OpenRaiser/Intern-Atlas huggingface dataset:
NANO3D: A Training-Free Approach for Efficient 3D Editing Without Masks
Venue: arXiv cs.CV 2025
Year: 2025

3D object editing is essential for interactive content creation in gaming, animation, and robotics, yet current approaches remain inefficient, inconsistent, and often fail to preserve unedited regions. Most methods rely on editing multi-view renderings followed by reconstruction, which introduces artifacts and limits practicality. To address these challenges, we propose Nano3D, a training-free framework for precise and coherent 3D object editing without masks. Nano3D integrates FlowEdit into TRELLIS to perform localized edits guided by front-view renderings, and further introduces region-aware merging strategies, Voxel/Slat-Merge, which adaptively preserve structural fidelity by ensuring consistency between edited and unedited areas. Experiments demonstrate that Nano3D achieves superior 3D consistency and visual quality compared with existing methods. Based on this framework, we construct the first large-scale 3D editing datasets Nano3D-Edit-100k, which contains over 100,000 high-quality 3D editing pairs. This work addresses long-standing challenges in both algorithm design and data availability, significantly improving the generality and reliability of 3D editing, and laying the groundwork for the development of feed-forward 3D editing models. Project Page:https://jamesyjl.github.io/Nano3D
IQNN-CS: Interpretable Quantum Neural Network for Credit Scoring
Venue: arXiv cs.LG 2025
Year: 2025

Credit scoring is a high-stakes task in financial services, where model decisions directly impact individuals' access to credit and are subject to strict regulatory scrutiny. While Quantum Machine Learning (QML) offers new computational capabilities, its black-box nature poses challenges for adoption in domains that demand transparency and trust. In this work, we present IQNN-CS, an interpretable quantum neural network framework designed for multiclass credit risk classification. The architecture combines a variational QNN with a suite of post-hoc explanation techniques tailored for structured data. To address the lack of structured interpretability in QML, we introduce Inter-Class Attribution Alignment (ICAA), a novel metric that quantifies attribution divergence across predicted classes, revealing how the model distinguishes between credit risk categories. Evaluated on two real-world credit datasets, IQNN-CS demonstrates stable training dynamics, competitive predictive performance, and enhanced interpretability. Our results highlight a practical path toward transparent and accountable QML models for financial decision-making.
Physical Layer Deception based on Semantic Distortion
Venue: arXiv cs.CR 2025
Year: 2025

Physical layer deception (PLD) is a framework we previously introduced that integrates physical layer security (PLS) with deception techniques, enabling proactive countermeasures against eavesdropping rather than relying solely on passive defense. We extend this framework to a semantic communication model and conduct a theoretical analysis using semantic distortion as the performance metric. In this work, we further investigate the receiver's selection of decryption strategies and the transmitter's optimization of encryption strategies. By anticipating the decryption strategy likely to be employed by the legitimate receiver and eavesdropper, the transmitter can optimize resource allocation and encryption parameters, thereby maximizing the semantic distortion at the eavesdropper while maintaining a low level of semantic distortion for the legitimate receiver. We present a rigorous analysis of the resulting optimization problem, propose an efficient optimization algorithm, and derive closed-form optimal solutions for multiple scenarios. Finally, we corroborate the theoretical findings with numerical simulations, which also confirm the practicality of the proposed algorithm.
OpenEstimate: Evaluating LLMs on Reasoning Under Uncertainty with Real-World Data
Venue: arXiv cs.AI 2025
Year: 2025

