OpenRaiser/Intern-Atlas huggingface dataset:
MuJoCo Playground
Venue: arXiv cs.RO 2025
Year: 2025

We introduce MuJoCo Playground, a fully open-source framework for robot learning built with MJX, with the express goal of streamlining simulation, training, and sim-to-real transfer onto robots. With a simple "pip install playground", researchers can train policies in minutes on a single GPU. Playground supports diverse robotic platforms, including quadrupeds, humanoids, dexterous hands, and robotic arms, enabling zero-shot sim-to-real transfer from both state and pixel inputs. This is achieved through an integrated stack comprising a physics engine, batch renderer, and training environments. Along with video results, the entire framework is freely available at playground.mujoco.org
BrainWavLM: Fine-tuning Speech Representations with Brain Responses to Language
Venue: arXiv cs.CL 2025
Year: 2025

Speech encoding models use auditory representations to predict how the human brain responds to spoken language stimuli. Most performant encoding models linearly map the hidden states of artificial neural networks to brain data, but this linear restriction may limit their effectiveness. In this work, we use low-rank adaptation (LoRA) to fine-tune a WavLM-based encoding model end-to-end on a brain encoding objective, producing a model we name BrainWavLM. We show that fine-tuning across all of cortex improves average encoding performance with greater stability than without LoRA. This improvement comes at the expense of low-level regions like auditory cortex (AC), but selectively fine-tuning on these areas improves performance in AC, while largely retaining gains made in the rest of cortex. Fine-tuned models generalized across subjects, indicating that they learned robust brain-like representations of the speech stimuli. Finally, by training linear probes, we showed that the brain data strengthened semantic representations in the speech model without any explicit annotations. Our results demonstrate that brain fine-tuning produces best-in-class speech encoding models, and that non-linear methods have the potential to bridge the gap between artificial and biological representations of semantics.
Robust Graph-Based Semi-Supervised Learning via $p$-Conductances
Venue: arXiv cs.LG 2025
Year: 2025

We study the problem of semi-supervised learning on graphs in the regime where data labels are scarce or possibly corrupted. We propose an approach called $p$-conductance learning that generalizes the $p$-Laplace and Poisson learning methods by introducing an objective reminiscent of $p$-Laplacian regularization and an affine relaxation of the label constraints. This leads to a family of probability measure mincut programs that balance sparse edge removal with accurate distribution separation. Our theoretical analysis connects these programs to well-known variational and probabilistic problems on graphs (including randomized cuts, effective resistance, and Wasserstein distance) and provides motivation for robustness when labels are diffused via the heat kernel. Computationally, we develop a semismooth Newton-conjugate gradient algorithm and extend it to incorporate class-size estimates when converting the continuous solutions into label assignments. Empirical results on computer vision and citation datasets demonstrate that our approach achieves state-of-the-art accuracy in low label-rate, corrupted-label, and partial-label regimes.
Generative AI for Internet of Things Security: Challenges and Opportunities
Venue: arXiv cs.CR 2025
Year: 2025

As Generative AI (GenAI) continues to gain prominence and utility across various sectors, their integration into the realm of Internet of Things (IoT) security evolves rapidly. This work delves into an examination of the state-of-the-art literature and practical applications on how GenAI could improve and be applied in the security landscape of IoT. Our investigation aims to map the current state of GenAI implementation within IoT security, exploring their potential to fortify security measures further. Through the compilation, synthesis, and analysis of the latest advancements in GenAI technologies applied to IoT, this paper not only introduces fresh insights into the field, but also lays the groundwork for future research directions. It explains the prevailing challenges within IoT security, discusses the effectiveness of GenAI in addressing these issues, and identifies significant research gaps through MITRE Mitigations. Accompanied with three case studies, we provide a comprehensive overview of the progress and future prospects of GenAI applications in IoT security. This study serves as a foundational resource to improve IoT security through the innovative application of GenAI, thus contributing to the broader discourse on IoT security and technology integration.
Uncovering Disparities in Rideshare Drivers Earning and Work Patterns: A Case Study of Chicago
Venue: arXiv cs.HC 2025
Year: 2025

Ride-sharing services are revolutionizing urban mobility while simultaneously raising significant concerns regarding fairness and driver equity. This study employs Chicago Trip Network Provider dataset to investigate disparities in ride-sharing earnings between 2018 and 2023. Our analysis reveals marked temporal shifts, including an earnings surge in early 2021 followed by fluctuations and a decline in inflation-adjusted income, as well as pronounced spatial disparities, with drivers in Central and airport regions earning substantially more than those in peripheral areas. Recognizing the limitations of trip-level data, we introduce a novel trip-driver assignment algorithm to reconstruct plausible daily work patterns, uncovering distinct driver clusters with varied earning profiles. Notably, drivers operating during late-evening and overnight hours secure higher per-trip and hourly rates, while emerging groups in low-demand regions face significant earnings deficits. Our findings call for more transparent pricing models and a re-examination of platform design to promote equitable driver outcomes.
Towards Automated Fact-Checking of Real-World Claims: Exploring Task Formulation and Assessment with LLMs
Venue: arXiv cs.CL 2025
Year: 2025

