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2502.08649 | Principles for Open Data Curation: A Case Study with the New York City
311 Service Request Data | cs.DB cs.CY stat.ME | In the early 21st century, the open data movement began to transform
societies and governments by promoting transparency, innovation, and public
engagement. The City of New York (NYC) has been at the forefront of this
movement since the enactment of the Open Data Law in 2012, creating the NYC
Open Data portal. The po... |
2502.08651 | Democratizing AI Governance: Balancing Expertise and Public
Participation | cs.CY cs.LG | The development and deployment of artificial intelligence (AI) systems, with
their profound societal impacts, raise critical challenges for governance.
Historically, technological innovations have been governed by concentrated
expertise with limited public input. However, AI's pervasive influence across
domains such ... |
2502.08652 | LegalScore: Development of a Benchmark for Evaluating AI Models in Legal
Career Exams in Brazil | cs.CY cs.AI | This research introduces LegalScore, a specialized index for assessing how
generative artificial intelligence models perform in a selected range of career
exams that require a legal background in Brazil. The index evaluates fourteen
different types of artificial intelligence models' performance, from
proprietary to o... |
2502.08655 | Personalizing Education through an Adaptive LMS with Integrated LLMs | cs.AI | The widespread adoption of large language models (LLMs) marks a
transformative era in technology, especially within the educational sector.
This paper explores the integration of LLMs within learning management systems
(LMSs) to develop an adaptive learning management system (ALMS) personalized
for individual learner... |
2502.08657 | Refining Positive and Toxic Samples for Dual Safety Self-Alignment of
LLMs with Minimal Human Interventions | cs.CL cs.AI | Recent AI agents, such as ChatGPT and LLaMA, primarily rely on instruction
tuning and reinforcement learning to calibrate the output of large language
models (LLMs) with human intentions, ensuring the outputs are harmless and
helpful. Existing methods heavily depend on the manual annotation of
high-quality positive s... |
2502.08658 | Analyzable Parameters Dominated Vehicle Platoon Dynamics Modeling and
Analysis: A Physics-Encoded Deep Learning Approach | cs.RO cs.AI | Recently, artificial intelligence (AI)-enabled nonlinear vehicle platoon
dynamics modeling plays a crucial role in predicting and optimizing the
interactions between vehicles. Existing efforts lack the extraction and capture
of vehicle behavior interaction features at the platoon scale. More
importantly, maintaining ... |
2502.08659 | Deployment-friendly Lane-changing Intention Prediction Powered by
Brain-inspired Spiking Neural Networks | cs.RO | Accurate and real-time prediction of surrounding vehicles' lane-changing
intentions is a critical challenge in deploying safe and efficient autonomous
driving systems in open-world scenarios. Existing high-performing methods
remain hard to deploy due to their high computational cost, long training
times, and excessiv... |
2502.08660 | Semantic Role Labeling: A Systematical Survey | cs.CL | Semantic role labeling (SRL) is a central natural language processing (NLP)
task aiming to understand the semantic roles within texts, facilitating a wide
range of downstream applications. While SRL has garnered extensive and enduring
research, there is currently a lack of a comprehensive survey that thoroughly
organ... |
2502.08661 | Few-shot LLM Synthetic Data with Distribution Matching | cs.CL cs.AI | As large language models (LLMs) advance, their ability to perform in-context
learning and few-shot language generation has improved significantly. This has
spurred using LLMs to produce high-quality synthetic data to enhance the
performance of smaller models like online retrievers or weak LLMs. However,
LLM-generated... |
2502.08662 | RoToR: Towards More Reliable Responses for Order-Invariant Inputs | cs.CL cs.AI | Mitigating positional bias of language models (LMs) for listwise inputs is a
well-known and important problem (e.g., lost-in-the-middle). While zero-shot
order-invariant LMs have been proposed to solve this issue, their success on
practical listwise problems has been limited. In this work, as a first
contribution, we... |
2502.08663 | Hallucination Detection: A Probabilistic Framework Using Embeddings
Distance Analysis | cs.CL cs.AI cs.CY | Hallucinations are one of the major issues affecting LLMs, hindering their
wide adoption in production systems. While current research solutions for
detecting hallucinations are mainly based on heuristics, in this paper we
introduce a mathematically sound methodology to reason about hallucination, and
leverage it to ... |
2502.08664 | Motion Forecasting for Autonomous Vehicles: A Survey | cs.RO cs.AI | In recent years, the field of autonomous driving has attracted increasingly
significant public interest. Accurately forecasting the future behavior of
various traffic participants is essential for the decision-making of Autonomous
Vehicles (AVs). In this paper, we focus on both scenario-based and
perception-based mot... |
2502.08666 | Hallucination, Monofacts, and Miscalibration: An Empirical Investigation | cs.CL cs.AI | Recent theoretical work by [Kalai and Vempala 2024] proves that a particular
notion of hallucination rate in LLMs must be lower bounded by the training data
monofact rate (related to the classical Good-Turing missing mass estimator)
minus model miscalibration. Through systematic experiments with n-gram models
and in-... |
2502.08667 | Unpaired Image-to-Image Translation with Content Preserving Perspective:
A Review | eess.IV cs.CV | Image-to-image translation (I2I) transforms an image from a source domain to
a target domain while preserving source content. Most computer vision
applications are in the field of image-to-image translation, such as style
transfer, image segmentation, and photo enhancement. The degree of preservation
of the content o... |
2502.08668 | Style Extraction on Text Embeddings Using VAE and Parallel Dataset | cs.CL | This study investigates the stylistic differences among various Bible
translations using a Variational Autoencoder (VAE) model. By embedding textual
data into high-dimensional vectors, the study aims to detect and analyze
stylistic variations between translations, with a specific focus on
distinguishing the American ... |
2502.08669 | Assessing the Impact of the Quality of Textual Data on Feature
Representation and Machine Learning Models | cs.CL | Background: Data collected in controlled settings typically results in
high-quality datasets. However, in real-world applications, the quality of data
collection is often compromised. It is well established that the quality of a
dataset significantly impacts the performance of machine learning models.
