OpenRaiser/Intern-Atlas huggingface dataset:
FailSafe: High-performance Resilient Serving
Venue: arXiv cs.DC 2025
Year: 2025

Tensor parallelism (TP) enables large language models (LLMs) to scale inference efficiently across multiple GPUs, but its tight coupling makes systems fragile: a single GPU failure can halt execution, trigger costly KVCache recomputation, and introduce long-term compute and memory imbalance. We present FailSafe, a fault-tolerant TP serving system that sustains high performance under irregular GPU availability. FailSafe introduces three techniques to balance computation and memory across GPUs: (1) Cyclic KVCache Placement for uniform memory utilization, (2) Hybrid Attention combining tensor- and data-parallel attention to eliminate stragglers, and (3) Fine-Grained Load-Aware Routing to dynamically balance requests. It further employs proactive KVCache backup and on-demand weight recovery to avoid expensive recomputation and redundant data transfers. We implement these techniques in a lightweight serving engine compatible with existing LLM infrastructures. Evaluated on an 8xH100 DGX system with real-world fault traces and representative workloads, FailSafe achieves up to 2x higher throughput and two orders of magnitude lower recovery latency compared to standard fault handling approaches. Even with up to three GPU failures, FailSafe sustains high throughput and balanced utilization, demonstrating robust and efficient LLM serving under dynamic and unreliable hardware conditions.
Real-Time Mobile Video Analytics for Pre-arrival Emergency Medical Services
Venue: arXiv cs.MM 2025
Year: 2025

Timely and accurate pre-arrival video streaming and analytics are critical for emergency medical services (EMS) to deliver life-saving interventions. Yet, current-generation EMS infrastructure remains constrained by one-to-one video streaming and limited analytics capabilities, leaving dispatchers and EMTs to manually interpret overwhelming, often noisy or redundant information in high-stress environments. We present TeleEMS, a mobile live video analytics system that enables pre-arrival multimodal inference by fusing audio and video into a unified decision-making pipeline before EMTs arrive on scene. TeleEMS comprises two key components: TeleEMS Client and TeleEMS Server. The TeleEMS Client runs across phones, smart glasses, and desktops to support bystanders, EMTs en route, and 911 dispatchers. The TeleEMS Server, deployed at the edge, integrates EMS-Stream, a communication backbone that enables smooth multi-party video streaming. On top of EMSStream, the server hosts three real-time analytics modules: (1) audio-to-symptom analytics via EMSLlama, a domain-specialized LLM for robust symptom extraction and normalization; (2) video-to-vital analytics using state-of-the-art rPPG methods for heart rate estimation; and (3) joint text-vital analytics via PreNet, a multimodal multitask model predicting EMS protocols, medication types, medication quantities, and procedures. Evaluation shows that EMSLlama outperforms GPT-4o (exact-match 0.89 vs. 0.57) and that text-vital fusion improves inference robustness, enabling reliable pre-arrival intervention recommendations. TeleEMS demonstrates the potential of mobile live video analytics to transform EMS operations, bridging the gap between bystanders, dispatchers, and EMTs, and paving the way for next-generation intelligent EMS infrastructure.
Multi-view Phase-aware Pedestrian-Vehicle Incident Reasoning Framework with Vision-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Pedestrian-vehicle incidents remain a critical urban safety challenge, with pedestrians accounting for over 20% of global traffic fatalities. Although existing video-based systems can detect when incidents occur, they provide little insight into how these events unfold across the distinct cognitive phases of pedestrian behavior. Recent vision-language models (VLMs) have shown strong potential for video understanding, but they remain limited in that they typically process videos in isolation, without explicit temporal structuring or multi-view integration. This paper introduces Multi-view Phase-aware Pedestrian-Vehicle Incident Reasoning (MP-PVIR), a unified framework that systematically processes multi-view video streams into structured diagnostic reports through four stages: (1) event-triggered multi-view video acquisition, (2) pedestrian behavior phase segmentation, (3) phase-specific multi-view reasoning, and (4) hierarchical synthesis and diagnostic reasoning. The framework operationalizes behavioral theory by automatically segmenting incidents into cognitive phases, performing synchronized multi-view analysis within each phase, and synthesizing results into causal chains with targeted prevention strategies. Particularly, two specialized VLMs underpin the MP-PVIR pipeline: TG-VLM for behavioral phase segmentation (mIoU = 0.4881) and PhaVR-VLM for phase-aware multi-view analysis, achieving a captioning score of 33.063 and up to 64.70% accuracy on question answering. Finally, a designated large language model is used to generate comprehensive reports detailing scene understanding, behavior interpretation, causal reasoning, and prevention recommendations. Evaluation on the Woven Traffic Safety dataset shows that MP-PVIR effectively translates multi-view video data into actionable insights, advancing AI-driven traffic safety analytics for vehicle-infrastructure cooperative systems.
Fair-GNE : Generalized Nash Equilibrium-Seeking Fairness in Multiagent Healthcare Automation
Venue: arXiv cs.LG 2025
Year: 2025

