OpenRaiser/Intern-Atlas huggingface dataset:
Deep Reinforcement Learning for Ranking Utility Tuning in the Ad Recommender System at Pinterest
Venue: arXiv cs.LG 2025
Year: 2025

The ranking utility function in an ad recommender system, which linearly combines predictions of various business goals, plays a central role in balancing values across the platform, advertisers, and users. Traditional manual tuning, while offering simplicity and interpretability, often yields suboptimal results due to its unprincipled tuning objectives, the vast amount of parameter combinations, and its lack of personalization and adaptability to seasonality. In this work, we propose a general Deep Reinforcement Learning framework for Personalized Utility Tuning (DRL-PUT) to address the challenges of multi-objective optimization within ad recommender systems. Our key contributions include: 1) Formulating the problem as a reinforcement learning task: given the state of an ad request, we predict the optimal hyperparameters to maximize a pre-defined reward. 2) Developing an approach to directly learn an optimal policy model using online serving logs, avoiding the need to estimate a value function, which is inherently challenging due to the high variance and unbalanced distribution of immediate rewards. We evaluated DRL-PUT through an online A/B experiment in Pinterest's ad recommender system. Compared to the baseline manual utility tuning approach, DRL-PUT improved the click-through rate by 9.7% and the long click-through rate by 7.7% on the treated segment. We conducted a detailed ablation study on the impact of different reward definitions and analyzed the personalization aspect of the learned policy model.
Towards Log Analysis with AI Agents: Cowrie Case Study
Venue: arXiv cs.CR 2025
Year: 2025

The scarcity of real-world attack data significantly hinders progress in cybersecurity research and education. Although honeypots like Cowrie effectively collect live threat intelligence, they generate overwhelming volumes of unstructured and heterogeneous logs, rendering manual analysis impractical. As a first step in our project on secure and efficient AI automation, this study explores the use of AI agents for automated log analysis. We present a lightweight and automated approach to process Cowrie honeypot logs. Our approach leverages AI agents to intelligently parse, summarize, and extract insights from raw data, while also considering the security implications of deploying such an autonomous system. Preliminary results demonstrate the pipeline's effectiveness in reducing manual effort and identifying attack patterns, paving the way for more advanced autonomous cybersecurity analysis in future work.
Label Smoothing++: Enhanced Label Regularization for Training Neural Networks
Venue: arXiv cs.CV 2025
Year: 2025

Training neural networks with one-hot target labels often results in overconfidence and overfitting. Label smoothing addresses this issue by perturbing the one-hot target labels by adding a uniform probability vector to create a regularized label. Although label smoothing improves the network's generalization ability, it assigns equal importance to all the non-target classes, which destroys the inter-class relationships. In this paper, we propose a novel label regularization training strategy called Label Smoothing++, which assigns non-zero probabilities to non-target classes and accounts for their inter-class relationships. Our approach uses a fixed label for the target class while enabling the network to learn the labels associated with non-target classes. Through extensive experiments on multiple datasets, we demonstrate how Label Smoothing++ mitigates overconfident predictions while promoting inter-class relationships and generalization capabilities.
Context-Aware Knowledge Distillation with Adaptive Weighting for Image Classification
Venue: arXiv cs.CV 2025
Year: 2025

Knowledge distillation (KD) is a widely used technique to transfer knowledge from a large teacher network to a smaller student model. Traditional KD uses a fixed balancing factor alpha as a hyperparameter to combine the hard-label cross-entropy loss with the soft-label distillation loss. However, a static alpha is suboptimal because the optimal trade-off between hard and soft supervision can vary during training. In this work, we propose an Adaptive Knowledge Distillation (AKD) framework. First we try to make alpha as learnable parameter that can be automatically learned and optimized during training. Then we introduce a formula to reflect the gap between the student and the teacher to compute alpha dynamically, guided by student-teacher discrepancies, and further introduce a Context-Aware Module (CAM) using MLP + Attention to adaptively reweight class-wise teacher outputs. Experiments on CIFAR-10 with ResNet-50 as teacher and ResNet-18 as student demonstrate that our approach achieves superior accuracy compared to fixed-weight KD baselines, and yields more stable convergence.
A Real-Time, Vision-Based System for Badminton Smash Speed Estimation on Mobile Devices
Venue: arXiv cs.CV 2025
Year: 2025

