id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.04356 | Open Foundation Models in Healthcare: Challenges, Paradoxes, and
Opportunities with GenAI Driven Personalized Prescription | cs.CL cs.AI cs.LG | In response to the success of proprietary Large Language Models (LLMs) such
as OpenAI's GPT-4, there is a growing interest in developing open,
non-proprietary LLMs and AI foundation models (AIFMs) for transparent use in
academic, scientific, and non-commercial applications. Despite their inability
to match the refine... |
2502.04357 | Reusing Embeddings: Reproducible Reward Model Research in Large Language
Model Alignment without GPUs | cs.CL cs.AI cs.LG | Large Language Models (LLMs) have made substantial strides in structured
tasks through Reinforcement Learning (RL), demonstrating proficiency in
mathematical reasoning and code generation. However, applying RL in broader
domains like chatbots and content generation -- through the process known as
Reinforcement Learni... |
2502.04358 | Position: Scaling LLM Agents Requires Asymptotic Analysis with LLM
Primitives | cs.CL cs.AI cs.CC cs.LG cs.NE | Decomposing hard problems into subproblems often makes them easier and more
efficient to solve. With large language models (LLMs) crossing critical
reliability thresholds for a growing slate of capabilities, there is an
increasing effort to decompose systems into sets of LLM-based agents, each of
whom can be delegate... |
2502.04359 | Exploring Spatial Language Grounding Through Referring Expressions | cs.CL cs.AI cs.CV | Spatial Reasoning is an important component of human cognition and is an area
in which the latest Vision-language models (VLMs) show signs of difficulty. The
current analysis works use image captioning tasks and visual question
answering. In this work, we propose using the Referring Expression
Comprehension task inst... |
2502.04360 | MARAGE: Transferable Multi-Model Adversarial Attack for
Retrieval-Augmented Generation Data Extraction | cs.CL cs.CR cs.LG | Retrieval-Augmented Generation (RAG) offers a solution to mitigate
hallucinations in Large Language Models (LLMs) by grounding their outputs to
knowledge retrieved from external sources. The use of private resources and
data in constructing these external data stores can expose them to risks of
extraction attacks, in... |
2502.04361 | Predicting 3D Motion from 2D Video for Behavior-Based VR Biometrics | cs.CV cs.AI cs.HC | Critical VR applications in domains such as healthcare, education, and
finance that use traditional credentials, such as PIN, password, or
multi-factor authentication, stand the chance of being compromised if a
malicious person acquires the user credentials or if the user hands over their
credentials to an ally. Rece... |
2502.04362 | LLMs can be easily Confused by Instructional Distractions | cs.CL cs.AI | Despite the fact that large language models (LLMs) show exceptional skill in
instruction following tasks, this strength can turn into a vulnerability when
the models are required to disregard certain instructions.
Instruction-following tasks typically involve a clear task description and
input text containing the tar... |
2502.04363 | On-device Sora: Enabling Diffusion-Based Text-to-Video Generation for
Mobile Devices | cs.CV | We present On-device Sora, a first pioneering solution for diffusion-based
on-device text-to-video generation that operates efficiently on
smartphone-grade devices. Building on Open-Sora, On-device Sora applies three
novel techniques to address the challenges of diffusion-based text-to-video
generation on computation... |
2502.04364 | Lost in Edits? A $\lambda$-Compass for AIGC Provenance | cs.CV cs.AI cs.HC cs.LG | Recent advancements in diffusion models have driven the growth of text-guided
image editing tools, enabling precise and iterative modifications of
synthesized content. However, as these tools become increasingly accessible,
they also introduce significant risks of misuse, emphasizing the critical need
for robust attr... |
2502.04365 | AI-Based Thermal Video Analysis in Privacy-Preserving Healthcare: A Case
Study on Detecting Time of Birth | cs.CV cs.AI | Approximately 10% of newborns need some assistance to start breathing and 5\%
proper ventilation. It is crucial that interventions are initiated as soon as
possible after birth. Accurate documentation of Time of Birth (ToB) is thereby
essential for documenting and improving newborn resuscitation performance.
However,... |
2502.04366 | Contrastive Token-level Explanations for Graph-based Rumour Detection | cs.CL cs.AI cs.LG | The widespread use of social media has accelerated the dissemination of
information, but it has also facilitated the spread of harmful rumours, which
can disrupt economies, influence political outcomes, and exacerbate public
health crises, such as the COVID-19 pandemic. While Graph Neural Network
(GNN)-based approach... |
2502.04367 | Hybrid Deep Learning Framework for Classification of Kidney CT Images:
Diagnosis of Stones, Cysts, and Tumors | eess.IV cs.CV cs.LG | Medical image classification is a vital research area that utilizes advanced
computational techniques to improve disease diagnosis and treatment planning.
Deep learning models, especially Convolutional Neural Networks (CNNs), have
transformed this field by providing automated and precise analysis of complex
medical i... |
2502.04369 | HSI: A Holistic Style Injector for Arbitrary Style Transfer | cs.CV cs.LG eess.IV | Attention-based arbitrary style transfer methods have gained significant
attention recently due to their impressive ability to synthesize style details.
