arXiv:2601.20498v1 Announce Type: cross Abstract: Diffusion models provide a principled framework for generative modeling via stochastic differential equations and time-reversed dynamics. Extending spectral diffusion approaches to spherical data, however, raises nontrivial geometric and stochastic issues that are absent in the Euclidean setting. In this work, we develop a diffusion modeling framework defined directly on finite-dimensional spherical harmonic representations of real-valued functions on the sphere. We show that the spherical discrete Fourier transform maps spatial Brownian motion to a […]

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arXiv:2603.28995v1 Announce Type: new Abstract: Hybrid quantum-classical machine learning offers a promising direction for advancing automated quality control in industrial settings. In this study, we investigate two hybrid quantum-classical approaches for classifying defects in aluminium TIG welding images and benchmarking their performance against a conventional deep learning model. A convolutional neural network is used to extract compact and informative feature vectors from weld images, effectively reducing the higher-dimensional pixel space to a lower-dimensional feature space. Our first quantum […]

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arXiv:2601.20037v1 Announce Type: new Abstract: Despite the ubiquity of tabular data in high-stakes domains, traditional deep learning architectures often struggle to match the performance of gradient-boosted decision trees while maintaining scientific interpretability. Standard neural networks typically treat features as independent entities, failing to exploit the inherent manifold structural dependencies that define tabular distributions. We propose Structural Compositional Function Networks (StructuralCFN), a novel architecture that imposes a Relation-Aware Inductive Bias via a differentiable structural prior. StructuralCFN explicitly models each […]

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For two decades, product design has revolved around a stable premise: users initiate, software responds. Even as AI crept into products such as recommendation engines, predictive text, and fraud detection, the interface still framed intelligence as reactive. AI agents break that contract. An agent does not wait for instruction. It monitors context, forms intentions, takes actions, and adapts its strategy over time. It delegates to APIs, coordinates across systems, and sometimes executes decisions without asking first. In other […]

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arXiv:2604.17010v1 Announce Type: new Abstract: We introduce a self-play framework for semantic equivalence in Haskell, utilizing formal verification to guide adversarial training between a generator and an evaluator. The framework leverages Liquid Haskell proofs for validating equivalence and execution-based counterexamples for inequivalence, organized via a difficulty-aware curriculum. To facilitate this, we release textbf{OpInstruct-HSx}, a synthetic dataset of $approx$28k validated Haskell programs. Empirical experiments show that our evaluator transfers effectively to downstream tasks, achieving up to 13.3pp accuracy gain […]

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arXiv:2506.16289v3 Announce Type: replace Abstract: This paper explores the relationship between the condition number of a neural network’s weight tensor and the extent of information encoded by the associated processing unit, viewed through the lens of information theory. It argues that a high condition number, though not sufficient for effective knowledge encoding, may indicate that the unit has learned to selectively amplify and compress information. This intuition is formalized for linear units with Gaussian inputs, linking the condition […]

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In the first part of this series, here, I feature a click fraud case that we are working on, litigated by one of the largest law firms in the US.  The input data comes from an Excel repository, automatically processed by an AI agent part of our BondingAI enterprise solutions. It comes with insights generation via automated SQL queries and pattern detection. In other contexts, the data may come from a PDF repository, the Internet, databases, or a […]

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How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## Enabling BDX On-Ramp and Off-Ramp to Onboard the Next Billion Crypto Users By @beldexcoin [ 3 Min read ] Beldex users can now buy BDX with fiat using Alchemy Pay’s seamless on-ramp payment methods. Read More. How OP_NET Enables Smart Contracts on Bitcoin Without […]

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arXiv:2603.20939v1 Announce Type: cross Abstract: Large language models are increasingly used as personal assistants, yet most lack a persistent user model, forcing users to repeatedly restate preferences across sessions. We propose Vector-Adapted Retrieval Scoring (VARS), a pipeline-agnostic, frozen-backbone framework that represents each user with long-term and short-term vectors in a shared preference space and uses these vectors to bias retrieval scoring over structured preference memory. The vectors are updated online from weak scalar rewards from users’ feedback, enabling […]

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