While Retrieval-Augmented Generation (RAG) augments Large Language Models (LLMs) with external knowledge, conventional single-agent RAG remains fundamentally limited in resolving complex queries demanding coordinated reasoning across heterogeneous data ecosystems. We present HM-RAG, a novel Hierarchical Multi-agent Multimodal RAG framework that pioneers collaborative intelligence for dynamic knowledge synthesis across structured, unstructured, and graph-based data. The framework is composed of a three-tiered architecture with specialized agents: a Decomposition Agent that dissects complex queries into contextually coherent sub-tasks via semantic-aware query rewriting and schema-guided context augmentation; Multi-source Retrieval Agents that carry out parallel, modality-specific retrieval using plug-and-play modules designed for vector, graph, and web-based databases; and a Decision Agent that uses consistency voting to integrate multi-source answers and resolve discrepancies in retrieval results through Expert Model Refinement. This architecture attains comprehensive query understanding by combining textual, graph-relational, and web-derived evidence, resulting in a remarkable 12.95% improvement in answer accuracy and a 3.56% boost in question classification accuracy over baseline RAG systems on the ScienceQA and CrisisMMD benchmarks. Notably, HM-RAG establishes state-of-the-art results in zero-shot settings on both datasets. Its modular architecture ensures seamless integration of new data modalities while maintaining strict data governance, marking a significant advancement in addressing the critical challenges of multimodal reasoning and knowledge synthesis in RAG systems.
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Existing few-shot action recognition (FSAR) studies predominantly follow a metric learning framework, where prototypes are generated directly from features extracted by an encoder, and classification is performed via distance-based matching. However, due to the limited number of available samples, significant variations exist between different video features of the same class. As a result, the same query video may yield different classification results when matched against different sets of support videos. To address this issue, we propose a novel Hierarchical Meta-Prototypes Network (HMP-Net). The key innovation of our approach lies in the introduction of a category-agnostic and feature-agnostic meta-prototype module, which guides video feature mapping into a more suitable feature space. To optimize this meta-prototype, we design an alternating meta-prototype training strategy, where the model first learns to transform features under a fixed meta-prototype, and then the meta-prototype is refined to better guide feature mapping. Additionally, to adapt image-based metric learning models to video-based FSAR tasks, we introduce a series of lightweight adaptation modules. Specifically, we integrate an adapter into the encoder to improve video frame feature extraction, design a hierarchical prototype generation mechanism to enhance overall video understanding, and incorporate a task-specific perception module to extract unique features for each task. These adaptations make our model better suited for FSAR, significantly improving performance. We evaluate HMP-Net on five challenging benchmarks, and experimental results demonstrate that our model achieves new state-of-the-art performance on HMDB51, UCF101, Kinetics, and SthSthV2-Small. Extensive empirical evaluations further highlight the effectiveness and robustness of HMP-Net.
Topological structures in image data, such as connected components and loops, play a crucial role in understanding image content (e.g., biomedical objects). Despite remarkable successes of numerous image processing methods that rely on appearance information, these methods often lack sensitivity to topological structures when used in general deep learning (DL) frameworks. In this paper, we introduce a new general approach, called TopoImages (for Topology Images), which computes a new representation of input images by encoding local topology of patches. In TopoImages, we leverage persistent homology (PH) to encode geometric and topological features inherent in image patches. Our main objective is to capture topological information in local patches of an input image into a vectorized form. Specifically, we first compute persistence diagrams (PDs) of the patches, and then vectorize and arrange these PDs into long vectors for pixels of the patches. The resulting multi-channel image-form representation is called a TopoImage. TopoImages offers a new perspective for data analysis. To garner diverse and significant topological features in image data and ensure a more comprehensive and enriched representation, we further generate multiple TopoImages of the input image using various filtration functions, which we call multi-view TopoImages. The multi-view TopoImages are fused with the input image for DL-based classification, with considerable improvement. Our TopoImages approach is highly versatile and can be seamlessly integrated into common DL frameworks. Experiments on three public medical image classification datasets demonstrate noticeably improved accuracy over state-of-the-art methods.
