id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.12499 | GPU Memory Usage Optimization for Backward Propagation in Deep Network
Training | cs.LG cs.DS | In modern Deep Learning, it has been a trend to design larger Deep Neural
Networks (DNNs) for the execution of more complex tasks and better accuracy. On
the other hand, Convolutional Neural Networks (CNNs) have become the standard
method for most of computer vision tasks. However, the memory allocation for
the inter... |
2502.12501 | Crowd Comparative Reasoning: Unlocking Comprehensive Evaluations for
LLM-as-a-Judge | cs.CL | LLM-as-a-Judge, which generates chain-of-thought (CoT) judgments, has become
a widely adopted auto-evaluation method. However, its reliability is
compromised by the CoT reasoning's inability to capture comprehensive and
deeper details, often leading to incomplete outcomes. Existing methods mainly
rely on majority vot... |
2502.12502 | Efficient OpAmp Adaptation for Zoom Attention to Golden Contexts | cs.CL | Large language models (LLMs) have shown significant promise in
question-answering (QA) tasks, particularly in retrieval-augmented generation
(RAG) scenarios and long-context applications. However, their performance is
hindered by noisy reference documents, which often distract from essential
information. Despite fine... |
2502.12507 | Mixture of Attention Yields Accurate Results for Tabular Data | cs.LG cs.AI | Tabular data inherently exhibits significant feature heterogeneity, but
existing transformer-based methods lack specialized mechanisms to handle this
property. To bridge the gap, we propose MAYA, an encoder-decoder
transformer-based framework. In the encoder, we design a Mixture of Attention
(MOA) that constructs mul... |
2502.12508 | Understanding Generalization in Transformers: Error Bounds and Training
Dynamics Under Benign and Harmful Overfitting | cs.LG | Transformers serve as the foundational architecture for many successful
large-scale models, demonstrating the ability to overfit the training data
while maintaining strong generalization on unseen data, a phenomenon known as
benign overfitting. However, research on how the training dynamics influence
error bounds wit... |
2502.12509 | LegalCore: A Dataset for Legal Documents Event Coreference Resolution | cs.CL cs.AI | Recognizing events and their coreferential mentions in a document is
essential for understanding semantic meanings of text. The existing research on
event coreference resolution is mostly limited to news articles. In this paper,
we present the first dataset for the legal domain, LegalCore, which has been
annotated wi... |
2502.12510 | Aspect-Guided Multi-Level Perturbation Analysis of Large Language Models
in Automated Peer Review | cs.CL | We propose an aspect-guided, multi-level perturbation framework to evaluate
the robustness of Large Language Models (LLMs) in automated peer review. Our
framework explores perturbations in three key components of the peer review
process-papers, reviews, and rebuttals-across several quality aspects,
including contribu... |
2502.12511 | Myna: Masking-Based Contrastive Learning of Musical Representations | cs.SD cs.AI cs.LG | We present Myna, a simple yet effective approach for self-supervised musical
representation learning. Built on a contrastive learning framework, Myna
introduces two key innovations: (1) the use of a Vision Transformer (ViT) on
mel-spectrograms as the backbone and (2) a novel data augmentation strategy,
token masking,... |
2502.12513 | RealSyn: An Effective and Scalable Multimodal Interleaved Document
Transformation Paradigm | cs.CV | After pre-training on extensive image-text pairs, Contrastive Language-Image
Pre-training (CLIP) demonstrates promising performance on a wide variety of
benchmarks. However, a substantial volume of non-paired data, such as
multimodal interleaved documents, remains underutilized for vision-language
representation lear... |
2502.12514 | Memory-updated-based Framework for 100% Reliable Flexible Flat Cables
Insertion | cs.RO | Automatic assembly lines have increasingly replaced human labor in various
tasks; however, the automation of Flexible Flat Cable (FFC) insertion remains
unrealized due to its high requirement for effective feedback and dynamic
operation, limiting approximately 11% of global industrial capacity. Despite
lots of approa... |
2502.12516 | Can LLMs Extract Frame-Semantic Arguments? | cs.CL | Frame-semantic parsing is a critical task in natural language understanding,
yet the ability of large language models (LLMs) to extract frame-semantic
arguments remains underexplored. This paper presents a comprehensive evaluation
of LLMs on frame-semantic argument identification, analyzing the impact of
input repres... |
2502.12518 | New Constant Dimension Codes From the Inserting Mixed Dimension
Construction and Multilevel Construction | cs.IT math.IT | Constant dimension codes (CDCs) are essential for error correction in random
network coding. A fundamental problem of CDCs is to determine their maximal
possible size for given parameters. Inserting construction and multilevel
construction are two effective techniques for constructing CDCs. We first
provide a suffici... |
2502.12520 | SAFEERASER: Enhancing Safety in Multimodal Large Language Models through
