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High reliability for scientific claims." }, "corroboration": { "score": 15, "label": "low", "source_count": 3, "unique_orgs": 1, "unique_countries": 1, "covering_orgs": [ "nature" ] }, "topic_group": "9cfb34fd881c523e", "verified_at": "2026-04-22T21:06:47.771560+00:00" }, { "id": "201e56679ce4a9f7", "source_name": "rss:nature", "source_country": "GB", "source_type": "news", "title": "Stop overlooking librarians’ expertise", "body": "Nature, Published online: 14 April 2026; doi:10.1038/d41586-026-01217-0 Stop overlooking librarians’ expertise", "url": "https://www.nature.com/articles/d41586-026-01217-0", "published_at": "2026-04-14", "galaxy_hints": [ "science" ], "raw_metadata": { "feed_url": "https://www.nature.com/nature.rss", "author": "Joseph G. L. Lee", "tags": [] }, "source_bias": { "label": "Nature", "bias": "centre", "funding": "private", "country": "GB", "note": "Peer-reviewed science journal. High reliability for scientific claims." }, "corroboration": { "score": 15, "label": "low", "source_count": 3, "unique_orgs": 1, "unique_countries": 1, "covering_orgs": [ "nature" ] }, "topic_group": "9cfb34fd881c523e", "verified_at": "2026-04-22T21:06:47.771560+00:00" }, { "id": "ce69322be1735e49", "source_name": "rss:nature", "source_country": "GB", "source_type": "news", "title": "Deep-sea mining mustn’t go ahead until there are baseline data", "body": "Nature, Published online: 14 April 2026; doi:10.1038/d41586-026-01215-2 Deep-sea mining mustn’t go ahead until there are baseline data", "url": "https://www.nature.com/articles/d41586-026-01215-2", "published_at": "2026-04-14", "galaxy_hints": [ "science" ], "raw_metadata": { "feed_url": "https://www.nature.com/nature.rss", "author": "Zeming Shi", "tags": [] }, "source_bias": { "label": "Nature", "bias": "centre", "funding": "private", "country": "GB", "note": "Peer-reviewed science journal. High reliability for scientific claims." }, "corroboration": { "score": 15, "label": "low", "source_count": 1, "unique_orgs": 1, "unique_countries": 1, "covering_orgs": [ "nature" ] }, "topic_group": "ce69322be1735e49", "verified_at": "2026-04-22T21:06:47.771560+00:00" }, { "id": "9cd37d5a4fecdbc4", "source_name": "rss:nature", "source_country": "GB", "source_type": "news", "title": "AI agents replicate human social dynamics in days", "body": "Nature, Published online: 14 April 2026; doi:10.1038/d41586-026-01218-z AI agents replicate human social dynamics in days", "url": "https://www.nature.com/articles/d41586-026-01218-z", "published_at": "2026-04-14", "galaxy_hints": [ "science" ], "raw_metadata": { "feed_url": "https://www.nature.com/nature.rss", "author": "Karen Spinner", "tags": [] }, "source_bias": { "label": "Nature", "bias": "centre", "funding": "private", "country": "GB", "note": "Peer-reviewed science journal. High reliability for scientific claims." }, "corroboration": { "score": 15, "label": "low", "source_count": 1, "unique_orgs": 1, "unique_countries": 1, "covering_orgs": [ "nature" ] }, "topic_group": "9cd37d5a4fecdbc4", "verified_at": "2026-04-22T21:06:47.771560+00:00" }, { "id": "abca867c8d7790e3", "source_name": "rss:nature", "source_country": "GB", "source_type": "news", "title": "Marine heatwaves can supercharge cyclones", "body": "Nature, Published online: 14 April 2026; doi:10.1038/d41586-026-01201-8 Storms that cross exceptionally warm ocean waters intensify more quickly and do more damage than storms that do not.", "url": "https://www.nature.com/articles/d41586-026-01201-8", "published_at": "2026-04-14", "galaxy_hints": [ "science" ], "raw_metadata": { "feed_url": "https://www.nature.com/nature.rss", "author": "", "tags": [] }, "source_bias": { "label": "Nature", "bias": "centre", "funding": "private", "country": "GB", "note": "Peer-reviewed science journal. High reliability for scientific claims." }, "corroboration": { "score": 15, "label": "low", "source_count": 3, "unique_orgs": 1, "unique_countries": 1, "covering_orgs": [ "nature" ] }, "topic_group": "9cfb34fd881c523e", "verified_at": "2026-04-22T21:06:47.771560+00:00" }, { "id": "3cf795bcde91bacb", "source_name": "rss:nature", "source_country": "GB", "source_type": "news", "title": "China discontinues prominent journal ranking list", "body": "Nature, Published online: 14 April 2026; doi:10.1038/d41586-026-01216-1 China discontinues prominent journal ranking list", "url": "https://www.nature.com/articles/d41586-026-01216-1", "published_at": "2026-04-14", "galaxy_hints": [ "science" ], "raw_metadata": { "feed_url": "https://www.nature.com/nature.rss", "author": "Zheng Liu", "tags": [] }, "source_bias": { "label": "Nature", "bias": "centre", "funding": "private", "country": "GB", "note": "Peer-reviewed science journal. High reliability for scientific claims." }, "corroboration": { "score": 15, "label": "low", "source_count": 1, "unique_orgs": 1, "unique_countries": 1, "covering_orgs": [ "nature" ] }, "topic_group": "3cf795bcde91bacb", "verified_at": "2026-04-22T21:06:47.771560+00:00" }, { "id": "e84cc3684b2cd923", "source_name": "rss:arxiv_cs", "source_country": "INT", "source_type": "news", "title": "LABBench2: An Improved Benchmark for AI Systems Performing Biology Research", "body": "arXiv:2604.09554v1 Announce Type: new \nAbstract: Optimism for accelerating scientific discovery with AI continues to grow. Current applications of AI in scientific research range from training dedicated foundation models on scientific data to agentic autonomous hypothesis generation systems to AI-driven autonomous labs. The need to measure progress of AI systems in scientific domains correspondingly must not only accelerate, but increasingly shift focus to more real-world capabilities. Beyond rote knowledge and even just reasoning to actually measuring the ability to perform meaningful work. Prior work introduced the Language Agent Biology Benchmark LAB-Bench as an initial attempt at measuring these abilities. Here we introduce an evolution of that benchmark, LABBench2, for measuring real-world capabilities of AI systems performing useful scientific tasks. LABBench2 comprises nearly 1,900 tasks and is, for the most part, a continuation of LAB-Bench, measuring similar capabilities but i", "url": "https://arxiv.org/abs/2604.09554", "published_at": "Tue, 14 Apr 2026 00:00:00 -0400", "galaxy_hints": [ "science", "technology" ], "raw_metadata": { "feed_url": "https://export.arxiv.org/rss/cs.AI", "author": "Jon M Laurent, Albert Bou, Michael Pieler, Conor Igoe, Alex Andonian, Siddharth Narayanan, James Braza, Alexandros Sanchez Vassopoulos, Jacob L Steenwyk, Blake Lash, Andrew D White, Samuel G Rodriques", "tags": [ "cs.AI", "cs.CL", "cs.LG" ] }, "source_bias": { "label": "arXiv CS", "bias": "centre", "funding": "nonprofit", "country": "INT", "note": "Preprint server. Not peer-reviewed but direct from researchers." }, "corroboration": { "score": 15, "label": "low", "source_count": 1, "unique_orgs": 1, "unique_countries": 1, "covering_orgs": [ "arxiv" ] }, "topic_group": "e84cc3684b2cd923", "verified_at": "2026-04-22T21:06:47.771560+00:00" }, { "id": "4b532b3d34bdd0ab", "source_name": "rss:arxiv_cs", "source_country": "INT", "source_type": "news", "title": "Linear Programming for Multi-Criteria Assessment with Cardinal and Ordinal Data: A Pessimistic Virtual Gap Analysis", "body": "arXiv:2604.09555v1 Announce Type: new \nAbstract: Multi-criteria Analysis (MCA) is used to rank alternatives based on various criteria. Key MCA methods, such as Multiple Criteria Decision Making (MCDM) methods, estimate parameters for criteria to compute the performance of each alternative. Nonetheless, subjective evaluations and biases frequently influence the reliability of results, while the diversity of data affects the precision of the parameters. The novel linear programming-based Virtual Gap Analysis (VGA) models tackle these issues. This paper outlines a two-step method that integrates two novel VGA models to assess each alternative from a pessimistic perspective, using both quantitative and qualitative criteria, and employing cardinal and ordinal