loop mechanism. Experiments on the Tennessee Eastman Process dataset with injected drifts and faults demonstrate high fault detection rates, fewer false alarms, and efficient adaptation to benign changes.
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2510.0019ViewHierarchical Adaptive Normalization: A Placement-Conditioned Cascade for Robust Wearable Activity RecognitionWearable Human Activity Recognition (HAR) systems face significant performance degradation when sensors are placed at different body locations or orientations. We introduce a hierarchical adaptive normalization method that addresses these challenges through a two-stage cascade. The first stage combines gravity-based orientation correction with placement context inference using signal variance analysis, while a novel stability gate prevents harmful adaptation during unstable periods. The second stage employs placement-conditioned adaptive Batch Normalization to refine feature representations in real-time. Comprehensive evaluations on public and custom datasets show that our method achieves 0.847Β±0.023 macro F1-score, outperforming static baselines by 36\% and state-of-the-art unsupervised domain adaptation methods by 13.7\%. The approach maintains real-time performance with only 2.3ms inference time and 45.2MB memory usage, demonstrating practical viability for on-device deployment in dynamic real-world scenarios.
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2510.0018ViewAdaptive Evidential Meta-Learning with Hyper-Conditioned Priors for Calibrated ECG PersonalisationThis research addresses a fundamental gap in uncertainty calibration during electrocardiogram (ECG) model personalisation. We propose \emph{Adaptive Evidential Meta-Learning}, a framework that attaches a lightweight evidential head with hyper-network-conditioned priors to a frozen ECG foundation model. The hyper-network dynamically sets the evidential prior using robust, class-conditional statistics computed from a few patient-specific ECG samples. Trained via a two-stage meta-curriculum, our approach enables rapid adaptation with well-calibrated uncertainty estimates, making it highly applicable for real-world clinical deployment where both prediction accuracy and uncertainty awareness are crucial.
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2510.0017ViewEREA: Enhanced Research Exploration and AnalysisThe increasing volume of scientific publications poses challenges for researchers in efficiently identifying relevant literature, synthesizing research trends, and exploring emerging ideas. Manual search and analysis processes are time-consuming and often insufficient for capturing complex citation relationships. This project presents an open-source Python-based system, EREA (Enhanced Research Exploration and Analysis), that integrates generative artificial intelligence, automated information retrieval, semantic vector search, and citation-based visualization to support enhanced research exploration. User-defined queries are processed to extract structured keywords, retrieve scholarly articles from Google Scholar, and supplement metadata using OpenAlex. Retrieved data are structured, and embedded in a vector database for semantic retrieval, and visualized through interactive, offline HTML graphs. A research report is generated through large language model-assisted synthesis. Developed according to the FAIR (Findability, Accessibility, Interoperability, and Reusability) Data Principles, the system accelerates research exploration, provides structured thematic insights, facilitates understanding through visual citation networks, and supports the identification of research gaps and future directions.
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2510.0016ViewA Data-Driven Energy Consumption Prediction Model for 5G Base Stations: Addressing Static and Dynamic Power ComponentsThe rapid deployment of 5G networks has intensified concerns about energy consumption in mobile communication systems. Unlike previous generations, 5G base stations (BSs) exhibit significant power draw even under zero traffic conditions, with static power accounting for $30\sim 40\%$ of total energy consumption. This paper proposes a novel data-driven framework that decouples total base station energy consumption into static and dynamic components, enabling more precise energy optimization. For static consumption modeling, we introduce a hybrid ResNet-XGBoost architecture that processes configuration parameters including bandwidth, antenna elements, transmit power, carrier count, and tilt angle. For dynamic consumption, we implement a Tabular Probabilistic Function Network (TabPFN) to capture the nonlinear relationship between resource utilization and energy demand. Experimental results using real-world data from a provincial Chinese telecom operator demonstrate that our model achieves a $15.5\%$ reduction in Mean Absolute Error (MAE) and an $R^2$ of 0.91 compared to conventional approaches.
