id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
|---|---|---|---|
2502.04530 | Robust Probabilistic Model Checking with Continuous Reward Domains | cs.AI cs.FL cs.LG | Probabilistic model checking traditionally verifies properties on the
expected value of a measure of interest. This restriction may fail to capture
the quality of service of a significant proportion of a system's runs,
especially when the probability distribution of the measure of interest is
poorly represented by it... |
2502.04531 | AnyPlace: Learning Generalized Object Placement for Robot Manipulation | cs.RO cs.AI cs.CV | Object placement in robotic tasks is inherently challenging due to the
diversity of object geometries and placement configurations. To address this,
we propose AnyPlace, a two-stage method trained entirely on synthetic data,
capable of predicting a wide range of feasible placement poses for real-world
tasks. Our key ... |
2502.04533 | Polarization-Dependent Loss Mitigation with Orthogonal-Design-Based
Precoding and Interference Cancellation | cs.IT math.IT | Recent work by Shehadeh and Kschischang provides a simple capacity-achieving
scheme for channels with polarization-dependent loss (PDL) under common
modeling assumptions via a careful choice of orthogonal-design-based precoding
and interference cancellation. This letter extends that work with a
simulation-based demon... |
2502.04535 | A Decoding Algorithm for Length-Control Summarization Based on Directed
Acyclic Transformers | cs.CL | Length-control summarization aims to condense long texts into a short one
within a certain length limit. Previous approaches often use autoregressive
(AR) models and treat the length requirement as a soft constraint, which may
not always be satisfied. In this study, we propose a novel length-control
decoding algorith... |
2502.04537 | Multilingual Non-Autoregressive Machine Translation without Knowledge
Distillation | cs.CL | Multilingual neural machine translation (MNMT) aims at using one single model
for multiple translation directions. Recent work applies non-autoregressive
Transformers to improve the efficiency of MNMT, but requires expensive
knowledge distillation (KD) processes. To this end, we propose an M-DAT
approach to non-autor... |
2502.04541 | The Phantom of the Elytra -- Phylogenetic Trait Extraction from Images
of Rove Beetles Using Deep Learning -- Is the Mask Enough? | cs.CV | Phylogenetic analysis traditionally relies on labor-intensive manual
extraction of morphological traits, limiting its scalability for large
datasets. Recent advances in deep learning offer the potential to automate this
process, but the effectiveness of different morphological representations for
phylogenetic trait e... |
2502.04543 | Sparsity-Based Interpolation of External, Internal and Swap Regret | stat.ML cs.LG | Focusing on the expert problem in online learning, this paper studies the
interpolation of several performance metrics via $\phi$-regret minimization,
which measures the performance of an algorithm by its regret with respect to an
arbitrary action modification rule $\phi$. With $d$ experts and $T\gg d$ rounds
in tota... |
2502.04544 | Solvability of Approximate Reach-Avoid Games | eess.SY cs.LO cs.SY | Objective: In a companion paper, we propose a parametric hybrid automaton
model and an algorithm for the online synthesis of robustly correct and
near-optimal controllers for cyber-physical system with reach-avoid guarantees.
A key part of this synthesis problem is based on a weighted discretised game
and solved via ... |
2502.04548 | Contextual Gradient Flow Modeling for Large Language Model
Generalization in Multi-Scale Feature Spaces | cs.CL | Optimization methodologies for training large-scale neural architectures
often rely on uniform gradient propagation mechanisms that fail to align with
hierarchical linguistic structures, limiting their capacity to generalize
across diverse language distributions. A structured gradient refinement
framework was introdu... |
2502.04549 | Mechanisms of Projective Composition of Diffusion Models | cs.LG | We study the theoretical foundations of composition in diffusion models, with
a particular focus on out-of-distribution extrapolation and
length-generalization. Prior work has shown that composing distributions via
linear score combination can achieve promising results, including
length-generalization in some cases (... |
2502.04552 | Reinforcement Learning Based Prediction of PID Controller Gains for
Quadrotor UAVs | eess.SY cs.RO cs.SY | A reinforcement learning (RL) based methodology is proposed and implemented
for online fine-tuning of PID controller gains, thus, improving quadrotor
effective and accurate trajectory tracking. The RL agent is first trained
offline on a quadrotor PID attitude controller and then validated through
simulations and expe... |
2502.04554 | Unifying and Optimizing Data Values for Selection via
Sequential-Decision-Making | cs.AI | Data selection has emerged as a crucial downstream application of data
valuation. While existing data valuation methods have shown promise in
selection tasks, the theoretical foundations and full potential of using data
values for selection remain largely unexplored. In this work, we first
demonstrate that data value... |
2502.04555 | Decomposing Multivariate Information Rates in Networks of Random
Processes | stat.ME cs.IT math.IT | The Partial Information Decomposition (PID) framework has emerged as a
powerful tool for analyzing high-order interdependencies in complex network
systems. However, its application to dynamic processes remains challenging due
to the implicit assumption of memorylessness, which often falls in real-world
scenarios. In ... |
2502.04556 | TruthFlow: Truthful LLM Generation via Representation Flow Correction | cs.CL cs.AI cs.LG | Large language models (LLMs) are known to struggle with consistently
generating truthful responses. While various representation intervention
techniques have been proposed, these methods typically apply a universal
representation correction vector to all input queries, limiting their
effectiveness against diverse que... |
2502.04557 | Speeding up Speculative Decoding via Approximate Verification | cs.LG cs.IT math.IT | Speculative Decoding (SD) is a recently proposed technique for faster
inference using Large Language Models (LLMs). SD operates by using a smaller
draft LLM for autoregressively generating a sequence of tokens and a larger
target LLM for parallel verification to ensure statistical consistency.
