technocracy

Agentic artificial intelligence systems increasingly combine language models, memory, retrieval, tool use, orchestration, and human oversight. For software engineering, this creates a variability problem that feature-oriented product line methods only partly address: organizations are configuring not only functions or components, but permitted patterns of agency. Unlike MAS-SPL, which mainly structures families of agent roles and interactions, the proposed approach targets LLM-based agents whose prompts, retrieval sources, tool authority, runtime monitoring, and governance boundaries vary together. This paper proposes […]

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Unplanned engine failures in helicopters can lead to severe operational disruptions, safety hazards, and costly repairs. To mitigate these risks, this study compares two predictive maintenance strategies for helicopter engines: a supervised classification pipeline and an unsupervised anomaly detection approach based on autoencoders (AEs). The supervised method relies on labelled examples of both normal and faulty behaviour, while the unsupervised approach learns a model of normal operation using only healthy engine data, flagging deviations as potential faults. Both […]

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arXiv:2601.02407v1 Announce Type: new Abstract: Standard simulations of the Iterated Prisoners Dilemma (IPD) operate in deterministic, noise-free environments, producing strategies that may be theoretically optimal but fragile when confronted with real-world uncertainty. This paper addresses two critical gaps in evolutionary game theory research: (1) the absence of realistic environmental stressors during strategy evolution, and (2) the Interpretability Gap, where evolved genetic strategies remain opaque binary sequences devoid of semantic meaning. We introduce a novel framework combining stochastic environmental […]

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Hello. First of all, I’m not sure if it’s okay for me to write a post in this community, but I decided to gather my courage and leave one. To begin with, I’m not an English speaker, so I leave all the English translation work to AI. It’s a great time to live in, where communication is possible even if you don’t speak the language. Since this is my first post, I thought I would also share a […]

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Recent research shows that Preference Alignment (PA) objectives act as divergence estimators between aligned (chosen) and unaligned (rejected) response distributions. In this work, we extend this divergence-based perspective to general alignment settings, such as reinforcement learning with verifiable rewards (RLVR), where only environmental rewards are available. Within this unified framework, we propose $f$-Group Relative Policy Optimization ($f$-GRPO), a class of on-policy reinforcement learning, and $f$-Hybrid Alignment Loss ($f$-HAL), a hybrid on/off policy objectives, for general LLM alignment based […]

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arXiv:2604.02369v2 Announce Type: new Abstract: Agent communication protocols are becoming critical infrastructure for large language model (LLM) systems that must use tools, coordinate with other agents, and operate across heterogeneous environments. This work presents a human-inspired perspective on this emerging landscape by organizing agent communication into three layers: communication, syntactic, and semantic. Under this framework, we systematically analyze 18 representative protocols and compare how they support reliable transport, structured interaction, and meaning-level coordination. Our analysis shows a clear […]

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arXiv:2503.12354v2 Announce Type: replace-cross Abstract: Traditional neural network regression models provide only point estimates, failing to capture predictive uncertainty. Probabilistic neural networks (PNNs) address this limitation by producing output distributions, enabling the construction of prediction intervals. However, the common assumption of Gaussian output distributions often results in overly wide intervals, particularly in the presence of outliers or deviations from normality. To enhance the adaptability of PNNs, we propose t-Distributed Neural Networks (TDistNNs), which generate t-distributed outputs, parameterized by […]

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arXiv:2604.07422v1 Announce Type: new Abstract: Recent advances in text-to-image (T2I) generation have enabled visually coherent image synthesis from descriptions, but generating images containing multiple given subjects remains challenging. As the number of reference identities increases, existing methods often suffer from subject missing and semantic drift. To address this problem, we propose MUSIC, the first MLLM specifically designed for textbf{MU}lti-textbf{S}ubject textbf{I}n-textbf{C}ontext image generation. To overcome the data scarcity, we introduce an automatic and scalable data generation pipeline that eliminates […]

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