Quantum generative modeling, where the Born rule naturally defines probability distributions through measurement of parameterized quantum states, is a promising near-term application of quantum computing. We propose a Quantum Scrambling Born Machine in which a fixed entangling unitary — acting as a scrambling reservoir — provides multi-qubit entanglement, while only single-qubit rotations are optimized. We consider three entangling unitaries — a Haar random unitary and two physically realizable approximations, a finite-depth brickwork random circuit and analog time evolution […]

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arXiv:2512.06297v2 Announce Type: replace-cross Abstract: Modern neural networks exhibit a striking property: basins of attraction in the loss landscape are often connected by low-loss paths, yet optimization dynamics generally remain confined to a single convex basin and rarely explore intermediate points. We resolve this paradox by identifying entropic barriers arising from the interplay between curvature variations along these paths and noise in optimization dynamics. Empirically, we find that curvature systematically rises away from minima, producing effective forces that […]

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arXiv:2603.03316v1 Announce Type: new Abstract: Most sign language recognition research relies on Transfer Learning (TL) from vision-based datasets such as ImageNet. Some extend this to alternatively available language datasets, often focusing on signs with cross-linguistic similarities. This body of work examines the necessity of these likenesses on effective knowledge transfer by comparing TL performance between iconic signs of two different sign language pairs: Chinese to Arabic and Greek to Flemish. Google Mediapipe was utilised as an input feature […]

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Real-world data streams, such as news articles and social media posts, are dynamic and nonstationary, creating challenges for real-time structured representation via knowledge graphs, where relation extraction is a key component. Continual relation extraction (CRE) addresses this setting by incrementally learning new relations while preserving previously acquired knowledge. This work investigates the use of pretrained language models for CRE, focusing on large language models (LLMs) and the effectiveness of memory replay in mitigating forgetting. We evaluated decoder-only models […]

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How execution stays correct long after interpretation expires When a service system fails, leaders look for execution gaps. But most modern service failures don’t come from bad execution. n They come from expired interpretation. The system keeps answering the same question. n The environment quietly changes the question. Everything still runs. n Scripts are followed. n Metrics stay calm. But the answers no longer fit the moment. And that’s how service systems drift. n Not by breaking, but […]

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arXiv:2601.22302v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training while preserving data privacy, yet both centralized and decentralized approaches face challenges in scalability, security, and update validation. We propose ZK-HybridFL, a secure decentralized FL framework that integrates a directed acyclic graph (DAG) ledger with dedicated sidechains and zero-knowledge proofs (ZKPs) for privacy-preserving model validation. The framework uses event-driven smart contracts and an oracle-assisted sidechain to verify local model updates without exposing sensitive data. A built-in […]

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Why does RLVR (RL with verifiable rewards) seem to work well for LLMs? My intuition was that sparse rewards are usually bad because exploration is hard and gradients get noisy. But RLVR papers/blogs make it look pretty effective in practice submitted by /u/Parking_Throat_9125 [link] [comments]

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In this paper we introduce the CDF manifold algorithm that operates on data sets where a single target dimension is strictly increasing given a minimum of two or more number of input dimension which is very common in telco data. The manifold can then be used to compute the closest upper and lower limit to a given new point as well as its CDF. Training takes O(n.ln[n]) steps in the best case and O(n3/2) in the worst case. […]

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arXiv:2602.11711v1 Announce Type: new Abstract: This article addresses the issue of estimating observation parameters (response and error parameters) in inverse problems. The focus is on cases where regularization is introduced in a Bayesian framework and the prior is modeled by a diffusion process. In this context, the issue of posterior sampling is well known to be thorny, and a recent paper proposes a notably simple and effective solution. Consequently, it offers an remarkable additional flexibility when it comes […]

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