Graph Neural Networks (GNNs) are effective for processing graph data; however, their heavy reliance on label information limits their generalization across domains. At the same time, Large Language Models (LLMs) have made significant progress across diverse domains, sparking growing interest in their potential for processing and understanding graph data. Nevertheless, their effectiveness is constrained by challenges such as space misalignment and global topology blindness, which arise because LLMs are trained primarily on Euclidean data rather than non-Euclidean structures. To address these challenges, we propose BridgeGLM, a novel framework that bridges graph and language spaces to improve domain generalization. BridgeGLM integrates topology-aware graph representations to capture higher-order structural relationships and employs a semantic-aware tokenizer to generate enriched node representations. Furthermore, we introduce three contrastive learning strategies based on graph interactions to effectively align graph and language representations. During testing, task-specific instruction templates facilitate zero-shot node classification. Extensive experiments on six datasets, covering both academic and recommendation graphs, show that BridgeGLM consistently outperforms state-of-the-art baselines across in-dataset, intra-domain, and cross-domain settings. In cross-domain settings, BridgeGLM achieves a 2-7% improvement in key performance metrics compared to the existing state-of-the-art method.
Multimodal models integrate visual, textual, and other data to achieve human-like understanding, but this fusion creates a conflict between cross-modal alignment and modality-specific expertise.The pursuit of unified feature spaces often undermines specialized knowledge in individual modalities, as shown by performance drops in unimodal tasks. To resolve this contradiction, we propose VAMP (Variational Alignment with Modality Preservation), a novel multimodal framework featuring a Dynamic Feature Diversion mechanism that partitions modal representations into two components-one preserving modality-specific expertise and the other enabling cross-modal alignment. Inspired by Variational Canonical Correlation Analysis, we introduce a shared space projection layer that maps features into a common representational space while preserving modality-specific characteristics. We further implement a Progressive Training Strategy that sequentially freezes different components before full fine-tuning, preventing mode collapse and enhancing generalization capabilities. Experimental results demonstrate VAMP's significant performance improvements across zero-shot image classification, cross-modal retrieval, and visual question answering, while simultaneously outperforming baseline models on unimodal tasks. This research provides an engineered solution to the ''knowledge dilution'' problem in cross-modal alignment.
Symbolic music understanding is a fundamental task in multimedia interpretation, which aims to decode musical attributes from symbolic music representations. Existing methods usually handle musical sequences as linguistic data, ignoring intrinsic musical properties. For instance, recurring melodies often appear in musical pieces with subtle variations, which requires methods that are aware of both local musical details and overall repetitive patterns, yet current methods can not fit the request. To address this issue, we introduce FG-Midiformer, a modified transformer framework that incorporates a multi-scale-aware feature learning (MSAFL) module and a local feature enhanced classification (LFEC) module for fine-grained understanding of multi-attributes. Specifically, the MSAFL module is designed to capture multi-scale musical relationships by embedding efficient multi-scale attention for long-term dependency modeling. In order to improve the classification accuracy of musical attributes, we devise the LFEC module, in which an attention mechanism with full 3-D weights is first introduced to efficiently highlight and leverage important local musical features. The LFEC module strengthens local feature representation and improves the sensitivity of FG-Midiformer to subtle differences between musical attributes. Extensive experiments show that FG-Midiformer achieves state-of-the-art performance in multi-attribute understanding tasks such as melody identification, velocity prediction, composer categorization, and emotion classification. The code will be released at https://github.com/Viki66666/FG-Midiformer.
To achieve audio and visual action detections in a given video accompanying with only video-level labels, a group of weakly-supervised audio-visual video parsing methods have been explored. Throughout their training processes, the action categories are typically assumed to be static, which is not always satisfied. Consequently, these methods can not be employed to handle dynamic scenarios involving continuously growing novel classes. To alleviate the above issue, we introduce a novel Continual Weakly-Supervised Audio-Visual Video Parsing (C-WSAVVP) task, where maintaining the knowledge of historic categories remains the eternal topic. Distinctly, owing to the weakly-supervised and multi-modal characteristics, two core challenges are more obvious in C-WSAVVP: (1) Compared with the continual audio-visual video classification task, where distilling video-level coarse action semantic of trimmed videos is sufficient for mitigating catastrophic forgetting, C-WSAVVP has to retain more fine-grained temporal semantic information of untrimmed videos containing both actions and backgrounds. (2) The semantics of different actions generally exhibit a certain degree of correlation, which is beneficial for understanding related actions, but how to maintain the semantic correlations? To address the specific challenges, the Semantic Prototype-based Action Refinement (SPAR) and Inter-Class Relation Topology Preservation (IRTP) modules are explored, where the former devotes to utilizing various semantic prototypes to refine more reliable temporal action intervals for distillation and the latter focuses on retaining semantic correlations between different actions in both modalities during continual learning. Comprehensive experiments on our reconstructed C-LLP dataset demonstrate the effectiveness and generalization capability of our proposed method.