Multimodal Machine Unlearning | cs.CV | As Multimodal Large Language Models (MLLMs) develop, their potential security
issues have become increasingly prominent. Machine Unlearning (MU), as an
effective strategy for forgetting specific knowledge in training data, has been
widely used in privacy protection. However, MU for safety in MLLM has yet to be
fully ... |
2502.12521 | Inference-Time Computations for LLM Reasoning and Planning: A Benchmark
and Insights | cs.AI cs.LG | We examine the reasoning and planning capabilities of large language models
(LLMs) in solving complex tasks. Recent advances in inference-time techniques
demonstrate the potential to enhance LLM reasoning without additional training
by exploring intermediate steps during inference. Notably, OpenAI's o1 model
shows pr... |
2502.12523 | Cohesive Subgraph Discovery in Hypergraphs: A Locality-Driven Indexing
Framework | cs.SI | Hypergraphs are increasingly employed to model complex, diverse relationships
in modern networks, effectively capturing higher-order interactions. A critical
challenge in this domain is the discovery of cohesive subgraphs, which provides
valuable insights into hypergraph structures. However, selecting suitable
parame... |
2502.12524 | YOLOv12: Attention-Centric Real-Time Object Detectors | cs.CV cs.AI | Enhancing the network architecture of the YOLO framework has been crucial for
a long time, but has focused on CNN-based improvements despite the proven
superiority of attention mechanisms in modeling capabilities. This is because
attention-based models cannot match the speed of CNN-based models. This paper
proposes a... |
2502.12525 | From Abstract to Actionable: Pairwise Shapley Values for Explainable AI | cs.LG cs.AI | Explainable AI (XAI) is critical for ensuring transparency, accountability,
and trust in machine learning systems as black-box models are increasingly
deployed within high-stakes domains. Among XAI methods, Shapley values are
widely used for their fairness and consistency axioms. However, prevalent
Shapley value appr... |
2502.12527 | Comprehensive Assessment and Analysis for NSFW Content Erasure in
Text-to-Image Diffusion Models | cs.CV | Text-to-image (T2I) diffusion models have gained widespread application
across various domains, demonstrating remarkable creative potential. However,
the strong generalization capabilities of these models can inadvertently led
they to generate NSFW content even with efforts on filtering NSFW content from
the training... |
2502.12528 | Contextual Linear Bandits with Delay as Payoff | cs.LG | A recent work by Schlisselberg et al. (2024) studies a delay-as-payoff model
for stochastic multi-armed bandits, where the payoff (either loss or reward) is
delayed for a period that is proportional to the payoff itself. While this
captures many real-world applications, the simple multi-armed bandit setting
limits th... |
2502.12529 | Alternating Regret for Online Convex Optimization | cs.LG | Motivated by alternating learning dynamics in two-player games, a recent work
by Cevher et al.(2024) shows that $o(\sqrt{T})$ alternating regret is possible
for any $T$-round adversarial Online Linear Optimization (OLO) problem, and
left as an open question whether the same is true for general Online Convex
Optimizat... |
2502.12530 | Policy-to-Language: Train LLMs to Explain Decisions with Flow-Matching
Generated Rewards | cs.CL cs.LG | As humans increasingly share environments with diverse agents powered by RL,
LLMs, and beyond, the ability to explain their policies in natural language
will be vital for reliable coexistence. In this paper, we build a
model-agnostic explanation generator based on an LLM. The technical novelty is
that the rewards for... |
2502.12531 | GSCE: A Prompt Framework with Enhanced Reasoning for Reliable LLM-driven
Drone Control | cs.RO cs.AI | The integration of Large Language Models (LLMs) into robotic control,
including drones, has the potential to revolutionize autonomous systems.
Research studies have demonstrated that LLMs can be leveraged to support
robotic operations. However, when facing tasks with complex reasoning, concerns
and challenges are rai... |
2502.12532 | CityEQA: A Hierarchical LLM Agent on Embodied Question Answering
Benchmark in City Space | cs.AI | Embodied Question Answering (EQA) has primarily focused on indoor
environments, leaving the complexities of urban settings - spanning
environment, action, and perception - largely unexplored. To bridge this gap,
we introduce CityEQA, a new task where an embodied agent answers
open-vocabulary questions through active ... |
2502.12534 | NoKSR: Kernel-Free Neural Surface Reconstruction via Point Cloud
Serialization | cs.CV | We present a novel approach to large-scale point cloud surface reconstruction
by developing an efficient framework that converts an irregular point cloud
into a signed distance field (SDF). Our backbone builds upon recent
transformer-based architectures (i.e., PointTransformerV3), that serializes the
point cloud into... |
2502.12535 | Learning Transformation-Isomorphic Latent Space for Accurate Hand Pose
Estimation | cs.CV | Vision-based regression tasks, such as hand pose estimation, have achieved
higher accuracy and faster convergence through representation learning.
