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GeoChemAD: Benchmarking Unsupervised Geochemical Anomaly Detection for Mineral Exploration
2026-03-13 15:14:57Yihao Ding, Yiran Zhang, Chris Gonzalez, Eun-Jung Holden, Wei LiuAbstract
Geochemical anomaly detection plays a critical role in mineral exploration as deviations from regional geochemical baselines may indicate mineralization. Existing studies suffer from two key limitations: (1) single region scenarios which limit model generalizability; (2) proprietary datasets, which makes result reproduction unattainable. In this work, we introduce \textbf{GeoChemAD}, an open-source benchmark dataset compiled from government-led geological surveys, covering multiple regions, sampling sources, and target elements. The dataset comprises eight subsets representing diverse spatial scales and sampling conditions. To establish strong baselines, we reproduce and benchmark a range of unsupervised anomaly detection methods, including statistical models, generative and transformer-based approaches. Furthermore, we propose \textbf{GeoChemFormer}, a transformer-based framework that leverages self-supervised pretraining to learn target-element-aware geochemical representations for spatial samples. Extensive experiments demonstrate that GeoChemFormer consistently achieves superior and robust performance across all eight subsets, outperforming existing unsupervised methods in both anomaly detection accuracy and generalization capability. The proposed dataset and framework provide a foundation for reproducible research and future development in this direction.
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URL
https://arxiv.org/abs/2603.13068
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Continual Learning in Large Language Models: Methods, Challenges, and Opportunities
2026-03-13 05:01:25Hongyang Chen, Zhongwu Sun, Hongfei Ye, Kunchi Li, Xuemin LinAbstract
Continual learning (CL) has emerged as a pivotal paradigm to enable large language models (LLMs) to dynamically adapt to evolving knowledge and sequential tasks while mitigating catastrophic forgetting-a critical limitation of the static pre-training paradigm inherent to modern LLMs. This survey presents a comprehensive overview of CL methodologies tailored for LLMs, structured around three core training stages: continual pre-training, continual fine-tuning, and continual this http URL the canonical taxonomy of rehearsal-, regularization-, and architecture-based methods, we further subdivide each category by its distinct forgetting mitigation mechanisms and conduct a rigorous comparative analysis of the adaptability and critical improvements of traditional CL methods for LLMs. In doing so, we explicitly highlight core distinctions between LLM CL and traditional machine learning, particularly with respect to scale, parameter efficiency, and emergent capabilities. Our analysis covers essential evaluation metrics, including forgetting rates and knowledge transfer efficiency, along with emerging benchmarks for assessing CL performance. This survey reveals that while current methods demonstrate promising results in specific domains, fundamental challenges persist in achieving seamless knowledge integration across diverse tasks and temporal scales. This systematic review contributes to the growing body of knowledge on LLM adaptation, providing researchers and practitioners with a structured framework for understanding current achievements and future opportunities in lifelong learning for language models.
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URL
https://arxiv.org/abs/2603.12658
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AS-Bridge: A Bidirectional Generative Framework Bridging Next-Generation Astronomical Surveys
2026-03-12 13:41:42Dichang Zhang, Yixuan Shao, Simon Birrer, Dimitris SamarasAbstract
The upcoming decade of observational cosmology will be shaped by large sky surveys, such as the ground-based LSST at the Vera C. Rubin Observatory and the space-based Euclid mission. While they promise an unprecedented view of the Universe across depth, resolution, and wavelength, their differences in observational modality, sky coverage, point-spread function, and scanning cadence make joint analysis beneficial, but also challenging. To facilitate joint analysis, we introduce A(stronomical)S(urvey)-Bridge, a bidirectional generative model that translates between ground- and space-based observations. AS-Bridge learns a diffusion model that employs a stochastic Brownian Bridge process between the LSST and Euclid observations. The two surveys have overlapping sky regions, where we can explicitly model the conditional probabilistic distribution between them. We show that this formulation enables new scientific capabilities beyond single-survey analysis, including faithful probabilistic predictions of missing survey observations and inter-survey detection of rare events. These results establish the feasibility of inter-survey generative modeling. AS-Bridge is therefore well-positioned to serve as a complementary component of future LSST-Euclid joint data pipelines, enhancing the scientific return once data from both surveys become available. Data and code are available at \href{this https URL}{this https URL}.
Abstract (translated)
即将到来的十年，观测宇宙学将受到大型天空调查的影响，例如地面基线的LSST（泛星系天文台，原名Vera C. Rubin Observatory）和基于太空的欧几里得任务。尽管它们承诺提供前所未有的深度、分辨率和波长范围内的宇宙视图，但其在观测方式、天空覆盖范围、点扩散函数以及扫描频率等方面的差异使得联合分析既有益又具有挑战性。为了促进这种联合分析，我们引入了A（天文学）S（调查）-Bridge（AS-Bridge），这是一种双向生成模型，它可以在地面和基于太空的观测之间进行转换。AS-Bridge 学习了一个扩散模型，该模型利用在LSST 和欧几里得观测之间的随机布朗桥过程。这两个调查项目拥有重叠的天空区域，在这些区域内我们可以明确地对它们之间的条件概率分布建模。我们展示了这一公式能够提供超越单一调查分析的新科学能力，包括对缺失调查观测进行忠实的概率预测以及跨调查罕见事件的检测。这些结果确立了跨调查生成模型的可能性。因此，AS-Bridge 有潜力成为未来LSST-欧几里得联合数据管道的一个补充组件，在两个调查的数据可用时增强科学研究的价值。相关数据和代码可在\href{this https URL}{此链接}获取。
URL
https://arxiv.org/abs/2603.11928
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Social, Legal, Ethical, Empathetic and Cultural Norm Operationalisation for AI Agents
2026-03-12 12:37:39Radu Calinescu, Ana Cavalcanti, Marsha Chechik, Lina Marsso, Beverley TownsendAbstract
As AI agents are increasingly used in high-stakes domains like healthcare and law enforcement, aligning their behaviour with social, legal, ethical, empathetic, and cultural (SLEEC) norms has become a critical engineering challenge. While international frameworks have established high-level normative principles for AI, a significant gap remains in translating these abstract principles into concrete, verifiable requirements. To address this gap, we propose a systematic SLEEC-norm operationalisation process for determining, validating, implementing, and verifying normative requirements. Furthermore, we survey the landscape of methods and tools supporting this process, and identify key remaining challenges and research avenues for addressing them. We thus establish a framework - and define a research and policy agenda - for developing AI agents that are not only functionally useful but also demonstrably aligned with human norms and values.
