OpenRaiser/Intern-Atlas huggingface dataset:
Nonconvex Obstacle Avoidance using Efficient Sampling-Based Distance Functions
Venue: arXiv cs.RO 2025
Year: 2025

We consider nonconvex obstacle avoidance where a robot described by nonlinear dynamics and a nonconvex shape has to avoid nonconvex obstacles. Obstacle avoidance is a fundamental problem in robotics and well studied in control. However, existing solutions are computationally expensive (e.g., model predictive controllers), neglect nonlinear dynamics (e.g., graph-based planners), use diffeomorphic transformations into convex domains (e.g., for star shapes), or are conservative due to convex overapproximations. The key challenge here is that the computation of the distance between the shapes of the robot and the obstacles is a nonconvex problem. We propose efficient computation of this distance via sampling-based distance functions. We quantify the sampling error and show that, for certain systems, such sampling-based distance functions are valid nonsmooth control barrier functions. We also study how to deal with disturbances on the robot dynamics in our setting. Finally, we illustrate our method on a robot navigation task involving an omnidirectional robot and nonconvex obstacles. We also analyze performance and computational efficiency of our controller as a function of the number of samples.
Multi-Robot Coordination with Adversarial Perception
Venue: arXiv cs.RO 2025
Year: 2025

This paper investigates the resilience of perception-based multi-robot coordination with wireless communication to online adversarial perception. A systematic study of this problem is essential for many safety-critical robotic applications that rely on the measurements from learned perception modules. We consider a (small) team of quadrotor robots that rely only on an Inertial Measurement Unit (IMU) and the visual data measurements obtained from a learned multi-task perception module (e.g., object detection) for downstream tasks, including relative localization and coordination. We focus on a class of adversarial perception attacks that cause misclassification, mislocalization, and latency. We propose that the effects of adversarial misclassification and mislocalization can be modeled as sporadic (intermittent) and spurious measurement data for the downstream tasks. To address this, we present a framework for resilience analysis of multi-robot coordination with adversarial measurements. The framework integrates data from Visual-Inertial Odometry (VIO) and the learned perception model for robust relative localization and state estimation in the presence of adversarially sporadic and spurious measurements. The framework allows for quantifying the degradation in system observability and stability in relation to the success rate of adversarial perception. Finally, experimental results on a multi-robot platform demonstrate the real-world applicability of our methodology for resource-constrained robotic platforms.
From Punchlines to Predictions: A Metric to Assess LLM Performance in Identifying Humor in Stand-Up Comedy
Venue: arXiv cs.CL 2025
Year: 2025

Comedy serves as a profound reflection of the times we live in and is a staple element of human interactions. In light of the widespread adoption of Large Language Models (LLMs), the intersection of humor and AI has become no laughing matter. Advancements in the naturalness of human-computer interaction correlates with improvements in AI systems' abilities to understand humor. In this study, we assess the ability of models in accurately identifying humorous quotes from a stand-up comedy transcript. Stand-up comedy's unique comedic narratives make it an ideal dataset to improve the overall naturalness of comedic understanding. We propose a novel humor detection metric designed to evaluate LLMs amongst various prompts on their capability to extract humorous punchlines. The metric has a modular structure that offers three different scoring methods - fuzzy string matching, sentence embedding, and subspace similarity - to provide an overarching assessment of a model's performance. The model's results are compared against those of human evaluators on the same task. Our metric reveals that regardless of prompt engineering, leading models, ChatGPT, Claude, and DeepSeek, achieve scores of at most 51% in humor detection. Notably, this performance surpasses that of humans who achieve a score of 41%. The analysis of human evaluators and LLMs reveals variability in agreement, highlighting the subjectivity inherent in humor and the complexities involved in extracting humorous quotes from live performance transcripts. Code available at https://github.com/swaggirl9000/humor.
Large Language Models integration in Smart Grids
Venue: arXiv cs.CY 2025
Year: 2025

