id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.12674 | SATA: Safe and Adaptive Torque-Based Locomotion Policies Inspired by
Animal Learning | [
"cs.RO",
"cs.LG"
] | Despite recent advances in learning-based controllers for legged robots, deployments in human-centric environments remain limited by safety concerns. Most of these approaches use position-based control, where policies output target joint angles that must be processed by a low-level controller (e.g., PD or impedance con... | {
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2502.12677 | Spiking Vision Transformer with Saccadic Attention | [
"cs.CV",
"cs.AI"
] | The combination of Spiking Neural Networks (SNNs) and Vision Transformers (ViTs) holds potential for achieving both energy efficiency and high performance, particularly suitable for edge vision applications. However, a significant performance gap still exists between SNN-based ViTs and their ANN counterparts. Here, we ... | {
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2502.12678 | Multi-Step Alignment as Markov Games: An Optimistic Online Gradient
Descent Approach with Convergence Guarantees | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Reinforcement Learning from Human Feedback (RLHF) has been highly successful in aligning large language models with human preferences. While prevalent methods like DPO have demonstrated strong performance, they frame interactions with the language model as a bandit problem, which limits their applicability in real-worl... | {
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2502.12680 | Introducing ROADS: A Systematic Comparison of Remote Control Interaction
Concepts for Automated Vehicles at Road Works | [
"cs.HC",
"cs.RO"
] | As vehicle automation technology continues to mature, there is a necessity for robust remote monitoring and intervention features. These are essential for intervening during vehicle malfunctions, challenging road conditions, or in areas that are difficult to navigate. This evolution in the role of the human operator - ... | {
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2502.12682 | K-n\'ucleo: Una herramienta para detectar la estructura conceptual de
los campos de investigaci\'on. El caso pr\'actico de la Altmetr\'ia | [
"stat.ME",
"cs.SI",
"physics.soc-ph"
] | In Social Network Analysis (SNA), k-core decomposition is used to detect hierarchical shells in networks. The application of the K-core decomposition to a network of keywords allows us to represent the conceptual structure of a research field. The objective of this work was to propose the application of k-core decompos... | {
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2502.12684 | Federated Variational Inference for Bayesian Mixture Models | [
"stat.ML",
"cs.LG",
"stat.ME"
] | We present a federated learning approach for Bayesian model-based clustering of large-scale binary and categorical datasets. We introduce a principled 'divide and conquer' inference procedure using variational inference with local merge and delete moves within batches of the data in parallel, followed by 'global' merge... | {
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2502.12685 | Theoretical Guarantees for Minimum Bayes Risk Decoding | [
"cs.CL"
] | Minimum Bayes Risk (MBR) decoding optimizes output selection by maximizing the expected utility value of an underlying human distribution. While prior work has shown the effectiveness of MBR decoding through empirical evaluation, few studies have analytically investigated why the method is effective. As a result of our... | {
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2502.12689 | Role extraction by matrix equations and generalized random walks | [
"math.NA",
"cs.NA",
"cs.SI"
] | The nodes in a network can be grouped into 'roles' based on similar connection patterns. This is usually achieved by defining a pairwise node similarity matrix and then clustering rows and columns of this matrix. This paper presents a new similarity matrix for solving role extraction problems in directed networks, whic... | {
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2502.12690 | Fast Data Aware Neural Architecture Search via Supernet Accelerated
Evaluation | [
"cs.NE",
"cs.AI",
"cs.CV",
"cs.LG"
] | Tiny machine learning (TinyML) promises to revolutionize fields such as healthcare, environmental monitoring, and industrial maintenance by running machine learning models on low-power embedded systems. However, the complex optimizations required for successful TinyML deployment continue to impede its widespread adopti... | {
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2502.12691 | Spherical Dense Text-to-Image Synthesis | [
"cs.CV"
] | Recent advancements in text-to-image (T2I) have improved synthesis results, but challenges remain in layout control and generating omnidirectional panoramic images. Dense T2I (DT2I) and spherical T2I (ST2I) models address these issues, but so far no unified approach exists. Trivial approaches, like prompting a DT2I mod... | {
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2502.12692 | Channel Estimation for Stacked Intelligent Metasurfaces in Rician Fading
Channels | [
"cs.IT",
"math.IT"
] | The recent combination of the rising architectures, known as stacked intelligent metasurface (SIM) and holographic multiple-input multiple-output (HMIMO), drives toward breakthroughs for next-generation wireless communication systems. Given the fact that the number of elements per surface of the SIM is much larger than... | {
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2502.12693 | Neuromorphic Readout for Hadron Calorimeters | [
"hep-ex",
"cs.ET",
"cs.LG",
"cs.NE"
] | We simulate hadrons impinging on a homogeneous lead-tungstate (PbWO4) calorimeter to investigate how the resulting light yield and its temporal structure, as detected by an array of light-sensitive sensors, can be processed by a neuromorphic computing system. Our model encodes temporal photon distributions as spike tra... | {
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2502.12696 | Radar Network for Gait Monitoring: Technology and Validation | [
"eess.SP",
"cs.SY",
"eess.SY"
