id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.13734 | Sample complexity of data-driven tuning of model hyperparameters in
neural networks with structured parameter-dependent dual function | [
"cs.LG"
] | Modern machine learning algorithms, especially deep learning based techniques, typically involve careful hyperparameter tuning to achieve the best performance. Despite the surge of intense interest in practical techniques like Bayesian optimization and random search based approaches to automating this laborious and com... | {
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2501.13735 | A Study of the Plausibility of Attention between RNN Encoders in Natural
Language Inference | [
"cs.CL"
] | Attention maps in neural models for NLP are appealing to explain the decision made by a model, hopefully emphasizing words that justify the decision. While many empirical studies hint that attention maps can provide such justification from the analysis of sound examples, only a few assess the plausibility of explanatio... | {
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2501.13736 | Discrete Layered Entropy, Conditional Compression and a Tighter Strong
Functional Representation Lemma | [
"cs.IT",
"math.IT"
] | We study a quantity called discrete layered entropy, which approximates the Shannon entropy within a logarithmic gap. Compared to the Shannon entropy, the discrete layered entropy is piecewise linear, approximates the expected length of the optimal one-to-one non-prefix-free encoding, and satisfies an elegant condition... | {
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2501.13743 | GPT-HTree: A Decision Tree Framework Integrating Hierarchical Clustering
and Large Language Models for Explainable Classification | [
"cs.LG"
] | This paper introduces GPT-HTree, a framework combining hierarchical clustering, decision trees, and large language models (LLMs) to address this challenge. By leveraging hierarchical clustering to segment individuals based on salient features, resampling techniques to balance class distributions, and decision trees to ... | {
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2501.13744 | Centralized Versus Distributed Routing for Large-Scale Satellite
Networks | [
"cs.NI",
"cs.SY",
"eess.SY"
] | An important choice in the design of satellite networks is whether the routing decisions are made in a distributed manner onboard the satellite, or centrally on a ground-based controller. We study the tradeoff between centralized and distributed routing in large-scale satellite networks. In particular, we consider a ce... | {
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2501.13746 | EICopilot: Search and Explore Enterprise Information over Large-scale
Knowledge Graphs with LLM-driven Agents | [
"cs.IR",
"cs.AI"
] | The paper introduces EICopilot, an novel agent-based solution enhancing search and exploration of enterprise registration data within extensive online knowledge graphs like those detailing legal entities, registered capital, and major shareholders. Traditional methods necessitate text-based queries and manual subgraph ... | {
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2501.13748 | Exact Soft Analytical Side-Channel Attacks using Tractable Circuits | [
"cs.LG",
"cs.CR"
] | Detecting weaknesses in cryptographic algorithms is of utmost importance for designing secure information systems. The state-of-the-art soft analytical side-channel attack (SASCA) uses physical leakage information to make probabilistic predictions about intermediate computations and combines these "guesses" with the kn... | {
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2501.13751 | On Disentangled Training for Nonlinear Transform in Learned Image
Compression | [
"eess.IV",
"cs.CV"
] | Learned image compression (LIC) has demonstrated superior rate-distortion (R-D) performance compared to traditional codecs, but is challenged by training inefficiency that could incur more than two weeks to train a state-of-the-art model from scratch. Existing LIC methods overlook the slow convergence caused by compact... | {
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2501.13756 | Solving the long-tailed distribution problem by exploiting the synergies
and balance of different techniques | [
"cs.CV",
"cs.AI",
"cs.LG"
] | In real-world data, long-tailed data distribution is common, making it challenging for models trained on empirical risk minimisation to learn and classify tail classes effectively. While many studies have sought to improve long tail recognition by altering the data distribution in the feature space and adjusting model ... | {
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2501.13758 | 2-Tier SimCSE: Elevating BERT for Robust Sentence Embeddings | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Effective sentence embeddings that capture semantic nuances and generalize well across diverse contexts are crucial for natural language processing tasks. We address this challenge by applying SimCSE (Simple Contrastive Learning of Sentence Embeddings) using contrastive learning to fine-tune the minBERT model for senti... | {
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2501.13762 | On Deciding the Data Complexity of Answering Linear Monadic Datalog
Queries with LTL Operators(Extended Version) | [
"cs.AI",
"cs.CC",
"cs.LO"
] | Our concern is the data complexity of answering linear monadic datalog queries whose atoms in the rule bodies can be prefixed by operators of linear temporal logic LTL. We first observe that, for data complexity, answering any connected query with operators $\bigcirc/\bigcirc^-$ (at the next/previous moment) is either ... | {
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2501.13763 | Integrating Causality with Neurochaos Learning: Proposed Approach and
Research Agenda | [
"cs.LG",
"cs.AI"
] | Deep learning implemented via neural networks, has revolutionized machine learning by providing methods for complex tasks such as object detection/classification and prediction. However, architectures based on deep neural networks have started to yield diminishing returns, primarily due to their statistical nature and ... | {
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2501.13766 | UGMathBench: A Diverse and Dynamic Benchmark for Undergraduate-Level
