id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
|---|---|---|---|
2502.12674 | SATA: Safe and Adaptive Torque-Based Locomotion Policies Inspired by
Animal Learning | cs.RO cs.LG | Despite recent advances in learning-based controllers for legged robots,
deployments in human-centric environments remain limited by safety concerns.
Most of these approaches use position-based control, where policies output
target joint angles that must be processed by a low-level controller (e.g., PD
or impedance c... |
2502.12677 | Spiking Vision Transformer with Saccadic Attention | cs.CV cs.AI | The combination of Spiking Neural Networks (SNNs) and Vision Transformers
(ViTs) holds potential for achieving both energy efficiency and high
performance, particularly suitable for edge vision applications. However, a
significant performance gap still exists between SNN-based ViTs and their ANN
counterparts. Here, w... |
2502.12678 | Multi-Step Alignment as Markov Games: An Optimistic Online Gradient
Descent Approach with Convergence Guarantees | cs.LG cs.AI cs.CL | Reinforcement Learning from Human Feedback (RLHF) has been highly successful
in aligning large language models with human preferences. While prevalent
methods like DPO have demonstrated strong performance, they frame interactions
with the language model as a bandit problem, which limits their applicability
in real-wo... |
2502.12680 | Introducing ROADS: A Systematic Comparison of Remote Control Interaction
Concepts for Automated Vehicles at Road Works | cs.HC cs.RO | As vehicle automation technology continues to mature, there is a necessity
for robust remote monitoring and intervention features. These are essential for
intervening during vehicle malfunctions, challenging road conditions, or in
areas that are difficult to navigate. This evolution in the role of the human
operator ... |
2502.12682 | K-n\'ucleo: Una herramienta para detectar la estructura conceptual de
los campos de investigaci\'on. El caso pr\'actico de la Altmetr\'ia | stat.ME cs.SI physics.soc-ph | In Social Network Analysis (SNA), k-core decomposition is used to detect
hierarchical shells in networks. The application of the K-core decomposition to
a network of keywords allows us to represent the conceptual structure of a
research field. The objective of this work was to propose the application of
k-core decomp... |
2502.12684 | Federated Variational Inference for Bayesian Mixture Models | stat.ML cs.LG stat.ME | We present a federated learning approach for Bayesian model-based clustering
of large-scale binary and categorical datasets. We introduce a principled
'divide and conquer' inference procedure using variational inference with local
merge and delete moves within batches of the data in parallel, followed by
'global' mer... |
2502.12685 | Theoretical Guarantees for Minimum Bayes Risk Decoding | cs.CL | Minimum Bayes Risk (MBR) decoding optimizes output selection by maximizing
the expected utility value of an underlying human distribution. While prior
work has shown the effectiveness of MBR decoding through empirical evaluation,
few studies have analytically investigated why the method is effective. As a
result of o... |
2502.12689 | Role extraction by matrix equations and generalized random walks | math.NA cs.NA cs.SI | The nodes in a network can be grouped into 'roles' based on similar
connection patterns. This is usually achieved by defining a pairwise node
similarity matrix and then clustering rows and columns of this matrix. This
paper presents a new similarity matrix for solving role extraction problems in
directed networks, wh... |
2502.12690 | Fast Data Aware Neural Architecture Search via Supernet Accelerated
Evaluation | cs.NE cs.AI cs.CV cs.LG | Tiny machine learning (TinyML) promises to revolutionize fields such as
healthcare, environmental monitoring, and industrial maintenance by running
machine learning models on low-power embedded systems. However, the complex
optimizations required for successful TinyML deployment continue to impede its
widespread adop... |
2502.12691 | Spherical Dense Text-to-Image Synthesis | cs.CV | Recent advancements in text-to-image (T2I) have improved synthesis results,
but challenges remain in layout control and generating omnidirectional
panoramic images. Dense T2I (DT2I) and spherical T2I (ST2I) models address
these issues, but so far no unified approach exists. Trivial approaches, like
prompting a DT2I m... |
2502.12692 | Channel Estimation for Stacked Intelligent Metasurfaces in Rician Fading
Channels | cs.IT math.IT | The recent combination of the rising architectures, known as stacked
intelligent metasurface (SIM) and holographic multiple-input multiple-output
(HMIMO), drives toward breakthroughs for next-generation wireless communication
systems. Given the fact that the number of elements per surface of the SIM is
much larger th... |
2502.12693 | Neuromorphic Readout for Hadron Calorimeters | hep-ex cs.ET cs.LG cs.NE | We simulate hadrons impinging on a homogeneous lead-tungstate (PbWO4)
calorimeter to investigate how the resulting light yield and its temporal
structure, as detected by an array of light-sensitive sensors, can be processed
by a neuromorphic computing system. Our model encodes temporal photon
distributions as spike t... |
2502.12696 | Radar Network for Gait Monitoring: Technology and Validation | eess.SP cs.SY eess.SY | In recent years, radar-based devices have emerged as an alternative approach
for gait monitoring. However, the radar configuration and the algorithms used
to extract the gait parameters often differ between contributions, lacking a
systematic evaluation of the most appropriate setup. Additionally, radar-based