] | In recent years, radar-based devices have emerged as an alternative approach for gait monitoring. However, the radar configuration and the algorithms used to extract the gait parameters often differ between contributions, lacking a systematic evaluation of the most appropriate setup. Additionally, radar-based studies o... | {
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2502.12700 | Multi-Novelty: Improve the Diversity and Novelty of Contents Generated
by Large Language Models via inference-time Multi-Views Brainstorming | [
"cs.CL"
] | Large Language Models (LLMs) demonstrate remarkable proficiency in generating accurate and fluent text. However, they often struggle with diversity and novelty, leading to repetitive or overly deterministic responses. These limitations stem from constraints in training data, including gaps in specific knowledge domains... | {
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2502.12701 | Translate Smart, not Hard: Cascaded Translation Systems with
Quality-Aware Deferral | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Larger models often outperform smaller ones but come with high computational costs. Cascading offers a potential solution. By default, it uses smaller models and defers only some instances to larger, more powerful models. However, designing effective deferral rules remains a challenge. In this paper, we propose a simpl... | {
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2502.12704 | Maximizing Truth Learning in a Social Network is NP-hard | [
"cs.SI"
] | Sequential learning models situations where agents predict a ground truth in sequence, by using their private, noisy measurements, and the predictions of agents who came earlier in the sequence. We study sequential learning in a social network, where agents only see the actions of the previous agents in their own neigh... | {
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2502.12706 | Scalable Model Merging with Progressive Layer-wise Distillation | [
"cs.LG"
] | Model merging offers an effective way to integrate the capabilities of multiple fine-tuned models. However, the performance degradation of the merged model remains a challenge, particularly when none or few data are available. This paper first highlights the necessity of domain-specific data for model merging by provin... | {
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2502.12707 | CausalMan: A physics-based simulator for large-scale causality | [
"cs.LG",
"stat.ML"
] | A comprehensive understanding of causality is critical for navigating and operating within today's complex real-world systems. The absence of realistic causal models with known data generating processes complicates fair benchmarking. In this paper, we present the CausalMan simulator, modeled after a real-world producti... | {
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2502.12710 | TREND: A Whitespace Replacement Information Hiding Method | [
"cs.CR",
"cs.AI",
"cs.SE"
] | Large Language Models (LLMs) have gained significant popularity in recent years. Differentiating between a text written by a human and a text generated by an LLM has become almost impossible. Information hiding techniques such as digital watermarking or steganography can help by embedding information inside text withou... | {
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2502.12713 | Uncertainty Propagation for Echocardiography Clinical Metric Estimation
via Contour Sampling | [
"cs.CV"
] | Echocardiography plays a fundamental role in the extraction of important clinical parameters (e.g. left ventricular volume and ejection fraction) required to determine the presence and severity of heart-related conditions. When deploying automated techniques for computing these parameters, uncertainty estimation is cru... | {
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2502.12714 | Playing with Voices: Tabletop Role-Playing Game Recordings as a
Diarization Challenge | [
"cs.CL",
"cs.SD"
] | This paper provides a proof of concept that audio of tabletop role-playing games (TTRPG) could serve as a challenge for diarization systems. TTRPGs are carried out mostly by conversation. Participants often alter their voices to indicate that they are talking as a fictional character. Audio processing systems are susce... | {
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2502.12716 | Soft Arm-Motor Thrust Characterization for a Pneumatically Actuated Soft
Morphing Quadrotor | [
"cs.RO",
"cs.SY",
"eess.SY"
] | In this work, an experimental characterization of the configuration space of a soft, pneumatically actuated morphing quadrotor is presented, with a focus on precise thrust characterization of its flexible arms, considering the effect of downwash. Unlike traditional quadrotors, the soft drone has pneumatically actuated ... | {
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2502.12717 | Learning the symmetric group: large from small | [
"cs.LG",
"math.CO",
"math.RT"
] | Machine learning explorations can make significant inroads into solving difficult problems in pure mathematics. One advantage of this approach is that mathematical datasets do not suffer from noise, but a challenge is the amount of data required to train these models and that this data can be computationally expensive ... | {
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2502.12723 | myEye2Wheeler: A Two-Wheeler Indian Driver Real-World Eye-Tracking
Dataset | [
"cs.CV"
] | This paper presents the myEye2Wheeler dataset, a unique resource of real-world gaze behaviour of two-wheeler drivers navigating complex Indian traffic. Most datasets are from four-wheeler drivers on well-planned roads and homogeneous traffic. Our dataset offers a critical lens into the unique visual attention patterns ... | {
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2502.12724 | Responsive Noise-Relaying Diffusion Policy: Responsive and Efficient
Visuomotor Control | [
"cs.RO"
] | Imitation learning is an efficient method for teaching robots a variety of tasks. Diffusion Policy, which uses a conditional denoising diffusion process to generate actions, has demonstrated superior performance, particularly in learning from multi-modal demonstrates. However, it relies on executing multiple actions to... | {
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2502.12732 | Circuit Representation Learning with Masked Gate Modeling and