Mathematical Reasoning with Large Language Models | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) have made significant strides in mathematical reasoning, underscoring the need for a comprehensive and fair evaluation of their capabilities. However, existing benchmarks often fall short, either lacking extensive coverage of undergraduate-level mathematical problems or probably suffering f... | {
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2501.13767 | An Efficient Diffusion-based Non-Autoregressive Solver for Traveling
Salesman Problem | [
"cs.LG"
] | Recent advances in neural models have shown considerable promise in solving Traveling Salesman Problems (TSPs) without relying on much hand-crafted engineering. However, while non-autoregressive (NAR) approaches benefit from faster inference through parallelism, they typically deliver solutions of inferior quality comp... | {
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2501.13772 | Tune In, Act Up: Exploring the Impact of Audio Modality-Specific Edits
on Large Audio Language Models in Jailbreak | [
"cs.SD",
"cs.AI",
"cs.LG",
"cs.MM",
"eess.AS"
] | Large Language Models (LLMs) demonstrate remarkable zero-shot performance across various natural language processing tasks. The integration of multimodal encoders extends their capabilities, enabling the development of Multimodal Large Language Models that process vision, audio, and text. However, these capabilities al... | {
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2501.13773 | Do Large Language Models Truly Understand Geometric Structures? | [
"cs.CL"
] | Geometric ability is a significant challenge for large language models (LLMs) due to the need for advanced spatial comprehension and abstract thinking. Existing datasets primarily evaluate LLMs on their final answers, but they cannot truly measure their true understanding of geometric structures, as LLMs can arrive at ... | {
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2501.13776 | Crossfire: An Elastic Defense Framework for Graph Neural Networks Under
Bit Flip Attacks | [
"cs.LG"
] | Bit Flip Attacks (BFAs) are a well-established class of adversarial attacks, originally developed for Convolutional Neural Networks within the computer vision domain. Most recently, these attacks have been extended to target Graph Neural Networks (GNNs), revealing significant vulnerabilities. This new development natur... | {
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2501.13778 | Explainable XR: Understanding User Behaviors of XR Environments using
LLM-assisted Analytics Framework | [
"cs.HC",
"cs.CL"
] | We present Explainable XR, an end-to-end framework for analyzing user behavior in diverse eXtended Reality (XR) environments by leveraging Large Language Models (LLMs) for data interpretation assistance. Existing XR user analytics frameworks face challenges in handling cross-virtuality - AR, VR, MR - transitions, multi... | {
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2501.13779 | Not Every AI Problem is a Data Problem: We Should Be Intentional About
Data Scaling | [
"cs.LG",
"cs.AI"
] | While Large Language Models require more and more data to train and scale, rather than looking for any data to acquire, we should consider what types of tasks are more likely to benefit from data scaling. We should be intentional in our data acquisition. We argue that the topology of data itself informs which tasks to ... | {
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2501.13780 | Matrix Completion in Group Testing: Bounds and Simulations | [
"cs.IT",
"cs.LG",
"math.IT"
] | The main goal of group testing is to identify a small number of defective items in a large population of items. A test on a subset of items is positive if the subset contains at least one defective item and negative otherwise. In non-adaptive design, all tests can be tested simultaneously and represented by a measureme... | {
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2501.13782 | Defending against Adversarial Malware Attacks on ML-based Android
Malware Detection Systems | [
"cs.CR",
"cs.AI",
"cs.LG",
"cs.SE"
] | Android malware presents a persistent threat to users' privacy and data integrity. To combat this, researchers have proposed machine learning-based (ML-based) Android malware detection (AMD) systems. However, adversarial Android malware attacks compromise the detection integrity of the ML-based AMD systems, raising sig... | {
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2501.13784 | Rate-Distortion Region for Distributed Indirect Source Coding with
Decoder Side Information | [
"cs.IT",
"math.IT"
] | This paper studies a variant of the rate-distortion problem motivated by task-oriented semantic communication and distributed learning systems, where $M$ correlated sources are independently encoded for a central decoder. The decoder has access to correlated side information in addition to the messages received from th... | {
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2501.13786 | Fast Iterative and Task-Specific Imputation with Online Learning | [
"cs.LG"
] | Missing feature values are a significant hurdle for downstream machine-learning tasks such as classification and regression. However, they are pervasive in multiple real-life use cases, for instance, in drug discovery research. Moreover, imputation methods might be time-consuming and offer few guarantees on the imputat... | {
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2501.13787 | Parameter-Efficient Fine-Tuning for Foundation Models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | This survey delves into the realm of Parameter-Efficient Fine-Tuning (PEFT) within the context of Foundation Models (FMs). PEFT, a cost-effective fine-tuning technique, minimizes parameters and computational complexity while striving for optimal downstream task performance. FMs, like ChatGPT, DALL-E, and LLaVA speciali... | {
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2501.13790 | Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic
Regression | [
"cs.LG"
] | We analyze two variants of Local Gradient Descent applied to distributed logistic regression with heterogeneous, separable data and show convergence at the rate $O(1/KR)$ for $K$ local steps and sufficiently large $R$ communication rounds. In contrast, all existing convergence guarantees for Local GD applied to any pro... | {
