id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.05215 | Watermarking across Modalities for Content Tracing and Generative AI | cs.CR cs.AI cs.LG | Watermarking embeds information into digital content like images, audio, or
text, imperceptible to humans but robustly detectable by specific algorithms.
This technology has important applications in many challenges of the industry
such as content moderation, tracing AI-generated content, and monitoring the
usage of ... |
2502.05218 | FactorGCL: A Hypergraph-Based Factor Model with Temporal Residual
Contrastive Learning for Stock Returns Prediction | q-fin.ST cs.AI cs.LG | As a fundamental method in economics and finance, the factor model has been
extensively utilized in quantitative investment. In recent years, there has
been a paradigm shift from traditional linear models with expert-designed
factors to more flexible nonlinear machine learning-based models with
data-driven factors, a... |
2502.05219 | Enabling External Scrutiny of AI Systems with Privacy-Enhancing
Technologies | cs.CY cs.AI cs.CR | This article describes how technical infrastructure developed by the
nonprofit OpenMined enables external scrutiny of AI systems without
compromising sensitive information.
Independent external scrutiny of AI systems provides crucial transparency
into AI development, so it should be an integral component of any app... |
2502.05220 | Aero-LLM: A Distributed Framework for Secure UAV Communication and
Intelligent Decision-Making | cs.CR cs.AI | Increased utilization of unmanned aerial vehicles (UAVs) in critical
operations necessitates secure and reliable communication with Ground Control
Stations (GCS). This paper introduces Aero-LLM, a framework integrating
multiple Large Language Models (LLMs) to enhance UAV mission security and
operational efficiency. U... |
2502.05221 | Blackout DIFUSCO | math.OC cs.AI | This study explores the integration of Blackout Diffusion into the DIFUSCO
framework for combinatorial optimization, specifically targeting the Traveling
Salesman Problem (TSP). Inspired by the success of discrete-time diffusion
models (D3PM) in maintaining structural integrity, we extend the paradigm to a
continuous... |
2502.05222 | VistaFlow: Photorealistic Volumetric Reconstruction with Dynamic
Resolution Management via Q-Learning | cs.CV cs.GR | We introduce VistaFlow, a scalable three-dimensional imaging technique
capable of reconstructing fully interactive 3D volumetric images from a set of
2D photographs. Our model synthesizes novel viewpoints through a differentiable
rendering system capable of dynamic resolution management on photorealistic 3D
scenes. W... |
2502.05223 | KDA: A Knowledge-Distilled Attacker for Generating Diverse Prompts to
Jailbreak LLMs | cs.CR cs.AI cs.CL cs.LG | Jailbreak attacks exploit specific prompts to bypass LLM safeguards, causing
the LLM to generate harmful, inappropriate, and misaligned content. Current
jailbreaking methods rely heavily on carefully designed system prompts and
numerous queries to achieve a single successful attack, which is costly and
impractical fo... |
2502.05224 | A Survey on Backdoor Threats in Large Language Models (LLMs): Attacks,
Defenses, and Evaluations | cs.CR cs.AI | Large Language Models (LLMs) have achieved significantly advanced
capabilities in understanding and generating human language text, which have
gained increasing popularity over recent years. Apart from their
state-of-the-art natural language processing (NLP) performance, considering
their widespread usage in many ind... |
2502.05225 | BitAbuse: A Dataset of Visually Perturbed Texts for Defending Phishing
Attacks | cs.CR cs.AI | Phishing often targets victims through visually perturbed texts to bypass
security systems. The noise contained in these texts functions as an
adversarial attack, designed to deceive language models and hinder their
ability to accurately interpret the content. However, since it is difficult to
obtain sufficient phish... |
2502.05227 | Robotouille: An Asynchronous Planning Benchmark for LLM Agents | cs.RO cs.AI cs.CL | Effective asynchronous planning, or the ability to efficiently reason and
plan over states and actions that must happen in parallel or sequentially, is
essential for agents that must account for time delays, reason over diverse
long-horizon tasks, and collaborate with other agents. While large language
model (LLM) ag... |
2502.05228 | Multi-Objective Mobile Damped Wave Algorithm (MOMDWA): A Novel Approach
For Quantum System Control | quant-ph cs.AI cs.SY | In this paper, we introduce a novel multi-objective optimization algorithm,
the Multi-Objective Mobile Damped Wave Algorithm (MOMDWA), specifically
designed to address complex quantum control problems. Our approach extends the
capabilities of the original Mobile Damped Wave Algorithm (MDWA) by
incorporating multiple ... |
2502.05229 | L2GNet: Optimal Local-to-Global Representation of Anatomical Structures
for Generalized Medical Image Segmentation | cs.CV | Continuous Latent Space (CLS) and Discrete Latent Space (DLS) models, like
AttnUNet and VQUNet, have excelled in medical image segmentation. In contrast,
Synergistic Continuous and Discrete Latent Space (CDLS) models show promise in
handling fine and coarse-grained information. However, they struggle with
modeling lo... |
2502.05230 | DiffNMR2: NMR Guided Sampling Acquisition Through Diffusion Model
Uncertainty | q-bio.QM cs.AI | Nuclear Magnetic Resonance (NMR) spectrometry uses electro-frequency pulses
to probe the resonance of a compound's nucleus, which is then analyzed to
determine its structure. The acquisition time of high-resolution NMR spectra
remains a significant bottleneck, especially for complex biological samples
such as protein... |
2502.05231 | Thin ring wing as a means of flow improvement upstream of a propeller | physics.flu-dyn cs.AI | There are numerous devices currently known with the purpose of reducing the
irregularity of the flow upstream of the propeller and to decrease by that
means the propeller-induced vibration and noise. Many of these devices are
wing-shaped vortex-generators that affect the flow with their induced (i.e.