studies... |
2502.12700 | Multi-Novelty: Improve the Diversity and Novelty of Contents Generated
by Large Language Models via inference-time Multi-Views Brainstorming | cs.CL | Large Language Models (LLMs) demonstrate remarkable proficiency in generating
accurate and fluent text. However, they often struggle with diversity and
novelty, leading to repetitive or overly deterministic responses. These
limitations stem from constraints in training data, including gaps in specific
knowledge domai... |
2502.12701 | Translate Smart, not Hard: Cascaded Translation Systems with
Quality-Aware Deferral | cs.CL cs.AI cs.LG | Larger models often outperform smaller ones but come with high computational
costs. Cascading offers a potential solution. By default, it uses smaller
models and defers only some instances to larger, more powerful models. However,
designing effective deferral rules remains a challenge. In this paper, we
propose a sim... |
2502.12704 | Maximizing Truth Learning in a Social Network is NP-hard | cs.SI | Sequential learning models situations where agents predict a ground truth in
sequence, by using their private, noisy measurements, and the predictions of
agents who came earlier in the sequence. We study sequential learning in a
social network, where agents only see the actions of the previous agents in
their own nei... |
2502.12706 | Scalable Model Merging with Progressive Layer-wise Distillation | cs.LG | Model merging offers an effective way to integrate the capabilities of
multiple fine-tuned models. However, the performance degradation of the merged
model remains a challenge, particularly when none or few data are available.
This paper first highlights the necessity of domain-specific data for model
merging by prov... |
2502.12707 | CausalMan: A physics-based simulator for large-scale causality | cs.LG stat.ML | A comprehensive understanding of causality is critical for navigating and
operating within today's complex real-world systems. The absence of realistic
causal models with known data generating processes complicates fair
benchmarking. In this paper, we present the CausalMan simulator, modeled after
a real-world produc... |
2502.12710 | TREND: A Whitespace Replacement Information Hiding Method | cs.CR cs.AI cs.SE | Large Language Models (LLMs) have gained significant popularity in recent
years. Differentiating between a text written by a human and a text generated
by an LLM has become almost impossible. Information hiding techniques such as
digital watermarking or steganography can help by embedding information inside
text with... |
2502.12713 | Uncertainty Propagation for Echocardiography Clinical Metric Estimation
via Contour Sampling | cs.CV | Echocardiography plays a fundamental role in the extraction of important
clinical parameters (e.g. left ventricular volume and ejection fraction)
required to determine the presence and severity of heart-related conditions.
When deploying automated techniques for computing these parameters, uncertainty
estimation is c... |
2502.12714 | Playing with Voices: Tabletop Role-Playing Game Recordings as a
Diarization Challenge | cs.CL cs.SD | This paper provides a proof of concept that audio of tabletop role-playing
games (TTRPG) could serve as a challenge for diarization systems. TTRPGs are
carried out mostly by conversation. Participants often alter their voices to
indicate that they are talking as a fictional character. Audio processing
systems are sus... |
2502.12716 | Soft Arm-Motor Thrust Characterization for a Pneumatically Actuated Soft
Morphing Quadrotor | cs.RO cs.SY eess.SY | In this work, an experimental characterization of the configuration space of
a soft, pneumatically actuated morphing quadrotor is presented, with a focus on
precise thrust characterization of its flexible arms, considering the effect of
downwash. Unlike traditional quadrotors, the soft drone has pneumatically
actuate... |
2502.12717 | Learning the symmetric group: large from small | cs.LG math.CO math.RT | Machine learning explorations can make significant inroads into solving
difficult problems in pure mathematics. One advantage of this approach is that
mathematical datasets do not suffer from noise, but a challenge is the amount
of data required to train these models and that this data can be
computationally expensiv... |
2502.12723 | myEye2Wheeler: A Two-Wheeler Indian Driver Real-World Eye-Tracking
Dataset | cs.CV | This paper presents the myEye2Wheeler dataset, a unique resource of
real-world gaze behaviour of two-wheeler drivers navigating complex Indian
traffic. Most datasets are from four-wheeler drivers on well-planned roads and
homogeneous traffic. Our dataset offers a critical lens into the unique visual
attention pattern... |
2502.12724 | Responsive Noise-Relaying Diffusion Policy: Responsive and Efficient
Visuomotor Control | cs.RO | Imitation learning is an efficient method for teaching robots a variety of
tasks. Diffusion Policy, which uses a conditional denoising diffusion process
to generate actions, has demonstrated superior performance, particularly in
learning from multi-modal demonstrates. However, it relies on executing
multiple actions ... |
2502.12732 | Circuit Representation Learning with Masked Gate Modeling and
Verilog-AIG Alignment | cs.LG | Understanding the structure and function of circuits is crucial for
electronic design automation (EDA). Circuits can be formulated as And-Inverter
graphs (AIGs), enabling efficient implementation of representation learning
through graph neural networks (GNNs). Masked modeling paradigms have been
proven effective in g... |
2502.12734 | Iron Sharpens Iron: Defending Against Attacks in Machine-Generated Text
