id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.10122 | Integrating Mediumband with Emerging Technologies: Unified Vision for 6G
and Beyond Physical Layer | [
"cs.IT",
"math.IT"
] | In this paper, we present a vision for the physical layer of 6G and beyond, where emerging physical layer technologies integrate to drive wireless links toward mediumband operation, addressing a major challenge: deep fading, a prevalent, and perhaps the most consequential, obstacle in wireless communication link perfor... | {
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2501.10124 | Gene Regulatory Network Inference in the Presence of Selection Bias and
Latent Confounders | [
"cs.LG"
] | Gene Regulatory Network Inference (GRNI) aims to identify causal relationships among genes using gene expression data, providing insights into regulatory mechanisms. A significant yet often overlooked challenge is selection bias, a process where only cells meeting specific criteria, such as gene expression thresholds, ... | {
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2501.10128 | FECT: Classification of Breast Cancer Pathological Images Based on
Fusion Features | [
"eess.IV",
"cs.CV"
] | Breast cancer is one of the most common cancers among women globally, with early diagnosis and precise classification being crucial. With the advancement of deep learning and computer vision, the automatic classification of breast tissue pathological images has emerged as a research focus. Existing methods typically re... | {
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2501.10129 | Spatio-temporal Graph Learning on Adaptive Mined Key Frames for
High-performance Multi-Object Tracking | [
"cs.CV",
"cs.AI"
] | In the realm of multi-object tracking, the challenge of accurately capturing the spatial and temporal relationships between objects in video sequences remains a significant hurdle. This is further complicated by frequent occurrences of mutual occlusions among objects, which can lead to tracking errors and reduced perfo... | {
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2501.10131 | ACE: Anatomically Consistent Embeddings in Composition and Decomposition | [
"cs.CV"
] | Medical images acquired from standardized protocols show consistent macroscopic or microscopic anatomical structures, and these structures consist of composable/decomposable organs and tissues, but existing self-supervised learning (SSL) methods do not appreciate such composable/decomposable structure attributes inhere... | {
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2501.10132 | ComplexFuncBench: Exploring Multi-Step and Constrained Function Calling
under Long-Context Scenario | [
"cs.CL"
] | Enhancing large language models (LLMs) with real-time APIs can help generate more accurate and up-to-date responses. However, evaluating the function calling abilities of LLMs in real-world scenarios remains under-explored due to the complexity of data collection and evaluation. In this work, we introduce ComplexFuncBe... | {
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2501.10134 | Exploring the Impact of Generative Artificial Intelligence in Education:
A Thematic Analysis | [
"cs.AI",
"cs.HC",
"cs.LG"
] | The recent advancements in Generative Artificial intelligence (GenAI) technology have been transformative for the field of education. Large Language Models (LLMs) such as ChatGPT and Bard can be leveraged to automate boilerplate tasks, create content for personalised teaching, and handle repetitive tasks to allow more ... | {
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2501.10137 | Visual Exploration of Stopword Probabilities in Topic Models | [
"cs.HC",
"cs.LG"
] | Stopword removal is a critical stage in many Machine Learning methods but often receives little consideration, it interferes with the model visualizations and disrupts user confidence. Inappropriately chosen or hastily omitted stopwords not only lead to suboptimal performance but also significantly affect the quality o... | {
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2501.10139 | Conformal Prediction Sets with Improved Conditional Coverage using Trust
Scores | [
"cs.LG",
"cs.AI",
"stat.ME",
"stat.ML"
] | Standard conformal prediction offers a marginal guarantee on coverage, but for prediction sets to be truly useful, they should ideally ensure coverage conditional on each test point. Unfortunately, it is impossible to achieve exact, distribution-free conditional coverage in finite samples. In this work, we propose an a... | {
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2501.10141 | Enhancing UAV Path Planning Efficiency Through Accelerated Learning | [
"cs.LG",
"cs.AI"
] | Unmanned Aerial Vehicles (UAVs) are increasingly essential in various fields such as surveillance, reconnaissance, and telecommunications. This study aims to develop a learning algorithm for the path planning of UAV wireless communication relays, which can reduce storage requirements and accelerate Deep Reinforcement L... | {
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2501.10143 | A Worrying Reproducibility Study of Intent-Aware Recommendation Models | [
"cs.IR"
] | Lately, we have observed a growing interest in intent-aware recommender systems (IARS). The promise of such systems is that they are capable of generating better recommendations by predicting and considering the underlying motivations and short-term goals of consumers. From a technical perspective, various sophisticate... | {
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2501.10144 | A Vision-Language Framework for Multispectral Scene Representation Using
Language-Grounded Features | [
"cs.CV"
] | Scene understanding in remote sensing often faces challenges in generating accurate representations for complex environments such as various land use areas or coastal regions, which may also include snow, clouds, or haze. To address this, we present a vision-language framework named Spectral LLaVA, which integrates mul... | {
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2501.10150 | Dual Debiasing: Remove Stereotypes and Keep Factual Gender for Fair
Language Modeling and Translation | [
"cs.CL",
"cs.AI"
] | Mitigation of biases, such as language models' reliance on gender stereotypes, is a crucial endeavor required for the creation of reliable and useful language technology. The crucial aspect of debiasing is to ensure that the models preserve their versatile capabilities, including their ability to solve language tasks a... | {