Verilog-AIG Alignment | [
"cs.LG"
] | Understanding the structure and function of circuits is crucial for electronic design automation (EDA). Circuits can be formulated as And-Inverter graphs (AIGs), enabling efficient implementation of representation learning through graph neural networks (GNNs). Masked modeling paradigms have been proven effective in gra... | {
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2502.12734 | Iron Sharpens Iron: Defending Against Attacks in Machine-Generated Text
Detection with Adversarial Training | [
"cs.CR",
"cs.CL"
] | Machine-generated Text (MGT) detection is crucial for regulating and attributing online texts. While the existing MGT detectors achieve strong performance, they remain vulnerable to simple perturbations and adversarial attacks. To build an effective defense against malicious perturbations, we view MGT detection from a ... | {
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2502.12736 | Cross-Domain Continual Learning for Edge Intelligence in Wireless ISAC
Networks | [
"eess.SP",
"cs.LG"
] | In wireless networks with integrated sensing and communications (ISAC), edge intelligence (EI) is expected to be developed at edge devices (ED) for sensing user activities based on channel state information (CSI). However, due to the CSI being highly specific to users' characteristics, the CSI-activity relationship is ... | {
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2502.12737 | Beyond Seen Data: Improving KBQA Generalization Through Schema-Guided
Logical Form Generation | [
"cs.CL",
"cs.AI"
] | Knowledge base question answering (KBQA) aims to answer user questions in natural language using rich human knowledge stored in large KBs. As current KBQA methods struggle with unseen knowledge base elements at test time,we introduce SG-KBQA: a novel model that injects schema contexts into entity retrieval and logical ... | {
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2502.12742 | 3D Shape-to-Image Brownian Bridge Diffusion for Brain MRI Synthesis from
Cortical Surfaces | [
"cs.CV"
] | Despite recent advances in medical image generation, existing methods struggle to produce anatomically plausible 3D structures. In synthetic brain magnetic resonance images (MRIs), characteristic fissures are often missing, and reconstructed cortical surfaces appear scattered rather than densely convoluted. To address ... | {
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2502.12743 | "I know myself better, but not really greatly": Using LLMs to Detect and
Explain LLM-Generated Texts | [
"cs.CL",
"cs.AI"
] | Large language models (LLMs) have demonstrated impressive capabilities in generating human-like texts, but the potential misuse of such LLM-generated texts raises the need to distinguish between human-generated and LLM-generated content. This paper explores the detection and explanation capabilities of LLM-based detect... | {
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2502.12744 | Self-Enhanced Reasoning Training: Activating Latent Reasoning in Small
Models for Enhanced Reasoning Distillation | [
"cs.CL"
] | The rapid advancement of large language models (LLMs) has significantly enhanced their reasoning abilities, enabling increasingly complex tasks. However, these capabilities often diminish in smaller, more computationally efficient models like GPT-2. Recent research shows that reasoning distillation can help small model... | {
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2502.12745 | MediaMind: Revolutionizing Media Monitoring using Agentification | [
"cs.CL",
"cs.AI",
"cs.LG"
] | In an era of rapid technological advancements, agentification of software tools has emerged as a critical innovation, enabling systems to function autonomously and adaptively. This paper introduces MediaMind as a case study to demonstrate the agentification process, highlighting how existing software can be transformed... | {
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2502.12747 | ExoKit: A Toolkit for Rapid Prototyping of Interactions for Arm-based
Exoskeletons | [
"cs.HC",
"cs.RO"
] | Exoskeletons open up a unique interaction space that seamlessly integrates users' body movements with robotic actuation. Despite its potential, human-exoskeleton interaction remains an underexplored area in HCI, largely due to the lack of accessible prototyping tools that enable designers to easily develop exoskeleton ... | {
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2502.12751 | Architect of the Bits World: Masked Autoregressive Modeling for Circuit
Generation Guided by Truth Table | [
"cs.LG"
] | Logic synthesis, a critical stage in electronic design automation (EDA), optimizes gate-level circuits to minimize power consumption and area occupancy in integrated circuits (ICs). Traditional logic synthesis tools rely on human-designed heuristics, often yielding suboptimal results. Although differentiable architectu... | {
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2502.12752 | High-Fidelity Novel View Synthesis via Splatting-Guided Diffusion | [
"cs.CV"
] | Despite recent advances in Novel View Synthesis (NVS), generating high-fidelity views from single or sparse observations remains a significant challenge. Existing splatting-based approaches often produce distorted geometry due to splatting errors. While diffusion-based methods leverage rich 3D priors to achieve improve... | {
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2502.12753 | Green LIME: Improving AI Explainability through Design of Experiments | [
"stat.ML",
"cs.LG",
"stat.ME"
] | In artificial intelligence (AI), the complexity of many models and processes often surpasses human interpretability, making it challenging to understand why a specific prediction is made. This lack of transparency is particularly problematic in critical fields like healthcare, where trust in a model's predictions is pa... | {
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2502.12755 | Efficient Machine Translation Corpus Generation: Integrating
Human-in-the-Loop Post-Editing with Large Language Models | [
"cs.CL",
"cs.AI",
"cs.HC"