Methods: A ru... |
2502.08671 | Color Universal Design Neural Network for the Color Vision Deficiencies | eess.IV cs.CV | Information regarding images should be visually understood by anyone,
including those with color deficiency. However, such information is not
recognizable if the color that seems to be distorted to the color deficiencies
meets an adjacent object. The aim of this paper is to propose a color universal
design network, c... |
2502.08673 | High-Throughput SAT Sampling | cs.AI cs.LG | In this work, we present a novel technique for GPU-accelerated Boolean
satisfiability (SAT) sampling. Unlike conventional sampling algorithms that
directly operate on conjunctive normal form (CNF), our method transforms the
logical constraints of SAT problems by factoring their CNF representations into
simplified mul... |
2502.08674 | COutfitGAN: Learning to Synthesize Compatible Outfits Supervised by
Silhouette Masks and Fashion Styles | cs.CV cs.GR cs.MM | How to recommend outfits has gained considerable attention in both academia
and industry in recent years. Many studies have been carried out regarding
fashion compatibility learning, to determine whether the fashion items in an
outfit are compatible or not. These methods mainly focus on evaluating the
compatibility o... |
2502.08676 | LIR-LIVO: A Lightweight,Robust LiDAR/Vision/Inertial Odometry with
Illumination-Resilient Deep Features | cs.RO cs.CV cs.SY eess.SP eess.SY | In this paper, we propose LIR-LIVO, a lightweight and robust
LiDAR-inertial-visual odometry system designed for challenging illumination and
degraded environments. The proposed method leverages deep learning-based
illumination-resilient features and LiDAR-Inertial-Visual Odometry (LIVO). By
incorporating advanced tec... |
2502.08678 | Multispectral Remote Sensing for Weed Detection in West Australian
Agricultural Lands | cs.CV eess.IV | The Kondinin region in Western Australia faces significant agricultural
challenges due to pervasive weed infestations, causing economic losses and
ecological impacts. This study constructs a tailored multispectral remote
sensing dataset and an end-to-end framework for weed detection to advance
precision agriculture p... |
2502.08679 | Deep Learning-Driven Malware Classification with API Call Sequence
Analysis and Concept Drift Handling | cs.LG cs.AI cs.CR | Malware classification in dynamic environments presents a significant
challenge due to concept drift, where the statistical properties of malware
data evolve over time, complicating detection efforts. To address this issue,
we propose a deep learning framework enhanced with a genetic algorithm to
improve malware clas... |
2502.08680 | Mathematical Reasoning in Large Language Models: Assessing Logical and
Arithmetic Errors across Wide Numerical Ranges | cs.LG cs.AI cs.CL | Mathematical reasoning in Large Language Models (LLMs) is often evaluated
using benchmarks with limited numerical ranges, failing to reflect real-world
problem-solving across diverse scales. Furthermore, most existing evaluation
methods only compare model outputs to ground-truth answers, obscuring insights
into reaso... |
2502.08681 | Centrally Coordinated Multi-Agent Reinforcement Learning for Power Grid
Topology Control | cs.MA cs.AI cs.LG | Power grid operation is becoming more complex due to the increase in
generation of renewable energy. The recent series of Learning To Run a Power
Network (L2RPN) competitions have encouraged the use of artificial agents to
assist human dispatchers in operating power grids. However, the combinatorial
nature of the act... |
2502.08682 | On the Role of Pre-trained Embeddings in Binary Code Analysis | cs.LG cs.AI | Deep learning has enabled remarkable progress in binary code analysis. In
particular, pre-trained embeddings of assembly code have become a gold standard
for solving analysis tasks, such as measuring code similarity or recognizing
functions. These embeddings are capable of learning a vector representation
from unlabe... |
2502.08683 | A Deep Learning approach for parametrized and time dependent Partial
Differential Equations using Dimensionality Reduction and Neural ODEs | cs.LG | Partial Differential Equations (PDEs) are central to science and engineering.