However, periodic paral... |
2502.04558 | Probing a Vision-Language-Action Model for Symbolic States and
Integration into a Cognitive Architecture | cs.RO cs.AI | Vision-language-action (VLA) models hold promise as generalist robotics
solutions by translating visual and linguistic inputs into robot actions, yet
they lack reliability due to their black-box nature and sensitivity to
environmental changes. In contrast, cognitive architectures (CA) excel in
symbolic reasoning and ... |
2502.04562 | Mixture of neural operator experts for learning boundary conditions and
model selection | cs.LG cs.NA math.NA physics.flu-dyn | While Fourier-based neural operators are best suited to learning mappings
between functions on periodic domains, several works have introduced techniques
for incorporating non trivial boundary conditions. However, all previously
introduced methods have restrictions that limit their applicability. In this
work, we int... |
2502.04563 | WaferLLM: A Wafer-Scale LLM Inference System | cs.LG cs.AI cs.AR cs.DC cs.ET | Emerging AI accelerators increasingly adopt wafer-scale manufacturing
technologies, integrating hundreds of thousands of AI cores in a mesh-based
architecture with large distributed on-chip memory (tens of GB in total) and
ultra-high on-chip memory bandwidth (tens of PB/s). However, current LLM
inference systems, opt... |
2502.04564 | My LLM might Mimic AAE -- But When Should it? | cs.CL | We examine the representation of African American English (AAE) in large
language models (LLMs), exploring (a) the perceptions Black Americans have of
how effective these technologies are at producing authentic AAE, and (b) in
what contexts Black Americans find this desirable. Through both a survey of
Black Americans... |
2502.04565 | Private Federated Learning In Real World Application -- A Case Study | cs.LG cs.CR | This paper presents an implementation of machine learning model training
using private federated learning (PFL) on edge devices. We introduce a novel
framework that uses PFL to address the challenge of training a model using
users' private data. The framework ensures that user data remain on individual
devices, with ... |
2502.04566 | An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At
Road Intersections Using Fisheye Cameras | cs.CV | Real time vehicle detection is a challenging task for urban traffic
surveillance. Increase in urbanization leads to increase in accidents and
traffic congestion in junction areas resulting in delayed travel time. In order
to solve these problems, an intelligent system utilizing automatic detection
and tracking system... |
2502.04567 | Preference Optimization via Contrastive Divergence: Your Reward Model is
Secretly an NLL Estimator | cs.AI | Existing studies on preference optimization (PO) have centered on
constructing pairwise preference data following simple heuristics, such as
maximizing the margin between preferred and dispreferred completions based on
human (or AI) ranked scores. However, none of these heuristics has a full
theoretical justification... |
2502.04568 | Learning Semantics-aware Search Operators for Genetic Programming | cs.LG cs.NE | Fitness landscapes in test-based program synthesis are known to be extremely
rugged, with even minimal modifications of programs often leading to
fundamental changes in their behavior and, consequently, fitness values.