Temporal Intention Localization (TIL) is crucial for video surveillance, focusing on identifying varying levels of suspicious intention to enhance security monitoring. However, existing discrete classification methods fail to capture the continuous progression of suspicious intentions, limiting early intervention and explainability. In this paper, we reconceptualize hidden intention modeling by shifting from discrete classification to continuous regression and propose Suspicion Progression Analysis Network (SPAN), which capture the fluctuations and progression of hidden intentions over time. Specifically, when analyzing the temporal progression of suspicion, we discover that suspicion exhibits long-term dependency and cumulative effects across extended sequences, characteristics significantly similar to the settings in Temporal Point Process (TPP) theory. Based on these insights, we formalize a suspicion score formula that models continuous changes while accounting for temporal characteristics. We also propose Suspicion Coefficient Modulation to adjust suspicion coefficients using multimodal information, reflecting different effects of suspicious actions. Notably, we introduce a Concept-Anchored Mapping method to quantify associations between suspicious actions and predefined intention concepts, enabling understanding of not just actions occurring but also their potential underlying intentions. Extensive experiments on the HAI dataset show that SPAN significantly outperforms existing methods, reducing MSE by 19.8% and improving average mAP by 1.78%,. Notably, SPAN achieves a 2.74% mAP gain in low-frequency cases, indicating superior capability in capturing subtle behavioral changes.Compared to discrete classification systems, out continuous suspicion modeling method enables earlier detection and more proactive interventions, substantially enhancing both system explainability and practical utility in security applications.
Following Clues, Approaching the Truth: Explainable Micro-Video Rumor Detection via Chain-of-Thought Reasoning
PDF ↗The rapid spread of rumor content on online micro-video platforms poses significant threats to public health and safety. However, existing Micro-Video Rumor Detection (MVRD) methods are generally black-box, which lacks transparency and makes it difficult to understand the reasoning behind classification decisions. In this work, we introduce ExMRD, a novel Explainable Micro-video Rumor Detection framework designed to generate detailed and coherent explanations for enhancing MVRD. Inspired by the powerful reasoning capacity of Chain-of-Thought (CoT), we introduce a novel inference mechanism called R3CoT-- consisting of Refining, Retrieving, and Reasoning on MVRD. This mechanism enables Multimodal Large Language Models (MLLMs) to reorganize the original video content, retrieve domain knowledge related to rumors, and generate explainable conclusions regarding whether the micro-video contains rumor information. Instead of directly fine-tuning MLLMs for MVRD, which is computationally expensive, we propose a Small Language Reviewer (SLReviewer), which distills the outputs of R3CoT guided MLLMs to ensure efficient and reliable predictions. Extensive experiments on three real-world benchmarks demonstrate that ExMRD significantly outperforms competitive baselines while providing high-quality rationales.
Information leakage issues in machine learning-based Web applications have attracted increasing attention. While the risk of data privacy leakage has been rigorously analyzed, the theory of model function leakage, known as Model Extraction Attacks (MEAs), has not been well studied. In this paper, we are the first to understand MEAs theoretically from an attack-agnostic perspective and to propose analytical metrics for evaluating model extraction risks. By using the Neural Tangent Kernel (NTK) theory, we formulate the linearized MEA as a regularized kernel classification problem and then derive the fidelity gap and generalization error bounds of the attack performance. Based on these theoretical analyses, we propose a new theoretical metric called Model Recovery Complexity (MRC), which measures the distance of weight changes between the victim and surrogate models to quantify risk. Additionally, we find that victim model accuracy, which shows a strong positive correlation with model extraction risk, can serve as an empirical metric. By integrating these two metrics, we propose a framework, namely Model Extraction Risk Inspector (MER-Inspector), to compare the extraction risks of models under different model architectures by utilizing relative metric values. We conduct extensive experiments on 16 model architectures and 5 datasets. The experimental results demonstrate that the proposed metrics have a high correlation with model extraction risks, and MER-Inspector can accurately compare the extraction risks of any two models with up to 89.58%.
Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurement
PDF ↗The rapid development of large language models (LLMs), like ChatGPT, has resulted in the widespread presence of LLM-generated content on social media platforms, raising concerns about misinformation, data biases, and privacy violations, which can undermine trust in online discourse. While detecting LLM-generated content is crucial for mitigating these risks, current methods often focus on binary classification, failing to address the complexities of real-world scenarios like human-LLM collaboration. To move beyond binary classification and address these challenges, we propose a new paradigm for detecting LLM-generated content. This approach introduces two novel tasks: LLM Role Recognition (LLM-RR), a multi-class classification task that identifies specific roles of an LLM in content generation, and LLM Involvement Measurement (LLM-IM), a regression task that quantifies the extent of LLM involvement in content creation. To support these tasks, we propose LLMDetect, a benchmark designed to evaluate detectors' performance on these new tasks. LLMDetect includes the Hybrid News Detection Corpus (HNDC) for training detectors, as well as DetectEval, a comprehensive evaluation suite that considers five distinct cross-context variations and two multi-intensity variations within the same LLM role. This allows for a thorough assessment of detectors' generalization and robustness across diverse contexts. Our empirical validation of 10 baseline detection methods demonstrates that fine-tuned Pre-trained Language Model (PLM)-based models consistently outperform others on both tasks, while advanced LLMs face challenges in accurately detecting their own generated content. Our experimental results and analysis offer insights for developing more effective detection models for LLM-generated content. This research enhances the understanding of LLM-generated content and establishes a foundation for more nuanced detection methodologies.
Item Response Theory (IRT) has been widely used in educational psychometrics to assess student ability, as well as the difficulty and discrimination of test questions. In this context, discrimination specifically refers to how effectively a question distinguishes between students of different ability levels, and it does not carry any connotation related to fairness. In recent years, IRT has been successfully used to evaluate the predictive performance of Machine Learning (ML) models, but this paper marks its first application in fairness evaluation. In this paper, we propose a novel Fair-IRT framework to evaluate a set of predictive models on a set of individuals, while simultaneously eliciting specific parameters, namely, the ability to make fair predictions (a feature of predictive models), as well as the discrimination and difficulty of individuals that affect the prediction results. Furthermore, we conduct a series of experiments to comprehensively understand the implications of these parameters for fairness evaluation. Detailed explanations for item characteristic curves (ICCs) are provided for particular individuals. We propose the flatness of ICCs to disentangle the unfairness between individuals and predictive models. The experiments demonstrate the effectiveness of this framework as a fairness evaluation tool. Two real-world case studies illustrate its potential application in evaluating fairness in both classification and regression tasks.
Previous studies on multimodal affective computing primarily focus on approximating predictions to annotated labels, often neglecting the ordinal nature of affective states. In this paper, we address this issue by exploring ordinal learning, and a Multimodal Ordinal Affective Computing (MOAC) framework is designed to enhance the understanding of the nature of affective concepts. Specifically, we propose coarse-grained label-level ordinal learning that prompts the model to learn to compare in the label space, encouraging higher predictive values for samples annotated with larger labels over those with smaller labels. Moreover, a regularization loss is proposed to prevent the output distributions from deviating significantly from the annotated label distributions. Fine-grained feature-level ordinal learning is then performed via the feature difference operation and the neutral embedding. The former compares samples in the feature space, calculating the difference between features of different samples to generate 'new' features for a more robust training. The latter seeks to reduce the difficulty of prediction by estimating the difference between the target multimodal representations and a neutral reference. We first demonstrate MOAC in multimodal sentiment analysis, which is a regression task that aligns well with the function of ordinal learning. Then we extend MOAC to classification tasks including multimodal humor detection and sarcasm detection to evaluate its generalizability. Experiments suggest that MOAC outperforms state-of-the-art methods.
TELEClass: Taxonomy Enrichment and LLM-Enhanced Hierarchical Text Classification with Minimal Supervision
PDF ↗Hierarchical text classification aims to categorize each document into a set of classes in a label taxonomy, which is a fundamental web text mining task with broad applications such as web content analysis and semantic indexing. Most earlier works focus on fully or semi-supervised methods that require a large amount of human annotated data which is costly and time-consuming to acquire. To alleviate human efforts, in this paper, we work on hierarchical text classification with a minimal amount of supervision: using the sole class name of each node as the only supervision. Recently, large language models (LLM) have shown competitive performance on various tasks through zero-shot prompting, but this method performs poorly in the hierarchical setting because it is ineffective to include the large and structured label space in a prompt. On the other hand, previous weakly-supervised hierarchical text classification methods only utilize the raw taxonomy skeleton and ignore the rich information hidden in the text corpus that can serve as additional class-indicative features. To tackle the above challenges, we propose TELEClass, Taxonomy Enrichment and LLM-Enhanced weakly-supervised hierarchical text Classification, which combines the general knowledge of LLMs and task-specific features mined from an unlabeled corpus. TELEClass automatically enriches the raw taxonomy with class-indicative features for better label space understanding and utilizes novel LLM-based data annotation and generation methods specifically tailored for the hierarchical setting. Experiments show that TELEClass can significantly outperform previous baselines while achieving comparable performance to zero-shot prompting of LLMs with drastically less inference cost.