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2501.10151 | Topology-Driven Attribute Recovery for Attribute Missing Graph Learning
in Social Internet of Things | [
"cs.AI"
] | With the advancement of information technology, the Social Internet of Things (SIoT) has fostered the integration of physical devices and social networks, deepening the study of complex interaction patterns. Text Attribute Graphs (TAGs) capture both topological structures and semantic attributes, enhancing the analysis... | {
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2501.10152 | Quantum Advantage in Private Multiple Hypothesis Testing | [
"quant-ph",
"cs.IT",
"math.IT"
] | For multiple hypothesis testing based on classical data samples, we demonstrate a quantum advantage in the optimal privacy-utility trade-off (PUT), where the privacy and utility measures are set to (quantum) local differential privacy and the pairwise-minimum Chernoff information, respectively. To show the quantum adva... | {
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2501.10153 | Region-wise stacking ensembles for estimating brain-age using MRI | [
"cs.LG",
"cs.AI"
] | Predictive modeling using structural magnetic resonance imaging (MRI) data is a prominent approach to study brain-aging. Machine learning algorithms and feature extraction methods have been employed to improve predictions and explore healthy and accelerated aging e.g. neurodegenerative and psychiatric disorders. The hi... | {
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2501.10156 | Tethered Variable Inertial Attitude Control Mechanisms through a Modular
Jumping Limbed Robot | [
"cs.RO"
] | This paper presents the concept of a tethered variable inertial attitude control mechanism for a modular jumping-limbed robot designed for planetary exploration in low-gravity environments. The system, named SPLITTER, comprises two sub-10 kg quadrupedal robots connected by a tether, capable of executing successive jump... | {
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2501.10157 | Structure-guided Deep Multi-View Clustering | [
"cs.CV"
] | Deep multi-view clustering seeks to utilize the abundant information from multiple views to improve clustering performance. However, most of the existing clustering methods often neglect to fully mine multi-view structural information and fail to explore the distribution of multi-view data, limiting clustering performa... | {
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2501.10160 | CSSDM Ontology to Enable Continuity of Care Data Interoperability | [
"cs.AI"
] | The rapid advancement of digital technologies and recent global pandemic scenarios have led to a growing focus on how these technologies can enhance healthcare service delivery and workflow to address crises. Action plans that consolidate existing digital transformation programs are being reviewed to establish core inf... | {
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2501.10162 | Convex Physics Informed Neural Networks for the Monge-Amp\`ere Optimal
Transport Problem | [
"math.NA",
"cs.LG",
"cs.NA"
] | Optimal transportation of raw material from suppliers to customers is an issue arising in logistics that is addressed here with a continuous model relying on optimal transport theory. A physics informed neuralnetwork method is advocated here for the solution of the corresponding generalized Monge-Amp`ere equation. Conv... | {
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2501.10163 | Invariant Theory and Magic State Distillation | [
"quant-ph",
"cs.IT",
"hep-th",
"math.IT"
] | We show that the performance of a linear self-orthogonal $GF(4)$ code for magic state distillation of Bravyi and Kitaev's $|T\rangle$-state is characterized by its simple weight enumerator. We compute weight enumerators of all such codes with fewer than 20 qubits and find none whose threshold exceeds that of the 5-qubi... | {
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2501.10165 | MechIR: A Mechanistic Interpretability Framework for Information
Retrieval | [
"cs.IR"
] | Mechanistic interpretability is an emerging diagnostic approach for neural models that has gained traction in broader natural language processing domains. This paradigm aims to provide attribution to components of neural systems where causal relationships between hidden layers and output were previously uninterpretable... | {
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2501.10166 | Implementing Finite Impulse Response Filters on Quantum Computers | [
"eess.SP",
"cs.IT",
"math.IT",
"quant-ph"
] | While signal processing is a mature area, its connections with quantum computing have received less attention. In this work, we propose approaches that perform classical discrete-time signal processing using quantum systems. Our approaches encode the classical discrete-time input signal into quantum states, and design ... | {
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2501.10172 | Mean and Variance Estimation Complexity in Arbitrary Distributions via
Wasserstein Minimization | [
"cs.LG"
] | Parameter estimation is a fundamental challenge in machine learning, crucial for tasks such as neural network weight fitting and Bayesian inference. This paper focuses on the complexity of estimating translation $\boldsymbol{\mu} \in \mathbb{R}^l$ and shrinkage $\sigma \in \mathbb{R}_{++}$ parameters for a distribution... | {
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2501.10173 | Optimal Restart Strategies for Parameter-dependent Optimization
Algorithms | [
"math.OC",
"cs.NE"
] | This paper examines restart strategies for algorithms whose successful termination depends on an unknown parameter $\lambda$. After each restart, $\lambda$ is increased, until the algorithm terminates successfully. It is assumed that there is a unique, unknown, optimal value for $\lambda$. For the algorithm to run succ... | {
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2501.10175 | Multi-stage Training of Bilingual Islamic LLM for Neural Passage
Retrieval | [
"cs.CL"
] | This study examines the use of Natural Language Processing (NLP) technology within the Islamic domain, focusing on developing an Islamic neural retrieval model. By leveraging the robust XLM-R model, the research employs a language reduction technique to create a lightweight bilingual large language model (LLM). Our app... | {
