id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.13753 | SCALAR: Scientific Citation-based Live Assessment of Long-context
Academic Reasoning | [
"cs.CL"
] | Evaluating large language models' (LLMs) long-context understanding capabilities remains challenging. We present SCALAR (Scientific Citation-based Live Assessment of Long-context Academic Reasoning), a novel benchmark that leverages academic papers and their citation networks. SCALAR features automatic generation of hi... | {
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2502.13754 | Capturing Rich Behavior Representations: A Dynamic Action Semantic-Aware
Graph Transformer for Video Captioning | [
"cs.CV"
] | Existing video captioning methods merely provide shallow or simplistic representations of object behaviors, resulting in superficial and ambiguous descriptions. However, object behavior is dynamic and complex. To comprehensively capture the essence of object behavior, we propose a dynamic action semantic-aware graph tr... | {
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2502.13755 | GPA: Grover Policy Agent for Generating Optimal Quantum Sensor Circuits | [
"quant-ph",
"cs.AI"
] | This study proposes a GPA for designing optimal Quantum Sensor Circuits (QSCs) to address complex quantum physics problems. The GPA consists of two parts: the Quantum Policy Evaluation (QPE) and the Quantum Policy Improvement (QPI). The QPE performs phase estimation to generate the search space, while the QPI utilizes ... | {
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2502.13757 | Identifying metric structures of deep latent variable models | [
"stat.ML",
"cs.LG"
] | Deep latent variable models learn condensed representations of data that, hopefully, reflect the inner workings of the studied phenomena. Unfortunately, these latent representations are not statistically identifiable, meaning they cannot be uniquely determined. Domain experts, therefore, need to tread carefully when in... | {
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2502.13759 | Geolocation with Real Human Gameplay Data: A Large-Scale Dataset and
Human-Like Reasoning Framework | [
"cs.CV"
] | Geolocation, the task of identifying an image's location, requires complex reasoning and is crucial for navigation, monitoring, and cultural preservation. However, current methods often produce coarse, imprecise, and non-interpretable localization. A major challenge lies in the quality and scale of existing geolocation... | {
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2502.13760 | Muscle Activation Estimation by Optimizing the Musculoskeletal Model for
Personalized Strength and Conditioning Training | [
"physics.med-ph",
"cs.RO"
] | Musculoskeletal models are pivotal in the domains of rehabilitation and resistance training to analyze muscle conditions. However, individual variability in musculoskeletal parameters and the immeasurability of some internal biomechanical variables pose significant obstacles to accurate personalized modelling. Furtherm... | {
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2502.13763 | Unsupervised Graph Embeddings for Session-based Recommendation with Item
Features | [
"cs.IR"
] | In session-based recommender systems, predictions are based on the user's preceding behavior in the session. State-of-the-art sequential recommendation algorithms either use graph neural networks to model sessions in a graph or leverage the similarity of sessions by exploiting item features. In this paper, we combine t... | {
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2502.13764 | An Overall Real-Time Mechanism for Classification and Quality Evaluation
of Rice | [
"cs.CV",
"cs.AI"
] | Rice is one of the most widely cultivated crops globally and has been developed into numerous varieties. The quality of rice during cultivation is primarily determined by its cultivar and characteristics. Traditionally, rice classification and quality assessment rely on manual visual inspection, a process that is both ... | {
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2502.13766 | GIMMICK -- Globally Inclusive Multimodal Multitask Cultural Knowledge
Benchmarking | [
"cs.CL"
] | Large Vision-Language Models (LVLMs) have recently gained attention due to their distinctive performance and broad applicability. While it has been previously shown that their efficacy in usage scenarios involving non-Western contexts falls short, existing studies are limited in scope, covering just a narrow range of c... | {
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2502.13767 | AI Software Engineer: Programming with Trust | [
"cs.SE",
"cs.AI"
] | Large Language Models (LLMs) have shown surprising proficiency in generating code snippets, promising to automate large parts of software engineering via artificial intelligence (AI). We argue that successfully deploying AI software engineers requires a level of trust equal to or even greater than the trust established... | {
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2502.13769 | A consensus set for the aggregation of partial rankings: the case of the
Optimal Set of Bucket Orders Problem | [
"cs.AI"
] | In rank aggregation problems (RAP), the solution is usually a consensus ranking that generalizes a set of input orderings. There are different variants that differ not only in terms of the type of rankings that are used as input and output, but also in terms of the objective function employed to evaluate the quality of... | {
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2502.13773 | Multi-Covering a Point Set by $m$ Disks with Minimum Total Area | [
"cs.RO",
"cs.CG"
] | A common robotics sensing problem is to place sensors to robustly monitor a set of assets, where robustness is assured by requiring asset $p$ to be monitored by at least $\kappa(p)$ sensors. Given $n$ assets that must be observed by $m$ sensors, each with a disk-shaped sensing region, where should the sensors be placed... | {
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2502.13775 | VITAL: A New Dataset for Benchmarking Pluralistic Alignment in