Performance metrics in sports, such as shot speed and angle, provide crucial feedback for athlete development. However, the technology to capture these metrics has historically been expensive, complex, and largely inaccessible to amateur and recreational players. This paper addresses this gap in the context of badminton, one of the world's most popular sports, by introducing a novel, cost-effective, and user-friendly system for measuring smash speed using ubiquitous smartphone technology. Our approach leverages a custom-trained YOLOv5 model for shuttlecock detection, combined with a Kalman filter for robust trajectory tracking. By implementing a video-based kinematic speed estimation method with spatiotemporal scaling, the system automatically calculates the shuttlecock's velocity from a standard video recording. The entire process is packaged into an intuitive mobile application, democratizing access to high-level performance analytics and empowering players at all levels to analyze and improve their game.
Plantbot: Integrating Plant and Robot through LLM Modular Agent Networks
Venue: arXiv cs.RO 2025
Year: 2025

We introduce Plantbot, a hybrid lifeform that connects a living plant with a mobile robot through a network of large language model (LLM) modules. Each module - responsible for sensing, vision, dialogue, or action - operates asynchronously and communicates via natural language, enabling seamless interaction across biological and artificial domains. This architecture leverages the capacity of LLMs to serve as hybrid interfaces, where natural language functions as a universal protocol, translating multimodal data (soil moisture, temperature, visual context) into linguistic messages that coordinate system behaviors. The integrated network transforms plant states into robotic actions, installing normativity essential for agency within the sensor-motor loop. By combining biological and robotic elements through LLM-mediated communication, Plantbot behaves as an embodied, adaptive agent capable of responding autonomously to environmental conditions. This approach suggests possibilities for a new model of artificial life, where decentralized, LLM modules coordination enable novel interactions between biological and artificial systems.
INF-3DP: Implicit Neural Fields for Collision-Free Multi-Axis 3D Printing
Venue: arXiv cs.RO 2025
Year: 2025

We introduce a general, scalable computational framework for multi-axis 3D printing based on implicit neural fields (INFs) that unifies all stages of toolpath generation and global collision-free motion planning. In our pipeline, input models are represented as signed distance fields, with fabrication objectives such as support-free printing, surface finish quality, and extrusion control being directly encoded in the optimization of an implicit guidance field. This unified approach enables toolpath optimization across both surface and interior domains, allowing shell and infill paths to be generated via implicit field interpolation. The printing sequence and multi-axis motion are then jointly optimized over a continuous quaternion field. Our continuous formulation constructs the evolving printing object as a time-varying SDF, supporting differentiable global collision handling throughout INF-based motion planning. Compared to explicit-representation-based methods, INF-3DP achieves up to two orders of magnitude speedup and significantly reduces waypoint-to-surface error. We validate our framework on diverse, complex models and demonstrate its efficiency with physical fabrication experiments using a robot-assisted multi-axis system.
Constitutional Law and AI Governance: Constraints on Model Licensing and Research Classification
Venue: arXiv cs.CY 2025
Year: 2025

Transformative AI systems may pose unprecedented catastrophic risks, but the U.S. Constitution places significant constraints on the government's ability to govern this technology. This paper examines how the First Amendment, administrative law, and the Fourteenth Amendment shape the legal vulnerability of two regulatory proposals: model licensing and AI research classification. While the First Amendment may provide some degree of protection for model algorithms or outputs, this protection does not foreclose regulation. Policymakers must also consider administrative legal requirements, due to both agency review and authority. Finally, while substantive due process and equal protection pose minimal obstacles, procedural due process requires the government to clearly define when developers vest a legal interest in their models. Given this analysis, effective AI governance requires careful implementation to avoid these legal challenges.
SasAgent: Multi-Agent AI System for Small-Angle Scattering Data Analysis
Venue: arXiv cs.AI 2025
Year: 2025

