id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
|---|---|---|---|
2502.11913 | PreAdaptFWI: Pretrained-Based Adaptive Residual Learning for
Full-Waveform Inversion Without Dataset Dependency | physics.geo-ph cs.LG | Full-waveform inversion (FWI) is a method that utilizes seismic data to
invert the physical parameters of subsurface media by minimizing the difference
between simulated and observed waveforms. Due to its ill-posed nature, FWI is
susceptible to getting trapped in local minima. Consequently, various research
efforts h... |
2502.11915 | On the robustness of ChatGPT in teaching Korean Mathematics | cs.AI math.HO | ChatGPT, an Artificial Intelligence model, has the potential to revolutionize
education. However, its effectiveness in solving non-English questions remains
uncertain. This study evaluates ChatGPT's robustness using 586 Korean
mathematics questions. ChatGPT achieves 66.72% accuracy, correctly answering
391 out of 586... |
2502.11916 | EssayJudge: A Multi-Granular Benchmark for Assessing Automated Essay
Scoring Capabilities of Multimodal Large Language Models | cs.CL cs.AI | Automated Essay Scoring (AES) plays a crucial role in educational assessment
by providing scalable and consistent evaluations of writing tasks. However,
traditional AES systems face three major challenges: (1) reliance on
handcrafted features that limit generalizability, (2) difficulty in capturing
fine-grained trait... |
2502.11918 | VLP: Vision-Language Preference Learning for Embodied Manipulation | cs.LG cs.RO | Reward engineering is one of the key challenges in Reinforcement Learning
(RL). Preference-based RL effectively addresses this issue by learning from
human feedback. However, it is both time-consuming and expensive to collect
human preference labels. In this paper, we propose a novel
\textbf{V}ision-\textbf{L}anguage... |
2502.11919 | From Text to Trust: Empowering AI-assisted Decision Making with Adaptive
LLM-powered Analysis | cs.HC cs.CL | AI-assisted decision making becomes increasingly prevalent, yet individuals
often fail to utilize AI-based decision aids appropriately especially when the
AI explanations are absent, potentially as they do not %understand reflect on
AI's decision recommendations critically. Large language models (LLMs), with
their ex... |
2502.11921 | Joint Evaluation of Fairness and Relevance in Recommender Systems with
Pareto Frontier | cs.IR | Fairness and relevance are two important aspects of recommender systems
(RSs). Typically, they are evaluated either (i) separately by individual
measures of fairness and relevance, or (ii) jointly using a single measure that
accounts for fairness with respect to relevance. However, approach (i) often
does not provide... |
2502.11925 | GRAPHGPT-O: Synergistic Multimodal Comprehension and Generation on
Graphs | cs.AI cs.CV cs.LG | The rapid development of Multimodal Large Language Models (MLLMs) has enabled
the integration of multiple modalities, including texts and images, within the
large language model (LLM) framework. However, texts and images are usually
interconnected, forming a multimodal attributed graph (MMAG). It is
underexplored how... |
2502.11926 | BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion
Recognition Datasets for 28 Languages | cs.CL | People worldwide use language in subtle and complex ways to express emotions.
While emotion recognition -- an umbrella term for several NLP tasks --
significantly impacts different applications in NLP and other fields, most work
in the area is focused on high-resource languages. Therefore, this has led to
major dispa... |
2502.11927 | Continual Learning Should Move Beyond Incremental Classification | cs.LG | Continual learning (CL) is the sub-field of machine learning concerned with
accumulating knowledge in dynamic environments. So far, CL research has mainly
focused on incremental classification tasks, where models learn to classify new
categories while retaining knowledge of previously learned ones. Here, we argue
tha... |
2502.11932 | On Representational Dissociation of Language and Arithmetic in Large
Language Models | cs.CL | The association between language and (non-linguistic) thinking ability in
humans has long been debated, and recently, neuroscientific evidence of brain
activity patterns has been considered. Such a scientific context naturally
raises an interdisciplinary question -- what about such a language-thought
dissociation in ... |
2502.11937 | FitLight: Federated Imitation Learning for Plug-and-Play Autonomous
Traffic Signal Control | cs.LG cs.AI | Although Reinforcement Learning (RL)-based Traffic Signal Control (TSC)
methods have been extensively studied, their practical applications still raise
some serious issues such as high learning cost and poor generalizability. This
is because the ``trial-and-error'' training style makes RL agents extremely
dependent o... |
2502.11938 | QoS based resource management for concurrent operation using MCTS | eess.SP cs.SY eess.SY | Modern AESA technology enables RF systems to not only perform various radar,
communication and electronic warfare tasks on a single aperture, but even to
execute multiple tasks concurrently. These capabilities increase system
complexity and require intelligent or cognitive resource management. This paper
introduces s... |
2502.11940 | The Dynamic Model of the UR10 Robot and its ROS2 Integration | cs.RO | This paper presents the full dynamic model of the UR10 industrial robot. A
triple-stage identification approach is adopted to estimate the manipulator's
dynamic coefficients. First, linear parameters are computed using a standard
linear regression algorithm. Subsequently, nonlinear friction parameters are
estimated a... |
2502.11941 | Deep Spatio-Temporal Neural Network for Air Quality Reanalysis | cs.LG cs.AI | Air quality prediction is key to mitigating health impacts and guiding
decisions, yet existing models tend to focus on temporal trends while
overlooking spatial generalization. We propose AQ-Net, a spatiotemporal
reanalysis model for both observed and unobserved stations in the near future.