passive) longitu... |
2502.05232 | Aligner-Encoders: Self-Attention Transformers Can Be Self-Transducers | cs.SD cs.AI cs.LG eess.AS | Modern systems for automatic speech recognition, including the RNN-Transducer
and Attention-based Encoder-Decoder (AED), are designed so that the encoder is
not required to alter the time-position of information from the audio sequence
into the embedding; alignment to the final text output is processed during
decodin... |
2502.05233 | Efficient Knowledge Feeding to Language Models: A Novel Integrated
Encoder-Decoder Architecture | cs.CL cs.IR | This paper introduces a novel approach to efficiently feeding knowledge to
language models (LLMs) during prediction by integrating retrieval and
generation processes within a unified framework. While the Retrieval-Augmented
Generation (RAG) model addresses gaps in LLMs' training data and knowledge
limits, it is hinde... |
2502.05234 | Optimizing Temperature for Language Models with Multi-Sample Inference | cs.LG cs.AI cs.CL | Multi-sample aggregation strategies, such as majority voting and best-of-N
sampling, are widely used in contemporary large language models (LLMs) to
enhance predictive accuracy across various tasks. A key challenge in this
process is temperature selection, which significantly impacts model
performance. Existing appro... |
2502.05236 | Koel-TTS: Enhancing LLM based Speech Generation with Preference
Alignment and Classifier Free Guidance | cs.SD cs.AI cs.LG eess.AS | While autoregressive speech token generation models produce speech with
remarkable variety and naturalness, their inherent lack of controllability
often results in issues such as hallucinations and undesired vocalizations that
do not conform to conditioning inputs. We introduce Koel-TTS, a suite of
enhanced encoder-d... |
2502.05237 | PSM-SQL: Progressive Schema Learning with Multi-granularity Semantics
for Text-to-SQL | cs.DB cs.AI | It is challenging to convert natural language (NL) questions into executable
structured query language (SQL) queries for text-to-SQL tasks due to the vast
number of database schemas with redundancy, which interferes with semantic
learning, and the domain shift between NL and SQL. Existing works for schema
linking foc... |
2502.05239 | Enhancing Knowledge Graph Construction: Evaluating with Emphasis on
Hallucination, Omission, and Graph Similarity Metrics | cs.CL cs.AI | Recent advancements in large language models have demonstrated significant
potential in the automated construction of knowledge graphs from unstructured
text. This paper builds upon our previous work [16], which evaluated various
models using metrics like precision, recall, F1 score, triple matching, and
graph matchi... |
2502.05240 | Survey on AI-Generated Media Detection: From Non-MLLM to MLLM | cs.CV | The proliferation of AI-generated media poses significant challenges to
information authenticity and social trust, making reliable detection methods
highly demanded. Methods for detecting AI-generated media have evolved rapidly,
paralleling the advancement of Multimodal Large Language Models (MLLMs).
Current detectio... |
2502.05242 | SEER: Self-Explainability Enhancement of Large Language Models'
Representations | cs.CL cs.AI cs.CV cs.LG | Explaining the hidden representations of Large Language Models (LLMs) is a
perspective to understand LLMs' underlying inference logic and improve their
reliability in application scenarios. However, previous methods introduce
external ''black-box'' modules to explain ''black-box'' LLMs, increasing the
potential uncer... |
2502.05244 | Probabilistic Artificial Intelligence | cs.AI cs.LG | Artificial intelligence commonly refers to the science and engineering of
artificial systems that can carry out tasks generally associated with requiring
aspects of human intelligence, such as playing games, translating languages,
and driving cars. In recent years, there have been exciting advances in
learning-based,... |
2502.05246 | Optimizing Wealth by a Game within Cellular Automata | cs.GT cs.MA | The objective is to find a Cellular Automata (CA) rule that can evolve 2D
patterns that are optimal with respect to a global fitness function. The global
fitness is defined as the sum of local computed utilities. A utility or value
function computes a score depending on the states in the local neighborhood.
