id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.11113 | Valuable Hallucinations: Realizable Non-realistic Propositions | cs.CL | This paper introduces the first formal definition of valuable hallucinations
in large language models (LLMs), addressing a gap in the existing literature.
We provide a systematic definition and analysis of hallucination value,
proposing methods for enhancing the value of hallucinations. In contrast to
previous works,... |
2502.11114 | Beyond Pairwise: Global Zero-shot Temporal Graph Generation | cs.CL | Temporal relation extraction (TRE) is a fundamental task in natural language
processing (NLP) that involves identifying the temporal relationships between
events in a document. Despite the advances in large language models (LLMs),
their application to TRE remains limited. Most existing approaches rely on
pairwise cla... |
2502.11115 | Are Generative Models Underconfident? An Embarrassingly Simple Quality
Estimation Approach | cs.CL | Quality Estimation (QE) is estimating the quality of model output when the
ground truth reference is not available. Looking at model uncertainty from its
own output probabilities is the most trivial and low-effort way to estimate the
output quality. However, for generative model, output probabilities might not
be the... |
2502.11116 | Gumbel Reranking: Differentiable End-to-End Reranker Optimization | cs.CL cs.IR | RAG systems rely on rerankers to identify relevant documents. However,
fine-tuning these models remains challenging due to the scarcity of annotated
query-document pairs. Existing distillation-based approaches suffer from
training-inference misalignment and fail to capture interdependencies among
candidate documents.... |
2502.11122 | Hierarchical Expert Prompt for Large-Language-Model: An Approach Defeat
Elite AI in TextStarCraft II for the First Time | cs.AI | Since the emergence of the Large Language Model (LLM), LLM has been widely
used in fields such as writing, translating, and searching. However, there is
still great potential for LLM-based methods in handling complex tasks such as
decision-making in the StarCraft II environment. To address problems such as
lack of re... |
2502.11123 | DuplexMamba: Enhancing Real-time Speech Conversations with Duplex and
Streaming Capabilities | cs.CL | Real-time speech conversation is essential for natural and efficient
human-machine interactions, requiring duplex and streaming capabilities.
Traditional Transformer-based conversational chatbots operate in a turn-based
manner and exhibit quadratic computational complexity that grows as the input
size increases. In t... |
2502.11124 | AdaManip: Adaptive Articulated Object Manipulation Environments and
Policy Learning | cs.RO cs.AI | Articulated object manipulation is a critical capability for robots to
perform various tasks in real-world scenarios. Composed of multiple parts
connected by joints, articulated objects are endowed with diverse functional
mechanisms through complex relative motions. For example, a safe consists of a
door, a handle, a... |
2502.11127 | G-Safeguard: A Topology-Guided Security Lens and Treatment on LLM-based
Multi-agent Systems | cs.CR cs.LG cs.MA | Large Language Model (LLM)-based Multi-agent Systems (MAS) have demonstrated
remarkable capabilities in various complex tasks, ranging from collaborative
problem-solving to autonomous decision-making. However, as these systems become
increasingly integrated into critical applications, their vulnerability to
adversari... |
2502.11128 | FELLE: Autoregressive Speech Synthesis with Token-Wise Coarse-to-Fine
Flow Matching | cs.CL cs.SD eess.AS | To advance continuous-valued token modeling and temporal-coherence
enforcement, we propose FELLE, an autoregressive model that integrates language
modeling with token-wise flow matching. By leveraging the autoregressive nature
of language models and the generative efficacy of flow matching, FELLE
effectively predicts... |
2502.11131 | Improving Similar Case Retrieval Ranking Performance By Revisiting
RankSVM | cs.CL | Given the rapid development of Legal AI, a lot of attention has been paid to
one of the most important legal AI tasks--similar case retrieval, especially
with language models to use. In our paper, however, we try to improve the
ranking performance of current models from the perspective of learning to rank
instead of ... |
2502.11132 | UNITE-FND: Reframing Multimodal Fake News Detection through Unimodal
Scene Translation | cs.LG cs.AI | Multimodal fake news detection typically demands complex architectures and
substantial computational resources, posing deployment challenges in real-world
settings. We introduce UNITE-FND, a novel framework that reframes multimodal
fake news detection as a unimodal text classification task. We propose six
specialized... |
2502.11133 | MasRouter: Learning to Route LLMs for Multi-Agent Systems | cs.LG cs.MA | Multi-agent systems (MAS) powered by Large Language Models (LLMs) have been
demonstrated to push the boundaries of LLM capabilities, yet they often incur
significant costs and face challenges in dynamic LLM selection. Current LLM
routing methods effectively reduce overhead in single-agent scenarios by
customizing LLM... |
2502.11134 | Solving Online Resource-Constrained Scheduling for Follow-Up Observation
in Astronomy: a Reinforcement Learning Approach | cs.AI astro-ph.IM | In the astronomical observation field, determining the allocation of
observation resources of the telescope array and planning follow-up
observations for targets of opportunity (ToOs) are indispensable components of
astronomical scientific discovery. This problem is computationally challenging,
given the online obser... |
2502.11137 | Safety Evaluation of DeepSeek Models in Chinese Contexts | cs.CL cs.AI | Recently, the DeepSeek series of models, leveraging their exceptional
reasoning capabilities and open-source strategy, is reshaping the global AI
landscape. Despite these advantages, they exhibit significant safety
deficiencies. Research conducted by Robust Intelligence, a subsidiary of Cisco,
in collaboration with t... |
2502.11138 | Machine Learning-Based Intrusion Detection and Prevention System for
IIoT Smart Metering Networks: Challenges and Solutions | cs.LG | The Industrial Internet of Things (IIoT) has revolutionized industries by
enabling automation, real-time data exchange, and smart decision-making.