However, the point-wise matching within the attention mechanism may overly
focus on local patterns such that neglect the remarkable global features of
style images. ... |
2502.04370 | DreamDPO: Aligning Text-to-3D Generation with Human Preferences via
Direct Preference Optimization | cs.CL cs.GR cs.LG | Text-to-3D generation automates 3D content creation from textual
descriptions, which offers transformative potential across various fields.
However, existing methods often struggle to align generated content with human
preferences, limiting their applicability and flexibility. To address these
limitations, in this pa... |
2502.04371 | PerPO: Perceptual Preference Optimization via Discriminative Rewarding | cs.AI cs.CL cs.LG | This paper presents Perceptual Preference Optimization (PerPO), a perception
alignment method aimed at addressing the visual discrimination challenges in
generative pre-trained multimodal large language models (MLLMs). To align MLLMs
with human visual perception process, PerPO employs discriminative rewarding to
gath... |
2502.04372 | Mining Unstructured Medical Texts With Conformal Active Learning | cs.CL cs.LG stat.ML | The extraction of relevant data from Electronic Health Records (EHRs) is
crucial to identifying symptoms and automating epidemiological surveillance
processes. By harnessing the vast amount of unstructured text in EHRs, we can
detect patterns that indicate the onset of disease outbreaks, enabling faster,
more targete... |
2502.04375 | An Analysis for Reasoning Bias of Language Models with Small
Initialization | cs.CL cs.LG | Transformer-based Large Language Models (LLMs) have revolutionized Natural
Language Processing by demonstrating exceptional performance across diverse
tasks. This study investigates the impact of the parameter initialization scale
on the training behavior and task preferences of LLMs. We discover that smaller
initial... |
2502.04376 | MEETING DELEGATE: Benchmarking LLMs on Attending Meetings on Our Behalf | cs.CL cs.AI | In contemporary workplaces, meetings are essential for exchanging ideas and
ensuring team alignment but often face challenges such as time consumption,
scheduling conflicts, and inefficient participation. Recent advancements in
Large Language Models (LLMs) have demonstrated their strong capabilities in
natural langua... |
2502.04377 | MapFusion: A Novel BEV Feature Fusion Network for Multi-modal Map
Construction | cs.CV cs.AI | Map construction task plays a vital role in providing precise and
comprehensive static environmental information essential for autonomous driving
systems. Primary sensors include cameras and LiDAR, with configurations varying
between camera-only, LiDAR-only, or camera-LiDAR fusion, based on
cost-performance considera... |
2502.04378 | DILLEMA: Diffusion and Large Language Models for Multi-Modal
Augmentation | cs.CV cs.GR cs.LG cs.SE | Ensuring the robustness of deep learning models requires comprehensive and
diverse testing. Existing approaches, often based on simple data augmentation
techniques or generative adversarial networks, are limited in producing
realistic and varied test cases. To address these limitations, we present a
novel framework f... |
2502.04379 | Can Large Language Models Capture Video Game Engagement? | cs.CV cs.AI cs.CL cs.HC | Can out-of-the-box pretrained Large Language Models (LLMs) detect human
affect successfully when observing a video? To address this question, for the
first time, we evaluate comprehensively the capacity of popular LLMs to
annotate and successfully predict continuous affect annotations of videos when
prompted by a seq... |
2502.04380 | Diversity as a Reward: Fine-Tuning LLMs on a Mixture of
Domain-Undetermined Data | cs.CL cs.AI cs.LG | Fine-tuning large language models (LLMs) using diverse datasets is crucial
for enhancing their overall performance across various domains. In practical
scenarios, existing methods based on modeling the mixture proportions of data
composition often struggle with data whose domain labels are missing, imprecise
or non-n... |
2502.04381 | Limitations of Large Language Models in Clinical Problem-Solving Arising
from Inflexible Reasoning | cs.CL cs.AI | Large Language Models (LLMs) have attained human-level accuracy on medical
question-answer (QA) benchmarks. However, their limitations in navigating
open-ended clinical scenarios have recently been shown, raising concerns about
the robustness and generalizability of LLM reasoning across diverse, real-world
medical ta... |
2502.04382 | Sparse Autoencoders for Hypothesis Generation | cs.CL cs.AI cs.CY | We describe HypotheSAEs, a general method to hypothesize interpretable
relationships between text data (e.g., headlines) and a target variable (e.g.,
clicks). HypotheSAEs has three steps: (1) train a sparse autoencoder on text
embeddings to produce interpretable features describing the data distribution,
(2) select f... |
2502.04384 | Enhancing Reasoning to Adapt Large Language Models for Domain-Specific
Applications | cs.CL cs.AI cs.LG cs.SY eess.SY | This paper presents SOLOMON, a novel Neuro-inspired Large Language Model