However, existing representation learning methods often encounter the following
issues: the high semantic level of features extracted from images is inadequate
for regress... |
2502.12536 | An Algorithm Board in Neural Decoding | cs.NE cs.AI | Understanding the mechanisms of neural encoding and decoding has always been
a highly interesting research topic in fields such as neuroscience and
cognitive intelligence. In prior studies, some researchers identified a
symmetry in neural data decoded by unsupervised methods in motor scenarios and
constructed a cogni... |
2502.12537 | Finding Optimal Trading History in Reinforcement Learning for Stock
Market Trading | cs.LG cs.AI | This paper investigates the optimization of temporal windows in Financial
Deep Reinforcement Learning (DRL) models using 2D Convolutional Neural Networks
(CNNs). We introduce a novel approach to treating the temporal field as a
hyperparameter and examine its impact on model performance across various
datasets and fea... |
2502.12539 | Design and Implementation of a Dual Uncrewed Surface Vessel Platform for
Bathymetry Research under High-flow Conditions | cs.RO cs.LG cs.SY eess.SY | Bathymetry, the study of underwater topography, relies on sonar mapping of
submerged structures. These measurements, critical for infrastructure health
monitoring, often require expensive instrumentation. The high financial risk
associated with sensor damage or vessel loss creates a reluctance to deploy
uncrewed surf... |
2502.12541 | When Segmentation Meets Hyperspectral Image: New Paradigm for
Hyperspectral Image Classification | cs.CV | Hyperspectral image (HSI) classification is a cornerstone of remote sensing,
enabling precise material and land-cover identification through rich spectral
information. While deep learning has driven significant progress in this task,
small patch-based classifiers, which account for over 90% of the progress, face
limi... |
2502.12542 | Computing Voting Rules with Improvement Feedback | cs.GT cs.AI | Aggregating preferences under incomplete or constrained feedback is a
fundamental problem in social choice and related domains. While prior work has
established strong impossibility results for pairwise comparisons, this paper
extends the inquiry to improvement feedback, where voters express incremental
adjustments r... |
2502.12545 | IM360: Textured Mesh Reconstruction for Large-scale Indoor Mapping with
360$^\circ$ Cameras | cs.CV | We present a novel 3D reconstruction pipeline for 360$^\circ$ cameras for 3D
mapping and rendering of indoor environments. Traditional Structure-from-Motion
(SfM) methods may not work well in large-scale indoor scenes due to the
prevalence of textureless and repetitive regions. To overcome these challenges,
our appro... |
2502.12546 | Spatiotemporal Multi-Camera Calibration using Freely Moving People | cs.CV | We propose a novel method for spatiotemporal multi-camera calibration using
freely moving people in multiview videos. Since calibrating multiple cameras
and finding matches across their views are inherently interdependent,
performing both in a unified framework poses a significant challenge. We
address these issues a... |
2502.12548 | Improving the Stability of GNN Force Field Models by Reducing Feature
Correlation | cs.LG cs.AI | Recently, Graph Neural Network based Force Field (GNNFF) models are widely
used in Molecular Dynamics (MD) simulation, which is one of the most
cost-effective means in semiconductor material research. However, even such
models provide high accuracy in energy and force Mean Absolute Error (MAE) over
trained (in-distri... |
2502.12552 | LLM Safety for Children | cs.CY cs.AI | This paper analyzes the safety of Large Language Models (LLMs) in
interactions with children below age of 18 years. Despite the transformative
applications of LLMs in various aspects of children's lives such as education
and therapy, there remains a significant gap in understanding and mitigating
potential content ha... |
2502.12555 | Warm Starting of CMA-ES for Contextual Optimization Problems | cs.NE | Several practical applications of evolutionary computation possess objective
functions that receive the design variables and externally given parameters.