Relying on fitness as the only guidance in iterative search algorithms like
genetic programming is ... |
2502.04573 | Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially
Pre-trained Transformer | cs.LG cs.AI | We present an Adversarially Pre-trained Transformer (APT) that is able to
perform zero-shot meta-learning on tabular prediction tasks without
pre-training on any real-world dataset, extending on the recent development of
Prior-Data Fitted Networks (PFNs) and TabPFN. Specifically, APT is pre-trained
with adversarial s... |
2502.04574 | Dark Brain Energy: Toward an Integrative Model of Spontaneous Slow
Oscillations | q-bio.NC cs.IT math.IT stat.AP | Neural oscillations facilitate the functioning of the human brain in spatial
and temporal dimensions at various frequencies. These oscillations feature a
universal frequency architecture that is governed by brain anatomy, ensuring
frequency specificity remains invariant across different measurement
techniques. Initia... |
2502.04575 | Complexity Analysis of Normalizing Constant Estimation: from Jarzynski
Equality to Annealed Importance Sampling and beyond | stat.ML cs.LG cs.NA math.NA physics.comp-ph stat.CO | Given an unnormalized probability density $\pi\propto\mathrm{e}^{-V}$,
estimating its normalizing constant
$Z=\int_{\mathbb{R}^d}\mathrm{e}^{-V(x)}\mathrm{d}x$ or free energy $F=-\log Z$
is a crucial problem in Bayesian statistics, statistical mechanics, and machine
learning. It is challenging especially in high dime... |
2502.04576 | Self-Regulation and Requesting Interventions | cs.LG cs.CL | Human intelligence involves metacognitive abilities like self-regulation,
recognizing limitations, and seeking assistance only when needed. While LLM
Agents excel in many domains, they often lack this awareness. Overconfident
agents risk catastrophic failures, while those that seek help excessively
hinder efficiency.... |
2502.04577 | Position-aware Automatic Circuit Discovery | cs.LG cs.CL | A widely used strategy to discover and understand language model mechanisms
is circuit analysis. A circuit is a minimal subgraph of a model's computation
graph that executes a specific task. We identify a gap in existing circuit
discovery methods: they assume circuits are position-invariant, treating model
components... |
2502.04580 | Technical Debt in In-Context Learning: Diminishing Efficiency in Long
Context | cs.LG cs.AI | Transformers have demonstrated remarkable in-context learning (ICL)
capabilities, adapting to new tasks by simply conditioning on demonstrations
without parameter updates. Compelling empirical and theoretical evidence
suggests that ICL, as a general-purpose learner, could outperform task-specific
models. However, it ... |
2502.04582 | The Mini Wheelbot: A Testbed for Learning-based Balancing, Flips, and
Articulated Driving | cs.RO cs.SY eess.SY math.OC | The Mini Wheelbot is a balancing, reaction wheel unicycle robot designed as a
testbed for learning-based control. It is an unstable system with highly
nonlinear yaw dynamics, non-holonomic driving, and discrete contact switches in
a small, powerful, and rugged form factor. The Mini Wheelbot can use its wheels
to stan... |
2502.04583 | Overcoming Fake Solutions in Semi-Dual Neural Optimal Transport: A
Smoothing Approach for Learning the Optimal Transport Plan | cs.LG | We address the convergence problem in learning the Optimal Transport (OT)
map, where the OT Map refers to a map from one distribution to another while
minimizing the transport cost. Semi-dual Neural OT, a widely used approach for
learning OT Maps with neural networks, often generates fake solutions that fail
to trans... |
2502.04584 | Joint State and Noise Covariance Estimation | cs.RO math.OC | This paper tackles the problem of jointly estimating the noise covariance
matrix alongside primary parameters (such as poses and points) from
measurements corrupted by Gaussian noise. In such settings, the noise
covariance matrix determines the weights assigned to individual measurements in
the least squares problem.... |
2502.04586 | Automatic Ply Partitioning for Laminar Composite Process Planning | math.OC cs.CE | This work introduces an automated ply partitioning strategy for large-scale
laminar composite manufacturing. It specifically targets the problem of
fabricating large plies from available spooled materials, while minimizing the
adverse effects on part quality. The proposed method inserts fiber-aligned
seams sequential... |
2502.04591 | Rethinking Oversmoothing in Graph Neural Networks: A Rank-Based
Perspective | cs.LG cs.AI stat.ML | Oversmoothing is a fundamental challenge in graph neural networks (GNNs): as
the number of layers increases, node embeddings become increasingly similar,
and model performance drops sharply. Traditionally, oversmoothing has been
quantified using metrics that measure the similarity of neighbouring node
features, such ... |
2502.04592 | CAMEF: Causal-Augmented Multi-Modality Event-Driven Financial
Forecasting by Integrating Time Series Patterns and Salient Macroeconomic
Announcements | cs.LG cs.AI cs.CE | Accurately forecasting the impact of macroeconomic events is critical for
investors and policymakers. Salient events like monetary policy decisions and
employment reports often trigger market movements by shaping expectations of
economic growth and risk, thereby establishing causal relationships between
events and ma... |
2502.04593 | The $\alpha$-Alternator: Dynamic Adaptation To Varying Noise Levels In