(LLM) Reasoning Network architecture that enhances the adaptability of
foundation models for domain-specific applications. Through a case study in
semiconductor layout design, we demonstrate how SOLOMON enables swift
adaptation of general-purpos... |
2502.04385 | TexLiDAR: Automated Text Understanding for Panoramic LiDAR Data | cs.CV cs.AI | Efforts to connect LiDAR data with text, such as LidarCLIP, have primarily
focused on embedding 3D point clouds into CLIP text-image space. However, these
approaches rely on 3D point clouds, which present challenges in encoding
efficiency and neural network processing. With the advent of advanced LiDAR
sensors like O... |
2502.04386 | Towards Fair Medical AI: Adversarial Debiasing of 3D CT Foundation
Embeddings | cs.CV cs.AI cs.LG | Self-supervised learning has revolutionized medical imaging by enabling
efficient and generalizable feature extraction from large-scale unlabeled
datasets. Recently, self-supervised foundation models have been extended to
three-dimensional (3D) computed tomography (CT) data, generating compact,
information-rich embed... |
2502.04387 | FedP$^2$EFT: Federated Learning to Personalize Parameter Efficient
Fine-Tuning for Multilingual LLMs | cs.CL cs.AI | Federated learning (FL) has enabled the training of multilingual large
language models (LLMs) on diverse and decentralized multilingual data,
especially on low-resource languages. To improve client-specific performance,
personalization via the use of parameter-efficient fine-tuning (PEFT) modules
such as LoRA is comm... |
2502.04388 | Position: Emergent Machina Sapiens Urge Rethinking Multi-Agent Paradigms | cs.MA cs.AI | Artificially intelligent (AI) agents that are capable of autonomous learning
and independent decision-making hold great promise for addressing complex
challenges across domains like transportation, energy systems, and
manufacturing. However, the surge in AI systems' design and deployment driven
by various stakeholder... |
2502.04389 | Overcoming Vision Language Model Challenges in Diagram Understanding: A
Proof-of-Concept with XML-Driven Large Language Models Solutions | cs.SE cs.AI | Diagrams play a crucial role in visually conveying complex relationships and
processes within business documentation. Despite recent advances in
Vision-Language Models (VLMs) for various image understanding tasks, accurately
identifying and extracting the structures and relationships depicted in
diagrams continues to... |
2502.04390 | In Praise of Stubbornness: The Case for Cognitive-Dissonance-Aware
Knowledge Updates in LLMs | cs.CL cs.AI cs.LG q-bio.NC | Despite remarkable capabilities, large language models (LLMs) struggle to
continually update their knowledge without catastrophic forgetting. In
contrast, humans effortlessly integrate new information, detect conflicts with
existing beliefs, and selectively update their mental models. This paper
introduces a cognitiv... |
2502.04391 | Towards Fair and Robust Face Parsing for Generative AI: A
Multi-Objective Approach | cs.CV cs.AI | Face parsing is a fundamental task in computer vision, enabling applications
such as identity verification, facial editing, and controllable image
synthesis. However, existing face parsing models often lack fairness and
robustness, leading to biased segmentation across demographic groups and errors
under occlusions, ... |
2502.04392 | Division-of-Thoughts: Harnessing Hybrid Language Model Synergy for
Efficient On-Device Agents | cs.CL cs.AI | The rapid expansion of web content has made on-device AI assistants
indispensable for helping users manage the increasing complexity of online
tasks. The emergent reasoning ability in large language models offer a
promising path for next-generation on-device AI agents. However, deploying
full-scale Large Language Mod... |
2502.04393 | UniCP: A Unified Caching and Pruning Framework for Efficient Video
Generation | cs.CV | Diffusion Transformers (DiT) excel in video generation but encounter
significant computational challenges due to the quadratic complexity of
attention. Notably, attention differences between adjacent diffusion steps
follow a U-shaped pattern. Current methods leverage this property by caching
attention blocks, however... |
2502.04394 | DECT: Harnessing LLM-assisted Fine-Grained Linguistic Knowledge and
Label-Switched and Label-Preserved Data Generation for Diagnosis of
Alzheimer's Disease | cs.CL cs.AI | Alzheimer's Disease (AD) is an irreversible neurodegenerative disease
affecting 50 million people worldwide. Low-cost, accurate identification of key
markers of AD is crucial for timely diagnosis and intervention. Language
impairment is one of the earliest signs of cognitive decline, which can be used
to discriminate... |
2502.04395 | Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time
Series Forecasting | cs.CV cs.LG | Recent advancements in time series forecasting have explored augmenting
models with text or vision modalities to improve accuracy. While text provides
contextual understanding, it often lacks fine-grained temporal details.