Healthcare | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Alignment techniques have become central to ensuring that Large Language Models (LLMs) generate outputs consistent with human values. However, existing alignment paradigms often model an averaged or monolithic preference, failing to account for the diversity of perspectives across cultures, demographics, and communitie... | {
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2502.13776 | EHOP: A Dataset of Everyday NP-Hard Optimization Problems | [
"cs.CL",
"cs.CC"
] | We introduce the dataset of Everyday Hard Optimization Problems (EHOP), a collection of NP-hard optimization problems expressed in natural language. EHOP includes problem formulations that could be found in computer science textbooks, versions that are dressed up as problems that could arise in real life, and variants ... | {
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2502.13777 | Herglotz-NET: Implicit Neural Representation of Spherical Data with
Harmonic Positional Encoding | [
"cs.LG",
"eess.SP"
] | Representing and processing data in spherical domains presents unique challenges, primarily due to the curvature of the domain, which complicates the application of classical Euclidean techniques. Implicit neural representations (INRs) have emerged as a promising alternative for high-fidelity data representation; howev... | {
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2502.13778 | Poster: SpiderSim: Multi-Agent Driven Theoretical Cybersecurity
Simulation for Industrial Digitalization | [
"cs.CR",
"cs.AI"
] | Rapid industrial digitalization has created intricate cybersecurity demands that necessitate effective validation methods. While cyber ranges and simulation platforms are widely deployed, they frequently face limitations in scenario diversity and creation efficiency. In this paper, we present SpiderSim, a theoretical c... | {
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2502.13780 | Translation in the Hands of Many:Centering Lay Users in Machine
Translation Interactions | [
"cs.CL",
"cs.CY"
] | Converging societal and technical factors have transformed language technologies into user-facing applications employed across languages. Machine Translation (MT) has become a global tool, with cross-lingual services now also supported by dialogue systems powered by multilingual Large Language Models (LLMs). This acces... | {
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2502.13783 | Generative Large Recommendation Models: Emerging Trends in LLMs for
Recommendation | [
"cs.IR"
] | In the era of information overload, recommendation systems play a pivotal role in filtering data and delivering personalized content. Recent advancements in feature interaction and user behavior modeling have significantly enhanced the recall and ranking processes of these systems. With the rise of large language model... | {
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2502.13785 | Helix-mRNA: A Hybrid Foundation Model For Full Sequence mRNA
Therapeutics | [
"q-bio.GN",
"cs.AI"
] | mRNA-based vaccines have become a major focus in the pharmaceutical industry. The coding sequence as well as the Untranslated Regions (UTRs) of an mRNA can strongly influence translation efficiency, stability, degradation, and other factors that collectively determine a vaccine's effectiveness. However, optimizing mRNA... | {
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2502.13789 | From Correctness to Comprehension: AI Agents for Personalized Error
Diagnosis in Education | [
"cs.CV"
] | Large Language Models (LLMs), such as GPT-4, have demonstrated impressive mathematical reasoning capabilities, achieving near-perfect performance on benchmarks like GSM8K. However, their application in personalized education remains limited due to an overemphasis on correctness over error diagnosis and feedback generat... | {
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2502.13791 | From Tools to Teammates: Evaluating LLMs in Multi-Session Coding
Interactions | [
"cs.CL"
] | Large Language Models (LLMs) are increasingly used in working environments for a wide range of tasks, excelling at solving individual problems in isolation. However, are they also able to effectively collaborate over long-term interactions? To investigate this, we introduce MemoryCode, a synthetic multi-session dataset... | {
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2502.13794 | LESA: Learnable LLM Layer Scaling-Up | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Training Large Language Models (LLMs) from scratch requires immense computational resources, making it prohibitively expensive. Model scaling-up offers a promising solution by leveraging the parameters of smaller models to create larger ones. However, existing depth scaling-up methods rely on empirical heuristic rules ... | {
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2502.13801 | Learning to explore when mistakes are not allowed | [
"cs.LG",
"cs.SY",
"eess.SY"
] | Goal-Conditioned Reinforcement Learning (GCRL) provides a versatile framework for developing unified controllers capable of handling wide ranges of tasks, exploring environments, and adapting behaviors. However, its reliance on trial-and-error poses challenges for real-world applications, as errors can result in costly... | {
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2502.13803 | 3D Gaussian Splatting aided Localization for Large and Complex
Indoor-Environments | [
"cs.CV",
"cs.RO"
] | The field of visual localization has been researched for several decades and has meanwhile found many practical applications. Despite the strong progress in this field, there are still challenging situations in which established methods fail. We present an approach to significantly improve the accuracy and reliability ... | {
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2502.13805 | AnDB: Breaking Boundaries with an AI-Native Database for Universal
Semantic Analysis | [
"cs.DB",
"cs.AI",
"cs.LG"
] | In this demonstration, we present AnDB, an AI-native database that supports traditional OLTP workloads and innovative AI-driven tasks, enabling unified semantic analysis across structured and unstructured data. While structured data analytics is mature, challenges remain in bridging the semantic gap between user querie... | {