Enforcing a fair workload allocation among multiple agents tasked to achieve an objective in learning enabled demand side healthcare worker settings is crucial for consistent and reliable performance at runtime. Existing multi-agent reinforcement learning (MARL) approaches steer fairness by shaping reward through post hoc orchestrations, leaving no certifiable self-enforceable fairness that is immutable by individual agents at runtime. Contextualized within a setting where each agent shares resources with others, we address this shortcoming with a learning enabled optimization scheme among self-interested decision makers whose individual actions affect those of other agents. This extends the problem to a generalized Nash equilibrium (GNE) game-theoretic framework where we steer group policy to a safe and locally efficient equilibrium, so that no agent can improve its utility function by unilaterally changing its decisions. Fair-GNE models MARL as a constrained generalized Nash equilibrium-seeking (GNE) game, prescribing an ideal equitable collective equilibrium within the problem's natural fabric. Our hypothesis is rigorously evaluated in our custom-designed high-fidelity resuscitation simulator. Across all our numerical experiments, Fair-GNE achieves significant improvement in workload balance over fixed-penalty baselines (0.89 vs.\ 0.33 JFI, $p < 0.01$) while maintaining 86\% task success, demonstrating statistically significant fairness gains through adaptive constraint enforcement. Our results communicate our formulations, evaluation metrics, and equilibrium-seeking innovations in large multi-agent learning-based healthcare systems with clarity and principled fairness enforcement.
Beyond Fixed and Dynamic Prompts: Embedded Jailbreak Templates for Advancing LLM Security
Venue: arXiv cs.CR 2025
Year: 2025

As the use of large language models (LLMs) continues to expand, ensuring their safety and robustness has become a critical challenge. In particular, jailbreak attacks that bypass built-in safety mechanisms are increasingly recognized as a tangible threat across industries, driving the need for diverse templates to support red-teaming efforts and strengthen defensive techniques. However, current approaches predominantly rely on two limited strategies: (i) substituting harmful queries into fixed templates, and (ii) having the LLM generate entire templates, which often compromises intent clarity and reproductibility. To address this gap, this paper introduces the Embedded Jailbreak Template, which preserves the structure of existing templates while naturally embedding harmful queries within their context. We further propose a progressive prompt-engineering methodology to ensure template quality and consistency, alongside standardized protocols for generation and evaluation. Together, these contributions provide a benchmark that more accurately reflects real-world usage scenarios and harmful intent, facilitating its application in red-teaming and policy regression testing.
Applying Relation Extraction and Graph Matching to Answering Multiple Choice Questions
Venue: arXiv cs.CL 2025
Year: 2025

In this research, we combine Transformer-based relation extraction with matching of knowledge graphs (KGs) and apply them to answering multiple-choice questions (MCQs) while maintaining the traceability of the output process. KGs are structured representations of factual knowledge consisting of entities and relations. Due to the high construction cost, they had been regarded as static databases with validated links. However, the recent development of Transformer-based relation extraction (RE) methods has enabled us to generate KGs dynamically by giving them natural language texts, and thereby opened the possibility for representing the meaning of the input sentences with the created KGs. Using this effect, we propose a method that answers MCQs in the "fill-in-the-blank" format, taking care of the point that RE methods generate KGs that represent false information if provided with factually incorrect texts. We measure the truthfulness of each question sentence by (i) converting the sentence into a relational graph using an RE method and (ii) verifying it against factually correct KGs under the closed-world assumption. The experimental results demonstrate that our method correctly answers up to around 70% of the questions, while providing traceability of the procedure. We also highlight that the question category has a vast influence on the accuracy.
Wave-Former: Through-Occlusion 3D Reconstruction via Wireless Shape Completion
Venue: arXiv cs.CV 2025
Year: 2025

