OpenRaiser/Intern-Atlas huggingface dataset:
ReHome Earth: A VR-Based Concept Validation for AI-Driven Space Homesickness Interventions
Venue: arXiv cs.HC 2025
Year: 2025

Space exploration has advanced rapidly, but the emotional needs of astronauts on long-duration missions remain underexplored. We present ReHome Earth, a dual-component design approach addressing space homesickness: 1) a future-oriented installation concept integrating transparent OLED displays with spaceship windows for real-time Earth connectivity, and 2) a functional VR prototype simulating astronaut isolation for testing AI-generated content effectiveness. Since accessing astronauts during missions is impossible, we conducted concept validation with terrestrial participants experiencing geographic displacement. Through evaluation with 84 proxy participants and 6 HCI experts, we demonstrate strong emotional resonance and validate three design implications: emotional pacing mechanisms, explainable biophysical feedback systems, and evolution from individual tools to collective affective infrastructure. Our contributions include a technically feasible space installation concept, a functional VR prototype for space HCI research, and empirical insights into the design of AI-driven emotional support systems for extreme isolation environments.
It's a TRAP! Task-Redirecting Agent Persuasion Benchmark for Web Agents
Venue: arXiv cs.HC 2025
Year: 2025

Web-based agents powered by large language models are increasingly used for tasks such as email management or professional networking. Their reliance on dynamic web content, however, makes them vulnerable to prompt injection attacks: adversarial instructions hidden in interface elements that persuade the agent to divert from its original task. We introduce the Task-Redirecting Agent Persuasion Benchmark (TRAP), an evaluation for studying how persuasion techniques misguide autonomous web agents on realistic tasks. Across six frontier models, agents are susceptible to prompt injection in 25\% of tasks on average (13\% for GPT-5 to 43\% for DeepSeek-R1), with small interface or contextual changes often doubling success rates and revealing systemic, psychologically driven vulnerabilities in web-based agents. We also provide a modular social-engineering injection framework with controlled experiments on high-fidelity website clones, allowing for further benchmark expansion.
Multi-Agent Framework for Threat Mitigation and Resilience in AI-Based Systems
Venue: arXiv cs.CR 2025
Year: 2025

Machine learning (ML) underpins foundation models in finance, healthcare, and critical infrastructure, making them targets for data poisoning, model extraction, prompt injection, automated jailbreaking, and preference-guided black-box attacks that exploit model comparisons. Larger models can be more vulnerable to introspection-driven jailbreaks and cross-modal manipulation. Traditional cybersecurity lacks ML-specific threat modeling for foundation, multimodal, and RAG systems. Objective: Characterize ML security risks by identifying dominant TTPs, vulnerabilities, and targeted lifecycle stages. Methods: We extract 93 threats from MITRE ATLAS (26), AI Incident Database (12), and literature (55), and analyze 854 GitHub/Python repositories. A multi-agent RAG system (ChatGPT-4o, temp 0.4) mines 300+ articles to build an ontology-driven threat graph linking TTPs, vulnerabilities, and stages. Results: We identify unreported threats including commercial LLM API model stealing, parameter memorization leakage, and preference-guided text-only jailbreaks. Dominant TTPs include MASTERKEY-style jailbreaking, federated poisoning, diffusion backdoors, and preference optimization leakage, mainly impacting pre-training and inference. Graph analysis reveals dense vulnerability clusters in libraries with poor patch propagation. Conclusion: Adaptive, ML-specific security frameworks, combining dependency hygiene, threat intelligence, and monitoring, are essential to mitigate supply-chain and inference risks across the ML lifecycle.
Beyond URDF: The Universal Robot Description Directory for Shared, Extensible, and Standardized Robot Models
Venue: arXiv cs.RO 2025
Year: 2025