First the... |
2502.05248 | Evaluating Personality Traits in Large Language Models: Insights from
Psychological Questionnaires | cs.CL cs.AI cs.MA | Psychological assessment tools have long helped humans understand behavioural
patterns. While Large Language Models (LLMs) can generate content comparable to
that of humans, we explore whether they exhibit personality traits. To this
end, this work applies psychological tools to LLMs in diverse scenarios to
generate ... |
2502.05252 | GSM-Infinite: How Do Your LLMs Behave over Infinitely Increasing Context
Length and Reasoning Complexity? | cs.CL cs.AI | Long-context large language models (LLMs) have recently shown strong
performance in information retrieval and long-document QA. However, to tackle
the most challenging intellectual problems, LLMs must reason effectively in
long and complex contexts (e.g., frontier mathematical research). Studying how
LLMs handle incr... |
2502.05253 | LLMs Can Teach Themselves to Better Predict the Future | cs.CL cs.AI | We present an outcome-driven fine-tuning framework that enhances the
forecasting capabilities of large language models (LLMs) without relying on
human-curated reasoning samples. Our method leverages model self-play to
generate pairs of diverse reasoning trajectories and probabilistic forecasts
for a set of diverse qu... |
2502.05255 | Incivility and Contentiousness Spillover between COVID-19 and Climate
Science Engagement | cs.SI cs.CY physics.soc-ph | Affective polarization and its accompanying cleavage-based sorting drives
incivility and contentiousness around climate change and other science-related
issues. Looking at the COVID-19 period, we study cross-domain spillover of
incivility and contentiousness in public engagements with climate change and
climate scien... |
2502.05256 | Learned Offline Query Planning via Bayesian Optimization | cs.DB | Analytics database workloads often contain queries that are executed
repeatedly. Existing optimization techniques generally prioritize keeping
optimization cost low, normally well below the time it takes to execute a
single instance of a query. If a given query is going to be executed thousands
of times, could it be ... |
2502.05264 | Quantum automated learning with provable and explainable trainability | quant-ph cs.AI cs.LG | Machine learning is widely believed to be one of the most promising practical
applications of quantum computing. Existing quantum machine learning schemes
typically employ a quantum-classical hybrid approach that relies crucially on
gradients of model parameters. Such an approach lacks provable convergence to
global ... |
2502.05271 | RobotMover: Learning to Move Large Objects by Imitating the Dynamic
Chain | cs.RO | Moving large objects, such as furniture, is a critical capability for robots
operating in human environments. This task presents significant challenges due
to two key factors: the need to synchronize whole-body movements to prevent
collisions between the robot and the object, and the under-actuated dynamics
arising f... |
2502.05273 | Principles and Components of Federated Learning Architectures | cs.LG | Federated learning, also known as FL, is a machine learning framework in
which a significant amount of clients (such as mobile devices or whole
enterprises) collaborate to collaboratively train a model while keeping
decentralized training data, all overseen by a central server (such as a
service provider). There are ... |
2502.05275 | Interpretable Failure Detection with Human-Level Concepts | cs.CV | Reliable failure detection holds paramount importance in safety-critical
applications. Yet, neural networks are known to produce overconfident
predictions for misclassified samples. As a result, it remains a problematic
matter as existing confidence score functions rely on category-level signals,
the logits, to detec... |
2502.05277 | Invizo: Arabic Handwritten Document Optical Character Recognition
Solution | cs.CV | Converting images of Arabic text into plain text is a widely researched topic
in academia and industry. However, recognition of Arabic handwritten and
printed text presents difficult challenges due to the complex nature of
variations of the Arabic script. This work proposes an end-to-end solution for
recognizing Arab... |
2502.05282 | Homeomorphism Prior for False Positive and Negative Problem in Medical
Image Dense Contrastive Representation Learning | cs.CV cs.AI | Dense contrastive representation learning (DCRL) has greatly improved the
learning efficiency for image-dense prediction tasks, showing its great
potential to reduce the large costs of medical image collection and dense
annotation. However, the properties of medical images make unreliable
correspondence discovery, br... |
2502.05286 | Fairness and Sparsity within Rashomon sets: Enumeration-Free Exploration
and Characterization | cs.LG | We introduce an enumeration-free method based on mathematical programming to
precisely characterize various properties such as fairness or sparsity within
the set of "good models", known as Rashomon set. This approach is generically
applicable to any hypothesis class, provided that a mathematical formulation of
the m... |
2502.05290 | Switch-based Independent Antagonist Actuation with a Single Motor for a
Soft Exosuit | cs.RO | The use of a cable-driven soft exosuit poses challenges with regards to the
mechanical design of the actuation system, particularly when used for actuation
along multiple degrees of freedom (DoF). The simplest general solution requires
the use of two actuators to be capable of inducing movement along one DoF.