We present Wave-Former, a novel method capable of high-accuracy 3D shape reconstruction for completely occluded, diverse, everyday objects. This capability can open new applications spanning robotics, augmented reality, and logistics. Our approach leverages millimeter-wave (mmWave) wireless signals, which can penetrate common occlusions and reflect off hidden objects. In contrast to past mmWave reconstruction methods, which suffer from limited coverage and high noise, Wave-Former introduces a physics-aware shape completion model capable of inferring full 3D geometry. At the heart of Wave-Former's design is a novel three-stage pipeline which bridges raw wireless signals with recent advancements in vision-based shape completion by incorporating physical properties of mmWave signals. The pipeline proposes candidate geometric surfaces, employs a transformer-based shape completion model designed specifically for mmWave signals, and finally performs entropy-guided surface selection. This enables Wave-Former to be trained using entirely synthetic point-clouds, while demonstrating impressive generalization to real-world data. In head-to-head comparisons with state-of-the-art baselines, Wave-Former raises recall from 54% to 72% while maintaining a high precision of 85%.
MVI-Bench: A Comprehensive Benchmark for Evaluating Robustness to Misleading Visual Inputs in LVLMs
Venue: arXiv cs.CV 2025
Year: 2025

Evaluating the robustness of Large Vision-Language Models (LVLMs) is essential for their continued development and responsible deployment in real-world applications. However, existing robustness benchmarks typically focus on hallucination or misleading textual inputs, while largely overlooking the equally critical challenge posed by misleading visual inputs in assessing visual understanding. To fill this important gap, we introduce MVI-Bench, the first comprehensive benchmark specially designed for evaluating how Misleading Visual Inputs undermine the robustness of LVLMs. Grounded in fundamental visual primitives, the design of MVI-Bench centers on three hierarchical levels of misleading visual inputs: Visual Concept, Visual Attribute, and Visual Relationship. Using this taxonomy, we curate six representative categories and compile 1,248 expertly annotated VQA instances. To facilitate fine-grained robustness evaluation, we further introduce MVI-Sensitivity, a novel metric that characterizes LVLM robustness at a granular level. Empirical results across 18 state-of-the-art LVLMs uncover pronounced vulnerabilities to misleading visual inputs, and our in-depth analyses on MVI-Bench provide actionable insights that can guide the development of more reliable and robust LVLMs. The benchmark and codebase can be accessed at https://github.com/chenyil6/MVI-Bench.
Selective Weak-to-Strong Generalization
Venue: arXiv cs.CL 2025
Year: 2025

Future superhuman models will surpass the ability of humans and humans will only be able to \textit{weakly} supervise superhuman models. To alleviate the issue of lacking high-quality data for model alignment, some works on weak-to-strong generalization (W2SG) finetune a strong pretrained model with a weak supervisor so that it can generalize beyond weak supervision. However, the invariable use of weak supervision in existing methods exposes issues in robustness, with a proportion of weak labels proving harmful to models. In this paper, we propose a selective W2SG framework to avoid using weak supervision when unnecessary. We train a binary classifier P(IK) to identify questions that a strong model can answer and use its self-generated labels for alignment. We further refine weak labels with a graph smoothing method. Extensive experiments on three benchmarks show that our method consistently outperforms competitive baselines. Further analyses show that P(IK) can generalize across tasks and difficulties, which indicates selective W2SG can help superalignment.
A Longitudinal Study on the Attitudes of Gay Men in Beijing Towards Gay Social Media Platforms: Lonely Souls in the Digital Concrete Jungle
Venue: arXiv cs.HC 2025
Year: 2025