Large Language Models (LLMs) are changing the way we operate our society and will undoubtedly impact power systems as well - but how exactly? By integrating various data streams - including real-time grid data, market dynamics, and consumer behaviors - LLMs have the potential to make power system operations more adaptive, enhance proactive security measures, and deliver personalized energy services. This paper provides a comprehensive analysis of 30 real-world applications across eight key categories: Grid Operations and Management, Energy Markets and Trading, Personalized Energy Management and Customer Engagement, Grid Planning and Education, Grid Security and Compliance, Advanced Data Analysis and Knowledge Discovery, Emerging Applications and Societal Impact, and LLM-Enhanced Reinforcement Learning. Critical technical hurdles, such as data privacy and model reliability, are examined, along with possible solutions. Ultimately, this review illustrates how LLMs can significantly contribute to building more resilient, efficient, and sustainable energy infrastructures, underscoring the necessity of their responsible and equitable deployment.
Hyperlocal disaster damage assessment using bi-temporal street-view imagery and pre-trained vision models
Venue: arXiv cs.CV 2025
Year: 2025

Street-view images offer unique advantages for disaster damage estimation as they capture impacts from a visual perspective and provide detailed, on-the-ground insights. Despite several investigations attempting to analyze street-view images for damage estimation, they mainly focus on post-disaster images. The potential of time-series street-view images remains underexplored. Pre-disaster images provide valuable benchmarks for accurate damage estimations at building and street levels. These images could aid annotators in objectively labeling post-disaster impacts, improving the reliability of labeled data sets for model training, and potentially enhancing the model performance in damage evaluation. The goal of this study is to estimate hyperlocal, on-the-ground disaster damages using bi-temporal street-view images and advanced pre-trained vision models. Street-view images before and after 2024 Hurricane Milton in Horseshoe Beach, Florida, were collected for experiments. The objectives are: (1) to assess the performance gains of incorporating pre-disaster street-view images as a no-damage category in fine-tuning pre-trained models, including Swin Transformer and ConvNeXt, for damage level classification; (2) to design and evaluate a dual-channel algorithm that reads pair-wise pre- and post-disaster street-view images for hyperlocal damage assessment. The results indicate that incorporating pre-disaster street-view images and employing a dual-channel processing framework can significantly enhance damage assessment accuracy. The accuracy improves from 66.14% with the Swin Transformer baseline to 77.11% with the dual-channel Feature-Fusion ConvNeXt model. This research enables rapid, operational damage assessments at hyperlocal spatial resolutions, providing valuable insights to support effective decision-making in disaster management and resilience planning.
A Case for Kolmogorov-Arnold Networks in Prefetching: Towards Low-Latency, Generalizable ML-Based Prefetchers
Venue: arXiv cs.AR 2025
Year: 2025

The memory wall problem arises due to the disparity between fast processors and slower memory, causing significant delays in data access, even more so on edge devices. Data prefetching is a key strategy to address this, with traditional methods evolving to incorporate Machine Learning (ML) for improved accuracy. Modern prefetchers must balance high accuracy with low latency to further practicality. We explore the applicability of utilizing Kolmogorov-Arnold Networks (KAN) with learnable activation functions,a prefetcher we implemented called KANBoost, to further this aim. KANs are a novel, state-of-the-art model that work on breaking down continuous, bounded multi-variate functions into functions of their constituent variables, and use these constitutent functions as activations on each individual neuron. KANBoost predicts the next memory access by modeling deltas between consecutive addresses, offering a balance of accuracy and efficiency to mitigate the memory wall problem with minimal overhead, instead of relying on address-correlation prefetching. Initial results indicate that KAN-based prefetching reduces inference latency (18X lower than state-of-the-art ML prefetchers) while achieving moderate IPC improvements (2.5\% over no-prefetching). While KANs still face challenges in capturing long-term dependencies, we propose that future research should explore hybrid models that combine KAN efficiency with stronger sequence modeling techniques, paving the way for practical ML-based prefetching in edge devices and beyond.
crowd-hpo: Realistic Hyperparameter Optimization and Benchmarking for Learning from Crowds with Noisy Labels
Venue: arXiv cs.LG 2025
Year: 2025