Conversely, vision captures intricate temporal patterns but lacks semantic
context, limiting th... |
2502.04397 | Multimodal Medical Code Tokenizer | cs.CL cs.AI cs.LG | Foundation models trained on patient electronic health records (EHRs) require
tokenizing medical data into sequences of discrete vocabulary items. Existing
tokenizers treat medical codes from EHRs as isolated textual tokens. However,
each medical code is defined by its textual description, its position in
ontological... |
2502.04398 | XMTC: Explainable Early Classification of Multivariate Time Series in
Reach-to-Grasp Hand Kinematics | cs.LG cs.GR cs.HC | Hand kinematics can be measured in Human-Computer Interaction (HCI) with the
intention to predict the user's intention in a reach-to-grasp action. Using
multiple hand sensors, multivariate time series data are being captured. Given
a number of possible actions on a number of objects, the goal is to classify
the multi... |
2502.04399 | Online Location Planning for AI-Defined Vehicles: Optimizing Joint Tasks
of Order Serving and Spatio-Temporal Heterogeneous Model Fine-Tuning | cs.LG cs.AI cs.SY eess.SY | Advances in artificial intelligence (AI) including foundation models (FMs),
are increasingly transforming human society, with smart city driving the
evolution of urban living.Meanwhile, vehicle crowdsensing (VCS) has emerged as
a key enabler, leveraging vehicles' mobility and sensor-equipped capabilities.
In particul... |
2502.04400 | Adaptive Prototype Knowledge Transfer for Federated Learning with Mixed
Modalities and Heterogeneous Tasks | cs.LG cs.AI cs.CR cs.MM | Multimodal Federated Learning (MFL) enables multiple clients to
collaboratively train models on multimodal data while ensuring clients'
privacy. However, modality and task heterogeneity hinder clients from learning
a unified representation, weakening local model generalization, especially in
MFL with mixed modalities... |
2502.04402 | Beyond Interpolation: Extrapolative Reasoning with Reinforcement
Learning and Graph Neural Networks | cs.LG cs.AI | Despite incredible progress, many neural architectures fail to properly
generalize beyond their training distribution. As such, learning to reason in a
correct and generalizable way is one of the current fundamental challenges in
machine learning. In this respect, logic puzzles provide a great testbed, as we
can full... |
2502.04403 | Agency Is Frame-Dependent | cs.AI | Agency is a system's capacity to steer outcomes toward a goal, and is a
central topic of study across biology, philosophy, cognitive science, and
artificial intelligence. Determining if a system exhibits agency is a
notoriously difficult question: Dennett (1989), for instance, highlights the
puzzle of determining whi... |
2502.04404 | Step Back to Leap Forward: Self-Backtracking for Boosting Reasoning of
Language Models | cs.CL cs.AI | The integration of slow-thinking mechanisms into large language models (LLMs)
offers a promising way toward achieving Level 2 AGI Reasoners, as exemplified
by systems like OpenAI's o1. However, several significant challenges remain,
including inefficient overthinking and an overreliance on auxiliary reward
models. We... |
2502.04405 | FAS: Fast ANN-SNN Conversion for Spiking Large Language Models | cs.LG cs.AI cs.CL | Spiking Large Language Models have been shown as a good alternative to LLMs
in various scenarios. Existing methods for creating Spiking LLMs, i.e., direct
training and ANN-SNN conversion, often suffer from performance degradation and
relatively high computational costs. To address these issues, we propose a
novel Fas... |
2502.04406 | Calibrated Physics-Informed Uncertainty Quantification | cs.LG cs.AI physics.comp-ph | Neural PDEs offer efficient alternatives to computationally expensive
numerical PDE solvers for simulating complex physical systems. However, their
lack of robust uncertainty quantification (UQ) limits deployment in critical
applications. We introduce a model-agnostic, physics-informed conformal
prediction (CP) frame... |
2502.04407 | Illuminating Spaces: Deep Reinforcement Learning and Laser-Wall
Partitioning for Architectural Layout Generation | cs.LG cs.AI | Space layout design (SLD), occurring in the early stages of the design
process, nonetheless influences both the functionality and aesthetics of the
ultimate architectural outcome. The complexity of SLD necessitates innovative
approaches to efficiently explore vast solution spaces. While image-based
generative AI has ... |
2502.04408 | Transforming Multimodal Models into Action Models for Radiotherapy | cs.LG cs.AI | Radiotherapy is a crucial cancer treatment that demands precise planning to
balance tumor eradication and preservation of healthy tissue. Traditional
treatment planning (TP) is iterative, time-consuming, and reliant on human
expertise, which can potentially introduce variability and inefficiency. We
propose a novel f... |
2502.04409 | Learning low-dimensional representations of ensemble forecast fields
using autoencoder-based methods | cs.LG physics.ao-ph | Large-scale numerical simulations often produce high-dimensional gridded data
that is challenging to process for downstream applications. A prime example is
numerical weather prediction, where atmospheric processes are modeled using
discrete gridded representations of the physical variables and dynamics.