Abstract (translated)
随着人工智能代理在医疗保健和执法等高风险领域中的应用日益增多，确保其行为符合社会、法律、伦理、同理心及文化（SLEEC）规范已成为一项关键的工程挑战。尽管国际框架已确立了关于AI的基本原则，但在将这些抽象原则转化为具体且可验证的要求方面仍然存在重大差距。为解决这一缺口，我们提出了一种系统性的SLEEC规范操作化流程，用于确定、验证、实施和核实规范性要求。此外，我们还调查了支持此过程的方法和技术工具，并识别出应对剩余关键挑战及研究方向的重要问题。因此，我们建立了一个框架——并定义了研究与政策议程——旨在开发不仅在功能上实用且能够明确符合人类社会规范和价值观的AI代理。
URL
https://arxiv.org/abs/2603.11864
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Governing Evolving Memory in LLM Agents: Risks, Mechanisms, and the Stability and Safety Governed Memory Framework
2026-03-12 10:16:52Chingkwun Lam, Jiaxin Li, Lingfei Zhang, Kuo ZhaoAbstract
Long-term memory has emerged as a foundational component of autonomous Large Language Model (LLM) agents, enabling continuous adaptation, lifelong multimodal learning, and sophisticated reasoning. However, as memory systems transition from static retrieval databases to dynamic, agentic mechanisms, critical concerns regarding memory governance, semantic drift, and privacy vulnerabilities have surfaced. While recent surveys have focused extensively on memory retrieval efficiency, they largely overlook the emergent risks of memory corruption in highly dynamic environments. To address these emerging challenges, we propose the Stability and Safety-Governed Memory (SSGM) framework, a conceptual governance architecture. SSGM decouples memory evolution from execution by enforcing consistency verification, temporal decay modeling, and dynamic access control prior to any memory consolidation. Through formal analysis and architectural decomposition, we show how SSGM can mitigate topology-induced knowledge leakage where sensitive contexts are solidified into long-term storage, and help prevent semantic drift where knowledge degrades through iterative summarization. Ultimately, this work provides a comprehensive taxonomy of memory corruption risks and establishes a robust governance paradigm for deploying safe, persistent, and reliable agentic memory systems.
Abstract (translated)
长期记忆已成为自主大型语言模型（LLM）代理的基础组成部分，使它们能够实现持续适应、终身多模态学习和复杂推理。然而，随着存储系统从静态检索数据库转变为动态的、具有代理性质的机制，关于内存治理、语义漂移以及隐私漏洞的关键问题也逐渐浮现出来。尽管最近的研究主要集中在记忆检索效率上，但这些研究在很大程度上忽视了高度动态环境中出现的记忆腐败风险。为解决这些问题，我们提出了稳定性和安全监管型记忆（SSGM）框架，这是一个概念性的治理架构。 SSGM通过强制执行一致性验证、时间衰减建模和动态访问控制，在任何记忆巩固之前将记忆演化与执行过程分离，以此来应对这些挑战。通过对正式分析和架构分解的研究，我们展示了SSGM如何缓解由拓扑引起的知识泄漏问题，在这种情况下，敏感上下文被固定为长期存储，并且有助于防止语义漂移的问题，即通过迭代总结导致的知识退化。 最终，这项工作提供了一个全面的记忆腐败风险分类法，并确立了一种稳健的治理范式，用于部署安全、持久和可靠的代理记忆系统。
URL
https://arxiv.org/abs/2603.11768
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Examining Users' Behavioural Intention to Use OpenClaw Through the Cognition--Affect--Conation Framework
Abstract
This study examines users' behavioural intention to use OpenClaw through the Cognition--Affect--Conation (CAC) framework. The research investigates how cognitive perceptions of the system influence affective responses and subsequently shape behavioural intention. Enabling factors include perceived personalisation, perceived intelligence, and relative advantage, while inhibiting factors include privacy concern, algorithmic opacity, and perceived risk. Survey data from 436 OpenClaw users were analysed using structural equation modelling. The results show that positive perceptions strengthen users' attitudes toward OpenClaw, which increase behavioural intention, whereas negative perceptions increase distrust and reduce intention to use the system. The study provides insights into the psychological mechanisms influencing the adoption of autonomous AI agents.
