id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.13514 | Shall Your Data Strategy Work? Perform a Swift Study | [
"cs.CL"
] | This work presents a swift method to assess the efficacy of particular types of instruction-tuning data, utilizing just a handful of probe examples and eliminating the need for model retraining. This method employs the idea of gradient-based data influence estimation, analyzing the gradient projections of probe example... | {
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2502.13516 | SPPD: Self-training with Process Preference Learning Using Dynamic Value
Margin | [
"cs.AI"
] | Recently, enhancing the numerical and logical reasoning capability of Large Language Models (LLMs) has emerged as a research hotspot. Existing methods face several limitations: inference-phase techniques (e.g., Chain of Thoughts) rely on prompt selection and the pretrained knowledge; sentence-level Supervised Fine-Tuni... | {
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2502.13519 | MILE: Model-based Intervention Learning | [
"cs.RO",
"cs.AI",
"cs.LG"
] | Imitation learning techniques have been shown to be highly effective in real-world control scenarios, such as robotics. However, these approaches not only suffer from compounding error issues but also require human experts to provide complete trajectories. Although there exist interactive methods where an expert overse... | {
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2502.13520 | A Large and Balanced Corpus for Fine-grained Arabic Readability
Assessment | [
"cs.CL"
] | This paper introduces the Balanced Arabic Readability Evaluation Corpus BAREC, a large-scale, fine-grained dataset for Arabic readability assessment. BAREC consists of 68,182 sentences spanning 1+ million words, carefully curated to cover 19 readability levels, from kindergarten to postgraduate comprehension. The corpu... | {
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2502.13522 | Enhancing Machine Learning Potentials through Transfer Learning across
Chemical Elements | [
"cs.LG",
"cond-mat.mtrl-sci"
] | Machine Learning Potentials (MLPs) can enable simulations of ab initio accuracy at orders of magnitude lower computational cost. However, their effectiveness hinges on the availability of considerable datasets to ensure robust generalization across chemical space and thermodynamic conditions. The generation of such dat... | {
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2502.13524 | MobileViM: A Light-weight and Dimension-independent Vision Mamba for 3D
Medical Image Analysis | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.NI"
] | Efficient evaluation of three-dimensional (3D) medical images is crucial for diagnostic and therapeutic practices in healthcare. Recent years have seen a substantial uptake in applying deep learning and computer vision to analyse and interpret medical images. Traditional approaches, such as convolutional neural network... | {
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2502.13525 | AS-GCL: Asymmetric Spectral Augmentation on Graph Contrastive Learning | [
"cs.LG"
] | Graph Contrastive Learning (GCL) has emerged as the foremost approach for self-supervised learning on graph-structured data. GCL reduces reliance on labeled data by learning robust representations from various augmented views. However, existing GCL methods typically depend on consistent stochastic augmentations, which ... | {
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2502.13527 | Exploiting Prefix-Tree in Structured Output Interfaces for Enhancing
Jailbreak Attacking | [
"cs.CR",
"cs.AI"
] | The rise of Large Language Models (LLMs) has led to significant applications but also introduced serious security threats, particularly from jailbreak attacks that manipulate output generation. These attacks utilize prompt engineering and logit manipulation to steer models toward harmful content, prompting LLM provider... | {
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2502.13530 | Breaking the Clusters: Uniformity-Optimization for Text-Based Sequential
Recommendation | [
"cs.IR"
] | Traditional sequential recommendation (SR) methods heavily rely on explicit item IDs to capture user preferences over time. This reliance introduces critical limitations in cold-start scenarios and domain transfer tasks, where unseen items and new contexts often lack established ID mappings. To overcome these limitatio... | {
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2502.13531 | Quotients of skew polynomial rings: new constructions of division
algebras and MRD codes | [
"math.CO",
"cs.IT",
"math.IT",
"math.RA"
] | We achieve new results on skew polynomial rings and their quotients, including the first explicit example of a skew polynomial ring where the ratio of the degree of a skew polynomial to the degree of its bound is not extremal. These methods lead to the construction of new (not necessarily associative) division algebras... | {
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2502.13533 | Train Small, Infer Large: Memory-Efficient LoRA Training for Large
Language Models | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Large Language Models (LLMs) have significantly advanced natural language processing with exceptional task generalization capabilities. Low-Rank Adaption (LoRA) offers a cost-effective fine-tuning solution, freezing the original model parameters and training only lightweight, low-rank adapter matrices. However, the mem... | {
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2502.13534 | Solving the Encoding Bottleneck: Of the HHL Algorithm, By the HHL
Algorithm | [
"quant-ph",
"cs.AI",
"cs.LG"
] | The Harrow-Hassidim-Lloyd (HHL) algorithm offers exponential speedup for solving the quantum linear-system problem. But some caveats for the speedup could be hard to met. One of the difficulties is the encoding bottleneck, i.e., the efficient preparation of the initial quantum state. To prepare an arbitrary $N$-dimensi... | {
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2502.13539 | Bursting Filter Bubble: Enhancing Serendipity Recommendations with
Aligned Large Language Models | [
"cs.IR"