However, its increased connectivity introduces cybersecurity threats,
particularly in smart metering networks, which play a crucial role in
monitoring and optimizing energ... |
2502.11140 | VisPath: Automated Visualization Code Synthesis via Multi-Path Reasoning
and Feedback-Driven Optimization | cs.SE cs.AI cs.CL cs.HC | Unprecedented breakthroughs in Large Language Models (LLMs) has amplified its
penetration into application of automated visualization code generation.
Few-shot prompting and query expansion techniques have notably enhanced data
visualization performance, however, still fail to overcome ambiguity and
complexity of nat... |
2502.11141 | Cognitive Neural Architecture Search Reveals Hierarchical Entailment | cs.NE cs.AI q-bio.QM | Recent research has suggested that the brain is more shallow than previously
thought, challenging the traditionally assumed hierarchical structure of the
ventral visual pathway. Here, we demonstrate that optimizing convolutional
network architectures for brain-alignment via evolutionary neural architecture
search res... |
2502.11142 | NavRAG: Generating User Demand Instructions for Embodied Navigation
through Retrieval-Augmented LLM | cs.AI cs.CL cs.CV | Vision-and-Language Navigation (VLN) is an essential skill for embodied
agents, allowing them to navigate in 3D environments following natural language
instructions. High-performance navigation models require a large amount of
training data, the high cost of manually annotating data has seriously hindered
this field.... |
2502.11147 | Efficient Long-Decoding Inference with Reasoning-Aware Attention
Sparsity | cs.LG cs.AI | Large Language Models (LLMs) have demonstrated strong capabilities across
various domains, with recent advancements in challenging reasoning tasks such
as mathematics and programming. However, solving reasoning tasks often requires
long decoding chains (of thoughts), which incur $O(N)$ time and memory
consumption, wh... |
2502.11149 | Large Language-Geometry Model: When LLM meets Equivariance | cs.LG cs.AI | Accurately predicting 3D structures and dynamics of physical systems is
crucial in scientific applications. Existing approaches that rely on geometric
Graph Neural Networks (GNNs) effectively enforce $\mathrm{E}(3)$-equivariance,
but they often fall in leveraging extensive broader information. While direct
applicatio... |
2502.11150 | Surprisal Takes It All: Eye Tracking Based Cognitive Evaluation of Text
Readability Measures | cs.CL | Text readability measures are widely used in many real-world scenarios and in
NLP. These measures have primarily been developed by predicting reading
comprehension outcomes, while largely neglecting what is perhaps the core
aspect of a readable text: reading ease. In this work, we propose a new eye
tracking based met... |
2502.11152 | Error Bound Analysis for the Regularized Loss of Deep Linear Neural
Networks | math.OC cs.LG | The optimization foundations of deep linear networks have received
significant attention lately. However, due to the non-convexity and
hierarchical structure, analyzing the regularized loss of deep linear networks
remains a challenging task. In this work, we study the local geometric
landscape of the regularized squa... |
2502.11155 | Uncertainty-Aware Search and Value Models: Mitigating Search Scaling
Flaws in LLMs | cs.AI cs.CL | Value model-guided search is effective in steering the generation but suffers
from scaling flaws: Its superiority diminishes with larger sample sizes,
underperforming non-search baselines. This limitation arises from reliability
degradation in value models in unseen reasoning paths. To address this, we
propose an unc... |
2502.11157 | Dyve: Thinking Fast and Slow for Dynamic Process Verification | cs.AI | We present Dyve, a dynamic process verifier that enhances reasoning error
detection in large language models by integrating fast and slow thinking,
inspired by Kahneman's Systems Theory. Dyve adaptively applies immediate
token-level confirmation System 1 for straightforward steps and comprehensive
analysis System 2 f... |
2502.11158 | AnyRefill: A Unified, Data-Efficient Framework for Left-Prompt-Guided
Vision Tasks | cs.CV | In this paper, we present a novel Left-Prompt-Guided (LPG) paradigm to
address a diverse range of reference-based vision tasks. Inspired by the human
creative process, we reformulate these tasks using a left-right stitching
formulation to construct contextual input. Building upon this foundation, we
propose AnyRefill... |
2502.11161 | BFA: Best-Feature-Aware Fusion for Multi-View Fine-grained Manipulation | cs.RO cs.CV | In real-world scenarios, multi-view cameras are typically employed for
fine-grained manipulation tasks. Existing approaches (e.g., ACT) tend to treat
multi-view features equally and directly concatenate them for policy learning.