AQ-Net utilizes the LSTM a... |
2502.11942 | Sharp-PINNs: staggered hard-constrained physics-informed neural networks
for phase field modelling of corrosion | cs.LG physics.comp-ph | Physics-informed neural networks have shown significant potential in solving
partial differential equations (PDEs) across diverse scientific fields.
However, their performance often deteriorates when addressing PDEs with
intricate and strongly coupled solutions. In this work, we present a novel
Sharp-PINN framework t... |
2502.11946 | Step-Audio: Unified Understanding and Generation in Intelligent Speech
Interaction | cs.CL cs.AI cs.HC cs.SD eess.AS | Real-time speech interaction, serving as a fundamental interface for
human-machine collaboration, holds immense potential. However, current
open-source models face limitations such as high costs in voice data
collection, weakness in dynamic control, and limited intelligence. To address
these challenges, this paper in... |
2502.11948 | Can Your Uncertainty Scores Detect Hallucinated Entity? | cs.CL | To mitigate the impact of hallucination nature of LLMs, many studies propose
detecting hallucinated generation through uncertainty estimation. However,
these approaches predominantly operate at the sentence or paragraph level,
failing to pinpoint specific spans or entities responsible for hallucinated
content. This l... |
2502.11949 | Massively Scaling Explicit Policy-conditioned Value Functions | cs.LG cs.AI | We introduce a scaling strategy for Explicit Policy-Conditioned Value
Functions (EPVFs) that significantly improves performance on challenging
continuous-control tasks. EPVFs learn a value function V({\theta}) that is
explicitly conditioned on the policy parameters, enabling direct gradient-based
updates to the param... |
2502.11951 | Qubit-Based Framework for Quantum Machine Learning: Bridging Classical
Data and Quantum Algorithms | cs.CE cs.LG quant-ph | This paper dives into the exciting and rapidly growing field of quantum
computing, explaining its core ideas, current progress, and how it could
revolutionize the way we solve complex problems. It starts by breaking down the
basics, like qubits, quantum circuits, and how principles like superposition
and entanglement... |
2502.11953 | Refined PAC-Bayes Bounds for Offline Bandits | stat.ML cs.LG | In this paper, we present refined probabilistic bounds on empirical reward
estimates for off-policy learning in bandit problems. We build on the
PAC-Bayesian bounds from Seldin et al. (2010) and improve on their results
using a new parameter optimization approach introduced by Rodr\'iguez et al.
(2024). This techniqu... |
2502.11955 | pySLAM: An Open-Source, Modular, and Extensible Framework for SLAM | cs.RO cs.CV | pySLAM is an open-source Python framework for Visual SLAM, supporting
monocular, stereo, and RGB-D cameras. It provides a flexible interface for
integrating both classical and modern local features, making it adaptable to
various SLAM tasks. The framework includes different loop closure methods, a
volumetric reconstr... |
2502.11959 | STRIVE: Structured Reasoning for Self-Improvement in Claim Verification | cs.AI | Claim verification is the task of determining whether a claim is supported or
refuted by evidence. Self-improvement methods, where reasoning chains are
generated and those leading to correct results are selected for training, have
succeeded in tasks like mathematical problem solving. However, in claim
verification, t... |
2502.11962 | Navigating the Helpfulness-Truthfulness Trade-Off with Uncertainty-Aware
Instruction Fine-Tuning | cs.CL cs.AI | Instruction Fine-tuning (IFT) can enhance the helpfulness of Large Language
Models (LLMs), but it may lower their truthfulness. This trade-off arises
because IFT steers LLMs to generate responses with long-tail knowledge that is
not well covered during pre-training, leading to more informative but less
truthful answe... |
2502.11965 | A MIMO Wireless Channel Foundation Model via CIR-CSI Consistency | eess.SP cs.AI | In the field of artificial intelligence, self-supervised learning has
demonstrated superior generalization capabilities by leveraging large-scale
unlabeled datasets for pretraining, which is especially critical for wireless
communication models to adapt to a variety of scenarios. This paper
innovatively treats Channe... |
2502.11968 | Theoretical Barriers in Bellman-Based Reinforcement Learning | cs.LG cs.AI | Reinforcement Learning algorithms designed for high-dimensional spaces often
enforce the Bellman equation on a sampled subset of states, relying on
generalization to propagate knowledge across the state space. In this paper, we
identify and formalize a fundamental limitation of this common approach.