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2501.10179 | A Simple but Effective Closed-form Solution for Extreme Multi-label
Learning | [
"cs.IR",
"cs.AI",
"cs.CL",
"cs.LG"
] | Extreme multi-label learning (XML) is a task of assigning multiple labels from an extremely large set of labels to each data instance. Many current high-performance XML models are composed of a lot of hyperparameters, which complicates the tuning process. Additionally, the models themselves are adapted specifically to ... | {
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2501.10181 | Improved learning rates in multi-unit uniform price auctions | [
"cs.GT",
"cs.LG"
] | Motivated by the strategic participation of electricity producers in electricity day-ahead market, we study the problem of online learning in repeated multi-unit uniform price auctions focusing on the adversarial opposing bid setting. The main contribution of this paper is the introduction of a new modeling of the bid ... | {
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2501.10185 | Modeling the drying process in hard carbon electrodes based on the
phase-field method | [
"cs.CE"
] | The present work addresses the simulation of pore emptying during the drying of battery electrodes. For this purpose, a model based on the multiphase-field method (MPF) is used, since it is an established approach for modeling and simulating multiphysical problems. A model based on phase fields is introduced that takes... | {
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2501.10186 | Generative Artificial Intelligence: Implications for Biomedical and
Health Professions Education | [
"cs.AI"
] | Generative AI has had a profound impact on biomedicine and health, both in professional work and in education. Based on large language models (LLMs), generative AI has been found to perform as well as humans in simulated situations taking medical board exams, answering clinical questions, solving clinical cases, applyi... | {
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2501.10187 | Good things come in small packages: Should we adopt Lite-GPUs in AI
infrastructure? | [
"cs.AR",
"cs.AI",
"cs.DC"
] | To match the blooming demand of generative AI workloads, GPU designers have so far been trying to pack more and more compute and memory into single complex and expensive packages. However, there is growing uncertainty about the scalability of individual GPUs and thus AI clusters, as state-of-the-art GPUs are already di... | {
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2501.10190 | Temporal Causal Reasoning with (Non-Recursive) Structural Equation
Models | [
"cs.AI",
"cs.LO"
] | Structural Equation Models (SEM) are the standard approach to representing causal dependencies between variables in causal models. In this paper we propose a new interpretation of SEMs when reasoning about Actual Causality, in which SEMs are viewed as mechanisms transforming the dynamics of exogenous variables into the... | {
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2501.10193 | Surrogate-based multiscale analysis of experiments on thermoplastic
composites under off-axis loading | [
"math.NA",
"cond-mat.mtrl-sci",
"cs.LG",
"cs.NA"
] | In this paper, we present a surrogate-based multiscale approach to model constant strain-rate and creep experiments on unidirectional thermoplastic composites under off-axis loading. In previous contributions, these experiments were modeled through a single-scale micromechanical simulation under the assumption of macro... | {
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2501.10195 | Contributions to the Decision Theoretic Foundations of Machine Learning
and Robust Statistics under Weakly Structured Information | [
"stat.ML",
"cs.LG"
] | This habilitation thesis is cumulative and, therefore, is collecting and connecting research that I (together with several co-authors) have conducted over the last few years. Thus, the absolute core of the work is formed by the ten publications listed on page 5 under the name Contributions 1 to 10. The references to th... | {
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2501.10196 | Pricing Mechanisms versus Non-Pricing Mechanisms for Demand Side
Management in Microgrids | [
"eess.SY",
"cs.SY"
] | In this paper, we compare pricing and non-pricing mechanisms for implementing demand-side management (DSM) mechanisms in a neighborhood in Helsinki, Finland. We compare load steering based on peak load-reduction using the profile steering method, and load steering based on market price signals, in terms of peak loads, ... | {
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2501.10197 | CSHNet: A Novel Information Asymmetric Image Translation Method | [
"cs.CV"
] | Despite advancements in cross-domain image translation, challenges persist in asymmetric tasks such as SAR-to-Optical and Sketch-to-Instance conversions, which involve transforming data from a less detailed domain into one with richer content. Traditional CNN-based methods are effective at capturing fine details but st... | {
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2501.10199 | Adaptive Clustering for Efficient Phenotype Segmentation of UAV
Hyperspectral Data | [
"cs.CV",
"eess.IV"
] | Unmanned Aerial Vehicles (UAVs) combined with Hyperspectral imaging (HSI) offer potential for environmental and agricultural applications by capturing detailed spectral information that enables the prediction of invisible features like biochemical leaf properties. However, the data-intensive nature of HSI poses challen... | {
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2501.10201 | ODMA-Based Cell-Free Unsourced Random Access with Successive
Interference Cancellation | [
"cs.ET",
"cs.IT",
"cs.SY",
"eess.SY",
"math.IT"
] | We consider the unsourced random access problem with multiple receivers and propose a cell-free type solution for that. In our proposed scheme, the active users transmit their signals to the access points (APs) distributed in a geographical area and connected to a central processing unit (CPU). The transmitted signals ... | {
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2501.10202 | Provably Safeguarding a Classifier from OOD and Adversarial Samples: an