However, it will introduce redundant visual information and bring higher
computational co... |
2502.11162 | Logarithmic Width Suffices for Robust Memorization | cs.LG stat.ML | The memorization capacity of neural networks with a given architecture has
been thoroughly studied in many works. Specifically, it is well-known that
memorizing $N$ samples can be done using a network of constant width,
independent of $N$. However, the required constructions are often quite
delicate. In this paper, w... |
2502.11163 | VLMs as GeoGuessr Masters: Exceptional Performance, Hidden Biases, and
Privacy Risks | cs.CV cs.CL | Visual-Language Models (VLMs) have shown remarkable performance across
various tasks, particularly in recognizing geographic information from images.
However, significant challenges remain, including biases and privacy concerns.
To systematically address these issues in the context of geographic information
recogniti... |
2502.11164 | Quantifying the Capability Boundary of DeepSeek Models: An
Application-Driven Performance Analysis | cs.AI cs.LG | DeepSeek-R1, known for its low training cost and exceptional reasoning
capabilities, has achieved state-of-the-art performance on various benchmarks.
However, detailed evaluations from the perspective of real-world applications
are lacking, making it challenging for users to select the most suitable
DeepSeek models f... |
2502.11167 | SURGE: On the Potential of Large Language Models as General-Purpose
Surrogate Code Executors | cs.LG cs.CL | Large language models (LLMs) have demonstrated remarkable capabilities in
code-related tasks, such as code understanding and code generation. However, an
equally important yet underexplored question is whether LLMs can serve as
general-purpose surrogate code executors, to predict the output and behavior of
a program ... |
2502.11168 | Knowing Your Target: Target-Aware Transformer Makes Better
Spatio-Temporal Video Grounding | cs.CV cs.AI | Transformer has attracted increasing interest in STVG, owing to its
end-to-end pipeline and promising result. Existing Transformer-based STVG
approaches often leverage a set of object queries, which are initialized simply
using zeros and then gradually learn target position information via iterative
interactions with... |
2502.11169 | Leveraging Constrained Monte Carlo Tree Search to Generate Reliable Long
Chain-of-Thought for Mathematical Reasoning | cs.CL | Recently, Long Chain-of-Thoughts (CoTs) have gained widespread attention for
improving the reasoning capabilities of Large Language Models (LLMs). This
necessitates that existing LLMs, which lack the ability to generate Long CoTs,
to acquire such capability through post-training methods. Without additional
training, ... |
2502.11173 | Evaluating the Potential of Quantum Machine Learning in Cybersecurity: A
Case-Study on PCA-based Intrusion Detection Systems | quant-ph cs.CR cs.LG cs.NI | Quantum computing promises to revolutionize our understanding of the limits
of computation, and its implications in cryptography have long been evident.
Today, cryptographers are actively devising post-quantum solutions to counter
the threats posed by quantum-enabled adversaries. Meanwhile, quantum scientists
are inn... |
2502.11175 | Investigating Language Preference of Multilingual RAG Systems | cs.CL | Multilingual Retrieval-Augmented Generation (mRAG) systems enhance language
models by integrating external multilingual information to produce
context-aware responses. However, mRAG systems struggle with retrieving
relevant information due to linguistic variations between queries and
documents, generating inconsisten... |
2502.11176 | LogiDynamics: Unraveling the Dynamics of Logical Inference in Large
Language Model Reasoning | cs.CL | Modern large language models (LLMs) employ various forms of logical
inference, both implicitly and explicitly, when addressing reasoning tasks.
Understanding how to optimally leverage these inference paradigms is critical
for advancing LLMs' reasoning capabilities. This paper adopts an exploratory
approach by introdu... |
2502.11177 | The Mirage of Model Editing: Revisiting Evaluation in the Wild | cs.CL | Despite near-perfect results in artificial evaluations, the effectiveness of
model editing in real-world applications remains unexplored. To bridge this
gap, we propose to study model editing in question answering (QA) by
establishing a rigorous evaluation practice to assess the effectiveness of
editing methods in co... |
2502.11178 | DAViMNet: SSMs-Based Domain Adaptive Object Detection | cs.CV | Unsupervised domain adaptation (UDA) for object detection adapts models
trained on labeled source domains to unlabeled target domains, ensuring robust
performance across domain shifts. Transformer-based architectures excel at
capturing long-range dependencies but face efficiency challenges due to their
quadratic atte... |
2502.11179 | RT-DEMT: A hybrid real-time acupoint detection model combining mamba and
transformer | cs.CV cs.AI | Traditional Chinese acupuncture methods often face controversy in clinical
practice due to their high subjectivity. Additionally, current
intelligent-assisted acupuncture systems have two major limitations: slow
acupoint localization speed and low accuracy. To address these limitations, a
new method leverages the exc... |
2502.11181 | Improving Scientific Document Retrieval with Concept Coverage-based
Query Set Generation | cs.IR cs.AI | In specialized fields like the scientific domain, constructing large-scale
human-annotated datasets poses a significant challenge due to the need for
domain expertise. Recent methods have employed large language models to
generate synthetic queries, which serve as proxies for actual user queries.