Abstract (translated)
这项研究通过认知-情感-意志（CAC）框架考察了用户使用OpenClaw的意向行为。该研究探讨了系统认知感知如何影响情感反应，并进而塑造行为意图。促进因素包括感知个性化、感知智能和相对优势，而抑制因素则包括隐私担忧、算法不透明性和感知风险。通过对436名OpenClaw用户的调查数据进行结构方程建模分析，结果显示积极的感知会增强用户对OpenClaw的态度，从而增加使用意向；相反，消极的感知会增加怀疑并降低使用系统的意愿。该研究为自主AI代理采用的心理机制提供了见解。
URL
https://arxiv.org/abs/2603.11455
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Synthetic Data Generation for Brain-Computer Interfaces: Overview, Benchmarking, and Future Directions
2026-03-11 20:36:02Ziwei Wang, Zhentao He, Xingyi He, Hongbin Wang, Tianwang Jia, Jingwei Luo, Siyang Li, Xiaoqing Chen, Dongrui WuAbstract
Deep learning has achieved transformative performance across diverse domains, largely driven by the large-scale, high-quality training data. In contrast, the development of brain-computer interfaces (BCIs) is fundamentally constrained by the limited, heterogeneous, and privacy-sensitive neural recordings. Generating synthetic yet physiologically plausible brain signals has therefore emerged as a compelling way to mitigate data scarcity and enhance model capacity. This survey provides a comprehensive review of brain signal generation for BCIs, covering methodological taxonomies, benchmark experiments, evaluation metrics, and key applications. We systematically categorize existing generative algorithms into four types: knowledge-based, feature-based, model-based, and translation-based approaches. Furthermore, we benchmark existing brain signal generation approaches across four representative BCI paradigms to provide an objective performance comparison. Finally, we discuss the potentials and challenges of current generation approaches and prospect future research on accurate, data-efficient, and privacy-aware BCI systems. The benchmark codebase is publicized at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2603.12296
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Artificial Intelligence as a Catalyst for Innovation in Software Engineering
2026-03-11 17:20:30Carlos Alberto Fern\'andez-y-Fern\'andez, Jorge R. Aguilar-CisnerosAbstract
The rapid evolution and inherent complexity of modern software requirements demand highly flexible and responsive development methodologies. While Agile frameworks have become the industry standard for prioritizing iteration, collaboration, and adaptability, software development teams continue to face persistent challenges in managing constantly evolving requirements and maintaining product quality under tight deadlines. This article explores the intersection of Artificial Intelligence (AI) and Software Engineering (SE), to analyze how AI serves as a powerful catalyst for enhancing agility and fostering innovation. The research combines a comprehensive review of existing literature with an empirical study, utilizing a survey directed at Software Engineering professionals to assess the perception, adoption, and impact of AI-driven tools. Key findings reveal that the integration of AI (specifically through Machine Learning (ML) and Natural Language Processing (NLP) )facilitates the automation of tedious tasks, from requirement management to code generation and testing . This paper demonstrates that AI not only optimizes current Agile practices but also introduces new capabilities essential for sustaining quality, speed, and innovation in the future landscape of software development.
Abstract (translated)
现代软件需求的快速演变及其内在复杂性要求高度灵活和响应迅速的开发方法。虽然敏捷框架已成为优先考虑迭代、协作和适应性的行业标准，但软件开发团队仍在管理不断变化的需求以及在紧张的时间限制下保持产品质量方面面临持续挑战。本文探讨了人工智能（AI）与软件工程（SE）之间的交集，并分析了如何利用AI作为增强敏捷性和促进创新的强大催化剂。研究结合了对现有文献的全面回顾和实证研究，通过针对软件工程师的专业调查来评估对AI驱动工具的认知、采用及其影响。 关键发现表明，将人工智能（特别是机器学习(ML) 和自然语言处理(NLP) ）整合到开发流程中可以实现从需求管理到代码生成和测试等繁琐任务的自动化。本文展示了AI不仅优化了现有的敏捷实践，还引入了未来软件开发环境中保持质量、速度和创新所必需的新能力。
URL
https://arxiv.org/abs/2603.10994
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Assessing the Political Fairness of Multilingual LLMs: A Case Study based on a 21-way Multiparallel EuroParl Dataset
2026-03-11 08:44:52Paul Lerner, Fran\c{c}ois YvonAbstract
The political biases of Large Language Models (LLMs) are usually assessed by simulating their answers to English surveys. In this work, we propose an alternative framing of political biases, relying on principles of fairness in multilingual translation. We systematically compare the translation quality of speeches in the European Parliament (EP), observing systematic differences with majority parties from left and right being better translated than outsider parties. This study is made possible by a new, 21-way multiparallel version of EuroParl, the parliamentary proceedings of the EP, which includes the political affiliations of each speaker. The dataset consists of 1.5M sentences for a total of 40M words and 249M characters. It covers three years, 1000+ speakers, 7 countries, 12 EU parties, 25 EU committees, and hundreds of national parties.
Abstract (translated)
大型语言模型（LLMs）的政治偏见通常通过模拟它们对英文调查的回答来评估。在这项工作中，我们提出了一种基于多语种翻译中公平性原则的替代政治偏见框架。我们系统地比较了欧洲议会（EP）演讲的翻译质量，观察到多数派别从左翼和右翼的讲话被更好地翻译，而局外政党则不然。这项研究得益于一个新的、21种语言平行版本的EuroParl数据集的支持，该数据集包括每位发言人的政治归属信息。该数据集包含150万句句子，总共有4000万个单词和2.49亿个字符，并涵盖了三年时间跨度、超过1000名发言人、7个国家、12个欧盟政党、25个欧盟委员会以及数百个国家级政党。
URL
https://arxiv.org/abs/2510.20508
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A Survey of Reasoning in Autonomous Driving Systems: Open Challenges and Emerging Paradigms
2026-03-11 07:40:53Kejin Yu, Yuhan Sun, Taiqiang Wu, Ruixu Zhang, Zhiqiang Lin, Yuxin Meng, Junjie Wang, Yujiu YangAbstract
The development of high-level autonomous driving (AD) is shifting from perception-centric limitations to a more fundamental bottleneck, namely, a deficit in robust and generalizable reasoning. Although current AD systems manage structured environments, they consistently falter in long-tail scenarios and complex social interactions that require human-like judgment. Meanwhile, the advent of large language and multimodal models (LLMs and MLLMs) presents a transformative opportunity to integrate a powerful cognitive engine into AD systems, moving beyond pattern matching toward genuine comprehension. However, a systematic framework to guide this integration is critically lacking. To bridge this gap, we provide a comprehensive review of this emerging field and argue that reasoning should be elevated from a modular component to the system's cognitive core. Specifically, we first propose a novel Cognitive Hierarchy to decompose the monolithic driving task according to its cognitive and interactive complexity. Building on this, we further derive and systematize seven core reasoning challenges, such as the responsiveness-reasoning trade-off and social-game reasoning. Furthermore, we conduct a dual-perspective review of the state-of-the-art, analyzing both system-centric approaches to architecting intelligent agents and evaluation-centric practices for their validation. Our analysis reveals a clear trend toward holistic and interpretable "glass-box" agents. In conclusion, we identify a fundamental and unresolved tension between the high-latency, deliberative nature of LLM-based reasoning and the millisecond-scale, safety-critical demands of vehicle control. For future work, a primary objective is to bridge the symbolic-to-physical gap by developing verifiable neuro-symbolic architectures, robust reasoning under uncertainty, and scalable models for implicit social negotiation.