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2502.13808 | MGFI-Net: A Multi-Grained Feature Integration Network for Enhanced
Medical Image Segmentation | [
"eess.IV",
"cs.CV"
] | Medical image segmentation plays a crucial role in various clinical applications. A major challenge in medical image segmentation is achieving accurate delineation of regions of interest in the presence of noise, low contrast, or complex anatomical structures. Existing segmentation models often neglect the integration ... | {
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2502.13810 | Learning Is a Kan Extension | [
"math.CT",
"cs.LG"
] | Previous work has demonstrated that efficient algorithms exist for computing Kan extensions and that some Kan extensions have interesting similarities to various machine learning algorithms. This paper closes the gap by proving that all error minimisation algorithms may be presented as a Kan extension. This result prov... | {
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2502.13811 | On the Duality between Gradient Transformations and Adapters | [
"cs.LG",
"cs.CL"
] | We study memory-efficient optimization of neural networks with linear gradient transformations, where the gradients are linearly mapped to a lower dimensional space than the full parameter space, thus saving memory required for gradient accumulation and optimizer state persistence. The model parameters are updated by f... | {
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2502.13813 | Optimal Overlap Detection of Shotgun Reads | [
"cs.IT",
"math.IT",
"math.ST",
"stat.TH"
] | We consider the problem of detecting the overlap between a pair of short fragments sampled in random locations from an exponentially longer sequence, via their possibly noisy reads. We consider a noiseless setting, in which the reads are noiseless, and the sequence is only assumed to be stationary and ergodic. Under mi... | {
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2502.13816 | Exploring Embodied Emotional Communication: A Human-oriented Review of
Mediated Social Touch | [
"cs.HC",
"cs.RO"
] | This paper offers a structured understanding of mediated social touch (MST) using a human-oriented approach, through an extensive review of literature spanning tactile interfaces, emotional information, mapping mechanisms, and the dynamics of human-human and human-robot interactions. By investigating the existing and e... | {
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2502.13818 | Building Age Estimation: A New Multi-Modal Benchmark Dataset and
Community Challenge | [
"cs.CV",
"cs.LG"
] | Estimating the construction year of buildings is of great importance for sustainability. Sustainable buildings minimize energy consumption and are a key part of responsible and sustainable urban planning and development to effectively combat climate change. By using Artificial Intelligence (AI) and recently proposed Tr... | {
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2502.13820 | Scoring Verifiers: Evaluating Synthetic Verification in Code and
Reasoning | [
"cs.AI",
"cs.CL",
"cs.LG",
"cs.SE"
] | Code verification has recently found great success as a critical component in training large scale reasoning models for coding. Synthetic techniques such as self-generated test cases and reward models provide a way to enhance code capabilities beyond predefined tests. Building on these advancements, we propose new benc... | {
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2502.13822 | Uncertainty quantification for Markov chains with application to
temporal difference learning | [
"stat.ML",
"cs.LG"
] | Markov chains are fundamental to statistical machine learning, underpinning key methodologies such as Markov Chain Monte Carlo (MCMC) sampling and temporal difference (TD) learning in reinforcement learning (RL). Given their widespread use, it is crucial to establish rigorous probabilistic guarantees on their convergen... | {
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2502.13823 | An Online Optimization-Based Trajectory Planning Approach for
Cooperative Landing Tasks | [
"cs.RO"
] | This paper presents a real-time trajectory planning scheme for a heterogeneous multi-robot system (consisting of a quadrotor and a ground mobile robot) for a cooperative landing task, where the landing position, landing time, and coordination between the robots are determined autonomously under the consideration of fea... | {
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2502.13825 | Mixup Regularization: A Probabilistic Perspective | [
"cs.LG",
"stat.ML"
] | In recent years, mixup regularization has gained popularity as an effective way to improve the generalization performance of deep learning models by training on convex combinations of training data. While many mixup variants have been explored, the proper adoption of the technique to conditional density estimation and ... | {
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2502.13826 | In-Place Updates of a Graph Index for Streaming Approximate Nearest
Neighbor Search | [
"cs.IR"
] | Indices for approximate nearest neighbor search (ANNS) are a basic component for information retrieval and widely used in database, search, recommendation and RAG systems. In these scenarios, documents or other objects are inserted into and deleted from the working set at a high rate, requiring a stream of updates to t... | {
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2502.13827 | Bayesian Physics Informed Neural Networks for Linear Inverse problems | [
"cs.LG",
"cs.NA",
"math.NA"
] | Inverse problems arise almost everywhere in science and engineering where we need to infer on a quantity from indirect observation. The cases of medical, biomedical, and industrial imaging systems are the typical examples. A very high overview of classification of the inverse problems method can be: i) Analytical, ii) ... | {
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2502.13833 | Contrastive Learning-Based privacy metrics in Tabular Synthetic Datasets | [
"cs.LG",
"cs.CR"
] | Synthetic data has garnered attention as a Privacy Enhancing Technology (PET) in sectors such as healthcare and finance. When using synthetic data in practical applications, it is important to provide protection guarantees. In the literature, two family of approaches are proposed for tabular data: on the one hand, Simi... | {
