id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.11483 | No-regret incentive-compatible online learning under exact truthfulness
with non-myopic experts | cs.LG cs.GT stat.ML | We study an online forecasting setting in which, over $T$ rounds, $N$
strategic experts each report a forecast to a mechanism, the mechanism selects
one forecast, and then the outcome is revealed. In any given round, each expert
has a belief about the outcome, but the expert wishes to select its report so
as to maxim... |
2502.11484 | Dictionary-Learning-Based Data Pruning for System Identification | cs.LG cs.SY eess.SY | System identification is normally involved in augmenting time series data by
time shifting and nonlinearisation (via polynomial basis), which introduce
redundancy both feature-wise and sample-wise. Many research works focus on
reducing redundancy feature-wise, while less attention is paid to sample-wise
redundancy. T... |
2502.11486 | Anti-Degeneracy Scheme for Lidar SLAM based on Particle Filter in
Geometry Feature-Less Environments | cs.RO | Simultaneous localization and mapping (SLAM) based on particle filtering has
been extensively employed in indoor scenarios due to its high efficiency.
However, in geometry feature-less scenes, the accuracy is severely reduced due
to lack of constraints. In this article, we propose an anti-degeneracy system
based on d... |
2502.11487 | Non-Binary LDPC Arithmetic Error Correction For Processing-in-Memory | cs.AR cs.IT math.IT | Processing-in-memory (PIM) based on emerging devices such as memristors is
more vulnerable to noise than traditional memories, due to the physical
non-idealities and complex operations in analog domains. To ensure high
reliability, efficient error-correcting code (ECC) is highly desired. However,
state-of-the-art ECC... |
2502.11490 | GPU-accelerated Multi-relational Parallel Graph Retrieval for Web-scale
Recommendations | cs.LG cs.DC cs.IR | Web recommendations provide personalized items from massive catalogs for
users, which rely heavily on retrieval stages to trade off the effectiveness
and efficiency of selecting a small relevant set from billion-scale candidates
in online digital platforms. As one of the largest Chinese search engine and
news feed pr... |
2502.11491 | Ontology-Guided Reverse Thinking Makes Large Language Models Stronger on
Knowledge Graph Question Answering | cs.CL cs.AI | Large language models (LLMs) have shown remarkable capabilities in natural
language processing. However, in knowledge graph question answering tasks
(KGQA), there remains the issue of answering questions that require multi-hop
reasoning. Existing methods rely on entity vector matching, but the purpose of
the question... |
2502.11492 | Why Vision Language Models Struggle with Visual Arithmetic? Towards
Enhanced Chart and Geometry Understanding | cs.AI cs.CL cs.CV | Vision Language Models (VLMs) have achieved remarkable progress in multimodal
tasks, yet they often struggle with visual arithmetic, seemingly simple
capabilities like object counting or length comparison, which are essential for
relevant complex tasks like chart understanding and geometric reasoning. In
this work, w... |
2502.11493 | DAST: Context-Aware Compression in LLMs via Dynamic Allocation of Soft
Tokens | cs.CL | Large Language Models (LLMs) face computational inefficiencies and redundant
processing when handling long context inputs, prompting a focus on compression
techniques. While existing semantic vector-based compression methods achieve
promising performance, these methods fail to account for the intrinsic
information de... |
2502.11494 | Stop Looking for Important Tokens in Multimodal Language Models:
Duplication Matters More | cs.CL cs.CV | Vision tokens in multimodal large language models often dominate huge
computational overhead due to their excessive length compared to linguistic
modality. Abundant recent methods aim to solve this problem with token pruning,
which first defines an importance criterion for tokens and then prunes the
unimportant visio... |
2502.11495 | Balanced Multi-Factor In-Context Learning for Multilingual Large
Language Models | cs.CL | Multilingual large language models (MLLMs) are able to leverage in-context
learning (ICL) to achieve high performance by leveraging cross-lingual
knowledge transfer without parameter updates. However, their effectiveness is
highly sensitive to example selection, particularly in multilingual settings.
Based on the fin... |
2502.11501 | Token Pruning in Multimodal Large Language Models: Are We Solving the
Right Problem? | cs.CL cs.CV | Multimodal large language models (MLLMs) have shown remarkable performance
for cross-modal understanding and generation, yet still suffer from severe
inference costs. Recently, abundant works have been proposed to solve this
problem with token pruning, which identifies the redundant tokens in MLLMs and
then prunes th... |
2502.11504 | Accelerated Gradient-based Design Optimization Via Differentiable
Physics-Informed Neural Operator: A Composites Autoclave Processing Case
Study | cs.LG cs.AI cs.NA math.NA | Simulation and optimization are crucial for advancing the engineering design
of complex systems and processes. Traditional optimization methods require
substantial computational time and effort due to their reliance on
resource-intensive simulations, such as finite element analysis, and the
complexity of rigorous opt... |
2502.11505 | A GNN-based Spectral Filtering Mechanism for Imbalance Classification in
Network Digital Twin | cs.LG cs.NI | Graph Neural Networks are gaining attention in Fifth-Generation (5G) core
network digital twins, which are data-driven complex systems with numerous
components. Analyzing these data can be challenging due to rare failure types,
leading to imbalanced classification in multiclass settings. Digital twins of
5G networks ... |
2502.11506 | Learning Surrogate Potential Mean Field Games via Gaussian Processes: A
Data-Driven Approach to Ill-Posed Inverse Problems | cs.LG math.OC stat.ML | Mean field games (MFGs) describe the collective behavior of large populations
of interacting agents. In this work, we tackle ill-posed inverse problems in
potential MFGs, aiming to recover the agents' population, momentum, and
environmental setup from limited, noisy measurements and partial observations.