Extreme Value Theory Approach | [
"stat.ML",
"cs.LG"
] | This paper introduces a novel method, Sample-efficient Probabilistic Detection using Extreme Value Theory (SPADE), which transforms a classifier into an abstaining classifier, offering provable protection against out-of-distribution and adversarial samples. The approach is based on a Generalized Extreme Value (GEV) mod... | {
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2501.10209 | Hypercone Assisted Contour Generation for Out-of-Distribution Detection | [
"cs.CV",
"cs.LG"
] | Recent advances in the field of out-of-distribution (OOD) detection have placed great emphasis on learning better representations suited to this task. While there are distance-based approaches, distributional awareness has seldom been exploited for better performance. We present HAC$_k$-OOD, a novel OOD detection metho... | {
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2501.10212 | Disharmony: Forensics using Reverse Lighting Harmonization | [
"cs.CV"
] | Content generation and manipulation approaches based on deep learning methods have seen significant advancements, leading to an increased need for techniques to detect whether an image has been generated or edited. Another area of research focuses on the insertion and harmonization of objects within images. In this stu... | {
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2501.10214 | Temporal Graph MLP Mixer for Spatio-Temporal Forecasting | [
"cs.LG"
] | Spatiotemporal forecasting is critical in applications such as traffic prediction, climate modeling, and environmental monitoring. However, the prevalence of missing data in real-world sensor networks significantly complicates this task. In this paper, we introduce the Temporal Graph MLP-Mixer (T-GMM), a novel architec... | {
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2501.10216 | The Relevance of AWS Chronos: An Evaluation of Standard Methods for Time
Series Forecasting with Limited Tuning | [
"cs.LG"
] | A systematic comparison of Chronos, a transformer-based time series forecasting framework, against traditional approaches including ARIMA and Prophet. We evaluate these models across multiple time horizons and user categories, with a focus on the impact of historical context length. Our analysis reveals that while Chro... | {
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2501.10219 | Robust Egoistic Rigid Body Localization | [
"eess.SP",
"cs.CV"
] | We consider a robust and self-reliant (or "egoistic") variation of the rigid body localization (RBL) problem, in which a primary rigid body seeks to estimate the pose (i.e., location and orientation) of another rigid body (or "target"), relative to its own, without the assistance of external infrastructure, without pri... | {
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2501.10221 | Modelling Activity Scheduling Behaviour with Deep Generative Machine
Learning | [
"cs.LG"
] | We model human activity scheduling behaviour using a deep generative machine learning approach. Activity schedules, which represent the activities and associated travel behaviours of individuals, are a core component of many applied models in the transport, energy and epidemiology domains. Our data driven approach lear... | {
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2501.10227 | Joint Active and Passive Beamforming Optimization for Beyond Diagonal
RIS-aided Multi-User Communications | [
"eess.SP",
"cs.IT",
"math.IT"
] | Benefiting from its capability to generalize existing reconfigurable intelligent surface (RIS) architectures and provide additional design flexibility via interactions between RIS elements, beyond-diagonal RIS (BD-RIS) has attracted considerable research interests recently. However, due to the symmetric and unitary pas... | {
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2501.10229 | Amortized Bayesian Mixture Models | [
"stat.ML",
"cs.LG",
"stat.CO"
] | Finite mixtures are a broad class of models useful in scenarios where observed data is generated by multiple distinct processes but without explicit information about the responsible process for each data point. Estimating Bayesian mixture models is computationally challenging due to issues such as high-dimensional pos... | {
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2501.10234 | Counterfactual Explanations for k-means and Gaussian Clustering | [
"cs.LG"
] | Counterfactuals have been recognized as an effective approach to explain classifier decisions. Nevertheless, they have not yet been considered in the context of clustering. In this work, we propose the use of counterfactuals to explain clustering solutions. First, we present a general definition for counterfactuals for... | {
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2501.10235 | SpaceTime: Causal Discovery from Non-Stationary Time Series | [
"cs.LG"
] | Understanding causality is challenging and often complicated by changing causal relationships over time and across environments. Climate patterns, for example, shift over time with recurring seasonal trends, while also depending on geographical characteristics such as ecosystem variability. Existing methods for discove... | {
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2501.10236 | Actively Coupled Sensor Configuration and Planning in Unknown Dynamic
Environments | [
"eess.SY",
"cs.SY"
] | We address the problem of path-planning for an autonomous mobile vehicle, called the ego vehicle, in an unknown andtime-varying environment. The objective is for the ego vehicle to minimize exposure to a spatiotemporally-varying unknown scalar field called the threat field. Noisy measurements of the threat field are pr... | {
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2501.10240 | Challenges and recommendations for Electronic Health Records data
extraction and preparation for dynamic prediction modelling in hospitalized
patients -- a practical guide | [
"cs.LG",
"cs.AI"
] | Dynamic predictive modeling using electronic health record (EHR) data has gained significant attention in recent years. The reliability and trustworthiness of such models depend heavily on the quality of the underlying data, which is largely determined by the stages preceding the model development: data extraction from... | {
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2501.10243 | Random-Key Algorithms for Optimizing Integrated Operating Room
