id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.12320 | Towards Fusing Point Cloud and Visual Representations for Imitation
Learning | cs.RO cs.CV | Learning for manipulation requires using policies that have access to rich
sensory information such as point clouds or RGB images. Point clouds
efficiently capture geometric structures, making them essential for
manipulation tasks in imitation learning. In contrast, RGB images provide rich
texture and semantic inform... |
2502.12323 | Adversarial Debiasing for Unbiased Parameter Recovery | cs.LG stat.ML | Advances in machine learning and the increasing availability of
high-dimensional data have led to the proliferation of social science research
that uses the predictions of machine learning models as proxies for measures of
human activity or environmental outcomes. However, prediction errors from
machine learning mode... |
2502.12325 | From Dense to Dynamic: Token-Difficulty Driven MoEfication of
Pre-Trained LLMs | cs.CL | Training large language models (LLMs) for different inference constraints is
computationally expensive, limiting control over efficiency-accuracy
trade-offs. Moreover, once trained, these models typically process tokens
uniformly, regardless of their complexity, leading to static and inflexible
behavior. In this pape... |
2502.12326 | Stability Bounds for Smooth Optimal Transport Maps and their Statistical
Implications | math.ST cs.LG stat.ME stat.ML stat.TH | We study estimators of the optimal transport (OT) map between two probability
distributions. We focus on plugin estimators derived from the OT map between
estimates of the underlying distributions. We develop novel stability bounds
for OT maps which generalize those in past work, and allow us to reduce the
problem of... |
2502.12327 | Learning Plasma Dynamics and Robust Rampdown Trajectories with
Predict-First Experiments at TCV | physics.plasm-ph cs.AI cs.LG cs.SY eess.SY | The rampdown in tokamak operations is a difficult to simulate phase during
which the plasma is often pushed towards multiple instability limits. To
address this challenge, and reduce the risk of disrupting operations, we
leverage recent advances in Scientific Machine Learning (SciML) to develop a
neural state-space m... |
2502.12328 | LM Agents for Coordinating Multi-User Information Gathering | cs.CL cs.AI | This paper introduces PeopleJoin, a benchmark for evaluating LM-mediated
collaborative problem solving. Given a user request, PeopleJoin agents must
identify teammates who might be able to assist, converse with these teammates
to gather information, and finally compile a useful answer or summary for the
original user... |
2502.12329 | A Novel Unified Parametric Assumption for Nonconvex Optimization | cs.LG cs.AI math.OC stat.ML | Nonconvex optimization is central to modern machine learning, but the general
framework of nonconvex optimization yields weak convergence guarantees that are
too pessimistic compared to practice. On the other hand, while convexity
enables efficient optimization, it is of limited applicability to many
practical proble... |
2502.12330 | X-IL: Exploring the Design Space of Imitation Learning Policies | cs.RO cs.LG | Designing modern imitation learning (IL) policies requires making numerous
decisions, including the selection of feature encoding, architecture, policy
representation, and more. As the field rapidly advances, the range of available
options continues to grow, creating a vast and largely unexplored design space
for IL ... |
2502.12337 | Stochastic Real-Time Deception in Nash Equilibrium Seeking for Games
with Quadratic Payoffs | eess.SY cs.SY | In multi-agent autonomous systems, deception is a fundamental concept which
characterizes the exploitation of unbalanced information to mislead victims
into choosing oblivious actions. This effectively alters the system's long term
behavior, leading to outcomes that may be beneficial to the deceiver but
detrimental t... |
2502.12340 | Understanding Silent Data Corruption in LLM Training | cs.LG cs.DC | As the scale of training large language models (LLMs) increases, one emergent
failure is silent data corruption (SDC), where hardware produces incorrect
computations without explicit failure signals. In this work, we are the first
to investigate the impact of real-world SDCs on LLM training by comparing model
trainin... |
2502.12342 | REAL-MM-RAG: A Real-World Multi-Modal Retrieval Benchmark | cs.IR cs.CV | Accurate multi-modal document retrieval is crucial for Retrieval-Augmented
Generation (RAG), yet existing benchmarks do not fully capture real-world
challenges with their current design. We introduce REAL-MM-RAG, an
automatically generated benchmark designed to address four key properties
essential for real-world ret... |
2502.12343 | Energy-Efficient Flat Precoding for MIMO Systems | cs.IT math.IT | This paper addresses the suboptimal energy efficiency of conventional digital
precoding schemes in multiple-input multiple-output (MIMO) systems. Through an
analysis of the power amplifier (PA) output power distribution associated with
conventional precoders, it is observed that these power distributions can be
quite... |
2502.12346 | QuZO: Quantized Zeroth-Order Fine-Tuning for Large Language Models | cs.LG cs.AI | Language Models (LLMs) are often quantized to lower precision to reduce the
memory cost and latency in inference. However, quantization often degrades
model performance, thus fine-tuning is required for various down-stream tasks.