] | This paper introduces an advanced methodology for machine translation (MT) corpus generation, integrating semi-automated, human-in-the-loop post-editing with large language models (LLMs) to enhance efficiency and translation quality. Building upon previous work that utilized real-time training of a custom MT quality es... | {
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2502.12756 | Navigating Demand Uncertainty in Container Shipping: Deep Reinforcement
Learning for Enabling Adaptive and Feasible Master Stowage Planning | [
"cs.LG",
"math.OC"
] | Reinforcement learning (RL) has shown promise in solving various combinatorial optimization problems. However, conventional RL faces challenges when dealing with real-world constraints, especially when action space feasibility is explicit and dependent on the corresponding state or trajectory. In this work, we focus on... | {
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2502.12759 | High-Fidelity Music Vocoder using Neural Audio Codecs | [
"cs.SD",
"cs.LG"
] | While neural vocoders have made significant progress in high-fidelity speech synthesis, their application on polyphonic music has remained underexplored. In this work, we propose DisCoder, a neural vocoder that leverages a generative adversarial encoder-decoder architecture informed by a neural audio codec to reconstru... | {
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2502.12762 | One-bit Compressed Sensing using Generative Models | [
"cs.LG",
"eess.SP"
] | This paper addresses the classical problem of one-bit compressed sensing using a deep learning-based reconstruction algorithm that leverages a trained generative model to enhance the signal reconstruction performance. The generator, a pre-trained neural network, learns to map from a low-dimensional latent space to a hi... | {
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2502.12767 | R2-KG: General-Purpose Dual-Agent Framework for Reliable Reasoning on
Knowledge Graphs | [
"cs.CL",
"cs.AI"
] | Recent studies have combined Large Language Models (LLMs) with Knowledge Graphs (KGs) to enhance reasoning, improving inference accuracy without additional training while mitigating hallucination. However, existing frameworks are often rigid, struggling to adapt to KG or task changes. They also rely heavily on powerful... | {
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2502.12769 | How Much Do LLMs Hallucinate across Languages? On Multilingual
Estimation of LLM Hallucination in the Wild | [
"cs.CL",
"cs.AI"
] | In the age of misinformation, hallucination -- the tendency of Large Language Models (LLMs) to generate non-factual or unfaithful responses -- represents the main risk for their global utility. Despite LLMs becoming increasingly multilingual, the vast majority of research on detecting and quantifying LLM hallucination ... | {
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2502.12771 | Mind the Gap: Aligning the Brain with Language Models Requires a
Nonlinear and Multimodal Approach | [
"cs.CL",
"q-bio.NC"
] | Self-supervised language and audio models effectively predict brain responses to speech. However, traditional prediction models rely on linear mappings from unimodal features, despite the complex integration of auditory signals with linguistic and semantic information across widespread brain networks during speech comp... | {
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2502.12776 | Portable Reward Tuning: Towards Reusable Fine-Tuning across Different
Pretrained Models | [
"cs.LG",
"cs.AI",
"stat.ML"
] | While foundation models have been exploited for various expert tasks through fine-tuning, any foundation model will become outdated due to its old knowledge or limited capability. Thus the underlying foundation model should be eventually replaced by new ones, which leads to repeated cost of fine-tuning these new models... | {
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2502.12777 | Evaluating link prediction: New perspectives and recommendations | [
"cs.SI",
"cs.AI"
] | Link prediction (LP) is an important problem in network science and machine learning research. The state-of-the-art LP methods are usually evaluated in a uniform setup, ignoring several factors associated with the data and application specific needs. We identify a number of such factors, such as, network-type, problem-... | {
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2502.12779 | Dependence and Uncertainty: Information Measures using Tsallis Entropy | [
"stat.ME",
"cs.IT",
"math.IT"
] | In multivariate analysis, uncertainty arises from two sources: the marginal distributions of the variables and their dependence structure. Quantifying the dependence structure is crucial, as it provides valuable insights into the relationships among components of a random vector. Copula functions effectively capture th... | {
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2502.12782 | VidCapBench: A Comprehensive Benchmark of Video Captioning for
Controllable Text-to-Video Generation | [
"cs.AI"
] | The training of controllable text-to-video (T2V) models relies heavily on the alignment between videos and captions, yet little existing research connects video caption evaluation with T2V generation assessment. This paper introduces VidCapBench, a video caption evaluation scheme specifically designed for T2V generatio... | {
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2502.12786 | Composition and Control with Distilled Energy Diffusion Models and
Sequential Monte Carlo | [
"stat.ML",
"cs.LG"
] | Diffusion models may be formulated as a time-indexed sequence of energy-based models, where the score corresponds to the negative gradient of an energy function. As opposed to learning the score directly, an energy parameterization is attractive as the energy itself can be used to control generation via Monte Carlo sam... | {
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2502.12788 | Commonsense Reasoning in Arab Culture | [
"cs.CL"
] | Despite progress in Arabic large language models, such as Jais and AceGPT, their evaluation on commonsense reasoning has largely relied on machine-translated datasets, which lack cultural depth and may introduce Anglocentric biases. Commonsense reasoning is shaped by geographical and cultural contexts, and existing Eng... | {
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2502.12791 | Beyond Timesteps: A Novel Activation-wise Membrane Potential Propagation