] | Recommender systems (RSs) often suffer from the feedback loop phenomenon, e.g., RSs are trained on data biased by their recommendations. This leads to the filter bubble effect that reinforces homogeneous content and reduces user satisfaction. To this end, serendipity recommendations, which offer unexpected yet relevant... | {
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2502.13542 | Activation-aware Probe-Query: Effective Key-Value Retrieval for
Long-Context LLMs Inference | [
"cs.CL",
"cs.AI"
] | Recent advances in large language models (LLMs) have showcased exceptional performance in long-context tasks, while facing significant inference efficiency challenges with limited GPU memory. Existing solutions first proposed the sliding-window approach to accumulate a set of historical \textbf{key-value} (KV) pairs fo... | {
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2502.13544 | From Sub-Ability Diagnosis to Human-Aligned Generation: Bridging the Gap
for Text Length Control via MARKERGEN | [
"cs.CL",
"cs.AI"
] | Despite the rapid progress of large language models (LLMs), their length-controllable text generation (LCTG) ability remains below expectations, posing a major limitation for practical applications. Existing methods mainly focus on end-to-end training to reinforce adherence to length constraints. However, the lack of d... | {
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2502.13548 | Detecting Linguistic Bias in Government Documents Using Large language
Models | [
"cs.CL"
] | This paper addresses the critical need for detecting bias in government documents, an underexplored area with significant implications for governance. Existing methodologies often overlook the unique context and far-reaching impacts of governmental documents, potentially obscuring embedded biases that shape public poli... | {
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2502.13550 | STaR-SQL: Self-Taught Reasoner for Text-to-SQL | [
"cs.CL"
] | Generating step-by-step "chain-of-thought" rationales has proven effective for improving the performance of large language models on complex reasoning tasks. However, applying such techniques to structured tasks, such as text-to-SQL, remains largely unexplored. In this paper, we introduce Self-Taught Reasoner for text-... | {
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2502.13555 | Democratizing Large Language Model-Based Graph Data Augmentation via
Latent Knowledge Graphs | [
"cs.LG",
"cs.AI"
] | Data augmentation is necessary for graph representation learning due to the scarcity and noise present in graph data. Most of the existing augmentation methods overlook the context information inherited from the dataset as they rely solely on the graph structure for augmentation. Despite the success of some large langu... | {
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2502.13559 | Implementation of an IEEE 802.11ax-Based Maritime Mesh Network in the
Red Sea | [
"eess.SY",
"cs.SY"
] | In this article, we explore the limitations of satellite phones in meeting the communication needs of fishermen operating in the Red Sea. We propose AX-MMN, a maritime mesh network based on the IEEE 802.11ax standard, to address these shortcomings of satellite phones and outline AX-MMN's system architecture. To validat... | {
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2502.13562 | Are Large Language Models In-Context Graph Learners? | [
"cs.LG",
"cs.AI"
] | Large language models (LLMs) have demonstrated remarkable in-context reasoning capabilities across a wide range of tasks, particularly with unstructured inputs such as language or images. However, LLMs struggle to handle structured data, such as graphs, due to their lack of understanding of non-Euclidean structures. As... | {
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2502.13564 | PRIV-QA: Privacy-Preserving Question Answering for Cloud Large Language
Models | [
"cs.CL"
] | The rapid development of large language models (LLMs) is redefining the landscape of human-computer interaction, and their integration into various user-service applications is becoming increasingly prevalent. However, transmitting user data to cloud-based LLMs presents significant risks of data breaches and unauthoriz... | {
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2502.13566 | Extracting Social Connections from Finnish Karelian Refugee Interviews
Using LLMs | [
"cs.CL"
] | We performed a zero-shot information extraction study on a historical collection of 89,339 brief Finnish-language interviews of refugee families relocated post-WWII from Finnish Eastern Karelia. Our research objective is two-fold. First, we aim to extract social organizations and hobbies from the free text of the inter... | {
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2502.13568 | LSR-Adapt: Ultra-Efficient Parameter Tuning with Matrix Low Separation
Rank Kernel Adaptation | [
"cs.LG",
"cs.CL"
] | Imposing an effective structural assumption on neural network weight matrices has been the major paradigm for designing Parameter-Efficient Fine-Tuning (PEFT) systems for adapting modern large pre-trained models to various downstream tasks. However, low rank based adaptation has become increasingly challenging due to t... | {
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2502.13569 | Model Evolution Framework with Genetic Algorithm for Multi-Task
Reinforcement Learning | [
"cs.AI"
] | Multi-task reinforcement learning employs a single policy to complete various tasks, aiming to develop an agent with generalizability across different scenarios. Given the shared characteristics of tasks, the agent's learning efficiency can be enhanced through parameter sharing. Existing approaches typically use a rout... | {
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2502.13570 | An Efficient Permutation-Based Kernel Two-Sample Test | [
"stat.ML",
"cs.LG",
"math.ST",
"stat.ME",
"stat.TH"
] | Two-sample hypothesis testing-determining whether two sets of data are drawn from the same distribution-is a fundamental problem in statistics and machine learning with broad scientific applications. In the context of nonparametric testing, maximum mean discrepancy (MMD) has gained popularity as a test statistic due to... | {