These proble... |
2502.11508 | Chinese Spelling Correction: A Comprehensive Survey of Progress,
Challenges, and Opportunities | cs.CL cs.AI | Chinese Spelling Correction (CSC) is a critical task in natural language
processing, aimed at detecting and correcting spelling errors in Chinese text.
This survey provides a comprehensive overview of CSC, tracing its evolution
from pre-trained language models to large language models, and critically
analyzing their ... |
2502.11509 | DifCluE: Generating Counterfactual Explanations with Diffusion
Autoencoders and modal clustering | cs.LG cs.AI | Generating multiple counterfactual explanations for different modes within a
class presents a significant challenge, as these modes are distinct yet
converge under the same classification. Diffusion probabilistic models (DPMs)
have demonstrated a strong ability to capture the underlying modes of data
distributions. I... |
2502.11513 | MaZO: Masked Zeroth-Order Optimization for Multi-Task Fine-Tuning of
Large Language Models | cs.LG cs.AI | Large language models have demonstrated exceptional capabilities across
diverse tasks, but their fine-tuning demands significant memory, posing
challenges for resource-constrained environments. Zeroth-order (ZO)
optimization provides a memory-efficient alternative by eliminating the need
for backpropagation. However,... |
2502.11514 | Investigating Inference-time Scaling for Chain of Multi-modal Thought: A
Preliminary Study | cs.CL | Recently, inference-time scaling of chain-of-thought (CoT) has been
demonstrated as a promising approach for addressing multi-modal reasoning
tasks. While existing studies have predominantly centered on text-based
thinking, the integration of both visual and textual modalities within the
reasoning process remains une... |
2502.11515 | SayAnything: Audio-Driven Lip Synchronization with Conditional Video
Diffusion | cs.CV | Recent advances in diffusion models have led to significant progress in
audio-driven lip synchronization. However, existing methods typically rely on
constrained audio-visual alignment priors or multi-stage learning of
intermediate representations to force lip motion synthesis. This leads to
complex training pipeline... |
2502.11516 | CRB-Rate Tradeoff in RSMA-enabled Near-Field Integrated Multi-Target
Sensing and Multi-User Communications | cs.IT math.IT | Extremely large-scale antenna arrays enhance spectral efficiency and spatial
resolution in integrated sensing and communication (ISAC) networks while
expanding the Rayleigh distance, triggering a shift from conventional far-field
plane waves to near-field (NF) spherical waves. However, full-digital
beamforming is inf... |
2502.11517 | Learning to Keep a Promise: Scaling Language Model Decoding Parallelism
with Learned Asynchronous Decoding | cs.CL cs.DC cs.LG | Decoding with autoregressive large language models (LLMs) traditionally
occurs sequentially, generating one token after another. An emerging line of
work explored parallel decoding by identifying and simultaneously generating
semantically independent chunks of LLM responses. However, these techniques
rely on hand-cra... |
2502.11518 | Generative Multi-Agent Collaboration in Embodied AI: A Systematic Review | cs.MA cs.AI cs.LG | Embodied multi-agent systems (EMAS) have attracted growing attention for
their potential to address complex, real-world challenges in areas such as
logistics and robotics. Recent advances in foundation models pave the way for
generative agents capable of richer communication and adaptive problem-solving.