Mechanism for Spiking Neural Networks in 3D cloud | [
"cs.CV",
"cs.LG"
] | Due to the similar characteristics between event-based visual data and point clouds, recent studies have emerged that treat event data as event clouds to learn based on point cloud analysis. Additionally, some works approach point clouds from the perspective of event vision, employing Spiking Neural Network (SNN) due t... | {
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2502.12793 | Unsupervised Anomaly Detection through Mass Repulsing Optimal Transport | [
"stat.ML",
"cs.AI",
"cs.LG"
] | Detecting anomalies in datasets is a longstanding problem in machine learning. In this context, anomalies are defined as a sample that significantly deviates from the remaining data. Meanwhile, optimal transport (OT) is a field of mathematics concerned with the transportation, between two probability measures, at least... | {
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2502.12794 | RAPID: Retrieval Augmented Training of Differentially Private Diffusion
Models | [
"cs.CR",
"cs.CV",
"cs.LG"
] | Differentially private diffusion models (DPDMs) harness the remarkable generative capabilities of diffusion models while enforcing differential privacy (DP) for sensitive data. However, existing DPDM training approaches often suffer from significant utility loss, large memory footprint, and expensive inference cost, im... | {
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2502.12796 | Learning Counterfactually Fair Models via Improved Generation with
Neural Causal Models | [
"cs.LG"
] | One of the main concerns while deploying machine learning models in real-world applications is fairness. Counterfactual fairness has emerged as an intuitive and natural definition of fairness. However, existing methodologies for enforcing counterfactual fairness seem to have two limitations: (i) generating counterfactu... | {
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2502.12798 | Envious Explore and Exploit | [
"cs.GT",
"cs.AI",
"cs.LG"
] | Explore-and-exploit tradeoffs play a key role in recommendation systems (RSs), aiming at serving users better by learning from previous interactions. Despite their commercial success, the societal effects of explore-and-exploit mechanisms are not well understood, especially regarding the utility discrepancy they genera... | {
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2502.12799 | Towards Text-Image Interleaved Retrieval | [
"cs.CL",
"cs.CV",
"cs.IR"
] | Current multimodal information retrieval studies mainly focus on single-image inputs, which limits real-world applications involving multiple images and text-image interleaved content. In this work, we introduce the text-image interleaved retrieval (TIIR) task, where the query and document are interleaved text-image se... | {
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2502.12801 | Learning Wall Segmentation in 3D Vessel Trees using Sparse Annotations | [
"cs.CV"
] | We propose a novel approach that uses sparse annotations from clinical studies to train a 3D segmentation of the carotid artery wall. We use a centerline annotation to sample perpendicular cross-sections of the carotid artery and use an adversarial 2D network to segment them. These annotations are then transformed into... | {
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2502.12802 | PPGF: Probability Pattern-Guided Time Series Forecasting | [
"cs.LG"
] | Time series forecasting (TSF) is an essential branch of machine learning with various applications. Most methods for TSF focus on constructing different networks to extract better information and improve performance. However, practical application data contain different internal mechanisms, resulting in a mixture of mu... | {
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2502.12803 | Design Optimization of Musculoskeletal Humanoids with Maximization of
Redundancy to Compensate for Muscle Rupture | [
"cs.RO"
] | Musculoskeletal humanoids have various biomimetic advantages, and the redundant muscle arrangement allowing for variable stiffness control is one of the most important. In this study, we focus on one feature of the redundancy, which enables the humanoid to keep moving even if one of its muscles breaks, an advantage tha... | {
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2502.12804 | Reinforcement Learning for Dynamic Resource Allocation in Optical
Networks: Hype or Hope? | [
"cs.NI",
"cs.LG",
"cs.SY",
"eess.SY"
] | The application of reinforcement learning (RL) to dynamic resource allocation in optical networks has been the focus of intense research activity in recent years, with almost 100 peer-reviewed papers. We present a review of progress in the field, and identify significant gaps in benchmarking practices and reproducibili... | {
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2502.12807 | An improved wind power prediction via a novel wind ramp identification
algorithm | [
"cs.LG"
] | Authors: Yifan Xu Abstract: Conventional wind power prediction methods often struggle to provide accurate and reliable predictions in the presence of sudden changes in wind speed and power output. To address this challenge, this study proposes an integrated algorithm that combines a wind speed mutation identification a... | {
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2502.12808 | Exceeding the Maximum Speed Limit of the Joint Angle for the Redundant
Tendon-driven Structures of Musculoskeletal Humanoids | [
"cs.RO"
] | The musculoskeletal humanoid has various biomimetic benefits, and the redundant muscle arrangement is one of its most important characteristics. This redundancy can achieve fail-safe redundant actuation and variable stiffness control. However, there is a problem that the maximum joint angle velocity is limited by the s... | {
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2502.12810 | Frequency-domain alignment of heterogeneous, multidimensional
separations data through complex orthogonal Procrustes analysis | [
"math.NA",
"cs.LG",
"cs.NA"
] | Multidimensional separations data have the capacity to reveal detailed information about complex biological samples. However, data analysis has been an ongoing challenge in the area since the peaks that represent chemical factors may drift over the course of several analytical runs along the first and second dimension ... | {