Scheduling | [
"cs.NE",
"cs.AI",
"math.CO"
] | Efficient surgery room scheduling is essential for hospital efficiency, patient satisfaction, and resource utilization. This study addresses this challenge by introducing a novel concept of Random-Key Optimizer (RKO), rigorously tested on literature and new, real-world inspired instances. Our combinatorial optimization... | {
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2501.10245 | Over-the-Air Multi-Sensor Inference with Neural Networks Using
Memristor-Based Analog Computing | [
"cs.LG",
"cs.DC",
"cs.IT",
"math.IT"
] | Deep neural networks provide reliable solutions for many classification and regression tasks; however, their application in real-time wireless systems with simple sensor networks is limited due to high energy consumption and significant bandwidth needs. This study proposes a multi-sensor wireless inference system with ... | {
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2501.10251 | The Distributed Multi-User Point Function | [
"cs.IT",
"cs.CR",
"math.IT"
] | In this paper, we study the problem of information-theoretic distributed multi-user point function, involving a trusted master node, $N \in \mathbb{N}$ server nodes, and $K\in \mathbb{N}$ users, where each user has access to the contents of a subset of the storages of server nodes. Each user is associated with an indep... | {
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2501.10256 | Unsupervised Rhythm and Voice Conversion of Dysarthric to Healthy Speech
for ASR | [
"eess.AS",
"cs.AI",
"cs.LG",
"cs.SD"
] | Automatic speech recognition (ASR) systems are well known to perform poorly on dysarthric speech. Previous works have addressed this by speaking rate modification to reduce the mismatch with typical speech. Unfortunately, these approaches rely on transcribed speech data to estimate speaking rates and phoneme durations,... | {
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2501.10258 | DADA: Dual Averaging with Distance Adaptation | [
"math.OC",
"cs.LG"
] | We present a novel universal gradient method for solving convex optimization problems. Our algorithm -- Dual Averaging with Distance Adaptation (DADA) -- is based on the classical scheme of dual averaging and dynamically adjusts its coefficients based on observed gradients and the distance between iterates and the star... | {
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2501.10261 | Logarithmic Regret for Nonlinear Control | [
"cs.LG"
] | We address the problem of learning to control an unknown nonlinear dynamical system through sequential interactions. Motivated by high-stakes applications in which mistakes can be catastrophic, such as robotics and healthcare, we study situations where it is possible for fast sequential learning to occur. Fast sequenti... | {
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2501.10262 | Deployment of an Aerial Multi-agent System for Automated Task Execution
in Large-scale Underground Mining Environments | [
"cs.RO",
"cs.SY",
"eess.SY"
] | In this article, we present a framework for deploying an aerial multi-agent system in large-scale subterranean environments with minimal infrastructure for supporting multi-agent operations. The multi-agent objective is to optimally and reactively allocate and execute inspection tasks in a mine, which are entered by a ... | {
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2501.10266 | MutualForce: Mutual-Aware Enhancement for 4D Radar-LiDAR 3D Object
Detection | [
"cs.CV"
] | Radar and LiDAR have been widely used in autonomous driving as LiDAR provides rich structure information, and radar demonstrates high robustness under adverse weather. Recent studies highlight the effectiveness of fusing radar and LiDAR point clouds. However, challenges remain due to the modality misalignment and infor... | {
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2501.10273 | SEANN: A Domain-Informed Neural Network for Epidemiological Insights | [
"cs.LG",
"cs.AI"
] | In epidemiology, traditional statistical methods such as logistic regression, linear regression, and other parametric models are commonly employed to investigate associations between predictors and health outcomes. However, non-parametric machine learning techniques, such as deep neural networks (DNNs), coupled with ex... | {
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2501.10282 | Computational Protein Science in the Era of Large Language Models (LLMs) | [
"cs.CE",
"cs.CL",
"q-bio.BM"
] | Considering the significance of proteins, computational protein science has always been a critical scientific field, dedicated to revealing knowledge and developing applications within the protein sequence-structure-function paradigm. In the last few decades, Artificial Intelligence (AI) has made significant impacts in... | {
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2501.10283 | GSTAR: Gaussian Surface Tracking and Reconstruction | [
"cs.CV"
] | 3D Gaussian Splatting techniques have enabled efficient photo-realistic rendering of static scenes. Recent works have extended these approaches to support surface reconstruction and tracking. However, tracking dynamic surfaces with 3D Gaussians remains challenging due to complex topology changes, such as surfaces appea... | {
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2501.10290 | Pairwise Elimination with Instance-Dependent Guarantees for Bandits with
Cost Subsidy | [
"cs.LG"
] | Multi-armed bandits (MAB) are commonly used in sequential online decision-making when the reward of each decision is an unknown random variable. In practice however, the typical goal of maximizing total reward may be less important than minimizing the total cost of the decisions taken, subject to a reward constraint. F... | {
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2501.10300 | An Ontology for Social Determinants of Education (SDoEd) based on
Human-AI Collaborative Approach | [
"cs.AI"
] | The use of computational ontologies is well-established in the field of Medical Informatics. The topic of Social Determinants of Health (SDoH) has also received extensive attention. Work at the intersection of ontologies and SDoH has been published. However, a standardized framework for Social Determinants of Education... | {
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2501.10309 | Entropic versions of Bergstr\"om's and Bonnesen's inequalities | [