Crowdworking is a cost-efficient solution for acquiring class labels. Since these labels are subject to noise, various approaches to learning from crowds have been proposed. Typically, these approaches are evaluated with default hyperparameter configurations, resulting in unfair and suboptimal performance, or with hyperparameter configurations tuned via a validation set with ground truth class labels, representing an often unrealistic scenario. Moreover, both setups can produce different approach rankings, complicating study comparisons. Therefore, we introduce crowd-hpo as a framework for evaluating approaches to learning from crowds in combination with criteria to select well-performing hyperparameter configurations with access only to noisy crowd-labeled validation data. Extensive experiments with neural networks demonstrate that these criteria select hyperparameter configurations, which improve the learning from crowd approaches' generalization performances, measured on separate test sets with ground truth labels. Hence, incorporating such criteria into experimental studies is essential for enabling fairer and more realistic benchmarking.
VibWalk: Mapping Lower-limb Haptic Experiences of Everyday Walking
Venue: arXiv cs.HC 2025
Year: 2025

Walking is among the most common human activities where the feet can gather rich tactile information from the ground. The dynamic contact between the feet and the ground generates vibration signals that can be sensed by the foot skin. While existing research focuses on foot pressure sensing and lower-limb interactions, methods of decoding tactile information from foot vibrations remain underexplored. Here, we propose a foot-equipped wearable system capable of recording wideband vibration signals during walking activities. By enabling location-based recording, our system generates maps of haptic data that encode information on ground materials, lower-limb activities, and road conditions. Its efficacy was demonstrated through studies involving 31 users walking over 18 different ground textures, achieving an overall identification accuracy exceeding 95\% (cross-user accuracy of 87\%). Our system allows pedestrians to map haptic information through their daily walking activities, which has potential applications in creating digitalized walking experiences and monitoring road conditions.
Enhancing Dialogue Systems with Discourse-Level Understanding Using Deep Canonical Correlation Analysis
Venue: arXiv cs.CL 2025
Year: 2025

The evolution of conversational agents has been driven by the need for more contextually aware systems that can effectively manage dialogue over extended interactions. To address the limitations of existing models in capturing and utilizing long-term conversational history, we propose a novel framework that integrates Deep Canonical Correlation Analysis (DCCA) for discourse-level understanding. This framework learns discourse tokens to capture relationships between utterances and their surrounding context, enabling a better understanding of long-term dependencies. Experiments on the Ubuntu Dialogue Corpus demonstrate significant enhancement in response selection, based on the improved automatic evaluation metric scores. The results highlight the potential of DCCA in improving dialogue systems by allowing them to filter out irrelevant context and retain critical discourse information for more accurate response retrieval.
High dimensional online calibration in polynomial time
Venue: arXiv cs.LG 2025
Year: 2025

In online (sequential) calibration, a forecaster predicts probability distributions over a finite outcome space $[d]$ over a sequence of $T$ days, with the goal of being calibrated. While asymptotically calibrated strategies are known to exist, they suffer from the curse of dimensionality: the best known algorithms require $\exp(d)$ days to achieve non-trivial calibration. In this work, we present the first asymptotically calibrated strategy that guarantees non-trivial calibration after a polynomial number of rounds. Specifically, for any desired accuracy $ε> 0$, our forecaster becomes $ε$-calibrated after $T = d^{O(1/ε^2)}$ days. We complement this result with a lower bound, proving that at least $T = d^{Ω(\log(1/ε))}$ rounds are necessary to achieve $ε$-calibration. Our results resolve the open questions posed by [Abernethy-Mannor'11, Hazan-Kakade'12]. Our algorithm is inspired by recent breakthroughs in swap regret minimization [Peng-Rubinstein'24, Dagan et al.'24]. Despite its strong theoretical guarantees, the approach is remarkably simple and intuitive: it randomly selects among a set of sub-forecasters, each of which predicts the empirical outcome frequency over recent time windows.
Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning
Venue: arXiv cs.LG 2025
Year: 2025

