OpenRaiser/Intern-Atlas huggingface dataset:
Securing Cross-Domain Internet of Drones: An RFF-PUF Allied Authenticated Key Exchange Protocol With Over-the-Air Enrollment
Venue: arXiv cs.CR 2025
Year: 2025

The Internet of Drones (IoD) is an emerging and crucial paradigm enabling advanced applications that require seamless, secure communication across heterogeneous and untrusted domains. In such environments, access control and the transmission of sensitive data pose significant security challenges for IoD systems, necessitating the design of lightweight mutual authentication and key exchange protocols. Existing solutions are often hampered by high computation overhead, reliance on third parties, the requirement for secret storage in resource-constrained drones, and the need for a strictly controlled enrollment environment. These limitations make them impractical for dynamic cross-domain deployment. To address these limitations, we propose a lightweight mutual authentication mechanism that integrates Radio Frequency Fingerprint (RFF) and Physical Unclonable Function (PUF) technologies for secure drone-to-drone (D2D) and drone-to-ground station server (D2G) communication. RFF-based device identification is used to achieve over-the-air (OTA) enrollment, while the PUF serves as the root of trust for establishing mutual authentication among communication parties. Additionally, the on-the-fly key generation capability of the PUF is co-designed with One-Time-Pad (OTP) encryption to realize ephemeral keying and eliminate the need for storing secrets within drones. Both informal security analysis and ProVerif-based formal security verification comprehensively demonstrate the resilience of our protocol against common security attacks. The proposed protocol also outperforms existing IoD authentication schemes in terms of security features, as well as computation, communication, and storage overhead.
Beyond Content: How Author Network Centrality Drives Citation Disparities in Top AI Conferences
Venue: arXiv cs.DL 2025
Year: 2025

While scholarly citations are pivotal for assessing academic impact, they often reflect systemic biases beyond research quality. This study examines a critical yet underexplored driver of citation disparities: authors' structural positions within scientific collaboration networks. Through a large-scale analysis of 17,942 papers from three top-tier machine learning conferences (NeurIPS, ICML, ICLR) published between 2005 and 2024, we quantify the influence of author centrality on citations. Methodologically, we advance the field by employing beta regression to model citation percentiles, which appropriately accounts for the bounded nature of citation data. We also propose a novel centrality metric, Harmonic Closeness with Temporal and Collaboration Count Decay (HCTCD), which incorporates temporal decay and collaboration intensity. Our results robustly demonstrate that long-term centrality exerts a significantly stronger effect on citation percentiles than short-term metrics, with closeness centrality and HCTCD emerging as the most potent predictors. Importantly, team-level centrality aggregation, particularly through exponentially weighted summation, explains citation variance more effectively than conventional rank-based approaches, underscoring the primacy of collective network connectivity over individual prominence. Integrating centrality features into machine learning models yields a 2.4% to 4.8% reduction in prediction error (MSE), confirming their value beyond content-based benchmarks. These findings challenge entrenched evaluation paradigms and advocate for network-aware assessment frameworks to mitigate structural inequities in scientific recognition.
HeartBench: Probing Core Dimensions of Anthropomorphic Intelligence in LLMs
Venue: arXiv cs.CL 2025
Year: 2025

While Large Language Models (LLMs) have achieved remarkable success in cognitive and reasoning benchmarks, they exhibit a persistent deficit in anthropomorphic intelligence-the capacity to navigate complex social, emotional, and ethical nuances. This gap is particularly acute in the Chinese linguistic and cultural context, where a lack of specialized evaluation frameworks and high-quality socio-emotional data impedes progress. To address these limitations, we present HeartBench, a framework designed to evaluate the integrated emotional, cultural, and ethical dimensions of Chinese LLMs. Grounded in authentic psychological counseling scenarios and developed in collaboration with clinical experts, the benchmark is structured around a theory-driven taxonomy comprising five primary dimensions and 15 secondary capabilities. We implement a case-specific, rubric-based methodology that translates abstract human-like traits into granular, measurable criteria through a ``reasoning-before-scoring'' evaluation protocol. Our assessment of 13 state-of-the-art LLMs indicates a substantial performance ceiling: even leading models achieve only 60% of the expert-defined ideal score. Furthermore, analysis using a difficulty-stratified ``Hard Set'' reveals a significant performance decay in scenarios involving subtle emotional subtexts and complex ethical trade-offs. HeartBench establishes a standardized metric for anthropomorphic AI evaluation and provides a methodological blueprint for constructing high-quality, human-aligned training data.
Fast Inference of Visual Autoregressive Model with Adjacency-Adaptive Dynamical Draft Trees
Venue: arXiv cs.CV 2025
Year: 2025