data. Next, prioritize the alternatives to eliminate the least favorable one. The proposed method is dependable and scalable, enabling thorough assessments efficiently and effectively within decision support systems.", "url": "https://arxiv.org/abs/2604.09555", "published_at": "Tue, 14 Apr 2026 00:00:00 -0400", "galaxy_hints": [ "science", "technology" ], "raw_metadata": { "feed_url": "https://export.arxiv.org/rss/cs.AI", "author": "Fuh-Hwa Franklin Liu, Su-Chuan Shih", "tags": [ "cs.AI", "math.OC" ] }, "source_bias": { "label": "arXiv CS", "bias": "centre", "funding": "nonprofit", "country": "INT", "note": "Preprint server. Not peer-reviewed but direct from researchers." }, "corroboration": { "score": 15, "label": "low", "source_count": 1, "unique_orgs": 1, "unique_countries": 1, "covering_orgs": [ "arxiv" ] }, "topic_group": "4b532b3d34bdd0ab", "verified_at": "2026-04-22T21:06:47.771560+00:00" }, { "id": "c67a00e8152dbd26", "source_name": "rss:arxiv_cs", "source_country": "INT", "source_type": "news", "title": "Seven simple steps for log analysis in AI systems", "body": "arXiv:2604.09563v1 Announce Type: new \nAbstract: AI systems produce large volumes of logs as they interact with tools and users. Analysing these logs can help understand model capabilities, propensities, and behaviours, or assess whether an evaluation worked as intended. Researchers have started developing methods for log analysis, but a standardised approach is still missing. Here we suggest a pipeline based on current best practices. We illustrate it with concrete code examples in the Inspect Scout library, provide detailed guidance on each step, and highlight common pitfalls. Our framework provides researchers with a foundation for rigorous and reproducible log analysis.", "url": "https://arxiv.org/abs/2604.09563", "published_at": "Tue, 14 Apr 2026 00:00:00 -0400", "galaxy_hints": [ "science", "technology" ], "raw_metadata": { "feed_url": "https://export.arxiv.org/rss/cs.AI", "author": "Magda Dubois, Ekin Zorer, Maia Hamin, Joe Skinner, Alexandra Souly, Jerome Wynne, Harry Coppock, Lucas Satos, Sayash Kapoor, Sunischal Dev, Keno Juchems, Kimberly Mai, Timo Flesch, Lennart Luettgau, Charles Teague, Eric Patey, JJ Allaire, Lorenzo Pacchiardi, Jose Hernandez-Orallo, Cozmin Ududec", "tags": [ "cs.AI", "cs.CL", "cs.LG" ] }, "source_bias": { "label": "arXiv CS", "bias": "centre", "funding": "nonprofit", "country": "INT", "note": "Preprint server. Not peer-reviewed but direct from researchers." }, "corroboration": { "score": 15, "label": "low", "source_count": 1, "unique_orgs": 1, "unique_countries": 1, "covering_orgs": [ "arxiv" ] }, "topic_group": "c67a00e8152dbd26", "verified_at": "2026-04-22T21:06:47.771560+00:00" }, { "id": "baba07e3987b10ff", "source_name": "rss:arxiv_cs", "source_country": "INT", "source_type": "news", "title": "Turing Test on Screen: A Benchmark for Mobile GUI Agent Humanization", "body": "arXiv:2604.09574v1 Announce Type: new \nAbstract: The rise of autonomous GUI agents has triggered adversarial countermeasures from digital platforms, yet existing research prioritizes utility and robustness over the critical dimension of anti-detection. We argue that for agents to survive in human-centric ecosystems, they must evolve Humanization capabilities. We introduce the ``Turing Test on Screen,'' formally modeling the interaction as a MinMax optimization problem between a detector and an agent aiming to minimize behavioral divergence. We then collect a new high-fidelity dataset of mobile touch dynamics, and conduct our analysis that vanilla LMM-based agents are easily detectable due to unnatural kinematics. Consequently, we establish the Agent Humanization Benchmark (AHB) and detection metrics to quantify the trade-off between imitability and utility. Finally, we propose methods ranging from heuristic noise to data-driven behavioral matching, demonstrating that agents can achieve", "url": "https://arxiv.org/abs/2604.09574", "published_at": "Tue, 14 Apr 2026 00:00:00 -0400", "galaxy_hints": [ "science", "technology" ], "raw_metadata": { "feed_url": "https://export.arxiv.org/rss/cs.AI", "author": "Jiachen Zhu, Lingyu Yang, Rong Shan, Congmin Zheng, Zeyu Zheng, Weiwen Liu, Yong Yu, Weinan Zhang, Jianghao Lin", "tags": [ "cs.AI", "cs.LG" ] }, "source_bias": { "label": "arXiv CS", "bias": "centre", "funding": "nonprofit", "country": "INT", "note": "Preprint server. Not peer-reviewed but direct from researchers." }, "corroboration": { "score": 15, "label": "low", "source_count": 1, "unique_orgs": 1, "unique_countries": 1, "covering_orgs": [ "arxiv" ] }, "topic_group": "baba07e3987b10ff", "verified_at": "2026-04-22T21:06:47.771560+00:00" }, { "id": "f13459284a56b91c", "source_name": "rss:arxiv_cs", "source_country": "INT", "source_type": "news", "title": "AHC: Meta-Learned Adaptive Compression for Continual Object Detection on Memory-Constrained Microcontrollers", "body": "arXiv:2604.09576v1 Announce Type: new \nAbstract: Deploying continual object detection on microcontrollers (MCUs) with under 100KB memory requires efficient feature compression that can adapt to evolving task distributions. Existing approaches rely on fixed compression strategies (e.g., FiLM conditioning) that cannot adapt to heterogeneous task characteristics, leading to suboptimal memory utilization and catastrophic forgetting. We introduce Adaptive Hierarchical Compression (AHC), a meta-learning framework featuring three key innovations: (1) true MAML-based compression that adapts via gradient descent to each new task in just 5 inner-loop steps, (2) hierarchical multi-scale compression with scale-aware ratios (8:1 for P3, 6.4:1 for P4, 4:1 for P5) matching FPN redundancy patterns, and (3) a dual-memory architecture combining short-term and long-term banks with importance-based consolidation under a hard 100KB budget. We provide formal theoretical guarantees bounding catastrophic forg", "url": "https://arxiv.org/abs/2604.09576", "published_at": "Tue, 14 Apr 2026 00:00:00 -0400", "galaxy_hints": [ "science", "technology" ], "raw_metadata": { "feed_url": "https://export.arxiv.org/rss/cs.AI", "author": "Bibin Wilson", "tags": [ "cs.AI" ] }, "source_bias": { "label": "arXiv CS", "bias": "centre", "funding": "nonprofit", "country": "INT", "note": "Preprint server. Not peer-reviewed but direct from researchers." }, "corroboration": { "score": 15, "label": "low", "source_count": 1, "unique_orgs": 1, "unique_countries": 1, "covering_orgs": [ "arxiv" ] }, "topic_group": "f13459284a56b91c", "verified_at": "2026-04-22T21:06:47.771560+00:00" }, { "id": "4be430775773b4a9", "source_name": "rss:arxiv_cs", "source_country": "INT", "source_type": "news", "title": "Explainable Planning for Hybrid Systems", "body": "arXiv:2604.09578v1 Announce Type: new \nAbstract: The recent advancement in artificial intelligence (AI) technologies facilitates a paradigm shift toward automation. Autonomous systems are fully or partially replacing manually crafted ones. At the core of these systems is automated planning. With the advent of powerful planners, automated planning is now applied to many complex and safety-critical domains, including smart energy grids, self-driving cars, warehouse automation, urban and air traffic control, search and rescue operations, surveillance, robotics, and healthcare. There is a growing need to generate explanations of AI-based systems, which is one of the major challenges the planning community faces today. The thesis presents a comprehensive study on explainable artificial intelligence planning (XAIP) for hybrid systems that capture a representation of real-world problems closely.", "url": "https://arxiv.org/abs/2604.09578", "published_at": "Tue, 14 Apr 2026 00:00:00 -0400", "galaxy_hints": [ "science", "technology" ], "raw_metadata": { "feed_url": "https://export.arxiv.org/rss/cs.AI", "author": "Mir Md Sajid Sarwar", "tags": [ "cs.AI" ] }, "source_bias": { "label": "arXiv CS", "bias": "centre", "funding": "nonprofit", "country": "INT", "note": "Preprint server. Not peer-reviewed but direct from researchers." }, "corroboration": { "score": 