Such problems are termed contextual optimization problems. These problems
require finding the optimal solutions corresponding to the given context
vectors. Existing... |
2502.12556 | From Maneuver to Mishap: A Systematic Literature Review on U-Turn Safety
Risks | eess.SY cs.SY | Understanding the impacts of U-turn configurations on intersection safety and
traffic operations is essential for developing effective strategies to enhance
road safety and efficiency. Extensive research has been conducted to
investigate the role of geometric designs, driver behavior, and advanced
technologies in mit... |
2502.12558 | MomentSeeker: A Comprehensive Benchmark and A Strong Baseline For Moment
Retrieval Within Long Videos | cs.CV cs.AI | Retrieval augmented generation (RAG) holds great promise in addressing
challenges associated with long video understanding. These methods retrieve
useful moments from long videos for their presented tasks, thereby enabling
multimodal large language models (MLLMs) to generate high-quality answers in a
cost-effective w... |
2502.12560 | How does a Language-Specific Tokenizer affect LLMs? | cs.CL | The necessity of language-specific tokenizers intuitively appears crucial for
effective natural language processing, yet empirical analyses on their
significance and underlying reasons are lacking. This study explores how
language-specific tokenizers influence the behavior of Large Language Models
predominantly train... |
2502.12561 | UXAgent: An LLM Agent-Based Usability Testing Framework for Web Design | cs.HC cs.CL | Usability testing is a fundamental yet challenging (e.g., inflexible to
iterate the study design flaws and hard to recruit study participants) research
method for user experience (UX) researchers to evaluate a web design. Recent
advances in Large Language Model-simulated Agent (LLM-Agent) research inspired
us to desi... |
2502.12562 | SEA: Low-Resource Safety Alignment for Multimodal Large Language Models
via Synthetic Embeddings | cs.CL cs.CR cs.MM | Multimodal Large Language Models (MLLMs) have serious security
vulnerabilities.While safety alignment using multimodal datasets consisting of
text and data of additional modalities can effectively enhance MLLM's security,
it is costly to construct these datasets. Existing low-resource security
alignment methods, incl... |
2502.12563 | Evaluating Language Models on Grooming Risk Estimation Using Fuzzy
Theory | cs.CL cs.AI cs.LG | Encoding implicit language presents a challenge for language models,
especially in high-risk domains where maintaining high precision is important.
Automated detection of online child grooming is one such critical domain, where
predators manipulate victims using a combination of explicit and implicit
language to conv... |
2502.12564 | Sample Efficient Omniprediction and Downstream Swap Regret for
Non-Linear Losses | cs.LG cs.GT | We define "decision swap regret" which generalizes both prediction for
downstream swap regret and omniprediction, and give algorithms for obtaining it
for arbitrary multi-dimensional Lipschitz loss functions in online adversarial
settings. We also give sample complexity bounds in the batch setting via an
online-to-ba... |
2502.12565 | Self Iterative Label Refinement via Robust Unlabeled Learning | cs.CL | Recent advances in large language models (LLMs) have yielded impressive
performance on various tasks, yet they often depend on high-quality feedback
that can be costly. Self-refinement methods attempt to leverage LLMs' internal
evaluation mechanisms with minimal human supervision; however, these approaches
frequently... |
2502.12566 | Exploring the Impact of Personality Traits on LLM Bias and Toxicity | cs.AI | With the different roles that AI is expected to play in human life, imbuing
large language models (LLMs) with different personalities has attracted
increasing research interests. While the "personification" enhances human
experiences of interactivity and adaptability of LLMs, it gives rise to
critical concerns about ... |
2502.12567 | DeltaDiff: A Residual-Guided Diffusion Model for Enhanced Image
Super-Resolution | cs.CV | Recently, the application of diffusion models in super-resolution tasks has
become a popular research direction. Existing work is focused on fully
migrating diffusion models to SR tasks. The diffusion model is proposed in the
field of image generation, so in order to make the generated results diverse,
the diffusion ... |
2502.12568 | A Cognitive Writing Perspective for Constrained Long-Form Text
Generation | cs.CL cs.AI | Like humans, Large Language Models (LLMs) struggle to generate high-quality
long-form text that adheres to strict requirements in a single pass. This
challenge is unsurprising, as successful human writing, according to the
Cognitive Writing Theory, is a complex cognitive process involving iterative
planning, translat... |
2502.12569 | Maximizing Value in Challenge the Champ Tournaments | cs.DS cs.GT cs.MA | A tournament is a method to decide the winner in a competition, and describes
the overall sequence in which matches between the players are held. While
deciding a worthy winner is the primary goal of a tournament, a close second is
to maximize the value generated for the matches played, with value for a match
measure... |
2502.12570 | GVTNet: Graph Vision Transformer For Face Super-Resolution | cs.CV | Recent advances in face super-resolution research have utilized the
Transformer architecture. This method processes the input image into a series
of small patches. However, because of the strong correlation between different
facial components in facial images. When it comes to super-resolution of
low-resolution image... |
2502.12571 | A Novel Gain Modeling Technique for LLC Resonant Converters based on The
Hybrid Deep-Learning/GMDH Neural Network | eess.SY cs.SY | This paper presents a novel hybrid approach for modeling the voltage gain of
LLC resonant converters by combining deep-learning neural networks with the
polynomial based Group Method of Data Handling (GMDH). While deep learning
offers high accuracy in predicting nonlinear converter behavior, it produces
complex netwo... |
2502.12574 | HeadInfer: Memory-Efficient LLM Inference by Head-wise Offloading | cs.LG cs.AI | Transformer-based large language models (LLMs) demonstrate impressive
performance in long context generation. Extending the context length has
disproportionately shifted the memory footprint of LLMs during inference to the
key-value cache (KV cache). In this paper, we propose HEADINFER, which offloads
the KV cache to... |
2502.12575 | DemonAgent: Dynamically Encrypted Multi-Backdoor Implantation Attack on
LLM-based Agent | cs.CR cs.AI | As LLM-based agents become increasingly prevalent, backdoors can be implanted
into agents through user queries or environment feedback, raising critical
concerns regarding safety vulnerabilities. However, backdoor attacks are
typically detectable by safety audits that analyze the reasoning process of
agents. To this ... |
2502.12576 | A Fuzzy Evaluation of Sentence Encoders on Grooming Risk Classification | cs.CL cs.AI cs.LG | With the advent of social media, children are becoming increasingly
vulnerable to the risk of grooming in online settings. Detecting grooming
instances in an online conversation poses a significant challenge as the
interactions are not necessarily sexually explicit, since the predators take
time to build trust and a ... |
2502.12579 | CHATS: Combining Human-Aligned Optimization and Test-Time Sampling for
Text-to-Image Generation | cs.CV | Diffusion models have emerged as a dominant approach for text-to-image
generation. Key components such as the human preference alignment and
classifier-free guidance play a crucial role in ensuring generation quality.