SEHG: Bridging Interpretability and Prediction in Self-Explainable Heterogeneous Graph Neural Networks
PDF ↗Heterogeneous Graph Neural Networks (HGNNs) are extensively applied in modeling web-based applications that involve heterogeneous graph structures. Explanation models for HGNNs aim to address their ''black box'' nature. Enhancing the interpretability of HGNNs leads to a better understanding and can potentially improve predictive performance. However, existing post-hoc HGNN explanation methods cannot impact the HGNN's predictions. Self-explainable homogeneous models also perform poorly on heterogeneous graphs. To address these challenges, we present a Self-Explainable Heterogeneous Graph Neural Network (SEHG), a novel architecture that integrates explanation generation into the learning process of HGNN through two alternative stages. The first stage focuses on producing high-quality explanations while providing predictions alongside. The second stage enhances prediction accuracy by a contrastive learning strategy. Unlike the current methods that rely on manually defined metapaths for structural explanations, SEHG generates important structure and feature explanations by learnable heterogeneous masks. To ensure high-quality and sparsity explanation, these masks are regulated by a uniquely designed range-based penalty during training. Moreover, we introduce HetBA, a collection of synthetic heterogeneous datasets designed to quantify and visualize explanations or heterogeneous graphs. Extensive experiments demonstrate the effectiveness of SEHG, which surpasses strong baselines in real-world node classification tasks by notable margins of up to 3.91%. SEHG also achieves state-of-the-art performance on synthetic datasets with improvement of up to 9.44%, and records the highest fidelity scores in explanation tasks, improving by up to 46.57%. To our knowledge, SEHG is a pioneering self-explainable HGNN framework that achieves state-of-the-art performance on both heterogeneous graph explanation and prediction tasks.
Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification
PDF ↗The expression of mental health symptoms through non-traditional means, such as memes, has gained remarkable attention over the past few years, with users often highlighting their mental health struggles through figurative intricacies within memes. While humans rely on commonsense knowledge to interpret these complex expressions, current Multimodal Language Models (MLMs) struggle to capture these figurative aspects inherent in memes. To address this gap, we introduce a novel dataset, AxiOM, derived from the GAD anxiety questionnaire, which categorizes memes into six fine-grained anxiety symptoms. Next, we propose a commonsense and domain-enriched framework, M3H, to enhance MLMs' ability to interpret figurative language and commonsense knowledge. The overarching goal remains to first understand and then classify the mental health symptoms expressed in memes. We benchmark M3H against 6 competitive baselines (with 20 variations), demonstrating improvements in both quantitative and qualitative metrics, including a detailed human evaluation. We observe a clear improvement of 4.20% and 4.66% on weighted-F1 metric. To assess the generalizability, we perform extensive experiments on a public dataset, RESTORE, for depressive symptom identification, presenting an ablation study that highlights the contribution of each module. Our findings reveal limitations in existing models and the advantage of employing commonsense to enhance figurative understanding.
ZooplanktonBench: A Geo-Aware Zooplankton Recognition and Classification Dataset from Marine Observations
PDF ↗Plankton are small drifting organisms found throughout the world's oceans and can be indicators of ocean health. One component of this plankton community is the zooplankton, which includes gelatinous animals and crustaceans (e.g. shrimp), as well as the early life stages (i.e., eggs and larvae) of many commercially important fishes. Being able to monitor zooplankton abundances accurately and understand how populations change in relation to ocean conditions is invaluable to marine science research, with important implications for future marine seafood productivity. While new imaging technologies generate massive amounts of video data of zooplankton, analyzing them using general-purpose computer vision tools turns out to be highly challenging due to the high similarity in appearance between the zooplankton and its background (e.g., marine snow). In this work, we present the ZooplanktonBench, a benchmark dataset containing images and videos of zooplankton associated with rich geospatial metadata (e.g., geographic coordinates, depth, etc.) in various water ecosystems. ZooplanktonBench defines a collection of tasks to detect, classify, and track zooplankton in challenging settings, including highly cluttered environments, living vs non-living classification, objects with similar shapes, and relatively small objects. Our dataset presents unique challenges and opportunities for state-of-the-art computer vision systems to evolve and improve visual understanding in dynamic environments characterized by significant variation and the need for geo-awareness. The code and settings described in this paper can be found on our website: https://lfk118.github.io/ZooplanktonBench_Webpage.