Detection with Adversarial Training | cs.CR cs.CL | Machine-generated Text (MGT) detection is crucial for regulating and
attributing online texts. While the existing MGT detectors achieve strong
performance, they remain vulnerable to simple perturbations and adversarial
attacks. To build an effective defense against malicious perturbations, we view
MGT detection from ... |
2502.12736 | Cross-Domain Continual Learning for Edge Intelligence in Wireless ISAC
Networks | eess.SP cs.LG | In wireless networks with integrated sensing and communications (ISAC), edge
intelligence (EI) is expected to be developed at edge devices (ED) for sensing
user activities based on channel state information (CSI). However, due to the
CSI being highly specific to users' characteristics, the CSI-activity
relationship i... |
2502.12737 | Beyond Seen Data: Improving KBQA Generalization Through Schema-Guided
Logical Form Generation | cs.CL cs.AI | Knowledge base question answering (KBQA) aims to answer user questions in
natural language using rich human knowledge stored in large KBs. As current
KBQA methods struggle with unseen knowledge base elements at test time,we
introduce SG-KBQA: a novel model that injects schema contexts into entity
retrieval and logica... |
2502.12742 | 3D Shape-to-Image Brownian Bridge Diffusion for Brain MRI Synthesis from
Cortical Surfaces | cs.CV | Despite recent advances in medical image generation, existing methods
struggle to produce anatomically plausible 3D structures. In synthetic brain
magnetic resonance images (MRIs), characteristic fissures are often missing,
and reconstructed cortical surfaces appear scattered rather than densely
convoluted. To addres... |
2502.12743 | "I know myself better, but not really greatly": Using LLMs to Detect and
Explain LLM-Generated Texts | cs.CL cs.AI | Large language models (LLMs) have demonstrated impressive capabilities in
generating human-like texts, but the potential misuse of such LLM-generated
texts raises the need to distinguish between human-generated and LLM-generated
content. This paper explores the detection and explanation capabilities of
LLM-based dete... |
2502.12744 | Self-Enhanced Reasoning Training: Activating Latent Reasoning in Small
Models for Enhanced Reasoning Distillation | cs.CL | The rapid advancement of large language models (LLMs) has significantly
enhanced their reasoning abilities, enabling increasingly complex tasks.
However, these capabilities often diminish in smaller, more computationally
efficient models like GPT-2. Recent research shows that reasoning distillation
can help small mod... |
2502.12745 | MediaMind: Revolutionizing Media Monitoring using Agentification | cs.CL cs.AI cs.LG | In an era of rapid technological advancements, agentification of software
tools has emerged as a critical innovation, enabling systems to function
autonomously and adaptively. This paper introduces MediaMind as a case study to
demonstrate the agentification process, highlighting how existing software can
be transform... |
2502.12747 | ExoKit: A Toolkit for Rapid Prototyping of Interactions for Arm-based
Exoskeletons | cs.HC cs.RO | Exoskeletons open up a unique interaction space that seamlessly integrates
users' body movements with robotic actuation. Despite its potential,
human-exoskeleton interaction remains an underexplored area in HCI, largely due
to the lack of accessible prototyping tools that enable designers to easily
develop exoskeleto... |
2502.12751 | Architect of the Bits World: Masked Autoregressive Modeling for Circuit
Generation Guided by Truth Table | cs.LG | Logic synthesis, a critical stage in electronic design automation (EDA),
optimizes gate-level circuits to minimize power consumption and area occupancy
in integrated circuits (ICs). Traditional logic synthesis tools rely on
human-designed heuristics, often yielding suboptimal results. Although
differentiable architec... |
2502.12752 | High-Fidelity Novel View Synthesis via Splatting-Guided Diffusion | cs.CV | Despite recent advances in Novel View Synthesis (NVS), generating
high-fidelity views from single or sparse observations remains a significant
challenge. Existing splatting-based approaches often produce distorted geometry
due to splatting errors. While diffusion-based methods leverage rich 3D priors
to achieve impro... |
2502.12753 | Green LIME: Improving AI Explainability through Design of Experiments | stat.ML cs.LG stat.ME | In artificial intelligence (AI), the complexity of many models and processes
often surpasses human interpretability, making it challenging to understand why
a specific prediction is made. This lack of transparency is particularly
problematic in critical fields like healthcare, where trust in a model's
predictions is ... |
2502.12755 | Efficient Machine Translation Corpus Generation: Integrating
Human-in-the-Loop Post-Editing with Large Language Models | cs.CL cs.AI cs.HC | This paper introduces an advanced methodology for machine translation (MT)
corpus generation, integrating semi-automated, human-in-the-loop post-editing
with large language models (LLMs) to enhance efficiency and translation
quality. Building upon previous work that utilized real-time training of a
custom MT quality ... |
2502.12756 | Navigating Demand Uncertainty in Container Shipping: Deep Reinforcement
Learning for Enabling Adaptive and Feasible Master Stowage Planning | cs.LG math.OC | Reinforcement learning (RL) has shown promise in solving various
combinatorial optimization problems. However, conventional RL faces challenges
when dealing with real-world constraints, especially when action space
feasibility is explicit and dependent on the corresponding state or trajectory.