We introduce SasAgent, a multi-agent AI system powered by large language models (LLMs) that automates small-angle scattering (SAS) data analysis by leveraging tools from the SasView software and enables user interaction via text input. SasAgent features a coordinator agent that interprets user prompts and delegates tasks to three specialized agents for scattering length density (SLD) calculation, synthetic data generation, and experimental data fitting. These agents utilize LLM-friendly tools to execute tasks efficiently. These tools, including the model data tool, Retrieval-Augmented Generation (RAG) documentation tool, bump fitting tool, and SLD calculator tool, are derived from the SasView Python library. A user-friendly Gradio-based interface enhances user accessibility. Through diverse examples, we demonstrate SasAgent's ability to interpret complex prompts, calculate SLDs, generate accurate scattering data, and fit experimental datasets with high precision. This work showcases the potential of LLM-driven AI systems to streamline scientific workflows and enhance automation in SAS research.
Authorship Without Writing: Large Language Models and the Senior Author Analogy
Venue: arXiv cs.CY 2025
Year: 2025

The use of large language models (LLMs) in bioethical, scientific, and medical writing remains controversial. While there is broad agreement in some circles that LLMs cannot count as authors, there is no consensus about whether and how humans using LLMs can count as authors. In many fields, authorship is distributed among large teams of researchers, some of whom, including paradigmatic senior authors who guide and determine the scope of a project and ultimately vouch for its integrity, may not write a single word. In this paper, we argue that LLM use (under specific conditions) is analogous to a form of senior authorship. On this view, the use of LLMs, even to generate complete drafts of research papers, can be considered a legitimate form of authorship according to the accepted criteria in many fields. We conclude that either such use should be recognized as legitimate, or current criteria for authorship require fundamental revision. AI use declaration: GPT-5 was used to help format Box 1. AI was not used for any other part of the preparation or writing of this manuscript.
HapMorph: A Pneumatic Framework for Multi-Dimensional Haptic Property Rendering
Venue: arXiv cs.RO 2025
Year: 2025

Haptic interfaces that can simultaneously modulate multiple physical properties remain a fundamental challenge in human-robot interaction. Existing systems typically allow the rendering of either geometric features or mechanical properties, but rarely both, within wearable form factors. Here, we introduce HapMorph, a pneumatic framework that enables continuous, simultaneous modulation of object size and stiffness through antagonistic fabric-based pneumatic actuators (AFPAs). We implemented a HapMorph protoytpe designed for hands interaction achieving size variation from 50 to 104 mm, stiffness modulation up to 4.7 N/mm and mass of the wearable parts of just 21 g. Through systematic characterization, we demonstrate decoupled control of size and stiffness properties via dual-chamber pressure regulation. Human perception studies with 10 participants reveal that users can distinguish nine discrete states across three size categories and three stiffness levels with 89.4% accuracy and 6.7 s average response time. We further demonstrate extended architectures that combine AFPAs with complementary pneumatic structures to enable shape or geometry morphing with concurrent stiffness control. Our results establish antagonistic pneumatic principle as a pathway toward next-generation haptic interfaces, capable of multi-dimensiona rendering properties within practical wearable constraints.
Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks (Journal Version)
Venue: arXiv cs.NI 2025
Year: 2025

