arXiv:2604.06300v1 Announce Type: new Abstract: Large language model-based agents are increasingly being integrated into core battlefield functions, including intelligence analysis, data fusion, and battlefield management. This paper argues that the very features that make such agents operationally attractive, namely their capacity for initiative, interpretation, their goal-directedness, and dynamic memory, are the same features that render context-appropriate human judgment and control substantively ineffectual in those parts of the kill chain where agents operate. Drawing on specific use cases, the […]

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Humans are highly effective at utilizing prior knowledge to adapt to novel tasks, a capability that standard machine learning models struggle to replicate due to their reliance on task-specific training. Meta-learning overcomes this limitation by allowing models to acquire transferable knowledge from various tasks, enabling rapid adaptation to new challenges with minimal data. This survey provides a rigorous, task-based formalization of meta-learning and meta-reinforcement learning and uses that paradigm to chronicle the landmark algorithms that paved the way […]

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In many parts of the world, access to funding determines whether an idea becomes a company, a research initiative, or remains only a concept. Unfortunately, funding systems are often centralized, opaque, and limited to a small network of institutions. Over the past few years, blockchain technology has introduced an alternative model: decentralized governance. Instead of a small group deciding how funds are allocated, communities can collectively govern resources through on-chain voting. This idea has been explored in several projects […]

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arXiv:2604.01413v1 Announce Type: new Abstract: Large Language Models (LLMs) increasingly rely on multi-turn reasoning and interaction, such as adaptive retrieval-augmented generation (RAG) and ReAct-style agents, to answer difficult questions. These methods improve accuracy by iteratively retrieving information, reasoning, or acting, but introduce a key challenge: textbf{When should the model stop?} Existing approaches rely on heuristic stopping rules or fixed turn budgets and provide no formal guarantees that the final prediction still contains the correct answer. This limitation is […]

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Machine learning models are increasingly used in high-stakes domains where their predictions can actively shape the environments in which they operate, a phenomenon known as performative prediction. This dynamic, in which the deployment of the model influences the very outcome it seeks to predict, can lead to unintended consequences, including feedback loops, performance issues, and significant societal risks. While the literature in the field has grown rapidly in recent years, a socio-technical synthesis that systemises the phenomenon concepts […]

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This 2nd episode on a 2 part series analyzes how GenAI has evolved to deliver automation through AI Agents and Agentic AI. It highlights the differences between both of them citing many sources from IBM, Anthropic McKinsey and Gartner amongst others as well as their best use cases and differences and also mentioning some of the risks. 1. Generative AI (GenAI) Generative AI refers to reactive systems that wait for user input, typically a prompt, to generate content1. […]

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Agent skills, structured packages of procedural knowledge and executable resources that agents dynamically load at inference time, have become a reliable mechanism for augmenting LLM agents. Yet inference-time skill augmentation is fundamentally limited: retrieval noise introduces irrelevant guidance, injected skill content imposes substantial token overhead, and the model never truly acquires the knowledge it merely follows. We ask whether skills can instead be internalized into model parameters, enabling zero-shot autonomous behavior without any runtime skill retrieval. We introduce […]

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arXiv:2603.24772v1 Announce Type: new Abstract: Clinical documentation is a critical factor for patient safety, diagnosis, and continuity of care. The administrative burden of EHRs is a significant factor in physician burnout. This is a critical issue for low-resource languages, including Finnish. This study aims to investigate the effectiveness of a domain-aligned natural language processing (NLP); large language model for medical transcription in Finnish by fine-tuning LLaMA 3.1-8B on a small validated corpus of simulated clinical conversations by students […]

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