Uncertaintie... |
2502.04411 | Mediator: Memory-efficient LLM Merging with Less Parameter Conflicts and
Uncertainty Based Routing | cs.LG cs.AI cs.CL | Model merging aggregates Large Language Models (LLMs) finetuned on different
tasks into a stronger one. However, parameter conflicts between models leads to
performance degradation in averaging. While model routing addresses this issue
by selecting individual models during inference, it imposes excessive storage
and ... |
2502.04412 | Decoder-Only LLMs are Better Controllers for Diffusion Models | cs.CV cs.AI cs.CL | Groundbreaking advancements in text-to-image generation have recently been
achieved with the emergence of diffusion models. These models exhibit a
remarkable ability to generate highly artistic and intricately detailed images
based on textual prompts. However, obtaining desired generation outcomes often
necessitates ... |
2502.04413 | MedRAG: Enhancing Retrieval-augmented Generation with Knowledge
Graph-Elicited Reasoning for Healthcare Copilot | cs.CL cs.AI cs.IR | Retrieval-augmented generation (RAG) is a well-suited technique for
retrieving privacy-sensitive Electronic Health Records (EHR). It can serve as a
key module of the healthcare copilot, helping reduce misdiagnosis for
healthcare practitioners and patients. However, the diagnostic accuracy and
specificity of existing ... |
2502.04415 | TerraQ: Spatiotemporal Question-Answering on Satellite Image Archives | cs.CV cs.AI | TerraQ is a spatiotemporal question-answering engine for satellite image
archives. It is a natural language processing system that is built to process
requests for satellite images satisfying certain criteria. The requests can
refer to image metadata and entities from a specialized knowledge base (e.g.,
the Emilia-Ro... |
2502.04416 | CMoE: Fast Carving of Mixture-of-Experts for Efficient LLM Inference | cs.LG cs.AI | Large language models (LLMs) achieve impressive performance by scaling model
parameters, but this comes with significant inference overhead. Feed-forward
networks (FFNs), which dominate LLM parameters, exhibit high activation
sparsity in hidden neurons. To exploit this, researchers have proposed using a
mixture-of-ex... |
2502.04417 | NeuralMOVES: A lightweight and microscopic vehicle emission estimation
model based on reverse engineering and surrogate learning | cs.LG cs.AI | The transportation sector significantly contributes to greenhouse gas
emissions, necessitating accurate emission models to guide mitigation
strategies. Despite its field validation and certification, the
industry-standard Motor Vehicle Emission Simulator (MOVES) faces challenges
related to complexity in usage, high c... |
2502.04418 | Autotelic Reinforcement Learning: Exploring Intrinsic Motivations for
Skill Acquisition in Open-Ended Environments | cs.LG cs.AI | This paper presents a comprehensive overview of autotelic Reinforcement
Learning (RL), emphasizing the role of intrinsic motivations in the open-ended
formation of skill repertoires. We delineate the distinctions between
knowledge-based and competence-based intrinsic motivations, illustrating how
these concepts infor... |
2502.04419 | Understanding and Mitigating the Bias Inheritance in LLM-based Data
Augmentation on Downstream Tasks | cs.LG cs.AI cs.CL | Generating synthetic datasets via large language models (LLMs) themselves has
emerged as a promising approach to improve LLM performance. However, LLMs
inherently reflect biases present in their training data, leading to a critical
challenge: when these models generate synthetic data for training, they may
propagate ... |
2502.04420 | KVTuner: Sensitivity-Aware Layer-wise Mixed Precision KV Cache
Quantization for Efficient and Nearly Lossless LLM Inference | cs.LG cs.AI cs.CL | KV cache quantization can improve Large Language Models (LLMs) inference
throughput and latency in long contexts and large batch-size scenarios while
preserving LLMs effectiveness. However, current methods have three unsolved
issues: overlooking layer-wise sensitivity to KV cache quantization, high
overhead of online... |
2502.04421 | Assessing and Prioritizing Ransomware Risk Based on Historical Victim
Data | cs.CR cs.AI cs.LG | We present an approach to identifying which ransomware adversaries are most
likely to target specific entities, thereby assisting these entities in
formulating better protection strategies. Ransomware poses a formidable
cybersecurity threat characterized by profit-driven motives, a complex
underlying economy supporti... |
2502.04423 | Primary Care Diagnoses as a Reliable Predictor for Orthopedic Surgical
Interventions | cs.LG cs.AI cs.CL | Referral workflow inefficiencies, including misaligned referrals and delays,
contribute to suboptimal patient outcomes and higher healthcare costs. In this
study, we investigated the possibility of predicting procedural needs based on
primary care diagnostic entries, thereby improving referral accuracy,
streamlining ... |
2502.04424 | EmoBench-M: Benchmarking Emotional Intelligence for Multimodal Large
Language Models | cs.CL cs.AI | With the integration of Multimodal large language models (MLLMs) into robotic
systems and various AI applications, embedding emotional intelligence (EI)
capabilities into these models is essential for enabling robots to effectively
address human emotional needs and interact seamlessly in real-world scenarios.