This survey ... |
2502.11519 | UniGO: A Unified Graph Neural Network for Modeling Opinion Dynamics on
Graphs | cs.SI cs.AI | Polarization and fragmentation in social media amplify user biases, making it
increasingly important to understand the evolution of opinions. Opinion
dynamics provide interpretability for studying opinion evolution, yet
incorporating these insights into predictive models remains challenging. This
challenge arises due... |
2502.11520 | AURORA:Automated Training Framework of Universal Process Reward Models
via Ensemble Prompting and Reverse Verification | cs.CL | The reasoning capabilities of advanced large language models (LLMs) like o1
have revolutionized artificial intelligence applications. Nevertheless,
evaluating and optimizing complex reasoning processes remain significant
challenges due to diverse policy distributions and the inherent limitations of
human effort and a... |
2502.11521 | DeFiScope: Detecting Various DeFi Price Manipulations with LLM Reasoning | cs.CR cs.AI | DeFi (Decentralized Finance) is one of the most important applications of
today's cryptocurrencies and smart contracts. It manages hundreds of billions
in Total Value Locked (TVL) on-chain, yet it remains susceptible to common DeFi
price manipulation attacks. Despite state-of-the-art (SOTA) systems like
DeFiRanger an... |
2502.11525 | Training Large Language Models to be Better Rule Followers | cs.CL | Large language models (LLMs) have shown impressive performance across a wide
range of tasks. However, they often exhibit unexpected failures in seemingly
straightforward tasks, suggesting a reliance on case-based reasoning rather
than rule-based reasoning. While the vast training corpus of LLMs contains
numerous text... |
2502.11528 | A Survey of Personalized Large Language Models: Progress and Future
Directions | cs.AI | Large Language Models (LLMs) excel in handling general knowledge tasks, yet
they struggle with user-specific personalization, such as understanding
individual emotions, writing styles, and preferences. Personalized Large
Language Models (PLLMs) tackle these challenges by leveraging individual user
data, such as user ... |
2502.11532 | Control-CLIP: Decoupling Category and Style Guidance in CLIP for
Specific-Domain Generation | cs.CV | Text-to-image diffusion models have shown remarkable capabilities of
generating high-quality images closely aligned with textual inputs. However,
the effectiveness of text guidance heavily relies on the CLIP text encoder,
which is trained to pay more attention to general content but struggles to
capture semantics in ... |
2502.11533 | Be Cautious When Merging Unfamiliar LLMs: A Phishing Model Capable of
Stealing Privacy | cs.CL | Model merging is a widespread technology in large language models (LLMs) that
integrates multiple task-specific LLMs into a unified one, enabling the merged
model to inherit the specialized capabilities of these LLMs. Most task-specific
LLMs are sourced from open-source communities and have not undergone rigorous
aud... |
2502.11534 | SurgPose: a Dataset for Articulated Robotic Surgical Tool Pose
Estimation and Tracking | cs.RO cs.CV | Accurate and efficient surgical robotic tool pose estimation is of
fundamental significance to downstream applications such as augmented reality
(AR) in surgical training and learning-based autonomous manipulation. While
significant advancements have been made in pose estimation for humans and
animals, it is still a ... |
2502.11535 | Disentangled Iterative Surface Fitting for Contact-stable Grasp Planning | cs.RO | In this work, we address the limitation of surface fitting-based grasp
planning algorithm, which primarily focuses on geometric alignment between the
gripper and object surface while overlooking the stability of contact point
distribution, often resulting in unstable grasps due to inadequate contact
configurations. T... |
2502.11537 | $\text{M}^{\text{3}}$: A Modular World Model over Streams of Tokens | cs.LG cs.AI | Token-based world models emerged as a promising modular framework, modeling
dynamics over token streams while optimizing tokenization separately. While
successful in visual environments with discrete actions (e.g., Atari games),
their broader applicability remains uncertain. In this paper, we introduce
$\text{M}^{\te... |
2502.11538 | How to Divide: A Set Partitioning Strategy Balancing the Trade-off
Between Intra-Subset Correlation and Inter-Subset Gain Mutual Influence in
Distributed Attack Detection Scheduling Task | cs.DC cs.SY eess.SY | Recently, the efficiency of attack detection in large-scale sensor networks
has remained a critical research challenge. Studies have shown that while
distributed algorithms offer higher efficiency compared to centralized
approaches, they often come at the cost of reduced performance. To strike a
balance between detec... |
2502.11541 | MuSC: Improving Complex Instruction Following with Multi-granularity
Self-Contrastive Training | cs.CL cs.AI | Complex instruction-following with elaborate constraints is imperative for
Large Language Models (LLMs). While existing methods have constructed data for
complex instruction alignment, they all rely on a more advanced model,
especially GPT-4, limiting their application. In this paper, we propose a
Multi-granularity S... |
2502.11544 | Evaluating o1-Like LLMs: Unlocking Reasoning for Translation through
Comprehensive Analysis | cs.CL | The o1-Like LLMs are transforming AI by simulating human cognitive processes,
but their performance in multilingual machine translation (MMT) remains
underexplored. This study examines: (1) how o1-Like LLMs perform in MMT tasks
and (2) what factors influence their translation quality. We evaluate multiple
o1-Like LLM... |
2502.11546 | DCAD-2000: A Multilingual Dataset across 2000+ Languages with Data