Abstract (translated)
高级自动驾驶（AD）的发展正从感知为中心的限制转向更为根本性的瓶颈，即在稳健和通用推理方面的不足。尽管当前的AD系统能够处理结构化的环境，但在需要人类判断的长尾场景和复杂的社交互动中仍然表现不佳。与此同时，大型语言模型和多模态模型（LLMs 和 MMLLMs）的出现为将强大的认知引擎整合到AD系统中提供了变革性的机会，使其超越模式匹配走向真正的理解。然而，缺乏一个系统的框架来指导这种融合是关键性的问题。为了填补这一空白，我们对这个新兴领域进行了全面回顾，并主张将推理从模块化组件提升至系统的认知核心。具体而言，我们首先提出了一种新的认知层次结构，以根据其认知和交互复杂度分解单一的驾驶任务。在此基础上，我们进一步推导并系统地提出了七个核心推理挑战，如响应性-推理权衡和社会游戏推理。此外，我们从双重视角回顾了当前最先进（SOTA）的研究状态，分析了构建智能代理的以系统为中心的方法和验证这些系统的评估中心实践。我们的分析揭示了一个明显的趋势，即向全面且可解释的“玻璃箱”代理发展。最后，我们在结论中指出了一种根本性且未解决的矛盾，即基于LLM的推理的高度延迟、反思性质与车辆控制所需的毫秒级安全关键需求之间的紧张关系。对于未来的工作，主要目标是通过开发验证性的神经符号架构、不确定条件下的稳健推理以及用于隐式社会协商的大规模模型来弥合符号到物理领域的差距。
URL
https://arxiv.org/abs/2603.11093
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The Attack and Defense Landscape of Agentic AI: A Comprehensive Survey
2026-03-11 05:25:55Juhee Kim, Xiaoyuan Liu, Zhun Wang, Shi Qiu, Bo Li, Wenbo Guo, Dawn SongAbstract
AI agents that combine large language models with non-AI system components are rapidly emerging in real-world applications, offering unprecedented automation and flexibility. However, this unprecedented flexibility introduces complex security challenges fundamentally different from those in traditional software systems. This paper presents the first systematic and comprehensive survey of AI agent security, including an analysis of the design space, attack landscape, and defense mechanisms for secure AI agent systems. We further conduct case studies to point out existing gaps in securing agentic AI systems and identify open challenges in this emerging domain. Our work also introduces the first systematic framework for understanding the security risks and defense strategies of AI agents, serving as a foundation for building both secure agentic systems and advancing research in this critical area.
Abstract (translated)
结合大型语言模型与非AI系统组件的AI代理在现实世界应用中迅速兴起，为自动化和灵活性提供了前所未有的可能性。然而，这种前所未有的灵活性引入了复杂的安全挑战，这些挑战从根本上不同于传统软件系统的安全问题。本文首次对AI代理安全性进行了全面而系统的调查，包括设计空间、攻击面以及保障安全AI代理系统防御机制的分析。我们进一步通过案例研究指出现有保护代理式AI系统的不足，并识别这一新兴领域的开放性挑战。我们的工作还提出了首个用于理解AI代理的安全风险和防御策略的系统化框架，为构建安全的代理式系统及推进该关键领域内的研究奠定了基础。
URL
https://arxiv.org/abs/2603.11088
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Autonomous Search for Sparsely Distributed Visual Phenomena through Environmental Context Modeling
2026-03-10 19:11:34Eric Chen, Travis Manderson, Nare Karapetyan, Peter Edmunds, Nicholas Roy, Yogesh GirdharAbstract
Autonomous underwater vehicles (AUVs) are increasingly used to survey coral reefs, yet efficiently locating specific coral species of interest remains difficult: target species are often sparsely distributed across the reef, and an AUV with limited battery life cannot afford to search everywhere. When detections of the target itself are too sparse to provide directional guidance, the robot benefits from an additional signal to decide where to look next. We propose using the visual environmental context -- the habitat features that tend to co-occur with a target species -- as that signal. Because context features are spatially denser and often vary more smoothly than target detections, we hypothesize that a reward function targeted at broader environmental context will enable adaptive planners to make better decisions on where to go next, even in regions where no target has yet been observed. Starting from a single labeled image, our method uses patch-level DINOv2 embeddings to perform one-shot detections of both the target species and its surrounding context online. We validate our approach using real imagery collected by an AUV at two reef sites in St. John, U.S. Virgin Islands, simulating the robot's motion offline. Our results demonstrate that one-shot detection combined with adaptive context modeling enables efficient autonomous surveying, sampling up to 75$\%$ of the target in roughly half the time required by exhaustive coverage when the target is sparsely distributed, and outperforming search strategies that only use target detections.
