OpenRaiser/Intern-Atlas huggingface dataset:
The Passwordless Authentication with Passkey Technology from an Implementation Perspective
Venue: arXiv cs.CR 2025
Year: 2025

With the rise of sophisticated authentication bypass techniques, passwords are no longer considered a reliable method for securing authentication systems. In recent years, new authentication technologies have shifted from traditional password-based logins to passwordless security. Among these, Time-Based One-Time Passwords (TOTP) remain one of the most widely used mechanisms, while Passkeys are emerging as a promising alternative with growing adoption. This paper highlights the key techniques used during the implementation of the authentication system with Passkey technology. It also suggests considerations for integrating components during system development to ensure that users can securely access their accounts with minimal complexity, while still meeting the requirements of a robust authentication system that balances security, usability, and performance. Additionally, by examining TOTP and Passkey mechanisms from an implementation perspective, this work not only addresses major security concerns such as password leaks, phishing attacks, and susceptibility to brute-force attacks, but also evaluates the feasibility and effectiveness of these mechanisms in real-world implementations. This paper demonstrates the superior security of Passkey technology and its potential for broader adoption in secure authentication systems.
Design and Implementation of a Controlled Ransomware Framework for Educational Purposes Using Flutter Cryptographic APIs on Desktop PCs and Android Devices
Venue: arXiv cs.CR 2025
Year: 2025

This study focuses on the creation and implementation of ransomware for educational purposes that leverages Python's native cryptographic APIs in a controlled environment. Additionally, an Android version of the framework is implemented using Flutter and Dart. For both versions, open-source cryptographic libraries are utilized. With this framework, researchers can systematically explore the functionalities of ransomware, including file encryption processes, cryptographic key management, and victim interaction dynamics. To ensure safe experimentation, multiple safeguards are incorporated, such as the ability to restrict the encryption process to a specific directory, providing the RSA private key for immediate decryption, and narrowing the scope of targetable files to a carefully curated list (.txt, .jpg, .csv, .doc). This paper draws inspiration from the infamous WannaCry ransomware and aims to simulate its behaviour on Android devices. By making the codebase open-source, it enables users to study, modify, and extend the program for pedagogical purposes and offers a hands-on tool that can be used to train the next generation of cybersecurity professionals.
Trust@Health: A Trust-Based Multilayered Network for Scalable Healthcare Service Management
Venue: arXiv cs.SI 2025
Year: 2025

We study the intricate relationships within healthcare systems, focusing on interactions among doctors, departments, and hospitals. Leveraging an evolutionary graph framework, the proposed model emphasizes both intra-layer and inter-layer trust relationships to better understand and optimize healthcare services. The trust-based network facilitates the identification of key healthcare entities by integrating their social and professional interactions, culminating in a trust-based algorithm that quantifies the importance of these entities. Validation with a real-world dataset reveals a strong correlation (0.91) between the proposed trust measures and the ratings of hospitals and departments, though doctor ratings demonstrate skewed distributions due to potential biases. By modeling these relationships and trust dynamics, the framework supports scalable healthcare infrastructure, enabling effective patient referrals, personalized recommendations, and enhanced decision-making pathways.
Queen Domination by SAT Solving
Venue: arXiv cs.LO 2025
Year: 2025

The queen domination problem asks for the minimum number of queens needed to attack all squares on an $n\times n$ chessboard. Once this optimal number is known, determining the number of distinct solutions up to isomorphism has also attracted considerable attention. Previous work has introduced specialized and highly optimized search procedures to address open instances of the problem. While efficient in terms of runtime, these approaches have not provided proofs that can be independently verified by third-party checkers. In contrast, this paper aims to combine efficiency with verifiability. We reduce the problem to a propositional satisfiability problem (SAT) using a straightforward encoding, and solve the resulting formulas with modern SAT solvers capable of generating proof certificates. By improving the SAT encoding with a novel literal ordering strategy, and leveraging established techniques such as static symmetry breaking and the Cube-and-Conquer paradigm, this paper achieves both performance and trustworthiness. Our approach discovers and corrects a discrepancy in previous results for $n=16$ and resolves the previously open case $n=19$.
Exploring Spatial-Temporal Dynamics in Event-based Facial Micro-Expression Analysis
Venue: arXiv cs.CV 2025
Year: 2025