However, their independent application in current text-to-image models
continues to face significant... |
2502.12581 | The Majority Vote Paradigm Shift: When Popular Meets Optimal | stat.ML cs.AI cs.LG | Reliably labelling data typically requires annotations from multiple human
workers. However, humans are far from being perfect. Hence, it is a common
practice to aggregate labels gathered from multiple annotators to make a more
confident estimate of the true label. Among many aggregation methods, the
simple and well ... |
2502.12582 | Adaptive Prototype Model for Attribute-based Multi-label Few-shot Action
Recognition | cs.CV | In real-world action recognition systems, incorporating more attributes helps
achieve a more comprehensive understanding of human behavior. However, using a
single model to simultaneously recognize multiple attributes can lead to a
decrease in accuracy. In this work, we propose a novel method i.e. Adaptive
Attribute ... |
2502.12583 | LongFaith: Enhancing Long-Context Reasoning in LLMs with Faithful
Synthetic Data | cs.CL | Despite the growing development of long-context large language models (LLMs),
data-centric approaches relying on synthetic data have been hindered by issues
related to faithfulness, which limit their effectiveness in enhancing model
performance on tasks such as long-context reasoning and question answering
(QA). Thes... |
2502.12584 | Enhancing Semi-supervised Learning with Noisy Zero-shot Pseudolabels | cs.LG cs.AI | Semi-supervised learning (SSL) leverages limited labeled data alongside
abundant unlabeled data to address labeling costs in machine learning. While
recent foundation models enable zero-shot inference, attempts to integrate
these capabilities into SSL through pseudo-labeling have shown mixed results
due to unreliable... |
2502.12586 | G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable
Recommendation | cs.IR cs.CL | Explainable recommendation has demonstrated significant advantages in
informing users about the logic behind recommendations, thereby increasing
system transparency, effectiveness, and trustworthiness. To provide
personalized and interpretable explanations, existing works often combine the
generation capabilities of ... |
2502.12587 | RSMLP: A light Sampled MLP Structure for Incomplete Utterance Rewrite | cs.CL cs.AI | The Incomplete Utterance Rewriting (IUR) task has garnered significant
attention in recent years. Its goal is to reconstruct conversational utterances
to better align with the current context, thereby enhancing comprehension. In
this paper, we introduce a novel and versatile lightweight method,
Rewritten-Sampled MLP ... |
2502.12589 | RM-PoT: Reformulating Mathematical Problems and Solving via Program of
Thoughts | cs.AI | Recently, substantial advancements have been made in training language models
to carry out step-by-step reasoning for solving intricate numerical reasoning
tasks. Beyond the methods used to solve these problems, the structure and
formulation of the problems themselves also play a crucial role in determining
the perfo... |
2502.12591 | CutPaste&Find: Efficient Multimodal Hallucination Detector with
Visual-aid Knowledge Base | cs.CV cs.CL | Large Vision-Language Models (LVLMs) have demonstrated impressive multimodal
reasoning capabilities, but they remain susceptible to hallucination,
particularly object hallucination where non-existent objects or incorrect
attributes are fabricated in generated descriptions. Existing detection methods
achieve strong pe... |
2502.12594 | PASER: Post-Training Data Selection for Efficient Pruned Large Language
Model Recovery | cs.CL | Model pruning is an effective approach for compressing large language models.