Sequences Using The Vendi Score For Improved Robustness and Performance | cs.LG cs.AI cs.NE stat.ML | Current state-of-the-art dynamical models, such as Mamba, assume the same
level of noisiness for all elements of a given sequence, which limits their
performance on noisy temporal data. In this paper, we introduce the
$\alpha$-Alternator, a novel generative model for time-dependent data that
dynamically adapts to the... |
2502.04595 | A Fractional-Order Nonlinear Backstepping Controller Design for
Current-Controlled Maglev System | eess.SY cs.SY | The magnetic levitation system (Maglev) is a nonlinear system by which an
object is suspended with no support other than magnetic fields. The main
control perspective of the Maglev system is to levitate a steel ball in air by
the electromagnetic force. However, the Maglev system has highly nonlinear
dynamics which is... |
2502.04597 | Multiscale style transfer based on a Laplacian pyramid for traditional
Chinese painting | cs.CV | Style transfer is adopted to synthesize appealing stylized images that
preserve the structure of a content image but carry the pattern of a style
image. Many recently proposed style transfer methods use only western oil
paintings as style images to achieve image stylization. As a result, unnatural
messy artistic effe... |
2502.04600 | Cooperative Payload Estimation by a Team of Mocobots | cs.RO | Consider the following scenario: a human guides multiple mobile manipulators
to grasp a common payload. For subsequent high-performance autonomous
manipulation of the payload by the mobile manipulator team, or for
collaborative manipulation with the human, the robots should be able to
discover where the other robots ... |
2502.04601 | LATTEO: A Framework to Support Learning Asynchronously Tempered with
Trusted Execution and Obfuscation | cs.CR cs.LG | The privacy vulnerabilities of the federated learning (FL) paradigm,
primarily caused by gradient leakage, have prompted the development of various
defensive measures. Nonetheless, these solutions have predominantly been
crafted for and assessed in the context of synchronous FL systems, with minimal
focus on asynchro... |
2502.04602 | Extracting and Understanding the Superficial Knowledge in Alignment | cs.CL cs.AI | Alignment of large language models (LLMs) with human values and preferences,
often achieved through fine-tuning based on human feedback, is essential for
ensuring safe and responsible AI behaviors. However, the process typically
requires substantial data and computation resources. Recent studies have
revealed that al... |
2502.04609 | Force interaction, modeling and soft tissue deformation during
reciprocating insertion of multi-part probe | cs.RO cs.SY eess.SY | The bio-inspired engineering of ovipositing wasps, which employ a
reciprocating motion for soft tissue insertion, offers potential advantages in
reducing insertion force and minimizing tissue damage. However, the underlying
mechanisms of tissue interaction and sparing are not fully understood. In this
study, we aim t... |
2502.04615 | Neural Clustering for Prefractured Mesh Generation in Real-time Object
Destruction | cs.CV cs.GR | Prefracture method is a practical implementation for real-time object
destruction that is hardly achievable within performance constraints, but can
produce unrealistic results due to its heuristic nature. To mitigate it, we
approach the clustering of prefractured mesh generation as an unordered
segmentation on point ... |
2502.04623 | HetSSNet: Spatial-Spectral Heterogeneous Graph Learning Network for
Panchromatic and Multispectral Images Fusion | cs.CV | Remote sensing pansharpening aims to reconstruct spatial-spectral properties
during the fusion of panchromatic (PAN) images and low-resolution
multi-spectral (LR-MS) images, finally generating the high-resolution
multi-spectral (HR-MS) images. In the mainstream modeling strategies, i.e., CNN
and Transformer, the inpu... |
2502.04625 | Phonetic Reconstruction of the Consonant System of Middle Chinese via
Mixed Integer Optimization | cs.CL | This paper is concerned with phonetic reconstruction of the consonant system
of Middle Chinese. We propose to cast the problem as a Mixed Integer
Programming problem, which is able to automatically explore homophonic
information from ancient rhyme dictionaries and phonetic information from
modern Chinese dialects, th... |
2502.04628 | AIQViT: Architecture-Informed Post-Training Quantization for Vision
Transformers | cs.CV | Post-training quantization (PTQ) has emerged as a promising solution for
reducing the storage and computational cost of vision transformers (ViTs).
Recent advances primarily target at crafting quantizers to deal with peculiar
activations characterized by ViTs. However, most existing methods underestimate
the informat... |
2502.04630 | High-Speed Dynamic 3D Imaging with Sensor Fusion Splatting | cs.CV cs.GR | Capturing and reconstructing high-speed dynamic 3D scenes has numerous
applications in computer graphics, vision, and interdisciplinary fields such as
robotics, aerodynamics, and evolutionary biology. However, achieving this using
a single imaging modality remains challenging. For instance, traditional RGB
cameras su... |
2502.04632 | Tight Bounds for Noisy Computation of High-Influence Functions,
Connectivity, and Threshold | cs.DS cs.CC cs.IT math.IT | In the noisy query model, the (binary) return value of every query (possibly
repeated) is independently flipped with some fixed probability $p \in (0,
1/2)$. In this paper, we obtain tight bounds on the noisy query complexity of
several fundamental problems.