However... |
2502.05291 | Can LLMs Rank the Harmfulness of Smaller LLMs? We are Not There Yet | cs.CL | Large language models (LLMs) have become ubiquitous, thus it is important to
understand their risks and limitations. Smaller LLMs can be deployed where
compute resources are constrained, such as edge devices, but with different
propensity to generate harmful output. Mitigation of LLM harm typically depends
on annotat... |
2502.05292 | Drone Detection and Tracking with YOLO and a Rule-based Method | cs.CV cs.AI cs.LG | Drones or unmanned aerial vehicles are traditionally used for military
missions, warfare, and espionage. However, the usage of drones has
significantly increased due to multiple industrial applications involving
security and inspection, transportation, research purposes, and recreational
drone flying. Such an increas... |
2502.05295 | GST-UNet: Spatiotemporal Causal Inference with Time-Varying Confounders | cs.LG stat.ME | Estimating causal effects from spatiotemporal data is a key challenge in
fields such as public health, social policy, and environmental science, where
controlled experiments are often infeasible. However, existing causal inference
methods relying on observational data face significant limitations: they depend
on stro... |
2502.05300 | Parameter Symmetry Breaking and Restoration Determines the Hierarchical
Learning in AI Systems | cs.LG cond-mat.dis-nn cs.AI stat.ML | The dynamics of learning in modern large AI systems is hierarchical, often
characterized by abrupt, qualitative shifts akin to phase transitions observed
in physical systems. While these phenomena hold promise for uncovering the
mechanisms behind neural networks and language models, existing theories remain
fragmente... |
2502.05301 | Decentralized Online Ensembles of Gaussian Processes for Multi-Agent
Systems | cs.LG cs.MA eess.SP stat.ML | Flexible and scalable decentralized learning solutions are fundamentally
important in the application of multi-agent systems. While several recent
approaches introduce (ensembles of) kernel machines in the distributed setting,
Bayesian solutions are much more limited. We introduce a fully decentralized,
asymptoticall... |
2502.05305 | Online Covariance Estimation in Nonsmooth Stochastic Approximation | stat.ML cs.LG math.OC | We consider applying stochastic approximation (SA) methods to solve nonsmooth
variational inclusion problems. Existing studies have shown that the averaged
iterates of SA methods exhibit asymptotic normality, with an optimal limiting
covariance matrix in the local minimax sense of H\'ajek and Le Cam. However, no
meth... |
2502.05307 | Training Set Reconstruction from Differentially Private Forests: How
Effective is DP? | cs.LG cs.CR | Recent research has shown that machine learning models are vulnerable to
privacy attacks targeting their training data. Differential privacy (DP) has
become a widely adopted countermeasure, as it offers rigorous privacy
protections.
In this paper, we introduce a reconstruction attack targeting
state-of-the-art $\va... |
2502.05309 | Learning the Geometric Mechanics of Robot Motion Using Gaussian Mixtures | cs.RO | Data-driven models of robot motion constructed using principles from
Geometric Mechanics have been shown to produce useful predictions of robot
motion for a variety of robots. For robots with a useful number of DoF, these
geometric mechanics models can only be constructed in the neighborhood of a
gait. Here we show h... |
2502.05310 | Oracular Programming: A Modular Foundation for Building LLM-Enabled
Software | cs.PL cs.AI | Large Language Models have proved surprisingly effective at solving a wide
range of tasks from just a handful of examples. However, their lack of
reliability and modularity limits their capacity to tackle large problems that
require many steps of reasoning. In response, researchers have proposed
advanced pipelines th... |
2502.05311 | ParquetDB: A Lightweight Python Parquet-Based Database | cs.DB physics.data-an | Traditional data storage formats and databases often introduce complexities
and inefficiencies that hinder rapid iteration and adaptability. To address
these challenges, we introduce ParquetDB, a Python-based database framework
that leverages the Parquet file format's optimized columnar storage. ParquetDB
offers effi... |
2502.05312 | Towards the Development of Balanced Synthetic Data for Correcting
Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and
Synthetic Data Generating Model | cs.CL cs.AI | Synthetic data generation is widely recognized as a way to enhance the
quality of neural grammatical error correction (GEC) systems. However, current
approaches often lack diversity or are too simplistic to generate the wide
range of grammatical errors made by humans, especially for low-resource
languages such as Ara... |
2502.05315 | AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future
Possibilities | cs.LG | We present a review of high-performance automatic modulation recognition
(AMR) models proposed in the literature to classify various Radio Frequency
(RF) modulation schemes. We replicated these models and compared their
performance in terms of accuracy across a range of signal-to-noise ratios. To
ensure a fair compar... |
2502.05318 | Diagonal Symmetrization of Neural Network Solvers for the Many-Electron
Schr\"odinger Equation | cs.LG cond-mat.mtrl-sci | Incorporating group symmetries into neural networks has been a cornerstone of
success in many AI-for-science applications. Diagonal groups of isometries,
which describe the invariance under a simultaneous movement of multiple
