OpenRaiser/Intern-Atlas huggingface dataset:
ARMOR: Adaptive Meshing with Reinforcement Optimization for Real-time 3D Monitoring in Unexposed Scenes
Venue: arXiv cs.RO 2025
Year: 2025

Unexposed environments, such as lava tubes, mines, and tunnels, are among the most complex yet strategically significant domains for scientific exploration and infrastructure development. Accurate and real-time 3D meshing of these environments is essential for applications including automated structural assessment, robotic-assisted inspection, and safety monitoring. Implicit neural Signed Distance Fields (SDFs) have shown promising capabilities in online meshing; however, existing methods often suffer from large projection errors and rely on fixed reconstruction parameters, limiting their adaptability to complex and unstructured underground environments such as tunnels, caves, and lava tubes. To address these challenges, this paper proposes ARMOR, a scene-adaptive and reinforcement learning-based framework for real-time 3D meshing in unexposed environments. The proposed method was validated across more than 3,000 meters of underground environments, including engineered tunnels, natural caves, and lava tubes. Experimental results demonstrate that ARMOR achieves superior performance in real-time mesh reconstruction, reducing geometric error by 3.96\% compared to state-of-the-art baselines, while maintaining real-time efficiency. The method exhibits improved robustness, accuracy, and adaptability, indicating its potential for advanced 3D monitoring and mapping in challenging unexposed scenarios. The project page can be found at: https://yizhezhang0418.github.io/armor.github.io/
Diffusion Stochastic Learning Over Adaptive Competing Networks
Venue: arXiv cs.MA 2025
Year: 2025

This paper studies a stochastic dynamic game between two competing teams, each consisting of a network of collaborating agents. Unlike fully cooperative settings, where all agents share a common objective, each team in this game aims to minimize its own distinct objective. In the adversarial setting, their objectives could be conflicting as in zero-sum games. Throughout the competition, agents share strategic information within their own team while simultaneously inferring and adapting to the strategies of the opposing team. We propose diffusion learning algorithms to address two important classes of this network game: i) a zero-sum game characterized by weak cross-team subgraph interactions, and ii) a general non-zero-sum game exhibiting strong cross-team subgraph interactions. We analyze the stability performance of the proposed algorithms under reasonable assumptions and illustrate the theoretical results through experiments on Cournot team competition and decentralized GAN training.
A Unified Benchmark of Federated Learning with Kolmogorov-Arnold Networks for Medical Imaging
Venue: arXiv cs.LG 2025
Year: 2025

Federated Learning (FL) enables model training across decentralized devices without sharing raw data, thereby preserving privacy in sensitive domains like healthcare. In this paper, we evaluate Kolmogorov-Arnold Networks (KAN) architectures against traditional MLP across six state-of-the-art FL algorithms on a blood cell classification dataset. Notably, our experiments demonstrate that KAN can effectively replace MLP in federated environments, achieving superior performance with simpler architectures. Furthermore, we analyze the impact of key hyperparameters-grid size and network architecture-on KAN performance under varying degrees of Non-IID data distribution. In addition, our ablation studies reveal that optimizing KAN width while maintaining minimal depth yields the best performance in federated settings. As a result, these findings establish KAN as a promising alternative for privacy-preserving medical imaging applications in distributed healthcare. To the best of our knowledge, this is the first comprehensive benchmark of KAN in FL settings for medical imaging task.
Robot Motion Planning using One-Step Diffusion with Noise-Optimized Approximate Motions
Venue: arXiv cs.RO 2025
Year: 2025

This paper proposes an image-based robot motion planning method using a one-step diffusion model. While the diffusion model allows for high-quality motion generation, its computational cost is too expensive to control a robot in real time. To achieve high quality and efficiency simultaneously, our one-step diffusion model takes an approximately generated motion, which is predicted directly from input images. This approximate motion is optimized by additive noise provided by our novel noise optimizer. Unlike general isotropic noise, our noise optimizer adjusts noise anisotropically depending on the uncertainty of each motion element. Our experimental results demonstrate that our method outperforms state-of-the-art methods while maintaining its efficiency by one-step diffusion.
Guided Tensor Lifting
Venue: arXiv cs.SE 2025
Year: 2025

Domain-specific languages (DSLs) for machine learning are revolutionizing the speed and efficiency of machine learning workloads as they enable users easy access to high-performance compiler optimizations and accelerators. However, to take advantage of these capabilities, a user must first translate their legacy code from the language it is currently written in, into the new DSL. The process of automatically lifting code into these DSLs has been identified by several recent works, which propose program synthesis as a solution. However, synthesis is expensive and struggles to scale without carefully designed and hard-wired heuristics. In this paper, we present an approach for lifting that combines an enumerative synthesis approach with a Large Language Model used to automatically learn the domain-specific heuristics for program lifting, in the form of a probabilistic grammar. Our approach outperforms the state-of-the-art tools in this area, despite only using learned heuristics.
If Concept Bottlenecks are the Question, are Foundation Models the Answer?
Venue: arXiv cs.LG 2025
Year: 2025