Our first contribution is to show that any Boolean functi... |
2502.04635 | Exercise Specialists Evaluation of Robot-led Physical Therapy for People
with Parkinsons Disease | cs.RO | Robot-led physical therapy (PT) offers a promising avenue to enhance the care
provided by clinical exercise specialists (ES) and physical and occupational
therapists to improve patients' adherence to prescribed exercises outside of a
clinic, such as at home. Collaborative efforts among roboticists, ES, physical
and o... |
2502.04636 | An Empirical Study of Code Obfuscation Practices in the Google Play
Store | cs.CR cs.AI cs.SE | The Android ecosystem is vulnerable to issues such as app repackaging,
counterfeiting, and piracy, threatening both developers and users. To mitigate
these risks, developers often employ code obfuscation techniques. However,
while effective in protecting legitimate applications, obfuscation also hinders
security inve... |
2502.04638 | Learning Street View Representations with Spatiotemporal Contrast | cs.CV cs.AI | Street view imagery is extensively utilized in representation learning for
urban visual environments, supporting various sustainable development tasks
such as environmental perception and socio-economic assessment. However, it is
challenging for existing image representations to specifically encode the
dynamic urban ... |
2502.04640 | Building Rome with Convex Optimization | cs.RO cs.CV math.OC | Global bundle adjustment is made easy by depth prediction and convex
optimization. We (i) propose a scaled bundle adjustment (SBA) formulation that
lifts 2D keypoint measurements to 3D with learned depth, (ii) design an
empirically tight convex semidfinite program (SDP) relaxation that solves SBA
to certfiable global... |
2502.04642 | Dynamic Incentive Selection for Hierarchical Convex Model Predictive
Control | eess.SY cs.SY math.OC | In this paper, we discuss incentive design for hierarchical model predictive
control (MPC) systems viewed as Stackelberg games. We consider a hierarchical
MPC formulation where, given a lower-level convex MPC (LoMPC), the upper-level
system solves a bilevel MPC (BiMPC) subject to the constraint that the
lower-level s... |
2502.04643 | Confidence Elicitation: A New Attack Vector for Large Language Models | cs.LG cs.CL cs.CR | A fundamental issue in deep learning has been adversarial robustness. As
these systems have scaled, such issues have persisted. Currently, large
language models (LLMs) with billions of parameters suffer from adversarial
attacks just like their earlier, smaller counterparts. However, the threat
models have changed. Pr... |
2502.04644 | Agentic Reasoning: Reasoning LLMs with Tools for the Deep Research | cs.AI cs.CL | We introduce Agentic Reasoning, a framework that enhances large language
model (LLM) reasoning by integrating external tool-using agents. Unlike
conventional LLM-based reasoning approaches, which rely solely on internal
inference, Agentic Reasoning dynamically engages web search, code execution,
and structured reason... |
2502.04645 | Cross-Encoder Rediscovers a Semantic Variant of BM25 | cs.IR cs.AI | Neural Ranking Models (NRMs) have rapidly advanced state-of-the-art
performance on information retrieval tasks. In this work, we investigate a
Cross-Encoder variant of MiniLM to determine which relevance features it
computes and where they are stored. We find that it employs a semantic variant
of the traditional BM25... |
2502.04646 | Importance Sampling via Score-based Generative Models | cs.LG cs.AI | Importance sampling, which involves sampling from a probability density
function (PDF) proportional to the product of an importance weight function and
a base PDF, is a powerful technique with applications in variance reduction,
biased or customized sampling, data augmentation, and beyond. Inspired by the
growing ava... |
2502.04649 | End-to-End Learning Framework for Solving Non-Markovian Optimal Control | cs.SY cs.LG math.OC | Integer-order calculus often falls short in capturing the long-range
dependencies and memory effects found in many real-world processes. Fractional
calculus addresses these gaps via fractional-order integrals and derivatives,
but fractional-order dynamical systems pose substantial challenges in system
identification ... |
2502.04655 | Before It's Too Late: A State Space Model for the Early Prediction of
Misinformation and Disinformation Engagement | cs.CL | In today's digital age, conspiracies and information campaigns can emerge
rapidly and erode social and democratic cohesion. While recent deep learning
approaches have made progress in modeling engagement through language and
propagation models, they struggle with irregularly sampled data and early
trajectory assessme... |
2502.04656 | MHAF-YOLO: Multi-Branch Heterogeneous Auxiliary Fusion YOLO for accurate
object detection | cs.CV | Due to the effective multi-scale feature fusion capabilities of the Path
Aggregation FPN (PAFPN), it has become a widely adopted component in YOLO-based
detectors. However, PAFPN struggles to integrate high-level semantic cues with
low-level spatial details, limiting its performance in real-world applications,
especi... |
2502.04658 | Shifting Attention to You: Personalized Brain-Inspired AI Models | q-bio.NC cs.AI | The integration of human and artificial intelligence represents a scientific
opportunity to advance our understanding of information processing, as each
system offers unique computational insights that can enhance and inform the
other. The synthesis of human cognitive principles with artificial intelligence
has the p... |
2502.04662 | Adversarially-Robust TD Learning with Markovian Data: Finite-Time Rates
and Fundamental Limits | cs.LG cs.SY eess.SY math.OC | One of the most basic problems in reinforcement learning (RL) is policy
evaluation: estimating the long-term return, i.e., value function,
corresponding to a given fixed policy. The celebrated Temporal Difference (TD)
learning algorithm addresses this problem, and recent work has investigated
finite-time convergence ... |