Traditional fine-tuning methods such as stochastic gradient descent and Adam
optimizatio... |
2502.12347 | Improving Grip Stability Using Passive Compliant Microspine Arrays for
Soft Robots in Unstructured Terrain | cs.RO | Microspine grippers are small spines commonly found on insect legs that
reinforce surface interaction by engaging with asperities to increase shear
force and traction. An array of such microspines, when integrated into the
limbs or undercarriage of a robot, can provide the ability to maneuver uneven
terrains, travers... |
2502.12350 | Mamute: high-performance computing for geophysical methods | cs.CE | Due to their high computational cost, geophysical applications are typically
designed to run in large computing systems. Because of that, such applications
must implement several high-performance techniques to use the computational
resources better. In this paper, we present Mamute, a software that delivers
wave equa... |
2502.12352 | Towards Mechanistic Interpretability of Graph Transformers via Attention
Graphs | cs.LG cs.AI | We introduce Attention Graphs, a new tool for mechanistic interpretability of
Graph Neural Networks (GNNs) and Graph Transformers based on the mathematical
equivalence between message passing in GNNs and the self-attention mechanism in
Transformers. Attention Graphs aggregate attention matrices across Transformer
lay... |
2502.12353 | Stability-based Generalization Bounds for Variational Inference | cs.LG | Variational inference (VI) is widely used for approximate inference in
Bayesian machine learning. In addition to this practical success,
generalization bounds for variational inference and related algorithms have
been developed, mostly through the connection to PAC-Bayes analysis. A second
line of work has provided a... |
2502.12354 | Human-centered explanation does not fit all: The interplay of
sociotechnical, cognitive, and individual factors in the effect AI
explanations in algorithmic decision-making | cs.CY cs.AI cs.HC | Recent XAI studies have investigated what constitutes a \textit{good}
explanation in AI-assisted decision-making. Despite the widely accepted
human-friendly properties of explanations, such as contrastive and selective,
existing studies have yielded inconsistent findings. To address these gaps, our
study focuses on t... |
2502.12355 | Hovering Flight of Soft-Actuated Insect-Scale Micro Aerial Vehicles
using Deep Reinforcement Learning | cs.RO cs.LG cs.SY eess.SY | Soft-actuated insect-scale micro aerial vehicles (IMAVs) pose unique
challenges for designing robust and computationally efficient controllers. At
the millimeter scale, fast robot dynamics ($\sim$ms), together with system
delay, model uncertainty, and external disturbances significantly affect flight
performances. He... |
2502.12359 | LanP: Rethinking the Impact of Language Priors in Large Vision-Language
Models | cs.CV | Large Vision-Language Models (LVLMs) have shown impressive performance in
various tasks. However, LVLMs suffer from hallucination, which hinders their
adoption in the real world. Existing studies emphasized that the strong
language priors of LVLMs can overpower visual information, causing
hallucinations. However, the... |
2502.12360 | Detecting Systematic Weaknesses in Vision Models along Predefined
Human-Understandable Dimensions | cs.CV cs.AI cs.LG | Studying systematic weaknesses of DNNs has gained prominence in the last few
years with the rising focus on building safe AI systems. Slice discovery
methods (SDMs) are prominent algorithmic approaches for finding such systematic
weaknesses. They identify top-k semantically coherent slices/subsets of data
where a DNN... |
2502.12361 | ConFit v2: Improving Resume-Job Matching using Hypothetical Resume
Embedding and Runner-Up Hard-Negative Mining | cs.CL | A reliable resume-job matching system helps a company recommend suitable
candidates from a pool of resumes and helps a job seeker find relevant jobs
from a list of job posts. However, since job seekers apply only to a few jobs,
interaction labels in resume-job datasets are sparse. We introduce ConFit v2,
an improveme... |
2502.12362 | Classifiers of Data Sharing Statements in Clinical Trial Records | cs.CL cs.AI | Digital individual participant data (IPD) from clinical trials are
increasingly distributed for potential scientific reuse. The identification of
available IPD, however, requires interpretations of textual data-sharing
statements (DSS) in large databases. Recent advancements in computational
linguistics include pre-t... |
2502.12365 | On the Performance of Uplink Pinching Antenna Systems (PASS) | cs.IT math.IT | Pinching antenna (PA) is a flexible antenna composed of a waveguide and
multiple dielectric particles, which is capable of reconfiguring wireless
channels intelligently in line-of-sight links. By leveraging the unique
features of PAs, we exploit the uplink (UL) transmission in pinching antenna
systems (PASS). To comp... |
2502.12366 | ScriptoriumWS: A Code Generation Assistant for Weak Supervision | cs.LG | Weak supervision is a popular framework for overcoming the labeled data
bottleneck: the need to obtain labels for training data. In weak supervision,
multiple noisy-but-cheap sources are used to provide guesses of the label and
are aggregated to produce high-quality pseudolabels. These sources are often
expressed as ... |
2502.12370 | Positional Encoding in Transformer-Based Time Series Models: A Survey | cs.LG | Recent advancements in transformer-based models have greatly improved time
series analysis, providing robust solutions for tasks such as forecasting,
anomaly detection, and classification. A crucial element of these models is