Cleaning as Anomaly Detection | cs.CL | The rapid development of multilingual large language models (LLMs) highlights
the need for high-quality, diverse, and clean multilingual datasets. In this
paper, we introduce DCAD-2000 (Data Cleaning as Anomaly Detection), a
large-scale multilingual corpus built using newly extracted Common Crawl data
and existing mu... |
2502.11554 | Toward Metaphor-Fluid Conversation Design for Voice User Interfaces | cs.HC cs.AI cs.CL cs.CY cs.ET | Metaphors play a critical role in shaping user experiences with Voice User
Interfaces (VUIs), yet existing designs often rely on static, human-centric
metaphors that fail to adapt to diverse contexts and user needs. This paper
introduces Metaphor-Fluid Design, a novel approach that dynamically adjusts
metaphorical re... |
2502.11555 | Equilibrate RLHF: Towards Balancing Helpfulness-Safety Trade-off in
Large Language Models | cs.AI | Fine-tuning large language models (LLMs) based on human preferences, commonly
achieved through reinforcement learning from human feedback (RLHF), has been
effective in improving their performance. However, maintaining LLM safety
throughout the fine-tuning process remains a significant challenge, as
resolving conflict... |
2502.11557 | Fast Maximum Common Subgraph Search: A Redundancy-Reduced Backtracking
Approach | cs.DB cs.DS | Given two input graphs, finding the largest subgraph that occurs in both,
i.e., finding the maximum common subgraph, is a fundamental operator for
evaluating the similarity between two graphs in graph data analysis. Existing
works for solving the problem are of either theoretical or practical interest,
but not both. ... |
2502.11559 | Auto-Search and Refinement: An Automated Framework for Gender Bias
Mitigation in Large Language Models | cs.CL cs.AI | Pre-training large language models (LLMs) on vast text corpora enhances
natural language processing capabilities but risks encoding social biases,
particularly gender bias. While parameter-modification methods like fine-tuning
mitigate bias, they are resource-intensive, unsuitable for closed-source
models, and lack a... |
2502.11560 | A Survey of Automatic Prompt Engineering: An Optimization Perspective | cs.AI cs.LG | The rise of foundation models has shifted focus from resource-intensive
fine-tuning to prompt engineering, a paradigm that steers model behavior
through input design rather than weight updates. While manual prompt
engineering faces limitations in scalability, adaptability, and cross-modal
alignment, automated methods... |
2502.11562 | Reinforced Information Retrieval | cs.CL | While retrieval techniques are widely used in practice, they still face
significant challenges in cross-domain scenarios. Recently,
generation-augmented methods have emerged as a promising solution to this
problem. These methods enhance raw queries by incorporating additional
information from an LLM-based generator, ... |
2502.11563 | Leader and Follower: Interactive Motion Generation under Trajectory
Constraints | cs.RO cs.AI | With the rapid advancement of game and film production, generating
interactive motion from texts has garnered significant attention due to its
potential to revolutionize content creation processes. In many practical
applications, there is a need to impose strict constraints on the motion range
or trajectory of virtua... |
2502.11564 | Continuous Diffusion Model for Language Modeling | cs.LG | Diffusion models have emerged as a promising alternative to autoregressive
models in modeling discrete categorical data. Yet diffusion models that
directly work on discrete data space do not fully exploit the power of
iterative refinement, as the signals are lost during the transition between
discrete states. Existin... |
2502.11565 | STARS-Enabled Full-Duplex Two-Way mMIMO System Under
Spatially-Correlated Channels | cs.IT math.IT | \underline{S}imultaneous \underline{t}ransmitting \underline{a}nd
\underline{r}eflecting \underline{s}urface (STARS)-assisted systems have
emerged to fill this gap by providing $ 360^{\circ}$ wireless coverage. In
parallel,
full-duplex (FD) communication offers a higher achievable rate through
efficient spectrum ut... |
2502.11569 | Towards Reasoning Ability of Small Language Models | cs.CL cs.AI cs.LG | Reasoning has long been viewed as an emergent property of large language
models (LLMs), appearing at or above a certain scale ($\sim$100B parameters).
However, recent studies challenge this assumption, showing that small language
models (SLMs) can also achieve competitive reasoning performance. SLMs are
increasingly ... |
2502.11570 | Towards a Trustworthy Anomaly Detection for Critical Applications
through Approximated Partial AUC Loss | cs.LG cs.CV | Anomaly Detection is a crucial step for critical applications such in the
industrial, medical or cybersecurity domains. These sectors share the same
requirement of handling differently the different types of classification
errors. Indeed, even if false positives are acceptable, false negatives are
not, because it wou... |
2502.11571 | FaMTEB: Massive Text Embedding Benchmark in Persian Language | cs.CL cs.IR cs.LG | In this paper, we introduce a comprehensive benchmark for Persian (Farsi)
text embeddings, built upon the Massive Text Embedding Benchmark (MTEB). Our
benchmark includes 63 datasets spanning seven different tasks: classification,
clustering, pair classification, reranking, retrieval, summary retrieval, and
semantic t... |
2502.11573 | InfiR : Crafting Effective Small Language Models and Multimodal Small
Language Models in Reasoning | cs.CL cs.AI | Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs)
have made significant advancements in reasoning capabilities. However, they
still face challenges such as high computational demands and privacy concerns.