Over the past decade, specialized social networking applications have become a cornerstone of life for many gay men in China. This paper employs a longitudinal mixed-methods approach to investigate how Chinese men who have sex with men (MSM) have shifted their attitudes toward these platforms between approximately 2013 and 2023. Drawing on archival analysis of online discourses, a quantitative survey of 412 participants, and in-depth semi-structured interviews with 32 participants, we trace the complex trajectory of this evolution. Our findings reveal a clear pattern: from the initial embrace of these applications as revolutionary tools for community building and identity affirmation (2014--2017), to a period of growing ambivalence and critique centered on commercialization, ``hookup culture,'' and multiple forms of discrimination (2017--2020), and finally to the present era (2020--2023), characterized by pragmatic, fragmented, yet simultaneously critical and reconstructive uses. Today, users strategically employ a repertoire of applications -- including global platforms (e.g., Grindr and Tinder), domestic mainstream platforms (e.g., Blued), and niche alternatives (e.g., Aloha) -- to fulfill differentiated needs. We develop a detailed temporal framework to capture this attitudinal evolution and discuss its design implications for creating more supportive, secure, and community-oriented digital environments for marginalized groups.
A Bit Level Weight Reordering Strategy Based on Column Similarity to Explore Weight Sparsity in RRAM-based NN Accelerator
Venue: arXiv cs.AR 2025
Year: 2025

Compute-in-Memory (CIM) and weight sparsity are two effective techniques to reduce data movement during Neural Network (NN) inference. However, they can hardly be employed in the same accelerator simultaneously because CIM requires structural compute patterns which are disrupted in sparse NNs. In this paper, we partially solve this issue by proposing a bit level weight reordering strategy which can realize compact mapping of sparse NN weight matrices onto Resistive Random Access Memory (RRAM) based NN Accelerators (RRAM-Acc). In specific, when weights are mapped to RRAM crossbars in a binary complement manner, we can observe that, which can also be mathematically proven, bit-level sparsity and similarity commonly exist in the crossbars. The bit reordering method treats bit sparsity as a special case of bit similarity, reserve only one column in a pair of columns that have identical bit values, and then map the compressed weight matrices into Operation Units (OU). The performance of our design is evaluated with typical NNs. Simulation results show a 61.24% average performance improvement and 1.51x-2.52x energy savings under different sparsity ratios, with only slight overhead compared to the state-of-the-art design.
Orion: A Unified Visual Agent for Multimodal Perception, Advanced Visual Reasoning and Execution
Venue: arXiv cs.CV 2025
Year: 2025

We introduce Orion, a visual agent that integrates vision-based reasoning with tool-augmented execution to achieve powerful, precise, multi-step visual intelligence across images, video, and documents. Unlike traditional vision-language models that generate descriptive outputs, Orion orchestrates a suite of specialized computer vision tools, including object detection, keypoint localization, panoptic segmentation, Optical Character Recognition (OCR), and geometric analysis, to execute complex multi-step visual workflows. The system achieves competitive performance across MMMU, MMBench, DocVQA, and MMLongBench while extending monolithic VLM capabilities to production-grade visual intelligence. Through its agentic, tool-augmented approach, Orion enables autonomous visual reasoning that bridges neural perception with symbolic execution, marking the transition from passive visual understanding to active, tool-driven visual intelligence. Try Orion for free at: https://chat.vlm.run Learn more at: https://www.vlm.run/orion
Visionary Co-Driver: Enhancing Driver Perception of Potential Risks with LLM and HUD
Venue: arXiv cs.HC 2025
Year: 2025

Drivers' perception of risky situations has always been a challenge in driving. Existing risk-detection methods excel at identifying collisions but face challenges in assessing the behavior of road users in non-collision situations. This paper introduces Visionary Co-Driver, a system that leverages large language models to identify non-collision roadside risks and alert drivers based on their eye movements. Specifically, the system combines video processing algorithms and LLMs to identify potentially risky road users. These risks are dynamically indicated on an adaptive heads-up display interface to enhance drivers' attention. A user study with 41 drivers confirms that Visionary Co-Driver improves drivers' risk perception and supports their recognition of roadside risks.
Towards Authentic Movie Dubbing with Retrieve-Augmented Director-Actor Interaction Learning
Venue: arXiv cs.CL 2025
Year: 2025

