I’ve been working on detecting AI-generated music and ran into the same wall that Deezer’s team documented in their paper, CNN-based detection on mel-spectrograms breaks when audio is compressed to MP3. The problem: A ResNet18 trained on mel-spectrograms works well on WAV files, but real-world music is distributed as MP3/AAC. Compression destroys the subtle spectral artifacts the CNN relies on. What actually worked: Instead of trying to make the CNN more robust, I added a second engine based […]

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OpenClaw is an open-source AI agent with 320k+ stars on GitHub. If you haven’t seen it: it connects to your actual chat apps (Telegram, WhatsApp, Discord, Signal) and does real things on your behalf. Email, calendar, shell commands, browser automation, cron jobs, memory that persists between conversations. It runs around the clock. It’s not a chatbot you ask questions. It’s closer to a coworker who never logs off and never forgets what you told it last Tuesday. But […]

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:::info Astounding Stories of Super-Science October, 1994, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. The Picture of Dorian Gray – Chapter IV Astounding Stories of Super-Science October 1994: The Picture of Dorian Gray – Chapter IV By Oscar Wilde ::: One afternoon, a month later, Dorian Gray was reclining in a luxurious arm-chair, in the little library of Lord Henry’s house in Mayfair. It was, […]

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arXiv:2601.10887v1 Announce Type: new Abstract: We propose a structure-preserving scheme for a hybrid model that couples the time-dependent Ginzburg-Landau (TDGL) equation of superconducting vortex dynamics and the nonlinear Bardeen-Cooper-Schrieffer (BCS) gap equation. This formulation is consistent with the classical TDGL equation in the near-critical temperature, while extending the applicability of the existing TDGL model to regimes beyond the critical temperature. The resulting system poses significant computational challenges due to its nonlinear and coupled structure. To achieve stable and […]

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arXiv:2601.16440v1 Announce Type: new Abstract: Erratum to the paper (Zhang et al., 2025): corrections to Table IV and the data in Page 3, Section A. In the post-pandemic era, a high proportion of civil aviation passengers wear masks during security checks, posing significant challenges to traditional face recognition models. The backbone network serves as the core component of face recognition models. In standard tests, r100 series models excelled (98%+ accuracy at 0.01% FAR in face comparison, high top1/top5 […]

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Reinforcement learning (RL) post-training is a critical stage in modern language model development, playing a key role in improving alignment and reasoning ability. However, several phenomena remain poorly understood, including the reduction in output diversity. To gain a broader understanding of RL post-training, we analyze the learning dynamics of RL post-training from a perspective that has been studied in supervised learning but remains underexplored in RL. We adopt an empirical neural tangent kernel (NTK) framework and decompose the […]

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Preface: An Odd Sort of Loneliness There is a brand of loneliness that has no single word in everyday vocabulary. It’s not loneliness of isolation – of being entirely alone, of having no one to reach out to. It is a different kind of experience, and a more painful kind at that: it is loneliness of being among people who know you, or at least who profess to know you and who claim to love you – and […]

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arXiv:2603.25956v1 Announce Type: new Abstract: Time-series anomaly detection (TSAD) is a critical component in monitoring complex systems, yet modern deep learning-based detectors are often highly sensitive to localized input corruptions and structured noise. We propose ARTA (Adversarially Robust multivariate Time-series Anomaly detection via joint information retention), a joint training framework that improves detector robustness through a principled min-max optimization objective. ARTA comprises an anomaly detector and a sparsity-constrained mask generator that are trained simultaneously. The generator identifies minimal, […]

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arXiv:2603.09532v1 Announce Type: new Abstract: Bandits with noncompliance separate the learner’s recommendation from the treatment actually delivered, so the learning target itself must be chosen. A platform may care about recommendation welfare in the current mediated workflow, treatment learning for a future direct-control regime, or anytime-valid uncertainty for one of those targets. These objectives need not agree. We formalize this objective-choice problem, identify the direct-control regime in which recommendation and treatment objectives collapse, and show by example that […]

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arXiv:2601.22311v1 Announce Type: new Abstract: Large language model (LLM)-based agents exhibit strong step-by-step reasoning capabilities over short horizons, yet often fail to sustain coherent behavior over long planning horizons. We argue that this failure reflects a fundamental mismatch: step-wise reasoning induces a form of step-wise greedy policy that is adequate for short horizons but fails in long-horizon planning, where early actions must account for delayed consequences. From this planning-centric perspective, we study LLM-based agents in deterministic, fully structured […]

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