Autoregressive (AR) image models achieve diffusion-level quality but suffer from sequential inference, requiring approximately 2,000 steps for a 576x576 image. Speculative decoding with draft trees accelerates LLMs yet underperforms on visual AR models due to spatially varying token prediction difficulty. We identify a key obstacle in applying speculative decoding to visual AR models: inconsistent acceptance rates across draft trees due to varying prediction difficulties in different image regions. We propose Adjacency-Adaptive Dynamical Draft Trees (ADT-Tree), an adjacency-adaptive dynamic draft tree that dynamically adjusts draft tree depth and width by leveraging adjacent token states and prior acceptance rates. ADT-Tree initializes via horizontal adjacency, then refines depth/width via bisectional adaptation, yielding deeper trees in simple regions and wider trees in complex ones. The empirical evaluations on MS-COCO 2017 and PartiPrompts demonstrate that ADT-Tree achieves speedups of 3.13xand 3.05x, respectively. Moreover, it integrates seamlessly with relaxed sampling methods such as LANTERN, enabling further acceleration. Code is available at https://github.com/Haodong-Lei-Ray/ADT-Tree.
Training-free Conditional Image Embedding Framework Leveraging Large Vision Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Conditional image embeddings are feature representations that focus on specific aspects of an image indicated by a given textual condition (e.g., color, genre), which has been a challenging problem. Although recent vision foundation models, such as CLIP, offer rich representations of images, they are not designed to focus on a specified condition. In this paper, we propose DIOR, a method that leverages a large vision-language model (LVLM) to generate conditional image embeddings. DIOR is a training-free approach that prompts the LVLM to describe an image with a single word related to a given condition. The hidden state vector of the LVLM's last token is then extracted as the conditional image embedding. DIOR provides a versatile solution that can be applied to any image and condition without additional training or task-specific priors. Comprehensive experimental results on conditional image similarity tasks demonstrate that DIOR outperforms existing training-free baselines, including CLIP. Furthermore, DIOR achieves superior performance compared to methods that require additional training across multiple settings.
Online Inertia Parameter Estimation for Unknown Objects Grasped by a Manipulator Towards Space Applications
Venue: arXiv cs.RO 2025
Year: 2025

Knowing the inertia parameters of a grasped object is crucial for dynamics-aware manipulation, especially in space robotics with free-floating bases. This work addresses the problem of estimating the inertia parameters of an unknown target object during manipulation. We apply and extend an existing online identification method by incorporating momentum conservation, enabling its use for the floating-base robots. The proposed method is validated through numerical simulations, and the estimated parameters are compared with ground-truth values. Results demonstrate accurate identification in the scenarios, highlighting the method's applicability to on-orbit servicing and other space missions.
Patch as Node: Human-Centric Graph Representation Learning for Multimodal Action Recognition
Venue: arXiv cs.CV 2025
Year: 2025