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2502.13571 | Diffusion Model Agnostic Social Influence Maximization in Hyperbolic
Space | [
"cs.SI",
"cs.LG"
] | The Influence Maximization (IM) problem aims to find a small set of influential users to maximize their influence spread in a social network. Traditional methods rely on fixed diffusion models with known parameters, limiting their generalization to real-world scenarios. In contrast, graph representation learning-based ... | {
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2502.13573 | Noise May Contain Transferable Knowledge: Understanding Semi-supervised
Heterogeneous Domain Adaptation from an Empirical Perspective | [
"cs.LG"
] | Semi-supervised heterogeneous domain adaptation (SHDA) addresses learning across domains with distinct feature representations and distributions, where source samples are labeled while most target samples are unlabeled, with only a small fraction labeled. Moreover, there is no one-to-one correspondence between source a... | {
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2502.13574 | RestoreGrad: Signal Restoration Using Conditional Denoising Diffusion
Models with Jointly Learned Prior | [
"eess.IV",
"cs.LG",
"eess.AS"
] | Denoising diffusion probabilistic models (DDPMs) can be utilized for recovering a clean signal from its degraded observation(s) by conditioning the model on the degraded signal. The degraded signals are themselves contaminated versions of the clean signals; due to this correlation, they may encompass certain useful inf... | {
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2502.13575 | ETS: Efficient Tree Search for Inference-Time Scaling | [
"cs.LG"
] | Test-time compute scaling has emerged as a new axis along which to improve model accuracy, where additional computation is used at inference time to allow the model to think longer for more challenging problems. One promising approach for test-time compute scaling is search against a process reward model, where a model... | {
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2502.13576 | Beyond One-Size-Fits-All: Tailored Benchmarks for Efficient Evaluation | [
"cs.LG",
"cs.AI"
] | Evaluating models on large benchmarks is very resource-intensive, especially during the period of rapid model evolution. Existing efficient evaluation methods estimate the performance of target models by testing them only on a small and static coreset of the benchmark, which is derived from the publicly available evalu... | {
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2502.13577 | Unraveling the Localized Latents: Learning Stratified Manifold
Structures in LLM Embedding Space with Sparse Mixture-of-Experts | [
"cs.LG"
] | However, real-world data often exhibit complex local structures that can be challenging for single-model approaches with a smooth global manifold in the embedding space to unravel. In this work, we conjecture that in the latent space of these large language models, the embeddings live in a local manifold structure with... | {
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2502.13581 | ActionPiece: Contextually Tokenizing Action Sequences for Generative
Recommendation | [
"cs.IR",
"cs.LG"
] | Generative recommendation (GR) is an emerging paradigm where user actions are tokenized into discrete token patterns and autoregressively generated as predictions. However, existing GR models tokenize each action independently, assigning the same fixed tokens to identical actions across all sequences without considerin... | {
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2502.13584 | Multi-Target Radar Search and Track Using Sequence-Capable Deep
Reinforcement Learning | [
"cs.LG",
"cs.SY",
"eess.SY"
] | The research addresses sensor task management for radar systems, focusing on efficiently searching and tracking multiple targets using reinforcement learning. The approach develops a 3D simulation environment with an active electronically scanned array radar, using a multi-target tracking algorithm to improve observati... | {
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2502.13592 | Don't Stop the Multi-Party! On Generating Synthetic Multi-Party
Conversations with Constraints | [
"cs.CL"
] | Multi-Party Conversations (MPCs) are widely studied across disciplines, with social media as a primary data source due to their accessibility. However, these datasets raise privacy concerns and often reflect platform-specific properties. For example, interactions between speakers may be limited due to rigid platform st... | {
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2502.13593 | Toward Robust Non-Transferable Learning: A Survey and Benchmark | [
"cs.LG",
"cs.CR",
"cs.CV"
] | Over the past decades, researchers have primarily focused on improving the generalization abilities of models, with limited attention given to regulating such generalization. However, the ability of models to generalize to unintended data (e.g., harmful or unauthorized data) can be exploited by malicious adversaries in... | {
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2502.13595 | MMTEB: Massive Multilingual Text Embedding Benchmark | [
"cs.CL",
"cs.AI",
"cs.IR"
] | Text embeddings are typically evaluated on a limited set of tasks, which are constrained by language, domain, and task diversity. To address these limitations and provide a more comprehensive evaluation, we introduce the Massive Multilingual Text Embedding Benchmark (MMTEB) - a large-scale, community-driven expansion o... | {
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2502.13603 | Efficient Safety Retrofitting Against Jailbreaking for LLMs | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Direct Preference Optimization (DPO) is an efficient alignment technique that steers LLMs towards preferable outputs by training on preference data, bypassing the need for explicit reward models. Its simplicity enables easy adaptation to various domains and safety requirements. This paper examines DPO's effectiveness i... | {
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2502.13604 | BeamLoRA: Beam-Constraint Low-Rank Adaptation | [
"cs.CL"