Concept Bottleneck Models (CBMs) are neural networks designed to conjoin high performance with ante-hoc interpretability. CBMs work by first mapping inputs (e.g., images) to high-level concepts (e.g., visible objects and their properties) and then use these to solve a downstream task (e.g., tagging or scoring an image) in an interpretable manner. Their performance and interpretability, however, hinge on the quality of the concepts they learn. The go-to strategy for ensuring good quality concepts is to leverage expert annotations, which are expensive to collect and seldom available in applications. Researchers have recently addressed this issue by introducing "VLM-CBM" architectures that replace manual annotations with weak supervision from foundation models. It is however unclear what is the impact of doing so on the quality of the learned concepts. To answer this question, we put state-of-the-art VLM-CBMs to the test, analyzing their learned concepts empirically using a selection of significant metrics. Our results show that, depending on the task, VLM supervision can sensibly differ from expert annotations, and that concept accuracy and quality are not strongly correlated. Our code is available at https://github.com/debryu/CQA.
Contextures: The Mechanism of Representation Learning
Venue: arXiv cs.LG 2025
Year: 2025

This dissertation establishes the contexture theory to mathematically characterize the mechanism of representation learning, or pretraining. Despite the remarkable empirical success of foundation models, it is not very clear what representations they learn, and why these representations are useful for various downstream tasks. A scientific understanding of representation learning is critical, especially at this point when scaling up the model size is producing diminishing returns, and designing new pretraining methods is imperative for further progress. Prior work treated different representation learning methods quite differently, whereas the contexture theory provides a unified framework for analyzing these methods. The central argument is that a representation is learned from the association between the input X and a context variable A. We prove that if an encoder captures the maximum information of this association, in which case we say that the encoder learns the contexture, then it will be optimal on the class of tasks that are compatible with the context. We also show that a context is the most useful when the association between X and A is neither too strong nor too weak. The important implication of the contexture theory is that increasing the model size alone will achieve diminishing returns, and further advancements require better contexts. We demonstrate that many pretraining objectives can learn the contexture, including supervised learning, self-supervised learning, generative models, etc. Then, we introduce two general objectives -- SVME and KISE, for learning the contexture. We also show how to mix multiple contexts together, an effortless way to create better contexts from existing ones. Then, we prove statistical learning bounds for representation learning. Finally, we discuss the effect of the data distribution shift from pretraining to the downstream task.
Prompt Injection Attack to Tool Selection in LLM Agents
Venue: arXiv cs.CR 2025
Year: 2025

Tool selection is a key component of LLM agents. A popular approach follows a two-step process - \emph{retrieval} and \emph{selection} - to pick the most appropriate tool from a tool library for a given task. In this work, we introduce \textit{ToolHijacker}, a novel prompt injection attack targeting tool selection in no-box scenarios. ToolHijacker injects a malicious tool document into the tool library to manipulate the LLM agent's tool selection process, compelling it to consistently choose the attacker's malicious tool for an attacker-chosen target task. Specifically, we formulate the crafting of such tool documents as an optimization problem and propose a two-phase optimization strategy to solve it. Our extensive experimental evaluation shows that ToolHijacker is highly effective, significantly outperforming existing manual-based and automated prompt injection attacks when applied to tool selection. Moreover, we explore various defenses, including prevention-based defenses (StruQ and SecAlign) and detection-based defenses (known-answer detection, DataSentinel, perplexity detection, and perplexity windowed detection). Our experimental results indicate that these defenses are insufficient, highlighting the urgent need for developing new defense strategies.
Digital Twin-based Out-of-Distribution Detection in Autonomous Vessels
Venue: arXiv cs.RO 2025
Year: 2025

An autonomous vessel (AV) is a complex cyber-physical system (CPS) with software enabling many key functionalities, e.g., navigation software enables an AV to autonomously or semi-autonomously follow a path to its destination. Digital twins of such AVs enable advanced functionalities such as running what-if scenarios, performing predictive maintenance, and enabling fault diagnosis. Due to technological improvements, real-time analyses using continuous data from vessels' real-time operations have become increasingly possible. However, the literature has little explored developing advanced analyses in real-time data in AVs with digital twins built with machine learning techniques. To this end, we present a novel digital twin-based approach (ODDIT) to detect future out-of-distribution (OOD) states of an AV before reaching them, enabling proactive intervention. Such states may indicate anomalies requiring attention (e.g., manual correction by the ship master) and assist testers in scenario-centered testing. The digital twin consists of two machine-learning models predicting future vessel states and whether the predicted state will be OOD. We evaluated ODDIT with five vessels across waypoint and zigzag maneuvering under simulated conditions, including sensor and actuator noise and environmental disturbances i.e., ocean current. ODDIT achieved high accuracy in detecting OOD states, with AUROC and TNR@TPR95 scores reaching 99\% across multiple vessels.
Near-Optimal Minimum Cuts in Hypergraphs at Scale
Venue: arXiv cs.DS 2025
Year: 2025