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2502.12811 | Applications of Stretch Reflex for the Upper Limb of Musculoskeletal
Humanoids: Protective Behavior, Postural Stability, and Active Induction | [
"cs.RO"
] | The musculoskeletal humanoid has various biomimetic benefits, and it is important that we can embed and evaluate human reflexes in the actual robot. Although stretch reflex has been implemented in lower limbs of musculoskeletal humanoids, we apply it to the upper limb to discover its useful applications. We consider th... | {
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2502.12813 | Simulating User Diversity in Task-Oriented Dialogue Systems using Large
Language Models | [
"cs.CL"
] | In this study, we explore the application of Large Language Models (LLMs) for generating synthetic users and simulating user conversations with a task-oriented dialogue system and present detailed results and their analysis. We propose a comprehensive novel approach to user simulation technique that uses LLMs to create... | {
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2502.12819 | Carotid Artery Plaque Analysis in 3D Based on Distance Encoding in Mesh
Representations | [
"cs.CV"
] | Purpose: Enabling a comprehensive and robust assessment of carotid artery plaques in 3D through extraction and visualization of quantitative plaque parameters. These parameters have potential applications in stroke risk analysis, evaluation of therapy effectiveness, and plaque progression prediction. Methods: We propos... | {
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2502.12821 | Pitfalls of Scale: Investigating the Inverse Task of Redefinition in
Large Language Models | [
"cs.CL"
] | Inverse tasks can uncover potential reasoning gaps as Large Language Models (LLMs) scale up. In this work, we explore the redefinition task, in which we assign alternative values to well-known physical constants and units of measure, prompting LLMs to respond accordingly. Our findings show that not only does model perf... | {
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2502.12825 | Reasoning and the Trusting Behavior of DeepSeek and GPT: An Experiment
Revealing Hidden Fault Lines in Large Language Models | [
"cs.CL",
"cs.AI"
] | When encountering increasingly frequent performance improvements or cost reductions from a new large language model (LLM), developers of applications leveraging LLMs must decide whether to take advantage of these improvements or stay with older tried-and-tested models. Low perceived switching frictions can lead to choi... | {
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2502.12829 | KazMMLU: Evaluating Language Models on Kazakh, Russian, and Regional
Knowledge of Kazakhstan | [
"cs.CL"
] | Despite having a population of twenty million, Kazakhstan's culture and language remain underrepresented in the field of natural language processing. Although large language models (LLMs) continue to advance worldwide, progress in Kazakh language has been limited, as seen in the scarcity of dedicated models and benchma... | {
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2502.12834 | NTP-INT: Network Traffic Prediction-Driven In-band Network Telemetry for
High-load Switches | [
"cs.NI",
"cs.LG"
] | In-band network telemetry (INT) is essential to network management due to its real-time visibility. However, because of the rapid increase in network devices and services, it has become crucial to have targeted access to detailed network information in a dynamic network environment. This paper proposes an intelligent n... | {
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2502.12835 | Subword models struggle with word learning, but surprisal hides it | [
"cs.CL"
] | We study word learning in subword and character language models with the psycholinguistic lexical decision task. While subword LMs struggle to discern words and non-words with high accuracy, character LMs solve this task easily and consistently. Furthermore, when comparing word learning and syntactic learning, both pro... | {
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2502.12836 | An LLM-Powered Agent for Physiological Data Analysis: A Case Study on
PPG-based Heart Rate Estimation | [
"cs.CL"
] | Large language models (LLMs) are revolutionizing healthcare by improving diagnosis, patient care, and decision support through interactive communication. More recently, they have been applied to analyzing physiological time-series like wearable data for health insight extraction. Existing methods embed raw numerical se... | {
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2502.12838 | Towards Equitable AI: Detecting Bias in Using Large Language Models for
Marketing | [
"cs.CY",
"cs.CL"
] | The recent advances in large language models (LLMs) have revolutionized industries such as finance, marketing, and customer service by enabling sophisticated natural language processing tasks. However, the broad adoption of LLMs brings significant challenges, particularly in the form of social biases that can be embedd... | {
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2502.12842 | Towards Adaptive Feedback with AI: Comparing the Feedback Quality of
LLMs and Teachers on Experimentation Protocols | [
"cs.AI",
"cs.HC"
] | Effective feedback is essential for fostering students' success in scientific inquiry. With advancements in artificial intelligence, large language models (LLMs) offer new possibilities for delivering instant and adaptive feedback. However, this feedback often lacks the pedagogical validation provided by real-world pra... | {
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2502.12845 | MOLLM: Multi-Objective Large Language Model for Molecular Design --
Optimizing with Experts | [
"cs.LG"
] | Molecular design plays a critical role in advancing fields such as drug discovery, materials science, and chemical engineering. This work introduces the Multi-Objective Large Language Model for Molecular Design (MOLLM), a novel framework that combines domain-specific knowledge with the adaptability of Large Language Mo... | {