"cs.IT",
"math.FA",
"math.IT"
] | We establish analogues of the Bergstr\"om and Bonnesen inequalities, related to determinants and volumes respectively, for the entropy power and for the Fisher information. The obtained inequalities strengthen the well-known convolution inequality for the Fisher information as well as the entropy power inequality in di... | {
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2501.10316 | Know Your Mistakes: Towards Preventing Overreliance on Task-Oriented
Conversational AI Through Accountability Modeling | [
"cs.CL"
] | Recent LLMs have enabled significant advancements for conversational agents. However, they are also well known to hallucinate, producing responses that seem plausible but are factually incorrect. On the other hand, users tend to over-rely on LLM-based AI agents, accepting AI's suggestion even when it is wrong. Adding p... | {
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2501.10318 | HiMix: Reducing Computational Complexity in Large Vision-Language Models | [
"cs.CV"
] | Benefiting from recent advancements in large language models and modality alignment techniques, existing Large Vision-Language Models(LVLMs) have achieved prominent performance across a wide range of scenarios. However, the excessive computational complexity limits the widespread use of these models in practical applic... | {
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2501.10319 | Natural Language Processing of Privacy Policies: A Survey | [
"cs.CL"
] | Natural Language Processing (NLP) is an essential subset of artificial intelligence. It has become effective in several domains, such as healthcare, finance, and media, to identify perceptions, opinions, and misuse, among others. Privacy is no exception, and initiatives have been taken to address the challenges of usab... | {
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2501.10321 | Towards Human-Guided, Data-Centric LLM Co-Pilots | [
"cs.LG",
"stat.ML"
] | Machine learning (ML) has the potential to revolutionize various domains, but its adoption is often hindered by the disconnect between the needs of domain experts and translating these needs into robust and valid ML tools. Despite recent advances in LLM-based co-pilots to democratize ML for non-technical domain experts... | {
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2501.10322 | Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level
Processing for Robust, Adaptable Language Models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Tokenization is a fundamental step in natural language processing, breaking text into units that computational models can process. While learned subword tokenizers have become the de-facto standard, they present challenges such as large vocabularies, limited adaptability to new domains or languages, and sensitivity to ... | {
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2501.10324 | New Fashion Products Performance Forecasting: A Survey on Evolutions,
Models and Emerging Trends | [
"cs.LG",
"cs.CV"
] | The fast fashion industry's insatiable demand for new styles and rapid production cycles has led to a significant environmental burden. Overproduction, excessive waste, and harmful chemicals have contributed to the negative environmental impact of the industry. To mitigate these issues, a paradigm shift that prioritize... | {
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2501.10325 | DiffStereo: High-Frequency Aware Diffusion Model for Stereo Image
Restoration | [
"cs.CV"
] | Diffusion models (DMs) have achieved promising performance in image restoration but haven't been explored for stereo images. The application of DM in stereo image restoration is confronted with a series of challenges. The need to reconstruct two images exacerbates DM's computational cost. Additionally, existing latent ... | {
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2501.10326 | Large language models for automated scholarly paper review: A survey | [
"cs.AI",
"cs.CL",
"cs.DL"
] | Large language models (LLMs) have significantly impacted human society, influencing various domains. Among them, academia is not simply a domain affected by LLMs, but it is also the pivotal force in the development of LLMs. In academic publications, this phenomenon is represented during the incorporation of LLMs into t... | {
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2501.10328 | BoK: Introducing Bag-of-Keywords Loss for Interpretable Dialogue
Response Generation | [
"cs.CL"
] | The standard language modeling (LM) loss by itself has been shown to be inadequate for effective dialogue modeling. As a result, various training approaches, such as auxiliary loss functions and leveraging human feedback, are being adopted to enrich open-domain dialogue systems. One such auxiliary loss function is Bag-... | {
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2501.10332 | Agent4Edu: Generating Learner Response Data by Generative Agents for
Intelligent Education Systems | [
"cs.CY",
"cs.AI"
] | Personalized learning represents a promising educational strategy within intelligent educational systems, aiming to enhance learners' practice efficiency. However, the discrepancy between offline metrics and online performance significantly impedes their progress. To address this challenge, we introduce Agent4Edu, a no... | {
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2501.10337 | Uncertainty-Aware Digital Twins: Robust Model Predictive Control using
Time-Series Deep Quantile Learning | [
"eess.SY",
"cs.SY"
] | Digital Twins, virtual replicas of physical systems that enable real-time monitoring, model updates, predictions, and decision-making, present novel avenues for proactive control strategies for autonomous systems. However, achieving real-time decision-making in Digital Twins considering uncertainty necessitates an effi... | {
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2501.10342 | Hybrid Deep Learning Model for epileptic seizure classification by using
1D-CNN with multi-head attention mechanism | [
"cs.LG"
] | Epilepsy is a prevalent neurological disorder globally, impacting around 50 million people \cite{WHO_epilepsy_50million}. Epileptic seizures result from sudden abnormal electrical activity in the brain, which can be read as sudden and significant changes in the EEG signal of the brain. The signal can vary in severity a... | {