positional encoding, which allows transformers to capture the intrinsic
sequential nature of... |
2502.12371 | IMLE Policy: Fast and Sample Efficient Visuomotor Policy Learning via
Implicit Maximum Likelihood Estimation | cs.RO cs.AI cs.LG | Recent advances in imitation learning, particularly using generative
modelling techniques like diffusion, have enabled policies to capture complex
multi-modal action distributions. However, these methods often require large
datasets and multiple inference steps for action generation, posing challenges
in robotics whe... |
2502.12372 | Factual Inconsistency in Data-to-Text Generation Scales Exponentially
with LLM Size: A Statistical Validation | cs.CL cs.AI cs.LG | Monitoring factual inconsistency is essential for ensuring trustworthiness in
data-to-text generation (D2T). While large language models (LLMs) have
demonstrated exceptional performance across various D2T tasks, previous studies
on scaling laws have primarily focused on generalization error through power
law scaling ... |
2502.12373 | Soft Robotics for Search and Rescue: Advancements, Challenges, and
Future Directions | cs.RO cs.AI | Soft robotics has emerged as a transformative technology in Search and Rescue
(SAR) operations, addressing challenges in navigating complex, hazardous
environments that often limit traditional rigid robots. This paper critically
examines advancements in soft robotic technologies tailored for SAR
applications, focusin... |
2502.12375 | UltraGen: Extremely Fine-grained Controllable Generation via Attribute
Reconstruction and Global Preference Optimization | cs.CL | Fine granularity is an essential requirement for controllable text
generation, which has seen rapid growth with the ability of LLMs. However,
existing methods focus mainly on a small set of attributes like 3 to 5, and
their performance degrades significantly when the number of attributes
increases to the next order o... |
2502.12377 | Alignment and Adversarial Robustness: Are More Human-Like Models More
Secure? | cs.CV | Representational alignment refers to the extent to which a model's internal
representations mirror biological vision, offering insights into both neural
similarity and functional correspondence. Recently, some more aligned models
have demonstrated higher resiliency to adversarial examples, raising the
question of whe... |
2502.12378 | Pragmatics in the Era of Large Language Models: A Survey on Datasets,
Evaluation, Opportunities and Challenges | cs.CL | Understanding pragmatics-the use of language in context-is crucial for
developing NLP systems capable of interpreting nuanced language use. Despite
recent advances in language technologies, including large language models,
evaluating their ability to handle pragmatic phenomena such as implicatures and
references rema... |
2502.12379 | OCT Data is All You Need: How Vision Transformers with and without
Pre-training Benefit Imaging | cs.CV cs.LG | Optical Coherence Tomography (OCT) provides high-resolution cross-sectional
images useful for diagnosing various diseases, but their distinct
characteristics from natural images raise questions about whether large-scale
pre-training on datasets like ImageNet is always beneficial. In this paper, we
investigate the imp... |
2502.12381 | Linear Diffusion Networks: Harnessing Diffusion Processes for Global
Interactions | cs.LG | Diffusion kernels capture global dependencies. We present Linear Diffusion
Networks (LDNs), a novel architecture that reinterprets sequential data
processing as a unified diffusion process. Our model integrates adaptive
diffusion modules with localized nonlinear updates and a diffusion-inspired
attention mechanism. T... |
2502.12382 | Hybrid Machine Learning Models for Intrusion Detection in IoT:
Leveraging a Real-World IoT Dataset | cs.CR cs.AI | The rapid growth of the Internet of Things (IoT) has revolutionized
industries, enabling unprecedented connectivity and functionality. However,
this expansion also increases vulnerabilities, exposing IoT networks to
increasingly sophisticated cyberattacks. Intrusion Detection Systems (IDS) are
crucial for mitigating ... |
2502.12383 | Locally-Deployed Chain-of-Thought (CoT) Reasoning Model in Chemical
Engineering: Starting from 30 Experimental Data | cs.LG stat.AP | In the field of chemical engineering, traditional data-processing and
prediction methods face significant challenges. Machine-learning and
large-language models (LLMs) also have their respective limitations. This paper
explores the application of the Chain-of-Thought (CoT) reasoning model in
chemical engineering, sta... |
2502.12384 | Scalable Back-Propagation-Free Training of Optical Physics-Informed
Neural Networks | cs.LG | Physics-informed neural networks (PINNs) have shown promise in solving
partial differential equations (PDEs), with growing interest in their
energy-efficient, real-time training on edge devices. Photonic computing offers
a potential solution to achieve this goal because of its ultra-high operation
speed. However, the... |
2502.12386 | Bridging the Data Gap in AI Reliability Research and Establishing
DR-AIR, a Comprehensive Data Repository for AI Reliability | stat.AP cs.AI | Artificial intelligence (AI) technology and systems have been advancing
rapidly. However, ensuring the reliability of these systems is crucial for
fostering public confidence in their use. This necessitates the modeling and
analysis of reliability data specific to AI systems. A major challenge in AI
reliability resea... |
2502.12388 | Achieving Upper Bound Accuracy of Joint Training in Continual Learning | cs.LG | Continual learning has been an active research area in machine learning,