] | Due to the demand for efficient fine-tuning of large language models, Low-Rank Adaptation (LoRA) has been widely adopted as one of the most effective parameter-efficient fine-tuning methods. Nevertheless, while LoRA improves efficiency, there remains room for improvement in accuracy. Herein, we adopt a novel perspectiv... | {
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2502.13606 | LaVCa: LLM-assisted Visual Cortex Captioning | [
"q-bio.NC",
"cs.AI",
"cs.CL",
"cs.CV",
"cs.LG"
] | Understanding the property of neural populations (or voxels) in the human brain can advance our comprehension of human perceptual and cognitive processing capabilities and contribute to developing brain-inspired computer models. Recent encoding models using deep neural networks (DNNs) have successfully predicted voxel-... | {
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2502.13607 | Environmental Influences on Collaboration Network Evolution: A
Historical Analysis | [
"cs.SI",
"physics.soc-ph"
] | We analysed two large collaboration networks -- the Microsoft Academic Graph (1800-2020) and Internet Movie Database (1900-2020) -- to quantify network responses to major historical events. Our analysis revealed four properties of network-environment interaction. First, historical events can influence network evolution... | {
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2502.13619 | Complex Ontology Matching with Large Language Model Embeddings | [
"cs.CL",
"cs.AI"
] | Ontology, and more broadly, Knowledge Graph Matching is a challenging task in which expressiveness has not been fully addressed. Despite the increasing use of embeddings and language models for this task, approaches for generating expressive correspondences still do not take full advantage of these models, in particula... | {
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2502.13621 | Decentralized Planning Using Probabilistic Hyperproperties | [
"cs.LO",
"cs.AI"
] | Multi-agent planning under stochastic dynamics is usually formalised using decentralized (partially observable) Markov decision processes ( MDPs) and reachability or expected reward specifications. In this paper, we propose a different approach: we use an MDP describing how a single agent operates in an environment and... | {
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2502.13622 | REFIND: Retrieval-Augmented Factuality Hallucination Detection in Large
Language Models | [
"cs.CL",
"cs.AI"
] | Hallucinations in large language model (LLM) outputs severely limit their reliability in knowledge-intensive tasks such as question answering. To address this challenge, we introduce REFIND (Retrieval-augmented Factuality hallucINation Detection), a novel framework that detects hallucinated spans within LLM outputs by ... | {
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2502.13624 | CardiacMamba: A Multimodal RGB-RF Fusion Framework with State Space
Models for Remote Physiological Measurement | [
"cs.CV"
] | Heart rate (HR) estimation via remote photoplethysmography (rPPG) offers a non-invasive solution for health monitoring. However, traditional single-modality approaches (RGB or Radio Frequency (RF)) face challenges in balancing robustness and accuracy due to lighting variations, motion artifacts, and skin tone bias. In ... | {
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2502.13626 | AI-Empowered Catalyst Discovery: A Survey from Classical Machine
Learning Approaches to Large Language Models | [
"cs.CE"
] | Catalysts are essential for accelerating chemical reactions and enhancing selectivity, which is crucial for the sustainable production of energy, materials, and bioactive compounds. Catalyst discovery is fundamental yet challenging in computational chemistry and has garnered significant attention due to the promising p... | {
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2502.13628 | Non-Euclidean Hierarchical Representational Learning using Hyperbolic
Graph Neural Networks for Environmental Claim Detection | [
"cs.CL"
] | Transformer-based models dominate NLP tasks like sentiment analysis, machine translation, and claim verification. However, their massive computational demands and lack of interpretability pose challenges for real-world applications requiring efficiency and transparency. In this work, we explore Graph Neural Networks (G... | {
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2502.13632 | Concept Layers: Enhancing Interpretability and Intervenability via LLM
Conceptualization | [
"cs.LG",
"cs.AI",
"cs.CL"
] | The opaque nature of Large Language Models (LLMs) has led to significant research efforts aimed at enhancing their interpretability, primarily through post-hoc methods. More recent in-hoc approaches, such as Concept Bottleneck Models (CBMs), offer both interpretability and intervenability by incorporating explicit conc... | {
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2502.13634 | First Glimpse on Physical Layer Security in Internet of Vehicles:
Transformed from Communication Interference to Sensing Interference | [
"cs.IT",
"math.IT"
] | Integrated sensing and communication (ISAC) plays a crucial role in the Internet of Vehicles (IoV), serving as a key factor in enhancing driving safety and traffic efficiency. To address the security challenges of the confidential information transmission caused by the inherent openness nature of wireless medium, diffe... | {
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2502.13637 | Exploring Mutual Cross-Modal Attention for Context-Aware Human
Affordance Generation | [
"cs.CV",
"cs.MM"
] | Human affordance learning investigates contextually relevant novel pose prediction such that the estimated pose represents a valid human action within the scene. While the task is fundamental to machine perception and automated interactive navigation agents, the exponentially large number of probable pose and action va... | {
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2502.13638 | Integrating Inverse and Forward Modeling for Sparse Temporal Data from
Sensor Networks | [
"cs.LG",
"cs.AI"
] | We present CavePerception, a framework for the analysis of sparse data from sensor networks that incorporates elements of inverse modeling and forward modeling. By integrating machine learning with physical modeling in a hypotheses space, we aim to improve the interpretability of sparse, noisy, and potentially incomple... | {