However, they lack c... |
2502.11182 | Stacked Intelligent Metasurface-Based Transceiver Design for Near-Field
Wideband Systems | cs.IT math.IT | Intelligent metasurfaces may be harnessed for realizing efficient holographic
multiple-input and multiple-output (MIMO) systems, at a low hardware-cost and
high energy-efficiency. As part of this family, we propose a hybrid beamforming
design for stacked intelligent metasurfaces (SIM) aided wideband wireless
systems ... |
2502.11183 | Don't Get Lost in the Trees: Streamlining LLM Reasoning by Overcoming
Tree Search Exploration Pitfalls | cs.CL | Recent advancements in tree search algorithms guided by verifiers have
significantly enhanced the reasoning capabilities of large language models
(LLMs), but at the cost of increased computational resources. In this work, we
identify two key challenges contributing to this inefficiency:
$\textit{over-exploration}$ du... |
2502.11184 | Can't See the Forest for the Trees: Benchmarking Multimodal Safety
Awareness for Multimodal LLMs | cs.CL cs.AI cs.CV cs.MM | Multimodal Large Language Models (MLLMs) have expanded the capabilities of
traditional language models by enabling interaction through both text and
images. However, ensuring the safety of these models remains a significant
challenge, particularly in accurately identifying whether multimodal content is
safe or unsafe... |
2502.11187 | TituLLMs: A Family of Bangla LLMs with Comprehensive Benchmarking | cs.CL cs.AI | In this paper, we present TituLLMs, the first large pretrained Bangla LLMs,
available in 1B and 3B parameter sizes. Due to computational constraints during
both training and inference, we focused on smaller models. To train TituLLMs,
we collected a pretraining dataset of approximately 37 billion tokens. We
extended t... |
2502.11188 | Exploring information geometry: Recent Advances and Connections to
Topological Field Theory | math.DG cs.IT math.AG math.IT | This introductory text arises from a lecture given in G\"oteborg, Sweden,
given by the first author and is intended for undergraduate students, as well
as for any mathematically inclined reader wishing to explore a synthesis of
ideas connecting geometry and statistics. At its core, this work seeks to
illustrate the p... |
2502.11190 | ReLearn: Unlearning via Learning for Large Language Models | cs.CL cs.AI cs.CV cs.HC cs.LG | Current unlearning methods for large language models usually rely on reverse
optimization to reduce target token probabilities. However, this paradigm
disrupts the subsequent tokens prediction, degrading model performance and
linguistic coherence. Moreover, existing evaluation metrics overemphasize
contextual forgett... |
2502.11191 | Primus: A Pioneering Collection of Open-Source Datasets for
Cybersecurity LLM Training | cs.CR cs.AI cs.CL | Large Language Models (LLMs) have shown remarkable advancements in
specialized fields such as finance, law, and medicine. However, in
cybersecurity, we have noticed a lack of open-source datasets, with a
particular lack of high-quality cybersecurity pretraining corpora, even though
much research indicates that LLMs a... |
2502.11193 | Large Language Models Penetration in Scholarly Writing and Peer Review | cs.CL | While the widespread use of Large Language Models (LLMs) brings convenience,
it also raises concerns about the credibility of academic research and
scholarly processes. To better understand these dynamics, we evaluate the
penetration of LLMs across academic workflows from multiple perspectives and
dimensions, providi... |
2502.11195 | From Deception to Perception: The Surprising Benefits of Deepfakes for
Detecting, Measuring, and Mitigating Bias | cs.CV cs.AI | While deepfake technologies have predominantly been criticized for potential
misuse, our study demonstrates their significant potential as tools for
detecting, measuring, and mitigating biases in key societal domains. By
employing deepfake technology to generate controlled facial images, we extend
the scope of tradit... |
2502.11196 | How Do LLMs Acquire New Knowledge? A Knowledge Circuits Perspective on
Continual Pre-Training | cs.LG cs.AI cs.CL cs.CV cs.HC | Despite exceptional capabilities in knowledge-intensive tasks, Large Language
Models (LLMs) face a critical gap in understanding how they internalize new
knowledge, particularly how to structurally embed acquired knowledge in their
neural computations. We address this issue through the lens of knowledge
circuit evolu... |
2502.11197 | CSP: A Simulator For Multi-Agent Ranking Competitions | cs.IR cs.GT | In ranking competitions, document authors compete for the highest rankings by
modifying their content in response to past rankings. Previous studies focused
on human participants, primarily students, in controlled settings. The rise of
generative AI, particularly Large Language Models (LLMs), introduces a new
paradig... |
2502.11198 | ANCHOLIK-NER: A Benchmark Dataset for Bangla Regional Named Entity
Recognition | cs.CL cs.LG | ANCHOLIK-NER is a linguistically diverse dataset for Named Entity Recognition
(NER) in Bangla regional dialects, capturing variations across Sylhet,
Chittagong, and Barishal. The dataset has around 10,443 sentences, 3,481
sentences per region. The data was collected from two publicly available