2502.04664 | Implicit Bias of SignGD and Adam on Multiclass Separable Data | cs.LG math.OC | In the optimization of overparameterized models, different gradient-based
methods can achieve zero training error yet converge to distinctly different
solutions inducing different generalization properties. While a decade of
research on implicit optimization bias has illuminated this phenomenon in
various settings, e... |
2502.04666 | Enhancing Health Information Retrieval with RAG by Prioritizing Topical
Relevance and Factual Accuracy | cs.IR | The exponential surge in online health information, coupled with its
increasing use by non-experts, highlights the pressing need for advanced Health
Information Retrieval models that consider not only topical relevance but also
the factual accuracy of the retrieved information, given the potential risks
associated wi... |
2502.04667 | Unveiling the Mechanisms of Explicit CoT Training: How Chain-of-Thought
Enhances Reasoning Generalization | cs.LG cs.AI cs.CL | Training large language models (LLMs) with high-quality Chain-of-Thought
(CoT) annotations has become a widely adopted strategy due to its significant
enhancement of reasoning capabilities. To fully comprehend this approach, two
questions naturally arise: (Q1) What advantages does training with CoT offer
compared to ... |
2502.04668 | Machine-Learning Interatomic Potentials for Long-Range Systems | physics.chem-ph cs.LG | Machine-learning interatomic potentials have emerged as a revolutionary class
of force-field models in molecular simulations, delivering quantum-mechanical
accuracy at a fraction of the computational cost and enabling the simulation of
large-scale systems over extended timescales. However, they often focus on
modelin... |
2502.04669 | A Comprehensive Review on Noise Control of Diffusion Model | cs.LG cs.AI | Diffusion models have recently emerged as powerful generative frameworks for
producing high-quality images. A pivotal component of these models is the noise
schedule, which governs the rate of noise injection during the diffusion
process. Since the noise schedule substantially influences sampling quality and
training... |
2502.04670 | CCS: Controllable and Constrained Sampling with Diffusion Models via
Initial Noise Perturbation | cs.LG cs.AI | Diffusion models have emerged as powerful tools for generative tasks,
producing high-quality outputs across diverse domains. However, how the
generated data responds to the initial noise perturbation in diffusion models
remains under-explored, which hinders understanding the controllability of the
sampling process. I... |
2502.04671 | ProofWala: Multilingual Proof Data Synthesis and Theorem-Proving | cs.AI cs.LG cs.LO cs.PL | Neural networks have shown substantial promise at automatic theorem-proving
in interactive proof assistants (ITPs) like Lean and Coq. However, most neural
theorem-proving models are restricted to specific ITPs, leaving out
opportunities for cross-lingual $\textit{transfer}$ between ITPs. We address
this weakness with... |
2502.04673 | Optimistic Algorithms for Adaptive Estimation of the Average Treatment
Effect | stat.ML cs.LG stat.ME | Estimation and inference for the Average Treatment Effect (ATE) is a
cornerstone of causal inference and often serves as the foundation for
developing procedures for more complicated settings. Although traditionally
analyzed in a batch setting, recent advances in martingale theory have paved
the way for adaptive meth... |
2502.04674 | AdParaphrase: Paraphrase Dataset for Analyzing Linguistic Features
toward Generating Attractive Ad Texts | cs.CL cs.AI | Effective linguistic choices that attract potential customers play crucial
roles in advertising success. This study aims to explore the linguistic
features of ad texts that influence human preferences. Although the creation of
attractive ad texts is an active area of research, progress in understanding
the specific l... |
2502.04675 | Scalable Oversight for Superhuman AI via Recursive Self-Critiquing | cs.AI cs.CL cs.LG | As AI capabilities increasingly surpass human proficiency in complex tasks,
current alignment techniques including SFT and RLHF face fundamental challenges
in ensuring reliable oversight. These methods rely on direct human assessment
and become untenable when AI outputs exceed human cognitive thresholds. In
response ... |
2502.04678 | Nearly Tight Bounds for Cross-Learning Contextual Bandits with Graphical
Feedback | cs.LG | The cross-learning contextual bandit problem with graphical feedback has
recently attracted significant attention. In this setting, there is a
contextual bandit with a feedback graph over the arms, and pulling an arm
reveals the loss for all neighboring arms in the feedback graph across all
contexts. Initially propos... |
2502.04679 | Mechanistic Understandings of Representation Vulnerabilities and
Engineering Robust Vision Transformers | cs.CV cs.LG | While transformer-based models dominate NLP and vision applications, their
underlying mechanisms to map the input space to the label space semantically
are not well understood. In this paper, we study the sources of known
representation vulnerabilities of vision transformers (ViT), where perceptually
identical images... |
2502.04680 | Performance Evaluation of Image Enhancement Techniques on Transfer
Learning for Touchless Fingerprint Recognition | cs.CV cs.LG | Fingerprint recognition remains one of the most reliable biometric
technologies due to its high accuracy and uniqueness. Traditional systems rely
on contact-based scanners, which are prone to issues such as image degradation
from surface contamination and inconsistent user interaction. To address these
limitations, c... |
2502.04682 | AI-Driven Solutions for Falcon Disease Classification: Concatenated
ConvNeXt cum EfficientNet AI Model Approach | cs.CV | Falconry, an ancient practice of training and hunting with falcons,
emphasizes the need for vigilant health monitoring to ensure the well-being of
these highly valued birds, especially during hunting activities. This research
paper introduces a cutting-edge approach, which leverages the power of