focusing on incrementally learning a sequence of tasks. A key challenge is
catastrophic forgetting (CF), and most research efforts have been directed
toward mitigating this issue. However, a significant gap remains between the
accuracy achieved ... |
2502.12391 | Reward-Safety Balance in Offline Safe RL via Diffusion Regularization | cs.LG | Constrained reinforcement learning (RL) seeks high-performance policies under
safety constraints. We focus on an offline setting where the agent has only a
fixed dataset -- common in realistic tasks to prevent unsafe exploration. To
address this, we propose Diffusion-Regularized Constrained Offline
Reinforcement Lear... |
2502.12393 | Time Series Treatment Effects Analysis with Always-Missing Controls | stat.ME cs.AI cs.LG stat.ML | Estimating treatment effects in time series data presents a significant
challenge, especially when the control group is always unobservable. For
example, in analyzing the effects of Christmas on retail sales, we lack direct
observation of what would have occurred in late December without the Christmas
impact. To addr... |
2502.12395 | Efficient Neural SDE Training using Wiener-Space Cubature | cs.LG | A neural stochastic differential equation (SDE) is an SDE with drift and
diffusion terms parametrized by neural networks. The training procedure for
neural SDEs consists of optimizing the SDE vector field (neural network)
parameters to minimize the expected value of an objective functional on
infinite-dimensional pat... |
2502.12396 | Scientific Machine Learning of Flow Resistance Using Universal Shallow
Water Equations with Differentiable Programming | physics.flu-dyn cs.CE cs.LG | Shallow water equations (SWEs) are the backbone of most hydrodynamics models
for flood prediction, river engineering, and many other water resources
applications. The estimation of flow resistance, i.e., the Manning's roughness
coefficient $n$, is crucial for ensuring model accuracy, and has been
previously determine... |
2502.12397 | Could AI Leapfrog the Web? Evidence from Teachers in Sierra Leone | cs.CY cs.AI cs.HC econ.GN q-fin.EC | Access to digital information is a driver of economic development. But
although 85% of sub-Saharan Africa's population is covered by mobile broadband
signal, only 37% use the internet, and those who do seldom use the web. We
investigate whether AI can bridge this gap by analyzing how 469 teachers use an
AI chatbot in... |
2502.12398 | Solving the Cold Start Problem on One's Own as an End User via
Preference Transfer | cs.IR cs.AI cs.LG | We propose a new approach that enables end users to directly solve the cold
start problem by themselves. The cold start problem is a common issue in
recommender systems, and many methods have been proposed to address the problem
on the service provider's side. However, when the service provider does not
take action, ... |
2502.12401 | Risk Assessment of Transmission Lines Against Grid-ignited Wildfires | cs.CE | Wildfires ignited by the power lines have become increasingly common over the
past decade. Enhancing the operational and financial resilience of power grids
against wildfires involves a multifaceted approach. Key proactive measures
include meticulous vegetation management, strategic grid hardening such as
infrastruct... |
2502.12403 | Sensing-based Robustness Challenges in Agricultural Robotic Harvesting | cs.RO cs.SY eess.SY | This paper presents the challenges agricultural robotic harvesters face in
detecting and localising fruits under various environmental disturbances. In
controlled laboratory settings, both the traditional HSV (Hue Saturation Value)
transformation and the YOLOv8 (You Only Look Once) deep learning model were
employed. ... |
2502.12404 | WMT24++: Expanding the Language Coverage of WMT24 to 55 Languages &
Dialects | cs.CL | As large language models (LLM) become more and more capable in languages
other than English, it is important to collect benchmark datasets in order to
evaluate their multilingual performance, including on tasks like machine
translation (MT). In this work, we extend the WMT24 dataset to cover 55
languages by collectin... |
2502.12405 | An Investment Prioritization Model for Wildfire Risk Mitigation Through
Power Line Undergrounding | cs.CE | Grid-ignited wildfires are one of the most destructive catastrophic events,
profoundly affecting the built and natural environments. Burying power lines is
an effective solution for mitigating the risk of wildfire ignition. However, it
is a costly capital expenditure (CapEx) requiring meticulous planning and
investme... |
2502.12406 | Multi-vision-based Picking Point Localisation of Target Fruit for
Harvesting Robots | cs.RO cs.CV | This paper presents multi-vision-based localisation strategies for harvesting
robots. Identifying picking points accurately is essential for robotic
harvesting because insecure grasping can lead to economic loss through fruit
damage and dropping. In this study, two multi-vision-based localisation
methods, namely the ... |
2502.12408 | On the Robust Approximation of ASR Metrics | cs.CL | Recent advances in speech foundation models are largely driven by scaling
both model size and data, enabling them to perform a wide range of tasks,
including speech recognition. Traditionally, ASR models are evaluated using
metrics like Word Error Rate (WER) and Character Error Rate (CER), which depend
on ground trut... |
2502.12411 | Gradient Co-occurrence Analysis for Detecting Unsafe Prompts in Large
Language Models | cs.CL cs.AI | Unsafe prompts pose significant safety risks to large language models (LLMs).