While human action recognition has witnessed notable achievements, multimodal methods fusing RGB and skeleton modalities still suffer from their inherent heterogeneity and fail to fully exploit the complementary potential between them. In this paper, we propose PAN, the first human-centric graph representation learning framework for multimodal action recognition, in which token embeddings of RGB patches containing human joints are represented as spatiotemporal graphs. The human-centric graph modeling paradigm suppresses the redundancy in RGB frames and aligns well with skeleton-based methods, thus enabling a more effective and semantically coherent fusion of multimodal features. Since the sampling of token embeddings heavily relies on 2D skeletal data, we further propose attention-based post calibration to reduce the dependency on high-quality skeletal data at a minimal cost interms of model performance. To explore the potential of PAN in integrating with skeleton-based methods, we present two variants: PAN-Ensemble, which employs dual-path graph convolution networks followed by late fusion, and PAN-Unified, which performs unified graph representation learning within a single network. On three widely used multimodal action recognition datasets, both PAN-Ensemble and PAN-Unified achieve state-of-the-art (SOTA) performance in their respective settings of multimodal fusion: separate and unified modeling, respectively.
Positive Narrativity Enhances Sense of Agency toward a VR Avatar
Venue: arXiv cs.HC 2025
Year: 2025

The full-body illusion (FBI) refers to the experience of perceiving a virtual avatar as one's own body. In virtual reality (VR) environments, inducing the FBI has been shown to modulate users' bodily experiences and behavior. Previous studies have demonstrated that embodying avatars with specific characteristics can influence users' actions, largely through the activation of implicit stereotypes. However, few studies have explicitly manipulated users' impressions of an avatar by introducing narrative context. The present study investigated how avatar narrativity, induced through contextual narratives, affects the FBI. Healthy participants embodied a powerful artificial lifeform avatar in VR after listening to either a positive narrative, in which the avatar used its abilities to protect others, or a negative narrative, in which it misused its power. Participants' impressions of the avatar and indices of bodily self-consciousness were subsequently assessed. The results showed that positive narratives significantly enhanced the sense of agency (SoA), and that SoA was positively correlated with participants' perceived personal familiarity with the avatar. These findings suggest that the avatar narrativity can modulate embodiment in VR.
Look Closer! An Adversarial Parametric Editing Framework for Hallucination Mitigation in VLMs
Venue: arXiv cs.CV 2025
Year: 2025

While Vision-Language Models (VLMs) have garnered increasing attention in the AI community due to their promising practical applications, they exhibit persistent hallucination issues, generating outputs misaligned with visual inputs. Recent studies attribute these hallucinations to VLMs' over-reliance on linguistic priors and insufficient visual feature integration, proposing heuristic decoding calibration strategies to mitigate them. However, the non-trainable nature of these strategies inherently limits their optimization potential. To this end, we propose an adversarial parametric editing framework for Hallucination mitigation in VLMs, which follows an \textbf{A}ctivate-\textbf{L}ocate-\textbf{E}dit \textbf{A}dversarially paradigm. Specifically, we first construct an activation dataset that comprises grounded responses (positive samples attentively anchored in visual features) and hallucinatory responses (negative samples reflecting LLM prior bias and internal knowledge artifacts). Next, we identify critical hallucination-prone parameter clusters by analyzing differential hidden states of response pairs. Then, these clusters are fine-tuned using prompts injected with adversarial tuned prefixes that are optimized to maximize visual neglect, thereby forcing the model to prioritize visual evidence over inherent parametric biases. Evaluations on both generative and discriminative VLM tasks demonstrate the significant effectiveness of ALEAHallu in alleviating hallucinations. Our code is available at https://github.com/hujiayu1223/ALEAHallu.
iSHIFT: Lightweight Slow-Fast GUI Agent with Adaptive Perception
Venue: arXiv cs.CV 2025
Year: 2025