Abstract (translated)
自主水下航行器（AUV）在珊瑚礁调查中的使用日益增多，但在有限电池寿命的情况下高效定位特定珊瑚物种仍然困难。目标物种通常稀疏分布在珊瑚礁上，因此无法进行全面搜索。当目标自身的检测过于稀疏而不能提供方向指导时，机器人需要额外的信号来决定下一步去哪里搜寻。我们建议利用视觉环境上下文——与目标物种常常共存的栖息地特征作为该信号。因为上下文特征在空间上的密度更高且往往变化更为平缓，我们认为针对更广泛环境上下文的目标函数将使自适应规划者能够更好地做出下一个地点的选择决策，即使是在尚未观察到任何目标的地方也能如此。 从单张标记图像开始，我们的方法利用Patch级别的DINOv2嵌入技术在线进行一次检测，同时识别目标物种及其周围的背景环境。我们使用美国维尔京群岛圣约翰的两个珊瑚礁采集的真实影像数据来验证这种方法的有效性，并通过离线模拟机器人的运动来完成这一过程。 实验结果表明，在目标稀疏分布的情况下，结合一次性检测和自适应上下文建模能够实现高效的自主调查，在大约一半的时间内可以采样到高达75%的目标物种，优于仅使用目标检测的搜索策略。
URL
https://arxiv.org/abs/2603.10174
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The Generation-Recognition Asymmetry: Six Dimensions of a Fundamental Divide in Formal Language Theory
2026-03-10 18:21:53Romain PeyrichouAbstract
Every formal grammar defines a language and can in principle be used in three ways: to generate strings (production), to recognize them (parsing), or -- given only examples -- to infer the grammar itself (grammar induction). Generation and recognition are extensionally equivalent -- they characterize the same set -- but operationally asymmetric in multiple independent ways. Inference is a qualitatively harder problem: it does not have access to a known grammar. Despite the centrality of this triad to compiler design, natural language processing, and formal language theory, no survey has treated it as a unified, multidimensional phenomenon. We identify six dimensions along which generation and recognition diverge: computational complexity, ambiguity, directionality, information availability, grammar inference, and temporality. We show that the common characterization "generation is easy, parsing is hard" is misleading: unconstrained generation is trivial, but generation under constraints can be NP-hard. The real asymmetry is that parsing is always constrained (the input is given) while generation need not be. Two of these dimensions -- directionality and temporality -- have not previously been identified as dimensions of the generation-recognition asymmetry. We connect the temporal dimension to the surprisal framework of Hale (2001) and Levy (2008), arguing that surprisal formalizes the temporal asymmetry between a generator (surprisal = 0) and a parser that predicts under uncertainty (surprisal > 0). We review bidirectional systems in NLP and observe that bidirectionality has been available for fifty years yet has not transferred to most domain-specific applications. We conclude with a discussion of large language models, which architecturally unify generation and recognition while operationally preserving the asymmetry.
Abstract (translated)
每种正式语法都定义了一种语言，并且原则上可以以三种方式使用：生成字符串（产生）、识别它们（解析），或——仅凭示例——推断出该语法本身（语法归纳）。生成和识别在外延上是等价的——它们表征同一集合，但在操作层面上存在多种独立的不对称性。推理则是一个本质上更难的问题：它没有已知语法可以参考。尽管这种三元组在编译器设计、自然语言处理和形式语言理论中处于核心地位，但迄今为止没有任何综述将其视为一个统一的多维度现象。 我们识别出了六个维度，在这些维度上生成与识别存在差异：计算复杂性、歧义度、方向性、信息可用性、语法归纳以及时间维度。我们展示了这种普遍描述“生成容易而解析困难”是误导性的：无约束下的生成非常简单，但受限条件下的生成可以达到NP难题级别。真正的不对称在于解析总是受到限制的（输入已知），而生成则未必需要。 在这六个差异中，有两个——方向性和时间维度——以前未被识别为生成-解析不对称性中的关键维度。我们探讨了时间维度与Hale (2001)和Levy (2008)提出的Surprisal框架之间的联系，并认为Surprisal形式化了一个生成器（surprisal=0）与一个在不确定条件下预测的解析器（surprisal>0）之间的时间不对称性。我们还回顾了自然语言处理领域中双向系统的应用，指出尽管这种双向性已经存在五十年之久，但大多数特定领域的应用程序并未采用这种方法。 最后，我们将讨论大规模语言模型，这些模型在架构上统一了生成和识别的功能，但在操作层面上保持了两者之间的不对称性。
URL
https://arxiv.org/abs/2603.10139
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Model Merging in the Era of Large Language Models: Methods, Applications, and Future Directions
2026-03-10 17:31:55Mingyang Song, Mao ZhengAbstract
Model merging has emerged as a transformative paradigm for combining the capabilities of multiple neural networks into a single unified model without additional training. With the rapid proliferation of fine-tuned large language models~(LLMs), merging techniques offer a computationally efficient alternative to ensembles and full retraining, enabling practitioners to compose specialized capabilities at minimal cost. This survey presents a comprehensive and structured examination of model merging in the LLM era through the \textbf{FUSE} taxonomy, a four-dimensional framework organized along \textbf{F}oundations, \textbf{U}nification Strategies, \textbf{S}cenarios, and \textbf{E}cosystem. We first establish the theoretical underpinnings of merging, including loss landscape geometry, mode connectivity, and the linear mode connectivity hypothesis. We then systematically review the algorithmic landscape, spanning weight averaging, task vector arithmetic, sparsification-enhanced methods, mixture-of-experts architectures, and evolutionary optimization approaches. For each method family, we analyze the core formulation, highlight representative works, and discuss practical trade-offs. We further examine downstream applications across multi-task learning, safety alignment, domain specialization, multilingual transfer, and federated learning. Finally, we survey the supporting ecosystem of open-source tools, community platforms, and evaluation benchmarks, and identify key open challenges including theoretical gaps, scalability barriers, and standardization needs. This survey aims to equip researchers and practitioners with a structured foundation for advancing model merging.