However, this process often leads to significant degradation of model
capabilities. While post-training techniques such as instruction tuning are
commonly employed to recover model performance, existing methods often overlook
the uneven det... |
2502.12598 | Bring Your Own Knowledge: A Survey of Methods for LLM Knowledge
Expansion | cs.CL | Adapting large language models (LLMs) to new and diverse knowledge is
essential for their lasting effectiveness in real-world applications. This
survey provides an overview of state-of-the-art methods for expanding the
knowledge of LLMs, focusing on integrating various knowledge types, including
factual information, ... |
2502.12599 | Learning a High-quality Robotic Wiping Policy Using Systematic Reward
Analysis and Visual-Language Model Based Curriculum | cs.RO cs.LG | Autonomous robotic wiping is an important task in various industries, ranging
from industrial manufacturing to sanitization in healthcare. Deep reinforcement
learning (Deep RL) has emerged as a promising algorithm, however, it often
suffers from a high demand for repetitive reward engineering. Instead of
relying on m... |
2502.12600 | Revisiting the Generalization Problem of Low-level Vision Models Through
the Lens of Image Deraining | cs.CV | Generalization remains a significant challenge for low-level vision models,
which often struggle with unseen degradations in real-world scenarios despite
their success in controlled benchmarks. In this paper, we revisit the
generalization problem in low-level vision models. Image deraining is selected
as a case study... |
2502.12601 | COPU: Conformal Prediction for Uncertainty Quantification in Natural
Language Generation | cs.CL | Uncertainty Quantification (UQ) for Natural Language Generation (NLG) is
crucial for assessing the performance of Large Language Models (LLMs), as it
reveals confidence in predictions, identifies failure modes, and gauges output
reliability. Conformal Prediction (CP), a model-agnostic method that generates
prediction... |
2502.12602 | Learning-based Dynamic Robot-to-Human Handover | cs.RO | This paper presents a novel learning-based approach to dynamic robot-to-human
handover, addressing the challenges of delivering objects to a moving receiver.
We hypothesize that dynamic handover, where the robot adjusts to the receiver's
movements, results in more efficient and comfortable interaction compared to
sta... |
2502.12603 | Disentangling Long-Short Term State Under Unknown Interventions for
Online Time Series Forecasting | cs.LG cs.AI | Current methods for time series forecasting struggle in the online scenario,
since it is difficult to preserve long-term dependency while adapting
short-term changes when data are arriving sequentially. Although some recent
methods solve this problem by controlling the updates of latent states, they
cannot disentangl... |
2502.12604 | S2C: Learning Noise-Resistant Differences for Unsupervised Change
Detection in Multimodal Remote Sensing Images | cs.CV | Unsupervised Change Detection (UCD) in multimodal Remote Sensing (RS) images
remains a difficult challenge due to the inherent spatio-temporal complexity
within data, and the heterogeneity arising from different imaging sensors.
Inspired by recent advancements in Visual Foundation Models (VFMs) and
Contrastive Learni... |
2502.12605 | Hypernetwork-based approach for optimal composition design in partially
controlled multi-agent systems | cs.MA cs.LG | Partially Controlled Multi-Agent Systems (PCMAS) are comprised of
controllable agents, managed by a system designer, and uncontrollable agents,
operating autonomously. This study addresses an optimal composition design
problem in PCMAS, which involves the system designer's problem, determining the
optimal number and ... |
2502.12607 | Generalized Kernel Inducing Points by Duality Gap for Dataset
Distillation | stat.ML cs.LG | We propose Duality Gap KIP (DGKIP), an extension of the Kernel Inducing
Points (KIP) method for dataset distillation. While existing dataset
distillation methods often rely on bi-level optimization, DGKIP eliminates the
need for such optimization by leveraging duality theory in convex programming.
The KIP method has ... |
2502.12608 | Unveiling Mode Connectivity in Graph Neural Networks | cs.LG cs.AI | A fundamental challenge in understanding graph neural networks (GNNs) lies in
characterizing their optimization dynamics and loss landscape geometry,
critical for improving interpretability and robustness. While mode
connectivity, a lens for analyzing geometric properties of loss landscapes has
proven insightful for ... |
2502.12611 | Who Writes What: Unveiling the Impact of Author Roles on AI-generated
Text Detection | cs.CL | The rise of Large Language Models (LLMs) necessitates accurate AI-generated
text detection. However, current approaches largely overlook the influence of
author characteristics. We investigate how sociolinguistic attributes-gender,
CEFR proficiency, academic field, and language environment-impact
state-of-the-art AI ... |
2502.12614 | Label Drop for Multi-Aspect Relation Modeling in Universal Information
Extraction | cs.CL cs.AI | Universal Information Extraction (UIE) has garnered significant attention due
to its ability to address model explosion problems effectively. Extractive UIE
can achieve strong performance using a relatively small model, making it widely
adopted. Extractive UIEs generally rely on task instructions for different
tasks,... |
2502.12616 | Improving Chain-of-Thought Reasoning via Quasi-Symbolic Abstractions | cs.CL | Chain-of-Though (CoT) represents a common strategy for reasoning in Large
Language Models (LLMs) by decomposing complex tasks into intermediate inference
steps. However, explanations generated via CoT are susceptible to content
biases that negatively affect their robustness and faithfulness. To mitigate
existing limi... |
2502.12617 | A Graph-Enhanced Deep-Reinforcement Learning Framework for the Aircraft
Landing Problem | cs.LG cs.AI cs.SY eess.SY | The Aircraft Landing Problem (ALP) is one of the challenging problems in
aircraft transportation and management. The challenge is to schedule the
arriving aircraft in a sequence so that the cost and delays are optimized.
