technocracy

arXiv:2604.16888v1 Announce Type: new Abstract: Parameter-free stochastic optimization aims to design algorithms that are agnostic to the underlying problem parameters while still achieving convergence rates competitive with optimally tuned methods. While some parameter-free methods do not require the specific values of the problem parameters, they still rely on prior knowledge, such as the lower or upper bounds of them. We refer to such methods as “partially parameter-free”. In this work, we target achieving “fully parameter-free” methods, i.e., the […]

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arXiv:2602.15875v1 Announce Type: new Abstract: Current Visual-Language Navigation (VLN) methodologies face a trade-off between semantic understanding and control precision. While Multimodal Large Language Models (MLLMs) offer superior reasoning, deploying them as low-level controllers leads to high latency, trajectory oscillations, and poor generalization due to weak geometric grounding. To address these limitations, we propose Fly0, a framework that decouples semantic reasoning from geometric planning. The proposed method operates through a three-stage pipeline: (1) an MLLM-driven module for grounding natural […]

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John M. Smart suggests the universe may behave like a growing organism. It mixes random creativity (evolution) with guided development, gradually producing more complex intelligence—eventually technology and perhaps cosmic-scale minds. In this view, instead of being mere accidents, civilizations may actually be tiny steps as the universe learns how to think.

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arXiv:2602.15870v1 Announce Type: new Abstract: Autoregressive language models decode left-to-right with irreversible commitments, limiting revision during multi-step reasoning. We propose textbf{VDLM}, a modular variable diffusion language model that separates semantic planning from text rendering. VDLM applies LLaDA-style masked diffusion over semantic variable embeddings to enable iterative refinement in latent space, then post-trains the planner with trajectory-aware optimization using embedding-space rewards and values, avoiding text decoding inside the RL loop. To convert planned embeddings back to text, we use […]

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Radial basis function in machine learning serves as a powerful tool for handling non-linear data patterns through neural networks that use distance-based activations. This approach helps models approximate complex functions and make accurate predictions in tasks like classification and regression. This blog explains what radial basis function means, its formula, network structure, training steps, a solved example, and applications of radial basis function network. You will gain a clear understanding of how these networks work, their strengths, and […]

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As artificial intelligence accelerates the creation of content, the internet has entered an era defined less by scarcity and more by saturation. Millions of new posts, research papers, and media artifacts appear daily, making discovery, not creation, the primary challenge. For Lior Alexander, founder of AlphaSignal, this shift marked the beginning of his work. Rather than asking how to produce more content, Alexander focused on a more fundamental question: how do people find what actually matters when everything […]

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arXiv:2601.12254v1 Announce Type: new Abstract: Generative speech enhancement (GSE) models show great promise in producing high-quality clean speech from noisy inputs, enabling applications such as curating noisy text-to-speech (TTS) datasets into high-quality ones. However, GSE models are prone to hallucination errors, such as phoneme omissions and speaker inconsistency, which conventional error filtering based on non-intrusive speech quality metrics often fails to detect. To address this issue, we propose a non-intrusive method for filtering hallucination errors from discrete token-based […]

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arXiv:2603.02248v1 Announce Type: new Abstract: Table-text retrieval aims to retrieve relevant tables and text to support open-domain question answering. Existing studies use either early or late fusion, but face limitations. Early fusion pre-aligns a table row with its associated passages, forming “stars,” which often include irrelevant contexts and miss query-dependent relationships. Late fusion retrieves individual nodes, dynamically aligning them, but it risks missing relevant contexts. Both approaches also struggle with advanced reasoning tasks, such as column-wise aggregation and […]

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