Multimodal Large Language Models (MLLMs) show strong potential for interpreting and interacting with complex, pixel-rich Graphical User Interface (GUI) environments. However, building agents that are both efficient for high-level tasks and precise for fine-grained interactions remains challenging. GUI agents must perform routine actions efficiently while also handling tasks that demand exact visual grounding, yet existing approaches struggle when accuracy depends on identifying specific interface elements. These MLLMs also remain large and cannot adapt their reasoning depth to the task at hand. In this work, we introduce iSHIFT: Implicit Slow-fast Hybrid Inference with Flexible Tokens, a lightweight agent that integrates latent thinking (implicit chain-of-thought) with a perception control module. iSHIFT enables an MLLM to switch between a slow mode, which leverages detailed visual grounding for high precision and a fast mode that uses global cues for efficiency. Special perception tokens guide attention to relevant screen regions, allowing the model to decide both how to reason and where to focus. Despite its compact 2.5B size, iSHIFT matches state-of-the-art performance on multiple benchmark datasets.
HWL-HIN: A Hypergraph-Level Hypergraph Isomorphism Network as Powerful as the Hypergraph Weisfeiler-Lehman Test with Application to Higher-Order Network Robustness
Venue: arXiv cs.LG 2025
Year: 2025

Robustness in complex systems is of significant engineering and economic importance. However, conventional attack-based a posteriori robustness assessments incur prohibitive computational overhead. Recently, deep learning methods, such as Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs), have been widely employed as surrogates for rapid robustness prediction. Nevertheless, these methods neglect the complex higher-order correlations prevalent in real-world systems, which are naturally modeled as hypergraphs. Although Hypergraph Neural Networks (HGNNs) have been widely adopted for hypergraph learning, their topological expressive power has not yet reached the theoretical upper bound. To address this limitation, inspired by Graph Isomorphism Networks, this paper proposes a hypergraph-level Hypergraph Isomorphism Network framework. Theoretically, this approach is proven to possess an expressive power strictly equivalent to the Hypergraph Weisfeiler-Lehman test and is applied to predict hypergraph robustness. Experimental results demonstrate that while maintaining superior efficiency in training and prediction, the proposed method not only outperforms existing graph-based models but also significantly surpasses conventional HGNNs in tasks that prioritize topological structure representation.
Backdoor Attacks on Prompt-Driven Video Segmentation Foundation Models
Venue: arXiv cs.CV 2025
Year: 2025

Prompt-driven Video Segmentation Foundation Models (VSFMs) such as SAM2 are increasingly deployed in applications like autonomous driving and digital pathology, raising concerns about backdoor threats. Surprisingly, we find that directly transferring classic backdoor attacks (e.g., BadNet) to VSFMs is almost ineffective, with ASR below 5\%. To understand this, we study encoder gradients and attention maps and observe that conventional training keeps gradients for clean and triggered samples largely aligned, while attention still focuses on the true object, preventing the encoder from learning a distinct trigger-related representation. To address this challenge, we propose BadVSFM, the first backdoor framework tailored to prompt-driven VSFMs. BadVSFM uses a two-stage strategy: (1) steer the image encoder so triggered frames map to a designated target embedding while clean frames remain aligned with a clean reference encoder; (2) train the mask decoder so that, across prompt types, triggered frame-prompt pairs produce a shared target mask, while clean outputs stay close to a reference decoder. Extensive experiments on two datasets and five VSFMs show that BadVSFM achieves strong, controllable backdoor effects under diverse triggers and prompts while preserving clean segmentation quality. Ablations over losses, stages, targets, trigger settings, and poisoning rates demonstrate robustness to reasonable hyperparameter changes and confirm the necessity of the two-stage design. Finally, gradient-conflict analysis and attention visualizations show that BadVSFM separates triggered and clean representations and shifts attention to trigger regions, while four representative defenses remain largely ineffective, revealing an underexplored vulnerability in current VSFMs.
MAI-UI Technical Report: Real-World Centric Foundation GUI Agents
Venue: arXiv cs.CV 2025
Year: 2025