This paper focuses on developing efficient Small Language Models (SLMs) and
Multimodal Sm... |
2502.11574 | Large Language Models and Mathematical Reasoning Failures | cs.AI | This paper investigates the mathematical reasoning capabilities of large
language models (LLMs) using 50 newly constructed high-school-level word
problems. Unlike prior studies that focus solely on answer correctness, we
rigorously analyze both final answers and solution steps to identify reasoning
failures. Evaluati... |
2502.11578 | Language Complexity Measurement as a Noisy Zero-Shot Proxy for
Evaluating LLM Performance | cs.CL cs.AI | Large Language Models (LLMs) have made significant strides in natural
language generation but often face challenges in tasks requiring precise
calculations and structural analysis. This paper investigates the performance
of state-of-the-art LLMs on language complexity measurement tasks, through the
computation of the... |
2502.11583 | Distributional autoencoders know the score | stat.ML cs.LG | This work presents novel and desirable properties of a recently introduced
class of autoencoders -- the Distributional Principal Autoencoder (DPA) -- that
combines distributionally correct reconstruction with principal components-like
interpretability of the encodings.
First, we show that the level sets of the enco... |
2502.11584 | Runtime Enforcement of CPS against Signal Temporal Logic | eess.SY cs.SY | Cyber-Physical Systems (CPSs), especially those involving autonomy, need
guarantees of their safety. Runtime Enforcement (RE) is a lightweight method to
formally ensure that some specified properties are satisfied over the
executions of the system. Hence, there is recent interest in the RE of CPS.
However, existing m... |
2502.11585 | Calibration of Vehicular Traffic Simulation Models by Local Optimization | cs.AI | Simulation is a valuable tool for traffic management experts to assist them
in refining and improving transportation systems and anticipating the impact of
possible changes in the infrastructure network before their actual
implementation. Calibrating simulation models using traffic count data is
challenging because o... |
2502.11586 | Syllables to Scenes: Literary-Guided Free-Viewpoint 3D Scene Synthesis
from Japanese Haiku | cs.CV | In the era of the metaverse, where immersive technologies redefine human
experiences, translating abstract literary concepts into navigable 3D
environments presents a fundamental challenge in preserving semantic and
emotional fidelity. This research introduces HaikuVerse, a novel framework for
transforming poetic abs... |
2502.11588 | A Unified Modeling Framework for Automated Penetration Testing | cs.AI cs.NI | The integration of artificial intelligence into automated penetration testing
(AutoPT) has highlighted the necessity of simulation modeling for the training
of intelligent agents, due to its cost-efficiency and swift feedback
capabilities. Despite the proliferation of AutoPT research, there is a
recognized gap in the... |
2502.11594 | iMOVE: Instance-Motion-Aware Video Understanding | cs.CV | Enhancing the fine-grained instance spatiotemporal motion perception
capabilities of Video Large Language Models is crucial for improving their
temporal and general video understanding. However, current models struggle to
perceive detailed and complex instance motions. To address these challenges, we
have made improv... |
2502.11596 | LLM Embeddings for Deep Learning on Tabular Data | cs.LG cs.AI | Tabular deep-learning methods require embedding numerical and categorical
input features into high-dimensional spaces before processing them. Existing
methods deal with this heterogeneous nature of tabular data by employing
separate type-specific encoding approaches. This limits the cross-table
transfer potential and... |
2502.11598 | Can LLM Watermarks Robustly Prevent Unauthorized Knowledge Distillation? | cs.CL | The radioactive nature of Large Language Model (LLM) watermarking enables the
detection of watermarks inherited by student models when trained on the outputs
of watermarked teacher models, making it a promising tool for preventing
unauthorized knowledge distillation. However, the robustness of watermark
radioactivity... |
2502.11599 | Self-orthogonal codes from plateaued functions and their applications in
quantum codes and LCD codes | cs.IT math.IT | Self-orthogonal codes have received great attention due to their important
applications in quantum codes, LCD codes and lattices. Recently, several
families of self-orthogonal codes containing the all-$1$ vector were
constructed by augmentation technique. In this paper, utilizing plateaued
functions, we construct som... |
2502.11603 | DR.GAP: Mitigating Bias in Large Language Models using Gender-Aware
Prompting with Demonstration and Reasoning | cs.CL cs.AI | Large Language Models (LLMs) exhibit strong natural language processing
capabilities but also inherit and amplify societal biases, including gender
bias, raising fairness concerns. Existing debiasing methods face significant
limitations: parameter tuning requires access to model weights, prompt-based
approaches often... |
2502.11604 | An Actor-Critic Algorithm with Function Approximation for Risk Sensitive
Cost Markov Decision Processes | cs.LG stat.ML | In this paper, we consider the risk-sensitive cost criterion with
exponentiated costs for Markov decision processes and develop a model-free
policy gradient algorithm in this setting. Unlike additive cost criteria such
as average or discounted cost, the risk-sensitive cost criterion is less
studied due to the complex... |
2502.11607 | GraphThought: Graph Combinatorial Optimization with Thought Generation | cs.LG | Large language models (LLMs) have demonstrated remarkable capabilities across
various domains, especially in text processing and generative tasks. Recent
advancements in the reasoning capabilities of state-of-the-art LLMs, such as
OpenAI-o1, have significantly broadened their applicability, particularly in
