technocracy

Quantum circuit Born machines based on instantaneous quantum polynomial-time (IQP) circuits are natural candidates for quantum generative modeling, both because of their probabilistic structure and because IQP sampling is provably classically hard in certain regimes. Recent proposals focus on training IQP-QCBMs using Maximum Mean Discrepancy (MMD) losses built from low-body Pauli-$Z$ correlators, but the effect of initialization on the resulting optimization landscape remains poorly understood. In this work, we address this by first proving that the MMD loss […]

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For minimally rigid graphs, the same edge-length data can admit multiple realizations (up to translations and rotations). Finding graphs with exceptionally many realizations is an extremal problem in rigidity theory, but exhaustive search quickly becomes infeasible due to the super-exponential growth of the number of candidate graphs and the high cost of realization-count evaluation. We propose a reinforcement-learning approach that constructs minimally rigid graphs via 0- and 1-extensions, also known as Henneberg moves. We optimize realization-count invariants using […]

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The rapid proliferation of the Internet of Things (IoT) has positioned the Message Queuing Telemetry Transport (MQTT) protocol as a fundamental communication standard for large-scale, resource-constrained systems. Despite its lightweight design and scalability advantages, modern MQTT deployments operate under increasingly complex conditions characterized by intensive security enforcement, dynamic traffic patterns, and widespread use of wildcard subscriptions. These factors introduce tightly coupled challenges related to system performance, congestion, and security, which are often addressed independently in existing literature. This […]

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New York, United States, April 21st, 2026/CyberNewswire/–BreachLock, a global leader in offensive security, today announced it has been named a representative vendor in the 2026 Gartner Market Guide for Adversarial Exposure Validation. This recognition marks the first time BreachLock has been identified in the Adversarial Exposure Validation (AEV) category since launching its agentic AI-powered Adversarial Exposure Validation platform in 2025. Not only has the company gained recognition in the AEV market quickly, but BreachLock has also emerged as the only vendor […]

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Edge computing requires safe, efficient, and generalizable online decision-making, yet existing methods suffer from reactive constraint handling, fragmented scheduling frameworks, and poor generalization. We propose PACE, a unified framework shifting from reactive remediation to proactive anticipation. PACE integrates a Proactive Constrained Policy Optimizer with preemptive penalty and constraint-aware intrinsic rewards, a Nested Index Scheduler with closed-form policies for preemptive and non-preemptive AoI minimization, and a Generalizable Multi-Objective Offloading Network with histogram encoding and masking for single-policy generalization. Experiments […]

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while I understand the difference between first visit Monte Carlo and every visit, are there any particular cases where we’d strongly prefer first visit and vice versa? like from my understanding, there are situations where first and every visit can be identical, but some scenarios where every visit is much better( eg. blackjack where there are barely any chances for states to repeat versus scenarios like automated car driving, where episodes are scarce so it becomes valuable to […]

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compression-aware intelligence suggests the real problem isn’t just hallucinations, it’s instability under variation. a model can give a correct answer once but if small changes in phrasing cause it to resolve that underlying tension differently, you get contradictions. that’s the signal that the compression hasn’t actually reconciled the underlying patterns. instead of “is this answer correct?” the new question becomes: “does this answer stay consistent when the same meaning is expressed differently?” using compression tension score (CTS) submitted […]

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arXiv:2601.19919v1 Announce Type: new Abstract: Knowledge distillation is one of the most effective methods for model compression. Previous studies have focused on the student model effectively training the predictive distribution of the teacher model. However, during training, the student model may inherit the shortcomings of the teacher model, which can lead to a decline in generalization capacity. To mitigate this issue, we propose adaptive self-knowledge distillation (ASKD), which dynamically reduces the dependence of the teacher model to improve […]

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