objects, arise naturally in many-body quantum problems. Despite their
importance, diagonal g... |
2502.05320 | Towards Fine-grained Renal Vasculature Segmentation: Full-Scale
Hierarchical Learning with FH-Seg | cs.CV | Accurate fine-grained segmentation of the renal vasculature is critical for
nephrological analysis, yet it faces challenges due to diverse and
insufficiently annotated images. Existing methods struggle to accurately
segment intricate regions of the renal vasculature, such as the inner and outer
walls, arteries and le... |
2502.05321 | Using Federated Machine Learning in Predictive Maintenance of Jet
Engines | cs.LG | The goal of this paper is to predict the Remaining Useful Life (RUL) of
turbine jet engines using a federated machine learning framework. Federated
Learning enables multiple edge devices/nodes or servers to collaboratively
train a shared model without sharing sensitive data, thus preserving data
privacy and security.... |
2502.05325 | From Counterfactuals to Trees: Competitive Analysis of Model Extraction
Attacks | cs.LG cs.CR | The advent of Machine Learning as a Service (MLaaS) has heightened the
trade-off between model explainability and security. In particular,
explainability techniques, such as counterfactual explanations, inadvertently
increase the risk of model extraction attacks, enabling unauthorized
replication of proprietary model... |
2502.05330 | Multi-Class Segmentation of Aortic Branches and Zones in Computed
Tomography Angiography: The AortaSeg24 Challenge | eess.IV cs.AI cs.CV cs.LG | Multi-class segmentation of the aorta in computed tomography angiography
(CTA) scans is essential for diagnosing and planning complex endovascular
treatments for patients with aortic dissections. However, existing methods
reduce aortic segmentation to a binary problem, limiting their ability to
measure diameters acro... |
2502.05331 | Fine-Tuned LLMs are "Time Capsules" for Tracking Societal Bias Through
Books | cs.CL | Books, while often rich in cultural insights, can also mirror societal biases
of their eras - biases that Large Language Models (LLMs) may learn and
perpetuate during training. We introduce a novel method to trace and quantify
these biases using fine-tuned LLMs. We develop BookPAGE, a corpus comprising
593 fictional ... |
2502.05332 | Removing Neural Signal Artifacts with Autoencoder-Targeted Adversarial
Transformers (AT-AT) | cs.LG | Electromyogenic (EMG) noise is a major contamination source in EEG data that
can impede accurate analysis of brain-specific neural activity. Recent
literature on EMG artifact removal has moved beyond traditional linear
algorithms in favor of machine learning-based systems. However, existing deep
learning-based filtra... |
2502.05333 | A Tutorial On Intersectionality in Fair Rankings | cs.CY cs.IR cs.LG | We address the critical issue of biased algorithms and unfair rankings, which
have permeated various sectors, including search engines, recommendation
systems, and workforce management. These biases can lead to discriminatory
outcomes in a data-driven world, especially against marginalized and
underrepresented groups... |
2502.05334 | Geometric Machine Learning on EEG Signals | cs.LG | Brain-computer interfaces (BCIs) offer transformative potential, but decoding
neural signals presents significant challenges. The core premise of this paper
is built around demonstrating methods to elucidate the underlying
low-dimensional geometric structure present in high-dimensional brainwave data
in order to assi... |
2502.05335 | Towards Foundational Models for Dynamical System Reconstruction:
Hierarchical Meta-Learning via Mixture of Experts | cs.LG | As foundational models reshape scientific discovery, a bottleneck persists in
dynamical system reconstruction (DSR): the ability to learn across system
hierarchies. Many meta-learning approaches have been applied successfully to
single systems, but falter when confronted with sparse, loosely related
datasets requirin... |
2502.05343 | Towards Wearable Interfaces for Robotic Caregiving | cs.RO | Physically assistive robots in home environments can enhance the autonomy of
individuals with impairments, allowing them to regain the ability to conduct
self-care and household tasks. Individuals with physical limitations may find
existing interfaces challenging to use, highlighting the need for novel
interfaces tha... |
2502.05344 | RAG-Verus: Repository-Level Program Verification with LLMs using
Retrieval Augmented Generation | cs.SE cs.AI | Scaling automated formal verification to real-world projects requires
resolving cross-module dependencies and global contexts, which are challenges
overlooked by existing function-centric methods. We introduce RagVerus, a
framework that synergizes retrieval-augmented generation with context-aware
prompting to automat... |
2502.05345 | Estimating Voltage Drop: Models, Features and Data Representation
Towards a Neural Surrogate | cs.AR cs.AI | Accurate estimation of voltage drop (IR drop) in modern Application-Specific
Integrated Circuits (ASICs) is highly time and resource demanding, due to the
growing complexity and the transistor density in recent technology nodes. To
mitigate this challenge, we investigate how Machine Learning (ML) techniques,
includin... |
2502.05346 | Probabilistic Subspace Manifolds for Contextual Inference in Large
Language Models | cs.CL | Representing token embeddings as probability distributions over learned
manifolds allows for more flexible contextual inference, reducing