datasets and through we... |
2502.11201 | Bridging the Gap: Enabling Natural Language Queries for NoSQL Databases
through Text-to-NoSQL Translation | cs.DB cs.AI | NoSQL databases have become increasingly popular due to their outstanding
performance in handling large-scale, unstructured, and semi-structured data,
highlighting the need for user-friendly interfaces to bridge the gap between
non-technical users and complex database queries. In this paper, we introduce
the Text-to-... |
2502.11203 | Multiscale autonomous forecasting of plasma systems' dynamics using
neural networks | physics.plasm-ph cs.LG | Plasma systems exhibit complex multiscale dynamics, resolving which poses
significant challenges for conventional numerical simulations. Machine learning
(ML) offers an alternative by learning data-driven representations of these
dynamics. Yet existing ML time-stepping models suffer from error accumulation,
instabili... |
2502.11205 | Deep Contrastive Learning for Feature Alignment: Insights from
Housing-Household Relationship Inference | cs.LG cs.CY | Housing and household characteristics are key determinants of social and
economic well-being, yet our understanding of their interrelationships remains
limited. This study addresses this knowledge gap by developing a deep
contrastive learning (DCL) model to infer housing-household relationships using
the American Com... |
2502.11211 | A Survey of LLM-based Agents in Medicine: How far are we from Baymax? | cs.CL cs.AI cs.CV | Large Language Models (LLMs) are transforming healthcare through the
development of LLM-based agents that can understand, reason about, and assist
with medical tasks. This survey provides a comprehensive review of LLM-based
agents in medicine, examining their architectures, applications, and
challenges. We analyze th... |
2502.11213 | Stochastic Optimization of Inventory at Large-scale Supply Chains | math.OC cs.AI cs.LG | Today's global supply chains face growing challenges due to rapidly changing
market conditions, increased network complexity and inter-dependency, and
dynamic uncertainties in supply, demand, and other factors. To combat these
challenges, organizations employ Material Requirements Planning (MRP) software
solutions to... |
2502.11221 | PlanGenLLMs: A Modern Survey of LLM Planning Capabilities | cs.AI cs.CL | LLMs have immense potential for generating plans, transforming an initial
world state into a desired goal state. A large body of research has explored
the use of LLMs for various planning tasks, from web navigation to travel
planning and database querying. However, many of these systems are tailored to
specific probl... |
2502.11223 | Asymmetric Conflict and Synergy in Post-training for LLM-based
Multilingual Machine Translation | cs.CL | The emergence of Large Language Models (LLMs) has advanced the multilingual
machine translation (MMT), yet the Curse of Multilinguality (CoM) remains a
major challenge. Existing work in LLM-based MMT typically mitigates this issue
via scaling up training and computation budget, which raises a critical
question: Is sc... |
2502.11225 | METAFOR: A Hybrid Metaheuristics Software Framework for Single-Objective
Continuous Optimization Problems | cs.NE cs.AI | Hybrid metaheuristics are powerful techniques for solving difficult
optimization problems that exploit the strengths of different approaches in a
single implementation. For algorithm designers, however, creating hybrid
metaheuristic implementations has become increasingly challenging due to the
vast number of design ... |
2502.11227 | Integrating Retrospective Framework in Multi-Robot Collaboration | cs.RO | Recent advancements in Large Language Models (LLMs) have demonstrated
substantial capabilities in enhancing communication and coordination in
multi-robot systems. However, existing methods often struggle to achieve
efficient collaboration and decision-making in dynamic and uncertain
environments, which are common in ... |
2502.11228 | Vendi-RAG: Adaptively Trading-Off Diversity And Quality Significantly
Improves Retrieval Augmented Generation With LLMs | cs.CL cs.AI | Retrieval-augmented generation (RAG) enhances large language models (LLMs)
for domain-specific question-answering (QA) tasks by leveraging external
knowledge sources. However, traditional RAG systems primarily focus on
relevance-based retrieval and often struggle with redundancy, especially when
reasoning requires co... |
2502.11229 | Provable and Practical Online Learning Rate Adaptation with
Hypergradient Descent | math.OC cs.LG | This paper investigates the convergence properties of the hypergradient
descent method (HDM), a 25-year-old heuristic originally proposed for adaptive
stepsize selection in stochastic first-order methods. We provide the first
rigorous convergence analysis of HDM using the online learning framework of
[Gao24] and appl... |
2502.11234 | MaskFlow: Discrete Flows For Flexible and Efficient Long Video
Generation | cs.CV | Generating long, high-quality videos remains a challenge due to the complex
interplay of spatial and temporal dynamics and hardware limitations. In this
work, we introduce \textbf{MaskFlow}, a unified video generation framework that
combines discrete representations with flow-matching to enable efficient