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2501.13794 | Unveiling the Power of Noise Priors: Enhancing Diffusion Models for
Mobile Traffic Prediction | [
"cs.LG"
] | Accurate prediction of mobile traffic, \textit{i.e.,} network traffic from cellular base stations, is crucial for optimizing network performance and supporting urban development. However, the non-stationary nature of mobile traffic, driven by human activity and environmental changes, leads to both regular patterns and ... | {
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2501.13795 | Training-Free Zero-Shot Temporal Action Detection with Vision-Language
Models | [
"cs.CV"
] | Existing zero-shot temporal action detection (ZSTAD) methods predominantly use fully supervised or unsupervised strategies to recognize unseen activities. However, these training-based methods are prone to domain shifts and require high computational costs, which hinder their practical applicability in real-world scena... | {
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2501.13796 | PromptMono: Cross Prompting Attention for Self-Supervised Monocular
Depth Estimation in Challenging Environments | [
"cs.CV"
] | Considerable efforts have been made to improve monocular depth estimation under ideal conditions. However, in challenging environments, monocular depth estimation still faces difficulties. In this paper, we introduce visual prompt learning for predicting depth across different environments within a unified model, and p... | {
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2501.13804 | Towards Real-World Validation of a Physics-Based Ship Motion Prediction
Model | [
"eess.SY",
"cs.RO",
"cs.SY"
] | The maritime industry aims towards a sustainable future, which requires significant improvements in operational efficiency. Current approaches focus on minimising fuel consumption and emissions through greater autonomy. Efficient and safe autonomous navigation requires high-fidelity ship motion models applicable to rea... | {
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2501.13805 | EgoHand: Ego-centric Hand Pose Estimation and Gesture Recognition with
Head-mounted Millimeter-wave Radar and IMUs | [
"cs.CV"
] | Recent advanced Virtual Reality (VR) headsets, such as the Apple Vision Pro, employ bottom-facing cameras to detect hand gestures and inputs, which offers users significant convenience in VR interactions. However, these bottom-facing cameras can sometimes be inconvenient and pose a risk of unintentionally exposing sens... | {
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2501.13806 | Generation of reusable learning objects from digital medical
collections: An analysis based on the MASMDOA framework | [
"cs.CL",
"cs.HC"
] | Learning Objects represent a widespread approach to structuring instructional materials in a large variety of educational contexts. The main aim of this work consists of analyzing from a qualitative point of view the process of generating reusable learning objects (RLOs) followed by Clavy, a tool that can be used to re... | {
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2501.13810 | Learning to Help in Multi-Class Settings | [
"cs.LG",
"cs.AI"
] | Deploying complex machine learning models on resource-constrained devices is challenging due to limited computational power, memory, and model retrainability. To address these limitations, a hybrid system can be established by augmenting the local model with a server-side model, where samples are selectively deferred b... | {
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2501.13812 | By-Example Synthesis of Vector Textures | [
"cs.CV",
"cs.GR"
] | We propose a new method for synthesizing an arbitrarily sized novel vector texture given a single raster exemplar. Our method first segments the exemplar to extract the primary textons, and then clusters them based on visual similarity. We then compute a descriptor to capture each texton's neighborhood which contains t... | {
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2501.13814 | On entropy-constrained Gaussian channel capacity via the moment problem | [
"cs.IT",
"math.IT",
"math.PR"
] | We study the capacity of the power-constrained additive Gaussian channel with an entropy constraint at the input. In particular, we characterize this capacity in the low signal-to-noise ratio regime, as a corollary of the following general result on a moment matching problem: we show that for any continuous random vari... | {
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2501.13816 | Large Language Model driven Policy Exploration for Recommender Systems | [
"cs.IR"
] | Recent advancements in Recommender Systems (RS) have incorporated Reinforcement Learning (RL), framing the recommendation as a Markov Decision Process (MDP). However, offline RL policies trained on static user data are vulnerable to distribution shift when deployed in dynamic online environments. Additionally, excessiv... | {
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2501.13817 | Temporal Logic Guided Safe Navigation for Autonomous Vehicles | [
"cs.RO",
"cs.FL",
"cs.SY",
"eess.SY"
] | Safety verification for autonomous vehicles (AVs) and ground robots is crucial for ensuring reliable operation given their uncertain environments. Formal language tools provide a robust and sound method to verify safety rules for such complex cyber-physical systems. In this paper, we propose a hybrid approach that comb... | {
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2501.13818 | Ensuring Medical AI Safety: Explainable AI-Driven Detection and
Mitigation of Spurious Model Behavior and Associated Data | [
"cs.AI",
"cs.CV",
"cs.LG"
] | Deep neural networks are increasingly employed in high-stakes medical applications, despite their tendency for shortcut learning in the presence of spurious correlations, which can have potentially fatal consequences in practice. Detecting and mitigating shortcut behavior is a challenging task that often requires signi... | {
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2501.13820 | Consistent spectral clustering in sparse tensor block models | [
"math.ST",
"cs.LG",
"math.PR",
"stat.TH"