Real-world settings where language models (LMs) are deployed -- in domains spanning healthcare, finance, and other forms of knowledge work -- require models to grapple with incomplete information and reason under uncertainty. Yet most LM evaluations focus on problems with well-defined answers and success criteria. This gap exists in part because natural problems involving uncertainty are difficult to construct: given that LMs have access to most of the same knowledge as humans, it is non-trivial to design questions for which LMs will struggle to produce correct answers, but which humans can answer reliably. As a result, LM performance on reasoning under uncertainty remains poorly characterized. To address this gap, we introduce OpenEstimate, an extensible, multi-domain benchmark for evaluating LMs on numerical estimation tasks that require models to synthesize significant amounts of background information and express predictions as probabilistic priors. We assess these priors for accuracy and calibration, quantifying their usefulness relative to samples from the true distribution of interest. Across six frontier LMs, we find that LM-elicited priors are often inaccurate and overconfident. Performance improves modestly depending on how uncertainty is elicited from the model, but is largely unaffected by changes in sampling strategy, reasoning effort, or prompt design. The OpenEstimate benchmark thus offers a challenging evaluation for frontier LMs and a platform for developing models that are better at probabilistic estimation and reasoning under uncertainty.
Partitioning $\mathbb{Z}_{sp}$ in finite fields and groups of trees and cycles
Venue: arXiv cs.CR 2025
Year: 2025

This paper investigates the algebraic and graphical structure of the ring $\mathbb{Z}_{sp}$, with a focus on its decomposition into finite fields, kernels, and special subsets. We establish classical isomorphisms between $\mathbb{F}_s$ and $p\mathbb{F}_s$, as well as $p\mathbb{F}_s^{\star}$ and $p\mathbb{F}_s^{+1,\star}$. We introduce the notion of arcs and rooted trees to describe the pre-periodic structure of $\mathbb{Z}_{sp}$, and prove that trees rooted at elements not divisible by $s$ or $p$ can be generated from the tree of unity via multiplication by cyclic arcs. Furthermore, we define and analyze the set $\mathbb{D}_{sp}$, consisting of elements that are neither multiples of $s$ or $p$ nor "off-by-one" elements, and show that its graph decomposes into cycles and pre-periodic trees. Finally, we demonstrate that every cycle in $\mathbb{Z}_{sp}$ contains inner cycles that are derived predictably from the cycles of the finite fields $p\mathbb{F}_s$ and $s\mathbb{F}_p$, and we discuss the cryptographic relevance of $\mathbb{D}_{sp}$, highlighting its potential for analyzing cyclic attacks and factorization methods.
Targeted Attacks and Defenses for Distributed Federated Learning in Vehicular Networks
Venue: arXiv cs.NI 2025
Year: 2025

In emerging networked systems, mobile edge devices such as ground vehicles and unmanned aerial system (UAS) swarms collectively aggregate vast amounts of data to make machine learning decisions such as threat detection in remote, dynamic, and infrastructure-constrained environments where power and bandwidth are scarce. Federated learning (FL) addresses these constraints and privacy concerns by enabling nodes to share local model weights for deep neural networks instead of raw data, facilitating more reliable decision-making than individual learning. However, conventional FL relies on a central server to coordinate model updates in each learning round, which imposes significant computational burdens on the central node and may not be feasible due to the connectivity constraints. By eliminating dependence on a central server, distributed federated learning (DFL) offers scalability, resilience to node failures, learning robustness, and more effective defense strategies. Despite these advantages, DFL remains vulnerable to increasingly advanced and stealthy cyberattacks. In this paper, we design sophisticated targeted training data poisoning and backdoor (Trojan) attacks, and characterize the emerging vulnerabilities in a vehicular network. We analyze how DFL provides resilience against such attacks compared to individual learning and present effective defense mechanisms to further strengthen DFL against the emerging cyber threats.
DLER: Doing Length pEnalty Right - Incentivizing More Intelligence per Token via Reinforcement Learning
Venue: arXiv cs.LG 2025
Year: 2025