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2502.13834 | Proving Olympiad Inequalities by Synergizing LLMs and Symbolic Reasoning | [
"cs.AI"
] | Large language models (LLMs) can prove mathematical theorems formally by generating proof steps (\textit{a.k.a.} tactics) within a proof system. However, the space of possible tactics is vast and complex, while the available training data for formal proofs is limited, posing a significant challenge to LLM-based tactic ... | {
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2502.13836 | Quantifying Memorization and Retriever Performance in
Retrieval-Augmented Vision-Language Models | [
"cs.LG",
"cs.AI"
] | Large Language Models (LLMs) demonstrate remarkable capabilities in question answering (QA), but metrics for assessing their reliance on memorization versus retrieval remain underdeveloped. Moreover, while finetuned models are state-of-the-art on closed-domain tasks, general-purpose models like GPT-4o exhibit strong ze... | {
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2502.13838 | Generative Video Semantic Communication via Multimodal Semantic Fusion
with Large Model | [
"eess.SP",
"cs.CV",
"cs.IT",
"eess.IV",
"math.IT"
] | Despite significant advancements in traditional syntactic communications based on Shannon's theory, these methods struggle to meet the requirements of 6G immersive communications, especially under challenging transmission conditions. With the development of generative artificial intelligence (GenAI), progress has been ... | {
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2502.13840 | Mitigating Popularity Bias in Collaborative Filtering through Fair
Sampling | [
"cs.IR",
"cs.AI"
] | Recommender systems often suffer from popularity bias, where frequently interacted items are overrepresented in recommendations. This bias stems from propensity factors influencing training data, leading to imbalanced exposure. In this paper, we introduce a Fair Sampling (FS) approach to address this issue by ensuring ... | {
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2502.13842 | Inner Thinking Transformer: Leveraging Dynamic Depth Scaling to Foster
Adaptive Internal Thinking | [
"cs.CL"
] | Large language models (LLMs) face inherent performance bottlenecks under parameter constraints, particularly in processing critical tokens that demand complex reasoning. Empirical analysis reveals challenging tokens induce abrupt gradient spikes across layers, exposing architectural stress points in standard Transforme... | {
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2502.13843 | Enhancing Cross-Domain Recommendations with Memory-Optimized LLM-Based
User Agents | [
"cs.IR",
"cs.AI"
] | Large Language Model (LLM)-based user agents have emerged as a powerful tool for improving recommender systems by simulating user interactions. However, existing methods struggle with cross-domain scenarios due to inefficient memory structures, leading to irrelevant information retention and failure to account for soci... | {
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2502.13845 | Enhancing LLM-Based Recommendations Through Personalized Reasoning | [
"cs.IR",
"cs.AI"
] | Current recommendation systems powered by large language models (LLMs) often underutilize their reasoning capabilities due to a lack of explicit logical structuring. To address this limitation, we introduce CoT-Rec, a framework that integrates Chain-of-Thought (CoT) reasoning into LLM-driven recommendations by incorpor... | {
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2502.13847 | DH-RAG: A Dynamic Historical Context-Powered Retrieval-Augmented
Generation Method for Multi-Turn Dialogue | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Retrieval-Augmented Generation (RAG) systems have shown substantial benefits in applications such as question answering and multi-turn dialogue \citep{lewis2020retrieval}. However, traditional RAG methods, while leveraging static knowledge bases, often overlook the potential of dynamic historical information in ongoing... | {
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2502.13851 | Evaluation of EAS directions based on TAIGA HiSCORE data using fully
connected neural networks | [
"astro-ph.IM",
"astro-ph.HE",
"cs.LG"
] | The direction of extensive air showers can be used to determine the source of gamma quanta and plays an important role in estimating the energy of the primary particle. The data from an array of non-imaging Cherenkov detector stations HiSCORE in the TAIGA experiment registering the number of photoelectrons and detectio... | {
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2502.13852 | Minimally sufficient structures for information-feedback policies | [
"cs.RO"
] | In this paper, we consider robotic tasks which require a desirable outcome to be achieved in the physical world that the robot is embedded in and interacting with. Accomplishing this objective requires designing a filter that maintains a useful representation of the physical world and a policy over the filter states. A... | {
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2502.13853 | Fine-grained Fallacy Detection with Human Label Variation | [
"cs.CL"
] | We introduce Faina, the first dataset for fallacy detection that embraces multiple plausible answers and natural disagreement. Faina includes over 11K span-level annotations with overlaps across 20 fallacy types on social media posts in Italian about migration, climate change, and public health given by two expert anno... | {
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2502.13855 | MagicGeo: Training-Free Text-Guided Geometric Diagram Generation | [
"cs.CV"
] | Geometric diagrams are critical in conveying mathematical and scientific concepts, yet traditional diagram generation methods are often manual and resource-intensive. While text-to-image generation has made strides in photorealistic imagery, creating accurate geometric diagrams remains a challenge due to the need for p... | {
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2502.13859 | MSVCOD:A Large-Scale Multi-Scene Dataset for Video Camouflage Object