generation o... |
2502.11238 | Span-Agnostic Optimal Sample Complexity and Oracle Inequalities for
Average-Reward RL | cs.LG cs.IT math.IT math.OC stat.ML | We study the sample complexity of finding an $\varepsilon$-optimal policy in
average-reward Markov Decision Processes (MDPs) with a generative model. The
minimax optimal span-based complexity of $\widetilde{O}(SAH/\varepsilon^2)$,
where $H$ is the span of the optimal bias function, has only been achievable
with prior... |
2502.11239 | Towards identifying possible fault-tolerant advantage of quantum linear
system algorithms in terms of space, time and energy | quant-ph cs.AI cs.LG math.OC | Quantum computing, a prominent non-Von Neumann paradigm beyond Moore's law,
can offer superpolynomial speedups for certain problems. Yet its advantages in
efficiency for tasks like machine learning remain under investigation, and
quantum noise complicates resource estimations and classical comparisons. We
provide a d... |
2502.11244 | Soteria: Language-Specific Functional Parameter Steering for
Multilingual Safety Alignment | cs.CL cs.AI | Ensuring consistent safety across multiple languages remains a significant
challenge for large language models (LLMs). We introduce Soteria, a lightweight
yet powerful strategy that locates and minimally adjusts the "functional heads"
most responsible for harmful content generation in each language. By altering
only ... |
2502.11245 | Shortcuts and Identifiability in Concept-based Models from a
Neuro-Symbolic Lens | cs.LG cs.AI | Concept-based Models are neural networks that learn a concept extractor to
map inputs to high-level concepts and an inference layer to translate these
into predictions. Ensuring these modules produce interpretable concepts and
behave reliably in out-of-distribution is crucial, yet the conditions for
achieving this re... |
2502.11246 | MemeSense: An Adaptive In-Context Framework for Social Commonsense
Driven Meme Moderation | cs.IR cs.CL cs.CY | Memes present unique moderation challenges due to their subtle, multimodal
interplay of images, text, and social context. Standard systems relying
predominantly on explicit textual cues often overlook harmful content
camouflaged by irony, symbolism, or cultural references. To address this gap,
we introduce MemeSense,... |
2502.11248 | Prevalence, Sharing Patterns, and Spreaders of Multimodal AI-Generated
Content on X during the 2024 U.S. Presidential Election | cs.SI cs.CY | While concerns about the risks of AI-generated content (AIGC) to the
integrity of social media discussions have been raised, little is known about
its scale and the actors responsible for its dissemination online. In this
work, we identify and characterize the prevalence, sharing patterns, and
spreaders of AIGC in di... |
2502.11250 | Uncertainty-Aware Step-wise Verification with Generative Reward Models | cs.CL | Complex multi-step reasoning tasks, such as solving mathematical problems,
remain challenging for large language models (LLMs). While outcome supervision
is commonly used, process supervision via process reward models (PRMs) provides
intermediate rewards to verify step-wise correctness in solution traces.
However, as... |
2502.11251 | Explaining Necessary Truths | cs.AI cs.CC math.HO q-bio.NC | Knowing the truth is rarely enough -- we also seek out reasons why the fact
is true. While much is known about how we explain contingent truths, we
understand less about how we explain facts, such as those in mathematics, that
are true as a matter of logical necessity. We present a framework, based in
computational c... |
2502.11256 | Unveiling Environmental Impacts of Large Language Model Serving: A
Functional Unit View | cs.LG cs.AR cs.CL | Large language models (LLMs) offer powerful capabilities but come with
significant environmental costs, particularly in carbon emissions. Existing
studies benchmark these emissions but lack a standardized basis for comparison
across models. To address this, we introduce the concept of a functional unit
(FU) and devel... |
2502.11258 | Leveraging Conditional Mutual Information to Improve Large Language
Model Fine-Tuning For Classification | cs.CL | Although large language models (LLMs) have demonstrated remarkable
capabilities in recent years, the potential of information theory (IT) to
enhance LLM development remains underexplored. This paper introduces the
information theoretic principle of Conditional Mutual Information (CMI) to LLM
fine-tuning for classific... |
2502.11259 | Exploiting network optimization stability for enhanced PET image
denoising using deep image prior | physics.med-ph cs.CV | PET is affected by statistical noise due to constraints on tracer dose and
scan duration, impacting both diagnostic performance and quantitative accuracy.
While deep learning (DL)-based PET denoising methods have been used to improve
image quality, they may introduce over-smoothing, compromising quantitative
accuracy... |
2502.11260 | Scalable Multi-Agent Offline Reinforcement Learning and the Role of
Information | cs.LG | Offline Reinforcement Learning (RL) focuses on learning policies solely from
a batch of previously collected data. offering the potential to leverage such
datasets effectively without the need for costly or risky active exploration.