Existing methods for detecting unsafe prompts rely on data-driven fine-tuning
to train guardrail models, necessitating significant data and computational
resources. In contrast, recent few-shot gradient-based methods emerge,
requiring only ... |
2502.12412 | Incomplete Graph Learning: A Comprehensive Survey | cs.LG eess.IV | Graph learning is a prevalent field that operates on ubiquitous graph data.
Effective graph learning methods can extract valuable information from graphs.
However, these methods are non-robust and affected by missing attributes in
graphs, resulting in sub-optimal outcomes. This has led to the emergence of
incomplete ... |
2502.12413 | DivIL: Unveiling and Addressing Over-Invariance for Out-of- Distribution
Generalization | cs.LG | Out-of-distribution generalization is a common problem that expects the model
to perform well in the different distributions even far from the train data. A
popular approach to addressing this issue is invariant learning (IL), in which
the model is compiled to focus on invariant features instead of spurious
features ... |
2502.12414 | Lost in Transcription, Found in Distribution Shift: Demystifying
Hallucination in Speech Foundation Models | cs.CL | Speech foundation models trained at a massive scale, both in terms of model
and data size, result in robust systems capable of performing multiple speech
tasks, including automatic speech recognition (ASR). These models transcend
language and domain barriers, yet effectively measuring their performance
remains a chal... |
2502.12415 | Gaseous Object Detection | cs.CV | Object detection, a fundamental and challenging problem in computer vision,
has experienced rapid development due to the effectiveness of deep learning.
The current objects to be detected are mostly rigid solid substances with
apparent and distinct visual characteristics. In this paper, we endeavor on a
scarcely expl... |
2502.12418 | Boosting Illuminant Estimation in Deep Color Constancy through Enhancing
Brightness Robustness | cs.CV cs.AI | Color constancy estimates illuminant chromaticity to correct color-biased
images. Recently, Deep Neural Network-driven Color Constancy (DNNCC) models
have made substantial advancements. Nevertheless, the potential risks in DNNCC
due to the vulnerability of deep neural networks have not yet been explored. In
this pape... |
2502.12420 | Sens-Merging: Sensitivity-Guided Parameter Balancing for Merging Large
Language Models | cs.CL cs.AI | Recent advances in large language models have led to numerous
task-specialized fine-tuned variants, creating a need for efficient model
merging techniques that preserve specialized capabilities while avoiding costly
retraining. While existing task vector-based merging methods show promise, they
typically apply unifor... |
2502.12421 | Wi-Chat: Large Language Model Powered Wi-Fi Sensing | cs.CL | Recent advancements in Large Language Models (LLMs) have demonstrated
remarkable capabilities across diverse tasks. However, their potential to
integrate physical model knowledge for real-world signal interpretation remains
largely unexplored. In this work, we introduce Wi-Chat, the first LLM-powered
Wi-Fi-based huma... |
2502.12425 | Robust Disentangled Counterfactual Learning for Physical Audiovisual
Commonsense Reasoning | cs.CV | In this paper, we propose a new Robust Disentangled Counterfactual Learning
(RDCL) approach for physical audiovisual commonsense reasoning. The task aims
to infer objects' physics commonsense based on both video and audio input, with
the main challenge being how to imitate the reasoning ability of humans, even
under ... |
2502.12427 | Multi Image Super Resolution Modeling for Earth System Models | cs.CV | Super-resolution (SR) techniques are essential for improving Earth System
Model (ESM) data's spatial resolution, which helps better understand complex
environmental processes. This paper presents a new algorithm, ViFOR, which
combines Vision Transformers (ViT) and Implicit Neural Representation Networks
(INRs) to gen... |
2502.12430 | Bridge the Gaps between Machine Unlearning and AI Regulation | cs.LG cs.AI | The "right to be forgotten" and the data privacy laws that encode it have
motivated machine unlearning since its earliest days. Now, an inbound wave of
artificial intelligence regulations - like the European Union's Artificial
Intelligence Act (AIA) - potentially offer important new use cases for machine
unlearning. ... |
2502.12435 | A Survey on Large Language Models for Automated Planning | cs.AI cs.CL | The planning ability of Large Language Models (LLMs) has garnered increasing
attention in recent years due to their remarkable capacity for multi-step
reasoning and their ability to generalize across a wide range of domains. While
some researchers emphasize the potential of LLMs to perform complex planning
tasks, oth... |
2502.12436 | Should I Trust You? Detecting Deception in Negotiations using
Counterfactual RL | cs.CL | An increasingly prevalent socio-technical problem is people being taken in by
offers that sound ``too good to be true'', where persuasion and trust shape
decision-making. This paper investigates how \abr{ai} can help detect these
deceptive scenarios. We analyze how humans strategically deceive each other in
\textit{D... |
2502.12442 | HopRAG: Multi-Hop Reasoning for Logic-Aware Retrieval-Augmented
Generation | cs.IR cs.CL | Retrieval-Augmented Generation (RAG) systems often struggle with imperfect
retrieval, as traditional retrievers focus on lexical or semantic similarity
rather than logical relevance. To address this, we propose HopRAG, a novel RAG
framework that augments retrieval with logical reasoning through
graph-structured knowl... |
2502.12444 | SparAMX: Accelerating Compressed LLMs Token Generation on AMX-powered
CPUs | cs.LG cs.AI cs.AR cs.PF | Large language models have high compute, latency, and memory requirements.