] | High-order clustering aims to classify objects in multiway datasets that are prevalent in various fields such as bioinformatics, social network analysis, and recommendation systems. These tasks often involve data that is sparse and high-dimensional, presenting significant statistical and computational challenges. This ... | {
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2501.13824 | Hallucinations Can Improve Large Language Models in Drug Discovery | [
"cs.CL",
"cs.AI"
] | Concerns about hallucinations in Large Language Models (LLMs) have been raised by researchers, yet their potential in areas where creativity is vital, such as drug discovery, merits exploration. In this paper, we come up with the hypothesis that hallucinations can improve LLMs in drug discovery. To verify this hypothes... | {
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2501.13825 | Sample-Based Piecewise Linear Power Flow Approximations Using
Second-Order Sensitivities | [
"math.OC",
"cs.SY",
"eess.SY"
] | The inherent nonlinearity of the power flow equations poses significant challenges in accurately modeling power systems, particularly when employing linearized approximations. Although power flow linearizations provide computational efficiency, they can fail to fully capture nonlinear behavior across diverse operating ... | {
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2501.13826 | Video-MMMU: Evaluating Knowledge Acquisition from Multi-Discipline
Professional Videos | [
"cs.CV",
"cs.CL"
] | Humans acquire knowledge through three cognitive stages: perceiving information, comprehending knowledge, and adapting knowledge to solve novel problems. Videos serve as an effective medium for this learning process, facilitating a progression through these cognitive stages. However, existing video benchmarks fail to s... | {
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2501.13828 | PhotoGAN: Generative Adversarial Neural Network Acceleration with
Silicon Photonics | [
"cs.AR",
"cs.LG"
] | Generative Adversarial Networks (GANs) are at the forefront of AI innovation, driving advancements in areas such as image synthesis, medical imaging, and data augmentation. However, the unique computational operations within GANs, such as transposed convolutions and instance normalization, introduce significant ineffic... | {
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2501.13829 | MV-GMN: State Space Model for Multi-View Action Recognition | [
"cs.CV"
] | Recent advancements in multi-view action recognition have largely relied on Transformer-based models. While effective and adaptable, these models often require substantial computational resources, especially in scenarios with multiple views and multiple temporal sequences. Addressing this limitation, this paper introdu... | {
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2501.13830 | A space-decoupling framework for optimization on bounded-rank matrices
with orthogonally invariant constraints | [
"math.OC",
"cs.AI",
"cs.LG"
] | Imposing additional constraints on low-rank optimization has garnered growing interest. However, the geometry of coupled constraints hampers the well-developed low-rank structure and makes the problem intricate. To this end, we propose a space-decoupling framework for optimization on bounded-rank matrices with orthogon... | {
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2501.13831 | Predicting Compact Phrasal Rewrites with Large Language Models for ASR
Post Editing | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Large Language Models (LLMs) excel at rewriting tasks such as text style transfer and grammatical error correction. While there is considerable overlap between the inputs and outputs in these tasks, the decoding cost still increases with output length, regardless of the amount of overlap. By leveraging the overlap betw... | {
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2501.13833 | On the Reasoning Capacity of AI Models and How to Quantify It | [
"cs.AI",
"cs.CL",
"cs.IT",
"math.IT"
] | Recent advances in Large Language Models (LLMs) have intensified the debate surrounding the fundamental nature of their reasoning capabilities. While achieving high performance on benchmarks such as GPQA and MMLU, these models exhibit limitations in more complex reasoning tasks, highlighting the need for more rigorous ... | {
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2501.13836 | Think Outside the Data: Colonial Biases and Systemic Issues in Automated
Moderation Pipelines for Low-Resource Languages | [
"cs.CL",
"cs.HC"
] | Most social media users come from non-English speaking countries in the Global South. Despite the widespread prevalence of harmful content in these regions, current moderation systems repeatedly struggle in low-resource languages spoken there. In this work, we examine the challenges AI researchers and practitioners fac... | {
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2501.13848 | Where Do You Go? Pedestrian Trajectory Prediction using Scene Features | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Accurate prediction of pedestrian trajectories is crucial for enhancing the safety of autonomous vehicles and reducing traffic fatalities involving pedestrians. While numerous studies have focused on modeling interactions among pedestrians to forecast their movements, the influence of environmental factors and scene-ob... | {
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2501.13851 | Large Vision-Language Models for Knowledge-Grounded Data Annotation of
Memes | [
"cs.LG"
] | Memes have emerged as a powerful form of communication, integrating visual and textual elements to convey humor, satire, and cultural messages. Existing research has focused primarily on aspects such as emotion classification, meme generation, propagation, interpretation, figurative language, and sociolinguistics, but ... | {
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2501.13855 | First Lessons Learned of an Artificial Intelligence Robotic System for
Autonomous Coarse Waste Recycling Using Multispectral Imaging-Based Methods | [
"cs.CV",
"cs.LG",
"cs.RO"