Micro-expression analysis has applications in domains such as Human-Robot Interaction and Driver Monitoring Systems. Accurately capturing subtle and fast facial movements remains difficult when relying solely on RGB cameras, due to limitations in temporal resolution and sensitivity to motion blur. Event cameras offer an alternative, with microsecond-level precision, high dynamic range, and low latency. However, public datasets featuring event-based recordings of Action Units are still scarce. In this work, we introduce a novel, preliminary multi-resolution and multi-modal micro-expression dataset recorded with synchronized RGB and event cameras under variable lighting conditions. Two baseline tasks are evaluated to explore the spatial-temporal dynamics of micro-expressions: Action Unit classification using Spiking Neural Networks (51.23\% accuracy with events vs. 23.12\% with RGB), and frame reconstruction using Conditional Variational Autoencoders, achieving SSIM = 0.8513 and PSNR = 26.89 dB with high-resolution event input. These promising results show that event-based data can be used for micro-expression recognition and frame reconstruction.
Universal Learning of Nonlinear Dynamics
Venue: arXiv cs.LG 2025
Year: 2025

We study the fundamental problem of learning a marginally stable unknown nonlinear dynamical system. We describe an algorithm for this problem, based on the technique of spectral filtering, which learns a mapping from past observations to the next based on a spectral representation of the system. Using techniques from online convex optimization, we prove vanishing prediction error for any nonlinear dynamical system that has finitely many marginally stable modes, with rates governed by a novel quantitative control-theoretic notion of learnability. The main technical component of our method is a new spectral filtering algorithm for linear dynamical systems, which incorporates past observations and applies to general noisy and marginally stable systems. This significantly generalizes the original spectral filtering algorithm to both asymmetric dynamics as well as incorporating noise correction, and is of independent interest.
Optimizing Neural Architectures for Hindi Speech Separation and Enhancement in Noisy Environments
Venue: arXiv cs.SD 2025
Year: 2025

This paper addresses the challenges of Hindi speech separation and enhancement using advanced neural network architectures, with a focus on edge devices. We propose a refined approach leveraging the DEMUCS model to overcome limitations of traditional methods, achieving substantial improvements in speech clarity and intelligibility. The model is fine-tuned with U-Net and LSTM layers, trained on a dataset of 400,000 Hindi speech clips augmented with ESC-50 and MS-SNSD for diverse acoustic environments. Evaluation using PESQ and STOI metrics shows superior performance, particularly under extreme noise conditions. To ensure deployment on resource-constrained devices like TWS earbuds, we explore quantization techniques to reduce computational requirements. This research highlights the effectiveness of customized AI algorithms for speech processing in Indian contexts and suggests future directions for optimizing edge-based architectures.
ToxiEval-ZKP: A Structure-Private Verification Framework for Molecular Toxicity Repair Tasks
Venue: arXiv cs.CR 2025
Year: 2025

In recent years, generative artificial intelligence (GenAI) has demonstrated remarkable capabilities in high-stakes domains such as molecular science. However, challenges related to the verifiability and structural privacy of its outputs remain largely unresolved. This paper focuses on the task of molecular toxicity repair. It proposes a structure-private verification framework - ToxiEval-ZKP - which, for the first time, introduces zero-knowledge proof (ZKP) mechanisms into the evaluation process of this task. The system enables model developers to demonstrate to external verifiers that the generated molecules meet multidimensional toxicity repair criteria, without revealing the molecular structures themselves. To this end, we design a general-purpose circuit compatible with both classification and regression tasks, incorporating evaluation logic, Poseidon-based commitment hashing, and a nullifier-based replay prevention mechanism to build a complete end-to-end ZK verification system. Experimental results demonstrate that ToxiEval-ZKP facilitates adequate validation under complete structural invisibility, offering strong circuit efficiency, security, and adaptability, thereby opening up a novel paradigm for trustworthy evaluation in generative scientific tasks.
Mind the Generation Process: Fine-Grained Confidence Estimation During LLM Generation
Venue: arXiv cs.CL 2025
Year: 2025