Since solving them is computationally expensive, a lot of effort has been put
into approximating their solution operator via both traditional and recently
increasingly Deep Learning (DL) techniques. A conclusive methodology capable of
accou... |
2502.08684 | Self-Evaluation for Job-Shop Scheduling | cs.LG cs.AI | Combinatorial optimization problems, such as scheduling and route planning,
are crucial in various industries but are computationally intractable due to
their NP-hard nature. Neural Combinatorial Optimization methods leverage
machine learning to address these challenges but often depend on sequential
decision-making,... |
2502.08685 | Beyond Models! Explainable Data Valuation and Metric Adaption for
Recommendation | cs.LG cs.AI | User behavior records serve as the foundation for recommender systems. While
the behavior data exhibits ease of acquisition, it often suffers from varying
quality. Current methods employ data valuation to discern high-quality data
from low-quality data. However, they tend to employ black-box design, lacking
transpare... |
2502.08686 | EEG Artifact Detection and Correction with Deep Autoencoders | cs.LG cs.AI | EEG signals convey important information about brain activity both in healthy
and pathological conditions. However, they are inherently noisy, which poses
significant challenges for accurate analysis and interpretation. Traditional
EEG artifact removal methods, while effective, often require extensive expert
interven... |
2502.08687 | Data Augmentation to Improve Large Language Models in Food Hazard and
Product Detection | cs.CL | The primary objective of this study is to demonstrate the impact of data
augmentation using ChatGPT-4o-mini on food hazard and product analysis. The
augmented data is generated using ChatGPT-4o-mini and subsequently used to
train two large language models: RoBERTa-base and Flan-T5-base. The models are
evaluated on te... |
2502.08688 | FAST: A Future Aircraft Sizing Tool for Advanced Aircraft and Propulsion
System Design | cs.CE physics.comp-ph | Without radical technological advancements, the global aviation industry will
continue to be a major carbon emitter. To reduce aviation's carbon emissions,
innovative aircraft technology, including electrified aircraft propulsion, is
under development. However, current aircraft sizing tools require detailed
design in... |
2502.08689 | Advancing machine fault diagnosis: A detailed examination of
convolutional neural networks | cs.LG cs.AI | The growing complexity of machinery and the increasing demand for operational
efficiency and safety have driven the development of advanced fault diagnosis
techniques. Among these, convolutional neural networks (CNNs) have emerged as a
powerful tool, offering robust and accurate fault detection and classification
cap... |
2502.08690 | Skrr: Skip and Re-use Text Encoder Layers for Memory Efficient
Text-to-Image Generation | cs.LG cs.AI cs.CV | Large-scale text encoders in text-to-image (T2I) diffusion models have
demonstrated exceptional performance in generating high-quality images from
textual prompts. Unlike denoising modules that rely on multiple iterative
steps, text encoders require only a single forward pass to produce text
embeddings. However, desp... |
2502.08691 | AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents
Advances Understanding of Human Behaviors and Society | cs.SI cs.AI | Understanding human behavior and society is a central focus in social
sciences, with the rise of generative social science marking a significant
paradigmatic shift. By leveraging bottom-up simulations, it replaces costly and
logistically challenging traditional experiments with scalable, replicable, and
systematic co... |
2502.08692 | Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices | cs.LG cs.DC | Split Learning (SL) recently emerged as an efficient paradigm for distributed
Machine Learning (ML) suitable for the Internet Of Things (IoT)-Cloud systems.
However, deploying SL on resource-constrained edge IoT platforms poses a
significant challenge in terms of balancing the model performance against the
processing... |
2502.08695 | A Bayesian Nonparametric Perspective on Mahalanobis Distance for Out of
Distribution Detection | stat.ML cs.LG | Bayesian nonparametric methods are naturally suited to the problem of
out-of-distribution (OOD) detection. However, these techniques have largely
been eschewed in favor of simpler methods based on distances between
pre-trained or learned embeddings of data points. Here we show a formal
relationship between Bayesian n... |
2502.08696 | Scalable Discrete Diffusion Samplers: Combinatorial Optimization and
Statistical Physics | cs.LG cond-mat.stat-mech cs.AI physics.comp-ph stat.ML | Learning to sample from complex unnormalized distributions over discrete
domains emerged as a promising research direction with applications in
statistical physics, variational inference, and combinatorial optimization.
Recent work has demonstrated the potential of diffusion models in this domain.