In this work, we focus ... |
2502.12759 | High-Fidelity Music Vocoder using Neural Audio Codecs | cs.SD cs.LG | While neural vocoders have made significant progress in high-fidelity speech
synthesis, their application on polyphonic music has remained underexplored. In
this work, we propose DisCoder, a neural vocoder that leverages a generative
adversarial encoder-decoder architecture informed by a neural audio codec to
reconst... |
2502.12762 | One-bit Compressed Sensing using Generative Models | cs.LG eess.SP | This paper addresses the classical problem of one-bit compressed sensing
using a deep learning-based reconstruction algorithm that leverages a trained
generative model to enhance the signal reconstruction performance. The
generator, a pre-trained neural network, learns to map from a low-dimensional
latent space to a ... |
2502.12767 | R2-KG: General-Purpose Dual-Agent Framework for Reliable Reasoning on
Knowledge Graphs | cs.CL cs.AI | Recent studies have combined Large Language Models (LLMs) with Knowledge
Graphs (KGs) to enhance reasoning, improving inference accuracy without
additional training while mitigating hallucination. However, existing
frameworks are often rigid, struggling to adapt to KG or task changes. They
also rely heavily on powerf... |
2502.12769 | How Much Do LLMs Hallucinate across Languages? On Multilingual
Estimation of LLM Hallucination in the Wild | cs.CL cs.AI | In the age of misinformation, hallucination -- the tendency of Large Language
Models (LLMs) to generate non-factual or unfaithful responses -- represents the
main risk for their global utility. Despite LLMs becoming increasingly
multilingual, the vast majority of research on detecting and quantifying LLM
hallucinatio... |
2502.12771 | Mind the Gap: Aligning the Brain with Language Models Requires a
Nonlinear and Multimodal Approach | cs.CL q-bio.NC | Self-supervised language and audio models effectively predict brain responses
to speech. However, traditional prediction models rely on linear mappings from
unimodal features, despite the complex integration of auditory signals with
linguistic and semantic information across widespread brain networks during
speech co... |
2502.12776 | Portable Reward Tuning: Towards Reusable Fine-Tuning across Different
Pretrained Models | cs.LG cs.AI stat.ML | While foundation models have been exploited for various expert tasks through
fine-tuning, any foundation model will become outdated due to its old knowledge
or limited capability. Thus the underlying foundation model should be
eventually replaced by new ones, which leads to repeated cost of fine-tuning
these new mode... |
2502.12777 | Evaluating link prediction: New perspectives and recommendations | cs.SI cs.AI | Link prediction (LP) is an important problem in network science and machine
learning research. The state-of-the-art LP methods are usually evaluated in a
uniform setup, ignoring several factors associated with the data and
application specific needs. We identify a number of such factors, such as,
network-type, proble... |
2502.12779 | Dependence and Uncertainty: Information Measures using Tsallis Entropy | stat.ME cs.IT math.IT | In multivariate analysis, uncertainty arises from two sources: the marginal
distributions of the variables and their dependence structure. Quantifying the
dependence structure is crucial, as it provides valuable insights into the
relationships among components of a random vector. Copula functions effectively
capture ... |
2502.12782 | VidCapBench: A Comprehensive Benchmark of Video Captioning for
Controllable Text-to-Video Generation | cs.AI | The training of controllable text-to-video (T2V) models relies heavily on the
alignment between videos and captions, yet little existing research connects
video caption evaluation with T2V generation assessment. This paper introduces
VidCapBench, a video caption evaluation scheme specifically designed for T2V
generat... |
2502.12786 | Composition and Control with Distilled Energy Diffusion Models and
Sequential Monte Carlo | stat.ML cs.LG | Diffusion models may be formulated as a time-indexed sequence of energy-based
models, where the score corresponds to the negative gradient of an energy
function. As opposed to learning the score directly, an energy parameterization
is attractive as the energy itself can be used to control generation via Monte
Carlo s... |
2502.12788 | Commonsense Reasoning in Arab Culture | cs.CL | Despite progress in Arabic large language models, such as Jais and AceGPT,
their evaluation on commonsense reasoning has largely relied on
machine-translated datasets, which lack cultural depth and may introduce
Anglocentric biases. Commonsense reasoning is shaped by geographical and
cultural contexts, and existing E... |
2502.12791 | Beyond Timesteps: A Novel Activation-wise Membrane Potential Propagation
Mechanism for Spiking Neural Networks in 3D cloud | cs.CV cs.LG | Due to the similar characteristics between event-based visual data and point
clouds, recent studies have emerged that treat event data as event clouds to
learn based on point cloud analysis. Additionally, some works approach point
clouds from the perspective of event vision, employing Spiking Neural Network
(SNN) due... |
2502.12793 | Unsupervised Anomaly Detection through Mass Repulsing Optimal Transport | stat.ML cs.AI cs.LG | Detecting anomalies in datasets is a longstanding problem in machine
