technocracy

The transition from smart to intelligent cities allows for the deployment and management of information and communication technologies in the urban context to be driven by holistic sustainability requirements rather than technical ones such as feasibility and fragmented, siloed operational patterns. This work proposes a multi-dimensional decision-making framework to manage a smart-intelligent city as an urban Cyber-Physical System across environmental, economic, and social sustainability pillars, metrics and their tradeoffs. A methodology based on Deep Reinforcement Learning and reward-shaping […]

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Crypto wallets often force users to choose between security, privacy, and usability. Since 2017, Mixin Messenger has been quietly building a different paradigm. With over $1 billion in assets under management and support for 2,900+ assets across 42 blockchains, the platform represents a contrarian bet: that users shouldn’t have to sacrifice self-custody for a Web2-like experience. Today, we sit down with Sonny Liu, CMO at Mixin, to understand how they’re rethinking the relationship between social communication, asset privacy, […]

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arXiv:2603.08716v1 Announce Type: new Abstract: Design Conductor (DC) is an autonomous agent which applies the capabilities of frontier models to build semiconductors end-to-end — that is, from concept to verified, tape-out ready GDSII (layout CAD file). In 12 hours and fully autonomously, DC was able to build several micro-architecture variations of a complete RISC-V CPU (which we dub VerCore) that meet timing at 1.48 GHz (rv32i-zmmul; using the ASAP7 PDK), starting from a 219-word requirements document. The VerCore […]

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arXiv:2601.05245v1 Announce Type: cross Abstract: We prove tight lower bounds for online multicalibration, establishing an information-theoretic separation from marginal calibration. In the general setting where group functions can depend on both context and the learner’s predictions, we prove an $Omega(T^{2/3})$ lower bound on expected multicalibration error using just three disjoint binary groups. This matches the upper bounds of Noarov et al. (2025) up to logarithmic factors and exceeds the $O(T^{2/3-varepsilon})$ upper bound for marginal calibration (Dagan et al., […]

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arXiv:2604.02378v1 Announce Type: new Abstract: Forecasting startup success is notoriously difficult, partly because meaningful outcomes, such as exits, large funding rounds, and sustained revenue growth, are rare and can take years to materialize. As a result, signals are sparse and evaluation cycles are slow. Y Combinator batches offer a unique mitigation: each batch comprises around 200 startups, funded simultaneously, with evaluation at Demo Day only three months later. We introduce YC Bench, a live benchmark for forecasting early […]

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arXiv:2603.10059v1 Announce Type: new Abstract: Flexible sensors are increasingly employed in soft robotics and wearable devices to provide proprioception of freeform deformations.Although supervised learning can train shape predictors from sensor signals, prediction accuracy strongly depends on sensor layout, which is typically determined heuristically or through trial-and-error. This work introduces a model-free, data-driven computational pipeline that jointly optimizes the number, length, and placement of flexible length-measurement sensors together with the parameters of a shape prediction network for large freeform […]

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Metal additive manufacturing (AM) enables the fabrication of safety-critical components, but reliable quality assurance depends on high-fidelity sensor streams containing proprietary process information, limiting collaborative data sharing. Existing defect-detection models typically treat melt-pool observations as independent samples, ignoring layer-wise physical couplings. Moreover, conventional privacy-preserving techniques, particularly Local Differential Privacy (LDP), lead to severe utility degradation because they inject uniform noise across all feature dimensions. To address these interrelated challenges, we propose FI-LDP-HGAT. This computational framework combines two methodological […]

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The attribution of systemic financial stress to specific market sectors requires metrics that are simultaneously faithful to the model’s internal computations, statistically consistent as the sample size grows, and connected to a physically meaningful measure of directed information flow. This paper addresses all three requirements through the lens of information geometry. We present and empirically verify the Entropy-Saliency Equivalence Theorem: the Metabolic Saliency Sms(i, t) introduced in the companion paper (Paper 1 of this series) is an asymptotically […]

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A novel regularization technique, AdaCubic, is proposed that adapts the weight of the cubic term. The heart of AdaCubic is an auxiliary optimization problem with cubic constraints that dynamically adjusts the weight of the cubic term in Newton’s cubic regularized method. We use Hutchinson’s method to approximate the Hessian matrix, thereby reducing computational cost. We demonstrate that AdaCubic inherits the cubically regularized Newton method’s local convergence guarantees. Our experiments in Computer Vision, Natural Language Processing, and Signal Processing […]

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