Specifically, we ... |
2502.11969 | Learning Generalizable Prompt for CLIP with Class Similarity Knowledge | cs.AI cs.CV cs.LG | In vision-language models (VLMs), prompt tuning has shown its effectiveness
in adapting models to downstream tasks. However, learned prompts struggle to
generalize to unseen classes, as they tend to overfit to the classes that are
targeted during prompt tuning. Examining failure cases, we observed that
learned prompt... |
2502.11971 | Robust 6DoF Pose Tracking Considering Contour and Interior
Correspondence Uncertainty for AR Assembly Guidance | cs.CV | Augmented reality assembly guidance is essential for intelligent
manufacturing and medical applications, requiring continuous measurement of the
6DoF poses of manipulated objects. Although current tracking methods have made
significant advancements in accuracy and efficiency, they still face challenges
in robustness ... |
2502.11973 | Generating Text from Uniform Meaning Representation | cs.CL | Uniform Meaning Representation (UMR) is a recently developed graph-based
semantic representation, which expands on Abstract Meaning Representation (AMR)
in a number of ways, in particular through the inclusion of document-level
information and multilingual flexibility. In order to effectively adopt and
leverage UMR f... |
2502.11974 | Image Inversion: A Survey from GANs to Diffusion and Beyond | cs.CV | Image inversion is a fundamental task in generative models, aiming to map
images back to their latent representations to enable downstream applications
such as editing, restoration, and style transfer. This paper provides a
comprehensive review of the latest advancements in image inversion techniques,
focusing on two... |
2502.11975 | Spatial decay of perturbations in hyperbolic equations with optimal
boundary control | math.OC cs.SY eess.SY | Recently, domain-uniform stabilizability and detectability has been the
central assumption %in order robustness results on the to ensure robustness in
the sense of exponential decay of spatially localized perturbations in
optimally controlled evolution equations. In the present paper we analyze a
chain of transport e... |
2502.11981 | Machine Learning Should Maximize Welfare, Not (Only) Accuracy | cs.LG cs.AI cs.CY | Decades of research in machine learning have given us powerful tools for
making accurate predictions. But when used in social settings and on human
inputs, better accuracy does not immediately translate to better social
outcomes. This may not be surprising given that conventional learning
frameworks are not designed ... |
2502.11983 | Design Considerations Based on Stability for a Class of TCP Algorithms | cs.NI cs.SY eess.SY | Transmission Control Protocol (TCP) continues to be the dominant transport
protocol on the Internet. The stability of fluid models has been a key
consideration in the design of TCP and the performance evaluation of TCP
algorithms. Based on local stability analysis, we formulate some design
considerations for a class ... |
2502.11984 | Blank Space: Adaptive Causal Coding for Streaming Communications Over
Multi-Hop Networks | cs.IT cs.NI math.IT | In this work, we introduce Blank Space AC-RLNC (BS), a novel Adaptive and
Causal Network Coding (AC-RLNC) solution designed to mitigate the triplet
trade-off between throughput-delay-efficiency in multi-hop networks. BS
leverages the network's physical limitations considering the bottleneck from
each node to the dest... |
2502.11986 | Selective Task Group Updates for Multi-Task Optimization | cs.LG | Multi-task learning enables the acquisition of task-generic knowledge by
training multiple tasks within a unified architecture. However, training all
tasks together in a single architecture can lead to performance degradation,
known as negative transfer, which is a main concern in multi-task learning.
