id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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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 ... |
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 ... |
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 utilize... |
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 ... |
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
geolocati... |
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. Furthe... |
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... |
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 bot... |
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... |
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
establish... |
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 ... |
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 plac... |
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
communit... |
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 variant... |
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; how... |
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... |
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 acc... |
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 mod... |
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 mR... |
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 gener... |
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 datas... |
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 rule... |
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 cost... |
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
reliabilit... |
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 quer... |
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 integratio... |
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 pr... |
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... |
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 ... |
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... |
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 ... |
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 be... |
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
converg... |
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 f... |
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 an... |
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... |
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... |
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, Si... |
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 tacti... |
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 ... |
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 bee... |
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 ensurin... |
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
Transfor... |
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 so... |
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
incorp... |
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 ongoi... |
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 detect... |
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.... |
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 an... |
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... |
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 ... |
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. Addi... |
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 ($\ap... |
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),... |
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 (g... |
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 descriptio... |
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 ... |
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 s... |
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 proto... |
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 m... |
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 pe... |
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 disc... |
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 explorat... |
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 ... |
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 eval... |
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.... |
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 ... |
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:
addi... |
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 in... |
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... |
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 ... |
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 sc... |
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 ... |
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 imped... |
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... |
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 involvin... |
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 ... |
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 programmi... |
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... |
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,... |
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 benchm... |
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 ... |
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 Bui... |
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
mode... |
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,
especial... |
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 ... |
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. Ea... |
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 templa... |
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... |
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... |
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}, wh... |
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, m... |
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 critica... |
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 kern... |
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 m... |
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 impr... |
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 task... |
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 ... |
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 lo... |
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 gener... |
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