complex pr... |
2502.11609 | Exploiting Task Relationships for Continual Learning Using
Transferability-Aware Task Embeddings | cs.LG | Continual learning (CL) has been an essential topic in the contemporary
application of deep neural networks, where catastrophic forgetting (CF) can
impede a model's ability to acquire knowledge progressively. Existing CL
strategies primarily address CF by regularizing model updates or separating
task-specific and sha... |
2502.11610 | Accuracy Assessment of OpenAlex and Clarivate Scholar ID with an
LLM-Assisted Benchmark | cs.IR | In quantitative SciSci (science of science) studies, accurately identifying
individual scholars is paramount for scientific data analysis. However, the
variability in how names are represented-due to commonality, abbreviations, and
different spelling conventions-complicates this task. While identifier systems
like OR... |
2502.11611 | Identifying Gender Stereotypes and Biases in Automated Translation from
English to Italian using Similarity Networks | cs.CL cs.AI | This paper is a collaborative effort between Linguistics, Law, and Computer
Science to evaluate stereotypes and biases in automated translation systems. We
advocate gender-neutral translation as a means to promote gender inclusion and
improve the objectivity of machine translation. Our approach focuses on
identifying... |
2502.11612 | Maximum Entropy Reinforcement Learning with Diffusion Policy | cs.LG cs.AI | The Soft Actor-Critic (SAC) algorithm with a Gaussian policy has become a
mainstream implementation for realizing the Maximum Entropy Reinforcement
Learning (MaxEnt RL) objective, which incorporates entropy maximization to
encourage exploration and enhance policy robustness. While the Gaussian policy
performs well on... |
2502.11614 | Is Human-Like Text Liked by Humans? Multilingual Human Detection and
Preference Against AI | cs.CL cs.AI | Prior studies have shown that distinguishing text generated by large language
models (LLMs) from human-written one is highly challenging, and often no better
than random guessing. To verify the generalizability of this finding across
languages and domains, we perform an extensive case study to identify the upper
boun... |
2502.11617 | In-Context Parametric Inference: Point or Distribution Estimators? | cs.LG cs.AI stat.ML | Bayesian and frequentist inference are two fundamental paradigms in
statistical estimation. Bayesian methods treat hypotheses as random variables,
incorporating priors and updating beliefs via Bayes' theorem, whereas
frequentist methods assume fixed but unknown hypotheses, relying on estimators
like maximum likelihoo... |
2502.11618 | Real-time Neural Rendering of LiDAR Point Clouds | cs.CV cs.GR | Static LiDAR scanners produce accurate, dense, colored point clouds, but
often contain obtrusive artifacts which makes them ill-suited for direct
display. We propose an efficient method to render photorealistic images of such
scans without any expensive preprocessing or training of a scene-specific
model. A naive pro... |
2502.11619 | Membership Inference Attacks for Face Images Against Fine-Tuned Latent
Diffusion Models | cs.CV | The rise of generative image models leads to privacy concerns when it comes
to the huge datasets used to train such models. This paper investigates the
possibility of inferring if a set of face images was used for fine-tuning a
Latent Diffusion Model (LDM). A Membership Inference Attack (MIA) method is
presented for ... |
2502.11633 | CLASS: Enhancing Cross-Modal Text-Molecule Retrieval Performance and
Training Efficiency | cs.CL | Cross-modal text-molecule retrieval task bridges molecule structures and
natural language descriptions. Existing methods predominantly focus on aligning
text modality and molecule modality, yet they overlook adaptively adjusting the
learning states at different training stages and enhancing training efficiency.
To ta... |
2502.11638 | Enhancing Out-of-Distribution Detection in Medical Imaging with
Normalizing Flows | cs.CV | Out-of-distribution (OOD) detection is crucial in AI-driven medical imaging
to ensure reliability and safety by identifying inputs outside a model's
training distribution. Existing methods often require retraining or
modifications to pre-trained models, which is impractical for clinical
applications. This study intro... |
2502.11639 | Neural Interpretable Reasoning | cs.LG cs.AI cs.NE | We formalize a novel modeling framework for achieving interpretability in
deep learning, anchored in the principle of inference equivariance. While the
direct verification of interpretability scales exponentially with the number of
variables of the system, we show that this complexity can be mitigated by
treating int... |
2502.11641 | A Zero-Knowledge Proof for the Syndrome Decoding Problem in the Lee
Metric | cs.CR cs.IT math.IT | The syndrome decoding problem is one of the NP-complete problems lying at the
foundation of code-based cryptography. The variant thereof where the distance
between vectors is measured with respect to the Lee metric, rather than the
more commonly used Hamming metric, has been analyzed recently in several works
due to ... |
2502.11642 | GaussianMotion: End-to-End Learning of Animatable Gaussian Avatars with
Pose Guidance from Text | cs.CV | In this paper, we introduce GaussianMotion, a novel human rendering model
that generates fully animatable scenes aligned with textual descriptions using
Gaussian Splatting. Although existing methods achieve reasonable text-to-3D
generation of human bodies using various 3D representations, they often face
limitations ... |
2502.11644 | InTec: integrated things-edge computing: a framework for distributing
machine learning pipelines in edge AI systems | cs.DC cs.AI | With the rapid expansion of the Internet of Things (IoT), sensors,
smartphones, and wearables have become integral to daily life, powering smart
applications in home automation, healthcare, and intelligent transportation.