Previous works ... |
2502.11989 | Characterizing Photorealism and Artifacts in Diffusion Model-Generated
Images | cs.HC cs.AI cs.CV | Diffusion model-generated images can appear indistinguishable from authentic
photographs, but these images often contain artifacts and implausibilities that
reveal their AI-generated provenance. Given the challenge to public trust in
media posed by photorealistic AI-generated images, we conducted a large-scale
experi... |
2502.11992 | On the Logic Elements Associated with Round-Off Errors and Gaussian Blur
in Image Registration: A Simple Case of Commingling | cs.CV | Discrete image registration can be a strategy to reconstruct signals from
samples corrupted by blur and noise. We examine superresolution and discrete
image registration for one-dimensional spatially-limited piecewise constant
functions which are subject to blur which is Gaussian or a mixture of Gaussians
as well as ... |
2502.11993 | MultiFlow: A unified deep learning framework for multi-vessel
classification, segmentation and clustering of phase-contrast MRI validated
on a multi-site single ventricle patient cohort | cs.CV | This study presents a unified deep learning (DL) framework, MultiFlowSeg, for
classification and segmentation of velocity-encoded phase-contrast magnetic
resonance imaging data, and MultiFlowDTC for temporal clustering of flow
phenotypes. Applied to the FORCE registry of Fontan procedure patients,
MultiFlowSeg achiev... |
2502.11995 | Presumed Cultural Identity: How Names Shape LLM Responses | cs.CL cs.AI | Names are deeply tied to human identity. They can serve as markers of
individuality, cultural heritage, and personal history. However, using names as
a core indicator of identity can lead to over-simplification of complex
identities. When interacting with LLMs, user names are an important point of
information for per... |
2502.12001 | Merging Language and Domain Specific Models: The Impact on Technical
Vocabulary Acquisition | cs.CL cs.LG | This paper investigates the integration of technical vocabulary in merged
language models. We explore the knowledge transfer mechanisms involved when
combining a general-purpose language-specific model with a domain-specific
model, focusing on the resulting model's comprehension of technical jargon. Our
experiments a... |
2502.12002 | NaturalL2S: End-to-End High-quality Multispeaker Lip-to-Speech Synthesis
with Differential Digital Signal Processing | cs.SD cs.CV eess.AS | Recent advancements in visual speech recognition (VSR) have promoted progress
in lip-to-speech synthesis, where pre-trained VSR models enhance the
intelligibility of synthesized speech by providing valuable semantic
information. The success achieved by cascade frameworks, which combine
pseudo-VSR with pseudo-text-to-... |
2502.12003 | Predicting Next-Day Wildfire Spread with Time Series and Attention | cs.CV | Recent research has demonstrated the potential of deep neural networks (DNNs)
to accurately predict next-day wildfire spread, based upon the current extent
of a fire and geospatial rasters of influential environmental covariates e.g.,
vegetation, topography, climate, and weather. In this work, we investigate a
recent... |
2502.12005 | Feasibility Evaluation of Quadratic Programs for Constrained Control | math.OC cs.SY eess.SY | This paper presents a computationally-efficient method for evaluating the
feasibility of Quadratic Programs (QPs) for online constrained control. Based
on the duality principle, we first show that the feasibility of a QP can be
determined by the solution of a properly-defined Linear Program (LP). Our
analysis yields ... |
2502.12007 | Demographic Attributes Prediction from Speech Using WavLM Embeddings | cs.CL cs.AI | This paper introduces a general classifier based on WavLM features, to infer
demographic characteristics, such as age, gender, native language, education,
and country, from speech. Demographic feature prediction plays a crucial role
in applications like language learning, accessibility, and digital forensics,
enablin... |
2502.12009 | Beyond Sentiment: Examining the Role of Moral Foundations in User
Engagement with News on Twitter | cs.SI | This study uses sentiment analysis and the Moral Foundations Theory (MFT) to
characterise news content in social media and examine its association with user
engagement. We employ Natural Language Processing to quantify the moral and
affective linguistic markers. At the same time, we automatically define
thematic macr... |
2502.12011 | Reconfigurable Intelligent Surfaces-Assisted Integrated Access and
Backhaul | cs.IT cs.LG cs.NI math.IT | In this paper, we study the impact of reconfigurable intelligent surfaces
(RISs) on the coverage extension of integrated access and backhaul (IAB)
networks. Particularly, using a finite stochastic geometry model, with random
distributions of user equipments (UEs) in a finite region, and planned
hierachical architectu... |
2502.12012 | Evolving Hard Maximum Cut Instances for Quantum Approximate Optimization
Algorithms | cs.ET cs.AI cs.NE quant-ph | Variational quantum algorithms, such as the Recursive Quantum Approximate
Optimization Algorithm (RQAOA), have become increasingly popular, offering
promising avenues for employing Noisy Intermediate-Scale Quantum devices to
address challenging combinatorial optimization tasks like the maximum cut
problem. In this st... |
2502.12013 | Unsupervised Structural-Counterfactual Generation under Domain Shift | cs.LG stat.ML | Motivated by the burgeoning interest in cross-domain learning, we present a
novel generative modeling challenge: generating counterfactual samples in a
target domain based on factual observations from a source domain. Our approach
operates within an unsupervised paradigm devoid of parallel or joint datasets,
relying ... |
2502.12017 | Scalable and Cost-Efficient ML Inference: Parallel Batch Processing with
Serverless Functions | cs.DC cs.LG | As data-intensive applications grow, batch processing in limited-resource
environments faces scalability and resource management challenges. Serverless