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2510.0015ViewA Data-Driven Energy Consumption Prediction Model for 5G Base Stations: Addressing Static and Dynamic Power ComponentsThe rapid deployment of 5G networks has intensified concerns about energy consumption in mobile communication systems. Unlike previous generations, 5G base stations (BSs) exhibit significant power draw even under zero traffic conditions, with static power accounting for $30\sim 40\%$ of total energy consumption. This paper proposes a novel data-driven framework that decouples total base station energy consumption into static and dynamic components, enabling more precise energy optimization. For static consumption modeling, we introduce a hybrid ResNet-XGBoost architecture that processes configuration parameters including bandwidth, antenna elements, transmit power, carrier count, and tilt angle. For dynamic consumption, we implement a Tabular Probabilistic Function Network (TabPFN) to capture the nonlinear relationship between resource utilization and energy demand. Experimental results using real-world data from a provincial Chinese telecom operator demonstrate that our model achieves a $15.5\%$ reduction in Mean Absolute Error (MAE) and an $R^2$ of 0.91 compared to conventional approaches.
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2510.0014ViewLLM-empowered knowledge graph construction: A surveyKnowledge Graphs (KGs) have long served as a fundamental infrastructure for structured knowledge representation and reasoning. With the advent of Large Language Models (LLMs), the construction of KGs has entered a new paradigmβshifting from rule-based and statistical pipelines to language-driven and generative frameworks. This survey provides a comprehensive overview of recent progress in **LLM-empowered knowledge graph construction**, systematically analyzing how LLMs reshape the classical three-layered pipeline of ontology engineering, knowledge extraction, and knowledge fusion. We first revisit traditional KG methodologies to establish conceptual foundations, and then review emerging LLM-driven approaches from two complementary perspectives: *schema-based* paradigms, which emphasize structure, normalization, and consistency; and *schema-free* paradigms, which highlight flexibility, adaptability, and open discovery. Across each stage, we synthesize representative frameworks, analyze their technical mechanisms, and identify their limitations. Finally, the survey outlines key trends and future research directions, including KG-based reasoning for LLMs, dynamic knowledge memory for agentic systems, and multimodal KG construction. Through this systematic review, we aim to clarify the evolving interplay between LLMs and knowledge graphs, bridging symbolic knowledge engineering and neural semantic understanding toward the development of adaptive, explainable, and intelligent knowledge systems.
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2510.0013ViewA Review of Intelligent Rock Mechanics: From Methods to ApplicationsArtificial Intelligence (AI) has great potential to transform rock mechanics by tackling its inherent complexities, such as anisotropy, nonlinearity, discontinuous, and multiphase nature. This review explores the evolution of AI, from basic neural networks like the BP model to advanced architectures such as Transformers, and their applications in areas like microstructure reconstruction, prediction of mechanical parameters, and addressing engineering challenges such as rockburst prediction and tunnel deformation. Machine learning techniques, particularly Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), have been crucial in automating tasks like fracture detection and efficiently generating 3D digital rock models. However, the effectiveness of AI in rock mechanics is limited by data scarcity and the need for high-quality datasets. Hybrid approaches, such as combining physics-informed neural networks (PINNs) with traditional numerical methods, offer promising solutions for solving governing equations. Additionally, Large Language Models (LLMs) are emerging as valuable tools for code generation and decision-making support. Despite these advancements, challenges remain, including issues with reproducibility, model interpretability, and adapting AI models to specific domains. Future progress will hinge on the availability of improved datasets, greater interdisciplinary collaboration, and the integration of spatial intelligence frameworks to bridge the gap between AIβs theoretical potential and its practical application in rock engineering.