While recent advances in Offline Multi-Agent RL (MARL) have shown promise, most
exist... |
2502.11262 | Generating Skyline Datasets for Data Science Models | cs.DB cs.AI | Preparing high-quality datasets required by various data-driven AI and
machine learning models has become a cornerstone task in data-driven analysis.
Conventional data discovery methods typically integrate datasets towards a
single pre-defined quality measure that may lead to bias for downstream tasks.
This paper int... |
2502.11265 | Towards Automatic Identification of Missing Tissues using a
Geometric-Learning Correspondence Model | cs.CV physics.med-ph | Missing tissue presents a big challenge for dose mapping, e.g., in the
reirradiation setting. We propose a pipeline to identify missing tissue on
intra-patient structure meshes using a previously trained geometric-learning
correspondence model. For our application, we relied on the prediction
discrepancies between fo... |
2502.11266 | The Shrinking Landscape of Linguistic Diversity in the Age of Large
Language Models | cs.CL | Language is far more than a communication tool. A wealth of information -
including but not limited to the identities, psychological states, and social
contexts of its users - can be gleaned through linguistic markers, and such
insights are routinely leveraged across diverse fields ranging from product
development an... |
2502.11267 | Prompting in the Dark: Assessing Human Performance in Prompt Engineering
for Data Labeling When Gold Labels Are Absent | cs.HC cs.AI cs.CL cs.LG | Millions of users prompt large language models (LLMs) for various tasks, but
how good are people at prompt engineering? Do users actually get closer to
their desired outcome over multiple iterations of their prompts? These
questions are crucial when no gold-standard labels are available to measure
progress. This pape... |
2502.11268 | Improved Unbiased Watermark for Large Language Models | cs.CL | As artificial intelligence surpasses human capabilities in text generation,
the necessity to authenticate the origins of AI-generated content has become
paramount. Unbiased watermarks offer a powerful solution by embedding
statistical signals into language model-generated text without distorting the
quality. In this ... |
2502.11269 | Unlocking the Potential of Generative AI through Neuro-Symbolic
Architectures: Benefits and Limitations | cs.AI cs.LG cs.SC | Neuro-symbolic artificial intelligence (NSAI) represents a transformative
approach in artificial intelligence (AI) by combining deep learning's ability
to handle large-scale and unstructured data with the structured reasoning of
symbolic methods. By leveraging their complementary strengths, NSAI enhances
generalizati... |
2502.11271 | OctoTools: An Agentic Framework with Extensible Tools for Complex
Reasoning | cs.LG cs.CL cs.CV cs.MA | Solving complex reasoning tasks may involve visual understanding, domain
knowledge retrieval, numerical calculation, and multi-step reasoning. Existing
methods augment large language models (LLMs) with external tools but are
restricted to specialized domains, limited tool types, or require additional
training data. I... |
2502.11273 | FairFare: A Tool for Crowdsourcing Rideshare Data to Empower Labor
Organizers | cs.HC cs.AI cs.CY | Rideshare workers experience unpredictable working conditions due to gig work
platforms' reliance on opaque AI and algorithmic systems. In response to these
challenges, we found that labor organizers want data to help them advocate for
legislation to increase the transparency and accountability of these platforms.
To... |
2502.11275 | Cuckoo: An IE Free Rider Hatched by Massive Nutrition in LLM's Nest | cs.CL | Massive high-quality data, both pre-training raw texts and post-training
annotations, have been carefully prepared to incubate advanced large language
models (LLMs). In contrast, for information extraction (IE), pre-training data,
such as BIO-tagged sequences, are hard to scale up. We show that IE models can
act as f... |
2502.11276 | The Rotary Position Embedding May Cause Dimension Inefficiency in
Attention Heads for Long-Distance Retrieval | cs.CL cs.LG | The Rotary Position Embedding (RoPE) is widely used in the attention heads of
many large language models (LLM). It rotates dimensions in the query and the
key vectors by different angles according to their positions in the input
sequence. For long context modeling, the range of positions may vary a lot, and
thus RoPE... |
2502.11278 | Reducing Computational Complexity of Rigidity-Based UAV Trajectory
Optimization for Real-Time Cooperative Target Localization | eess.SY cs.SY | Accurate and swift localization of the target is crucial in emergencies.