computing offers a flexible alternative, enabling dynamic resource allocation
and automatic scaling. This paper explores how serverless architectures can
make large-s... |
2502.12018 | Atom of Thoughts for Markov LLM Test-Time Scaling | cs.CL cs.AI cs.LG | Large Language Models (LLMs) achieve superior performance through
training-time scaling, and test-time scaling further enhances their
capabilities by conducting effective reasoning during inference. However, as
the scale of reasoning increases, existing test-time scaling methods suffer
from accumulated historical inf... |
2502.12019 | Robotic CBCT Meets Robotic Ultrasound | cs.RO eess.IV | The multi-modality imaging system offers optimal fused images for safe and
precise interventions in modern clinical practices, such as computed tomography
- ultrasound (CT-US) guidance for needle insertion. However, the limited
dexterity and mobility of current imaging devices hinder their integration into
standardiz... |
2502.12020 | Learning in a Multifield Coherent Ising Machine | cond-mat.mes-hall cond-mat.dis-nn cs.ET cs.NE nlin.AO | Physical information processors can learn from examples if they are modified
according to an abstract parameter update equation, termed a learning rule. We
introduce a physical model for self-learning that encodes the learning rule in
the Hamiltonian of the system. The model consists of a network of multi-modal
reson... |
2502.12022 | Teaching LLMs According to Their Aptitude: Adaptive Reasoning for
Mathematical Problem Solving | cs.CL cs.AI | Existing approaches to mathematical reasoning with large language models
(LLMs) rely on Chain-of-Thought (CoT) for generalizability or Tool-Integrated
Reasoning (TIR) for precise computation. While efforts have been made to
combine these methods, they primarily rely on post-selection or predefined
strategies, leaving... |
2502.12025 | SafeChain: Safety of Language Models with Long Chain-of-Thought
Reasoning Capabilities | cs.AI cs.CL | Emerging large reasoning models (LRMs), such as DeepSeek-R1 models, leverage
long chain-of-thought (CoT) reasoning to generate structured intermediate
steps, enhancing their reasoning capabilities. However, long CoT does not
inherently guarantee safe outputs, potentially leading to harmful consequences
such as the in... |
2502.12027 | Enhancing Transparent Object Pose Estimation: A Fusion of GDR-Net and
Edge Detection | cs.CV | Object pose estimation of transparent objects remains a challenging task in
the field of robot vision due to the immense influence of lighting, background,
and reflections. However, the edges of clear objects have the highest contrast,
which leads to stable and prominent features. We propose a novel approach by
incor... |
2502.12029 | KnowPath: Knowledge-enhanced Reasoning via LLM-generated Inference Paths
over Knowledge Graphs | cs.AI | Large language models (LLMs) have demonstrated remarkable capabilities in
various complex tasks, yet they still suffer from hallucinations. Introducing
external knowledge, such as knowledge graph, can enhance the LLMs' ability to
provide factual answers. LLMs have the ability to interactively explore
knowledge graphs... |
2502.12031 | Masked Latent Prediction and Classification for Self-Supervised Audio
Representation Learning | cs.SD cs.AI | Recently, self-supervised learning methods based on masked latent prediction
have proven to encode input data into powerful representations. However, during
training, the learned latent space can be further transformed to extract
higher-level information that could be more suited for downstream
classification tasks. ... |
2502.12033 | The geometry of BERT | cs.LG | Transformer neural networks, particularly Bidirectional Encoder
Representations from Transformers (BERT), have shown remarkable performance
across various tasks such as classification, text summarization, and question
answering. However, their internal mechanisms remain mathematically obscure,
highlighting the need f... |
2502.12037 | Information geometry of tempered stable processes | math.DG cs.IT math.IT math.PR | We find information geometry of tempered stable processes. Starting with the
derivation of $\alpha$-divergence between two tempered stable processes, Fisher
information matrices of tempered stable processes and $\alpha$-connections of
their statistical manifolds are obtained. Additionally, we also provide
statistical... |
2502.12047 | Quantum Byzantine Multiple Access Channels | cs.IT math.IT math.QA | In communication theory, attacks like eavesdropping or jamming are typically
assumed to occur at the channel level, while communication parties are expected
to follow established protocols. But what happens if one of the parties turns
malicious? In this work, we investigate a compelling scenario: a
multiple-access ch... |
2502.12048 | A Survey on Bridging EEG Signals and Generative AI: From Image and Text
to Beyond | cs.AI cs.HC cs.LG | Integration of Brain-Computer Interfaces (BCIs) and Generative Artificial
Intelligence (GenAI) has opened new frontiers in brain signal decoding,
enabling assistive communication, neural representation learning, and
multimodal integration. BCIs, particularly those leveraging
Electroencephalography (EEG), provide a no... |
2502.12049 | Classifying the Stoichiometry of Virus-like Particles with Interpretable
Machine Learning | cs.LG q-bio.BM q-bio.QM | Virus-like particles (VLPs) are valuable for vaccine development due to their
immune-triggering properties. Understanding their stoichiometry, the number of
protein subunits to form a VLP, is critical for vaccine optimisation. However,
current experimental methods to determine stoichiometry are time-consuming and
req... |
2502.12050 | SpeechT: Findings of the First Mentorship in Speech Translation | cs.CL cs.SD | This work presents the details and findings of the first mentorship in speech
translation (SpeechT), which took place in December 2024 and January 2025. To
fulfil the requirements of the mentorship, the participants engaged in key
activities, including data preparation, modelling, and advanced research.