While large language models (LLMs) have demonstrated remarkable performance across diverse tasks, they fundamentally lack self-awareness and frequently exhibit overconfidence, assigning high confidence scores to incorrect predictions. Accurate confidence estimation is therefore critical for enhancing the trustworthiness and reliability of LLM-generated outputs. However, existing approaches suffer from coarse-grained scoring mechanisms that fail to provide fine-grained, continuous confidence estimates throughout the generation process. To address these limitations, we introduce FineCE, a novel confidence estimation method that delivers accurate, fine-grained confidence scores during text generation. Specifically, we first develop a comprehensive pipeline for constructing training data that effectively captures the underlying probabilistic distribution of LLM responses, and then train a model to predict confidence scores for arbitrary text sequences in a supervised manner. Furthermore, we propose a Backward Confidence Integration (BCI) strategy that leverages information from the subsequent text to enhance confidence estimation for the current sequence during inference. We also introduce three strategies for identifying optimal positions to perform confidence estimation within the generation process. Extensive experiments on multiple benchmark datasets demonstrate that FineCE consistently outperforms existing classical confidence estimation methods. Our code and all baselines used in the paper are available on GitHub.
J6: Jacobian-Driven Role Attribution for Multi-Objective Prompt Optimization in LLMs
Venue: arXiv cs.CL 2025
Year: 2025

In large language model (LLM) adaptation, balancing multiple optimization objectives such as improving factuality (heat) and increasing confidence (via low entropy) poses a fundamental challenge, especially when prompt parameters (e.g., hidden-layer insertions h and embedding modifications w) interact in non-trivial ways. Existing multi-objective optimization strategies often rely on scalar gradient aggregation, ignoring the deeper geometric structure between objectives and parameters. We propose J6, a structured Jacobian-based method that decomposes the gradient interaction matrix into six interpretable components. This decomposition enables both hard decision-making (e.g., choosing the dominant update direction via argmax) and soft strategies (e.g., attention-style weighting via softmax over J6), forming a dynamic update framework that adapts to local conflict and synergy. Moreover, the interpretable structure of J6 provides insight into parameter attribution, task interference, and geometry-aligned adaptation. Our work introduces a principled and extensible mechanism for conflict-aware prompt optimization, and opens a new avenue for incorporating structured Jacobian reasoning into multi-objective neural tuning.
Overcoming Knowledge Discrepancies: Structuring Reasoning Threads through Knowledge Balancing in Interactive Scenarios
Venue: arXiv cs.AI 2025
Year: 2025

Reasoning in interactive problem solving scenarios requires models to construct reasoning threads that reflect user understanding and align with structured domain knowledge. However, current reasoning models often lack explicit semantic hierarchies, user-domain knowledge alignment, and principled mechanisms to prune reasoning threads for effectiveness. These limitations result in lengthy generic output that does not guide users through goal-oriented reasoning steps. To address this, we propose a prototype-inspired, two-phases Reasoning-Threads-Evaluation (ReT-Eval) framework, drawing inspiration from human-like reasoning strategies that emphasize structured knowledge reuse. In the first phase, semantically relevant knowledge structures are extracted from a sparse domain knowledge graph using a graph neural network and enriched with intrinsic large language model knowledge to resolve knowledge discrepancies. In the second phase, these threads are evaluated and pruned using a reward-guided strategy aimed at maintaining semantic coherence to generate effective reasoning threads. Experiments and expert evaluations show that ReT-Eval enhances user understanding and outperforms state-of-the-art reasoning models.
Urban AI Governance Must Embed Legal Reasonableness for Democratic and Sustainable Cities
Venue: arXiv cs.CY 2025
Year: 2025