Reasoning language models such as OpenAI-o1, DeepSeek-R1, and Qwen achieve strong performance via extended chains of thought but often generate unnecessarily long outputs. Maximizing intelligence per token--accuracy relative to response length--remains an open problem. We revisit reinforcement learning (RL) with the simplest length penalty--truncation--and show that accuracy degradation arises not from the lack of sophisticated penalties but from inadequate RL optimization. We identify three key challenges: (i) large bias in advantage estimation, (ii) entropy collapse, and (iii) sparse reward signal. We address them with Doing Length pEnalty Right (DLER), a training recipe combining batch-wise reward normalization, higher clipping, dynamic sampling, and a simple truncation length penalty. DLER achieves state-of-the-art accuracy--efficiency trade-offs, cutting output length by over 70 percent while surpassing all previous baseline accuracy. It also improves test-time scaling: compared to DeepSeek-R1-7B, DLER-7B generates multiple concise responses in parallel with 28 percent higher accuracy and lower latency. We further introduce Difficulty-Aware DLER, which adaptively tightens truncation on easier questions for additional efficiency gains. We also propose an update-selective merging method that preserves baseline accuracy while retaining the concise reasoning ability of the DLER model, which is useful for scenarios where RL training data is scarce.
Extending Temporal Disturbance Estimations For Magnetic Anomaly Navigation and Mapping
Venue: arXiv cs.CE 2025
Year: 2025

Slow-moving vehicles relying on crustal magnetic anomaly navigation (MagNav) or vehicles revisiting the same location in a short time - such as those used for surveys in magnetic anomaly mapping - require fixed ground stations within 100 km of the vehicle's trajectory to measure and remove the geomagnetic disturbance field from magnetic readings. This approach is impractical due to the limited network of fixed-ground magnetometer stations, making long-range (several hundred kilometers long) aeromagnetic surveys for anomaly map-making infeasible. To address these challenges, we developed the Extended Reference Station Model (ERSM). ERSM applies a longitudinal correction and regression model to an extended reference ground magnetometer station (ERS) to produce an estimate of the local temporal disturbance field. ERSM is regression model-agnostic, so we implemented a linear regression, a k-nearest neighbors (kNN) regression, and a neural-network regression model to assess performance benefits. Our results show typical performance below 10nT root mean square error and median performance below 5nT for typical use with the kNN and neural-net model for farther distances and below 5nT performance using the linear regression model on stations with proximity. We also consider how space-weather events, water-body separation, and proximity to polar regions affect the model performance based on ERS selection.
Autonomous Reactive Masonry Construction using Collaborative Heterogeneous Aerial Robots with Experimental Demonstration
Venue: arXiv cs.RO 2025
Year: 2025

This article presents a fully autonomous aerial masonry construction framework using heterogeneous unmanned aerial vehicles (UAVs), supported by experimental validation. Two specialized UAVs were developed for the task: (i) a brick-carrier UAV equipped with a ball-joint actuation mechanism for precise brick manipulation, and (ii) an adhesion UAV integrating a servo-controlled valve and extruder nozzle for accurate adhesion application. The proposed framework employs a reactive mission planning unit that combines a dependency graph of the construction layout with a conflict graph to manage simultaneous task execution, while hierarchical state machines ensure robust operation and safe transitions during task execution. Dynamic task allocation allows real-time adaptation to environmental feedback, while minimum-jerk trajectory generation ensures smooth and precise UAV motion during brick pickup and placement. Additionally, the brick-carrier UAV employs an onboard vision system that estimates brick poses in real time using ArUco markers and a least-squares optimization filter, enabling accurate alignment during construction. To the best of the authors' knowledge, this work represents the first experimental demonstration of fully autonomous aerial masonry construction using heterogeneous UAVs, where one UAV precisely places the bricks while another autonomously applies adhesion material between them. The experimental results supported by the video showcase the effectiveness of the proposed framework and demonstrate its potential to serve as a foundation for future developments in autonomous aerial robotic construction.
NEMO: Faster Parallel Execution for Highly Contended Blockchain Workloads (Full version)
Venue: arXiv cs.DC 2025
Year: 2025