|
2502.12051 | How to Upscale Neural Networks with Scaling Law? A Survey and Practical
Guidelines | cs.CL cs.LG | Neural scaling laws have revolutionized the design and optimization of
large-scale AI models by revealing predictable relationships between model
size, dataset volume, and computational resources. Early research established
power-law relationships in model performance, leading to compute-optimal
scaling strategies. H... |
2502.12052 | A Dual-Perspective NLG Meta-Evaluation Framework with Automatic
Benchmark and Better Interpretability | cs.CL | In NLG meta-evaluation, evaluation metrics are typically assessed based on
their consistency with humans. However, we identify some limitations in
traditional NLG meta-evaluation approaches, such as issues in handling human
ratings and ambiguous selections of correlation measures, which undermine the
effectiveness of... |
2502.12054 | PhysReason: A Comprehensive Benchmark towards Physics-Based Reasoning | cs.AI | Large language models demonstrate remarkable capabilities across various
domains, especially mathematics and logic reasoning. However, current
evaluations overlook physics-based reasoning - a complex task requiring physics
theorems and constraints. We present PhysReason, a 1,200-problem benchmark
comprising knowledge... |
2502.12055 | Designing Role Vectors to Improve LLM Inference Behaviour | cs.CL | The influence of personas on Large Language Models (LLMs) has been widely
studied, yet their direct impact on performance remains uncertain. This work
explores a novel approach to guiding LLM behaviour through role vectors, an
alternative to persona-based prompting. We construct 29 role vectors derived
from model act... |
2502.12057 | Culture is Not Trivia: Sociocultural Theory for Cultural NLP | cs.CL cs.CY | The field of cultural NLP has recently experienced rapid growth, driven by a
pressing need to ensure that language technologies are effective and safe
across a pluralistic user base. This work has largely progressed without a
shared conception of culture, instead choosing to rely on a wide array of
cultural proxies. ... |
2502.12058 | A survey about perceptions of mobility to inform an agent-based
simulator of subjective modal choice | cs.MA cs.CY | In order to adapt to the issues of climate change and public health, urban
policies are trying to encourage soft mobility, but the share of the car
remains significant. Beyond known constraints, we study here the impact of
perception biases on individual choices. We designed a multi-criteria decision
model, integrati... |
2502.12063 | Low-Rank Thinning | stat.ML cs.LG math.OC math.ST stat.ME stat.TH | The goal in thinning is to summarize a dataset using a small set of
representative points. Remarkably, sub-Gaussian thinning algorithms like Kernel
Halving and Compress can match the quality of uniform subsampling while
substantially reducing the number of summary points. However, existing
guarantees cover only a res... |
2502.12064 | AI-generated Text Detection with a GLTR-based Approach | cs.CL cs.AI | The rise of LLMs (Large Language Models) has contributed to the improved
performance and development of cutting-edge NLP applications. However, these
can also pose risks when used maliciously, such as spreading fake news, harmful
content, impersonating individuals, or facilitating school plagiarism, among
others. Thi... |
2502.12065 | Formalizing Complex Mathematical Statements with LLMs: A Study on
Mathematical Definitions | cs.CL cs.FL | Thanks to their linguistic capabilities, LLMs offer an opportunity to bridge
the gap between informal mathematics and formal languages through
autoformalization. However, it is still unclear how well LLMs generalize to
sophisticated and naturally occurring mathematical statements. To address this
gap, we investigate ... |
2502.12066 | CONSTRUCTA: Automating Commercial Construction Schedules in Fabrication
Facilities with Large Language Models | cs.AI cs.LG cs.SE | Automating planning with LLMs presents transformative opportunities for
traditional industries, yet remains underexplored. In commercial construction,
the complexity of automated scheduling often requires manual intervention to
ensure precision. We propose CONSTRUCTA, a novel framework leveraging LLMs to
optimize con... |
2502.12067 | TokenSkip: Controllable Chain-of-Thought Compression in LLMs | cs.CL cs.AI | Chain-of-Thought (CoT) has been proven effective in enhancing the reasoning
capabilities of large language models (LLMs). Recent advancements, such as
OpenAI's o1 and DeepSeek-R1, suggest that scaling up the length of CoT
sequences during inference could further boost LLM reasoning performance.