Detection | [
"cs.CV"
] | Video Camouflaged Object Detection (VCOD) is a challenging task which aims to identify objects that seamlessly concealed within the background in videos. The dynamic properties of video enable detection of camouflaged objects through motion cues or varied perspectives. Previous VCOD datasets primarily contain animal ob... | {
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2502.13863 | The NavINST Dataset for Multi-Sensor Autonomous Navigation | [
"cs.RO"
] | The NavINST Laboratory has developed a comprehensive multisensory dataset from various road-test trajectories in urban environments, featuring diverse lighting conditions, including indoor garage scenarios with dense 3D maps. This dataset includes multiple commercial-grade IMUs and a high-end tactical-grade IMU. Additi... | {
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2502.13870 | SPEX: Scaling Feature Interaction Explanations for LLMs | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.IT",
"math.IT"
] | Large language models (LLMs) have revolutionized machine learning due to their ability to capture complex interactions between input features. Popular post-hoc explanation methods like SHAP provide marginal feature attributions, while their extensions to interaction importances only scale to small input lengths ($\appr... | {
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2502.13873 | NVR: Vector Runahead on NPUs for Sparse Memory Access | [
"cs.AR",
"cs.AI"
] | Deep Neural Networks are increasingly leveraging sparsity to reduce the scaling up of model parameter size. However, reducing wall-clock time through sparsity and pruning remains challenging due to irregular memory access patterns, leading to frequent cache misses. In this paper, we present NPU Vector Runahead (NVR), a... | {
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2502.13874 | The KnowWhereGraph: A Large-Scale Geo-Knowledge Graph for
Interdisciplinary Knowledge Discovery and Geo-Enrichment | [
"cs.DB"
] | Global challenges such as food supply chain disruptions, public health crises, and natural hazard responses require access to and integration of diverse datasets, many of which are geospatial. Over the past few years, a growing number of (geo)portals have been developed to address this need. However, most existing (geo... | {
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2502.13875 | MEX: Memory-efficient Approach to Referring Multi-Object Tracking | [
"cs.CV",
"cs.AI"
] | Referring Multi-Object Tracking (RMOT) is a relatively new concept that has rapidly gained traction as a promising research direction at the intersection of computer vision and natural language processing. Unlike traditional multi-object tracking, RMOT identifies and tracks objects and incorporates textual descriptions... | {
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2502.13877 | Near-Optimal List-Recovery of Linear Code Families | [
"cs.IT",
"math.CO",
"math.IT"
] | We prove several results on linear codes achieving list-recovery capacity. We show that random linear codes achieve list-recovery capacity with constant output list size (independent of the alphabet size and length). That is, over alphabets of size at least $\ell^{\Omega(1/\varepsilon)}$, random linear codes of rate $R... | {
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2502.13880 | Class E/EF Inductive Power Transfer to Achieve Stable Output under
Variable Low Coupling | [
"eess.SY",
"cs.SY"
] | This paper develops an inductive power transfer(IPT)system with stable output power based on a Class E/EF inverter. Load-independent design of Class E/EF inverter has recently attracted widespread interest. However, applying this design to IPT systems has proven challenging when the coupling coefficient is weak. To sol... | {
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2502.13881 | PSCon: Toward Conversational Product Search | [
"cs.CL",
"cs.AI",
"cs.IR"
] | Conversational Product Search (CPS) is confined to simulated conversations due to the lack of real-world CPS datasets that reflect human-like language. Additionally, current conversational datasets are limited to support cross-market and multi-lingual usage. In this paper, we introduce a new CPS data collection protoco... | {
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2502.13883 | Multi-view Video-Pose Pretraining for Operating Room Surgical Activity
Recognition | [
"cs.CV"
] | Understanding the workflow of surgical procedures in complex operating rooms requires a deep understanding of the interactions between clinicians and their environment. Surgical activity recognition (SAR) is a key computer vision task that detects activities or phases from multi-view camera recordings. Existing SAR mod... | {
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2502.13886 | Refining embeddings with fill-tuning: data-efficient generalised
performance improvements for materials foundation models | [
"cs.LG",
"cs.CE"
] | Pretrained foundation models learn embeddings that can be used for a wide range of downstream tasks. These embeddings optimise general performance, and if insufficiently accurate at a specific task the model can be fine-tuned to improve performance. For all current methodologies this operation necessarily degrades perf... | {
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2502.13891 | Highly Dynamic and Flexible Spatio-Temporal Spectrum Management with
AI-Driven O-RAN: A Multi-Granularity Marketplace Framework | [
"eess.SY",
"cs.LG",
"cs.SY"
] | Current spectrum-sharing frameworks struggle with adaptability, often being either static or insufficiently dynamic. They primarily emphasize temporal sharing while overlooking spatial and spectral dimensions. We propose an adaptive, AI-driven spectrum-sharing framework within the O-RAN architecture, integrating discri... | {
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2502.13894 | NavigateDiff: Visual Predictors are Zero-Shot Navigation Assistants | [
"cs.RO",
"cs.CV"
] | Navigating unfamiliar environments presents significant challenges for household robots, requiring the ability to recognize and reason about novel decoration and layout. Existing reinforcement learning methods cannot be directly transferred to new environments, as they typically rely on extensive mapping and exploratio... | {