Abstract (translated)
模型合并作为一种将多个神经网络的能力整合到单一统一模型中的范式，无需额外训练便已崭露头角。随着经过微调的大规模语言模型（LLMs）的迅速普及，合并技术为集合和重新训练提供了一个计算效率更高的替代方案，使实践者能够在成本最小的情况下组合出专业化的功能。本文综述通过**FUSE**分类法，在一个四维框架下对LLM时代的模型合并进行了全面而系统的审查。这个框架沿用了**基础（Foundations）**、**统一策略（Unification Strategies）**、**应用场景（Scenarios）**和**生态系统（Ecosystem）**四个维度。 首先，本文确立了合并的理论基础，包括损失景观几何学、模式连接性以及线性模式连接假设。接下来系统地回顾了算法领域，涵盖了权重平均法、任务向量算术、增强稀疏化的方法、专家混合架构和进化优化方法。对于每个方法家族，我们分析核心公式，强调代表性工作，并讨论实际的取舍。 此外，本文进一步考察了下游应用，包括多任务学习、安全对齐、领域专业化、跨语言迁移和联邦学习的应用。最后，文章调查了开源工具、社区平台以及评估基准的支持生态系统，并识别出了关键开放挑战，包括理论差距、可扩展性障碍和标准化需求。 这篇综述旨在为研究人员和实践者提供一个结构化的基础，以推动模型合并的发展。
URL
https://arxiv.org/abs/2603.09938
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Benchmarking Political Persuasion Risks Across Frontier Large Language Models
2026-03-10 16:42:05Zhongren Chen, Joshua Kalla, Quan LeAbstract
Concerns persist regarding the capacity of Large Language Models (LLMs) to sway political views. Although prior research has claimed that LLMs are not more persuasive than standard political campaign practices, the recent rise of frontier models warrants further study. In two survey experiments (N=19,145) across bipartisan issues and stances, we evaluate seven state-of-the-art LLMs developed by Anthropic, OpenAI, Google, and xAI. We find that LLMs outperform standard campaign advertisements, with heterogeneity in performance across models. Specifically, Claude models exhibit the highest persuasiveness, while Grok exhibits the lowest. The results are robust across issues and stances. Moreover, in contrast to the findings in Hackenburg et al. (2025b) and Lin et al. (2025) that information-based prompts boost persuasiveness, we find that the effectiveness of information-based prompts is model-dependent: they increase the persuasiveness of Claude and Grok while substantially reducing that of GPT. We introduce a data-driven and strategy-agnostic LLM-assisted conversation analysis approach to identify and assess underlying persuasive strategies. Our work benchmarks the persuasive risks of frontier models and provides a framework for cross-model comparative risk assessment.
Abstract (translated)
关于大型语言模型（LLM）影响政治观点的能力的担忧仍然存在。尽管之前的研究声称LLM并不比标准的政治竞选活动更具说服力，但前沿模型的兴起需要进一步研究。通过两项涉及两党议题和立场的调查实验（N=19,145），我们评估了由Anthropic、OpenAI、Google和xAI开发的七种最先进的LLM。我们发现，这些LLM的表现超过了标准竞选广告，并且不同模型之间的表现存在差异。具体来说，Claude系列表现出最高的说服力，而Grok则最低。研究结果在各种议题和立场上都是一致的。 此外，与Hackenburg等人（2025b）和Lin等人（2025）的研究发现相反，他们认为信息型提示可以提高说服力，我们发现信息型提示的有效性是模型依赖性的：它们增加了Claude和Grok的说服力，但显著降低了GPT的说服力。我们引入了一种基于数据驱动且策略无关的LLM辅助对话分析方法，以识别并评估潜在的说服策略。我们的工作为前沿模型的说服风险提供了基准，并提供了一个跨模型比较性风险评估框架。
URL
https://arxiv.org/abs/2603.09884
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POLISH'ing the Sky: Wide-Field and High-Dynamic Range Interferometric Image Reconstruction with Application to Strong Lens Discovery
2026-03-10 03:59:26Zihui Wu, Liam Connor, Samuel McCarty, Katherine L. BoumanAbstract
Radio interferometry enables high-resolution imaging of astronomical radio sources by synthesizing a large effective aperture from an array of antennas and solving a deconvolution problem to reconstruct the image. Deep learning has emerged as a promising solution to the imaging problem, reducing computational costs and enabling super-resolution. However, existing DL-based methods often fall short of the requirements for real-world deployment due to limitations in handling high dynamic range, large field of view, and mismatches between training and test conditions. In this work, we build upon and extend the POLISH framework, a recent DL model for radio interferometric imaging. We introduce key improvements to enable robust reconstruction and super-resolution under real-world conditions: (1) a patch-wise training and stitching strategy for scaling to wide-field imaging and (2) a nonlinear arcsinh-based intensity transformation to manage high dynamic range. We conduct comprehensive evaluations using the T-RECS simulation suite with realistic sky models and point spead functions (PSF), and demonstrate that our approach significantly improves reconstruction quality and robustness. We test the model on realistic simulated strong gravitational lenses and show that lens systems with Einstein radii near the PSF scale can be recovered after deconvolution with our POLISH model, potentially yielding 10$\times$ more galaxy-galaxy lensing systems from the Deep Synoptic Array (DSA) survey than with image-plane CLEAN. Our results highlight the potential of DL models as practical, scalable tools for next-generation radio astronomy.