While specialized accelerators such as GPUs and TPUs typically run these
workloads, CPUs are more widely available and consume less energy. Accelerating
LLMs with CPUs enables broader AI access at a lower cost and power consumption.
This accel... |
2502.12445 | Computational Safety for Generative AI: A Signal Processing Perspective | cs.AI cs.LG stat.ML | AI safety is a rapidly growing area of research that seeks to prevent the
harm and misuse of frontier AI technology, particularly with respect to
generative AI (GenAI) tools that are capable of creating realistic and
high-quality content through text prompts. Examples of such tools include large
language models (LLMs... |
2502.12446 | Multi-Attribute Steering of Language Models via Targeted Intervention | cs.CL cs.AI cs.LG | Inference-time intervention (ITI) has emerged as a promising method for
steering large language model (LLM) behavior in a particular direction (e.g.,
improving helpfulness) by intervening on token representations without costly
updates to the LLM's parameters. However, existing ITI approaches fail to scale
to multi-a... |
2502.12448 | From Principles to Applications: A Comprehensive Survey of Discrete
Tokenizers in Generation, Comprehension, Recommendation, and Information
Retrieval | cs.IR | Discrete tokenizers have emerged as indispensable components in modern
machine learning systems, particularly within the context of autoregressive
modeling and large language models (LLMs). These tokenizers serve as the
critical interface that transforms raw, unstructured data from diverse
modalities into discrete to... |
2502.12449 | YUNet: Improved YOLOv11 Network for Skyline Detection | cs.CV | Skyline detection plays an important role in geolocalizaion, flight control,
visual navigation, port security, etc. The appearance of the sky and non-sky
areas are variable, because of different weather or illumination environment,
which brings challenges to skyline detection. In this research, we proposed the
YUNet ... |
2502.12450 | Investigating and Extending Homans' Social Exchange Theory with Large
Language Model based Agents | cs.AI | Homans' Social Exchange Theory (SET) is widely recognized as a basic
framework for understanding the formation and emergence of human civilizations
and social structures. In social science, this theory is typically studied
based on simple simulation experiments or real-world human studies, both of
which either lack r... |
2502.12453 | UniMatch: Universal Matching from Atom to Task for Few-Shot Drug
Discovery | cs.LG cs.AI q-bio.BM | Drug discovery is crucial for identifying candidate drugs for various
diseases.However, its low success rate often results in a scarcity of
annotations, posing a few-shot learning problem. Existing methods primarily
focus on single-scale features, overlooking the hierarchical molecular
structures that determine diffe... |
2502.12454 | Benchmarking Zero-Shot Facial Emotion Annotation with Large Language
Models: A Multi-Class and Multi-Frame Approach in DailyLife | cs.CV cs.AI cs.LG | This study investigates the feasibility and performance of using large
language models (LLMs) to automatically annotate human emotions in everyday
scenarios. We conducted experiments on the DailyLife subset of the publicly
available FERV39k dataset, employing the GPT-4o-mini model for rapid, zero-shot
labeling of key... |
2502.12455 | DSMoE: Matrix-Partitioned Experts with Dynamic Routing for
Computation-Efficient Dense LLMs | cs.CL | As large language models continue to scale, computational costs and resource
consumption have emerged as significant challenges. While existing
sparsification methods like pruning reduce computational overhead, they risk
losing model knowledge through parameter removal. This paper proposes DSMoE
(Dynamic Sparse Mixtu... |
2502.12456 | Not-So-Optimal Transport Flows for 3D Point Cloud Generation | cs.CV cs.AI | Learning generative models of 3D point clouds is one of the fundamental
problems in 3D generative learning. One of the key properties of point clouds
is their permutation invariance, i.e., changing the order of points in a point
cloud does not change the shape they represent. In this paper, we analyze the
recently pr... |
2502.12458 | An Empirical Evaluation of Encoder Architectures for Fast Real-Time Long
Conversational Understanding | cs.CL | Analyzing long text data such as customer call transcripts is a
cost-intensive and tedious task. Machine learning methods, namely Transformers,
are leveraged to model agent-customer interactions. Unfortunately, Transformers
adhere to fixed-length architectures and their self-attention mechanism scales
quadratically w... |
2502.12459 | Stress Testing Generalization: How Minor Modifications Undermine Large
Language Model Performance | cs.CL cs.AI cs.LG | This paper investigates the fragility of Large Language Models (LLMs) in
generalizing to novel inputs, specifically focusing on minor perturbations in
well-established benchmarks (e.g., slight changes in question format or
