Papers
Event:
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2510.0040ViewA Fuzzy-based Approach to Predict Human Interaction by Functional Near-Infrared SpectroscopyIn this article, we introduce the Fuzzy logic-based attention (Fuzzy Attention Layer) mechanism, a novel computational approach designed to enhance the interpretability and efficacy of neural models in psychological research. The fuzzy attention layer integrated into the transformer encoder model to analyze complex psychological phenomena from neural signals captured by functional near-infrared spectroscopy (fNIRS). By leveraging fuzzy logic, the fuzzy attention layer learns and identifies interpretable patterns of neural activity. This addresses a significant challenge in using transformers: the lack of transparency in determining which specific brain activities most contribute to particular predictions. Our experimental results, obtained from fNIRS data engaged in social interactions involving handholding, reveal that the fuzzy attention layer not only learns interpretable patterns of neural activity but also enhances model performance. In addition, these patterns provide deeper insights into the neural correlates of interpersonal touch and emotional exchange. The application of our model shows promising potential in understanding the complex aspects of human social behavior, verify psychological theory with machine learning algorithms, thereby contributing significantly to the fields of social neuroscience and AI. Presented version based on the work published in IEEE TFS (2025)
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2510.0036ViewA Self-Driving Laboratory for Materials Science: An Autonomous Research Agent for Deep Data Analysis and InterpretationAs artificial intelligence increasingly permeates scientific research, the βAI for Scienceβ paradigm is evolving to enable more autonomous scientific workflows. Traditional research processes heavily rely on researchersβ expertise and manual operations, particularly in data analysis and interpretationβthe critical βlast mileβ from raw data to profound insights. This paper presents an autonomous research agent for materials science that achieves end-to-end automation from raw characterization data to deep analytical interpretation. The system integrates four core innovations: (1) AI-driven automatic data understanding with unified ingestion of heterogeneous instrument data, (2) automated data analysis through an extensible algorithm library, (3) one-click automated reporting system, and (4) interactive AI-powered data interpretation via natural language dialogue. We demonstrate the agentβs capabilities through real-world case studies across multiple characterization techniques (Raman, UPS, UV-Vis, TG), achieving remarkable performance: UV-Vis bandgap analysis is accelerated by 600Γ compared to manual processing, while maintaining exceptional accuracy with fitting precision R2 β₯ 0.999. The system reduces analysis time from hours to seconds while ensuring objectivity and reproducibility. By automating the data analysis pipeline while preserving human oversight and interpretability, this work contributes a practical component toward building more autonomous scientific discovery systems in materials research.
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2510.0035ViewMotivGraph-SoIQ: Integrating Motivational Knowledge Graphs and Socratic Dialogue for Enhanced LLM IdeationLarge Language Models (LLMs) hold substantial potential for accelerating academic ideation but face critical challenges in grounding ideas and mitigating confirmation bias for further refinement. We propose integrating motivational knowledge graphs and socratic dialogue to address these limitations in enhanced LLM ideation (MotivGraph-SoIQ). This novel framework provides essential grounding and practical idea improvement steps for LLM ideation by integrating a Motivational Knowledge Graph (MotivGraph) with a Q-Driven Socratic Ideator. The MotivGraph structurally stores three key node types-problem, challenge, and solutionβto offer motivation grounding for the LLM ideation process. The Ideator is a dual-agent system utilizing Socratic questioning, which facilitates a rigorous refinement process that mitigates confirmation bias and improves idea quality across novelty, experimental rigor, and motivational rationality dimensions. On the ICLR25 paper topics dataset, MotivGraph-SoIQ exhibits clear advantages over existing state-of-the-art approaches across LLM-based scoring, ELO ranking, and human evaluation metrics.