Robots are typically described in software by specification files (e.g., URDF, SDF, MJCF, USD) that encode only basic kinematic, dynamic, and geometric information. As a result, downstream applications such as simulation, planning, and control must repeatedly re-derive richer data, leading to redundant computations, fragmented implementations, and limited standardization. In this work, we introduce the Universal Robot Description Directory (URDD), a modular representation that organizes derived robot information into structured, easy-to-parse JSON and YAML modules. Our open-source toolkit automatically generates URDDs from URDFs, with a Rust implementation supporting Bevy-based visualization. Additionally, we provide a JavaScript/Three.js viewer for web-based inspection of URDDs. Experiments on multiple robot platforms show that URDDs can be generated efficiently, encapsulate substantially richer information than standard specification files, and directly enable the construction of core robotics subroutines. URDD provides a unified, extensible resource for reducing redundancy and establishing shared standards across robotics frameworks. We conclude with a discussion on the limitations and implications of our work.
Pole-centric Descriptors for Robust Robot Localization: Evaluation under Pole-at-Distance (PaD) Observations using the Small Pole Landmark (SPL) Dataset
Venue: arXiv cs.RO 2025
Year: 2025

While pole-like structures are widely recognized as stable geometric anchors for long-term robot localization, their identification reliability degrades significantly under Pole-at-Distance (Pad) observations typical of large-scale urban environments. This paper shifts the focus from descriptor design to a systematic investigation of descriptor robustness. Our primary contribution is the establishment of a specialized evaluation framework centered on the Small Pole Landmark (SPL) dataset. This dataset is constructed via an automated tracking-based association pipeline that captures multi-view, multi-distance observations of the same physical landmarks without manual annotation. Using this framework, we present a comparative analysis of Contrastive Learning (CL) and Supervised Learning (SL) paradigms. Our findings reveal that CL induces a more robust feature space for sparse geometry, achieving superior retrieval performance particularly in the 5--10m range. This work provides an empirical foundation and a scalable methodology for evaluating landmark distinctiveness in challenging real-world scenarios.
A Sequential Hermaphrodite Coupling Mechanism for Lattice-based Modular Robots
Venue: arXiv cs.RO 2025
Year: 2025

Lattice-based modular robot systems are envisioned for large-scale construction in extreme environments, such as space. Coupling mechanisms for heterogeneous structural modules should meet all of the following requirements: single-sided coupling and decoupling, flat surfaces when uncoupled, and coupling to passive coupling interfaces as well as coupling behavior between coupling mechanisms. The design requirements for such a coupling mechanism are complex. We propose a novel shape-matching mechanical coupling mechanism that satisfies these design requirements. This mechanism enables controlled, sequential transitions between male and female states. When uncoupled, all mechanisms are in the female state. To enable single-sided coupling, one side of the mechanisms switches to the male state during the coupling process. Single-sided decoupling is possible not only from the male side but also from the female side by forcibly switching the opposite mechanism's male state to the female state. This coupling mechanism can be applied to various modular robot systems and robot arm tool changers.
Energy and Memory-Efficient Federated Learning With Ordered Layer Freezing
Venue: arXiv cs.LG 2025
Year: 2025

Federated Learning (FL) has emerged as a privacy-preserving paradigm for training machine learning models across distributed edge devices in the Internet of Things (IoT). By keeping data local and coordinating model training through a central server, FL effectively addresses privacy concerns and reduces communication overhead. However, the limited computational power, memory, and bandwidth of IoT edge devices pose significant challenges to the efficiency and scalability of FL, especially when training deep neural networks. Various FL frameworks have been proposed to reduce computation and communication overheads through dropout or layer freezing. However, these approaches often sacrifice accuracy or neglect memory constraints. To this end, in this work, we introduce Federated Learning with Ordered Layer Freezing (FedOLF). FedOLF consistently freezes layers in a predefined order before training, significantly mitigating computation and memory requirements. To further reduce communication and energy costs, we incorporate Tensor Operation Approximation (TOA), a lightweight alternative to conventional quantization that better preserves model accuracy. Experimental results demonstrate that over non-iid data, FedOLF achieves at least 0.3%, 6.4%, 5.81%, 4.4%, 6.27% and 1.29% higher accuracy than existing works respectively on EMNIST (with CNN), CIFAR-10 (with AlexNet), CIFAR-100 (with ResNet20 and ResNet44), and CINIC-10 (with ResNet20 and ResNet44), along with higher energy efficiency and lower memory footprint.
LIMO: Low-Power In-Memory-Annealer and Matrix-Multiplication Primitive for Edge Computing
Venue: arXiv cs.ET 2025
Year: 2025