Fact-checking is necessary to address the increasing volume of misinformation. Traditional fact-checking relies on manual analysis to verify claims, but it is slow and resource-intensive. This study establishes baseline comparisons for Automated Fact-Checking (AFC) using Large Language Models (LLMs) across multiple labeling schemes (binary, three-class, five-class) and extends traditional claim verification by incorporating analysis, verdict classification, and explanation in a structured setup to provide comprehensive justifications for real-world claims. We evaluate Llama-3 models of varying sizes (3B, 8B, 70B) on 17,856 claims collected from PolitiFact (2007-2024) using evidence retrieved via restricted web searches. We utilize TIGERScore as a reference-free evaluation metric to score the justifications. Our results show that larger LLMs consistently outperform smaller LLMs in classification accuracy and justification quality without fine-tuning. We find that smaller LLMs in a one-shot scenario provide comparable task performance to fine-tuned Small Language Models (SLMs) with large context sizes, while larger LLMs consistently surpass them. Evidence integration improves performance across all models, with larger LLMs benefiting most. Distinguishing between nuanced labels remains challenging, emphasizing the need for further exploration of labeling schemes and alignment with evidences. Our findings demonstrate the potential of retrieval-augmented AFC with LLMs.
Exploring Emotion-Sensitive LLM-Based Conversational AI
Venue: arXiv cs.HC 2025
Year: 2025

Conversational AI chatbots have become increasingly common within the customer service industry. Despite improvements in their emotional development, they often lack the authenticity of real customer service interactions or the competence of service providers. By comparing emotion-sensitive and emotion-insensitive LLM-based chatbots across 30 participants, we aim to explore how emotional sensitivity in chatbots influences perceived competence and overall customer satisfaction in service interactions. Additionally, we employ sentiment analysis techniques to analyze and interpret the emotional content of user inputs. We highlight that perceptions of chatbot trustworthiness and competence were higher in the case of the emotion-sensitive chatbot, even if issue resolution rates were not affected. We discuss implications of improved user satisfaction from emotion-sensitive chatbots and potential applications in support services.
On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms
Venue: arXiv cs.AI 2025
Year: 2025

To create usable and deployable Artificial Intelligence (AI) systems, there requires a level of assurance in performance under many different conditions. Many times, deployed machine learning systems will require more classic logic and reasoning performed through neurosymbolic programs jointly with artificial neural network sensing. While many prior works have examined the assurance of a single component of the system solely with either the neural network alone or entire enterprise systems, very few works have examined the assurance of integrated neurosymbolic systems. Within this work, we assess the assurance of end-to-end fully differentiable neurosymbolic systems that are an emerging method to create data-efficient and more interpretable models. We perform this investigation using Scallop, an end-to-end neurosymbolic library, across classification and reasoning tasks in both the image and audio domains. We assess assurance across adversarial robustness, calibration, user performance parity, and interpretability of solutions for catching misaligned solutions. We find end-to-end neurosymbolic methods present unique opportunities for assurance beyond their data efficiency through our empirical results but not across the board. We find that this class of neurosymbolic models has higher assurance in cases where arithmetic operations are defined and where there is high dimensionality to the input space, where fully neural counterparts struggle to learn robust reasoning operations. We identify the relationship between neurosymbolic models' interpretability to catch shortcuts that later result in increased adversarial vulnerability despite performance parity. Finally, we find that the promise of data efficiency is typically only in the case of class imbalanced reasoning problems.
A Comprehensive Survey on Imbalanced Data Learning
Venue: arXiv cs.LG 2025
Year: 2025

With the expansion of data availability, machine learning (ML) has achieved remarkable breakthroughs in both academia and industry. However, imbalanced data distributions are prevalent in various types of raw data and severely hinder the performance of ML by biasing the decision-making processes. To deepen the understanding of imbalanced data and facilitate the related research and applications, this survey systematically analyzes various real-world data formats and concludes existing researches for different data formats into four distinct categories: data re-balancing, feature representation, training strategy, and ensemble learning. This structured analysis helps researchers comprehensively understand the pervasive nature of imbalance across diverse data formats, thereby paving a clearer path toward achieving specific research goals. We provide an overview of relevant open-source libraries, spotlight current challenges, and offer novel insights aimed at fostering future advancements in this critical area of study.
Low Complexity Artificial Noise Aided Beam Focusing Design in Near-Field Terahertz Communications
Venue: arXiv cs.IT 2025
Year: 2025