Abstract (translated)
无线电干涉测量通过合成大型有效孔径并解决去卷积问题来实现对天文射电源的高分辨率成像，从而利用一组天线阵列。深度学习已成为解决成像问题的一项有前景的技术，它能降低计算成本，并支持超分辨率成像。然而，现有的基于深度学习的方法在处理高动态范围、大视场以及训练和测试条件不匹配等问题时往往表现不足，难以满足实际应用的要求。 在此工作中，我们建立并扩展了POLISH框架——这是最近提出的一种用于无线电干涉测量成像的深度学习模型。我们引入了一些关键改进，旨在实现在现实世界条件下稳健重建和超分辨率：（1）一种基于补丁训练和拼接策略的方法，以适应宽视野成像；（2）一种非线性的arcsinh强度变换方法来管理高动态范围。 使用带有真实天空模型和点扩散函数(PSF)的T-RECS模拟套件进行了全面评估。我们证明了我们的方法显著提高了重建质量和鲁棒性。我们在现实世界的强引力透镜模拟上测试了该模型，并展示了在去卷积后，利用我们的POLISH模型可以恢复出接近PSF尺度的爱因斯坦半径的透镜系统，从而有可能从深巡天阵列(DSA)调查中获得比使用图像平面CLEAN多10倍的星系-星系透镜系统。 我们的结果强调了深度学习模型作为下一代射电天文领域实用、可扩展工具的巨大潜力。
URL
https://arxiv.org/abs/2603.09162
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Security Considerations for Multi-agent Systems
2026-03-09 22:46:27Tam Nguyen, Moses Ndebugre, Dheeraj ArremsettyAbstract
Multi-agent artificial intelligence systems or MAS are systems of autonomous agents that exercise delegated tool authority, share persistent memory, and coordinate via inter-agent communication. MAS introduces qualitatively distinct security vulnerabilities from those documented for singular AI models. Existing security and governance frameworks were not designed for these emerging attack surfaces. This study systematically characterizes the threat landscape of MAS and quantitatively evaluates 16 security frameworks for AI against it. A four-phase methodology is proposed: constructing a deep technical knowledge base of production multi-agent architectures; conducting generative AI-assisted threat modeling scoped to MAS cybersecurity risks and validated by domain experts; structuring survey plans at individual-threat granularity; and scoring each framework on a three-point scale against the cybersecurity risks. The risks were organized into 193 distinct main threat items across nine risk categories. The expected minimal average score is 2. No reviewed framework achieves majority coverage of any single category. Non-Determinism (mean score 1.231 across all 16 frameworks) and Data Leakage (1.340) are the most under-addressed domains. The OWASP Agentic Security Initiative leads overall at 65.3\% coverage and in the design phase; the CDAO Generative AI Responsible AI Toolkit leads in development and operational coverage. These results provide the first empirical cross-framework comparison for MAS security and offer evidence-based guidance for framework selection.
Abstract (translated)
多智能体人工智能系统（MAS）是由自主代理组成的系统，这些代理被授予了工具权限，并共享持久内存并通过代理间通信进行协调。与单个AI模型记录的安全漏洞相比，MAS引入了性质上不同的安全威胁。现有的安全和治理框架并未针对这些新兴的攻击面设计。这项研究系统地描述了MAS的安全威胁景观，并对其进行了定量评估，涉及16种人工智能安全框架。该研究提出了一个四阶段的方法论：构建生产多代理架构的技术知识库；通过领域专家验证、针对MAS网络安全风险进行生成AI辅助威胁建模；制定以个别威胁为粒度的调查计划；并在网络安全风险上对每个框架进行三评分制评估。 在九个风险类别下，将这些风险组织成193个主要威胁项目。预计最低平均得分为2分。没有审查过的框架能够覆盖任何单一类别的大多数内容。非确定性（所有16个框架的平均得分1.231）和数据泄漏（1.340）是解决不足的主要领域。OWASP代理安全倡议在总覆盖率上以65.3%领先，并且在设计阶段表现最佳；CDAO生成AI负责任AI工具包则在开发和运营覆盖方面领先。 这些结果为MAS的安全性提供了首个基于实证的跨框架比较，为框架选择提供依据。
URL
https://arxiv.org/abs/2603.09002
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Impact of Different Failures on a Robot's Perceived Reliability
2026-03-09 18:27:53Andrew Violette, Zhanxin Wu, Haruki Nishimura, Masha Itkina, Leticia Priebe Rocha, Mark Zolotas, Guy Hoffman, Hadas Kress-GazitAbstract
Robots fail, potentially leading to a loss in the robot's perceived reliability (PR), a measure correlated with trustworthiness. In this study we examine how various kinds of failures affect the PR of the robot differently, and how this measure recovers without explicit social repair actions by the robot. In a preregistered and controlled online video study, participants were asked to predict a robot's success in a pick-and-place task. We examined manipulation failures (slips), freezing (lapses), and three types of incorrect picked objects or place goals (mistakes). Participants were shown one of 11 videos -- one of five types of failure, one of five types of failure followed by a successful execution in the same video, or a successful execution video. This was followed by two additional successful execution videos. Participants bet money either on the robot or on a coin toss after each video. People's betting patterns along with a qualitative analysis of their survey responses highlight that mistakes are less damaging to PR than slips or lapses, and some mistakes are even perceived as successes. We also see that successes immediately following a failure have the same effect on PR as successes without a preceding failure. Finally, we show that successful executions recover PR after a failure. Our findings highlight which robot failures are in higher need of repair in a human-robot interaction, and how trust could be recovered by robot successes.