learning. In this context, anomalies are defined as a sample that significantly
deviates from the remaining data. Meanwhile, optimal transport (OT) is a field
of mathematics concerned with the transportation, between two probability
measures, at lea... |
2502.12794 | RAPID: Retrieval Augmented Training of Differentially Private Diffusion
Models | cs.CR cs.CV cs.LG | Differentially private diffusion models (DPDMs) harness the remarkable
generative capabilities of diffusion models while enforcing differential
privacy (DP) for sensitive data. However, existing DPDM training approaches
often suffer from significant utility loss, large memory footprint, and
expensive inference cost, ... |
2502.12796 | Learning Counterfactually Fair Models via Improved Generation with
Neural Causal Models | cs.LG | One of the main concerns while deploying machine learning models in
real-world applications is fairness. Counterfactual fairness has emerged as an
intuitive and natural definition of fairness. However, existing methodologies
for enforcing counterfactual fairness seem to have two limitations: (i)
generating counterfac... |
2502.12798 | Envious Explore and Exploit | cs.GT cs.AI cs.LG | Explore-and-exploit tradeoffs play a key role in recommendation systems
(RSs), aiming at serving users better by learning from previous interactions.
Despite their commercial success, the societal effects of explore-and-exploit
mechanisms are not well understood, especially regarding the utility
discrepancy they gene... |
2502.12799 | Towards Text-Image Interleaved Retrieval | cs.CL cs.CV cs.IR | Current multimodal information retrieval studies mainly focus on single-image
inputs, which limits real-world applications involving multiple images and
text-image interleaved content. In this work, we introduce the text-image
interleaved retrieval (TIIR) task, where the query and document are interleaved
text-image ... |
2502.12801 | Learning Wall Segmentation in 3D Vessel Trees using Sparse Annotations | cs.CV | We propose a novel approach that uses sparse annotations from clinical
studies to train a 3D segmentation of the carotid artery wall. We use a
centerline annotation to sample perpendicular cross-sections of the carotid
artery and use an adversarial 2D network to segment them. These annotations are
then transformed in... |
2502.12802 | PPGF: Probability Pattern-Guided Time Series Forecasting | cs.LG | Time series forecasting (TSF) is an essential branch of machine learning with
various applications. Most methods for TSF focus on constructing different
networks to extract better information and improve performance. However,
practical application data contain different internal mechanisms, resulting in
a mixture of ... |
2502.12803 | Design Optimization of Musculoskeletal Humanoids with Maximization of
Redundancy to Compensate for Muscle Rupture | cs.RO | Musculoskeletal humanoids have various biomimetic advantages, and the
redundant muscle arrangement allowing for variable stiffness control is one of
the most important. In this study, we focus on one feature of the redundancy,
which enables the humanoid to keep moving even if one of its muscles breaks, an
advantage t... |
2502.12804 | Reinforcement Learning for Dynamic Resource Allocation in Optical
Networks: Hype or Hope? | cs.NI cs.LG cs.SY eess.SY | The application of reinforcement learning (RL) to dynamic resource allocation
in optical networks has been the focus of intense research activity in recent
years, with almost 100 peer-reviewed papers. We present a review of progress in
the field, and identify significant gaps in benchmarking practices and
reproducibi... |
2502.12807 | An improved wind power prediction via a novel wind ramp identification
algorithm | cs.LG | Authors: Yifan Xu Abstract: Conventional wind power prediction methods often
struggle to provide accurate and reliable predictions in the presence of sudden
changes in wind speed and power output. To address this challenge, this study
proposes an integrated algorithm that combines a wind speed mutation
identification... |
2502.12808 | Exceeding the Maximum Speed Limit of the Joint Angle for the Redundant
Tendon-driven Structures of Musculoskeletal Humanoids | cs.RO | The musculoskeletal humanoid has various biomimetic benefits, and the
redundant muscle arrangement is one of its most important characteristics. This
redundancy can achieve fail-safe redundant actuation and variable stiffness
control. However, there is a problem that the maximum joint angle velocity is
limited by the... |
2502.12810 | Frequency-domain alignment of heterogeneous, multidimensional
separations data through complex orthogonal Procrustes analysis | math.NA cs.LG cs.NA | Multidimensional separations data have the capacity to reveal detailed
information about complex biological samples. However, data analysis has been
an ongoing challenge in the area since the peaks that represent chemical
factors may drift over the course of several analytical runs along the first
and second dimensio... |
2502.12811 | Applications of Stretch Reflex for the Upper Limb of Musculoskeletal
Humanoids: Protective Behavior, Postural Stability, and Active Induction | cs.RO | The musculoskeletal humanoid has various biomimetic benefits, and it is
important that we can embed and evaluate human reflexes in the actual robot.