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2502.13640 | Qorgau: Evaluating LLM Safety in Kazakh-Russian Bilingual Contexts | [
"cs.CL"
] | Large language models (LLMs) are known to have the potential to generate harmful content, posing risks to users. While significant progress has been made in developing taxonomies for LLM risks and safety evaluation prompts, most studies have focused on monolingual contexts, primarily in English. However, language- and ... | {
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2502.13641 | SLAMSpoof: Practical LiDAR Spoofing Attacks on Localization Systems
Guided by Scan Matching Vulnerability Analysis | [
"cs.RO"
] | Accurate localization is essential for enabling modern full self-driving services. These services heavily rely on map-based traffic information to reduce uncertainties in recognizing lane shapes, traffic light locations, and traffic signs. Achieving this level of reliance on map information requires centimeter-level lo... | {
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2502.13645 | Measuring the Effect of Transcription Noise on Downstream Language
Understanding Tasks | [
"cs.CL"
] | With the increasing prevalence of recorded human speech, spoken language understanding (SLU) is essential for its efficient processing. In order to process the speech, it is commonly transcribed using automatic speech recognition technology. This speech-to-text transition introduces errors into the transcripts, which s... | {
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2502.13646 | D.Va: Validate Your Demonstration First Before You Use It | [
"cs.CL"
] | In-context learning (ICL) has demonstrated significant potential in enhancing the capabilities of large language models (LLMs) during inference. It's well-established that ICL heavily relies on selecting effective demonstrations to generate outputs that better align with the expected results. As for demonstration selec... | {
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2502.13647 | Instruction Tuning on Public Government and Cultural Data for
Low-Resource Language: a Case Study in Kazakh | [
"cs.CL"
] | Instruction tuning in low-resource languages remains underexplored due to limited text data, particularly in government and cultural domains. To address this, we introduce and open-source a large-scale (10,600 samples) instruction-following (IFT) dataset, covering key institutional and cultural knowledge relevant to Ka... | {
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2502.13648 | Reliability Across Parametric and External Knowledge: Understanding
Knowledge Handling in LLMs | [
"cs.CL"
] | Large Language Models (LLMs) enhance their problem-solving capability by leveraging both parametric and external knowledge. Beyond leveraging external knowledge to improve response accuracy, they require key capabilities for reliable knowledge-handling: resolving conflicts between knowledge sources, avoiding distractio... | {
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2502.13652 | C2T: A Classifier-Based Tree Construction Method in Speculative Decoding | [
"cs.CL",
"cs.AI"
] | The growing scale of Large Language Models (LLMs) has exacerbated inference latency and computational costs. Speculative decoding methods, which aim to mitigate these issues, often face inefficiencies in the construction of token trees and the verification of candidate tokens. Existing strategies, including chain mode,... | {
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2502.13653 | A Query-Driven Approach to Space-Efficient Range Searching | [
"cs.DS",
"cs.CG",
"cs.LG"
] | We initiate a study of a query-driven approach to designing partition trees for range-searching problems. Our model assumes that a data structure is to be built for an unknown query distribution that we can access through a sampling oracle, and must be selected such that it optimizes a meaningful performance parameter ... | {
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2502.13656 | Refining Sentence Embedding Model through Ranking Sentences Generation
with Large Language Models | [
"cs.CL"
] | Sentence embedding is essential for many NLP tasks, with contrastive learning methods achieving strong performance using annotated datasets like NLI. Yet, the reliance on manual labels limits scalability. Recent studies leverage large language models (LLMs) to generate sentence pairs, reducing annotation dependency. Ho... | {
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2502.13660 | Towards Invariance to Node Identifiers in Graph Neural Networks | [
"cs.LG"
] | Message-Passing Graph Neural Networks (GNNs) are known to have limited expressive power, due to their message passing structure. One mechanism for circumventing this limitation is to add unique node identifiers (IDs), which break the symmetries that underlie the expressivity limitation. In this work, we highlight a key... | {
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2502.13662 | Generalization error bound for denoising score matching under relaxed
manifold assumption | [
"cs.LG",
"math.ST",
"stat.ML",
"stat.TH"
] | We examine theoretical properties of the denoising score matching estimate. We model the density of observations with a nonparametric Gaussian mixture. We significantly relax the standard manifold assumption allowing the samples step away from the manifold. At the same time, we are still able to leverage a nice distrib... | {
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2502.13663 | User Association and Coordinated Beamforming in Cognitive
Aerial-Terrestrial Networks: A Safe Reinforcement Learning Approach | [
"cs.IT",
"eess.SP",
"math.IT"
] | Cognitive aerial-terrestrial networks (CATNs) offer a solution to spectrum scarcity by sharing spectrum between aerial and terrestrial networks. However, aerial users (AUs) experience significant interference from numerous terrestrial base stations (BSs). To alleviate such interference, we investigate a user associatio... | {