] | Current disposal facilities for coarse-grained waste perform manual sorting of materials with heavy machinery. Large quantities of recyclable materials are lost to coarse waste, so more effective sorting processes must be developed to recover them. Two key aspects to automate the sorting process are object detection wi... | {
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2501.13858 | The Lock Generative Adversarial Network for Medical Waveform Anomaly
Detection | [
"cs.CE"
] | Waveform signal analysis is a complex and important task in medical care. For example, mechanical ventilators are critical life-support machines, but they can cause serious injury to patients if they are out of synchronization with the patients' own breathing reflex. This asynchrony is revealed by the waveforms showing... | {
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2501.13859 | Dual-Modal Prototype Joint Learning for Compositional Zero-Shot Learning | [
"cs.CV"
] | Compositional Zero-Shot Learning (CZSL) aims to recognize novel compositions of attributes and objects by leveraging knowledge learned from seen compositions. Recent approaches have explored the use of Vision-Language Models (VLMs) to align textual and visual modalities. These methods typically employ prompt engineerin... | {
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2501.13864 | Autoencoders for Anomaly Detection are Unreliable | [
"cs.LG",
"cs.AI"
] | Autoencoders are frequently used for anomaly detection, both in the unsupervised and semi-supervised settings. They rely on the assumption that when trained using the reconstruction loss, they will be able to reconstruct normal data more accurately than anomalous data. Some recent works have posited that this assumptio... | {
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2501.13865 | Threshold Selection for Iterative Decoding of $(v,w)$-regular Binary
Codes | [
"cs.CR",
"cs.IT",
"math.IT"
] | Iterative bit flipping decoders are an efficient and effective decoder choice for decoding codes which admit a sparse parity-check matrix. Among these, sparse $(v,w)$-regular codes, which include LDPC and MDPC codes are of particular interest both for efficient data correction and the design of cryptographic primitives... | {
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2501.13868 | Lost in Siting: The Hidden Carbon Cost of Inequitable Residential Solar
Installations | [
"cs.CE"
] | The declining cost of solar photovoltaics (PV) combined with strong federal and state-level incentives have resulted in a high number of residential solar PV installations in the US. However, these installations are concentrated in particular regions, such as California, and demographics, such as high-income Asian neig... | {
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2501.13876 | FAST-LIVO2 on Resource-Constrained Platforms: LiDAR-Inertial-Visual
Odometry with Efficient Memory and Computation | [
"cs.RO"
] | This paper presents a lightweight LiDAR-inertial-visual odometry system optimized for resource-constrained platforms. It integrates a degeneration-aware adaptive visual frame selector into error-state iterated Kalman filter (ESIKF) with sequential updates, improving computation efficiency significantly while maintainin... | {
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2501.13878 | Eye Gaze as a Signal for Conveying User Attention in Contextual AI
Systems | [
"cs.HC",
"cs.CV"
] | Advanced multimodal AI agents can now collaborate with users to solve challenges in the world. We explore eye tracking's role in such interaction to convey a user's attention relative to the physical environment. We hypothesize that this knowledge improves contextual understanding for AI agents. By observing hours of h... | {
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2501.13880 | A RAG-Based Institutional Assistant | [
"cs.CL"
] | Although large language models (LLMs) demonstrate strong text generation capabilities, they struggle in scenarios requiring access to structured knowledge bases or specific documents, limiting their effectiveness in knowledge-intensive tasks. To address this limitation, retrieval-augmented generation (RAG) models have ... | {
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2501.13883 | Utilizing Evolution Strategies to Train Transformers in Reinforcement
Learning | [
"cs.LG",
"cs.NE"
] | We explore a capability of evolution strategies to train an agent with its policy based on a transformer architecture in a reinforcement learning setting. We performed experiments using OpenAI's highly parallelizable evolution strategy to train Decision Transformer in Humanoid locomotion environment and in the environm... | {
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2501.13884 | Exploring Finetuned Audio-LLM on Heart Murmur Features | [
"eess.AS",
"cs.AI",
"cs.SD"
] | Large language models (LLMs) for audio have excelled in recognizing and analyzing human speech, music, and environmental sounds. However, their potential for understanding other types of sounds, particularly biomedical sounds, remains largely underexplored despite significant scientific interest. In this study, we focu... | {
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2501.13885 | Quantum model reduction for continuous-time quantum filters | [
"quant-ph",
"cs.SY",
"eess.SY",
"math-ph",
"math.MP"
] | The use of quantum stochastic models is widespread in dynamical reduction, simulation of open systems, feedback control and adaptive estimation. In many applications only part of the information contained in the filter's state is actually needed to reconstruct the target observable quantities; thus, filters of smaller ... | {
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2501.13887 | What Does an Audio Deepfake Detector Focus on? A Study in the Time
Domain | [
"cs.LG",
"cs.SD",
"eess.AS"
] | Adding explanations to audio deepfake detection (ADD) models will boost their real-world application by providing insight on the decision making process. In this paper, we propose a relevancy-based explainable AI (XAI) method to analyze the predictions of transformer-based ADD models. We compare against standard Grad-C... | {
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2501.13888 | Multimodal Sensor Dataset for Monitoring Older Adults Post Lower-Limb
Fractures in Community Settings | [
"cs.LG",
"cs.CV"
