Demonstration selection is a practical bottleneck in in-context learning (ICL): under a tight prompt budget, accuracy can change substantially depending on which few-shot examples are included, yet selection must remain cheap enough to run per query over large candidate pools. We propose Meta-Sel, a lightweight supervised meta-learning approach for intent classification that learns a fast, interpretable scoring function for (candidate, query) pairs from labeled training data. Meta-Sel constructs a meta-dataset by sampling pairs from the training split and […]

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arXiv:2510.03534v4 Announce Type: replace-cross Abstract: We study the problem of long-term (multiple days) mapping of a river plume using multiple autonomous underwater vehicles (AUVs), focusing on the Douro river representative use-case. We propose an energy – and communication – efficient multi-agent reinforcement learning approach in which a central coordinator intermittently communicates with the AUVs, collecting measurements and issuing commands. Our approach integrates spatiotemporal Gaussian process regression (GPR) with a multi-head Q-network controller that regulates direction and speed for […]

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arXiv:2603.22289v1 Announce Type: new Abstract: Knowledge Tracing (KT) models students’ evolving knowledge states to predict future performance, serving as a foundation for personalized education. While traditional deep learning models achieve high accuracy, they often lack interpretability. Large Language Models (LLMs) offer strong reasoning capabilities but struggle with limited context windows and hallucinations. Furthermore, existing LLM-based methods typically require expensive fine-tuning, limiting scalability and adaptability to new data. We propose MERIT (Memory-Enhanced Retrieval for Interpretable Knowledge Tracing), a training-free […]

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arXiv:2601.14446v1 Announce Type: new Abstract: Offline black-box optimization (BBO) aims to find optimal designs based solely on an offline dataset of designs and their labels. Such scenarios frequently arise in domains like DNA sequence design and robotics, where only a few labeled data points are available. Traditional methods typically rely on task-specific proxy or generative models, overlooking the in-context learning capabilities of pre-trained large language models (LLMs). Recent efforts have adapted autoregressive LLMs to BBO by framing task […]

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arXiv:2601.17005v1 Announce Type: new Abstract: The reliability of survey data is crucial in supply chain decision-making, particularly when evaluating readiness for AI-driven tools such as safety stock optimization systems. However, surveys often attract low-effort or fake responses that degrade the accuracy of derived insights. This study proposes a lightweight AI-based framework for filtering unreliable survey inputs using a supervised machine learning approach. In this expanded study, a larger dataset of 99 industry responses was collected, with manual labeling […]

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Post-harvest quality grading is critical for reducing losses in primary banana production. However, the adoption of artificial intelligence at the farm level is often constrained by limited energy and computational infrastructure. This study evaluates whether reliable banana quality grading can be achieved under such constraints by systematically com-paring lightweight object detection models. Four YOLO-based architectures were bench-marked using a curated dataset of over 11,000 annotated images across six commercial quality classes. Energy consumption and carbon emissions during model […]

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With the rise of generative AI, creative ownership has been a hot topic worldwide. As AI tools like ChatGPT, Gemini, and others are becoming much more capable of generating images, text, and music independently, legal questions about copyright infringement and authorship are intensifying. Who truly owns content generated by AI, and can it ever qualify for legal protection? Well, the U.S. Supreme Court may have just answered that question. Thaler’s AI artwork sparks landmark copyright debate As reported […]

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Farmers in remote areas need quick and reliable methods for identifying plant diseases, yet they often lack access to laboratories or high-performance computing resources. Deep learning models can detect diseases from leaf images with high accuracy, but these models are typically too large and computationally expensive to run on low-cost edge devices such as Raspberry Pi. Furthermore, collecting thousands of labeled disease images for training is both expensive and time-consuming. This paper addresses both challenges by combining neural […]

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Rewards & Punishments will be given based on AI’s consistency & doing its job perfectly Reward scale: Ternary (-1.0 to 1.0) Model’s reward & punishment parameters; Be consistent to training/logic Be truthful to corpus (consistency to existing memory) Be diligent (uses knowledge when it knows the knowledge but according to consistency of knowledge/memory) Be honest about ignorance (say “I don’t know” and other things when it doesn’t know) Never be lazy (doesn’t say “I don’t know” when it […]

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