arXiv:2603.10014v1 Announce Type: new Abstract: Learning at scale often requires domain-specific automation such as assessment and feedback. An organization locked in to a general learning platform without these specialist automations limits its pedagogical offering. An ecosystem of interoperable, platform-agnostic microservices for domain-specific automation would solve this problem. To develop an effective eco-system, a standard interface (API) for education microservices is required. We propose an initial specification for a standard, platform-independent API for educational microservices, $upmu$Ed. The API integrates […]

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Hi guys, I’m learning meta RL. I’m trying to try the RL2 algorithm with some Gymnasium environments. However, it seems that there is no implementation of this algorithm in current RL libraries like rllib, stable-baselines3, TorchRL. Do you have any ideas of implementing of this RL algorithm? Which library should I use? submitted by /u/ZitaLovesCats [link] [comments]

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arXiv:2604.01327v1 Announce Type: new Abstract: Macroscopic unmanned aerial vehicle (UAV) traffic organization in three-dimensional airspace faces significant challenges from static wind fields and complex obstacles. A critical difficulty lies in simultaneously capturing the strong anisotropy induced by wind while strictly preserving transport consistency and boundary semantics, which are often compromised in standard physics-informed learning approaches. To resolve this, we propose a constraint-preserving hybrid solver that integrates a physics-informed neural network for the anisotropic Eikonal value problem with a […]

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Agriculture is increasingly challenged by climate change, soil degradation, and resource depletion, and hence requires advanced data-driven crop classification and recommendation solutions. This work presents an explainable ensemble learning paradigm that fuses optimized feature pyramids, deep networks, self-attention mechanisms, and residual networks for bolstering crop suitability predictions based on soil characteristics (e.g., pH, nitrogen, potassium) and climatic conditions (e.g., temperature, rainfall). With a dataset comprising 3,867 instances and 29 features from the Ethiopian Agricultural Transformation Agency and NASA, […]

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Matter naturally complexifies, life evolves toward consciousness, and millions of galaxies exist. With these three facts, Teilhard logically implies millions of intelligent civilizations throughout the universe. This challenges Christian theology’s assumption of Earth’s uniqueness. Rather than retreat, he proposes embracing a cosmic Christ who centers all conscious life across the entire universe.

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arXiv:2601.03444v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as automated evaluators, yet prior works demonstrate that these LLM judges often lack consistency in scoring when the prompt is altered. However, the effect of the grading scale itself remains underexplored. We study the LLM-as-a-judge problem by comparing two kinds of raters: humans and LLMs. We collect ratings from both groups on three scales and across six benchmarks that include objective, open-ended subjective, and mixed tasks. […]

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Microservice-based architecture has become a dominant style for building cloud-native systems, but their rapid and continuous evolution makes it difficult to understand, verify, and control the impact of change. Although many works address microservices from the perspectives of design, deployment, and operation, there is still limited consolidated knowledge on how the evolution and verification of microservice-based systems are supported in the research literature. This paper reports on a systematic literature review (SLR) on methods and tools for analyzing […]

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arXiv:2601.21109v1 Announce Type: new Abstract: Recent advances in low-rank adaptation (LoRA) have enabled efficient fine-tuning of large language models (LLMs) with minimal additional parameters. However, existing LoRA methods apply static rank configurations uniformly across all input tokens, ignoring variation in token complexity and computational requirements. In this work, we propose ChunkWise LoRA, a dynamic and adaptive approach that partitions sequences into variable-length chunks based on token complexity and assigns each chunk a tailored low-rank configuration. Our system introduces […]

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