There are various solution approaches to solving this problem, most of which
are based on operati... |
2502.12618 | Uncertainty-Aware Graph Structure Learning | cs.LG | Graph Neural Networks (GNNs) have become a prominent approach for learning
from graph-structured data. However, their effectiveness can be significantly
compromised when the graph structure is suboptimal. To address this issue,
Graph Structure Learning (GSL) has emerged as a promising technique that
refines node conn... |
2502.12623 | DeepResonance: Enhancing Multimodal Music Understanding via
Music-centric Multi-way Instruction Tuning | cs.SD cs.AI cs.CL cs.MM eess.AS | Recent advancements in music large language models (LLMs) have significantly
improved music understanding tasks, which involve the model's ability to
analyze and interpret various musical elements. These improvements primarily
focused on integrating both music and text inputs. However, the potential of
incorporating ... |
2502.12624 | Implicit Repair with Reinforcement Learning in Emergent Communication | cs.LG cs.MA | Conversational repair is a mechanism used to detect and resolve
miscommunication and misinformation problems when two or more agents interact.
One particular and underexplored form of repair in emergent communication is
the implicit repair mechanism, where the interlocutor purposely conveys the
desired information in... |
2502.12627 | DAMamba: Vision State Space Model with Dynamic Adaptive Scan | cs.CV | State space models (SSMs) have recently garnered significant attention in
computer vision. However, due to the unique characteristics of image data,
adapting SSMs from natural language processing to computer vision has not
outperformed the state-of-the-art convolutional neural networks (CNNs) and
Vision Transformers ... |
2502.12629 | Rate Maximization for Downlink Pinching-Antenna Systems | cs.IT eess.SP math.IT | In this letter, we consider a new type of flexible-antenna system, termed
pinching-antenna, where multiple low-cost pinching antennas, realized by
activating small dielectric particles on a dielectric waveguide, are jointly
used to serve a single-antenna user. Our goal is to maximize the downlink
transmission rate by... |
2502.12630 | Automating Prompt Leakage Attacks on Large Language Models Using Agentic
Approach | cs.CR cs.AI | This paper presents a novel approach to evaluating the security of large
language models (LLMs) against prompt leakage-the exposure of system-level
prompts or proprietary configurations. We define prompt leakage as a critical
threat to secure LLM deployment and introduce a framework for testing the
robustness of LLMs... |
2502.12631 | Score-Based Diffusion Policy Compatible with Reinforcement Learning via
Optimal Transport | cs.LG cs.AI | Diffusion policies have shown promise in learning complex behaviors from
demonstrations, particularly for tasks requiring precise control and long-term
planning. However, they face challenges in robustness when encountering
distribution shifts. This paper explores improving diffusion-based imitation
learning models t... |
2502.12632 | MALT Diffusion: Memory-Augmented Latent Transformers for Any-Length
Video Generation | cs.CV cs.LG | Diffusion models are successful for synthesizing high-quality videos but are
limited to generating short clips (e.g., 2-10 seconds). Synthesizing sustained
footage (e.g. over minutes) still remains an open research question. In this
paper, we propose MALT Diffusion (using Memory-Augmented Latent Transformers),
a new ... |
2502.12633 | One Size doesn't Fit All: A Personalized Conversational Tutoring Agent
for Mathematics Instruction | cs.CL cs.AI | Large language models (LLMs) have been increasingly employed in various
intelligent educational systems, simulating human tutors to facilitate
effective human-machine interaction. However, previous studies often overlook
the significance of recognizing and adapting to individual learner
characteristics. Such adaptati... |
2502.12634 | Introducing Context Information in Lifelong Sequential Modeling using
Temporal Convolutional Networks | cs.IR | The importance of lifelong sequential modeling (LSM) is growing in the realm
of social media recommendation systems. A key component in this process is the
attention module, which derives interest representations with respect to
candidate items from the sequence. Typically, attention modules function in a
point-wise ... |
2502.12635 | Corrupted but Not Broken: Rethinking the Impact of Corrupted Data in
Visual Instruction Tuning | cs.CV | Visual Instruction Tuning (VIT) enhances Multimodal Large Language Models
(MLLMs) but it is hindered by corrupted datasets containing hallucinated
content, incorrect responses, and poor OCR quality. While prior works focus on
dataset refinement through high-quality data collection or rule-based
filtering, they are co... |
2502.12638 | NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule
Generation | q-bio.QM cs.LG q-bio.BM | 3D molecule generation is crucial for drug discovery and material design.