The hypergraph minimum cut problem aims to partition its vertices into two blocks while minimizing the total weight of the cut hyperedges. This fundamental problem arises in network reliability, VLSI design, and community detection. We present HeiCut, a scalable algorithm for computing near-optimal minimum cuts in both unweighted and weighted hypergraphs. HeiCut aggressively reduces the hypergraph size through a sequence of provably exact reductions that preserve the minimum cut, along with an optional heuristic contraction based on label propagation. It then solves a relaxed Binary Integer Linear Program (BIP) on the reduced hypergraph to compute a near-optimal minimum cut. Our extensive evaluation on over 500 real-world hypergraphs shows that HeiCut computes the exact minimum cut in over 85% of instances using our exact reductions alone, and offers the best solution quality across all instances. It solves over twice as many instances as the state-of-the-art within set computational limits, and is up to five orders of magnitude faster.
Foundation Model-Driven Framework for Human-Object Interaction Prediction with Segmentation Mask Integration
Venue: arXiv cs.CV 2025
Year: 2025

In this work, we introduce Segmentation to Human-Object Interaction (\textit{\textbf{Seg2HOI}}) approach, a novel framework that integrates segmentation-based vision foundation models with the human-object interaction task, distinguished from traditional detection-based Human-Object Interaction (HOI) methods. Our approach enhances HOI detection by not only predicting the standard triplets but also introducing quadruplets, which extend HOI triplets by including segmentation masks for human-object pairs. More specifically, Seg2HOI inherits the properties of the vision foundation model (e.g., promptable and interactive mechanisms) and incorporates a decoder that applies these attributes to HOI task. Despite training only for HOI, without additional training mechanisms for these properties, the framework demonstrates that such features still operate efficiently. Extensive experiments on two public benchmark datasets demonstrate that Seg2HOI achieves performance comparable to state-of-the-art methods, even in zero-shot scenarios. Lastly, we propose that Seg2HOI can generate HOI quadruplets and interactive HOI segmentation from novel text and visual prompts that were not used during training, making it versatile for a wide range of applications by leveraging this flexibility.
Do You Know the Way? Human-in-the-Loop Understanding for Fast Traversability Estimation in Mobile Robotics
Venue: arXiv cs.RO 2025
Year: 2025

The increasing use of robots in unstructured environments necessitates the development of effective perception and navigation strategies to enable field robots to successfully perform their tasks. In particular, it is key for such robots to understand where in their environment they can and cannot travel -- a task known as traversability estimation. However, existing geometric approaches to traversability estimation may fail to capture nuanced representations of traversability, whereas vision-based approaches typically either involve manually annotating a large number of images or require robot experience. In addition, existing methods can struggle to address domain shifts as they typically do not learn during deployment. To this end, we propose a human-in-the-loop (HiL) method for traversability estimation that prompts a human for annotations as-needed. Our method uses a foundation model to enable rapid learning on new annotations and to provide accurate predictions even when trained on a small number of quickly-provided HiL annotations. We extensively validate our method in simulation and on real-world data, and demonstrate that it can provide state-of-the-art traversability prediction performance.
NORA: A Small Open-Sourced Generalist Vision Language Action Model for Embodied Tasks
Venue: arXiv cs.RO 2025
Year: 2025

Existing Visual-Language-Action (VLA) models have shown promising performance in zero-shot scenarios, demonstrating impressive task execution and reasoning capabilities. However, a significant challenge arises from the limitations of visual encoding, which can result in failures during tasks such as object grasping. Moreover, these models typically suffer from high computational overhead due to their large sizes, often exceeding 7B parameters. While these models excel in reasoning and task planning, the substantial computational overhead they incur makes them impractical for real-time robotic environments, where speed and efficiency are paramount. To address the limitations of existing VLA models, we propose NORA, a 3B-parameter model designed to reduce computational overhead while maintaining strong task performance. NORA adopts the Qwen-2.5-VL-3B multimodal model as its backbone, leveraging its superior visual-semantic understanding to enhance visual reasoning and action grounding. Additionally, our \model{} is trained on 970k real-world robot demonstrations and equipped with the FAST+ tokenizer for efficient action sequence generation. Experimental results demonstrate that NORA outperforms existing large-scale VLA models, achieving better task performance with significantly reduced computational overhead, making it a more practical solution for real-time robotic autonomy.
The Automation Advantage in AI Red Teaming
Venue: arXiv cs.CR 2025
Year: 2025