This position paper argues that embedding the legal "reasonable person" standard in municipal AI systems is essential for democratic and sustainable urban governance. As cities increasingly deploy artificial intelligence (AI) systems, concerns around equity, accountability, and normative legitimacy are growing. This paper introduces the Urban Reasonableness Layer (URL), a conceptual framework that adapts the legal "reasonable person" standard for supervisory oversight in municipal AI systems, including potential future implementations of Artificial General Intelligence (AGI). Drawing on historical analogies, scenario mapping, and participatory norm-setting, we explore how legal and community-derived standards can inform AI decision-making in urban contexts. Rather than prescribing a fixed solution, the URL is proposed as an exploratory architecture for negotiating contested values, aligning automation with democratic processes, and interrogating the limits of technical alignment. Our key contributions include: (1) articulating the conceptual and operational architecture of the URL; (2) specifying participatory mechanisms for dynamic normative threshold-setting; (3) presenting a comparative scenario analysis of governance trajectories; and (4) outlining evaluation metrics and limitations. This work contributes to ongoing debates on urban AI governance by foregrounding pluralism, contestability, and the inherently political nature of socio-technical systems.
Invitation Is All You Need! Promptware Attacks Against LLM-Powered Assistants in Production Are Practical and Dangerous
Venue: arXiv cs.CR 2025
Year: 2025

The growing integration of LLMs into applications has introduced new security risks, notably known as Promptware - maliciously engineered prompts designed to manipulate LLMs to compromise the CIA triad of these applications. While prior research warned about a potential shift in the threat landscape for LLM-powered applications, the risk posed by Promptware is frequently perceived as low. In this paper, we investigate the risk Promptware poses to users of Gemini-powered assistants (web application, mobile application, and Google Assistant). We propose a novel Threat Analysis and Risk Assessment (TARA) framework to assess Promptware risks for end users. Our analysis focuses on a new variant of Promptware called Targeted Promptware Attacks, which leverage indirect prompt injection via common user interactions such as emails, calendar invitations, and shared documents. We demonstrate 14 attack scenarios applied against Gemini-powered assistants across five identified threat classes: Short-term Context Poisoning, Permanent Memory Poisoning, Tool Misuse, Automatic Agent Invocation, and Automatic App Invocation. These attacks highlight both digital and physical consequences, including spamming, phishing, disinformation campaigns, data exfiltration, unapproved user video streaming, and control of home automation devices. We reveal Promptware's potential for on-device lateral movement, escaping the boundaries of the LLM-powered application, to trigger malicious actions using a device's applications. Our TARA reveals that 73% of the analyzed threats pose High-Critical risk to end users. We discuss mitigations and reassess the risk (in response to deployed mitigations) and show that the risk could be reduced significantly to Very Low-Medium. We disclosed our findings to Google, which deployed dedicated mitigations.
Humanoid Motion Scripting with Postural Synergies
Venue: arXiv cs.RO 2025
Year: 2025

Generating sequences of human-like motions for humanoid robots presents challenges in collecting and analyzing reference human motions, synthesizing new motions based on these reference motions, and mapping the generated motion onto humanoid robots. To address these issues, we introduce SynSculptor, a humanoid motion analysis and editing framework that leverages postural synergies for training-free human-like motion scripting. To analyze human motion, we collect 3+ hours of motion capture data across 20 individuals where a real-time operational space controller mimics human motion on a simulated humanoid robot. The major postural synergies are extracted using principal component analysis (PCA) for velocity trajectories segmented by changes in robot momentum, constructing a style-conditioned synergy library for free-space motion generation. To evaluate generated motions using the synergy library, the foot-sliding ratio and proposed metrics for motion smoothness involving total momentum and kinetic energy deviations are computed for each generated motion, and compared with reference motions. Finally, we leverage the synergies with a motion-language transformer, where the humanoid, during execution of motion tasks with its end-effectors, adapts its posture based on the chosen synergy. Supplementary material, code, and videos are available at https://rhea-mal.github.io/humanoidsynergies.io.
AUTOVR: Automated UI Exploration for Detecting Sensitive Data Flow Exposures in Virtual Reality Apps
Venue: arXiv cs.CR 2025
Year: 2025