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2510.0012ViewA Review of Intelligent Rock Mechanics: From Methods to ApplicationsIntelligent rock mechanics represents the convergence of artificial intelligence (AI) and classical rock mechanics, providing new paradigms to understand, model, and predict the complex behaviors of geological materials. This review synthesizes recent progress from foundational AI methodologies to their practical applications in rock engineering. Traditional challengesβsuch as anisotropy, discontinuities, and multiphysics couplingβhave been re-examined through data-driven and hybrid approaches that integrate learning algorithms with physical principles. The study traces the evolution of AI in this field, from early backpropagation and support vector machines to modern deep learning frameworks such as convolutional and transformer architectures, highlighting their roles in microstructure reconstruction, mechanical parameter estimation, constitutive modeling, and real-time hazard prediction. Emerging techniques, including physics-informed neural networks and graph-based learning, bridge data-driven inference with physical interpretability, while large language models are beginning to facilitate automated code generation and decision support in geotechnical analysis. Despite remarkable progress, key challenges remain in data quality, model generalization, and interpretability. Addressing these issues requires standardized datasets, interdisciplinary collaboration, and the establishment of transparent, reproducible AI workflows. The paper concludes by outlining a forward-looking perspective on developing next-generation intelligent frameworks capable of coupling physical knowledge, spatial reasoning, and adaptive learning, thereby advancing rock mechanics from empirical modeling toward fully intelligent, autonomous systems.
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2510.0011ViewAutomated Algorithmic Discovery for Gravitational-Wave Detection Guided by LLM-Informed Evolutionary Monte Carlo Tree SearchGravitational-wave signal detection with unknown source parameters buried in dynamic detector noise remains a formidable computational challenge. Existing approaches face core limitations from restrictive assumptions: traditional methods rely on predefined theoretical priors, while neural networks introduce hidden biases and lack interpretability. We propose Evolutionary Monte Carlo Tree Search (Evo-MCTS), the first integration of large language model (LLM) guidance with domain-aware physical constraints for automated gravitational wave detection. This framework systematically explores algorithmic solution spaces through tree-structured search enhanced by evolutionary optimization, combining MCTS for strategic exploration with evolutionary algorithms for solution refinement. The LLM component provides domain-aware heuristics while maintaining interpretability through explicit algorithmic pathway generation. Experimental validation demonstrates substantial performance improvements, achieving a 20.2\% improvement over state-of-the-art gravitational wave detection algorithms on the MLGWSC-1 benchmark dataset and a remarkable 59.1\% improvement over other LLM-based algorithm optimization frameworks. Beyond performance improvements, our framework establishes a transferable methodology for automated algorithmic discovery across computational science domains.
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2510.0010ViewBioMARS: A Multi-Agent Robotic System for Autonomous Biological ExperimentsLarge language models (LLMs) and vision-language models (VLMs) have the potential to transform biological research by enabling autonomous experimentation. Yet, their application remains constrained by rigid protocol design, limited adaptability to dynamic lab conditions, inadequate error handling, and high operational complexity. Here we introduce BioMARS (Biological Multi-Agent Robotic System), an intelligent platform that integrates LLMs, VLMs, and modular robotics to autonomously design, plan, and execute biological experiments. BioMARS uses a hierarchical architecture: the Biologist Agent synthesizes protocols via retrieval-augmented generation; the Technician Agent translates them into executable robotic pseudo-code; and the Inspector Agent ensures procedural integrity through multimodal perception and anomaly detection. The system autonomously conducts cell passaging and culture tasks, matching or exceeding manual performance in viability, consistency, and morphological integrity. It also supports conte
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2510.0009ViewBioMARS: A Multi-Agent Robotic System for Autonomous Biological ExperimentsLarge language models (LLMs) and vision-language models (VLMs) have the potential to transform biological research by enabling autonomous experimentation. Yet, their application remains constrained by rigid protocol design, limited adaptability to dynamic lab conditions, inadequate error handling, and high operational complexity. Here we introduce BioMARS (Biological Multi-Agent Robotic System), an intelligent platform that integrates LLMs, VLMs, and modular robotics to autonomously design, plan, and execute biological experiments. BioMARS uses a hierarchical architecture: the Biologist Agent synthesizes protocols via retrieval-augmented generation; the Technician Agent translates them into executable robotic pseudo-code; and the Inspector Agent ensures procedural integrity through multimodal perception and anomaly detection. The system autonomously conducts cell passaging and culture tasks, matching or exceeding manual performance in viability, consistency, and morphological integrity. It also supports context-aware optimization, outperforming conventional strategies in differentiating retinal pigment epithelial cells. A web interface enables real-time human-AI collaboration, while a modular backend allows scalable integration with laboratory hardware. These results highlight the feasibility of generalizable, AI-driven laboratory automation and the transformative role of language-based reasoning in biological research.