In wireless networks characterized by dense connectivity, the significant signaling overhead generated by distributed link scheduling algorithms can exacerbate issues like congestion, energy consumption, and radio footprint expansion. To mitigate these challenges, we propose a distributed link sparsification scheme employing graph neural networks (GNNs) to reduce scheduling overhead for delay-tolerant traffic while maintaining network capacity. A GNN module is trained to adjust contention thresholds for individual links based on traffic statistics and network topology, enabling links to withdraw from scheduling contention when they are unlikely to succeed. Our approach is facilitated by a novel offline constrained {unsupervised} learning algorithm capable of balancing two competing objectives: minimizing scheduling overhead while ensuring that total utility meets the required level. In simulated wireless multi-hop networks with up to 500 links, our link sparsification technique effectively alleviates network congestion and reduces radio footprints across four distinct distributed link scheduling protocols.
Neural Breadcrumbs: Membership Inference Attacks on LLMs Through Hidden State and Attention Pattern Analysis
Venue: arXiv cs.LG 2025
Year: 2025

Membership inference attacks (MIAs) reveal whether specific data was used to train machine learning models, serving as important tools for privacy auditing and compliance assessment. Recent studies have reported that MIAs perform only marginally better than random guessing against large language models, suggesting that modern pre-training approaches with massive datasets may be free from privacy leakage risks. Our work offers a complementary perspective to these findings by exploring how examining LLMs' internal representations, rather than just their outputs, may provide additional insights into potential membership inference signals. Our framework, \emph{memTrace}, follows what we call \enquote{neural breadcrumbs} extracting informative signals from transformer hidden states and attention patterns as they process candidate sequences. By analyzing layer-wise representation dynamics, attention distribution characteristics, and cross-layer transition patterns, we detect potential memorization fingerprints that traditional loss-based approaches may not capture. This approach yields strong membership detection across several model families achieving average AUC scores of 0.85 on popular MIA benchmarks. Our findings suggest that internal model behaviors can reveal aspects of training data exposure even when output-based signals appear protected, highlighting the need for further research into membership privacy and the development of more robust privacy-preserving training techniques for large language models.
Calibrated Recommendations with Contextual Bandits
Venue: arXiv cs.LG 2025
Year: 2025

Spotify's Home page features a variety of content types, including music, podcasts, and audiobooks. However, historical data is heavily skewed toward music, making it challenging to deliver a balanced and personalized content mix. Moreover, users' preference towards different content types may vary depending on the time of day, the day of week, or even the device they use. We propose a calibration method that leverages contextual bandits to dynamically learn each user's optimal content type distribution based on their context and preferences. Unlike traditional calibration methods that rely on historical averages, our approach boosts engagement by adapting to how users interests in different content types varies across contexts. Both offline and online results demonstrate improved precision and user engagement with the Spotify Home page, in particular with under-represented content types such as podcasts.
PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series
Venue: arXiv cs.LG 2025
Year: 2025

Multivariate time series (MTS) are ubiquitous in domains such as healthcare, climate science, and industrial monitoring, but their high dimensionality, limited labeled data, and non-stationary nature pose significant challenges for conventional machine learning methods. While recent self-supervised learning (SSL) approaches mitigate label scarcity by data augmentations or time point-based contrastive strategy, they neglect the intrinsic periodic structure of MTS and fail to capture the dynamic evolution of latent states. We propose PLanTS, a periodicity-aware self-supervised learning framework that explicitly models irregular latent states and their transitions. We first designed a period-aware multi-granularity patching mechanism and a generalized contrastive loss to preserve both instance-level and state-level similarities across multiple temporal resolutions. To further capture temporal dynamics, we design a next-transition prediction pretext task that encourages representations to encode predictive information about future state evolution. We evaluate PLanTS across a wide range of downstream tasks-including multi-class and multi-label classification, forecasting, trajectory tracking and anomaly detection. PLanTS consistently improves the representation quality over existing SSL methods and demonstrates superior runtime efficiency compared to DTW-based methods.
Veriserum: A dual-plane fluoroscopic dataset with knee implant phantoms for deep learning in medical imaging
Venue: arXiv cs.CV 2025
Year: 2025