Although stretch reflex has been implemented in lower limbs of musculoskeletal
humanoids, we apply it to the upper limb to discover its useful applications.
We consider ... |
2502.12813 | Simulating User Diversity in Task-Oriented Dialogue Systems using Large
Language Models | cs.CL | In this study, we explore the application of Large Language Models (LLMs) for
generating synthetic users and simulating user conversations with a
task-oriented dialogue system and present detailed results and their analysis.
We propose a comprehensive novel approach to user simulation technique that
uses LLMs to crea... |
2502.12819 | Carotid Artery Plaque Analysis in 3D Based on Distance Encoding in Mesh
Representations | cs.CV | Purpose: Enabling a comprehensive and robust assessment of carotid artery
plaques in 3D through extraction and visualization of quantitative plaque
parameters. These parameters have potential applications in stroke risk
analysis, evaluation of therapy effectiveness, and plaque progression
prediction. Methods: We prop... |
2502.12821 | Pitfalls of Scale: Investigating the Inverse Task of Redefinition in
Large Language Models | cs.CL | Inverse tasks can uncover potential reasoning gaps as Large Language Models
(LLMs) scale up. In this work, we explore the redefinition task, in which we
assign alternative values to well-known physical constants and units of
measure, prompting LLMs to respond accordingly. Our findings show that not only
does model pe... |
2502.12825 | Reasoning and the Trusting Behavior of DeepSeek and GPT: An Experiment
Revealing Hidden Fault Lines in Large Language Models | cs.CL cs.AI | When encountering increasingly frequent performance improvements or cost
reductions from a new large language model (LLM), developers of applications
leveraging LLMs must decide whether to take advantage of these improvements or
stay with older tried-and-tested models. Low perceived switching frictions can
lead to ch... |
2502.12829 | KazMMLU: Evaluating Language Models on Kazakh, Russian, and Regional
Knowledge of Kazakhstan | cs.CL | Despite having a population of twenty million, Kazakhstan's culture and
language remain underrepresented in the field of natural language processing.
Although large language models (LLMs) continue to advance worldwide, progress
in Kazakh language has been limited, as seen in the scarcity of dedicated
models and bench... |
2502.12834 | NTP-INT: Network Traffic Prediction-Driven In-band Network Telemetry for
High-load Switches | cs.NI cs.LG | In-band network telemetry (INT) is essential to network management due to its
real-time visibility. However, because of the rapid increase in network devices
and services, it has become crucial to have targeted access to detailed network
information in a dynamic network environment. This paper proposes an
intelligent... |
2502.12835 | Subword models struggle with word learning, but surprisal hides it | cs.CL | We study word learning in subword and character language models with the
psycholinguistic lexical decision task. While subword LMs struggle to discern
words and non-words with high accuracy, character LMs solve this task easily
and consistently. Furthermore, when comparing word learning and syntactic
learning, both p... |
2502.12836 | An LLM-Powered Agent for Physiological Data Analysis: A Case Study on
PPG-based Heart Rate Estimation | cs.CL | Large language models (LLMs) are revolutionizing healthcare by improving
diagnosis, patient care, and decision support through interactive
communication. More recently, they have been applied to analyzing physiological
time-series like wearable data for health insight extraction. Existing methods
embed raw numerical ... |
2502.12838 | Towards Equitable AI: Detecting Bias in Using Large Language Models for
Marketing | cs.CY cs.CL | The recent advances in large language models (LLMs) have revolutionized
industries such as finance, marketing, and customer service by enabling
sophisticated natural language processing tasks. However, the broad adoption of
LLMs brings significant challenges, particularly in the form of social biases
that can be embe... |
2502.12842 | Towards Adaptive Feedback with AI: Comparing the Feedback Quality of
LLMs and Teachers on Experimentation Protocols | cs.AI cs.HC | Effective feedback is essential for fostering students' success in scientific
inquiry. With advancements in artificial intelligence, large language models
(LLMs) offer new possibilities for delivering instant and adaptive feedback.
However, this feedback often lacks the pedagogical validation provided by
real-world p... |
2502.12845 | MOLLM: Multi-Objective Large Language Model for Molecular Design --
Optimizing with Experts | cs.LG | Molecular design plays a critical role in advancing fields such as drug
discovery, materials science, and chemical engineering. This work introduces
the Multi-Objective Large Language Model for Molecular Design (MOLLM), a novel
framework that combines domain-specific knowledge with the adaptability of
Large Language ... |
2502.12847 | Characterizing the Interaction of Cultural Evolution Mechanisms in
Experimental Social Networks | cs.SI q-bio.NC q-bio.PE | Understanding how cognitive and social mechanisms shape the evolution of
complex artifacts such as songs is central to cultural evolution research.