Combinatorial optimization (CO) underpins applications in science and engineering, ranging from logistics to electronic design automation. A classic example is the NP-complete Traveling Salesman Problem (TSP). Finding exact solutions for large-scale TSP instances remains computationally intractable; on von Neumann architectures, such solvers are constrained by the memory wall, incurring compute-memory traffic that grows with instance size. Metaheuristics, such as simulated annealing implemented on compute-in-memory (CiM) architectures, offer a way to mitigate the von Neumann bottleneck. This is accomplished by performing in-memory optimization cycles to rapidly find approximate solutions for TSP instances. Yet this approach suffers from degrading solution quality as instance size increases, owing to inefficient state-space exploration. To address this, we present LIMO, a mixed-signal computational macro that implements an in-memory annealing algorithm with reduced search-space complexity. The annealing process is aided by the stochastic switching of spin-transfer-torque magnetic-tunnel-junctions (STT-MTJs) to escape local minima. For large instances, our macro co-design is complemented by a refinement-based divide-and-conquer algorithm amenable to parallel optimization in a spatial architecture. Consequently, our system comprising several LIMO macros achieves superior solution quality and faster time-to-solution on instances up to 85,900 cities compared to prior hardware annealers. The modularity of our annealing peripherals allows the LIMO macro to be reused for other applications, such as vector-matrix multiplications (VMMs). This enables our architecture to support neural network inference. As an illustration, we show image classification and face detection with software-comparable accuracy, while achieving lower latency and energy consumption than baseline CiM architectures.
Multiparty Authorization for Secure Data Storage in Cloud Environments using Improved Attribute-Based Encryption
Venue: arXiv cs.CR 2025
Year: 2025

In todays scenario, various organizations store their sensitive data in the cloud environment. Multiple problems are present while retrieving and storing vast amounts of data, such as the frequency of data requests (increasing the computational overhead of the server) and data leakage while storing. To cope with said problem, Attribute-Based Encryption (ABE) is one of the potential security and access control techniques for secure data storage and authorization. The proposed work divides into two objectives: (i) provide access to authorized users and (ii) secure data storage in a cloud environment. The improved ABE using Functional Based Stream Cipher (FBSE) is proposed for data storage. The proposed technique uses simple scalar points over a parabolic curve to provide multiparty authorization. The authorization points are generated and share only with the authorized recipients. The Shamir secret sharing technique generate the authorization points and 2D-Lagrange Interpolation is used to reconstruct the secret points from regular parabola. The proposed scheme has specified the threshold (Ts>3) legally authorized users to reconstruct the attribute-associated keys for decryption. The encryption of data is evaluated using Statistical analysis (NIST Statistical Test Suite, Correlation Coefficient, and Histogram) test to investigate image pixel deviation. The parameters like encryption and decryption are used for performance analysis, where an increase in the number of attributes for the authorization policy will increase the encryption time. The proposed scheme imposes minimal storage overhead, irrespective of the users identity. The security analysis evidence that it resists collision attacks. The security and performance analysis results demonstrate that the proposed scheme is more robust and secure.
RobustMask: Certified Robustness against Adversarial Neural Ranking Attack via Randomized Masking
Venue: arXiv cs.CR 2025
Year: 2025