Existin... |
2502.04426 | Decoding AI Judgment: How LLMs Assess News Credibility and Bias | cs.CL cs.AI cs.CY | Large Language Models (LLMs) are increasingly used to assess news
credibility, yet little is known about how they make these judgments. While
prior research has examined political bias in LLM outputs or their potential
for automated fact-checking, their internal evaluation processes remain largely
unexamined. Underst... |
2502.04428 | Confident or Seek Stronger: Exploring Uncertainty-Based On-device LLM
Routing From Benchmarking to Generalization | cs.CL cs.AI cs.LG | Large language models (LLMs) are increasingly deployed and democratized on
edge devices. To improve the efficiency of on-device deployment, small language
models (SLMs) are often adopted due to their efficient decoding latency and
reduced energy consumption. However, these SLMs often generate inaccurate
responses whe... |
2502.04457 | "In order that" -- a data driven study of symptoms and causes of
obsolescence | cs.CL cs.CY | The paper is an empirical case study of grammatical obsolescence in progress.
The main studied variable is the purpose subordinator in order that, which is
shown to be steadily decreasing in the frequency of use starting from the
beginning of the twentieth century. This work applies a data-driven approach
for the inv... |
2502.04463 | Training Language Models to Reason Efficiently | cs.LG cs.CL | Scaling model size and training data has led to great advances in the
performance of Large Language Models (LLMs). However, the diminishing returns
of this approach necessitate alternative methods to improve model capabilities,
particularly in tasks requiring advanced reasoning. Large reasoning models,
which leverage... |
2502.04465 | FocalCodec: Low-Bitrate Speech Coding via Focal Modulation Networks | cs.LG cs.AI cs.SD eess.AS | Large language models have revolutionized natural language processing through
self-supervised pretraining on massive datasets. Inspired by this success,
researchers have explored adapting these methods to speech by discretizing
continuous audio into tokens using neural audio codecs. However, existing
approaches face ... |
2502.04467 | Efficient variable-length hanging tether parameterization for marsupial
robot planning in 3D environments | cs.RO | This paper presents a novel approach to efficiently parameterize and estimate
the state of a hanging tether for path and trajectory planning of a UGV tied to
a UAV in a marsupial configuration. Most implementations in the state of the
art assume a taut tether or make use of the catenary curve to model the shape
of th... |
2502.04468 | Iterative Importance Fine-tuning of Diffusion Models | cs.LG eess.IV math.PR | Diffusion models are an important tool for generative modelling, serving as
effective priors in applications such as imaging and protein design. A key
challenge in applying diffusion models for downstream tasks is efficiently
sampling from resulting posterior distributions, which can be addressed using
the $h$-transf... |
2502.04469 | No Images, No Problem: Retaining Knowledge in Continual VQA with
Questions-Only Memory | cs.CV cs.AI | Continual Learning in Visual Question Answering (VQACL) requires models to
learn new visual-linguistic tasks (plasticity) while retaining knowledge from
previous tasks (stability). The multimodal nature of VQACL presents unique
challenges, requiring models to balance stability across visual and textual
domains while ... |
2502.04470 | Color in Visual-Language Models: CLIP deficiencies | cs.CV cs.AI | This work explores how color is encoded in CLIP (Contrastive Language-Image
Pre-training) which is currently the most influential VML (Visual Language
model) in Artificial Intelligence. After performing different experiments on
synthetic datasets created for this task, we conclude that CLIP is able to
attribute corre... |
2502.04471 | Identifying Flaky Tests in Quantum Code: A Machine Learning Approach | cs.SE cs.LG | Testing and debugging quantum software pose significant challenges due to the
inherent complexities of quantum mechanics, such as superposition and
entanglement. One challenge is indeterminacy, a fundamental characteristic of
quantum systems, which increases the likelihood of flaky tests in quantum
programs. To the b... |
2502.04475 | Augmented Conditioning Is Enough For Effective Training Image Generation | cs.CV cs.AI cs.LG | Image generation abilities of text-to-image diffusion models have
significantly advanced, yielding highly photo-realistic images from descriptive
text and increasing the viability of leveraging synthetic images to train
computer vision models. To serve as effective training data, generated images
must be highly reali... |
2502.04476 | ADIFF: Explaining audio difference using natural language | cs.SD cs.AI eess.AS | Understanding and explaining differences between audio recordings is crucial
for fields like audio forensics, quality assessment, and audio generation. This
involves identifying and describing audio events, acoustic scenes, signal
characteristics, and their emotional impact on listeners. This paper stands out
as the ... |
2502.04478 | OneTrack-M: A multitask approach to transformer-based MOT models | cs.CV cs.LG | Multi-Object Tracking (MOT) is a critical problem in computer vision,
essential for understanding how objects move and interact in videos. This field
faces significant challenges such as occlusions and complex environmental
dynamics, impacting model accuracy and efficiency. While traditional approaches
have relied on... |
2502.04480 | On Techniques for Barely Coupled Multiphysics | cs.CE | A technique to combine codes to solve barely coupled multiphysics problems