However, due to the au... |
2502.12073 | Can LLMs Simulate Social Media Engagement? A Study on Action-Guided
Response Generation | cs.CL | Social media enables dynamic user engagement with trending topics, and recent
research has explored the potential of large language models (LLMs) for
response generation. While some studies investigate LLMs as agents for
simulating user behavior on social media, their focus remains on practical
viability and scalabil... |
2502.12080 | HumanGif: Single-View Human Diffusion with Generative Prior | cs.CV | While previous single-view-based 3D human reconstruction methods made
significant progress in novel view synthesis, it remains a challenge to
synthesize both view-consistent and pose-consistent results for animatable
human avatars from a single image input. Motivated by the success of 2D
character animation, we propo... |
2502.12081 | Unhackable Temporal Rewarding for Scalable Video MLLMs | cs.CV cs.CL | In the pursuit of superior video-processing MLLMs, we have encountered a
perplexing paradox: the "anti-scaling law", where more data and larger models
lead to worse performance. This study unmasks the culprit: "temporal hacking",
a phenomenon where models shortcut by fixating on select frames, missing the
full video ... |
2502.12082 | AdaSplash: Adaptive Sparse Flash Attention | cs.CL cs.LG | The computational cost of softmax-based attention in transformers limits
their applicability to long-context tasks. Adaptive sparsity, of which
$\alpha$-entmax attention is an example, offers a flexible data-dependent
alternative, but existing implementations are inefficient and do not leverage
the sparsity to obtain... |
2502.12084 | VLM$^2$-Bench: A Closer Look at How Well VLMs Implicitly Link Explicit
Matching Visual Cues | cs.CL | Visually linking matching cues is a crucial ability in daily life, such as
identifying the same person in multiple photos based on their cues, even
without knowing who they are. Despite the extensive knowledge that
vision-language models (VLMs) possess, it remains largely unexplored whether
they are capable of perfor... |
2502.12085 | APB: Accelerating Distributed Long-Context Inference by Passing
Compressed Context Blocks across GPUs | cs.LG cs.CL | While long-context inference is crucial for advancing large language model
(LLM) applications, its prefill speed remains a significant bottleneck. Current
approaches, including sequence parallelism strategies and compute reduction
through approximate attention mechanisms, still fall short of delivering
optimal infere... |
2502.12086 | Unifying Explainable Anomaly Detection and Root Cause Analysis in
Dynamical Systems | cs.LG stat.ML | Dynamical systems, prevalent in various scientific and engineering domains,
are susceptible to anomalies that can significantly impact their performance
and reliability. This paper addresses the critical challenges of anomaly
detection, root cause localization, and anomaly type classification in
dynamical systems gov... |
2502.12088 | Meta-Statistical Learning: Supervised Learning of Statistical Inference | cs.LG cs.AI | This work demonstrates that the tools and principles driving the success of
large language models (LLMs) can be repurposed to tackle distribution-level
tasks, where the goal is to predict properties of the data-generating
distribution rather than labels for individual datapoints. These tasks
encompass statistical inf... |
2502.12089 | How compositional generalization and creativity improve as diffusion
models are trained | stat.ML cs.LG | Natural data is often organized as a hierarchical composition of features.
How many samples do generative models need to learn the composition rules, so
as to produce a combinatorial number of novel data? What signal in the data is
exploited to learn? We investigate these questions both theoretically and
empirically.... |
2502.12093 | WeVibe: Weight Change Estimation Through Audio-Induced Shelf Vibrations
In Autonomous Stores | eess.SP cs.SY eess.SY | Weight change estimation is crucial in various applications, particularly for
detecting pick-up and put-back actions when people interact with the shelf
while shopping in autonomous stores. Moreover, accurate weight change
estimation allows autonomous stores to automatically identify items being
picked up or put back... |
2502.12094 | A Study on Leveraging Search and Self-Feedback for Agent Reasoning | cs.AI cs.CL | Recent works have demonstrated that incorporating search during inference can
significantly improve reasoning capabilities of language agents. Some
approaches may make use of the ground truth or rely on model's own generated
feedback. The search algorithm uses this feedback to then produce values that
will update its... |
2502.12095 | Descriminative-Generative Custom Tokens for Vision-Language Models | cs.CV | This paper explores the possibility of learning custom tokens for
representing new concepts in Vision-Language Models (VLMs). Our aim is to learn
tokens that can be effective for both discriminative and generative tasks while
composing well with words to form new input queries. The targeted concept is
specified in te... |
2502.12096 | Token Communications: A Unified Framework for Cross-modal Context-aware
Semantic Communications | cs.IT cs.CV cs.MM eess.SP math.IT | In this paper, we introduce token communications (TokCom), a unified
framework to leverage cross-modal context information in generative semantic
communications (GenSC). TokCom is a new paradigm, motivated by the recent
success of generative foundation models and multimodal large language models
(GFM/MLLMs), where th... |
2502.12098 | Bandwidth-Adaptive Spatiotemporal Correspondence Identification for
Collaborative Perception | cs.RO | Correspondence identification (CoID) is an essential capability in
multi-robot collaborative perception, which enables a group of robots to
consistently refer to the same objects within their respective fields of view.