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2502.13668 | PeerQA: A Scientific Question Answering Dataset from Peer Reviews | [
"cs.CL",
"cs.AI",
"cs.IR"
] | We present PeerQA, a real-world, scientific, document-level Question Answering (QA) dataset. PeerQA questions have been sourced from peer reviews, which contain questions that reviewers raised while thoroughly examining the scientific article. Answers have been annotated by the original authors of each paper. The datas... | {
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2502.13674 | SCOPE: A Self-supervised Framework for Improving Faithfulness in
Conditional Text Generation | [
"cs.CL"
] | Large Language Models (LLMs), when used for conditional text generation, often produce hallucinations, i.e., information that is unfaithful or not grounded in the input context. This issue arises in typical conditional text generation tasks, such as text summarization and data-to-text generation, where the goal is to p... | {
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2502.13675 | A CFL condition for the finite cell method | [
"cs.CE",
"cs.NA",
"math.NA"
] | Immersed boundary finite element methods allow the user to bypass the potentially troublesome task of boundary-conforming mesh generation. However, they suffer from the influence of cut elements, i.e., elements that are intersected by the physical domain boundaries. When combined with explicit time integration, poorly ... | {
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2502.13676 | An Adaptive Data-Enabled Policy Optimization Approach for Autonomous
Bicycle Control | [
"eess.SY",
"cs.RO",
"cs.SY",
"math.OC"
] | This paper presents a unified control framework that integrates a Feedback Linearization (FL) controller in the inner loop with an adaptive Data-Enabled Policy Optimization (DeePO) controller in the outer loop to balance an autonomous bicycle. While the FL controller stabilizes and partially linearizes the inherently u... | {
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2502.13677 | A Framework for Semantics-based Situational Awareness during Mobile
Robot Deployments | [
"cs.RO"
] | Deployment of robots into hazardous environments typically involves a ``Human-Robot Teaming'' (HRT) paradigm, in which a human supervisor interacts with a remotely operating robot inside the hazardous zone. Situational Awareness (SA) is vital for enabling HRT, to support navigation, planning, and decision-making. This ... | {
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2502.13681 | An LLM-based Agent for Reliable Docker Environment Configuration | [
"cs.SE",
"cs.AI",
"cs.CL",
"cs.LG"
] | Environment configuration is a critical yet time-consuming step in software development, especially when dealing with unfamiliar code repositories. While Large Language Models (LLMs) demonstrate the potential to accomplish software engineering tasks, existing methods for environment configuration often rely on manual e... | {
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2502.13685 | MoM: Linear Sequence Modeling with Mixture-of-Memories | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Linear sequence modeling methods, such as linear attention, state space modeling, and linear RNNs, offer significant efficiency improvements by reducing the complexity of training and inference. However, these methods typically compress the entire input sequence into a single fixed-size memory state, which leads to sub... | {
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2502.13686 | Graph Signal Inference by Learning Narrowband Spectral Kernels | [
"stat.ML",
"cs.LG"
] | While a common assumption in graph signal analysis is the smoothness of the signals or the band-limitedness of their spectrum, in many instances the spectrum of real graph data may be concentrated at multiple regions of the spectrum, possibly including mid-to-high-frequency components. In this work, we propose a novel ... | {
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2502.13688 | Non-Linear Function Computation Broadcast | [
"cs.IT",
"math.IT"
] | This work addresses the $K$-user computation broadcast problem consisting of a master node, that holds all datasets and users for a general class of function demands, including linear and non-linear functions, over finite fields. The master node sends a broadcast message to enable each of $K$ distributed users to compu... | {
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2502.13691 | Is This Collection Worth My LLM's Time? Automatically Measuring
Information Potential in Text Corpora | [
"cs.CL"
] | As large language models (LLMs) converge towards similar capabilities, the key to advancing their performance lies in identifying and incorporating valuable new information sources. However, evaluating which text collections are worth the substantial investment required for digitization, preprocessing, and integration ... | {
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2502.13692 | Tight Generalization Bounds for Large-Margin Halfspaces | [
"cs.LG",
"math.ST",
"stat.TH"
] | We prove the first generalization bound for large-margin halfspaces that is asymptotically tight in the tradeoff between the margin, the fraction of training points with the given margin, the failure probability and the number of training points. | {
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2502.13693 | Medical Image Classification with KAN-Integrated Transformers and
Dilated Neighborhood Attention | [
"cs.CV"
] | Convolutional networks, transformers, hybrid models, and Mamba-based architectures have demonstrated strong performance across various medical image classification tasks. However, these methods were primarily designed to classify clean images using labeled data. In contrast, real-world clinical data often involve image... | {
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2502.13699 | Secure and Green Rate-Splitting Multiple Access Integrated Sensing and
Communications | [
"cs.IT",
"math.IT"
] | In this paper, we investigate the sensing, communication, security, and energy efficiency of integrated sensing and communication (ISAC)-enabled cognitive radio networks (CRNs) in a challenging scenario where communication quality, security, and sensing accuracy are affected by interference and eavesdropping. Specifica... | {