has been developed. Each field is advanced separately until a stop is
triggered. This could be due to a preset time increment, a preset number of
timesteps, a preset decrease of residuals, a preset change in unknowns, a
preset change in geomet... |
2502.04483 | Measuring Physical Plausibility of 3D Human Poses Using Physics
Simulation | cs.CV | Modeling humans in physical scenes is vital for understanding
human-environment interactions for applications involving augmented reality or
assessment of human actions from video (e.g. sports or physical
rehabilitation). State-of-the-art literature begins with a 3D human pose, from
monocular or multiple views, and u... |
2502.04484 | The ML Supply Chain in the Era of Software 2.0: Lessons Learned from
Hugging Face | cs.SE cs.LG | The last decade has seen widespread adoption of Machine Learning (ML)
components in software systems. This has occurred in nearly every domain, from
natural language processing to computer vision. These ML components range from
relatively simple neural networks to complex and resource-intensive large
language models.... |
2502.04485 | Active Task Disambiguation with LLMs | cs.CL cs.AI cs.LG | Despite the impressive performance of large language models (LLMs) across
various benchmarks, their ability to address ambiguously specified
problems--frequent in real-world interactions--remains underexplored. To
address this gap, we introduce a formal definition of task ambiguity and frame
the problem of task disam... |
2502.04488 | Building A Unified AI-centric Language System: analysis, framework and
future work | cs.CL cs.AI | Recent advancements in large language models have demonstrated that extended
inference through techniques can markedly improve performance, yet these gains
come with increased computational costs and the propagation of inherent biases
found in natural languages. This paper explores the design of a unified
AI-centric ... |
2502.04489 | CNN Autoencoders for Hierarchical Feature Extraction and Fusion in
Multi-sensor Human Activity Recognition | cs.LG cs.AI | Deep learning methods have been widely used for Human Activity Recognition
(HAR) using recorded signals from Iner-tial Measurement Units (IMUs) sensors
that are installed on various parts of the human body. For this type of HAR,
sev-eral challenges exist, the most significant of which is the analysis of
multivarious ... |
2502.04491 | Provable Sample-Efficient Transfer Learning Conditional Diffusion Models
via Representation Learning | cs.LG math.ST stat.ML stat.TH | While conditional diffusion models have achieved remarkable success in
various applications, they require abundant data to train from scratch, which
is often infeasible in practice. To address this issue, transfer learning has
emerged as an essential paradigm in small data regimes. Despite its empirical
success, the ... |
2502.04492 | Multi-Agent Reinforcement Learning with Focal Diversity Optimization | cs.CL | The advancement of Large Language Models (LLMs) and their finetuning
strategies has triggered the renewed interests in multi-agent reinforcement
learning. In this paper, we introduce a focal diversity-optimized multi-agent
reinforcement learning approach, coined as MARL-Focal, with three unique
characteristics. First... |
2502.04493 | LUND-PROBE -- LUND Prostate Radiotherapy Open Benchmarking and
Evaluation dataset | physics.med-ph cs.CV eess.IV | Radiotherapy treatment for prostate cancer relies on computed tomography (CT)
and/or magnetic resonance imaging (MRI) for segmentation of target volumes and
organs at risk (OARs). Manual segmentation of these volumes is regarded as the
gold standard for ground truth in machine learning applications but to acquire
suc... |
2502.04495 | Discovering Physics Laws of Dynamical Systems via Invariant Function
Learning | cs.LG | We consider learning underlying laws of dynamical systems governed by
ordinary differential equations (ODE). A key challenge is how to discover
intrinsic dynamics across multiple environments while circumventing
environment-specific mechanisms. Unlike prior work, we tackle more complex
environments where changes exte... |
2502.04497 | Distributed Resilient Asymmetric Bipartite Consensus: A Data-Driven
Event-Triggered Mechanism | eess.SY cs.SY | The problem of asymmetric bipartite consensus control is investigated within
the context of nonlinear, discrete-time, networked multi-agent systems (MAS)
subject to aperiodic denial-of-service (DoS) attacks. To address the challenges
posed by these aperiodic DoS attacks, a data-driven event-triggered (DDET)
mechanism... |
2502.04498 | Verifiable Format Control for Large Language Model Generations | cs.CL | Recent Large Language Models (LLMs) have demonstrated satisfying general
instruction following ability. However, small LLMs with about 7B parameters
still struggle fine-grained format following (e.g., JSON format), which
seriously hinder the advancements of their applications. Most existing methods
focus on benchmark... |
2502.04499 | Revisiting Intermediate-Layer Matching in Knowledge Distillation:
Layer-Selection Strategy Doesn't Matter (Much) | cs.LG cs.AI cs.CL | Knowledge distillation (KD) is a popular method of transferring knowledge
from a large "teacher" model to a small "student" model. KD can be divided into
two categories: prediction matching and intermediate-layer matching. We explore
an intriguing phenomenon: layer-selection strategy does not matter (much) in
interme... |
2502.04501 | ULPT: Prompt Tuning with Ultra-Low-Dimensional Optimization | cs.CL | Large language models achieve state-of-the-art performance but are costly to
fine-tune due to their size. Parameter-efficient fine-tuning methods, such as
prompt tuning, address this by reducing trainable parameters while maintaining