Concatenated ConvNeXt... |
2502.04684 | G2PDiffusion: Genotype-to-Phenotype Prediction with Diffusion Models | cs.LG cs.AI | Discovering the genotype-phenotype relationship is crucial for genetic
engineering, which will facilitate advances in fields such as crop breeding,
conservation biology, and personalized medicine. Current research usually
focuses on single species and small datasets due to limitations in phenotypic
data collection, e... |
2502.04686 | Learning Strategic Language Agents in the Werewolf Game with Iterative
Latent Space Policy Optimization | cs.AI | Large language model (LLM)-based agents have recently shown impressive
progress in a variety of domains, including open-ended conversation and
multi-step decision-making. However, applying these agents to social deduction
games such as Werewolf, which requires both strategic decision-making and
free-form language int... |
2502.04688 | M-IFEval: Multilingual Instruction-Following Evaluation | cs.CL cs.AI | Instruction following is a core capability of modern Large language models
(LLMs), making evaluating this capability essential to understanding these
models. The Instruction Following Evaluation (IFEval) benchmark from the
literature does this using objective criteria, offering a measure of LLM
performance without su... |
2502.04689 | ARR: Question Answering with Large Language Models via Analyzing,
Retrieving, and Reasoning | cs.CL cs.AI cs.LG | Large language models (LLMs) achieve remarkable performance on challenging
benchmarks that are often structured as multiple-choice question-answering (QA)
tasks. Zero-shot Chain-of-Thought (CoT) prompting enhances reasoning in LLMs
but provides only vague and generic guidance ("think step by step"). This paper
introd... |
2502.04692 | STRIDE: Automating Reward Design, Deep Reinforcement Learning Training
and Feedback Optimization in Humanoid Robotics Locomotion | cs.RO cs.LG | Humanoid robotics presents significant challenges in artificial intelligence,
requiring precise coordination and control of high-degree-of-freedom systems.
Designing effective reward functions for deep reinforcement learning (DRL) in
this domain remains a critical bottleneck, demanding extensive manual effort,
domain... |
2502.04693 | Be Water, My Antennas: Riding on Radio Wave Fluctuation in Nature for
Spatial Multiplexing using Programmable Meta-Fluid Antenna | physics.app-ph cs.SY eess.SY | Interference and scattering, often deemed undesirable, are inevitable in
wireless communications, especially when the current mobile networks and
upcoming sixth generation (6G) have turned into ultra-dense networks. Current
approaches relying on multiple-input multiple-output (MIMO) combined with
artificial-intellige... |
2502.04695 | Bridging the Gap in XAI-Why Reliable Metrics Matter for Explainability
and Compliance | cs.AI cs.CE cs.ET cs.LG | This position paper emphasizes the critical gap in the evaluation of
Explainable AI (XAI) due to the lack of standardized and reliable metrics,
which diminishes its practical value, trustworthiness, and ability to meet
regulatory requirements. Current evaluation methods are often fragmented,
subjective, and biased, m... |
2502.04696 | Adaptive Learning-based Model Predictive Control Strategy for Drift
Vehicles | cs.RO | Drift vehicle control offers valuable insights to support safe autonomous
driving in extreme conditions, which hinges on tracking a particular path while
maintaining the vehicle states near the drift equilibrium points (DEP).
However, conventional tracking methods are not adaptable for drift vehicles due
to their opp... |
2502.04697 | Multi-Agent Coverage Control in Non-Convex Annulus Region with Conformal
Mapping | eess.SY cs.SY | Efficiently fulfilling coverage tasks in non-convex regions has long been a
significant challenge for multi-agent systems (MASs). By leveraging conformal
mapping, this paper introduces a novel sectorial coverage formulation to
transform a non-convex annulus region into a topologically equivalent one. This
approach en... |
2502.04699 | A Meta-learner for Heterogeneous Effects in Difference-in-Differences | stat.ML cs.LG | We address the problem of estimating heterogeneous treatment effects in panel
data, adopting the popular Difference-in-Differences (DiD) framework under the
conditional parallel trends assumption. We propose a novel doubly robust
meta-learner for the Conditional Average Treatment Effect on the Treated
(CATT), reducin... |
2502.04700 | EigenLoRAx: Recycling Adapters to Find Principal Subspaces for
Resource-Efficient Adaptation and Inference | cs.LG cs.AI | The rapid growth of large models has raised concerns about their
environmental impact and equity in accessibility due to significant
computational costs. Low-Rank Adapters (LoRA) offer a lightweight solution for
finetuning large models, resulting in an abundance of publicly available
adapters tailored to diverse doma... |
2502.04703 | Symbolic Regression of Data-Driven Reduced Order Model Closures for
Under-Resolved, Convection-Dominated Flows | math.NA cs.LG cs.NA physics.flu-dyn | Data-driven closures correct the standard reduced order models (ROMs) to
increase their accuracy in under-resolved, convection-dominated flows. There
are two types of data-driven ROM closures in current use: (i) structural, with
simple ansatzes (e.g., linear or quadratic); and (ii) machine learning-based,
with neural... |
2502.04706 | Enhancing Impression Change Prediction in Speed Dating Simulations Based
on Speakers' Personalities | cs.CL cs.HC | This paper focuses on simulating text dialogues in which impressions between
speakers improve during speed dating. This simulation involves selecting an
utterance from multiple candidates generated by a text generation model that
replicates a specific speaker's utterances, aiming to improve the impression of
the spea... |
2502.04713 | Leveraging band diversity for feature selection in EO data | eess.IV cs.CV | Hyperspectral imaging (HSI) is a powerful earth observation technology that
captures and processes information across a wide spectrum of wavelengths.