However, existing m... |
2502.08697 | Bilevel Learning for Bilevel Planning | cs.RO | A robot that learns from demonstrations should not just imitate what it sees
-- it should understand the high-level concepts that are being demonstrated and
generalize them to new tasks. Bilevel planning is a hierarchical model-based
approach where predicates (relational state abstractions) can be leveraged to
achiev... |
2502.08728 | A Comparative Study of Machine Learning Algorithms for Stock Price
Prediction Using Insider Trading Data | cs.LG | The research paper empirically investigates several machine learning
algorithms to forecast stock prices depending on insider trading information.
Insider trading offers special insights into market sentiment, pointing to
upcoming changes in stock prices. This study examines the effectiveness of
algorithms like decis... |
2502.08729 | Policy Selection and Schedules for Exclusive Bus Lane and High Occupancy
Vehicle Lane in a Bi-modal Transportation Corridor | math.OC cs.SY eess.SY | Efficient management of transportation corridors is critical for sustaining
urban mobility, directly influencing transportation efficiency. Two prominent
strategies for enhancing public transit services and alleviating congestion,
Exclusive Bus Lane (EBL) and High Occupancy Vehicle Lane (HOVL), are gaining
increasing... |
2502.08730 | New Bounds for Sparse Variational Gaussian Processes | cs.LG stat.ME stat.ML | Sparse variational Gaussian processes (GPs) construct tractable posterior
approximations to GP models. At the core of these methods is the assumption
that the true posterior distribution over training function values ${\bf f}$
and inducing variables ${\bf u}$ is approximated by a variational distribution
that incorpo... |
2502.08731 | Equity-aware Design and Timing of Fare-free Transit Zoning under Demand
Uncertainty | math.OC cs.SY eess.SY | We propose the first analytical stochastic model for optimizing the
configuration and implementation policies of fare-free transit. The model
focuses on a transportation corridor with two transportation modes: automobiles
and buses. The corridor is divided into two sections, an inner one with
fare-free transit servic... |
2502.08736 | Recurrent Memory for Online Interdomain Gaussian Processes | cs.LG stat.ML | We propose a novel online Gaussian process (GP) model that is capable of
capturing long-term memory in sequential data in an online regression setting.
Our model, Online HiPPO Sparse Variational Gaussian Process Regression
(OHSGPR), leverages the HiPPO (High-order Polynomial Projection Operators)
framework, which is ... |
2502.08742 | "Active Neighbour": A Novel Monitoring Model for Cyber-Physical Systems | eess.SY cs.SY | Over the past decade, advancements in technology have enabled Cyber-Physical
Systems (CPS) to monitor sensor networks through various methodologies.
However, these developments have concurrently introduced significant security
challenges, necessitating robust protective measures. As a result, securing CPS
has become ... |
2502.08744 | Are Expressions for Music Emotions the Same Across Cultures? | cs.CL cs.HC cs.SD eess.AS | Music evokes profound emotions, yet the universality of emotional descriptors
across languages remains debated. A key challenge in cross-cultural research on
music emotion is biased stimulus selection and manual curation of taxonomies,
predominantly relying on Western music and languages. To address this, we
propose ... |
2502.08745 | IHEval: Evaluating Language Models on Following the Instruction
Hierarchy | cs.CL | The instruction hierarchy, which establishes a priority order from system
messages to user messages, conversation history, and tool outputs, is essential
for ensuring consistent and safe behavior in language models (LMs). Despite its
importance, this topic receives limited attention, and there is a lack of
comprehens... |
2502.08754 | HistoSmith: Single-Stage Histology Image-Label Generation via
Conditional Latent Diffusion for Enhanced Cell Segmentation and
Classification | cs.CV cs.AI | Precise segmentation and classification of cell instances are vital for
analyzing the tissue microenvironment in histology images, supporting medical
diagnosis, prognosis, treatment planning, and studies of brain
cytoarchitecture. However, the creation of high-quality annotated datasets for
training remains a major c... |
2502.08756 | From PowerPoint UI Sketches to Web-Based Applications: Pattern-Driven
Code Generation for GIS Dashboard Development Using Knowledge-Augmented LLMs,
Context-Aware Visual Prompting, and the React Framework | cs.AI cs.SE | Developing web-based GIS applications, commonly known as CyberGIS dashboards,
for querying and visualizing GIS data in environmental research often demands
repetitive and resource-intensive efforts. While Generative AI offers
automation potential for code generation, it struggles with complex scientific
applications ... |
2502.08757 | A Low-Complexity Plug-and-Play Deep Learning Model for Massive MIMO
Precoding Across Sites | eess.SP cs.LG | Massive multiple-input multiple-output (mMIMO) technology has transformed
wireless communication by enhancing spectral efficiency and network capacity.