distractor length). Despite high benchmark scores, LLMs exhibit significant
accuracy drops and ... |
2502.12460 | LMN: A Tool for Generating Machine Enforceable Policies from Natural
Language Access Control Rules using LLMs | cs.CR cs.LG | Organizations often lay down rules or guidelines called Natural Language
Access Control Policies (NLACPs) for specifying who gets access to which
information and when. However, these cannot be directly used in a target access
control model like Attribute-based Access Control (ABAC). Manually translating
the NLACP rul... |
2502.12462 | Emulating Retrieval Augmented Generation via Prompt Engineering for
Enhanced Long Context Comprehension in LLMs | cs.CL | This paper addresses the challenge of comprehending very long contexts in
Large Language Models (LLMs) by proposing a method that emulates Retrieval
Augmented Generation (RAG) through specialized prompt engineering and
chain-of-thought (CoT) reasoning. While recent LLMs support over 100,000 tokens
in a single prompt,... |
2502.12464 | SafeRoute: Adaptive Model Selection for Efficient and Accurate Safety
Guardrails in Large Language Models | cs.CL | Deploying large language models (LLMs) in real-world applications requires
robust safety guard models to detect and block harmful user prompts. While
large safety guard models achieve strong performance, their computational cost
is substantial. To mitigate this, smaller distilled models are used, but they
often under... |
2502.12465 | Computational-Statistical Tradeoffs at the Next-Token Prediction
Barrier: Autoregressive and Imitation Learning under Misspecification | cs.LG cs.DS | Next-token prediction with the logarithmic loss is a cornerstone of
autoregressive sequence modeling, but, in practice, suffers from error
amplification, where errors in the model compound and generation quality
degrades as sequence length $H$ increases. From a theoretical perspective, this
phenomenon should not appe... |
2502.12466 | EquiBench: Benchmarking Code Reasoning Capabilities of Large Language
Models via Equivalence Checking | cs.LG cs.AI cs.CL cs.PL cs.SE | Equivalence checking, i.e., determining whether two programs produce
identical outputs for all possible inputs, underpins a broad range of
applications, including software refactoring, testing, and optimization. We
present the task of equivalence checking as a new way to evaluate the code
reasoning abilities of large... |
2502.12468 | MCTS-Judge: Test-Time Scaling in LLM-as-a-Judge for Code Correctness
Evaluation | cs.LG cs.AI | The LLM-as-a-Judge paradigm shows promise for evaluating generative content
but lacks reliability in reasoning-intensive scenarios, such as programming.
Inspired by recent advances in reasoning models and shifts in scaling laws, we
pioneer bringing test-time computation into LLM-as-a-Judge, proposing
MCTS-Judge, a re... |
2502.12470 | Reasoning on a Spectrum: Aligning LLMs to System 1 and System 2 Thinking | cs.CL | Large Language Models (LLMs) exhibit impressive reasoning abilities, yet
their reliance on structured step-by-step processing reveals a critical
limitation. While human cognition fluidly adapts between intuitive, heuristic
(System 1) and analytical, deliberative (System 2) reasoning depending on the
context, LLMs lac... |
2502.12476 | CoCo-CoLa: Evaluating Language Adherence in Multilingual LLMs | cs.CL | Multilingual Large Language Models (LLMs) develop cross-lingual abilities
despite being trained on limited parallel data. However, they often struggle to
generate responses in the intended language, favoring high-resource languages
such as English. In this work, we introduce CoCo-CoLa (Correct Concept -
Correct Langu... |
2502.12477 | Savaal: Scalable Concept-Driven Question Generation to Enhance Human
Learning | cs.CL | Assessing and enhancing human learning through question-answering is vital,
yet automating this process remains challenging. While large language models
(LLMs) excel at summarization and query responses, their ability to generate
meaningful questions for learners is underexplored.
We propose Savaal, a scalable ques... |
2502.12478 | MSE-Adapter: A Lightweight Plugin Endowing LLMs with the Capability to
Perform Multimodal Sentiment Analysis and Emotion Recognition | cs.CL | Current Multimodal Sentiment Analysis (MSA) and Emotion Recognition in
Conversations (ERC) methods based on pre-trained language models exhibit two
primary limitations:
1) Once trained for MSA and ERC tasks, these pre-trained language models lose
their original generalized capabilities. 2) They demand considerable
... |
2502.12479 | MotifBench: A standardized protein design benchmark for
motif-scaffolding problems | cs.LG q-bio.BM | The motif-scaffolding problem is a central task in computational protein
design: Given the coordinates of atoms in a geometry chosen to confer a desired
biochemical function (a motif), the task is to identify diverse protein
structures (scaffolds) that include the motif and maintain its geometry.