Following the design of more efficient blockchain consensus algorithms, the execution layer has emerged as the new performance bottleneck of blockchains, especially under high contention. Current parallel execution frameworks either rely on optimistic concurrency control (OCC) or on pessimistic concurrency control (PCC), both of which see their performance decrease when workloads are highly contended, albeit for different reasons. In this work, we present NEMO, a new blockchain execution engine that combines OCC with the object data model to address this challenge. NEMO introduces four core innovations: (i) a greedy commit rule for transactions using only owned objects; (ii) refined handling of dependencies to reduce re-executions; (iii) the use of incomplete but statically derivable read/write hints to guide execution; and (iv) a priority-based scheduler that favors transactions that unblock others. Through simulated execution experiments, we demonstrate that NEMO significantly reduces redundant computation and achieves higher throughput than representative approaches. For example, with 16 workers NEMO's throughput is up to 42% higher than the one of Block-STM, the state-of-the-art OCC approach, and 61% higher than the pessimistic concurrency control baseline used.
Revisiting UTAUT for the Age of AI: Understanding Employees AI Adoption and Usage Patterns Through an Extended UTAUT Framework
Venue: arXiv cs.CY 2025
Year: 2025

This study investigates whether demographic factors shape adoption and attitudes among employees toward artificial intelligence (AI) technologies at work. Building on an extended Unified Theory of Acceptance and Use of Technology (UTAUT), which reintroduces affective dimensions such as attitude, self-efficacy, and anxiety, we surveyed 2,257 professionals across global regions and organizational levels within a multinational consulting firm. Non-parametric tests examined whether three demographic factors (i.e., years of experience, hierarchical level in the organization, and geographic region) were associated with AI adoption, usage intensity, and eight UTAUT constructs. Organizational level significantly predicted AI adoption, with senior employees showing higher usage rates, while experience and region were unrelated to adoption. Among AI users (n = 1,256), frequency and duration of use showed minimal demographic variation. However, omnibus tests revealed small but consistent group differences across several UTAUT constructs, particularly anxiety, performance expectancy, and behavioral intention, suggesting that emotional and cognitive responses to AI vary modestly across contexts. These findings highlight that demographic factors explain limited variance in AI acceptance but remain relevant for understanding contextual nuances in technology-related attitudes. The results underscore the need to integrate affective and organizational factors into models of technology acceptance to support equitable, confident, and sustainable engagement with AI in modern workplaces.
Hyperparameter Optimization and Reproducibility in Deep Learning Model Training
Venue: arXiv cs.CV 2025
Year: 2025

Reproducibility remains a critical challenge in foundation model training for histopathology, often hindered by software randomness, hardware non-determinism, and inconsistent hyperparameter reporting. To investigate these issues, we trained a CLIP model on the QUILT-1M dataset and systematically evaluated the impact of different hyperparameter settings and augmentation strategies across three downstream histopathology datasets (PatchCamelyon, LC25000-Lung, and LC25000-Colon). Despite variability across runs, we identified clear trends: RandomResizedCrop values of 0.7-0.8 outperformed more aggressive (0.6) or conservative (0.9) settings, distributed training without local loss improved stability, and learning rates below 5.0e-5 consistently degraded performance across all datasets. The LC25000 (Colon) dataset consistently provided the most reproducible benchmark. These findings highlight that reproducibility in computational pathology depends not only on transparent documentation but also on carefully chosen experimental configurations, and we provide practical rules to guide future efforts in developing reproducible foundation models for digital pathology.
A simple mean field model of feature learning
Venue: arXiv cs.LG 2025
Year: 2025

Feature learning (FL), where neural networks adapt their internal representations during training, remains poorly understood. Using methods from statistical physics, we derive a tractable, self-consistent mean-field (MF) theory for the Bayesian posterior of two-layer non-linear networks trained with stochastic gradient Langevin dynamics (SGLD). At infinite width, this theory reduces to kernel ridge regression, but at finite width it predicts a symmetry breaking phase transition where networks abruptly align with target functions. While the basic MF theory provides theoretical insight into the emergence of FL in the finite-width regime, semi-quantitatively predicting the onset of FL with noise or sample size, it substantially underestimates the improvements in generalisation after the transition. We trace this discrepancy to a key mechanism absent from the plain MF description: \textit{self-reinforcing input feature selection}. Incorporating this mechanism into the MF theory allows us to quantitatively match the learning curves of SGLD-trained networks and provides mechanistic insight into FL.
Visualizing miniKanren Search with a Fine-Grained Small-Step Semantics
Venue: arXiv cs.PL 2025
Year: 2025