Social network topology (what artifacts are available?), selection (which are
chosen?), and reproduction (how are they copied?) have all been proposed as key
influencing ... |
2502.12849 | Leveraging Intermediate Representations for Better Out-of-Distribution
Detection | cs.LG cs.CV | In real-world applications, machine learning models must reliably detect
Out-of-Distribution (OoD) samples to prevent unsafe decisions. Current OoD
detection methods often rely on analyzing the logits or the embeddings of the
penultimate layer of a neural network. However, little work has been conducted
on the exploi... |
2502.12851 | MeMo: Towards Language Models with Associative Memory Mechanisms | cs.CL cs.AI | Memorization is a fundamental ability of Transformer-based Large Language
Models, achieved through learning. In this paper, we propose a paradigm shift
by designing an architecture to memorize text directly, bearing in mind the
principle that memorization precedes learning. We introduce MeMo, a novel
architecture for... |
2502.12852 | MVL-SIB: A Massively Multilingual Vision-Language Benchmark for
Cross-Modal Topical Matching | cs.CL | Existing multilingual vision-language (VL) benchmarks often only cover a
handful of languages. Consequently, evaluations of large vision-language models
(LVLMs) predominantly target high-resource languages, underscoring the need for
evaluation data for low-resource languages. To address this limitation, we
introduce ... |
2502.12853 | S$^2$R: Teaching LLMs to Self-verify and Self-correct via Reinforcement
Learning | cs.CL cs.LG | Recent studies have demonstrated the effectiveness of LLM test-time scaling.
However, existing approaches to incentivize LLMs' deep thinking abilities
generally require large-scale data or significant training efforts. Meanwhile,
it remains unclear how to improve the thinking abilities of less powerful base
models. I... |
2502.12855 | Integrating Arithmetic Learning Improves Mathematical Reasoning in
Smaller Models | cs.CL cs.AI cs.LG | While large models pre-trained on high-quality data exhibit excellent
performance across various reasoning tasks, including mathematical reasoning
(e.g. GSM8k, MultiArith), specializing smaller models to excel at mathematical
reasoning remains a challenging problem. Common approaches to address this
challenge include... |
2502.12858 | Rejected Dialects: Biases Against African American Language in Reward
Models | cs.CL cs.AI cs.CY | Preference alignment via reward models helps build safe, helpful, and
reliable large language models (LLMs). However, subjectivity in preference
judgments and the lack of representative sampling in preference data collection
can introduce new biases, hindering reward models' fairness and equity. In this
work, we intr... |
2502.12859 | PAFT: Prompt-Agnostic Fine-Tuning | cs.CL cs.AI | While Large Language Models (LLMs) adapt well to downstream tasks after
fine-tuning, this adaptability often compromises prompt robustness, as even
minor prompt variations can significantly degrade performance. To address this,
we propose Prompt-Agnostic Fine-Tuning(PAFT), a simple yet effective approach
that dynamic... |
2502.12860 | An Experimental Study of SOTA LiDAR Segmentation Models | cs.CV | Point cloud segmentation (PCS) is to classify each point in point clouds. The
task enables robots to parse their 3D surroundings and run autonomously.
According to different point cloud representations, existing PCS models can be
roughly divided into point-, voxel-, and range image-based models. However, no
work has ... |
2502.12861 | InstructRobot: A Model-Free Framework for Mapping Natural Language
Instructions into Robot Motion | cs.RO | The ability to communicate with robots using natural language is a
significant step forward in human-robot interaction. However, accurately
translating verbal commands into physical actions is promising, but still
presents challenges. Current approaches require large datasets to train the
models and are limited to ro... |
2502.12862 | RobotIQ: Empowering Mobile Robots with Human-Level Planning for
Real-World Execution | cs.RO cs.SY eess.SY | This paper introduces RobotIQ, a framework that empowers mobile robots with
human-level planning capabilities, enabling seamless communication via natural
language instructions through any Large Language Model. The proposed framework
is designed in the ROS architecture and aims to bridge the gap between humans
and ro... |
2502.12863 | Malware Detection based on API calls | cs.CR cs.LG | Malware attacks pose a significant threat in today's interconnected digital
landscape, causing billions of dollars in damages. Detecting and identifying
families as early as possible provides an edge in protecting against such
malware. We explore a lightweight, order-invariant approach to detecting and
mitigating mal... |
2502.12874 | Testing for Causal Fairness | cs.LG | Causality is widely used in fairness analysis to prevent discrimination on
sensitive attributes, such as genders in career recruitment and races in crime
prediction. However, the current data-based Potential Outcomes Framework (POF)
often leads to untrustworthy fairness analysis results when handling
high-dimensional... |
2502.12876 | Continuous Learning Conversational AI: A Personalized Agent Framework