Veriserum is an open-source dataset designed to support the training of deep learning registration for dual-plane fluoroscopic analysis. It comprises approximately 110,000 X-ray images of 10 knee implant pair combinations (2 femur and 5 tibia implants) captured during 1,600 trials, incorporating poses associated with daily activities such as level gait and ramp descent. Each image is annotated with an automatically registered ground-truth pose, while 200 images include manually registered poses for benchmarking. Key features of Veriserum include dual-plane images and calibration tools. The dataset aims to support the development of applications such as 2D/3D image registration, image segmentation, X-ray distortion correction, and 3D reconstruction. Freely accessible, Veriserum aims to advance computer vision and medical imaging research by providing a reproducible benchmark for algorithm development and evaluation. The Veriserum dataset used in this study is publicly available via https://movement.ethz.ch/data-repository/veriserum.html, with the data stored at ETH Zürich Research Collections: https://doi.org/10.3929/ethz-b-000701146.
An Analysis of Layer-Freezing Strategies for Enhanced Transfer Learning in YOLO Architectures
Venue: arXiv cs.CV 2025
Year: 2025

The You Only Look Once (YOLO) architecture is crucial for real-time object detection. However, deploying it in resource-constrained environments such as unmanned aerial vehicles (UAVs) requires efficient transfer learning. Although layer freezing is a common technique, the specific impact of various freezing configurations on contemporary YOLOv8 and YOLOv10 architectures remains unexplored, particularly with regard to the interplay between freezing depth, dataset characteristics, and training dynamics. This research addresses this gap by presenting a detailed analysis of layer-freezing strategies. We systematically investigate multiple freezing configurations across YOLOv8 and YOLOv10 variants using four challenging datasets that represent critical infrastructure monitoring. Our methodology integrates a gradient behavior analysis (L2 norm) and visual explanations (Grad-CAM) to provide deeper insights into training dynamics under different freezing strategies. Our results reveal that there is no universal optimal freezing strategy but, rather, one that depends on the properties of the data. For example, freezing the backbone is effective for preserving general-purpose features, while a shallower freeze is better suited to handling extreme class imbalance. These configurations reduce graphics processing unit (GPU) memory consumption by up to 28% compared to full fine-tuning and, in some cases, achieve mean average precision (mAP@50) scores that surpass those of full fine-tuning. Gradient analysis corroborates these findings, showing distinct convergence patterns for moderately frozen models. Ultimately, this work provides empirical findings and practical guidelines for selecting freezing strategies. It offers a practical, evidence-based approach to balanced transfer learning for object detection in scenarios with limited resources.
Bi-Level Game-Theoretic Planning of Cyber Deception for Cognitive Arbitrage
Venue: arXiv cs.GT 2025
Year: 2025

Cognitive vulnerabilities shape human decision-making and arise primarily from two sources: (1) cognitive capabilities, which include disparities in knowledge, education, expertise, or access to information, and (2) cognitive biases, such as rational inattention, confirmation bias, and base rate neglect, which influence how individuals perceive and process information. Exploiting these vulnerabilities allows an entity with superior cognitive awareness to gain a strategic advantage, a concept referred to as cognitive arbitrage. This paper investigates how to exploit the cognitive vulnerabilities of Advanced Persistent Threat (APT) attackers and proposes cognition-aware defenses that leverage windows of superiority to counteract attacks. Specifically, the proposed bi-level cyber warfare game focuses on "strategic-level" design for defensive deception mechanisms, which then facilitates "operational-level" actions and tactical-level execution of Tactics, Techniques, and Procedures (TTPs). Game-theoretic reasoning and analysis play a significant role in the cross-echelon quantitative modeling and design of cognitive arbitrage strategies. Our numerical results demonstrate that although the defender's initial advantage diminishes over time, strategically timed and deployed deception techniques can turn a negative value for the attacker into a positive one during the planning phase, and achieve at least a 40% improvement in total rewards during execution. This demonstrates that the defender can amplify even small initial advantages, sustain a strategic edge over the attacker, and secure long-term objectives, such as protecting critical assets throughout the attacker's lifecycle.