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2510.0034ViewCognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object DetectionDesigning high-performance object detection architectures is a complex task, where traditional manual design is time-consuming and labor-intensive, and Neural Architecture Search (NAS) is computationally prohibitive. While recent approaches using Large Language Models (LLMs) show promise, they often function as iterative optimizers within a search loop, rather than generating architectures directly from a holistic understanding of the data. To address this gap, we propose Cognitive-YOLO, a novel framework for LLM-driven architecture synthesis that generates network configurations directly from the intrinsic characteristics of the dataset. Our method consists of three stages: first, an analysis module extracts key meta-features (e.g., object scale distribution and scene density) from the target dataset; second, the LLM reasons upon these features, augmented with state-of-the-art components retrieved via Retrieval-Augmented Generation (RAG), to synthesize the architecture into a structured neural network description, which we term the Neural Architecture Description Language (NADL); finally, a compiler instantiates this description into a deployable model. Extensive experiments on five diverse object detection datasets demonstrate that our proposed Cognitive-YOLO consistently generates superior architectures, achieving state-of-the-art (SOTA) performance by outperforming strong baseline models across multiple benchmarks.
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2510.0030ViewLatent-Diffusion Guided Cross-View Alignment for Heterogeneous Graph RecommendationRecommender systems operating on heterogeneous, multi-relational graphs contend with noise and incompleteness in auxiliary signals, which can destabilize learning and degrade ranking performance when targeting robust representations. Naive cross-view training risks propagating noise across views, and existing contrastive or augmentation-based schemes often hinge on design choices and can struggle to scale to large, complex graphs. We propose a latent-diffusion guided cross-view alignment framework for heterogeneous graph recommendation that jointly learns a relation-aware heterogeneous GNN encoder, producing paired target and auxiliary embeddings, and a compact, time-conditioned latent-space denoiser that maps noisy auxiliary latents toward target-view semantics. The denoiser provides principled supervision to disentangle structured noise, with its residual outputs fused into target embeddings to refine ranking-relevant representations. Training optimizes a joint denoising objective and a ranking objective, enabling scalable, robust cross-view alignment without ad-hoc augmentations. Empirical results on implicit-feedback data demonstrate improved robustness and ranking accuracy under noisy auxiliary signals, with flexible gradient-flow and fusion strategies supporting stable end-to-end training on large graphs. Ablations highlight the benefits of explicit noise modeling in auxiliary views, diffusion-based supervision for stability, and scalable, relation-aware encoding of practical significance for recommender systems.
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2510.0026ViewGeometry-Aware Optimal Flow Matching via Convex PotentialsGenerative modeling under quadratic optimal transport (OT) aims to learn deterministic maps that push mass from a simple source distribution \(p_0\) to a target distribution \(p_1\) along the Wasserstein-2 (W2) geodesics. While flow-based models and neural differential equations offer flexible transports, existing approaches typically rely on multi-step integration and yield trajectories whose curvature deviates from W2 geodesics, reducing efficiency, interpretability, and stability. We propose a geometry-aware framework that parameterizes time-dependent velocity fields as gradients of convex potentials modeled by Input Convex Neural Networks (ICNNs). This convex-potential representation guarantees transport along straight lines, exactly matching the W2 map under quadratic cost. Training uses a Flow Matching objective tailored to the convex setting, with explicit gradient computations and a dedicated inversion subproblem to recover preimages under the convex-potential flow; an optional amortization network provides favorable initializations for the inversion and accelerates optimization. The method is agnostic to the specific transport plan and can condition on arbitrary couplings between \(p_0\) and \(p_1\). Empirically, the approach yields geometry-faithful transports along W2 geodesics, enabling fast sampling with one-step or few-step updates and controlled curvature. Diagnostics on representative datasets confirm geometric fidelity and trainability, and we discuss initialization and transport-plan considerations for scalable, stable generative modeling under quadratic OT.
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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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2510.0022ViewAdaptive 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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2510.0011ViewAutomated 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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2510.0009ViewBioMARS: 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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2510.0007ViewHEAL: 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.