Neural ranking models have achieved remarkable progress and are now widely deployed in real-world applications such as Retrieval-Augmented Generation (RAG). However, like other neural architectures, they remain vulnerable to adversarial manipulations: subtle character-, word-, or phrase-level perturbations can poison retrieval results and artificially promote targeted candidates, undermining the integrity of search engines and downstream systems. Existing defenses either rely on heuristics with poor generalization or on certified methods that assume overly strong adversarial knowledge, limiting their practical use. To address these challenges, we propose RobustMask, a novel defense that combines the context-prediction capability of pretrained language models with a randomized masking-based smoothing mechanism. Our approach strengthens neural ranking models against adversarial perturbations at the character, word, and phrase levels. Leveraging both the pairwise comparison ability of ranking models and probabilistic statistical analysis, we provide a theoretical proof of RobustMask's certified top-K robustness. Extensive experiments further demonstrate that RobustMask successfully certifies over 20% of candidate documents within the top-10 ranking positions against adversarial perturbations affecting up to 30% of their content. These results highlight the effectiveness of RobustMask in enhancing the adversarial robustness of neural ranking models, marking a significant step toward providing stronger security guarantees for real-world retrieval systems.
ISOPO: Proximal policy gradients without pi-old
Venue: arXiv cs.LG 2025
Year: 2025

This note introduces Isometric Policy Optimization (ISOPO), an efficient method to approximate the natural policy gradient in a single gradient step. In comparison, existing proximal policy methods such as GRPO or CISPO use multiple gradient steps with variants of importance ratio clipping to approximate a natural gradient step relative to a reference policy. In its simplest form, ISOPO normalizes the log-probability gradient of each sequence in the Fisher metric before contracting with the advantages. Another variant of ISOPO transforms the microbatch advantages based on the neural tangent kernel in each layer. ISOPO applies this transformation layer-wise in a single backward pass and can be implemented with negligible computational overhead compared to vanilla REINFORCE.
Task-driven Heterophilic Graph Structure Learning
Venue: arXiv cs.LG 2025
Year: 2025

Graph neural networks (GNNs) often struggle to learn discriminative node representations for heterophilic graphs, where connected nodes tend to have dissimilar labels and feature similarity provides weak structural cues. We propose frequency-guided graph structure learning (FgGSL), an end-to-end graph inference framework that jointly learns homophilic and heterophilic graph structures along with a spectral encoder. FgGSL employs a learnable, symmetric, feature-driven masking function to infer said complementary graphs, which are processed using pre-designed low- and high-pass graph filter banks. A label-based structural loss explicitly promotes the recovery of homophilic and heterophilic edges, enabling task-driven graph structure learning. We derive stability bounds for the structural loss and establish robustness guarantees for the filter banks under graph perturbations. Experiments on six heterophilic benchmarks demonstrate that FgGSL consistently outperforms state-of-the-art GNNs and graph rewiring methods, highlighting the benefits of combining frequency information with supervised topology inference.
Entropy-Guided Token Dropout: Training Autoregressive Language Models with Limited Domain Data
Venue: arXiv cs.CL 2025
Year: 2025

As access to high-quality, domain-specific data grows increasingly scarce, multi-epoch training has become a practical strategy for adapting large language models (LLMs). However, autoregressive models often suffer from performance degradation under repeated data exposure, where overfitting leads to a marked decline in model capability. Through empirical analysis, we trace this degradation to an imbalance in learning dynamics: predictable, low-entropy tokens are learned quickly and come to dominate optimization, while the model's ability to generalize on high-entropy tokens deteriorates with continued training. To address this, we introduce EntroDrop, an entropy-guided token dropout method that functions as structured data regularization. EntroDrop selectively masks low-entropy tokens during training and employs a curriculum schedule to adjust regularization strength in alignment with training progress. Experiments across model scales from 0.6B to 8B parameters show that EntroDrop consistently outperforms standard regularization baselines and maintains robust performance throughout extended multi-epoch training. These findings underscore the importance of aligning regularization with token-level learning dynamics when training on limited data. Our approach offers a promising pathway toward more effective adaptation of LLMs in data-constrained domains.
Local Rendezvous Hashing: Bounded Loads and Minimal Churn via Cache-Local Candidates
Venue: arXiv cs.DC 2025
Year: 2025