representational rigidity while enhancing semantic granularity. Comparative
evaluations demonstrate that probabilistic embeddings improve neighborhood
consistency and decrease redu... |
2502.05349 | Contextual Scenario Generation for Two-Stage Stochastic Programming | math.OC cs.LG | Two-stage stochastic programs (2SPs) are important tools for making decisions
under uncertainty. Decision-makers use contextual information to generate a set
of scenarios to represent the true conditional distribution. However, the
number of scenarios required is a barrier to implementing 2SPs, motivating the
problem... |
2502.05351 | Deep Generative model that uses physical quantities to generate and
retrieve solar magnetic active regions | astro-ph.SR cs.LG stat.ML | Deep generative models have shown immense potential in generating unseen data
that has properties of real data. These models learn complex data-generating
distributions starting from a smaller set of latent dimensions. However,
generative models have encountered great skepticism in scientific domains due
to the disco... |
2502.05352 | ITBench: Evaluating AI Agents across Diverse Real-World IT Automation
Tasks | cs.AI cs.DC cs.MA | Realizing the vision of using AI agents to automate critical IT tasks depends
on the ability to measure and understand effectiveness of proposed solutions.
We introduce ITBench, a framework that offers a systematic methodology for
benchmarking AI agents to address real-world IT automation tasks. Our initial
release t... |
2502.05360 | Curse of Dimensionality in Neural Network Optimization | cs.LG math.OC stat.ML | The curse of dimensionality in neural network optimization under the
mean-field regime is studied. It is demonstrated that when a shallow neural
network with a Lipschitz continuous activation function is trained using either
empirical or population risk to approximate a target function that is $r$ times
continuously ... |
2502.05364 | Hypencoder: Hypernetworks for Information Retrieval | cs.IR cs.LG | The vast majority of retrieval models depend on vector inner products to
produce a relevance score between a query and a document. This naturally limits
the expressiveness of the relevance score that can be employed. We propose a
new paradigm, instead of producing a vector to represent the query we produce a
small ne... |
2502.05365 | Low Dimensional Koopman Generalized Eigenfunctions Representation: An
Approach to Address Koopman High-Dimensionality Problem | eess.SY cs.SY | This Paper introduces a methodology to achieve a lower dimensional Koopman
quasi linear representation of nonlinear dynamics using Koopman generalized
eigenfunctions. The methodology is presented for the analytically derived
Koopman formulation of rigid body dynamics but can be generalized to any
data-driven or analy... |
2502.05367 | Detecting APT Malware Command and Control over HTTP(S) Using Contextual
Summaries | cs.CR cs.LG cs.NI | Advanced Persistent Threats (APTs) are among the most sophisticated threats
facing critical organizations worldwide. APTs employ specific tactics,
techniques, and procedures (TTPs) which make them difficult to detect in
comparison to frequent and aggressive attacks. In fact, current network
intrusion detection system... |
2502.05368 | Otter: Generating Tests from Issues to Validate SWE Patches | cs.SE cs.LG | While there has been plenty of work on generating tests from existing code,
there has been limited work on generating tests from issues. A correct test
must validate the code patch that resolves the issue. In this work, we focus on
the scenario where the code patch does not exist yet. This approach supports
two major... |
2502.05369 | DobLIX: A Dual-Objective Learned Index for Log-Structured Merge Trees | cs.DB cs.LG math.OC | In this paper, we introduce DobLIX, a dual-objective learned index
specifically designed for Log-Structured Merge(LSM) tree-based key-value
stores. Although traditional learned indexes focus exclusively on optimizing
index lookups, they often overlook the impact of data access from storage,
resulting in performance b... |
2502.05370 | fMoE: Fine-Grained Expert Offloading for Large Mixture-of-Experts
Serving | cs.LG cs.AI cs.DC | Large Language Models (LLMs) have gained immense success in revolutionizing
various applications, including content generation, search and recommendation,
and AI-assisted operation. To reduce high training costs, Mixture-of-Experts
(MoE) architecture has become a popular backbone for modern LLMs. However,
despite the... |
2502.05371 | Cumulant Structures of Entanglement Entropy | math-ph cs.IT math.IT math.MP quant-ph | We present a new method to derive exact cumulant expressions of any order of
von Neumann entropy over Hilbert-Schmidt ensemble. The new method uncovers
hidden cumulant structures that decouple each cumulant in a summation-free
manner into its lower-order joint cumulants involving families of ancillary
statistics. Imp... |
2502.05372 | Active Learning of Model Discrepancy with Bayesian Experimental Design | cs.LG | Digital twins have been actively explored in many engineering applications,
such as manufacturing and autonomous systems. However, model discrepancy is
ubiquitous in most digital twin models and has significant impacts on the
performance of using those models. In recent years, data-driven modeling
techniques have bee... |
2502.05374 | Towards LLM Unlearning Resilient to Relearning Attacks: A
Sharpness-Aware Minimization Perspective and Beyond | cs.LG cs.CL | The LLM unlearning technique has recently been introduced to comply with data