Hyperspectral imaging provides comprehensive and detailed spectral data that is
invaluable for a wide range of reconstruction problems. However due to
complexity in ana... |
2502.04718 | Evaluating Text Style Transfer Evaluation: Are There Any Reliable
Metrics? | cs.CL | Text Style Transfer (TST) is the task of transforming a text to reflect a
particular style while preserving its original content. Evaluating TST outputs
is a multidimensional challenge, requiring the assessment of style transfer
accuracy, content preservation, and naturalness. Using human evaluation is
ideal but cost... |
2502.04719 | Tolerance-Aware Deep Optics | cs.CV cs.GR | Deep optics has emerged as a promising approach by co-designing optical
elements with deep learning algorithms. However, current research typically
overlooks the analysis and optimization of manufacturing and assembly
tolerances. This oversight creates a significant performance gap between
designed and fabricated opt... |
2502.04722 | Singing Voice Conversion with Accompaniment Using Self-Supervised
Representation-Based Melody Features | cs.SD cs.LG eess.AS | Melody preservation is crucial in singing voice conversion (SVC). However, in
many scenarios, audio is often accompanied with background music (BGM), which
can cause audio distortion and interfere with the extraction of melody and
other key features, significantly degrading SVC performance. Previous methods
have atte... |
2502.04725 | Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images? | cs.CV cs.AI | Despite the remarkable success of diffusion models (DMs) in data generation,
they exhibit specific failure cases with unsatisfactory outputs. We focus on
one such limitation: the ability of DMs to learn hidden rules between image
features. Specifically, for image data with dependent features ($\mathbf{x}$)
and ($\mat... |
2502.04728 | Generating Symbolic World Models via Test-time Scaling of Large Language
Models | cs.AI | Solving complex planning problems requires Large Language Models (LLMs) to
explicitly model the state transition to avoid rule violations, comply with
constraints, and ensure optimality-a task hindered by the inherent ambiguity of
natural language. To overcome such ambiguity, Planning Domain Definition
Language (PDDL... |
2502.04729 | The "negative end" of change in grammar: terminology, concepts and
causes | cs.CL cs.CY | The topic of "negative end" of change is, contrary to the fields of
innovation and emergence, largely under-researched. Yet, it has lately started
to gain an increasing attention from language scholars worldwide. The main
focus of this article is threefold, namely to discuss the i) terminology; ii)
concepts and iii) ... |
2502.04730 | PhyloVAE: Unsupervised Learning of Phylogenetic Trees via Variational
Autoencoders | stat.ML cs.LG q-bio.PE | Learning informative representations of phylogenetic tree structures is
essential for analyzing evolutionary relationships. Classical distance-based
methods have been widely used to project phylogenetic trees into Euclidean
space, but they are often sensitive to the choice of distance metric and may
lack sufficient r... |
2502.04734 | SC-OmniGS: Self-Calibrating Omnidirectional Gaussian Splatting | cs.CV cs.GR | 360-degree cameras streamline data collection for radiance field 3D
reconstruction by capturing comprehensive scene data. However, traditional
radiance field methods do not address the specific challenges inherent to
360-degree images. We present SC-OmniGS, a novel self-calibrating
omnidirectional Gaussian splatting ... |
2502.04737 | Learning Universal Multi-level Market Irrationality Factors to Improve
Stock Return Forecasting | cs.LG | Recent years have witnessed the perfect encounter of deep learning and
quantitative trading has achieved great success in stock investment. Numerous
deep learning-based models have been developed for forecasting stock returns,
leveraging the powerful representation capabilities of neural networks to
identify patterns... |
2502.04740 | SelaFD:Seamless Adaptation of Vision Transformer Fine-tuning for
Radar-based Human Activity | cs.CV cs.LG | Human Activity Recognition (HAR) such as fall detection has become
increasingly critical due to the aging population, necessitating effective
monitoring systems to prevent serious injuries and fatalities associated with
falls. This study focuses on fine-tuning the Vision Transformer (ViT) model
specifically for HAR u... |
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