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2502.13701 | Causes and Strategies in Multiagent Systems | [
"cs.AI",
"cs.MA"
] | Causality plays an important role in daily processes, human reasoning, and artificial intelligence. There has however not been much research on causality in multi-agent strategic settings. In this work, we introduce a systematic way to build a multi-agent system model, represented as a concurrent game structure, for a ... | {
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2502.13703 | Parameterized Complexity of Hedonic Games with Enemy-Oriented
Preferences | [
"cs.GT",
"cs.MA"
] | Hedonic games model settings in which a set of agents have to be partitioned into groups which we call coalitions. In the enemy aversion model, each agent has friends and enemies, and an agent prefers to be in a coalition with as few enemies as possible and, subject to that, as many friends as possible. A partition sho... | {
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2502.13707 | Human-Like Robot Impedance Regulation Skill Learning from Human-Human
Demonstrations | [
"cs.RO"
] | Humans are experts in collaborating with others physically by regulating compliance behaviors based on the perception of their partner states and the task requirements. Enabling robots to develop proficiency in human collaboration skills can facilitate more efficient human-robot collaboration (HRC). This paper introduc... | {
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2502.13708 | Active Illumination for Visual Ego-Motion Estimation in the Dark | [
"cs.RO"
] | Visual Odometry (VO) and Visual SLAM (V-SLAM) systems often struggle in low-light and dark environments due to the lack of robust visual features. In this paper, we propose a novel active illumination framework to enhance the performance of VO and V-SLAM algorithms in these challenging conditions. The developed approac... | {
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2502.13713 | TALKPLAY: Multimodal Music Recommendation with Large Language Models | [
"cs.IR",
"cs.SD",
"eess.AS"
] | We present TalkPlay, a multimodal music recommendation system that reformulates the recommendation task as large language model token generation. TalkPlay represents music through an expanded token vocabulary that encodes multiple modalities - audio, lyrics, metadata, semantic tags, and playlist co-occurrence. Using th... | {
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2502.13714 | Hierarchical RL-MPC for Demand Response Scheduling | [
"eess.SY",
"cs.SY"
] | This paper presents a hierarchical framework for demand response optimization in air separation units (ASUs) that combines reinforcement learning (RL) with linear model predictive control (LMPC). We investigate two control architectures: a direct RL approach and a control-informed methodology where an RL agent provides... | {
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2502.13716 | Event-Based Video Frame Interpolation With Cross-Modal Asymmetric
Bidirectional Motion Fields | [
"cs.CV"
] | Video Frame Interpolation (VFI) aims to generate intermediate video frames between consecutive input frames. Since the event cameras are bio-inspired sensors that only encode brightness changes with a micro-second temporal resolution, several works utilized the event camera to enhance the performance of VFI. However, e... | {
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2502.13718 | Multi-Scale and Multi-Objective Optimization for Cross-Lingual
Aspect-Based Sentiment Analysis | [
"cs.CL"
] | Aspect-based sentiment analysis (ABSA) is a sequence labeling task that has garnered growing research interest in multilingual contexts. However, recent studies lack more robust feature alignment and finer aspect-level alignment. In this paper, we propose a novel framework, Multi-Scale and Multi-Objective optimization ... | {
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2502.13719 | TrustRAG: An Information Assistant with Retrieval Augmented Generation | [
"cs.IR",
"cs.AI"
] | \Ac{RAG} has emerged as a crucial technique for enhancing large models with real-time and domain-specific knowledge. While numerous improvements and open-source tools have been proposed to refine the \ac{RAG} framework for accuracy, relatively little attention has been given to improving the trustworthiness of generate... | {
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2502.13721 | Learning Novel Transformer Architecture for Time-series Forecasting | [
"cs.LG",
"cs.CL"
] | Despite the success of Transformer-based models in the time-series prediction (TSP) tasks, the existing Transformer architecture still face limitations and the literature lacks comprehensive explorations into alternative architectures. To address these challenges, we propose AutoFormer-TS, a novel framework that levera... | {
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2502.13722 | Deep Learning for VWAP Execution in Crypto Markets: Beyond the Volume
Curve | [
"q-fin.ST",
"cs.LG"
] | Volume-Weighted Average Price (VWAP) is arguably the most prevalent benchmark for trade execution as it provides an unbiased standard for comparing performance across market participants. However, achieving VWAP is inherently challenging due to its dependence on two dynamic factors, volumes and prices. Traditional appr... | {
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2502.13723 | Direct Value Optimization: Improving Chain-of-Thought Reasoning in LLMs
with Refined Values | [
"cs.CL",
"cs.AI"
] | We introduce Direct Value Optimization (DVO), an innovative reinforcement learning framework for enhancing large language models in complex reasoning tasks. Unlike traditional methods relying on preference labels, DVO utilizes value signals at individual reasoning steps, optimizing models via a mean squared error loss.... | {
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2502.13725 | Adapting Large Language Models for Time Series Modeling via a Novel
Parameter-efficient Adaptation Method | [
"cs.CL"