] | Lower-Limb Fractures (LLF) are a major health concern for older adults, often leading to reduced mobility and prolonged recovery, potentially impairing daily activities and independence. During recovery, older adults frequently face social isolation and functional decline, complicating rehabilitation and adversely affe... | {
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2501.13889 | Generating Realistic Forehead-Creases for User Verification via
Conditioned Piecewise Polynomial Curves | [
"cs.CV"
] | We propose a trait-specific image generation method that models forehead creases geometrically using B-spline and B\'ezier curves. This approach ensures the realistic generation of both principal creases and non-prominent crease patterns, effectively constructing detailed and authentic forehead-crease images. These geo... | {
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2501.13890 | Federated Granger Causality Learning for Interdependent Clients with
State Space Representation | [
"cs.LG",
"stat.ML"
] | Advanced sensors and IoT devices have improved the monitoring and control of complex industrial enterprises. They have also created an interdependent fabric of geographically distributed process operations (clients) across these enterprises. Granger causality is an effective approach to detect and quantify interdepende... | {
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2501.13893 | Pix2Cap-COCO: Advancing Visual Comprehension via Pixel-Level Captioning | [
"cs.CV",
"cs.AI",
"cs.LG"
] | We present Pix2Cap-COCO, the first panoptic pixel-level caption dataset designed to advance fine-grained visual understanding. To achieve this, we carefully design an automated annotation pipeline that prompts GPT-4V to generate pixel-aligned, instance-specific captions for individual objects within images, enabling mo... | {
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2501.13896 | GUI-Bee: Align GUI Action Grounding to Novel Environments via Autonomous
Exploration | [
"cs.CL",
"cs.AI",
"cs.CV",
"cs.LG"
] | Graphical User Interface (GUI) action grounding is a critical step in GUI automation that maps language instructions to actionable elements on GUI screens. Most recent works of GUI action grounding leverage large GUI datasets to fine-tune MLLMs. However, the fine-tuning data always covers limited GUI environments, and ... | {
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2501.13898 | PointOBB-v3: Expanding Performance Boundaries of Single Point-Supervised
Oriented Object Detection | [
"cs.CV",
"cs.AI"
] | With the growing demand for oriented object detection (OOD), recent studies on point-supervised OOD have attracted significant interest. In this paper, we propose PointOBB-v3, a stronger single point-supervised OOD framework. Compared to existing methods, it generates pseudo rotated boxes without additional priors and ... | {
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2501.13904 | Privacy-Preserving Personalized Federated Prompt Learning for Multimodal
Large Language Models | [
"cs.LG"
] | Multimodal Large Language Models (LLMs) are pivotal in revolutionizing customer support and operations by integrating multiple modalities such as text, images, and audio. Federated Prompt Learning (FPL) is a recently proposed approach that combines pre-trained multimodal LLMs such as vision-language models with federat... | {
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2501.13905 | On Learning Representations for Tabular Data Distillation | [
"cs.LG"
] | Dataset distillation generates a small set of information-rich instances from a large dataset, resulting in reduced storage requirements, privacy or copyright risks, and computational costs for downstream modeling, though much of the research has focused on the image data modality. We study tabular data distillation, w... | {
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2501.13906 | Universal optimality of $T$-avoiding spherical codes and designs | [
"math.CO",
"cs.IT",
"math.IT",
"math.MG"
] | Given an open set (a union of open intervals), $T\subset [-1,1]$ we introduce the concepts of $T$-avoiding spherical codes and designs, that is, spherical codes that have no inner products in the set $T$. We show that certain codes found in the minimal vectors of the Leech lattices, as well as the minimal vectors of th... | {
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2501.13908 | Graph Neural Controlled Differential Equations For Collaborative
Filtering | [
"cs.IR"
] | Graph Convolution Networks (GCNs) are widely considered state-of-the-art for recommendation systems. Several studies in the field of recommendation systems have attempted to apply collaborative filtering (CF) into the Neural ODE framework. These studies follow the same idea as LightGCN, which removes the weight matrix ... | {
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2501.13912 | Analysis of Indic Language Capabilities in LLMs | [
"cs.CL"
] | This report evaluates the performance of text-in text-out Large Language Models (LLMs) to understand and generate Indic languages. This evaluation is used to identify and prioritize Indic languages suited for inclusion in safety benchmarks. We conduct this study by reviewing existing evaluation studies and datasets; an... | {
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2501.13915 | Binary Diffusion Probabilistic Model | [
"cs.CV"
] | We introduce the Binary Diffusion Probabilistic Model (BDPM), a novel generative model optimized for binary data representations. While denoising diffusion probabilistic models (DDPMs) have demonstrated notable success in tasks like image synthesis and restoration, traditional DDPMs rely on continuous data representati... | {
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2501.13916 | PBM-VFL: Vertical Federated Learning with Feature and Sample Privacy | [
"cs.LG"
] | We present Poisson Binomial Mechanism Vertical Federated Learning (PBM-VFL), a communication-efficient Vertical Federated Learning algorithm with Differential Privacy guarantees. PBM-VFL combines Secure Multi-Party Computation with the recently introduced Poisson Binomial Mechanism to protect parties' private datasets ... | {
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2501.13918 | Improving Video Generation with Human Feedback | [
"cs.CV",
"cs.AI",