Graph mining has become crucial in fields such as social science, finance, and cybersecurity. Many large-scale real-world networks exhibit both heterogeneity, where multiple node and edge types exist in the graph, and heterophily, where connected nodes may have dissimilar labels and attributes. However, existing benchmarks primarily focus on either heterophilic homogeneous graphs or homophilic heterogeneous graphs, leaving a significant gap in understanding how models perform on graphs with both heterogeneity and heterophily. To bridge this gap, we introduce H2GB, a large-scale node-classification graph benchmark that brings together the complexities of both the heterophily and heterogeneity properties of real-world graphs. H2GB encompasses 9 real-world datasets spanning 5 diverse domains, 28 baseline models, and a unified benchmarking library with a standardized data loader, evaluator, unified modeling framework, and an extensible framework for reproducibility. We establish a standardized workflow supporting both model selection and development, enabling researchers to easily benchmark graph learning methods. Extensive experiments across 28 baselines reveal that current methods struggle with heterophilic and heterogeneous graphs, underscoring the need for improved approaches. Finally, we present a new variant of the model, H2G-former, developed following our standardized workflow, that excels at this challenging benchmark. Both the benchmark and the framework are publicly available at Github and PyPI, with documentation hosted at https://junhongmit.github.io/H2GB.
COEF-VQ: Cost-Efficient Video Quality Understanding through a Cascaded Multimodal LLM Framework
PDF ↗Recently, with the emergence of recent Multimodal Large Language Model (MLLM) technology, it has become possible to exploit its video understanding capability on different classification tasks. In practice, we face the difficulty of huge requirements for GPU resource if we need to deploy MLLMs online. In this paper, we propose COEF-VQ, a novel cascaded MLLM framework designed to enhance video quality understanding on the short-video platform while optimizing computational efficiency. Our approach integrates an entropy-based pre-filtering stage, where a lightweight model assesses uncertainty and selectively filters cases before passing them to the more computationally intensive MLLM for final evaluation. By prioritizing high-uncertainty samples for deeper analysis, our framework significantly reduces GPU usage while maintaining the strong classification performance of a full MLLM deployment. To demonstrate the effectiveness of COEF-VQ, we deploy this new framework onto the video management platform (VMP) at the short-video platform, and perform a series of detailed experiments on two in-house tasks related to video quality understanding. We show that COEF-VQ leads to substantial performance gains from the offline evaluation in these two tasks and effectively enhances platform safety with limit resource consumption, significantly reducing inappropriate content video view rate by 9.9% in a online A/B test without affecting engagement. Post-launch monitoring confirmed sustained improvements, validating its real-world impact.
Understanding Why Large Language Models Can Be Ineffective in Time Series Analysis: The Impact of Modality Alignment
PDF ↗Large Language Models (LLMs) have demonstrated impressive performance in time series analysis and seems to understand the time temporal relationship well than traditional transformer-based approaches. However, since LLMs are not designed for time series tasks, simpler models-like linear regressions can often achieve comparable performance with far less complexity. In this study, we perform extensive experiments to assess the effectiveness of applying LLMs to key time series tasks, including forecasting, classification, imputation, and anomaly detection. We compare the performance of LLMs against simpler baseline models, such as single-layer linear models and randomly initialized LLMs. Our results reveal that LLMs offer minimal advantages for these core time series tasks and may even distort the temporal structure of the data. In contrast, simpler models consistently outperform LLMs while requiring far fewer parameters. Furthermore, we analyze existing reprogramming techniques and show, through data manifold analysis, that these methods fail to effectively align time series data with language and display ''pseudo-alignment'' behavior in embedding space. Our findings suggest that the performance of LLM-based methods in time series tasks arises from the intrinsic characteristics and structure of time series data, rather than any meaningful alignment with the language model architecture. We release the code for experiments here: https://github.com/IcurasLW/Official-Repository_Understanding_LLM_for_Time_Series_Analysis.git