This paper proposes a novel deep learning-based mMIMO precoder to tackle the
complexity challenges of existing approaches, such as weighted minimum mean
square error ... |
2502.08758 | Compression of Site-Specific Deep Neural Networks for Massive MIMO
Precoding | eess.SP cs.LG | The deployment of deep learning (DL) models for precoding in massive
multiple-input multiple-output (mMIMO) systems is often constrained by high
computational demands and energy consumption. In this paper, we investigate the
compute energy efficiency of mMIMO precoders using DL-based approaches,
comparing them to con... |
2502.08759 | Contextual bandits with entropy-based human feedback | cs.AI | In recent years, preference-based human feedback mechanisms have become
essential for enhancing model performance across diverse applications,
including conversational AI systems such as ChatGPT. However, existing
approaches often neglect critical aspects, such as model uncertainty and the
variability in feedback qua... |
2502.08764 | Demand Response Optimization MILP Framework for Microgrids with DERs | eess.SY cs.LG cs.SY | The integration of renewable energy sources in microgrids introduces
significant operational challenges due to their intermittent nature and the
mismatch between generation and demand patterns. Effective demand response (DR)
strategies are crucial for maintaining system stability and economic
efficiency, particularly... |
2502.08766 | Unlocking Mental Health: Exploring College Students' Well-being through
Smartphone Behaviors | cs.CY cs.HC cs.LG cs.SE | The global mental health crisis is a pressing concern, with college students
particularly vulnerable to rising mental health disorders. The widespread use
of smartphones among young adults, while offering numerous benefits, has also
been linked to negative outcomes such as addiction and regret, significantly
impactin... |
2502.08767 | SelfElicit: Your Language Model Secretly Knows Where is the Relevant
Evidence | cs.CL cs.AI | Providing Language Models (LMs) with relevant evidence in the context (either
via retrieval or user-provided) can significantly improve their ability to
provide factually correct grounded responses. However, recent studies have
found that LMs often struggle to fully comprehend and utilize key evidence from
the contex... |
2502.08769 | Cluster and Predict Latent Patches for Improved Masked Image Modeling | cs.CV cs.AI | Masked Image Modeling (MIM) offers a promising approach to self-supervised
representation learning, however existing MIM models still lag behind the
state-of-the-art. In this paper, we systematically analyze target
representations, loss functions, and architectures, to introduce CAPI - a novel
pure-MIM framework that... |
2502.08773 | Universal Model Routing for Efficient LLM Inference | cs.CL cs.LG | Large language models' significant advances in capabilities are accompanied
by significant increases in inference costs. Model routing is a simple
technique for reducing inference cost, wherein one maintains a pool of
candidate LLMs, and learns to route each prompt to the smallest feasible LLM.
Existing works focus o... |
2502.08774 | Exploring Test Time Adaptation for Subcortical Segmentation of the Fetal
Brain in 3D Ultrasound | cs.CV cs.AI cs.LG | Monitoring the growth of subcortical regions of the fetal brain in ultrasound
(US) images can help identify the presence of abnormal development. Manually
segmenting these regions is a challenging task, but recent work has shown that
it can be automated using deep learning. However, applying pretrained models to
unse... |
2502.08776 | Treatment response as a latent variable | stat.ME cs.LG stat.ML | Scientists often need to analyze the samples in a study that responded to
treatment in order to refine their hypotheses and find potential causal drivers
of response. Natural variation in outcomes makes teasing apart responders from
non-responders a statistical inference problem. To handle latent responses, we
introd... |
2502.08777 | Zero-Shot Belief: A Hard Problem for LLMs | cs.CL | We present two LLM-based approaches to zero-shot source-and-target belief
prediction on FactBank: a unified system that identifies events, sources, and
belief labels in a single pass, and a hybrid approach that uses a fine-tuned
DeBERTa tagger for event detection. We show that multiple open-sourced,
closed-source, an... |
2502.08779 | SB-Bench: Stereotype Bias Benchmark for Large Multimodal Models | cs.CV | Stereotype biases in Large Multimodal Models (LMMs) perpetuate harmful
societal prejudices, undermining the fairness and equity of AI applications. As
LMMs grow increasingly influential, addressing and mitigating inherent biases
related to stereotypes, harmful generations, and ambiguous assumptions in
real-world scen... |
2502.08782 | A comparative study of different TSO-DSO coordination in the reserve
market | eess.SY cs.SY | The increasing penetration of Distributed Energy Resources (DERs) in the
distribution system has led to the emergence of a new market actor - the
aggregator. The aggregator serves as a facilitator, enabling flexibility asset
owners to get access to different markets. In which, EVs aggregators are
gaining more attenti... |
2502.08783 | Learning Discontinuous Galerkin Solutions to Elliptic Problems via Small
Linear Convolutional Neural Networks | cs.LG cs.NA math.NA | In recent years, there has been an increasing interest in using deep learning
and neural networks to tackle scientific problems, particularly in solving
partial differential equations (PDEs). However, many neural network-based
methods, such as physics-informed neural networks, depend on automatic
differentiation and ... |
2502.08784 | Acoustic Wave Manipulation Through Sparse Robotic Actuation | cs.RO cs.AI | Recent advancements in robotics, control, and machine learning have
facilitated progress in the challenging area of object manipulation. These
advancements include, among others, the use of deep neural networks to
represent dynamics that are partially observed by robot sensors, as well as
effective control using spar... |
2502.08785 | Decision Tree Based Wrappers for Hearing Loss | cs.LG cs.SD | Audiology entities are using Machine Learning (ML) models to guide their
screening towards people at risk. Feature Engineering (FE) focuses on