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2502.13895 | Geometric Principles for Machine Learning of Dynamical Systems | [
"cs.LG"
] | Mathematical descriptions of dynamical systems are deeply rooted in topological spaces defined by non-Euclidean geometry. This paper proposes leveraging structure-rich geometric spaces for machine learning to achieve structural generalization when modeling physical systems from data, in contrast to embedding physics bi... | {
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2502.13897 | DataSciBench: An LLM Agent Benchmark for Data Science | [
"cs.CL",
"cs.AI",
"cs.LG"
] | This paper presents DataSciBench, a comprehensive benchmark for evaluating Large Language Model (LLM) capabilities in data science. Recent related benchmarks have primarily focused on single tasks, easily obtainable ground truth, and straightforward evaluation metrics, which limits the scope of tasks that can be evalua... | {
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2502.13898 | GroundCap: A Visually Grounded Image Captioning Dataset | [
"cs.CV",
"cs.CL"
] | Current image captioning systems lack the ability to link descriptive text to specific visual elements, making their outputs difficult to verify. While recent approaches offer some grounding capabilities, they cannot track object identities across multiple references or ground both actions and objects simultaneously. W... | {
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2502.13899 | AI-Driven Discovery of High Performance Polymer Electrodes for
Next-Generation Batteries | [
"cond-mat.mtrl-sci",
"cs.LG",
"physics.app-ph"
] | The use of transition group metals in electric batteries requires extensive usage of critical elements like lithium, cobalt and nickel, which poses significant environmental challenges. Replacing these metals with redox-active organic materials offers a promising alternative, thereby reducing the carbon footprint of ba... | {
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2502.13900 | Optimistically Optimistic Exploration for Provably Efficient
Infinite-Horizon Reinforcement and Imitation Learning | [
"cs.LG"
] | We study the problem of reinforcement learning in infinite-horizon discounted linear Markov decision processes (MDPs), and propose the first computationally efficient algorithm achieving near-optimal regret guarantees in this setting. Our main idea is to combine two classic techniques for optimistic exploration: additi... | {
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2502.13905 | Partially Observable Gaussian Process Network and Doubly Stochastic
Variational Inference | [
"cs.LG",
"cs.AI"
] | To reduce the curse of dimensionality for Gaussian processes (GP), they can be decomposed into a Gaussian Process Network (GPN) of coupled subprocesses with lower dimensionality. In some cases, intermediate observations are available within the GPN. However, intermediate observations are often indirect, noisy, and inco... | {
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2502.13908 | Judging the Judges: A Collection of LLM-Generated Relevance Judgements | [
"cs.IR"
] | Using Large Language Models (LLMs) for relevance assessments offers promising opportunities to improve Information Retrieval (IR), Natural Language Processing (NLP), and related fields. Indeed, LLMs hold the promise of allowing IR experimenters to build evaluation collections with a fraction of the manual human labor c... | {
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2502.13909 | Lost in Sequence: Do Large Language Models Understand Sequential
Recommendation? | [
"cs.IR",
"cs.AI"
] | Large Language Models (LLMs) have recently emerged as promising tools for recommendation thanks to their advanced textual understanding ability and context-awareness. Despite the current practice of training and evaluating LLM-based recommendation (LLM4Rec) models under a sequential recommendation scenario, we found th... | {
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2502.13912 | Optimizing Research Portfolio For Semantic Impact | [
"cs.IR",
"cs.SI"
] | Citation metrics are widely used to assess academic impact but suffer from social biases, including institutional prestige and journal visibility. Here we introduce rXiv Semantic Impact (XSI), a novel framework that predicts research impact by analyzing how scientific semantic graphs evolve in underlying fabric of scie... | {
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2502.13913 | How Do LLMs Perform Two-Hop Reasoning in Context? | [
"cs.CL",
"cs.AI"
] | "Socrates is human. All humans are mortal. Therefore, Socrates is mortal." This classical example demonstrates two-hop reasoning, where a conclusion logically follows from two connected premises. While transformer-based Large Language Models (LLMs) can make two-hop reasoning, they tend to collapse to random guessing wh... | {
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2502.13915 | Conveniently Identify Coils in Inductive Power Transfer System Using
Machine Learning | [
"eess.SY",
"cs.SY"
] | High-frequency inductive power transfer (IPT) has garnered significant attention in recent years due to its long transmission distance and high efficiency. The inductance values L and quality factors Q of the transmitting and receiving coils greatly influence the system's operation. Traditional methods involved impedan... | {
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2502.13917 | TESS 2: A Large-Scale Generalist Diffusion Language Model | [
"cs.CL"
] | We introduce TESS 2, a general instruction-following diffusion language model that outperforms contemporary instruction-tuned diffusion models, as well as matches and sometimes exceeds strong autoregressive (AR) models. We train TESS 2 by first adapting a strong AR model via continued pretraining with the usual cross-e... | {
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2502.13918 | Playing Hex and Counter Wargames using Reinforcement Learning and
Recurrent Neural Networks | [
"cs.LG"