Large Artificial Intelligence Models (LAMs) powered by massive datasets, extensive parameter scales, and extensive computational resources, leading to significant transformations across various industries. Yet, their practical deployment on resource-limited mobile edge devices is hindered by critical challenges such as data privacy, constrained resources, and high overhead costs. Addressing this gap, this paper proposes a novel framework, named Quantized Split Federated Fine-Tuning Large AI Model (SFLAM). By partitioning the training load between edge devices and servers using a split learning paradigm, SFLAM can facilitate the operation of large models on devices and significantly lowers the memory requirements on edge devices. Additionally, SFLAM incorporates quantization management, power control, and bandwidth allocation strategies to enhance training efficiency while concurrently reducing energy consumption and communication latency. A theoretical analysis exploring the latency-energy trade-off is presented, and the framework's efficacy is validated via comprehensive simulations. The findings indicate that SFLAM achieves superior performance in terms of learning efficiency and scalability compared to conventional methods, thereby providing a valuable approach for enabling advanced AI services in resource-constrained scenarios.
Exploring Modality Disruption in Multimodal Fake News Detection
Venue: arXiv cs.MM 2025
Year: 2025

The rapid growth of social media has led to the widespread dissemination of fake news across multiple content forms, including text, images, audio, and video. Compared to unimodal fake news detection, multimodal fake news detection benefits from the increased availability of information across multiple modalities. However, in the context of social media, certain modalities in multimodal fake news detection tasks may contain disruptive or over-expressive information. These elements often include exaggerated or embellished content. We define this phenomenon as modality disruption and explore its impact on detection models through experiments. To address the issue of modality disruption in a targeted manner, we propose a multimodal fake news detection framework, FND-MoE. Additionally, we design a two-pass feature selection mechanism to further mitigate the impact of modality disruption. Extensive experiments on the FakeSV and FVC-2018 datasets demonstrate that FND-MoE significantly outperforms state-of-the-art methods, with accuracy improvements of 3.45% and 3.71% on the respective datasets compared to baseline models.
A Multi-Layered Security Analysis of Blockchain Systems: From Attack Vectors to Defense and System Hardening
Venue: arXiv cs.CR 2025
Year: 2025

The application of Bitcoin enables people to understand blockchain technology gradually. Bitcoin is a decentralized currency that does not rely on third-party credit institutions, and the core of Bitcoin's underlying technology is blockchain. With the increasing value of Bitcoin and the vigorous development of decentralization, people's research on blockchain is also increasing day by day. Today's blockchain technology has not only made great achievements in the application of Bitcoin, but has also been preliminarily applied in other fields, such as finance, medical treatment, the Internet of Things, and so on. However, with the initial application of blockchain technology on the Internet, the security of blockchain technology has also been widely concerned by people in the industry. For example, whether currency trading platforms, smart contracts, blockchain consensus mechanisms, and other technologies are vulnerable to attacks, and how we can defend against these attacks digitally and optimize the blockchain system is exactly the subject we want to study. For the security of appeal blockchain, this paper first analyzes the security threats faced by the application digital currency trading platform of the blockchain system, then analyzes the security problems of smart contract closely related to blockchain 2.0, and then analyzes and studies the security threats of blockchain public chain, consensus mechanism, and P2P. Finally, combined with the security problems at all levels of the blockchain system we analyze and study how to optimize the security of the blockchain system.
RSLAQ -- A Robust SLA-driven 6G O-RAN QoS xApp using deep reinforcement learning
Venue: arXiv cs.NI 2025
Year: 2025

