id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
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 informat... | {
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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 models... | {
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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 paper,... | {
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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 o... | {
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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 mod... | {
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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. ... | {
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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 problems... | {
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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 po... | {
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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 to ... | {
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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 training ... | {
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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 retri... | {
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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 u... | {
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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 optimization ... | {
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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, traverse ... | {
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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 equati... | {
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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 layer... | {
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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 alg... | {
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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 the... | {
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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. Here... | {
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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 p... | {
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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-u... | {
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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 improvement... | {
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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-tra... | {
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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 compre... | {
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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 sm... | {
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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 t... | {
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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 where... | {
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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 to... | {
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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, focusing ... | {
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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 of ... | {
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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 wheth... | {
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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 remain... | {
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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 impac... | {
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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. Thi... | {
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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 th... | {
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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, start... | {
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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 l... | {
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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 researc... | {
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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 by... | {
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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 Learni... | {
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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 addres... | {
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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 path-... | {
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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 determined ... | {
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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 S... | {
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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, us... | {
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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 infrastructur... | {
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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. Ho... | {
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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 collecting ... | {
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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 investment... | {
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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 an... | {
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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 truth ... | {
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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 fe... | {
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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 gr... | {
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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 by... | {
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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 challe... | {
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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 explor... | {
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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 paper,... | {
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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 uniform ... | {
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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 human ... | {
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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 th... | {
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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 gener... | {
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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. Ho... | {
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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, other... | {
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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{Dip... | {
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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 knowled... | {
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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 acceler... | {
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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) ... | {
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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-att... | {
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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 toke... | {
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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 al... | {
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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 rea... | {
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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 differe... | {
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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 f... | {
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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 Mixture... | {
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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 prop... | {
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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 wit... | {
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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 un... | {
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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 rules... | {
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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, s... | {
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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 underpe... | {
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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 appear... | {
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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 l... | {
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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 reso... | {
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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 lack ... | {
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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 Languag... | {
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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 questi... | {
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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 co... | {
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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 pro... | {
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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 class... | {
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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 to ... | {
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} |
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 d... | {
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} |
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 for... | {
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} |
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 environment... | {
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} |
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 prim... | {
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} |
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 g... | {
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} |
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 sequence... | {
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} |
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 knowle... | {
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} |
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 $q+2\sqrt{q}$. In ... | {
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} |
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 app... | {
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} |
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 this ... | {
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} |
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