] | Time series modeling holds significant importance in many real-world applications and has been extensively studied. While pre-trained foundation models have made impressive strides in the fields of natural language processing (NLP) and computer vision (CV), their development in time series domains has been constrained ... | {
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2502.13728 | Secure Federated Data Distillation | [
"cs.CR",
"cs.AI"
] | Dataset Distillation (DD) is a powerful technique for reducing large datasets into compact, representative synthetic datasets, accelerating Machine Learning training. However, traditional DD methods operate in a centralized manner, which poses significant privacy threats and reduces its applicability. To mitigate these... | {
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2502.13729 | Emergence of the Primacy Effect in Structured State-Space Models | [
"cs.LG",
"cs.NE",
"q-bio.NC"
] | Human and animal memory for sequentially presented items is well-documented to be more accurate for those at the beginning and end of the sequence, phenomena known as the primacy and recency effects, respectively. By contrast, artificial neural network (ANN) models are typically designed with a memory that decays monot... | {
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2502.13730 | Cascading CMA-ES Instances for Generating Input-diverse Solution Batches | [
"cs.NE"
] | Rather than obtaining a single good solution for a given optimization problem, users often seek alternative design choices, because the best-found solution may perform poorly with respect to additional objectives or constraints that are difficult to capture into the modeling process. Aiming for batches of diverse sol... | {
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} |
2502.13731 | Robust Counterfactual Inference in Markov Decision Processes | [
"cs.AI"
] | This paper addresses a key limitation in existing counterfactual inference methods for Markov Decision Processes (MDPs). Current approaches assume a specific causal model to make counterfactuals identifiable. However, there are usually many causal models that align with the observational and interventional distribution... | {
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} |
2502.13732 | Homophily Heterogeneity Matters in Graph Federated Learning: A Spectrum
Sharing and Complementing Perspective | [
"cs.LG"
] | Since heterogeneity presents a fundamental challenge in graph federated learning, many existing methods are proposed to deal with node feature heterogeneity and structure heterogeneity. However, they overlook the critical homophily heterogeneity, which refers to the substantial variation in homophily levels across grap... | {
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} |
2502.13734 | CARE: Confidence-Aware Regression Estimation of building density
fine-tuning EO Foundation Models | [
"cs.CV",
"cs.LG"
] | Performing accurate confidence quantification and assessment is important for deep neural networks to predict their failures, improve their performance and enhance their capabilities in real-world applications, for their practical deployment in real life. For pixel-wise regression tasks, confidence quantification and a... | {
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} |
2502.13737 | PEDRO-V: From a concurrent engineering case study to a promising phase
zero mission definition | [
"eess.SY",
"cs.SY"
] | Each year, the European Space Agency (ESA) organizes challenges for university students, from BSc to PhD levels. The ESA Concurrent Engineering Challange 2024 was hosted by four Concurrent Design Facilites (CDF) across Europe: ESEC Galazia, ISAE SUPAERO, the University of Athens, and the University of Portsmouth. A tot... | {
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} |
2502.13738 | Enhancing Input-Label Mapping in In-Context Learning with Contrastive
Decoding | [
"cs.CL"
] | Large language models (LLMs) excel at a range of tasks through in-context learning (ICL), where only a few task examples guide their predictions. However, prior research highlights that LLMs often overlook input-label mapping information in ICL, relying more on their pre-trained knowledge. To address this issue, we int... | {
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} |
2502.13740 | Benchmarking of Different YOLO Models for CAPTCHAs Detection and
Classification | [
"cs.CV"
] | This paper provides an analysis and comparison of the YOLOv5, YOLOv8 and YOLOv10 models for webpage CAPTCHAs detection using the datasets collected from the web and darknet as well as synthetized data of webpages. The study examines the nano (n), small (s), and medium (m) variants of YOLO architectures and use metrics ... | {
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} |
2502.13743 | Inference of Abstraction for Grounded Predicate Logic | [
"cs.AI"
] | An important open question in AI is what simple and natural principle enables a machine to reason logically for meaningful abstraction with grounded symbols. This paper explores a conceptually new approach to combining probabilistic reasoning and predicative symbolic reasoning over data. We return to the era of reasoni... | {
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} |
2502.13747 | Reverse Markov Learning: Multi-Step Generative Models for Complex
Distributions | [
"cs.LG",
"stat.ME",
"stat.ML"
] | Learning complex distributions is a fundamental challenge in contemporary applications. Generative models, such as diffusion models, have demonstrated remarkable success in overcoming many limitations of traditional statistical methods. Shen and Meinshausen (2024) introduced engression, a generative approach based on s... | {
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} |
2502.13751 | RobustX: Robust Counterfactual Explanations Made Easy | [
"cs.LG",
"cs.AI"
] | The increasing use of Machine Learning (ML) models to aid decision-making in high-stakes industries demands explainability to facilitate trust. Counterfactual Explanations (CEs) are ideally suited for this, as they can offer insights into the predictions of an ML model by illustrating how changes in its input data may ... | {
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} |
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