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2501.10343 | 3rd Workshop on Maritime Computer Vision (MaCVi) 2025: Challenge Results | [
"cs.CV",
"cs.AI"
] | The 3rd Workshop on Maritime Computer Vision (MaCVi) 2025 addresses maritime computer vision for Unmanned Surface Vehicles (USV) and underwater. This report offers a comprehensive overview of the findings from the challenges. We provide both statistical and qualitative analyses, evaluating trends from over 700 submissi... | {
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2501.10344 | FC-Datalog as a Framework for Efficient String Querying | [
"cs.LO",
"cs.DB",
"cs.FL"
] | Core spanners are a class of document spanners that capture the core functionality of IBM's AQL. FC is a logic on strings built around word equations that when extended with constraints for regular languages can be seen as a logic for core spanners. The recently introduced FC-Datalog extends FC with recursion, which al... | {
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2501.10347 | ColNet: Collaborative Optimization in Decentralized Federated Multi-task
Learning Systems | [
"cs.LG"
] | The integration of Federated Learning (FL) and Multi-Task Learning (MTL) has been explored to address client heterogeneity, with Federated Multi-Task Learning (FMTL) treating each client as a distinct task. However, most existing research focuses on data heterogeneity (e.g., addressing non-IID data) rather than task he... | {
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2501.10348 | Credit Risk Identification in Supply Chains Using Generative Adversarial
Networks | [
"cs.LG"
] | Credit risk management within supply chains has emerged as a critical research area due to its significant implications for operational stability and financial sustainability. The intricate interdependencies among supply chain participants mean that credit risks can propagate across networks, with impacts varying by in... | {
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2501.10356 | DexForce: Extracting Force-informed Actions from Kinesthetic
Demonstrations for Dexterous Manipulation | [
"cs.RO"
] | Imitation learning requires high-quality demonstrations consisting of sequences of state-action pairs. For contact-rich dexterous manipulation tasks that require fine-grained dexterity, the actions in these state-action pairs must produce the right forces. Current widely-used methods for collecting dexterous manipulati... | {
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2501.10357 | Zero-Shot Monocular Scene Flow Estimation in the Wild | [
"cs.CV"
] | Large models have shown generalization across datasets for many low-level vision tasks, like depth estimation, but no such general models exist for scene flow. Even though scene flow has wide potential use, it is not used in practice because current predictive models do not generalize well. We identify three key challe... | {
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2501.10360 | FaceXBench: Evaluating Multimodal LLMs on Face Understanding | [
"cs.CV"
] | Multimodal Large Language Models (MLLMs) demonstrate impressive problem-solving abilities across a wide range of tasks and domains. However, their capacity for face understanding has not been systematically studied. To address this gap, we introduce FaceXBench, a comprehensive benchmark designed to evaluate MLLMs on co... | {
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2501.10361 | How Large Language Models (LLMs) Extrapolate: From Guided Missiles to
Guided Prompts | [
"cs.CY",
"cs.CL"
] | This paper argues that we should perceive LLMs as machines of extrapolation. Extrapolation is a statistical function for predicting the next value in a series. Extrapolation contributes to both GPT successes and controversies surrounding its hallucination. The term hallucination implies a malfunction, yet this paper co... | {
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2501.10362 | Reviewing Uses of Regulatory Compliance Monitoring | [
"cs.CY",
"cs.DB"
] | In order to deliver their services and products to customers, organizations need to manage numerous business processes. One important consideration thereby lies in the adherence to regulations such as laws, guidelines, or industry standards. In order to monitor adherence of their business processes to regulations - in ... | {
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2501.10365 | Can LLMs Identify Gaps and Misconceptions in Students' Code
Explanations? | [
"cs.CY",
"cs.AI",
"cs.SE"
] | This paper investigates various approaches using Large Language Models (LLMs) to identify gaps and misconceptions in students' self-explanations of specific instructional material, in our case explanations of code examples. This research is a part of our larger effort to automate the assessment of students' freely gene... | {
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2501.10366 | Participatory Assessment of Large Language Model Applications in an
Academic Medical Center | [
"cs.CY",
"cs.AI",
"cs.LG"
] | Although Large Language Models (LLMs) have shown promising performance in healthcare-related applications, their deployment in the medical domain poses unique challenges of ethical, regulatory, and technical nature. In this study, we employ a systematic participatory approach to investigate the needs and expectations r... | {
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2501.10367 | GTDE: Grouped Training with Decentralized Execution for Multi-agent
Actor-Critic | [
"cs.MA",
"cs.AI"
] | The rapid advancement of multi-agent reinforcement learning (MARL) has given rise to diverse training paradigms to learn the policies of each agent in the multi-agent system. The paradigms of decentralized training and execution (DTDE) and centralized training with decentralized execution (CTDE) have been proposed and ... | {
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2501.10368 | The Potential of Answer Classes in Large-scale Written Computer-Science
Exams -- Vol. 2 | [
"cs.CY",
"cs.AI"
] | Students' answers to tasks provide a valuable source of information in teaching as they result from applying cognitive processes to a learning content addressed in the task. Due to steadily increasing course sizes, analyzing student answers is frequently the only means of obtaining evidence about student performance. H... | {