strong performance. However, prior methods tie prompt embeddings to the model's
dime... |
2502.04506 | When One LLM Drools, Multi-LLM Collaboration Rules | cs.CL | This position paper argues that in many realistic (i.e., complex,
contextualized, subjective) scenarios, one LLM is not enough to produce a
reliable output. We challenge the status quo of relying solely on a single
general-purpose LLM and argue for multi-LLM collaboration to better represent
the extensive diversity o... |
2502.04507 | Fast Video Generation with Sliding Tile Attention | cs.CV | Diffusion Transformers (DiTs) with 3D full attention power state-of-the-art
video generation, but suffer from prohibitive compute cost -- when generating
just a 5-second 720P video, attention alone takes 800 out of 945 seconds of
total inference time. This paper introduces sliding tile attention (STA) to
address this... |
2502.04510 | Heterogeneous Swarms: Jointly Optimizing Model Roles and Weights for
Multi-LLM Systems | cs.CL | We propose Heterogeneous Swarms, an algorithm to design multi-LLM systems by
jointly optimizing model roles and weights. We represent multi-LLM systems as
directed acyclic graphs (DAGs) of LLMs with topological message passing for
collaborative generation. Given a pool of LLM experts and a utility function,
Heterogen... |
2502.04511 | Beyond Sample-Level Feedback: Using Reference-Level Feedback to Guide
Data Synthesis | cs.CL | LLMs demonstrate remarkable capabilities in following natural language
instructions, largely due to instruction-tuning on high-quality datasets. While
synthetic data generation has emerged as a scalable approach for creating such
datasets, maintaining consistent quality standards remains challenging. Recent
approache... |
2502.04512 | Safety is Essential for Responsible Open-Ended Systems | cs.AI | AI advancements have been significantly driven by a combination of foundation
models and curiosity-driven learning aimed at increasing capability and
adaptability. A growing area of interest within this field is Open-Endedness -
the ability of AI systems to continuously and autonomously generate novel and
diverse art... |
2502.04515 | MedGNN: Towards Multi-resolution Spatiotemporal Graph Learning for
Medical Time Series Classification | cs.LG cs.AI | Medical time series has been playing a vital role in real-world healthcare
systems as valuable information in monitoring health conditions of patients.
Accurate classification for medical time series, e.g., Electrocardiography
(ECG) signals, can help for early detection and diagnosis. Traditional methods
towards medi... |
2502.04517 | Towards Cost-Effective Reward Guided Text Generation | cs.LG cs.CL | Reward-guided text generation (RGTG) has emerged as a viable alternative to
offline reinforcement learning from human feedback (RLHF). RGTG methods can
align baseline language models to human preferences without further training
like in standard RLHF methods. However, they rely on a reward model to score
each candida... |
2502.04519 | GenVC: Self-Supervised Zero-Shot Voice Conversion | eess.AS cs.LG | Zero-shot voice conversion has recently made substantial progress, but many
models still depend on external supervised systems to disentangle speaker
identity and linguistic content. Furthermore, current methods often use
parallel conversion, where the converted speech inherits the source utterance's
temporal structu... |
2502.04520 | Linear Correlation in LM's Compositional Generalization and
Hallucination | cs.CL | The generalization of language models (LMs) is undergoing active debates,
contrasting their potential for general intelligence with their struggles with
basic knowledge composition (e.g., reverse/transition curse). This paper
uncovers the phenomenon of linear correlations in LMs during knowledge
composition. For expl... |
2502.04521 | Generative Autoregressive Transformers for Model-Agnostic Federated MRI
Reconstruction | eess.IV cs.CV | Although learning-based models hold great promise for MRI reconstruction,
single-site models built on limited local datasets often suffer from poor
generalization. This challenge has spurred interest in collaborative model
training on multi-site datasets via federated learning (FL) -- a
privacy-preserving framework t... |
2502.04522 | ImprovNet: Generating Controllable Musical Improvisations with Iterative
Corruption Refinement | cs.SD cs.AI eess.AS | Deep learning has enabled remarkable advances in style transfer across
various domains, offering new possibilities for creative content generation.
However, in the realm of symbolic music, generating controllable and expressive
performance-level style transfers for complete musical works remains
challenging due to li... |
2502.04528 | Group-Adaptive Threshold Optimization for Robust AI-Generated Text
Detection | cs.CL cs.LG | The advancement of large language models (LLMs) has made it difficult to
differentiate human-written text from AI-generated text. Several AI-text
detectors have been developed in response, which typically utilize a fixed
global threshold (e.g., {\theta} = 0.5) to classify machine-generated text.
However, we find that... |
2502.04529 | Agricultural Field Boundary Detection through Integration of "Simple
Non-Iterative Clustering (SNIC) Super Pixels" and "Canny Edge Detection
Method" | cs.LG cs.CV | Efficient use of cultivated areas is a necessary factor for sustainable
development of agriculture and ensuring food security. Along with the rapid
development of satellite technologies in developed countries, new methods are
being searched for accurate and operational identification of cultivated areas.
In this cont... |
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