We present a deterministic small-step operational semantics for miniKanren that explicitly represents the evolving search tree during execution. This semantics models interleaving and goal scheduling at fine granularity, allowing each evaluation step-goal activation, suspension, resumption, and success -- to be visualized precisely. Building on this model, we implement an interactive visualizer that renders the search tree as it develops and lets users step through execution. The tool acts as a pedagogical notional machine for reasoning about miniKanren's fair search behavior, helping users understand surprising answer orders and operational effects. Our semantics and tool are validated through property-based testing and illustrated with several examples.
Game mechanics for cyber-harm awareness in the metaverse
Venue: arXiv cs.MM 2025
Year: 2025

Educating children and young people to be safe online is essential, especially as the metaverse, a next-generation internet blending immersive technologies, promises to reshape their interactions and amplify their experiences. While virtual reality offers fully immersive, highly interactive, and multi-sensory engagement, it also heightens cyber harm risks for young or vulnerable users. To address this, the CyberNinjas VR experience was developed to educate children aged 8 to 16 on safe metaverse behaviours, providing clear referral steps for harmful interactions. Understanding user engagement in metaverse gaming will aid the design of future VR environments which prioritize safety and inclusivity. This project analyses CyberNinjas to understand how game mechanics can foster cyber-safe behaviours.
OCR-APT: Reconstructing APT Stories from Audit Logs using Subgraph Anomaly Detection and LLMs
Venue: arXiv cs.CR 2025
Year: 2025

Advanced Persistent Threats (APTs) are stealthy cyberattacks that often evade detection in system-level audit logs. Provenance graphs model these logs as connected entities and events, revealing relationships that are missed by linear log representations. Existing systems apply anomaly detection to these graphs but often suffer from high false positive rates and coarse-grained alerts. Their reliance on node attributes like file paths or IPs leads to spurious correlations, reducing detection robustness and reliability. To fully understand an attack's progression and impact, security analysts need systems that can generate accurate, human-like narratives of the entire attack. To address these challenges, we introduce OCR-APT, a system for APT detection and reconstruction of human-like attack stories. OCR-APT uses Graph Neural Networks (GNNs) for subgraph anomaly detection, learning behavior patterns around nodes rather than fragile attributes such as file paths or IPs. This approach leads to a more robust anomaly detection. It then iterates over detected subgraphs using Large Language Models (LLMs) to reconstruct multi-stage attack stories. Each stage is validated before proceeding, reducing hallucinations and ensuring an interpretable final report. Our evaluations on the DARPA TC3, OpTC, and NODLINK datasets show that OCR-APT outperforms state-of-the-art systems in both detection accuracy and alert interpretability. Moreover, OCR-APT reconstructs human-like reports that comprehensively capture the attack story.
Toward Black Scholes for Prediction Markets: A Unified Kernel and Market Maker's Handbook
Venue: arXiv cs.CE 2025
Year: 2025

Prediction markets, such as Polymarket, aggregate dispersed information into tradable probabilities, but they still lack a unifying stochastic kernel comparable to the one options gained from Black-Scholes. As these markets scale with institutional participation, exchange integrations, and higher volumes around elections and macro prints, market makers face belief volatility, jump, and cross-event risks without standardized tools for quoting or hedging. We propose such a foundation: a logit jump-diffusion with risk-neutral drift that treats the traded probability p_t as a Q-martingale and exposes belief volatility, jump intensity, and dependence as quotable risk factors. On top, we build a calibration pipeline that filters microstructure noise, separates diffusion from jumps using expectation-maximization, enforces the risk-neutral drift, and yields a stable belief-volatility surface. We then define a coherent derivative layer (variance, correlation, corridor, and first-passage instruments) analogous to volatility and correlation products in option markets. In controlled experiments on synthetic risk-neutral paths and real event data, the model reduces short-horizon belief-variance forecast error relative to diffusion-only and probability-space baselines, supporting both causal calibration and economic interpretability. Conceptually, the logit jump-diffusion kernel supplies an implied-volatility analogue for prediction markets: a tractable, tradable language for quoting, hedging, and transferring belief risk across venues such as Polymarket.