regulations and address the safety and ethical concerns of LLMs by removing the
undesired data-model influence. However, state-of-the-art unlearning methods
face a critical vulnerability: they are susceptible to ``relearning'' the
removed i... |
2502.05376 | BCQ: Block Clustered Quantization for 4-bit (W4A4) LLM Inference | cs.LG | Post-training quantization (PTQ) is a promising approach to reducing the
storage and computational requirements of large language models (LLMs) without
additional training cost. Recent PTQ studies have primarily focused on
quantizing only weights to sub-8-bits while maintaining activations at 8-bits
or higher. Accura... |
2502.05378 | NextBestPath: Efficient 3D Mapping of Unseen Environments | cs.CV cs.RO | This work addresses the problem of active 3D mapping, where an agent must
find an efficient trajectory to exhaustively reconstruct a new scene. Previous
approaches mainly predict the next best view near the agent's location, which
is prone to getting stuck in local areas. Additionally, existing indoor
datasets are in... |
2502.05383 | Is attention all you need to solve the correlated electron problem? | cond-mat.str-el cond-mat.mes-hall cs.AI | The attention mechanism has transformed artificial intelligence research by
its ability to learn relations between objects. In this work, we explore how a
many-body wavefunction ansatz constructed from a large-parameter self-attention
neural network can be used to solve the interacting electron problem in solids.
By ... |
2502.05384 | Demonstrating CavePI: Autonomous Exploration of Underwater Caves by
Semantic Guidance | cs.RO | Enabling autonomous robots to safely and efficiently navigate, explore, and
map underwater caves is of significant importance to water resource management,
hydrogeology, archaeology, and marine robotics. In this work, we demonstrate
the system design and algorithmic integration of a visual servoing framework
for sema... |
2502.05387 | Coarse-to-Fine Structure-Aware Artistic Style Transfer | cs.CV cs.AI | Artistic style transfer aims to use a style image and a content image to
synthesize a target image that retains the same artistic expression as the
style image while preserving the basic content of the content image. Many
recently proposed style transfer methods have a common problem; that is, they
simply transfer th... |
2502.05389 | The Role of Prosody in Spoken Question Answering | cs.CL | Spoken language understanding research to date has generally carried a heavy
text perspective. Most datasets are derived from text, which is then
subsequently synthesized into speech, and most models typically rely on
automatic transcriptions of speech. This is to the detriment of
prosody--additional information carr... |
2502.05390 | Learning Task Representations from In-Context Learning | cs.CL cs.LG | Large language models (LLMs) have demonstrated remarkable proficiency in
in-context learning (ICL), where models adapt to new tasks through
example-based prompts without requiring parameter updates. However,
understanding how tasks are internally encoded and generalized remains a
challenge. To address some of the emp... |
2502.05391 | Beyond and Free from Diffusion: Invertible Guided Consistency Training | cs.CV | Guidance in image generation steers models towards higher-quality or more
targeted outputs, typically achieved in Diffusion Models (DMs) via
Classifier-free Guidance (CFG). However, recent Consistency Models (CMs), which
offer fewer function evaluations, rely on distilling CFG knowledge from
pretrained DMs to achieve... |
2502.05392 | Open Challenges in Time Series Anomaly Detection: An Industry
Perspective | cs.LG | Current research in time-series anomaly detection is using definitions that
miss critical aspects of how anomaly detection is commonly used in practice. We
list several areas that are of practical relevance and that we believe are
either under-investigated or missing entirely from the current discourse. Based
on an i... |
2502.05395 | Hierarchical Lexical Manifold Projection in Large Language Models: A
Novel Mechanism for Multi-Scale Semantic Representation | cs.CL | The integration of structured hierarchical embeddings into transformer-based
architectures introduces a refined approach to lexical representation, ensuring
that multi-scale semantic relationships are preserved without compromising
computational efficiency. A projection mechanism that maps tokens onto a
structured ma... |
2502.05396 | A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation | eess.IV cs.CV | Segmentation of 3D medical images is a critical task for accurate diagnosis
and treatment planning. Convolutional neural networks (CNNs) have dominated the
field, achieving significant success in 3D medical image segmentation. However,
CNNs struggle with capturing long-range dependencies and global context,
limiting ... |
2502.05397 | Imitation Learning from a Single Temporally Misaligned Video | cs.LG | We examine the problem of learning sequential tasks from a single visual
demonstration. A key challenge arises when demonstrations are temporally
misaligned due to variations in timing, differences in embodiment, or
inconsistencies in execution. Existing approaches treat imitation as a
distribution-matching problem, ... |
2502.05398 | Probabilistic Foundations for Metacognition via Hybrid-AI | cs.AI | Metacognition is the concept of reasoning about an agent's own internal
processes, and it has recently received renewed attention with respect to
artificial intelligence (AI) and, more specifically, machine learning systems.