In this paper, we develop a novel low-complexity artificial noise (AN) aided beam focusing scheme in a near-field terahertz wiretap communication system. In this system, the base station (BS) equipped with a large-scale array transmits signals to a legitimate user, while mitigating information leakage to an eavesdropper. We formulate an optimization problem to maximize the secrecy rate achieved at the legitimate user and solve it by designing the optimal beam focusing and power allocation. Numerical results demonstrate the significant performance improvement achieved by the proposed AN aided beam focusing scheme, especially when the eavesdropper is located closer to the BS than the legitimate user.
What exactly has TabPFN learned to do?
Venue: arXiv cs.LG 2025
Year: 2025

TabPFN [Hollmann et al., 2023], a Transformer model pretrained to perform in-context learning on fresh tabular classification problems, was presented at the last ICLR conference. To better understand its behavior, we treat it as a black-box function approximator generator and observe its generated function approximations on a varied selection of training datasets. Exploring its learned inductive biases in this manner, we observe behavior that is at turns either brilliant or baffling. We conclude this post with thoughts on how these results might inform the development, evaluation, and application of prior-data fitted networks (PFNs) in the future.
Outback: Fast and Communication-efficient Index for Key-Value Store on Disaggregated Memory
Venue: arXiv cs.DB 2025
Year: 2025

Disaggregated memory systems achieve resource utilization efficiency and system scalability by distributing computation and memory resources into distinct pools of nodes. RDMA is an attractive solution to support high-throughput communication between different disaggregated resource pools. However, existing RDMA solutions face a dilemma: one-sided RDMA completely bypasses computation at memory nodes, but its communication takes multiple round trips; two-sided RDMA achieves one-round-trip communication but requires non-trivial computation for index lookups at memory nodes, which violates the principle of disaggregated memory. This work presents Outback, a novel indexing solution for key-value stores with a one-round-trip RDMA-based network that does not incur computation-heavy tasks at memory nodes. Outback is the first to utilize dynamic minimal perfect hashing and separates its index into two components: one memory-efficient and compute-heavy component at compute nodes and the other memory-heavy and compute-efficient component at memory nodes. We implement a prototype of Outback and evaluate its performance in a public cloud. The experimental results show that Outback achieves higher throughput than both the state-of-the-art one-sided RDMA and two-sided RDMA-based in-memory KVS by 1.06-5.03x, due to the unique strength of applying a separated perfect hashing index.
End-to-End triplet loss based fine-tuning for network embedding in effective PII detection
Venue: arXiv cs.LG 2025
Year: 2025

There are many approaches in mobile data ecosystem that inspect network traffic generated by applications running on user's device to detect personal data exfiltration from the user's device. State-of-the-art methods rely on features extracted from HTTP requests and in this context, machine learning involves training classifiers on these features and making predictions using labelled packet traces. However, most of these methods include external feature selection before model training. Deep learning, on the other hand, typically does not require such techniques, as it can autonomously learn and identify patterns in the data without external feature extraction or selection algorithms. In this article, we propose a novel deep learning based end-to-end learning framework for prediction of exposure of personally identifiable information (PII) in mobile packets. The framework employs a pre-trained large language model (LLM) and an autoencoder to generate embedding of network packets and then uses a triplet-loss based fine-tuning method to train the model, increasing detection effectiveness using two real-world datasets. We compare our proposed detection framework with other state-of-the-art works in detecting PII leaks from user's device.
A Contextual-Aware Position Encoding for Sequential Recommendation
Venue: arXiv cs.IR 2025
Year: 2025

Sequential recommendation (SR), which encodes user activity to predict the next action, has emerged as a widely adopted strategy in developing commercial personalized recommendation systems. A critical component of modern SR models is the attention mechanism, which synthesizes users' historical activities. This mechanism is typically order-invariant and generally relies on position encoding (PE). Conventional SR models simply assign a learnable vector to each position, resulting in only modest gains compared to traditional recommendation models. Moreover, limited research has been conducted on position encoding tailored for sequential recommendation, leaving a significant gap in addressing its unique requirements. To bridge this gap, we propose a novel Contextual-Aware Position Encoding method for sequential recommendation, abbreviated as CAPE. To the best of our knowledge, CAPE is the first PE method specifically designed for sequential recommendation. Comprehensive experiments conducted on benchmark SR datasets demonstrate that CAPE consistently enhances multiple mainstream backbone models and achieves state-of-the-art performance, across small and large scale model size. Furthermore, we deployed CAPE in an industrial setting on a real-world commercial platform, clearly showcasing the effectiveness of our approach. Our source code is available at https://github.com/yjdy/CAPE.
Implementation of a Fuzzy Relational Database. Case Study: Chilean Cardboard Industry in the Maule Region
Venue: arXiv cs.CY 2025
Year: 2025