However, accurate position data of a target mobile device, typically obtained
from global navigation satellite systems (GNSS), cellular networks, or WiFi,
may not always be accessible to first responders. For instance, 1) accuracy and
availabili... |
2502.11279 | Neural Operators for Stochastic Modeling of Nonlinear Structural System
Response to Natural Hazards | cs.LG | Traditionally, neural networks have been employed to learn the mapping
between finite-dimensional Euclidean spaces. However, recent research has
opened up new horizons, focusing on the utilization of deep neural networks to
learn operators capable of mapping infinite-dimensional function spaces. In
this work, we empl... |
2502.11284 | Balancing the Budget: Understanding Trade-offs Between Supervised and
Preference-Based Finetuning | cs.LG | Post-training of Large Language Models often involves a pipeline of
Supervised Finetuning (SFT) followed by Preference Finetuning (PFT) using
methods like Direct Preference Optimization. Both stages require annotated data
that are very different in structure and costs. We study how to optimally
allocate a fixed train... |
2502.11287 | MC-BEVRO: Multi-Camera Bird Eye View Road Occupancy Detection for
Traffic Monitoring | cs.CV | Single camera 3D perception for traffic monitoring faces significant
challenges due to occlusion and limited field of view. Moreover, fusing
information from multiple cameras at the image feature level is difficult
because of different view angles. Further, the necessity for practical
implementation and compatibility... |
2502.11291 | Dialogue-based Explanations for Logical Reasoning using Structured
Argumentation | cs.AI cs.DB cs.HC cs.LO | The problem of explaining inconsistency-tolerant reasoning in knowledge bases
(KBs) is a prominent topic in Artificial Intelligence (AI). While there is some
work on this problem, the explanations provided by existing approaches often
lack critical information or fail to be expressive enough for non-binary
conflicts.... |
2502.11295 | Game-Of-Goals: Using adversarial games to achieve strategic resilience | cs.AI cs.GT | Our objective in this paper is to develop a machinery that makes a given
organizational strategic plan resilient to the actions of competitor agents
(adverse environmental actions). We assume that we are given a goal tree
representing strategic goals (can also be seen business requirements for a
software systems) wit... |
2502.11298 | Integrating Language Models for Enhanced Network State Monitoring in
DRL-Based SFC Provisioning | cs.NI cs.AI cs.CL | Efficient Service Function Chain (SFC) provisioning and Virtual Network
Function (VNF) placement are critical for enhancing network performance in
modern architectures such as Software-Defined Networking (SDN) and Network
Function Virtualization (NFV). While Deep Reinforcement Learning (DRL) aids
decision-making in d... |
2502.11299 | Grassroots Platforms with Atomic Transactions: Social Networks,
Cryptocurrencies, and Democratic Federations | cs.DC cs.NI cs.SI | Grassroots platforms aim to offer an egalitarian alternative to global
platforms -- centralized/autocratic (Facebook etc.) and
decentralized/plutocratic (Bitcoin etc.) alike. Key grassroots platforms
include grassroots social networks, grassroots cryptocurrencies, and grassroots
democratic federations. Previously, gr... |
2502.11300 | CORDIAL: Can Multimodal Large Language Models Effectively Understand
Coherence Relationships? | cs.CL cs.AI cs.CV | Multimodal Large Language Models (MLLMs) are renowned for their superior
instruction-following and reasoning capabilities across diverse problem
domains. However, existing benchmarks primarily focus on assessing factual and
logical correctness in downstream tasks, with limited emphasis on evaluating
MLLMs' ability to... |
2502.11304 | Leveraging Multimodal-LLMs Assisted by Instance Segmentation for
Intelligent Traffic Monitoring | cs.AI cs.CL cs.CV | A robust and efficient traffic monitoring system is essential for smart
cities and Intelligent Transportation Systems (ITS), using sensors and cameras
to track vehicle movements, optimize traffic flow, reduce congestion, enhance
road safety, and enable real-time adaptive traffic control. Traffic monitoring
models mus... |
2502.11305 | Non-Uniform Memory Sampling in Experience Replay | cs.LG | Continual learning is the process of training machine learning models on a
sequence of tasks where data distributions change over time. A well-known
obstacle in this setting is catastrophic forgetting, a phenomenon in which a
model drastically loses performance on previously learned tasks when learning
new ones. A po... |
2502.11306 | Smoothing Out Hallucinations: Mitigating LLM Hallucination with Smoothed
Knowledge Distillation | cs.CL cs.LG | Large language models (LLMs) often suffer from hallucination, generating
factually incorrect or ungrounded content, which limits their reliability in
high-stakes applications. A key factor contributing to hallucination is the use
of hard labels during training, which enforce deterministic supervision,
encourage overc... |
2502.11307 | Exploiting Point-Language Models with Dual-Prompts for 3D Anomaly
Detection | cs.CV cs.AI | Anomaly detection (AD) in 3D point clouds is crucial in a wide range of
industrial applications, especially in various forms of precision
manufacturing. Considering the industrial demand for reliable 3D AD, several
methods have been developed. However, most of these approaches typically
require training separate mode... |
2502.11308 | ALGEN: Few-shot Inversion Attacks on Textual Embeddings using Alignment
and Generation | cs.CR cs.AI cs.CL | With the growing popularity of Large Language Models (LLMs) and vector
databases, private textual data is increasingly processed and stored as
numerical embeddings. However, recent studies have proven that such embeddings
are vulnerable to inversion attacks, where original text is reconstructed to
reveal sensitive in... |
2502.11310 | Generalized Factor Neural Network Model for High-dimensional Regression | stat.ML cs.LG q-fin.ST | We tackle the challenges of modeling high-dimensional data sets, particularly
those with latent low-dimensional structures hidden within complex, non-linear,
and noisy relationships. Our approach enables a seamless integration of
concepts from non-parametric regression, factor models, and neural networks for
high-dim... |
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