via A2C Reinforcement Learning | cs.AI | Creating personalized and adaptable conversational AI remains a key
challenge. This paper introduces a Continuous Learning Conversational AI (CLCA)
approach, implemented using A2C reinforcement learning, to move beyond static
Large Language Models (LLMs). We use simulated sales dialogues, generated by
LLMs, to train ... |
2502.12877 | Pushing the Limits of the Reactive Affine Shaker Algorithm to Higher
Dimensions | math.NA cs.LG cs.NA | Bayesian Optimization (BO) for the minimization of expensive functions of
continuous variables uses all the knowledge acquired from previous samples
(${\boldsymbol x}_i$ and $f({\boldsymbol x}_i)$ values) to build a surrogate
model based on Gaussian processes. The surrogate is then exploited to define
the next point ... |
2502.12884 | How desirable is alignment between LLMs and linguistically diverse human
users? | cs.CL | We discuss how desirable it is that Large Language Models (LLMs) be able to
adapt or align their language behavior with users who may be diverse in their
language use. User diversity may come about among others due to i) age
differences; ii) gender characteristics, and/or iii) multilingual experience,
and associated ... |
2502.12886 | Are Multilingual Language Models an Off-ramp for Under-resourced
Languages? Will we arrive at Digital Language Equality in Europe in 2030? | cs.CL | Large language models (LLMs) demonstrate unprecedented capabilities and
define the state of the art for almost all natural language processing (NLP)
tasks and also for essentially all Language Technology (LT) applications. LLMs
can only be trained for languages for which a sufficient amount of pre-training
data is av... |
2502.12892 | Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept
Extraction in Large Vision Models | cs.CV | Sparse Autoencoders (SAEs) have emerged as a powerful framework for machine
learning interpretability, enabling the unsupervised decomposition of model
representations into a dictionary of abstract, human-interpretable concepts.
However, we reveal a fundamental limitation: existing SAEs exhibit severe
instability, as... |
2502.12893 | H-CoT: Hijacking the Chain-of-Thought Safety Reasoning Mechanism to
Jailbreak Large Reasoning Models, Including OpenAI o1/o3, DeepSeek-R1, and
Gemini 2.0 Flash Thinking | cs.CL | Large Reasoning Models (LRMs) have recently extended their powerful reasoning
capabilities to safety checks-using chain-of-thought reasoning to decide
whether a request should be answered. While this new approach offers a
promising route for balancing model utility and safety, its robustness remains
underexplored. To... |
2502.12894 | CAST: Component-Aligned 3D Scene Reconstruction from an RGB Image | cs.CV | Recovering high-quality 3D scenes from a single RGB image is a challenging
task in computer graphics. Current methods often struggle with domain-specific
limitations or low-quality object generation. To address these, we propose CAST
(Component-Aligned 3D Scene Reconstruction from a Single RGB Image), a novel
method ... |
2502.12895 | Multilingual European Language Models: Benchmarking Approaches and
Challenges | cs.CL | The breakthrough of generative large language models (LLMs) that can solve
different tasks through chat interaction has led to a significant increase in
the use of general benchmarks to assess the quality or performance of these
models beyond individual applications. There is also a need for better methods
to evaluat... |
2502.12896 | None of the Others: a General Technique to Distinguish Reasoning from
Memorization in Multiple-Choice LLM Evaluation Benchmarks | cs.CL | In LLM evaluations, reasoning is often distinguished from recall/memorization
by performing numerical variations to math-oriented questions. Here we
introduce a general variation method for multiple-choice questions that
completely dissociates the correct answer from previously seen tokens or
concepts, requiring LLMs... |
2502.12897 | On Zero Skip-Cost Generalized Fractional-Repetition Codes from Covering
Designs | cs.IT math.CO math.IT | We study generalized fractional repetition codes that have zero skip cost,
and which are based on covering designs. We show that a zero skip cost is
always attainable, perhaps at a price of an expansion factor compared with the
optimal size of fractional repetition codes based on Steiner systems. We
provide three con... |
2502.12898 | The Relationship Between Head Injury and Alzheimer's Disease: A Causal
Analysis with Bayesian Networks | cs.LG | This study examines the potential causal relationship between head injury and
the risk of developing Alzheimer's disease (AD) using Bayesian networks and
regression models. Using a dataset of 2,149 patients, we analyze key medical
history variables, including head injury history, memory complaints,
cardiovascular dis... |
2502.12900 | Soundwave: Less is More for Speech-Text Alignment in LLMs | cs.CL cs.AI cs.SD | Existing end-to-end speech large language models (LLMs) usually rely on
large-scale annotated data for training, while data-efficient training has not
been discussed in depth. We focus on two fundamental problems between speech
and text: the representation space gap and sequence length inconsistency. We
propose Sound... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.