optimizing data for ML models, with evolutionary methods being effective in
feature selection and construction tasks. This work aims to benchmark an
evolutionary FE wrapper, u... |
2502.08786 | MRUCT: Mixed Reality Assistance for Acupuncture Guided by Ultrasonic
Computed Tomography | cs.HC cs.CV cs.GR | Chinese acupuncture practitioners primarily depend on muscle memory and
tactile feedback to insert needles and accurately target acupuncture points, as
the current workflow lacks imaging modalities and visual aids. Consequently,
new practitioners often learn through trial and error, requiring years of
experience to b... |
2502.08788 | If Multi-Agent Debate is the Answer, What is the Question? | cs.CL cs.LG | Multi-agent debate (MAD) has emerged as a promising approach to enhance the
factual accuracy and reasoning quality of large language models (LLMs) by
engaging multiple agents in iterative discussions during inference. Despite its
potential, we argue that current MAD research suffers from critical
shortcomings in eval... |
2502.08789 | Delay Analysis of 5G HARQ in the Presence of Decoding and Feedback
Latencies | cs.IT cs.SY eess.SY math.IT | The growing demand for stringent quality of service (QoS) guarantees in 5G
networks requires accurate characterisation of delay performance, often
measured using Delay Violation Probability (DVP) for a given target delay.
Widely used retransmission schemes like Automatic Repeat reQuest (ARQ) and
Hybrid ARQ (HARQ) imp... |
2502.08791 | ClipRover: Zero-shot Vision-Language Exploration and Target Discovery by
Mobile Robots | cs.RO | Vision-language navigation (VLN) has emerged as a promising paradigm,
enabling mobile robots to perform zero-shot inference and execute tasks without
specific pre-programming. However, current systems often separate map
exploration and path planning, with exploration relying on inefficient
algorithms due to limited (... |
2502.08792 | Auction Design using Value Prediction with Hallucinations | cs.GT cs.AI | We investigate a Bayesian mechanism design problem where a seller seeks to
maximize revenue by selling an indivisible good to one of n buyers,
incorporating potentially unreliable predictions (signals) of buyers' private
values derived from a machine learning model. We propose a framework where
these signals are some... |
2502.08794 | Spectral Journey: How Transformers Predict the Shortest Path | cs.LG | Decoder-only transformers lead to a step-change in capability of large
language models. However, opinions are mixed as to whether they are really
planning or reasoning. A path to making progress in this direction is to study
the model's behavior in a setting with carefully controlled data. Then
interpret the learned ... |
2502.08795 | Low-Resolution Neural Networks | cs.LG | The expanding scale of large neural network models introduces significant
challenges, driving efforts to reduce memory usage and enhance computational
efficiency. Such measures are crucial to ensure the practical implementation
and effective application of these sophisticated models across a wide array of
use cases. ... |
2502.08796 | A Systematic Review on the Evaluation of Large Language Models in Theory
of Mind Tasks | cs.CL cs.CY cs.HC | In recent years, evaluating the Theory of Mind (ToM) capabilities of large
language models (LLMs) has received significant attention within the research
community. As the field rapidly evolves, navigating the diverse approaches and
methodologies has become increasingly complex. This systematic review
synthesizes curr... |
2502.08803 | Deep EEG Super-Resolution: Upsampling EEG Spatial Resolution with
Generative Adversarial Networks | cs.LG | Electroencephalography (EEG) activity contains a wealth of information about
what is happening within the human brain. Recording more of this data has the
potential to unlock endless future applications. However, the cost of EEG
hardware is increasingly expensive based upon the number of EEG channels being
recorded s... |
2502.08806 | CLOVER: A Test Case Generation Benchmark with Coverage, Long-Context,
and Verification | cs.SE cs.AI cs.LG | Software testing is a critical aspect of software development, yet generating
test cases remains a routine task for engineers. This paper presents a
benchmark, CLOVER, to evaluate models' capabilities in generating and
completing test cases under specific conditions. Spanning from simple assertion
completions to writ... |
2502.08807 | InTAR: Inter-Task Auto-Reconfigurable Accelerator Design for High Data
Volume Variation in DNNs | cs.AR cs.LG | The rise of deep neural networks (DNNs) has driven a boom in AI services,
which results in an increased demand for computing power and memory. In modern
DNNs, the data sizes produced and consumed are highly varied across operations
(high data volume variation, HDV). Because existing design paradigms use fixed
executi... |
2502.08808 | A First-order Generative Bilevel Optimization Framework for Diffusion
Models | cs.LG math.OC stat.ML | Diffusion models, which iteratively denoise data samples to synthesize
high-quality outputs, have achieved empirical success across domains. However,
optimizing these models for downstream tasks often involves nested bilevel
structures, such as tuning hyperparameters for fine-tuning tasks or noise
schedules in traini... |
2502.08813 | Measuring Anxiety Levels with Head Motion Patterns in Severe Depression
Population | cs.CV | Depression and anxiety are prevalent mental health disorders that frequently
cooccur, with anxiety significantly influencing both the manifestation and
treatment of depression. An accurate assessment of anxiety levels in
individuals with depression is crucial to develop effective and personalized
treatment plans. Thi... |
2502.08818 | Lexical Manifold Reconfiguration in Large Language Models: A Novel
Architectural Approach for Contextual Modulation | cs.CL | Contextual adaptation in token embeddings plays a central role in determining
how well language models maintain coherence and retain semantic relationships
over extended text sequences. Static embeddings often impose constraints on
lexical flexibility, leading to suboptimal performance when faced with complex
sentenc... |
2502.08820 | Can a Single Model Master Both Multi-turn Conversations and Tool Use?