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2501.10369 | Creative Loss: Ambiguity, Uncertainty and Indeterminacy | [
"cs.CY",
"cs.AI",
"cs.HC",
"cs.LG"
] | This article evaluates how creative uses of machine learning can address three adjacent terms: ambiguity, uncertainty and indeterminacy. Through the progression of these concepts it reflects on increasing ambitions for machine learning as a creative partner, illustrated with research from Unit 21 at the Bartlett School... | {
"Other": 0,
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} |
2501.10370 | Harnessing Large Language Models for Mental Health: Opportunities,
Challenges, and Ethical Considerations | [
"cs.CY",
"cs.AI",
"cs.LG"
] | Large Language Models (LLMs) are transforming mental health care by enhancing accessibility, personalization, and efficiency in therapeutic interventions. These AI-driven tools empower mental health professionals with real-time support, improved data integration, and the ability to encourage care-seeking behaviors, par... | {
"Other": 0,
"cs.AI": 1,
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"cs.NE": 0,
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"cs.SD": 0,
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"cs.SY": 0
} |
2501.10371 | What we learned while automating bias detection in AI hiring systems for
compliance with NYC Local Law 144 | [
"cs.CY",
"cs.AI"
] | Since July 5, 2023, New York City's Local Law 144 requires employers to conduct independent bias audits for any automated employment decision tools (AEDTs) used in hiring processes. The law outlines a minimum set of bias tests that AI developers and implementers must perform to ensure compliance. Over the past few mont... | {
"Other": 0,
"cs.AI": 1,
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"cs.SY": 0
} |
2501.10373 | DK-PRACTICE: An Intelligent Educational Platform for Personalized
Learning Content Recommendations Based on Students Knowledge State | [
"cs.CY",
"cs.AI"
] | This study introduces DK-PRACTICE (Dynamic Knowledge Prediction and Educational Content Recommendation System), an intelligent online platform that leverages machine learning to provide personalized learning recommendations based on student knowledge state. Students participate in a short, adaptive assessment using the... | {
"Other": 0,
"cs.AI": 1,
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} |
2501.10375 | DAOP: Data-Aware Offloading and Predictive Pre-Calculation for Efficient
MoE Inference | [
"cs.DC",
"cs.LG"
] | Mixture-of-Experts (MoE) models, though highly effective for various machine learning tasks, face significant deployment challenges on memory-constrained devices. While GPUs offer fast inference, their limited memory compared to CPUs means not all experts can be stored on the GPU simultaneously, necessitating frequent,... | {
"Other": 1,
"cs.AI": 0,
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"cs.CL": 0,
"cs.CR": 0,
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"cs.SD": 0,
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"cs.SY": 0
} |
2501.10376 | Energy-Constrained Information Storage on Memristive Devices in the
Presence of Resistive Drift | [
"cs.ET",
"cs.IT",
"cs.LG",
"eess.SP",
"math.IT"
] | In this paper, we examine the problem of information storage on memristors affected by resistive drift noise under energy constraints. We introduce a novel, fundamental trade-off between the information lifetime of memristive states and the energy that must be expended to bring the device into a particular state. We th... | {
"Other": 1,
"cs.AI": 0,
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"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
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"cs.SY": 0
} |
2501.10377 | The Three Social Dimensions of Chatbot Technology | [
"cs.CY",
"cs.AI",
"cs.CL"
] | The development and deployment of chatbot technology, while spanning decades and employing different techniques, require innovative frameworks to understand and interrogate their functionality and implications. A mere technocentric account of the evolution of chatbot technology does not fully illuminate how conversatio... | {
"Other": 0,
"cs.AI": 1,
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"cs.NE": 0,
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"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.10384 | Nirvana AI Governance: How AI Policymaking Is Committing Three Old
Fallacies | [
"cs.CY",
"cs.HC",
"cs.LG"
] | This research applies Harold Demsetz's concept of the nirvana approach to the realm of AI governance and debunks three common fallacies in various AI policy proposals--"the grass is always greener on the other side," "free lunch," and "the people could be different." Through this, I expose fundamental flaws in the curr... | {
"Other": 0,
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"cs.SD": 0,
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"cs.SY": 0
} |
2501.10385 | Autonomous Microscopy Experiments through Large Language Model Agents | [
"cs.CY",
"cond-mat.mtrl-sci",
"cs.AI",
"physics.ins-det"
] | The emergence of large language models (LLMs) has accelerated the development of self-driving laboratories (SDLs) for materials research. Despite their transformative potential, current SDL implementations rely on rigid, predefined protocols that limit their adaptability to dynamic experimental scenarios across differe... | {
"Other": 0,
"cs.AI": 1,
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"cs.NE": 0,
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"cs.SD": 0,
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"cs.SY": 0
} |
2501.10388 | Beyond the Sum: Unlocking AI Agents Potential Through Market Forces | [
"cs.CY",
"cs.AI",
"cs.CL",
"cs.GT",
"cs.MA"
] | The emergence of Large Language Models has fundamentally transformed the capabilities of AI agents, enabling a new class of autonomous agents capable of interacting with their environment through dynamic code generation and execution. These agents possess the theoretical capacity to operate as independent economic acto... | {
"Other": 1,
"cs.AI": 1,
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"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
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