Author(s): Code Experts Originally published on Towards AI. Stop Building Chatbots. Start Building AI Agents That Actually Work. The landscape of artificial intelligence is rapidly evolving beyond simple chatbots and question-answering systems. Enter AI agents — autonomous systems that can perceive their environment, make decisions, and take actions to achieve specific goals. In this blog series, we’ll explore how to build these intelligent agents using the Agno framework. AI agents are systems that go beyond passive information retrieval. […]

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This paper presents a scalable and low-cost Retrieval Augmented Generation (RAG) architecture designed to enhance learning in university-level courses, with a particular focus on supporting students from economically disadvantaged backgrounds. Recent advances in large language models (LLMs) have demonstrated considerable potential in educational contexts; however, their adoption is often limited by computational costs and the need for stable broadband access, issues that disproportionately affect low-income learners. To address this challenge, we propose a lightweight, mobile, and friendly RAG […]

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We often treat current LLMs as massive “reflex” engines (Model-Free policies) – they react to the context based on training habits, but they don’t really “plan” or verify their moves unless we force them with Chain-of-Thought. I’ve been looking at the architecture behind the new Logical Intelligence lab, which is pivoting to Energy-Based Models. From an RL perspective, this feels exactly like the shift from “habit-based” action to “planning-based” control. Instead of just guessing the next token, the […]

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arXiv:2601.04257v1 Announce Type: new Abstract: We study multilingual speaker attribute prediction under linguistic variation, domain mismatch, and data imbalance across languages. We propose RLMIL-DAT, a multilingual extension of the reinforced multiple instance learning framework that combines reinforcement learning based instance selection with domain adversarial training to encourage language invariant utterance representations. We evaluate the approach on a five language Twitter corpus in a few shot setting and on a VoxCeleb2 derived corpus covering forty languages in a zero […]

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arXiv:2505.18269v4 Announce Type: replace-cross Abstract: We study the problem of selecting a subset from a large action space shared by a family of bandits, with the goal of achieving performance nearly matching that of using the full action space. Indeed, in many natural situations, while the nominal set of actions may be large, there also exist significant correlations between the rewards of different actions. In this paper we propose an algorithm that can significantly reduce the action space […]

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arXiv:2602.07143v1 Announce Type: new Abstract: Audio is a critical component of multimodal perception, and any truly intelligent system must demonstrate a wide range of auditory capabilities. These capabilities include transcription, classification, retrieval, reasoning, segmentation, clustering, reranking, and reconstruction. Fundamentally, each task involves transforming a raw audio signal into a meaningful ’embedding’ – be it a single vector, a sequence of continuous or discrete representations, or another structured form – which then serves as the basis for generating the […]

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arXiv:2602.15901v1 Announce Type: new Abstract: Autonomous sailboats are well suited for long-duration ocean observation due to their wind-driven endurance. However, their performance is highly anisotropic and strongly influenced by inhomogeneous and time-varying wind and current fields, limiting the effectiveness of existing coverage methods such as boustrophedon sweeping. Planning under these environmental and maneuvering constraints remains underexplored. This paper presents a spatiotemporal coverage path planning framework tailored to autonomous sailboats, combining (1) topology-based morphological constraints in the spatial domain […]

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