CoALM: A Unified Conversational Agentic Language Model | cs.AI cs.CL | Large Language Models (LLMs) with API-calling capabilities enabled building
effective Language Agents (LA), while also revolutionizing the conventional
task-oriented dialogue (TOD) paradigm. However, current approaches face a
critical dilemma: TOD systems are often trained on a limited set of target
APIs, requiring n... |
2502.08821 | DejAIvu: Identifying and Explaining AI Art on the Web in Real-Time with
Saliency Maps | cs.CV cs.AI cs.LG | The recent surge in advanced generative models, such as diffusion models and
generative adversarial networks (GANs), has led to an alarming rise in
AI-generated images across various domains on the web. While such technologies
offer benefits such as democratizing artistic creation, they also pose
challenges in misinf... |
2502.08822 | $\mathsf{CSMAE~}$:~Cataract Surgical Masked Autoencoder (MAE) based
Pre-training | cs.CV | Automated analysis of surgical videos is crucial for improving surgical
training, workflow optimization, and postoperative assessment. We introduce a
CSMAE, Masked Autoencoder (MAE)-based pretraining approach, specifically
developed for Cataract Surgery video analysis, where instead of randomly
selecting tokens for m... |
2502.08825 | Examining and Adapting Time for Multilingual Classification via Mixture
of Temporal Experts | cs.CL | Time is implicitly embedded in classification process: classifiers are
usually built on existing data while to be applied on future data whose
distributions (e.g., label and token) may change. However, existing
state-of-the-art classification models merely consider the temporal variations
and primarily focus on Engli... |
2502.08826 | Ask in Any Modality: A Comprehensive Survey on Multimodal
Retrieval-Augmented Generation | cs.CL cs.AI cs.IR | Large Language Models (LLMs) struggle with hallucinations and outdated
knowledge due to their reliance on static training data. Retrieval-Augmented
Generation (RAG) mitigates these issues by integrating external dynamic
information enhancing factual and updated grounding. Recent advances in
multimodal learning have l... |
2502.08828 | A Survey on Data-Centric AI: Tabular Learning from Reinforcement
Learning and Generative AI Perspective | cs.LG cs.AI | Tabular data is one of the most widely used data formats across various
domains such as bioinformatics, healthcare, and marketing. As artificial
intelligence moves towards a data-centric perspective, improving data quality
is essential for enhancing model performance in tabular data-driven
applications. This survey f... |
2502.08829 | PLayer-FL: A Principled Approach to Personalized Layer-wise Cross-Silo
Federated Learning | cs.LG | Non-identically distributed data is a major challenge in Federated Learning
(FL). Personalized FL tackles this by balancing local model adaptation with
global model consistency. One variant, partial FL, leverages the observation
that early layers learn more transferable features by federating only early
layers. Howev... |
2502.08832 | LSM Trees in Adversarial Environments | cs.DB cs.CR | The Log Structured Merge (LSM) Tree is a popular choice for key-value stores
that focus on optimized write throughput while maintaining performant,
production-ready read latencies. To optimize read performance, LSM stores rely
on a probabilistic data structure called the Bloom Filter (BF). In this paper,
we focus on ... |
2502.08834 | A Reversible Solver for Diffusion SDEs | cs.LG cs.AI stat.ML | Diffusion models have quickly become the state-of-the-art for generation
tasks across many different data modalities. An important ability of diffusion
models is the ability to encode samples from the data distribution back into
the sampling prior distribution. This is useful for performing alterations to
real data s... |
2502.08835 | A Bundle-based Augmented Lagrangian Framework: Algorithm, Convergence,
and Primal-dual Principles | math.OC cs.SY eess.SY | We propose a new bundle-based augmented Lagrangian framework for solving
constrained convex problems. Unlike the classical (inexact) augmented
Lagrangian method (ALM) that has a nested double-loop structure, our framework
features a $\textit{single-loop}$ process. Motivated by the proximal bundle
method (PBM), we use... |
2502.08836 | Survey on Single-Image Reflection Removal using Deep Learning Techniques | cs.CV | The phenomenon of reflection is quite common in digital images, posing
significant challenges for various applications such as computer vision,
photography, and image processing. Traditional methods for reflection removal
often struggle to achieve clean results while maintaining high fidelity and
robustness, particul... |
2502.08840 | Thresholds for Reconstruction of Random Hypergraphs From Graph
Projections | math.ST cs.IT math.IT math.PR stat.TH | The graph projection of a hypergraph is a simple graph with the same vertex
set and with an edge between each pair of vertices that appear in a hyperedge.