] | Hex and Counter Wargames are adversarial two-player simulations of real military conflicts requiring complex strategic decision-making. Unlike classical board games, these games feature intricate terrain/unit interactions, unit stacking, large maps of varying sizes, and simultaneous move and combat decisions involving ... | {
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2502.13920 | Exploring Personalized Health Support through Data-Driven, Theory-Guided
LLMs: A Case Study in Sleep Health | [
"cs.HC",
"cs.CL"
] | Despite the prevalence of sleep-tracking devices, many individuals struggle to translate data into actionable improvements in sleep health. Current methods often provide data-driven suggestions but may not be feasible and adaptive to real-life constraints and individual contexts. We present HealthGuru, a novel large la... | {
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2502.13921 | Exploring Code Language Models for Automated HLS-based Hardware
Generation: Benchmark, Infrastructure and Analysis | [
"cs.LG",
"cs.AR",
"cs.SE"
] | Recent advances in code generation have illuminated the potential of employing large language models (LLMs) for general-purpose programming languages such as Python and C++, opening new opportunities for automating software development and enhancing programmer productivity. The potential of LLMs in software programming... | {
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2502.13922 | LongPO: Long Context Self-Evolution of Large Language Models through
Short-to-Long Preference Optimization | [
"cs.CL",
"cs.LG"
] | Large Language Models (LLMs) have demonstrated remarkable capabilities through pretraining and alignment. However, superior short-context LLMs may underperform in long-context scenarios due to insufficient long-context alignment. This alignment process remains challenging due to the impracticality of human annotation f... | {
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2502.13923 | Qwen2.5-VL Technical Report | [
"cs.CV",
"cs.CL"
] | We introduce Qwen2.5-VL, the latest flagship model of Qwen vision-language series, which demonstrates significant advancements in both foundational capabilities and innovative functionalities. Qwen2.5-VL achieves a major leap forward in understanding and interacting with the world through enhanced visual recognition, p... | {
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2502.13925 | Beyond Single Frames: Can LMMs Comprehend Temporal and Contextual
Narratives in Image Sequences? | [
"cs.CL"
] | Large Multimodal Models (LMMs) have achieved remarkable success across various visual-language tasks. However, existing benchmarks predominantly focus on single-image understanding, leaving the analysis of image sequences largely unexplored. To address this limitation, we introduce StripCipher, a comprehensive benchmar... | {
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2502.13928 | Symmetrical Visual Contrastive Optimization: Aligning Vision-Language
Models with Minimal Contrastive Images | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG"
] | Recent studies have shown that Large Vision-Language Models (VLMs) tend to neglect image content and over-rely on language-model priors, resulting in errors in visually grounded tasks and hallucinations. We hypothesize that this issue arises because existing VLMs are not explicitly trained to generate texts that are ac... | {
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} |
2502.13935 | Continually Learning Structured Visual Representations via Network
Refinement with Rerelation | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Current machine learning paradigm relies on continuous representations like neural networks, which iteratively adjust parameters to approximate outcomes rather than directly learning the structure of problem. This spreads information across the network, causing issues like information loss and incomprehensibility Build... | {
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2502.13936 | Image compositing is all you need for data augmentation | [
"cs.CV",
"cs.LG"
] | This paper investigates the impact of various data augmentation techniques on the performance of object detection models. Specifically, we explore classical augmentation methods, image compositing, and advanced generative models such as Stable Diffusion XL and ControlNet. The objective of this work is to enhance model ... | {
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2502.13942 | A Chain-of-Thought Subspace Meta-Learning for Few-shot Image Captioning
with Large Vision and Language Models | [
"cs.CV"
] | A large-scale vision and language model that has been pretrained on massive data encodes visual and linguistic prior, which makes it easier to generate images and language that are more natural and realistic. Despite this, there is still a significant domain gap between the modalities of vision and language, especially... | {
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2502.13943 | AdaptiveStep: Automatically Dividing Reasoning Step through Model
Confidence | [
"cs.AI",
"cs.CL",
"cs.LG"
] | Current approaches for training Process Reward Models (PRMs) often involve breaking down responses into multiple reasoning steps using rule-based techniques, such as using predefined placeholder tokens or setting the reasoning step's length into a fixed size. These approaches overlook the fact that specific words do no... | {
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2502.13945 | GPU-Friendly Laplacian Texture Blending | [
"cs.GR",
"cs.CV"
] | Texture and material blending is one of the leading methods for adding variety to rendered virtual worlds, creating composite materials, and generating procedural content. When done naively, it can introduce either visible seams or contrast loss, leading to an unnatural look not representative of blended textures. Earl... | {
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2502.13946 | Why Safeguarded Ships Run Aground? Aligned Large Language Models' Safety
Mechanisms Tend to Be Anchored in The Template Region | [
"cs.CL",
"cs.AI",
"cs.CR"
] | The safety alignment of large language models (LLMs) remains vulnerable, as their initial behavior can be easily jailbroken by even relatively simple attacks. Since infilling a fixed template between the input instruction and initial model output is a common practice for existing LLMs, we hypothesize that this template... | {