15, "label": "low", "source_count": 1, "unique_orgs": 1, "unique_countries": 1, "covering_orgs": [ "arxiv" ] }, "topic_group": "4be430775773b4a9", "verified_at": "2026-04-22T21:06:47.771560+00:00" }, { "id": "63f0f50cac453dc0", "source_name": "rss:arxiv_cs", "source_country": "INT", "source_type": "news", "title": "Help Without Being Asked: A Deployed Proactive Agent System for On-Call Support with Continuous Self-Improvement", "body": "arXiv:2604.09579v1 Announce Type: new \nAbstract: In large-scale cloud service platforms, thousands of customer tickets are generated daily and are typically handled through on-call dialogues. This high volume of on-call interactions imposes a substantial workload on human support analysts. Recent studies have explored reactive agents that leverage large language models as a first line of support to interact with customers directly and resolve issues. However, when issues remain unresolved and are escalated to human support, these agents are typically disengaged. As a result, they cannot assist with follow-up inquiries, track resolution progress, or learn from the cases they fail to address. In this paper, we introduce Vigil, a novel proactive agent system designed to operate throughout the entire on-call life-cycle. Unlike reactive agents, Vigil focuses on providing assistance during the phase in which human support is already involved. It integrates into the dialogue between the custo", "url": "https://arxiv.org/abs/2604.09579", "published_at": "Tue, 14 Apr 2026 00:00:00 -0400", "galaxy_hints": [ "science", "technology" ], "raw_metadata": { "feed_url": "https://export.arxiv.org/rss/cs.AI", "author": "Fengrui Liu, Xiao He, Tieying Zhang", "tags": [ "cs.AI", "cs.SE" ] }, "source_bias": { "label": "arXiv CS", "bias": "centre", "funding": "nonprofit", "country": "INT", "note": "Preprint server. Not peer-reviewed but direct from researchers." }, "corroboration": { "score": 15, "label": "low", "source_count": 1, "unique_orgs": 1, "unique_countries": 1, "covering_orgs": [ "arxiv" ] }, "topic_group": "63f0f50cac453dc0", "verified_at": "2026-04-22T21:06:47.771560+00:00" }, { "id": "c1042fb194166810", "source_name": "rss:arxiv_cs", "source_country": "INT", "source_type": "news", "title": "OOWM: Structuring Embodied Reasoning and Planning via Object-Oriented Programmatic World Modeling", "body": "arXiv:2604.09580v1 Announce Type: new \nAbstract: Standard Chain-of-Thought (CoT) prompting empowers Large Language Models (LLMs) with reasoning capabilities, yet its reliance on linear natural language is inherently insufficient for effective world modeling in embodied tasks. While text offers flexibility, it fails to explicitly represent the state-space, object hierarchies, and causal dependencies required for robust robotic planning. To address these limitations, we propose Object-Oriented World Modeling (OOWM), a novel framework that structures embodied reasoning through the lens of software engineering formalisms. We redefine the world model not as a latent vector space, but as an explicit symbolic tuple $W = \\langle S, T \\rangle$: a State Abstraction ($G_\\text{state}$) instantiating the environmental state $S$, coupled with a Control Policy ($G_\\text{control}$) representing the transition logic $T: S \\times A \\rightarrow S'$. OOWM leverages the Unified Modeling Language (UML) to m", "url": "https://arxiv.org/abs/2604.09580", "published_at": "Tue, 14 Apr 2026 00:00:00 -0400", "galaxy_hints": [ "science", "technology" ], "raw_metadata": { "feed_url": "https://export.arxiv.org/rss/cs.AI", "author": "Hongyu Chen, Liang Lin, Guangrun Wang", "tags": [ "cs.AI", "cs.LG" ] }, "source_bias": { "label": "arXiv CS", "bias": "centre", "funding": "nonprofit", "country": "INT", "note": "Preprint server. Not peer-reviewed but direct from researchers." }, "corroboration": { "score": 15, "label": "low", "source_count": 1, "unique_orgs": 1, "unique_countries": 1, "covering_orgs": [ "arxiv" ] }, "topic_group": "c1042fb194166810", "verified_at": "2026-04-22T21:06:47.771560+00:00" } ]