The evolution of 6G envisions a wide range of applications and services characterized by highly differentiated and stringent Quality of Service (QoS) requirements. Open Radio Access Network (O-RAN) technology has emerged as a transformative approach that enables intelligent software-defined management of the RAN. A cornerstone of O-RAN is the RAN Intelligent Controller (RIC), which facilitates the deployment of intelligent applications (xApps and rApps) near the radio unit. In this context, QoS management through O-RAN has been explored using network slice and machine learning (ML) techniques. Although prior studies have demonstrated the ability to optimize RAN resource allocation and prioritize slices effectively, they have not considered the critical integration of Service Level Agreements (SLAs) into the ML learning process. This omission can lead to suboptimal resource utilization and, in many cases, service outages when target Key Performance Indicators (KPIs) are not met. This work introduces RSLAQ, an innovative xApp designed to ensure robust QoS management for RAN slicing while incorporating SLAs directly into its operational framework. RSLAQ translates operator policies into actionable configurations, guiding resource distribution and scheduling for RAN slices. Using deep reinforcement learning (DRL), RSLAQ dynamically monitors RAN performance metrics and computes optimal actions, embedding SLA constraints to mitigate conflicts and prevent outages. Extensive system-level simulations validate the efficacy of the proposed solution, demonstrating its ability to optimize resource allocation, improve SLA adherence, and maintain operational reliability (>95%) in challenging scenarios.
Feature-Aware Malicious Output Detection and Mitigation
Venue: arXiv cs.CL 2025
Year: 2025

The rapid advancement of large language models (LLMs) has brought significant benefits to various domains while introducing substantial risks. Despite being fine-tuned through reinforcement learning, LLMs lack the capability to discern malicious content, limiting their defense against jailbreak. To address these safety concerns, we propose a feature-aware method for harmful response rejection (FMM), which detects the presence of malicious features within the model's feature space and adaptively adjusts the model's rejection mechanism. By employing a simple discriminator, we detect potential malicious traits during the decoding phase. Upon detecting features indicative of toxic tokens, FMM regenerates the current token. By employing activation patching, an additional rejection vector is incorporated during the subsequent token generation, steering the model towards a refusal response. Experimental results demonstrate the effectiveness of our approach across multiple language models and diverse attack techniques, while crucially maintaining the models' standard generation capabilities.
Towards More Efficient, Robust, Instance-adaptive, and Generalizable Sequential Decision making
Venue: arXiv cs.LG 2025
Year: 2025

The primary goal of my Ph.D. study is to develop provably efficient and practical algorithms for data-driven sequential decision-making under uncertainty. My work focuses on reinforcement learning (RL), multi-armed bandits, and their applications, including recommendation systems, computer networks, video analytics, and large language models (LLMs). Sequential decision-making methods, such as bandits and RL, have demonstrated remarkable success - ranging from outperforming human players in complex games like Atari and Go to advancing robotics, recommendation systems, and fine-tuning LLMs. Despite these successes, many established algorithms rely on idealized models that can fail under model misspecifications or adversarial perturbations, particularly in settings where accurate prior knowledge of the underlying model class is unavailable or where malicious users operate within dynamic systems. These challenges are pervasive in real-world applications, where robust and adaptive solutions are critical. Furthermore, while worst-case guarantees provide theoretical reliability, they often fail to capture instance-dependent performance, which can lead to more efficient and practical solutions. Another key challenge lies in generalizing to new, unseen environments, a crucial requirement for deploying these methods in dynamic and unpredictable settings. To address these limitations, my research aims to develop more efficient, robust, instance-adaptive, and generalizable sequential decision-making algorithms for both reinforcement learning and bandits. Towards this end, I focus on developing more efficient, robust, instance-adaptive, and generalizable for both general reinforcement learning (RL) and bandits.