However, these advancements face significant challenges due to latency and
bandwidth constraints... |
2502.11645 | Deviation Ratings: A General, Clone-Invariant Rating Method | cs.GT cs.CL cs.MA stat.OT | Many real-world multi-agent or multi-task evaluation scenarios can be
naturally modelled as normal-form games due to inherent strategic (adversarial,
cooperative, and mixed motive) interactions. These strategic interactions may
be agentic (e.g. players trying to win), fundamental (e.g. cost vs quality), or
complement... |
2502.11646 | Hyperspherical Energy Transformer with Recurrent Depth | cs.LG | Transformer-based foundation models have achieved unprecedented success with
a gigantic amount of parameters and computational resources. Yet, the core
building blocks of these models, the Transformer layers, and how they are
arranged and configured are primarily engineered from the bottom up and driven
by heuristics... |
2502.11647 | DELMAN: Dynamic Defense Against Large Language Model Jailbreaking with
Model Editing | cs.CR cs.AI | Large Language Models (LLMs) are widely applied in decision making, but their
deployment is threatened by jailbreak attacks, where adversarial users
manipulate model behavior to bypass safety measures. Existing defense
mechanisms, such as safety fine-tuning and model editing, either require
extensive parameter modifi... |
2502.11649 | Competing LLM Agents in a Non-Cooperative Game of Opinion Polarisation | cs.AI cs.SI | We introduce a novel non-cooperative game to analyse opinion formation and
resistance, incorporating principles from social psychology such as
confirmation bias, resource constraints, and influence penalties. Our
simulation features Large Language Model (LLM) agents competing to influence a
population, with penalties... |
2502.11651 | MMXU: A Multi-Modal and Multi-X-ray Understanding Dataset for Disease
Progression | cs.CV cs.AI | Large vision-language models (LVLMs) have shown great promise in medical
applications, particularly in visual question answering (MedVQA) and diagnosis
from medical images. However, existing datasets and models often fail to
consider critical aspects of medical diagnostics, such as the integration of
historical recor... |
2502.11655 | Object-Centric Image to Video Generation with Language Guidance | cs.CV | Accurate and flexible world models are crucial for autonomous systems to
understand their environment and predict future events. Object-centric models,
with structured latent spaces, have shown promise in modeling object dynamics
and interactions, but often face challenges in scaling to complex datasets and
incorpora... |
2502.11656 | Uncovering the Impact of Chain-of-Thought Reasoning for Direct
Preference Optimization: Lessons from Text-to-SQL | cs.CL cs.DB | Direct Preference Optimization (DPO) has proven effective in complex
reasoning tasks like math word problems and code generation. However, when
applied to Text-to-SQL datasets, it often fails to improve performance and can
even degrade it. Our investigation reveals the root cause: unlike math and code
tasks, which na... |
2502.11657 | How does ion temperature gradient turbulence depend on magnetic
geometry? Insights from data and machine learning | physics.plasm-ph cs.LG | Magnetic geometry has a significant effect on the level of turbulent
transport in fusion plasmas. Here, we model and analyze this dependence using
multiple machine learning methods and a dataset of > 200,000 nonlinear
simulations of ion-temperature-gradient turbulence in diverse non-axisymmetric
geometries. The datas... |
2502.11658 | "I'm not for sale" -- Perceptions and limited awareness of privacy risks
by digital natives about location data | cs.CY cs.AI cs.CR | Although mobile devices benefit users in their daily lives in numerous ways,
they also raise several privacy concerns. For instance, they can reveal
sensitive information that can be inferred from location data. This location
data is shared through service providers as well as mobile applications.