"cs.GR",
"cs.LG"
] | Video generation has achieved significant advances through rectified flow techniques, but issues like unsmooth motion and misalignment between videos and prompts persist. In this work, we develop a systematic pipeline that harnesses human feedback to mitigate these problems and refine the video generation model. Specif... | {
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2501.13919 | Temporal Preference Optimization for Long-Form Video Understanding | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG",
"cs.RO"
] | Despite significant advancements in video large multimodal models (video-LMMs), achieving effective temporal grounding in long-form videos remains a challenge for existing models. To address this limitation, we propose Temporal Preference Optimization (TPO), a novel post-training framework designed to enhance the tempo... | {
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2501.13920 | IMAGINE-E: Image Generation Intelligence Evaluation of State-of-the-art
Text-to-Image Models | [
"cs.CV",
"cs.CL",
"cs.LG"
] | With the rapid development of diffusion models, text-to-image(T2I) models have made significant progress, showcasing impressive abilities in prompt following and image generation. Recently launched models such as FLUX.1 and Ideogram2.0, along with others like Dall-E3 and Stable Diffusion 3, have demonstrated exceptiona... | {
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2501.13921 | The Breeze 2 Herd of Models: Traditional Chinese LLMs Based on Llama
with Vision-Aware and Function-Calling Capabilities | [
"cs.CL"
] | Llama-Breeze2 (hereinafter referred to as Breeze2) is a suite of advanced multi-modal language models, available in 3B and 8B parameter configurations, specifically designed to enhance Traditional Chinese language representation. Building upon the Llama 3.2 model family, we continue the pre-training of Breeze2 on an ex... | {
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2501.13923 | Efficient Mitigation of Error Floors in Quantum Error Correction using
Non-Binary Low-Density Parity-Check Codes | [
"quant-ph",
"cs.IT",
"math.IT"
] | In this paper, we propose an efficient method to reduce error floors in quantum error correction using non-binary low-density parity-check (LDPC) codes. We identify and classify cycle structures in the parity-check matrix where estimated noise becomes trapped, and develop tailored decoding methods for each cycle type. ... | {
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2501.13924 | Towards Robust Multimodal Open-set Test-time Adaptation via Adaptive
Entropy-aware Optimization | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Test-time adaptation (TTA) has demonstrated significant potential in addressing distribution shifts between training and testing data. Open-set test-time adaptation (OSTTA) aims to adapt a source pre-trained model online to an unlabeled target domain that contains unknown classes. This task becomes more challenging whe... | {
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2501.13925 | GeoPixel: Pixel Grounding Large Multimodal Model in Remote Sensing | [
"cs.CV"
] | Recent advances in large multimodal models (LMMs) have recognized fine-grained grounding as an imperative factor of visual understanding and dialogue. However, the benefits of such representation in LMMs are limited to the natural image domain, and these models perform poorly for remote sensing (RS). The distinct overh... | {
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2501.13926 | Can We Generate Images with CoT? Let's Verify and Reinforce Image
Generation Step by Step | [
"cs.CV",
"cs.AI",
"cs.CL"
] | Chain-of-Thought (CoT) reasoning has been extensively explored in large models to tackle complex understanding tasks. However, it still remains an open question whether such strategies can be applied to verifying and reinforcing image generation scenarios. In this paper, we provide the first comprehensive investigation... | {
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2501.13927 | CRPO: Confidence-Reward Driven Preference Optimization for Machine
Translation | [
"cs.CL",
"cs.AI",
"cs.CV"
] | Large language models (LLMs) have shown great potential in natural language processing tasks, but their application to machine translation (MT) remains challenging due to pretraining on English-centric data and the complexity of reinforcement learning from human feedback (RLHF). Direct Preference Optimization (DPO) has... | {
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2501.13928 | Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass | [
"cs.CV",
"cs.AI",
"cs.GR",
"cs.RO"
] | Multi-view 3D reconstruction remains a core challenge in computer vision, particularly in applications requiring accurate and scalable representations across diverse perspectives. Current leading methods such as DUSt3R employ a fundamentally pairwise approach, processing images in pairs and necessitating costly global ... | {
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2501.13935 | Low rank matrix completion and realization of graphs: results and
problems | [
"math.HO",
"cs.DM",
"cs.LG",
"math.CO",
"math.GT"
] | The Netflix problem (from machine learning) asks the following. Given a ratings matrix in which each entry $(i,j)$ represents the rating of movie $j$ by customer $i$, if customer $i$ has watched movie $j$, and is otherwise missing, we would like to predict the remaining entries in order to make good recommendations to ... | {
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2501.13936 | Evaluating Computational Accuracy of Large Language Models in Numerical
Reasoning Tasks for Healthcare Applications | [
"cs.AI",
"cs.CL",
"cs.LG"
] | Large Language Models (LLMs) have emerged as transformative tools in the healthcare sector, demonstrating remarkable capabilities in natural language understanding and generation. However, their proficiency in numerical reasoning, particularly in high-stakes domains like in clinical applications, remains underexplored.... | {
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2501.13941 | GaussMark: A Practical Approach for Structural Watermarking of Language
Models | [
"cs.CR",
"cs.AI",
"cs.CL",
"cs.LG"