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2502.12847 | Characterizing the Interaction of Cultural Evolution Mechanisms in
Experimental Social Networks | [
"cs.SI",
"q-bio.NC",
"q-bio.PE"
] | Understanding how cognitive and social mechanisms shape the evolution of complex artifacts such as songs is central to cultural evolution research. Social network topology (what artifacts are available?), selection (which are chosen?), and reproduction (how are they copied?) have all been proposed as key influencing fa... | {
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2502.12849 | Leveraging Intermediate Representations for Better Out-of-Distribution
Detection | [
"cs.LG",
"cs.CV"
] | In real-world applications, machine learning models must reliably detect Out-of-Distribution (OoD) samples to prevent unsafe decisions. Current OoD detection methods often rely on analyzing the logits or the embeddings of the penultimate layer of a neural network. However, little work has been conducted on the exploita... | {
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2502.12851 | MeMo: Towards Language Models with Associative Memory Mechanisms | [
"cs.CL",
"cs.AI"
] | Memorization is a fundamental ability of Transformer-based Large Language Models, achieved through learning. In this paper, we propose a paradigm shift by designing an architecture to memorize text directly, bearing in mind the principle that memorization precedes learning. We introduce MeMo, a novel architecture for l... | {
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2502.12852 | MVL-SIB: A Massively Multilingual Vision-Language Benchmark for
Cross-Modal Topical Matching | [
"cs.CL"
] | Existing multilingual vision-language (VL) benchmarks often only cover a handful of languages. Consequently, evaluations of large vision-language models (LVLMs) predominantly target high-resource languages, underscoring the need for evaluation data for low-resource languages. To address this limitation, we introduce MV... | {
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2502.12853 | S$^2$R: Teaching LLMs to Self-verify and Self-correct via Reinforcement
Learning | [
"cs.CL",
"cs.LG"
] | Recent studies have demonstrated the effectiveness of LLM test-time scaling. However, existing approaches to incentivize LLMs' deep thinking abilities generally require large-scale data or significant training efforts. Meanwhile, it remains unclear how to improve the thinking abilities of less powerful base models. In ... | {
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2502.12855 | Integrating Arithmetic Learning Improves Mathematical Reasoning in
Smaller Models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | While large models pre-trained on high-quality data exhibit excellent performance across various reasoning tasks, including mathematical reasoning (e.g. GSM8k, MultiArith), specializing smaller models to excel at mathematical reasoning remains a challenging problem. Common approaches to address this challenge include k... | {
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2502.12858 | Rejected Dialects: Biases Against African American Language in Reward
Models | [
"cs.CL",
"cs.AI",
"cs.CY"
] | Preference alignment via reward models helps build safe, helpful, and reliable large language models (LLMs). However, subjectivity in preference judgments and the lack of representative sampling in preference data collection can introduce new biases, hindering reward models' fairness and equity. In this work, we introd... | {
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2502.12859 | PAFT: Prompt-Agnostic Fine-Tuning | [
"cs.CL",
"cs.AI"
] | While Large Language Models (LLMs) adapt well to downstream tasks after fine-tuning, this adaptability often compromises prompt robustness, as even minor prompt variations can significantly degrade performance. To address this, we propose Prompt-Agnostic Fine-Tuning(PAFT), a simple yet effective approach that dynamical... | {
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2502.12860 | An Experimental Study of SOTA LiDAR Segmentation Models | [
"cs.CV"
] | Point cloud segmentation (PCS) is to classify each point in point clouds. The task enables robots to parse their 3D surroundings and run autonomously. According to different point cloud representations, existing PCS models can be roughly divided into point-, voxel-, and range image-based models. However, no work has be... | {
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2502.12861 | InstructRobot: A Model-Free Framework for Mapping Natural Language
Instructions into Robot Motion | [
"cs.RO"
] | The ability to communicate with robots using natural language is a significant step forward in human-robot interaction. However, accurately translating verbal commands into physical actions is promising, but still presents challenges. Current approaches require large datasets to train the models and are limited to robo... | {
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2502.12862 | RobotIQ: Empowering Mobile Robots with Human-Level Planning for
Real-World Execution | [
"cs.RO",
"cs.SY",
"eess.SY"
] | This paper introduces RobotIQ, a framework that empowers mobile robots with human-level planning capabilities, enabling seamless communication via natural language instructions through any Large Language Model. The proposed framework is designed in the ROS architecture and aims to bridge the gap between humans and robo... | {
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2502.12863 | Malware Detection based on API calls | [
"cs.CR",
"cs.LG"
] | Malware attacks pose a significant threat in today's interconnected digital landscape, causing billions of dollars in damages. Detecting and identifying families as early as possible provides an edge in protecting against such malware. We explore a lightweight, order-invariant approach to detecting and mitigating malwa... | {
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2502.12874 | Testing for Causal Fairness | [
"cs.LG"
] | Causality is widely used in fairness analysis to prevent discrimination on sensitive attributes, such as genders in career recruitment and races in crime prediction. However, the current data-based Potential Outcomes Framework (POF) often leads to untrustworthy fairness analysis results when handling high-dimensional d... | {