Significant recent p... |
2502.12481 | Predicate Hierarchies Improve Few-Shot State Classification | cs.CV cs.AI cs.LG cs.RO | State classification of objects and their relations is core to many
long-horizon tasks, particularly in robot planning and manipulation. However,
the combinatorial explosion of possible object-predicate combinations, coupled
with the need to adapt to novel real-world environments, makes it a desideratum
for state cla... |
2502.12483 | The Knowledge Microscope: Features as Better Analytical Lenses than
Neurons | cs.CL | Previous studies primarily utilize MLP neurons as units of analysis for
understanding the mechanisms of factual knowledge in Language Models (LMs);
however, neurons suffer from polysemanticity, leading to limited knowledge
expression and poor interpretability. In this paper, we first conduct
preliminary experiments t... |
2502.12484 | LocalEscaper: A Weakly-supervised Framework with Regional Reconstruction
for Scalable Neural TSP Solvers | cs.LG cs.AI | Neural solvers have shown significant potential in solving the Traveling
Salesman Problem (TSP), yet current approaches face significant challenges.
Supervised learning (SL)-based solvers require large amounts of high-quality
labeled data, while reinforcement learning (RL)-based solvers, though less
dependent on such... |
2502.12485 | Safe at the Margins: A General Approach to Safety Alignment in
Low-Resource English Languages -- A Singlish Case Study | cs.CL cs.AI | To ensure safe usage, Large Language Models (LLMs) typically undergo
alignment with human-defined values. However, this alignment often relies on
primarily English data and is biased towards Western-centric values, limiting
its effectiveness in low-resource language settings. In this paper, we describe
our approach f... |
2502.12486 | EPO: Explicit Policy Optimization for Strategic Reasoning in LLMs via
Reinforcement Learning | cs.CL | Large Language Models (LLMs) have shown impressive reasoning capabilities in
well-defined problems with clear solutions, such as mathematics and coding.
However, they still struggle with complex real-world scenarios like business
negotiations, which require strategic reasoning-an ability to navigate dynamic
environme... |
2502.12488 | Enhancing Audio-Visual Spiking Neural Networks through
Semantic-Alignment and Cross-Modal Residual Learning | cs.CV | Humans interpret and perceive the world by integrating sensory information
from multiple modalities, such as vision and hearing. Spiking Neural Networks
(SNNs), as brain-inspired computational models, exhibit unique advantages in
emulating the brain's information processing mechanisms. However, existing SNN
models pr... |
2502.12489 | A Comprehensive Survey on Generative AI for Video-to-Music Generation | eess.AS cs.AI cs.MM | The burgeoning growth of video-to-music generation can be attributed to the
ascendancy of multimodal generative models. However, there is a lack of
literature that comprehensively combs through the work in this field. To fill
this gap, this paper presents a comprehensive review of video-to-music
generation using deep... |
2502.12490 | UniGenCoder: Merging Seq2Seq and Seq2Tree Paradigms for Unified Code
Generation | cs.CL | Deep learning-based code generation has completely transformed the way
developers write programs today. Existing approaches to code generation have
focused either on the Sequence-to-Sequence paradigm, which generates target
code as a sequence of tokens, or the Sequence-to-Tree paradigm, which outputs
code as a sequen... |
2502.12492 | Boost, Disentangle, and Customize: A Robust System2-to-System1 Pipeline
for Code Generation | cs.AI | Large language models (LLMs) have demonstrated remarkable capabilities in
various domains, particularly in system 1 tasks, yet the intricacies of their
problem-solving mechanisms in system 2 tasks are not sufficiently explored.
Recent research on System2-to-System1 methods surge, exploring the System 2
reasoning know... |
2502.12493 | Optimal and Almost Optimal Locally Repairable Codes from Hyperelliptic
Curves | cs.IT math.IT | Locally repairable codes are widely applicable in contemporary large-scale
distributed cloud storage systems and various other areas. By making use of
some algebraic structures of elliptic curves, Li et al. developed a series of
$q$-ary optimal locally repairable codes with lengths that can extend to
$q+2\sqrt{q}$. I... |
2502.12494 | EDGE: Efficient Data Selection for LLM Agents via Guideline
Effectiveness | cs.LG cs.AI | Large Language Models (LLMs) have shown remarkable capabilities as AI agents.
However, existing methods for enhancing LLM-agent abilities often lack a focus
on data quality, leading to inefficiencies and suboptimal results in both
fine-tuning and prompt engineering. To address this issue, we introduce EDGE, a
novel a... |
2502.12498 | USPilot: An Embodied Robotic Assistant Ultrasound System with Large
Language Model Enhanced Graph Planner | cs.RO | In the era of Large Language Models (LLMs), embodied artificial intelligence
presents transformative opportunities for robotic manipulation tasks.
Ultrasound imaging, a widely used and cost-effective medical diagnostic
procedure, faces challenges due to the global shortage of professional
sonographers. To address thi... |
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