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2502.13951 | IP-Composer: Semantic Composition of Visual Concepts | [
"cs.CV",
"cs.GR"
] | Content creators often draw inspiration from multiple visual sources, combining distinct elements to craft new compositions. Modern computational approaches now aim to emulate this fundamental creative process. Although recent diffusion models excel at text-guided compositional synthesis, text as a medium often lacks p... | {
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2502.13953 | Neurosymbolic artificial intelligence via large language models and
coherence-driven inference | [
"cs.AI"
] | We devise an algorithm to generate sets of propositions that objectively instantiate graphs that support coherence-driven inference. We then benchmark the ability of large language models (LLMs) to reconstruct coherence graphs from (a straightforward transformation of) propositions expressed in natural language, with p... | {
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2502.13954 | Latent Distribution Decoupling: A Probabilistic Framework for
Uncertainty-Aware Multimodal Emotion Recognition | [
"cs.CL",
"cs.LG"
] | Multimodal multi-label emotion recognition (MMER) aims to identify the concurrent presence of multiple emotions in multimodal data. Existing studies primarily focus on improving fusion strategies and modeling modality-to-label dependencies. However, they often overlook the impact of \textbf{aleatoric uncertainty}, whic... | {
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} |
2502.13957 | RAG-Gym: Optimizing Reasoning and Search Agents with Process Supervision | [
"cs.CL",
"cs.AI"
] | Retrieval-augmented generation (RAG) has shown great potential for knowledge-intensive tasks, but its traditional architectures rely on static retrieval, limiting their effectiveness for complex questions that require sequential information-seeking. While agentic reasoning and search offer a more adaptive approach, mos... | {
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} |
2502.13959 | LIDDIA: Language-based Intelligent Drug Discovery Agent | [
"cs.CL"
] | Drug discovery is a long, expensive, and complex process, relying heavily on human medicinal chemists, who can spend years searching the vast space of potential therapies. Recent advances in artificial intelligence for chemistry have sought to expedite individual drug discovery tasks; however, there remains a critical ... | {
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} |
2502.13961 | The Computational Advantage of Depth: Learning High-Dimensional
Hierarchical Functions with Gradient Descent | [
"stat.ML",
"cs.LG"
] | Understanding the advantages of deep neural networks trained by gradient descent (GD) compared to shallow models remains an open theoretical challenge. While the study of multi-index models with Gaussian data in high dimensions has provided analytical insights into the benefits of GD-trained neural networks over kernel... | {
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} |
2502.13962 | Is That Your Final Answer? Test-Time Scaling Improves Selective Question
Answering | [
"cs.CL"
] | Scaling the test-time compute of large language models has demonstrated impressive performance on reasoning benchmarks. However, existing evaluations of test-time scaling make the strong assumption that a reasoning system should always give an answer to any question provided. This overlooks concerns about whether a mod... | {
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} |
2502.13963 | MuDAF: Long-Context Multi-Document Attention Focusing through
Contrastive Learning on Attention Heads | [
"cs.CL"
] | Large Language Models (LLMs) frequently show distracted attention due to irrelevant information in the input, which severely impairs their long-context capabilities. Inspired by recent studies on the effectiveness of retrieval heads in long-context factutality, we aim at addressing this distraction issue through improv... | {
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} |
2502.13964 | A Training-Free Framework for Precise Mobile Manipulation of Small
Everyday Objects | [
"cs.RO",
"cs.AI",
"cs.CV",
"cs.LG"
] | Many everyday mobile manipulation tasks require precise interaction with small objects, such as grasping a knob to open a cabinet or pressing a light switch. In this paper, we develop Servoing with Vision Models (SVM), a closed-loop training-free framework that enables a mobile manipulator to tackle such precise tasks ... | {
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} |
2502.13965 | Autellix: An Efficient Serving Engine for LLM Agents as General Programs | [
"cs.LG",
"cs.AI",
"cs.DC"
] | Large language model (LLM) applications are evolving beyond simple chatbots into dynamic, general-purpose agentic programs, which scale LLM calls and output tokens to help AI agents reason, explore, and solve complex tasks. However, existing LLM serving systems ignore dependencies between programs and calls, missing si... | {
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} |
2502.13966 | Where's the Bug? Attention Probing for Scalable Fault Localization | [
"cs.SE",
"cs.LG"
] | Ensuring code correctness remains a challenging problem even as large language models (LLMs) become increasingly capable at code-related tasks. While LLM-based program repair systems can propose bug fixes using only a user's bug report, their effectiveness is fundamentally limited by their ability to perform fault loca... | {
"Other": 1,
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} |
2502.13967 | FlexTok: Resampling Images into 1D Token Sequences of Flexible Length | [
"cs.CV",
"cs.LG"
] | Image tokenization has enabled major advances in autoregressive image generation by providing compressed, discrete representations that are more efficient to process than raw pixels. While traditional approaches use 2D grid tokenization, recent methods like TiTok have shown that 1D tokenization can achieve high generat... | {
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} |
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