We consider the problem of reconstructing a random $d$-uniform hypergraph from
its projection. Feasibility of this task depends on $d$ and the density of
hyperedge... |
2502.08841 | Delayed takedown of illegal content on social media makes moderation
ineffective | cs.SI cs.CY | Social media platforms face legal and regulatory demands to swiftly remove
illegal content, sometimes under strict takedown deadlines. However, the
effects of moderation speed and the impact of takedown deadlines remain
underexplored. This study models the relationship between the timeliness of
illegal content remova... |
2502.08844 | MuJoCo Playground | cs.RO | We introduce MuJoCo Playground, a fully open-source framework for robot
learning built with MJX, with the express goal of streamlining simulation,
training, and sim-to-real transfer onto robots. With a simple "pip install
playground", researchers can train policies in minutes on a single GPU.
Playground supports dive... |
2502.08845 | Optimal Dataset Size for Recommender Systems: Evaluating Algorithms'
Performance via Downsampling | cs.IR | This thesis investigates dataset downsampling as a strategy to optimize
energy efficiency in recommender systems while maintaining competitive
performance. With increasing dataset sizes posing computational and
environmental challenges, this study explores the trade-offs between energy
efficiency and recommendation q... |
2502.08856 | A Systematic Evaluation of Generative Models on Tabular Transportation
Data | cs.LG | The sharing of large-scale transportation data is beneficial for
transportation planning and policymaking. However, it also raises significant
security and privacy concerns, as the data may include identifiable personal
information, such as individuals' home locations. To address these concerns,
synthetic data genera... |
2502.08858 | Estimating Probabilities of Causation with Machine Learning Models | cs.AI | Probabilities of causation play a crucial role in modern decision-making.
This paper addresses the challenge of predicting probabilities of causation for
subpopulations with insufficient data using machine learning models. Tian and
Pearl first defined and derived tight bounds for three fundamental
probabilities of ca... |
2502.08859 | EnigmaEval: A Benchmark of Long Multimodal Reasoning Challenges | cs.AI cs.CL | As language models master existing reasoning benchmarks, we need new
challenges to evaluate their cognitive frontiers. Puzzle-solving events are
rich repositories of challenging multimodal problems that test a wide range of
advanced reasoning and knowledge capabilities, making them a unique testbed for
evaluating fro... |
2502.08860 | Brain in the Dark: Design Principles for Neuromimetic Inference under
the Free Energy Principle | cs.NE | Deep learning has revolutionised artificial intelligence (AI) by enabling
automatic feature extraction and function approximation from raw data. However,
it faces challenges such as a lack of out-of-distribution generalisation,
catastrophic forgetting and poor interpretability. In contrast, biological
neural networks... |
2502.08864 | Off-Switching Not Guaranteed | cs.AI | Hadfield-Menell et al. (2017) propose the Off-Switch Game, a model of
Human-AI cooperation in which AI agents always defer to humans because they are
uncertain about our preferences. I explain two reasons why AI agents might not
defer. First, AI agents might not value learning. Second, even if AI agents
value learnin... |
2502.08866 | BrainWavLM: Fine-tuning Speech Representations with Brain Responses to
Language | cs.CL | Speech encoding models use auditory representations to predict how the human
brain responds to spoken language stimuli. Most performant encoding models
linearly map the hidden states of artificial neural networks to brain data, but
this linear restriction may limit their effectiveness. In this work, we use
low-rank a... |
2502.08869 | Harnessing Vision Models for Time Series Analysis: A Survey | cs.LG cs.AI cs.CV | Time series analysis has witnessed the inspiring development from traditional
autoregressive models, deep learning models, to recent Transformers and Large
Language Models (LLMs). Efforts in leveraging vision models for time series
analysis have also been made along the way but are less visible to the
community due t... |
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