This paper reviews a hybrid-AI approach known as "error detecting and
correcting rules" (EDCR... |
2502.05400 | Dynamic Noise Preference Optimization for LLM Self-Improvement via
Synthetic Data | cs.CL | Although LLMs have achieved significant success, their reliance on large
volumes of human-annotated data has limited their potential for further
scaling. In this situation, utilizing self-generated synthetic data has become
crucial for fine-tuning LLMs without extensive human annotation. However,
current methods ofte... |
2502.05402 | Convolutional Deep Colorization for Image Compression: A Color Grid
Based Approach | cs.CV cs.AI | The search for image compression optimization techniques is a topic of
constant interest both in and out of academic circles. One method that shows
promise toward future improvements in this field is image colorization since
image colorization algorithms can reduce the amount of color data that needs to
be stored for... |
2502.05403 | Analyzing public sentiment to gauge key stock events and determine
volatility in conjunction with time and options premiums | cs.LG | Analyzing stocks and making higher accurate predictions on where the price is
heading continues to become more and more challenging therefore, we designed a
new financial algorithm that leverages social media sentiment analysis to
enhance the prediction of key stock earnings and associated volatility. Our
model integ... |
2502.05407 | The Complexity of Learning Sparse Superposed Features with Feedback | cs.LG cs.AI stat.ML | The success of deep networks is crucially attributed to their ability to
capture latent features within a representation space. In this work, we
investigate whether the underlying learned features of a model can be
efficiently retrieved through feedback from an agent, such as a large language
model (LLM), in the form... |
2502.05409 | Vision-in-the-loop Simulation for Deep Monocular Pose Estimation of UAV
in Ocean Environment | cs.CV cs.AI cs.LG cs.RO cs.SY eess.SY | This paper proposes a vision-in-the-loop simulation environment for deep
monocular pose estimation of a UAV operating in an ocean environment. Recently,
a deep neural network with a transformer architecture has been successfully
trained to estimate the pose of a UAV relative to the flight deck of a research
vessel, o... |
2502.05414 | Graph-based Molecular In-context Learning Grounded on Morgan
Fingerprints | cs.LG cs.CL | In-context learning (ICL) effectively conditions large language models (LLMs)
for molecular tasks, such as property prediction and molecule captioning, by
embedding carefully selected demonstration examples into the input prompt. This
approach avoids the computational overhead of extensive pertaining and
fine-tuning.... |
2502.05415 | Show-o Turbo: Towards Accelerated Unified Multimodal Understanding and
Generation | cs.CV cs.AI | There has been increasing research interest in building unified multimodal
understanding and generation models, among which Show-o stands as a notable
representative, demonstrating great promise for both text-to-image and
image-to-text generation. The inference of Show-o involves progressively
denoising image tokens ... |
2502.05416 | Deep Generative Models with Hard Linear Equality Constraints | cs.LG | While deep generative models~(DGMs) have demonstrated remarkable success in
capturing complex data distributions, they consistently fail to learn
constraints that encode domain knowledge and thus require constraint
integration. Existing solutions to this challenge have primarily relied on
heuristic methods and often ... |
2502.05423 | LRA-GNN: Latent Relation-Aware Graph Neural Network with Initial and
Dynamic Residual for Facial Age Estimation | cs.CV | Face information is mainly concentrated among facial key points, and frontier
research has begun to use graph neural networks to segment faces into patches
as nodes to model complex face representations. However, these methods
construct node-to-node relations based on similarity thresholds, so there is a
problem that... |
2502.05424 | SAMGPT: Text-free Graph Foundation Model for Multi-domain Pre-training
and Cross-domain Adaptation | cs.CL cs.AI | Graphs are able to model interconnected entities in many online services,
supporting a wide range of applications on the Web. This raises an important
question: How can we train a graph foundational model on multiple source
domains and adapt to an unseen target domain? A major obstacle is that graphs
from different d... |
2502.05425 | Toward Copyright Integrity and Verifiability via Multi-Bit Watermarking
for Intelligent Transportation Systems | cs.CR cs.CL | Intelligent transportation systems (ITS) use advanced technologies such as
artificial intelligence to significantly improve traffic flow management
efficiency, and promote the intelligent development of the transportation
industry. However, if the data in ITS is attacked, such as tampering or
forgery, it will endange... |
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