Consistent hashing is fundamental to distributed systems, but ring-based schemes can exhibit high peak-to-average load ratios unless they use many virtual nodes, while multi-probe methods improve balance at the cost of scattered memory accesses. This paper introduces Local Rendezvous Hashing (LRH), which preserves a token ring but restricts Highest Random Weight (HRW) selection to a cache-local window of C distinct neighboring physical nodes. LRH locates a key by one binary search, enumerates exactly C distinct candidates using precomputed next-distinct offsets, and chooses the HRW winner (optionally weighted). Lookup cost is O(log|R| + C). Under fixed-topology liveness changes, fixed-candidate filtering remaps only keys whose original winner is down, yielding zero excess churn. In a benchmark with N=5000, V=256 (|R|=1.28M), K=50M and C=8, LRH reduces Max/Avg load from 1.2785 to 1.0947 and achieves 60.05 Mkeys/s, about 6.8x faster than multi-probe consistent hashing with 8 probes (8.80 Mkeys/s) while approaching its balance (Max/Avg 1.0697). A microbenchmark indicates multi-probe assignment is dominated by repeated ring searches and memory traffic rather than probe-generation arithmetic.
Eliminating Inductive Bias in Reward Models with Information-Theoretic Guidance
Venue: arXiv cs.LG 2025
Year: 2025

Reward models (RMs) are essential in reinforcement learning from human feedback (RLHF) to align large language models (LLMs) with human values. However, RM training data is commonly recognized as low-quality, containing inductive biases that can easily lead to overfitting and reward hacking. For example, more detailed and comprehensive responses are usually human-preferred but with more words, leading response length to become one of the inevitable inductive biases. A limited number of prior RM debiasing approaches either target a single specific type of bias or model the problem with only simple linear correlations, \textit{e.g.}, Pearson coefficients. To mitigate more complex and diverse inductive biases in reward modeling, we introduce a novel information-theoretic debiasing method called \textbf{D}ebiasing via \textbf{I}nformation optimization for \textbf{R}M (DIR). Inspired by the information bottleneck (IB), we maximize the mutual information (MI) between RM scores and human preference pairs, while minimizing the MI between RM outputs and biased attributes of preference inputs. With theoretical justification from information theory, DIR can handle more sophisticated types of biases with non-linear correlations, broadly extending the real-world application scenarios for RM debiasing methods. In experiments, we verify the effectiveness of DIR with three types of inductive biases: \textit{response length}, \textit{sycophancy}, and \textit{format}. We discover that DIR not only effectively mitigates target inductive biases but also enhances RLHF performance across diverse benchmarks, yielding better generalization abilities. The code and training recipes are available at https://github.com/Qwen-Applications/DIR.
Dynamic Channel Knowledge Map Construction in MIMO-OFDM Systems
Venue: arXiv cs.IT 2025
Year: 2025

Channel knowledge map (CKM) is a promising paradigm for environment-aware communications by establishing a deterministic mapping between physical locations and channel parameters. Existing CKM construction methods focus on quasi-static propagation environment. This paper develops a dynamic CKM construction method for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. We establish a dynamic channel model that captures the coexistence of quasi-static and dynamic scatterers, as well as the impacts of antenna rotation and synchronization errors. Based on this model, we formulate the problem of dynamic CKM construction within a Bayesian inference framework and design a two-stage approximate Bayesian inference algorithm. In stage I, a high-performance algorithm is developed to jointly infer quasi-static channel parameters and calibrate synchronization errors from historical measurements. In stage II, by leveraging the quasi-static parameters as informative priors, a low-complexity algorithm is designed to estimate dynamic parameters from limited real-time measurements. Simulation results validate the superiority of the proposed method and demonstrate its effectiveness in enabling low-overhead, high-performance channel estimation in dynamic environments.