In real-world applications, such as connected autonomous driving, vehicles face
challenges in direc... |
2502.12102 | Relational Norms for Human-AI Cooperation | cs.AI cs.ET | How we should design and interact with social artificial intelligence depends
on the socio-relational role the AI is meant to emulate or occupy. In human
society, relationships such as teacher-student, parent-child, neighbors,
siblings, or employer-employee are governed by specific norms that prescribe or
proscribe c... |
2502.12108 | Using the Path of Least Resistance to Explain Deep Networks | cs.LG cs.AI stat.ML | Integrated Gradients (IG), a widely used axiomatic path-based attribution
method, assigns importance scores to input features by integrating model
gradients along a straight path from a baseline to the input. While effective
in some cases, we show that straight paths can lead to flawed attributions. In
this paper, we... |
2502.12109 | Personality Structured Interview for Large Language Model Simulation in
Personality Research | cs.CL cs.AI | Although psychometrics researchers have recently explored the use of large
language models (LLMs) as proxies for human participants, LLMs often fail to
generate heterogeneous data with human-like diversity, which diminishes their
value in advancing social science research. To address these challenges, we
explored the... |
2502.12110 | A-MEM: Agentic Memory for LLM Agents | cs.CL cs.HC | While large language model (LLM) agents can effectively use external tools
for complex real-world tasks, they require memory systems to leverage
historical experiences. Current memory systems enable basic storage and
retrieval but lack sophisticated memory organization, despite recent attempts
to incorporate graph da... |
2502.12113 | A Monocular Event-Camera Motion Capture System | cs.RO cs.CV | Motion capture systems are a widespread tool in research to record
ground-truth poses of objects. Commercial systems use reflective markers
attached to the object and then triangulate pose of the object from multiple
camera views. Consequently, the object must be visible to multiple cameras
which makes such multi-vie... |
2502.12115 | SWE-Lancer: Can Frontier LLMs Earn $1 Million from Real-World Freelance
Software Engineering? | cs.LG cs.SE | We introduce SWE-Lancer, a benchmark of over 1,400 freelance software
engineering tasks from Upwork, valued at \$1 million USD total in real-world
payouts. SWE-Lancer encompasses both independent engineering tasks--ranging
from \$50 bug fixes to \$32,000 feature implementations--and managerial tasks,
where models cho... |
2502.12118 | Scaling Test-Time Compute Without Verification or RL is Suboptimal | cs.LG cs.CL | Despite substantial advances in scaling test-time compute, an ongoing debate
in the community is how it should be scaled up to enable continued and
efficient improvements with scaling. There are largely two approaches: first,
distilling successful search or thinking traces; and second, using verification
(e.g., 0/1 o... |
2502.12119 | PRISM: Self-Pruning Intrinsic Selection Method for Training-Free
Multimodal Data Selection | cs.CV cs.AI cs.CL | Visual instruction tuning refines pre-trained Multimodal Large Language
Models (MLLMs) to enhance their real-world task performance. However, the rapid
expansion of visual instruction datasets introduces significant data
redundancy, leading to excessive computational costs. Existing data selection
methods predominant... |
2502.12120 | LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws | cs.LG cs.AI cs.CL | Scaling laws guide the development of large language models (LLMs) by
offering estimates for the optimal balance of model size, tokens, and compute.
More recently, loss-to-loss scaling laws that relate losses across pretraining
datasets and downstream tasks have emerged as a powerful tool for understanding
and improv... |
2502.12122 | Minimal Ranks, Maximum Confidence: Parameter-efficient Uncertainty
Quantification for LoRA | cs.LG | Low-Rank Adaptation (LoRA) enables parameter-efficient fine-tuning of large
language models by decomposing weight updates into low-rank matrices,
significantly reducing storage and computational overhead. While effective,
standard LoRA lacks mechanisms for uncertainty quantification, leading to
overconfident and poor... |
2502.12123 | On the Query Complexity of Verifier-Assisted Language Generation | cs.CL cs.LG | Recently, a plethora of works have proposed inference-time algorithms (e.g.
best-of-n), which incorporate verifiers to assist the generation process. Their
quality-efficiency trade-offs have been empirically benchmarked on a variety of
constrained generation tasks, but the algorithmic design landscape is still
largel... |
2502.12124 | RA-MTR: A Retrieval Augmented Multi-Task Reader based Approach for
Inspirational Quote Extraction from Long Documents | cs.CL | Inspirational quotes from famous individuals are often used to convey
thoughts in news articles, essays, and everyday conversations. In this paper,
we propose a novel context-based quote extraction system that aims to extract
the most relevant quote from a long text. We formulate this quote extraction as
an open doma... |
2502.12125 | Hypernym Bias: Unraveling Deep Classifier Training Dynamics through the
Lens of Class Hierarchy | cs.AI cs.LG | We investigate the training dynamics of deep classifiers by examining how
hierarchical relationships between classes evolve during training. Through
extensive experiments, we argue that the learning process in classification
problems can be understood through the lens of label clustering. Specifically,
we observe tha... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.