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2510.0008ViewToward a Federated Model of AI Scientists: Architecture, Pipeline, and RoadmapThis paper proposes a federated model of AI Scientists, integrating a layered stack architecture, an iterative discovery pipeline, and a governance-aligned roadmap. We argue that AI Scientists should not only accelerate discovery but also serve as custodians of epistemic integrity. Through case studies in drug discovery, climate modeling, and materials science, we demonstrate how federation enables cross-domain synthesis while embedding reproducibility, incentive alignment, and participatory governance. We conclude with a research roadmap toward Trusted AI Scientists, highlighting technical, incentive, and governance challenges.
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2510.0007ViewHEAL: Learning-Free Source Free Unsupervised Domain Adaptation for Cross-Modality Medical Image SegmentationGrowing demands for clinical data privacy and storage constraints have spurred advances in Source Free Unsupervised Domain Adaptation (SFUDA). SFUDA addresses the domain shift by adapting models from the source domain to the unseen target domain without accessing source data, even when target-domain labels are unavailable. However, SFUDA faces significant challenges: the absence of source domain data and label supervision in the target domain due to source free and unsupervised settings. To address these issues, we propose HEAL, a novel SFUDA framework that integrates Hierarchical denoising, Edge-guided selection, sizeAware fusion, and Learning-free characteristic. Large-scale cross-modality experiments demonstrate that our method outperforms existing SFUDA approaches,achieving state-of-the-art (SOTA) performance. The source code is publicly available at: https://anonymous.4open.science/r/HEAL-10C5.
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2510.0006ViewHEAL: Learning-Free Source Free Unsupervised Domain Adaptation for Cross-Modality Medical Image SegmentationGrowing demands for clinical data privacy and storage constraints have spurred advances in Source Free Unsupervised Domain Adaptation (SFUDA). SFUDA addresses the domain shift by adapting models from the source domain to the unseen target domain without accessing source data, even when target-domain labels are unavailable. However, SFUDA faces significant challenges: the absence of source domain data and label supervision in the target domain due to source free and unsupervised settings. To address these issues, we propose HEAL, a novel SFUDA framework that integrates Hierarchical denoising, Edge-guided selection, size-Aware fusion, and Learning-free characteristic. Large-scale cross-modality experiments demonstrate that our method outperforms existing SFUDA approaches, achieving state-of-the-art (SOTA) performance. The source code is publicly available at: https://anonymous.4open.science/r/HEAL-10C5.
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2510.0005ViewSynergistic Space-Vision Processing for Predicate InferenceScene graph generation, which parses images into structured graph, is a fundamental task for scene understanding. Most existing SGG models are dedicated to generating predicate representations based on appearance, relative position, and contextual cues. However, due to the predicate representation ambiguity arising from spatial co-occurrence, the generated scene graphs are often factually correct, but semantically shallow. To address this problem, we propose inferring predicates by synergistically processing spatial and visual information. Our core insight is that acknowledging the coexistence of geometric and non-geometric predicates, rather than struggling to disentangle them, is better suited for predicate inference than existing single-stream architectures. To this end, we introduce a novel method, Dual-stream Synergistic Network (DS-Net). Specifically, it contains two parallel streams: a space stream to predict geometric predicates from spatial layouts and edge features, and a vision stream to predict non-geometric predicates from fine-grained visual cues and linguistic priors. Based on them, we then design Cross-Stream Fusion module to enhance the corresponding predicate representation by using the mutual information of the two types. Through the collaborative processing of these streams, our DS-Net no longer treats the two predicate types as conflicting signals that need to be disentangled. Instead, it utilizes their synergy to facilitate predicate inference, providing a new perspective on resolving predicate ambiguity. Experiments have demonstrated the effectiveness of our method. Furthermore, our approach exhibits strong versatility and can be efficiently integrated with various existing models to enhance their performance. For instance, the 2.3\% $\sim$ 8.2\% increase in mR@100 on PredCls task demonstrates this capability.