arXiv:2602.13298v1 Announce Type: new Abstract: Increasing convolutional depth has been central to advances in image recognition, yet deeper networks do not uniformly yield higher accuracy, stable optimization, or efficient computation. We present a controlled comparative study of three canonical convolutional neural network architectures – VGG, ResNet, and GoogLeNet – to isolate how depth influences classification performance, convergence behavior, and computational efficiency. By standardizing training protocols and explicitly distinguishing between nominal and effective depth, we show that the benefits […]

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Before you can govern agentic identities, you have to see them. All of them. The rise of Microsoft Copilot Studio and Power Virtual Agents has quietly created a new class of identity problem. Every agent an employee builds gets its own Entra ID object — sometimes two. At scale, a large enterprise can accumulate thousands of these registrations before anyone in the security or identity team has written a single governance policy for them. The instinct is to jump straight to policy: […]

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arXiv:2604.06327v1 Announce Type: new Abstract: Recent diffusion-based text-to-speech (TTS) models achieve high naturalness and expressiveness, yet often suffer from speaker drift, a subtle, gradual shift in perceived speaker identity within a single utterance. This underexplored phenomenon undermines the coherence of synthetic speech, especially in long-form or interactive settings. We introduce the first automatic framework for detecting speaker drift by formulating it as a binary classification task over utterance-level speaker consistency. Our method computes cosine similarity across overlapping segments […]

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arXiv:2512.07578v2 Announce Type: replace Abstract: We propose $phi$-test, a global feature-selection and significance procedure for black-box predictors that combines Shapley attributions with selective inference. Given a trained model and an evaluation dataset, $phi$-test performs SHAP-guided screening and fits a linear surrogate on the screened features via a selection rule with a tractable selective-inference form. For each retained feature, it outputs a Shapley-based global score, a surrogate coefficient, and post-selection $p$-values and confidence intervals in a global feature-importance table. […]

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This article explores a conservative looping strategy on Fluid using USDe/USDT on Arbitrum. With ~8.9% APY and a liquidation buffer of ~12%, the strategy prioritizes liquidity and risk-adjusted returns over maximum yield. Break-even is reached after ~6–8 weeks. Assets Use reliable stablecoins from trusted issuers, where the probability of a depeg is very low. The final choice is always yours. Personally, I’d start with: USDC USDT USDe Later, this list of reliable stablecoins can be expanded. Fluid strategy: […]

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Overview We have recently built and launched our multi-seller SaaS B2B platform with Stripe Connect. Our platform is intended for small businesses with mainly offline presence to easily start selling gift cards online. Stripe Connect Express makes it possible to build a multi-seller platform surprisingly quickly. You can onboard sellers, take application fees, and route payments directly to third-party accounts with relatively little code. Stripe can handle compliance, payouts, and much of the operational complexity. However, in practice, […]

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Emergent communication offers insight into how agents develop shared structured representations, yet most research assumes homogeneous modalities or aligned representational spaces, overlooking the perceptual heterogeneity of real-world settings. We study a heterogeneous multi-step binary communication game where agents differ in modality and lack perceptual grounding. Despite perceptual misalignment, multimodal systems converge to class-consistent messages grounded in perceptual input. Unimodal systems communicate more efficiently, using fewer bits and achieving lower classification entropy, while multimodal agents require greater information exchange […]

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arXiv:2602.22268v1 Announce Type: new Abstract: Quantization followed by parameter-efficient fine-tuning has emerged as a promising paradigm for downstream adaptation under tight GPU memory constraints. However, this sequential pipeline fails to leverage the intricate interaction between quantization bit-width and LoRA rank. Specifically, a carefully optimized quantization allocation with low quantization error does not always translate to strong fine-tuning performance, and different bit-width and rank configurations can lead to significantly varying outcomes under the same memory budget. To address this […]

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arXiv:2601.01223v1 Announce Type: cross Abstract: Clinical decision-making demands uncertainty quantification that provides both distribution-free coverage guarantees and risk-adaptive precision, requirements that existing methods fail to jointly satisfy. We present a hybrid Bayesian-conformal framework that addresses this fundamental limitation in healthcare predictions. Our approach integrates Bayesian hierarchical random forests with group-aware conformal calibration, using posterior uncertainties to weight conformity scores while maintaining rigorous coverage validity. Evaluated on 61,538 admissions across 3,793 U.S. hospitals and 4 regions, our method achieves […]

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