] | Recent advances in Large Language Models (LLMs) have led to significant improvements in natural language processing tasks, but their ability to generate human-quality text raises significant ethical and operational concerns in settings where it is important to recognize whether or not a given text was generated by a hu... | {
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2501.13942 | Prompt-Based Monte Carlo Tree Search for Mitigating Hallucinations in
Large Models | [
"cs.AI"
] | With the rapid development of large models in the field of artificial intelligence, how to enhance their application capabilities in handling complex problems in the field of scientific research remains a challenging problem to be solved. This study proposes an improved Monte Carlo Tree Search (MCTS) method based on pr... | {
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2501.13943 | Language Representation Favored Zero-Shot Cross-Domain Cognitive
Diagnosis | [
"cs.CL",
"cs.AI",
"cs.CY",
"cs.LG"
] | Cognitive diagnosis aims to infer students' mastery levels based on their historical response logs. However, existing cognitive diagnosis models (CDMs), which rely on ID embeddings, often have to train specific models on specific domains. This limitation may hinder their directly practical application in various target... | {
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} |
2501.13944 | Fanar: An Arabic-Centric Multimodal Generative AI Platform | [
"cs.CL",
"cs.AI"
] | We present Fanar, a platform for Arabic-centric multimodal generative AI systems, that supports language, speech and image generation tasks. At the heart of Fanar are Fanar Star and Fanar Prime, two highly capable Arabic Large Language Models (LLMs) that are best in the class on well established benchmarks for similar ... | {
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} |
2501.13945 | Self-Explanation in Social AI Agents | [
"cs.CL",
"cs.AI",
"cs.CY"
] | Social AI agents interact with members of a community, thereby changing the behavior of the community. For example, in online learning, an AI social assistant may connect learners and thereby enhance social interaction. These social AI assistants too need to explain themselves in order to enhance transparency and trust... | {
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} |
2501.13946 | Hallucination Mitigation using Agentic AI Natural Language-Based
Frameworks | [
"cs.CL",
"cs.AI",
"cs.MA"
] | Hallucinations remain a significant challenge in current Generative AI models, undermining trust in AI systems and their reliability. This study investigates how orchestrating multiple specialized Artificial Intelligent Agents can help mitigate such hallucinations, with a focus on systems leveraging Natural Language Pr... | {
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} |
2501.13947 | A Comprehensive Survey on Integrating Large Language Models with
Knowledge-Based Methods | [
"cs.CL",
"cs.AI"
] | The rapid development of artificial intelligence has brought about substantial advancements in the field. One promising direction is the integration of Large Language Models (LLMs) with structured knowledge-based systems. This approach aims to enhance AI capabilities by combining the generative language understanding o... | {
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} |
2501.13948 | Longitudinal Abuse and Sentiment Analysis of Hollywood Movie Dialogues
using LLMs | [
"cs.CL",
"cs.AI"
] | Over the past decades, there has been an increasing concern about the prevalence of abusive and violent content in Hollywood movies. This study uses Large Language Models (LLMs) to explore the longitudinal abuse and sentiment analysis of Hollywood Oscar and blockbuster movie dialogues from 1950 to 2024. By employing fi... | {
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} |
2501.13949 | Can OpenAI o1 Reason Well in Ophthalmology? A 6,990-Question
Head-to-Head Evaluation Study | [
"cs.CL",
"cs.AI"
] | Question: What is the performance and reasoning ability of OpenAI o1 compared to other large language models in addressing ophthalmology-specific questions? Findings: This study evaluated OpenAI o1 and five LLMs using 6,990 ophthalmological questions from MedMCQA. O1 achieved the highest accuracy (0.88) and macro-F1 ... | {
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} |
2501.13950 | DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco
Addiction Prevention | [
"cs.CV"
] | While tobacco advertising innovates at unprecedented speed, traditional surveillance methods remain frozen in time, especially in the context of social media. The lack of large-scale, comprehensive datasets and sophisticated monitoring systems has created a widening gap between industry advancement and public health ov... | {
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} |
2501.13951 | A Layered Multi-Expert Framework for Long-Context Mental Health
Assessments | [
"cs.CL",
"cs.AI"
] | Long-form mental health assessments pose unique challenges for large language models (LLMs), which often exhibit hallucinations or inconsistent reasoning when handling extended, domain-specific contexts. We introduce Stacked Multi-Model Reasoning (SMMR), a layered framework that leverages multiple LLMs and specialized ... | {
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} |
2501.13952 | The Dual-use Dilemma in LLMs: Do Empowering Ethical Capacities Make a
Degraded Utility? | [
"cs.CL",
"cs.AI"
] | Recent years have witnessed extensive efforts to enhance Large Language Models (LLMs) across various domains, alongside growing attention to their ethical implications. However, a critical challenge remains largely overlooked: LLMs must balance between rejecting harmful requests for safety and accommodating legitimate ... | {
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} |
2501.13953 | Redundancy Principles for MLLMs Benchmarks | [
"cs.CL",
"cs.AI"
] | With the rapid iteration of Multi-modality Large Language Models (MLLMs) and the evolving demands of the field, the number of benchmarks produced annually has surged into the hundreds. The rapid growth has inevitably led to significant redundancy among benchmarks. Therefore, it is crucial to take a step back and critic... | {
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} |
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