While prior efforts focus on 3D diffusion models for their benefits in modeling
continuous 3D conformers, they overlook the advantages of 1D SELFIES-based
Language Models (LMs), which can generate 100% valid molecules and leverage the
billion-s... |
2502.12640 | RecDreamer: Consistent Text-to-3D Generation via Uniform Score
Distillation | cs.CV | Current text-to-3D generation methods based on score distillation often
suffer from geometric inconsistencies, leading to repeated patterns across
different poses of 3D assets. This issue, known as the Multi-Face Janus
problem, arises because existing methods struggle to maintain consistency
across varying poses and ... |
2502.12654 | Free Energy and Network Structure: Breaking Scale-Free Behaviour Through
Information Processing Constraints | cs.SI physics.soc-ph | In this paper we show how The Free Energy Principle (FEP) can provide an
explanation for why real-world networks deviate from scale-free behaviour, and
how these characteristic deviations can emerge from constraints on information
processing. We propose a minimal FEP model for node behaviour reveals three
distinct re... |
2502.12655 | LiMo-Calib: On-Site Fast LiDAR-Motor Calibration for Quadruped
Robot-Based Panoramic 3D Sensing System | cs.RO | Conventional single LiDAR systems are inherently constrained by their limited
field of view (FoV), leading to blind spots and incomplete environmental
awareness, particularly on robotic platforms with strict payload limitations.
Integrating a motorized LiDAR offers a practical solution by significantly
expanding the ... |
2502.12658 | R.R.: Unveiling LLM Training Privacy through Recollection and Ranking | cs.CL | Large Language Models (LLMs) pose significant privacy risks, potentially
leaking training data due to implicit memorization. Existing privacy attacks
primarily focus on membership inference attacks (MIAs) or data extraction
attacks, but reconstructing specific personally identifiable information (PII)
in LLM's traini... |
2502.12659 | The Hidden Risks of Large Reasoning Models: A Safety Assessment of R1 | cs.CY cs.AI | The rapid development of large reasoning models, such as OpenAI-o3 and
DeepSeek-R1, has led to significant improvements in complex reasoning over
non-reasoning large language models~(LLMs). However, their enhanced
capabilities, combined with the open-source access of models like DeepSeek-R1,
raise serious safety conc... |
2502.12663 | Demystifying Multilingual Chain-of-Thought in Process Reward Modeling | cs.CL | Large language models (LLMs) are designed to perform a wide range of tasks.
To improve their ability to solve complex problems requiring multi-step
reasoning, recent research leverages process reward modeling to provide
fine-grained feedback at each step of the reasoning process for reinforcement
learning (RL), but i... |
2502.12665 | A$^2$ATS: Retrieval-Based KV Cache Reduction via Windowed Rotary
Position Embedding and Query-Aware Vector Quantization | cs.CL | Long context large language models (LLMs) pose significant challenges for
efficient serving due to the large memory footprint and high access overhead of
KV cache. Retrieval-based KV cache reduction methods can mitigate these
challenges, typically by offloading the complete KV cache to CPU and retrieving
necessary to... |
2502.12668 | Evaluation of Best-of-N Sampling Strategies for Language Model Alignment | cs.CL | Best-of-N (BoN) sampling with a reward model has been shown to be an
effective strategy for aligning Large Language Models (LLMs) with human
preferences at the time of decoding. BoN sampling is susceptible to a problem
known as reward hacking. Since the reward model is an imperfect proxy for the
true objective, an ex... |
2502.12669 | Perovskite-LLM: Knowledge-Enhanced Large Language Models for Perovskite
Solar Cell Research | cs.AI | The rapid advancement of perovskite solar cells (PSCs) has led to an
exponential growth in research publications, creating an urgent need for
efficient knowledge management and reasoning systems in this domain. We present
a comprehensive knowledge-enhanced system for PSCs that integrates three key
components. First, ... |
2502.12671 | Baichuan-M1: Pushing the Medical Capability of Large Language Models | cs.CL | The current generation of large language models (LLMs) is typically designed
for broad, general-purpose applications, while domain-specific LLMs, especially
in vertical fields like medicine, remain relatively scarce. In particular, the
development of highly efficient and practical LLMs for the medical domain is
chall... |
2502.12672 | Speech-FT: A Fine-tuning Strategy for Enhancing Speech Representation
Models Without Compromising Generalization Ability | cs.CL cs.AI | Speech representation models are highly effective at extracting general
features for various tasks. While fine-tuning can enhance these representations
for specific applications, it often compromises their generalization ability.
To address this challenge, we propose Speech-FT, a fine-tuning strategy for
speech repre... |
2502.12673 | ROI-NeRFs: Hi-Fi Visualization of Objects of Interest within a Scene by
NeRFs Composition | cs.CV cs.GR | Efficient and accurate 3D reconstruction is essential for applications in
cultural heritage. This study addresses the challenge of visualizing objects
within large-scale scenes at a high level of detail (LOD) using Neural Radiance
Fields (NeRFs). The aim is to improve the visual fidelity of chosen objects
while maint... |
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