This paper analyzes Large Language Model (LLM) security vulnerabilities based on data from Crucible, encompassing 214,271 attack attempts by 1,674 users across 30 LLM challenges. Our findings reveal automated approaches significantly outperform manual techniques (69.5% vs 47.6% success rate), despite only 5.2% of users employing automation. We demonstrate that automated approaches excel in systematic exploration and pattern matching challenges, while manual approaches retain speed advantages in certain creative reasoning scenarios, often solving problems 5x faster when successful. Challenge categories requiring systematic exploration are most effectively targeted through automation, while intuitive challenges sometimes favor manual techniques for time-to-solve metrics. These results illuminate how algorithmic testing is transforming AI red-teaming practices, with implications for both offensive security research and defensive measures. Our analysis suggests optimal security testing combines human creativity for strategy development with programmatic execution for thorough exploration.
Can AI Agents Design and Implement Drug Discovery Pipelines?
Venue: arXiv cs.AI 2025
Year: 2025

The rapid advancement of artificial intelligence, particularly autonomous agentic systems based on Large Language Models (LLMs), presents new opportunities to accelerate drug discovery by improving in-silico modeling and reducing dependence on costly experimental trials. Current AI agent-based systems demonstrate proficiency in solving programming challenges and conducting research, indicating an emerging potential to develop software capable of addressing complex problems such as pharmaceutical design and drug discovery. This paper introduces DO Challenge, a benchmark designed to evaluate the decision-making abilities of AI agents in a single, complex problem resembling virtual screening scenarios. The benchmark challenges systems to independently develop, implement, and execute efficient strategies for identifying promising molecular structures from extensive datasets, while navigating chemical space, selecting models, and managing limited resources in a multi-objective context. We also discuss insights from the DO Challenge 2025, a competition based on the proposed benchmark, which showcased diverse strategies explored by human participants. Furthermore, we present the Deep Thought multi-agent system, which demonstrated strong performance on the benchmark, outperforming most human teams. Among the language models tested, Claude 3.7 Sonnet, Gemini 2.5 Pro and o3 performed best in primary agent roles, and GPT-4o, Gemini 2.0 Flash were effective in auxiliary roles. While promising, the system's performance still fell short of expert-designed solutions and showed high instability, highlighting both the potential and current limitations of AI-driven methodologies in transforming drug discovery and broader scientific research.
Enhancing Quality for VVC Compressed Videos with Omniscient Quality Enhancement Model
Venue: arXiv cs.CV 2025
Year: 2025

The latest video coding standard H.266/VVC has shown its great improvement in terms of compression performance when compared to its predecessor HEVC standard. Though VVC was implemented with many advanced techniques, it still met the same challenges as its predecessor due to the need for even higher perceptual quality demand at the decoder side as well as the compression performance at the encoder side. The advancement of Artificial Intelligence (AI) technology, notably the deep learning-based video quality enhancement methods, was shown to be a promising approach to improving the perceptual quality experience. In this paper, we propose a novel Omniscient video quality enhancement Network for VVC compressed Videos. The Omniscient Network for compressed video quality enhancement was originally designed for HEVC compressed videos in which not only the spatial-temporal features but also cross-frequencies information were employed to augment the visual quality. Inspired by this work, we propose a modification of the OVQE model and integrate it into the lasted STD-VVC (Standard Versatile Video Coding) decoder architecture. As assessed in a rich set of test conditions, the proposed OVQE-VVC solution is able to achieve significant PSNR improvement, notably around 0.74 dB and up to 1.2 dB with respect to the original STD-VVC codec. This also corresponds to around 19.6% of bitrate saving while keeping a similar quality observation.
Transfer Learning Under High-Dimensional Network Convolutional Regression Model
Venue: arXiv cs.LG 2025
Year: 2025

Transfer learning enhances model performance by utilizing knowledge from related domains, particularly when labeled data is scarce. While existing research addresses transfer learning under various distribution shifts in independent settings, handling dependencies in networked data remains challenging. To address this challenge, we propose a high-dimensional transfer learning framework based on network convolutional regression (NCR), inspired by the success of graph convolutional networks (GCNs). The NCR model incorporates random network structure by allowing each node's response to depend on its features and the aggregated features of its neighbors, capturing local dependencies effectively. Our methodology includes a two-step transfer learning algorithm that addresses domain shift between source and target networks, along with a source detection mechanism to identify informative domains. Theoretically, we analyze the lasso estimator in the context of a random graph based on the Erdos-Renyi model assumption, demonstrating that transfer learning improves convergence rates when informative sources are present. Empirical evaluations, including simulations and a real-world application using Sina Weibo data, demonstrate substantial improvements in prediction accuracy, particularly when labeled data in the target domain is limited.