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2502.12876 | Continuous Learning Conversational AI: A Personalized Agent Framework
via A2C Reinforcement Learning | [
"cs.AI"
] | Creating personalized and adaptable conversational AI remains a key challenge. This paper introduces a Continuous Learning Conversational AI (CLCA) approach, implemented using A2C reinforcement learning, to move beyond static Large Language Models (LLMs). We use simulated sales dialogues, generated by LLMs, to train an... | {
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2502.12877 | Pushing the Limits of the Reactive Affine Shaker Algorithm to Higher
Dimensions | [
"math.NA",
"cs.LG",
"cs.NA"
] | Bayesian Optimization (BO) for the minimization of expensive functions of continuous variables uses all the knowledge acquired from previous samples (${\boldsymbol x}_i$ and $f({\boldsymbol x}_i)$ values) to build a surrogate model based on Gaussian processes. The surrogate is then exploited to define the next point to... | {
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2502.12884 | How desirable is alignment between LLMs and linguistically diverse human
users? | [
"cs.CL"
] | We discuss how desirable it is that Large Language Models (LLMs) be able to adapt or align their language behavior with users who may be diverse in their language use. User diversity may come about among others due to i) age differences; ii) gender characteristics, and/or iii) multilingual experience, and associated di... | {
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"cs.SY": 0
} |
2502.12886 | Are Multilingual Language Models an Off-ramp for Under-resourced
Languages? Will we arrive at Digital Language Equality in Europe in 2030? | [
"cs.CL"
] | Large language models (LLMs) demonstrate unprecedented capabilities and define the state of the art for almost all natural language processing (NLP) tasks and also for essentially all Language Technology (LT) applications. LLMs can only be trained for languages for which a sufficient amount of pre-training data is avai... | {
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} |
2502.12892 | Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept
Extraction in Large Vision Models | [
"cs.CV"
] | Sparse Autoencoders (SAEs) have emerged as a powerful framework for machine learning interpretability, enabling the unsupervised decomposition of model representations into a dictionary of abstract, human-interpretable concepts. However, we reveal a fundamental limitation: existing SAEs exhibit severe instability, as i... | {
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} |
2502.12893 | H-CoT: Hijacking the Chain-of-Thought Safety Reasoning Mechanism to
Jailbreak Large Reasoning Models, Including OpenAI o1/o3, DeepSeek-R1, and
Gemini 2.0 Flash Thinking | [
"cs.CL"
] | Large Reasoning Models (LRMs) have recently extended their powerful reasoning capabilities to safety checks-using chain-of-thought reasoning to decide whether a request should be answered. While this new approach offers a promising route for balancing model utility and safety, its robustness remains underexplored. To a... | {
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} |
2502.12894 | CAST: Component-Aligned 3D Scene Reconstruction from an RGB Image | [
"cs.CV"
] | Recovering high-quality 3D scenes from a single RGB image is a challenging task in computer graphics. Current methods often struggle with domain-specific limitations or low-quality object generation. To address these, we propose CAST (Component-Aligned 3D Scene Reconstruction from a Single RGB Image), a novel method fo... | {
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} |
2502.12895 | Multilingual European Language Models: Benchmarking Approaches and
Challenges | [
"cs.CL"
] | The breakthrough of generative large language models (LLMs) that can solve different tasks through chat interaction has led to a significant increase in the use of general benchmarks to assess the quality or performance of these models beyond individual applications. There is also a need for better methods to evaluate ... | {
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} |
2502.12896 | None of the Others: a General Technique to Distinguish Reasoning from
Memorization in Multiple-Choice LLM Evaluation Benchmarks | [
"cs.CL"
] | In LLM evaluations, reasoning is often distinguished from recall/memorization by performing numerical variations to math-oriented questions. Here we introduce a general variation method for multiple-choice questions that completely dissociates the correct answer from previously seen tokens or concepts, requiring LLMs t... | {
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} |
2502.12897 | On Zero Skip-Cost Generalized Fractional-Repetition Codes from Covering
Designs | [
"cs.IT",
"math.CO",
"math.IT"
] | We study generalized fractional repetition codes that have zero skip cost, and which are based on covering designs. We show that a zero skip cost is always attainable, perhaps at a price of an expansion factor compared with the optimal size of fractional repetition codes based on Steiner systems. We provide three const... | {
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} |
2502.12898 | The Relationship Between Head Injury and Alzheimer's Disease: A Causal
Analysis with Bayesian Networks | [
"cs.LG"
] | This study examines the potential causal relationship between head injury and the risk of developing Alzheimer's disease (AD) using Bayesian networks and regression models. Using a dataset of 2,149 patients, we analyze key medical history variables, including head injury history, memory complaints, cardiovascular disea... | {
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} |
2502.12900 | Soundwave: Less is More for Speech-Text Alignment in LLMs | [
"cs.CL",
"cs.AI",
"cs.SD"
] | Existing end-to-end speech large language models (LLMs) usually rely on large-scale annotated data for training, while data-efficient training has not been discussed in depth. We focus on two fundamental problems between speech and text: the representation space gap and sequence length inconsistency. We propose Soundwa... | {
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} |
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