The rise of Virtual Reality (VR) has provided developers with an unprecedented platform for creating games and applications (apps) that require distinct inputs, different from those of conventional devices like smartphones. The Meta Quest VR platform, driven by Meta, has democratized VR app publishing and attracted millions of users worldwide. However, as the number of published apps grows, there is a notable lack of robust headless tools for user interface (UI) exploration and user event testing. To address this need, we present AUTOVR, an automatic framework for dynamic UI and user event interaction in VR apps built on the Unity Engine. Unlike conventional Android and GUI testers, AUTOVR analyzes the app's internal binary to reveal hidden events, resolves generative event dependencies, and utilizes them for comprehensive exploration of VR apps. Using sensitive data exposure as a performance metric, we compare AUTOVR with Android Monkey, a widely used headless Android GUI stress testing tool. Our empirical evaluation demonstrates AUTOVR's superior performance, triggering an order of magnitude of more sensitive data exposures and significantly enhancing the privacy of VR apps.
DHG-Bench: A Comprehensive Benchmark for Deep Hypergraph Learning
Venue: arXiv cs.LG 2025
Year: 2025

Deep graph models have achieved great success in network representation learning. However, their focus on pairwise relationships restricts their ability to learn pervasive higher-order interactions in real-world systems, which can be naturally modeled as hypergraphs. To tackle this issue, Hypergraph Neural Networks (HNNs) have garnered substantial attention in recent years. Despite the proposal of numerous HNNs, the absence of consistent experimental protocols and multi-dimensional empirical analysis impedes deeper understanding and further development of HNN research. While several toolkits for deep hypergraph learning (DHGL) have been introduced to facilitate algorithm evaluation, they provide only limited quantitative evaluation results and insufficient coverage of advanced algorithms, datasets, and benchmark tasks. To fill the gap, we introduce DHG-Bench, the first comprehensive benchmark for HNNs. Specifically, DHG-Bench systematically investigates the characteristics of HNNs in terms of four dimensions: effectiveness, efficiency, robustness, and fairness. We comprehensively evaluate 17 state-of-the-art HNN algorithms on 22 diverse datasets spanning node-, edge-, and graph-level tasks, under unified experimental settings. Extensive experiments reveal both the strengths and limitations of existing algorithms, offering valuable insights and directions for future research. Furthermore, to facilitate reproducible research, we have developed an easy-to-use library for training and evaluating different HNN methods. The DHG-Bench library is available at: https://github.com/Coco-Hut/DHG-Bench.
Robot Trains Robot: Automatic Real-World Policy Adaptation and Learning for Humanoids
Venue: arXiv cs.RO 2025
Year: 2025

Simulation-based reinforcement learning (RL) has significantly advanced humanoid locomotion tasks, yet direct real-world RL from scratch or adapting from pretrained policies remains rare, limiting the full potential of humanoid robots. Real-world learning, despite being crucial for overcoming the sim-to-real gap, faces substantial challenges related to safety, reward design, and learning efficiency. To address these limitations, we propose Robot-Trains-Robot (RTR), a novel framework where a robotic arm teacher actively supports and guides a humanoid robot student. The RTR system provides protection, learning schedule, reward, perturbation, failure detection, and automatic resets. It enables efficient long-term real-world humanoid training with minimal human intervention. Furthermore, we propose a novel RL pipeline that facilitates and stabilizes sim-to-real transfer by optimizing a single dynamics-encoded latent variable in the real world. We validate our method through two challenging real-world humanoid tasks: fine-tuning a walking policy for precise speed tracking and learning a humanoid swing-up task from scratch, illustrating the promising capabilities of real-world humanoid learning realized by RTR-style systems. See https://robot-trains-robot.github.io/ for more info.