Abstract (translated)
机器人出现故障可能会导致其感知可靠度（PR）的下降，而这一指标与信任程度相关。在本研究中，我们探讨了不同类型故障如何对机器人的PR产生不同影响，并分析了这种测量标准在没有明确的社会修复行动的情况下如何恢复。这项预注册且受控的在线视频研究表明，参与者被要求预测机器人在抓取和放置任务中的成功率。研究观察了操作失误（小过失）、冻结（中断）以及三种类型的选择错误对象或放置目标（错误）。参与者观看十一段不同的视频——五种类型的故障、五种故障后接成功的执行视频，或者单纯的成功的执行视频。之后，还播放了两段额外的成功执行视频。在每次视频结束后，参与者需要选择下注机器人还是硬币翻转的结果。 分析参与者的选择模式以及他们调查问卷的定性反馈表明，错误对PR的损害程度小于小过失或中断，并且某些错误甚至被人们视为成功的一部分。此外，我们还发现故障后紧接的成功执行对PR的影响与未经历前一次失败的直接成功相同。最后，研究展示了成功的执行可以修复先前出现的故障后的PR下降。 这些研究成果强调了在人机交互中哪些机器人的故障需要更多的修复措施，并且说明机器人后续的成功如何恢复人们的信任度。
URL
https://arxiv.org/abs/2603.08821
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Hierarchical Multi-Modal Planning for Fixed-Altitude Sparse Target Search and Sampling
2026-03-09 12:53:04Lingpeng Chen, Yuchen Zheng, Apple Pui-Yi Chui, Junfeng Wu, Ziyang HongAbstract
Efficient monitoring of sparse benthic phenomena, such as coral colonies, presents a great challenge for Autonomous Underwater Vehicles. Traditional exhaustive coverage strategies are energy-inefficient, while recent adaptive sampling approaches rely on costly vertical maneuvers. To address these limitations, we propose HIMoS (Hierarchical Informative Multi-Modal Search), a fixed-altitude framework for sparse coral search-and-sample missions. The system integrates a heterogeneous sensor suite within a two-layer planning architecture. At the strategic level, a Global Planner optimizes topological routes to maximize potential discovery. At the tactical level, a receding-horizon Local Planner leverages differentiable belief propagation to generate kinematically feasible trajectories that balance acoustic substrate exploration, visual coral search, and close-range sampling. Validated in high-fidelity simulations derived from real-world coral reef benthic surveys, our approach demonstrates superior mission efficiency compared to state-of-the-art baselines.
Abstract (translated)
对稀疏海底现象（如珊瑚群落）的有效监测对自主水下航行器来说是一个重大挑战。传统的全面覆盖策略能耗效率低，而最近的自适应采样方法则依赖于成本高昂的垂直机动操作。为了解决这些问题，我们提出了HIMoS（分层信息多模式搜索），这是一种用于稀疏珊瑚搜索和采集任务的固定高度框架。该系统在两层规划架构中整合了异构传感器套件。在战略层面，全局规划器优化拓扑路线以最大化潜在发现的机会；而在战术层面，一个具有前瞻性的局部规划器利用可微信念传播技术生成可行的动力学轨迹，这些轨迹平衡声纳底质探索、视觉珊瑚搜索以及近距离采集任务。 我们的方法通过基于真实世界珊瑚礁海底调查的高保真模拟验证了其有效性，并且在与最新基准方法相比时展示了更优的任务效率。
URL
https://arxiv.org/abs/2603.08336
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Examining the Role of YouTube Production and Consumption Dynamics on the Formation of Extreme Ideologies
2026-03-09 07:40:20Sarmad Chandio, Rishab NithyanandAbstract
The relationship between content production and consumption on algorithm-driven platforms like YouTube plays a critical role in shaping ideological behaviors. While prior work has largely focused on user behavior and algorithmic recommendations, the interplay between what is produced and what gets consumed, and its role in ideological shifts remains understudied. In this paper, we present a longitudinal, mixed-methods analysis combining one year of YouTube watch history with two waves of ideological surveys from 1,100 U.S. participants. We identify users who exhibited significant shifts toward more extreme ideologies and compare their content consumption and the production patterns of YouTube channels they engaged with to ideologically stable users. Our findings show that users who became more extreme consumed have different consumption habits from those who do not. This gets amplified by the fact that channels favored by users with extreme ideologies also have a higher affinity to produce content with a higher anger, grievance and other such markers. Lastly, using time series analysis, we examine whether content producers are the primary drivers of consumption behavior or merely responding to user demand.
Abstract (translated)
算法驱动平台（如YouTube）上内容生产和消费之间的关系在塑造意识形态行为中扮演着关键角色。尽管先前的研究主要集中在用户行为和算法推荐上，但关于生成的内容与被消费的内容之间相互作用及其在意识形态转变中的作用仍鲜有研究。本文通过结合1000名美国参与者一年的YouTube观看历史记录及两次意识形态调查波次，采用纵向、混合方法分析进行探讨。 我们识别出那些表现出向更极端意识形态方向显著变化的用户，并将其内容消费和他们参与的YouTube频道的内容生产模式与意识形态稳定的用户进行了对比。研究发现显示，转向更加极端意识形态的用户有不同于其他用户的消费习惯。这一现象进一步被放大，因为受到极端意识形态支持者的青睐的频道也倾向于制作愤怒、怨恨等情绪更强烈的视频内容。 最后，我们利用时间序列分析来探讨内容生产者是否是推动消费行为的主要驱动力，还是仅仅在回应用户需求。
URL
https://arxiv.org/abs/2603.08049
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