The international database community refers to the manipulation of data with inaccuracy and uncertainty using the term fuzzy, which has been translated into Spanish as "borroso" and into French as "flou". Semantically, this term conveys two main ideas: first, the natural concept of ambiguity or vagueness in human reasoning, and second, its connection to fuzzy set theory, fuzzy logic, and possibility theory, as developed by Zadeh between 1965 and 1977. This article explores two key aspects: the attributes of the fuzzy data model GEFRED (GENeralized model for Fuzzy RElational Database) and their implementation in a Relational Database (RDB). The modeling of these attributes was conducted in a Chilian cardboard manufacturing company located in the Maule Region, where the described phenomena involve imprecise and uncertain attributes and values. Specifically, our focus is on the knowledge related to the manufacturing process of coated cardboard, particularly the quality control process for finished products in the company's Conversion Department. The quality of these products, categorized as either stacks or rolls, is characterized using both classical and fuzzy attributes. Classical attributes are typically measured with physical instruments, whereas fuzzy attributes are assessed through human senses, primarily sight and touch, as perceived by the operators.
Typhoon T1: An Open Thai Reasoning Model
Venue: arXiv cs.CL 2025
Year: 2025

This paper introduces Typhoon T1, an open effort to develop an open Thai reasoning model. A reasoning model is a relatively new type of generative model built on top of large language models (LLMs). A reasoning model generates a long chain of thought before arriving at a final answer, an approach found to improve performance on complex tasks. However, details on developing such a model are limited, especially for reasoning models that can generate traces in a low-resource language. Typhoon T1 presents an open effort that dives into the details of developing a reasoning model in a more cost-effective way by leveraging supervised fine-tuning using open datasets, instead of reinforcement learning. This paper shares the details about synthetic data generation and training, as well as our dataset and model weights. Additionally, we provide insights gained from developing a reasoning model that generalizes across domains and is capable of generating reasoning traces in a low-resource language, using Thai as an example. We hope this open effort provides a foundation for further research in this field.
Evolution of Data-driven Single- and Multi-Hazard Susceptibility Mapping and Emergence of Deep Learning Methods
Venue: arXiv cs.CV 2025
Year: 2025

Data-driven susceptibility mapping of natural hazards has harnessed the advances in classification methods used on heterogeneous sources represented as raster images. Susceptibility mapping is an important step towards risk assessment for any natural hazard. Increasingly, multiple hazards co-occur spatially, temporally, or both, which calls for an in-depth study on multi-hazard susceptibility mapping. In recent years, single-hazard susceptibility mapping algorithms have become well-established and have been extended to multi-hazard susceptibility mapping. Deep learning is also emerging as a promising method for single-hazard susceptibility mapping. Here, we discuss the evolution of methods for a single hazard, their extensions to multi-hazard maps as a late fusion of decisions, and the use of deep learning methods in susceptibility mapping. We finally propose a vision for adapting data fusion strategies in multimodal deep learning to multi-hazard susceptibility mapping. From the background study of susceptibility methods, we demonstrate that deep learning models are promising, untapped methods for multi-hazard susceptibility mapping. Data fusion strategies provide a larger space of deep learning models applicable to multi-hazard susceptibility mapping.
Anchor Sponsor Firms in Open Source Software Ecosystems
Venue: arXiv cs.SE 2025
Year: 2025

Firms are intensifying their involvement with open source software (OSS), going beyond contributing to individual projects and releasing their own core technologies as OSS. These technologies, from web frameworks to programming languages, are the foundations of large and growing ecosystems. Yet we know little about how these anchor sponsors shape the behavior of OSS contributors. We examine Mozilla Corporation's role as incubator and anchor sponsor in the Rust programming language ecosystem, leveraging data on nearly 30,000 developers and 40,000 OSS projects from 2015 to 2022. When Mozilla abruptly exited Rust in August 2020, event-study models estimate a negative impact on ecosystem activity: a 9\% immediate drop in weekly commits and a 0.6 percentage point decline in trend. We observe an asymmetry in the shock's effects: former Mozilla developers and close collaborators continued contributing relatively quickly, whereas more distant developers showed reduced or ceased activity even six months later. An agent-based model of an OSS ecosystem with an anchor sponsor replicates these patterns. We also find a marked slowdown in new developers and projects entering Rust post-shock. Our results suggest that Mozilla served as a critical signal of Rust's quality and stability. Once withdrawn, newcomers and less-embedded developers were the most discouraged, raising concerns about long-term ecosystem sustainability.