Understanding how a... |
2502.11663 | MaskGWM: A Generalizable Driving World Model with Video Mask
Reconstruction | cs.CV | World models that forecast environmental changes from actions are vital for
autonomous driving models with strong generalization. The prevailing driving
world model mainly build on video prediction model. Although these models can
produce high-fidelity video sequences with advanced diffusion-based generator,
they are... |
2502.11664 | VRoPE: Rotary Position Embedding for Video Large Language Models | cs.AI | Rotary Position Embedding (RoPE) has shown strong performance in text-based
Large Language Models (LLMs), but extending it to video remains a challenge due
to the intricate spatiotemporal structure of video frames. Existing
adaptations, such as RoPE-3D, attempt to encode spatial and temporal dimensions
separately but... |
2502.11665 | On the kernel learning problem | stat.ML cs.LG math.CA math.FA math.OC | The classical kernel ridge regression problem aims to find the best fit for
the output $Y$ as a function of the input data $X\in \mathbb{R}^d$, with a
fixed choice of regularization term imposed by a given choice of a reproducing
kernel Hilbert space, such as a Sobolev space. Here we consider a
generalization of the ... |
2502.11669 | Deep Subspace Learning for Surface Anomaly Classification Based on 3D
Point Cloud Data | stat.ML cs.LG | Surface anomaly classification is critical for manufacturing system fault
diagnosis and quality control. However, the following challenges always hinder
accurate anomaly classification in practice: (i) Anomaly patterns exhibit
intra-class variation and inter-class similarity, presenting challenges in the
accurate cla... |
2502.11671 | Diversity-Oriented Data Augmentation with Large Language Models | cs.CL cs.AI cs.LG | Data augmentation is an essential technique in natural language processing
(NLP) for enriching training datasets by generating diverse samples. This
process is crucial for improving the robustness and generalization capabilities
of NLP models. However, a significant challenge remains: \textit{Insufficient
Attention t... |
2502.11672 | Exact Upper and Lower Bounds for the Output Distribution of Neural
Networks with Random Inputs | cs.LG stat.ME stat.ML | We derive exact upper and lower bounds for the cumulative distribution
function (cdf) of the output of a neural network over its entire support
subject to noisy (stochastic) inputs. The upper and lower bounds converge to
the true cdf over its domain as the resolution increases. Our method applies to
any feedforward N... |
2502.11673 | Best of Both Worlds: Regret Minimization versus Minimax Play | cs.LG stat.ML | In this paper, we investigate the existence of online learning algorithms
with bandit feedback that simultaneously guarantee $O(1)$ regret compared to a
given comparator strategy, and $O(\sqrt{T})$ regret compared to the best
strategy in hindsight, where $T$ is the number of rounds. We provide the first
affirmative a... |
2502.11677 | Towards Fully Exploiting LLM Internal States to Enhance Knowledge
Boundary Perception | cs.CL | Large language models (LLMs) exhibit impressive performance across diverse
tasks but often struggle to accurately gauge their knowledge boundaries,
leading to confident yet incorrect responses. This paper explores leveraging
LLMs' internal states to enhance their perception of knowledge boundaries from
efficiency and... |
2502.11678 | Exploring LLM-based Student Simulation for Metacognitive Cultivation | cs.CY cs.CL | Metacognitive education plays a crucial role in cultivating students'
self-regulation and reflective thinking, providing essential support for those
with learning difficulties through academic advising. Simulating students with
insufficient learning capabilities using large language models offers a
promising approach... |
2502.11680 | Spectral structure learning for clinical time series | cs.LG | We develop and evaluate a structure learning algorithm for clinical time
series. Clinical time series are multivariate time series observed in multiple
patients and irregularly sampled, challenging existing structure learning
algorithms. We assume that our times series are realizations of StructGP, a
k-dimensional mu... |
2502.11681 | RIDE: Enhancing Large Language Model Alignment through Restyled
In-Context Learning Demonstration Exemplars | cs.CL cs.AI | Alignment tuning is crucial for ensuring large language models (LLMs) behave
ethically and helpfully. Current alignment approaches require high-quality
annotations and significant training resources. This paper proposes a low-cost,
tuning-free method using in-context learning (ICL) to enhance LLM alignment.
Through a... |
2502.11682 | Double Momentum and Error Feedback for Clipping with Fast Rates and
Differential Privacy | cs.LG math.OC stat.ML | Strong Differential Privacy (DP) and Optimization guarantees are two
desirable properties for a method in Federated Learning (FL). However, existing
algorithms do not achieve both properties at once: they either have optimal DP
guarantees but rely on restrictive assumptions such as bounded
gradients/bounded data hete... |
2502.11684 | MathFimer: Enhancing Mathematical Reasoning by Expanding Reasoning Steps
through Fill-in-the-Middle Task | cs.CL cs.AI | Mathematical reasoning represents a critical frontier in advancing large
language models (LLMs). While step-by-step approaches have emerged as the
dominant paradigm for mathematical problem-solving in LLMs, the quality of
reasoning steps in training data fundamentally constrains the performance of
the models. Recent ... |
2502.11687 | ReVeil: Unconstrained Concealed Backdoor Attack on Deep Neural Networks
using Machine Unlearning | cs.CR cs.AI cs.LG | Backdoor attacks embed hidden functionalities in deep neural networks (DNN),
triggering malicious behavior with specific inputs. Advanced defenses monitor
anomalous DNN inferences to detect such attacks. However, concealed backdoors
evade detection by maintaining a low pre-deployment attack success rate (ASR)
and res... |
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