The automatic movie dubbing model generates vivid speech from given scripts, replicating a speaker's timbre from a brief timbre prompt while ensuring lip-sync with the silent video. Existing approaches simulate a simplified workflow where actors dub directly without preparation, overlooking the critical director-actor interaction. In contrast, authentic workflows involve a dynamic collaboration: directors actively engage with actors, guiding them to internalize the context cues, specifically emotion, before performance. To address this issue, we propose a new Retrieve-Augmented Director-Actor Interaction Learning scheme to achieve authentic movie dubbing, termed Authentic-Dubber, which contains three novel mechanisms: (1) We construct a multimodal Reference Footage library to simulate the learning footage provided by directors. Note that we integrate Large Language Models (LLMs) to achieve deep comprehension of emotional representations across multimodal signals. (2) To emulate how actors efficiently and comprehensively internalize director-provided footage during dubbing, we propose an Emotion-Similarity-based Retrieval-Augmentation strategy. This strategy retrieves the most relevant multimodal information that aligns with the target silent video. (3) We develop a Progressive Graph-based speech generation approach that incrementally incorporates the retrieved multimodal emotional knowledge, thereby simulating the actor's final dubbing process. The above mechanisms enable the Authentic-Dubber to faithfully replicate the authentic dubbing workflow, achieving comprehensive improvements in emotional expressiveness. Both subjective and objective evaluations on the V2C Animation benchmark dataset validate the effectiveness. The code and demos are available at https://github.com/AI-S2-Lab/Authentic-Dubber.
PathMind: A Retrieve-Prioritize-Reason Framework for Knowledge Graph Reasoning with Large Language Models
Venue: arXiv cs.AI 2025
Year: 2025

Knowledge graph reasoning (KGR) is the task of inferring new knowledge by performing logical deductions on knowledge graphs. Recently, large language models (LLMs) have demonstrated remarkable performance in complex reasoning tasks. Despite promising success, current LLM-based KGR methods still face two critical limitations. First, existing methods often extract reasoning paths indiscriminately, without assessing their different importance, which may introduce irrelevant noise that misleads LLMs. Second, while many methods leverage LLMs to dynamically explore potential reasoning paths, they require high retrieval demands and frequent LLM calls. To address these limitations, we propose PathMind, a novel framework designed to enhance faithful and interpretable reasoning by selectively guiding LLMs with important reasoning paths. Specifically, PathMind follows a "Retrieve-Prioritize-Reason" paradigm. First, it retrieves a query subgraph from KG through the retrieval module. Next, it introduces a path prioritization mechanism that identifies important reasoning paths using a semantic-aware path priority function, which simultaneously considers the accumulative cost and the estimated future cost for reaching the target. Finally, PathMind generates accurate and logically consistent responses via a dual-phase training strategy, including task-specific instruction tuning and path-wise preference alignment. Extensive experiments on benchmark datasets demonstrate that PathMind consistently outperforms competitive baselines, particularly on complex reasoning tasks with fewer input tokens, by identifying essential reasoning paths.
Object-Centric World Models for Causality-Aware Reinforcement Learning
Venue: arXiv cs.LG 2025
Year: 2025

World models have been developed to support sample-efficient deep reinforcement learning agents. However, it remains challenging for world models to accurately replicate environments that are high-dimensional, non-stationary, and composed of multiple objects with rich interactions since most world models learn holistic representations of all environmental components. By contrast, humans perceive the environment by decomposing it into discrete objects, facilitating efficient decision-making. Motivated by this insight, we propose \emph{Slot Transformer Imagination with CAusality-aware reinforcement learning} (STICA), a unified framework in which object-centric Transformers serve as the world model and causality-aware policy and value networks. STICA represents each observation as a set of object-centric tokens, together with tokens for the agent action and the resulting reward, enabling the world model to predict token-level dynamics and interactions. The policy and value networks then estimate token-level cause--effect relations and use them in the attention layers, yielding causality-guided decision-making. Experiments on object-rich benchmarks demonstrate that STICA consistently outperforms state-of-the-art agents in both sample efficiency and final performance.