The development of GUI agents could revolutionize the next generation of human-computer interaction. Motivated by this vision, we present MAI-UI, a family of foundation GUI agents spanning the full spectrum of sizes, including 2B, 8B, 32B, and 235B-A22B variants. We identify four key challenges to realistic deployment: the lack of native agent-user interaction, the limits of UI-only operation, the absence of a practical deployment architecture, and brittleness in dynamic environments. MAI-UI addresses these issues with a unified methodology: a self-evolving data pipeline that expands the navigation data to include user interaction and MCP tool calls, a native device-cloud collaboration system routes execution by task state, and an online RL framework with advanced optimizations to scale parallel environments and context length. MAI-UI establishes new state-of-the-art across GUI grounding and mobile navigation. On grounding benchmarks, it reaches 73.5% on ScreenSpot-Pro, 91.3% on MMBench GUI L2, 70.9% on OSWorld-G, and 49.2% on UI-Vision, surpassing Gemini-3-Pro and Seed1.8 on ScreenSpot-Pro. On mobile GUI navigation, it sets a new SOTA of 76.7% on AndroidWorld, surpassing UI-Tars-2, Gemini-2.5-Pro and Seed1.8. On MobileWorld, MAI-UI obtains 41.7% success rate, significantly outperforming end-to-end GUI models and competitive with Gemini-3-Pro based agentic frameworks. Our online RL experiments show significant gains from scaling parallel environments from 32 to 512 (+5.2 points) and increasing environment step budget from 15 to 50 (+4.3 points). Finally, the native device-cloud collaboration system improves on-device performance by 33%, reduces cloud model calls by over 40%, and preserves user privacy.
On the Ergodic Capacity for SIM-Aided Holographic MIMO Communications
Venue: arXiv cs.IT 2025
Year: 2025

We derive a novel closed-form lower bound on the ergodic capacity of holographic multiple-input multiple-output (HMIMO) systems enhanced by stacked intelligent metasurfaces (SIMs) under Rayleigh fading conditions. The proposed expression is valid for systems with a finite number of antennas and SIM elements and exhibits tightness throughout the whole signal-to-noise ratio (SNR) range. Furthermore, we conduct a comprehensive low-SNR analysis, offering meaningful observations on how key system parameters influence the capacity performance.
Context as a Tool: Context Management for Long-Horizon SWE-Agents
Venue: arXiv cs.CL 2025
Year: 2025

Agents based on large language models have recently shown strong potential on real-world software engineering (SWE) tasks that require long-horizon interaction with repository-scale codebases. However, most existing agents rely on append-only context maintenance or passively triggered compression heuristics, which often lead to context explosion, semantic drift, and degraded reasoning in long-running interactions. We propose CAT, a new context management paradigm that elevates context maintenance to a callable tool integrated into the decision-making process of agents. CAT formalizes a structured context workspace consisting of stable task semantics, condensed long-term memory, and high-fidelity short-term interactions, and enables agents to proactively compress historical trajectories into actionable summaries at appropriate milestones. To support context management for SWE-agents, we propose a trajectory-level supervision framework, CAT-GENERATOR, based on an offline data construction pipeline that injects context-management actions into complete interaction trajectories. Using this framework, we train a context-aware model, SWE-Compressor. Experiments on SWE-Bench-Verified demonstrate that SWE-Compressor reaches a 57.6% solved rate and significantly outperforms ReAct-based agents and static compression baselines, while maintaining stable and scalable long-horizon reasoning under a bounded context budget.
Abstraction of Trusted Execution Environments as the Missing Layer for Broad Confidential Computing Adoption: A Systematization of Knowledge
Venue: arXiv cs.CR 2025
Year: 2025

Trusted Execution Environments (TEEs) protect sensitive code and data from the operating system, hypervisor, or other untrusted software. Different solutions exist, each proposing different features. Abstraction layers aim to unify the ecosystem, allowing application developers and system administrators to leverage confidential computing as broadly and efficiently as possible. We start with an overview of representative available TEE technologies. We describe and summarize each TEE ecosystem, classifying them in different categories depending on their main design choices. Then, we propose a systematization of knowledge focusing on different abstraction layers around each design choice. We describe the underlying technologies of each design, as well as the inner workings and features of each abstraction layer. Our study reveals opportunities for improving existing abstraction layer solutions. It also highlights WebAssembly, a promising approach that supports the largest set of features. We close with a discussion on future directions for research, such as how future abstraction layers may evolve and integrate with the confidential computing ecosystem.