AI made code generation cheap. But it also made silent breakage cheap. Large diffs. Subtle logic removals. Relaxed validations. Expensive cloud calls. Secrets slipping into logs. Most of this doesn’t get caught while generating code; it gets discovered in PR review, or worse, in production. That’s exactly where git-lrc changes the game. What developers actually gain Review at the right moment. Every staged diff is reviewed before it gets committed. No extra dashboard. No context switching. Faster understanding of AI-generated diffs. […]

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The electric vehicle routing problem with time windows (EVRPTW) is a complex optimization problem in sustainable logistics, where routing decisions must minimize total travel distance, fleet size, and battery usage while satisfying strict customer time constraints. Although deep reinforcement learning (DRL) has shown great potential as an alternative to classical heuristics and exact solvers, existing DRL models often struggle to maintain training stability-failing to converge or generalize when constraints are dense. In this study, we propose a curriculum-based […]

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Private Companies Are Becoming Strategic Targets in Global Cyber Operations In the shadowed corridors of cyberspace, where digital frontiers stretch beyond the horizon of imagination, a profound transformation has taken root. For much of the past two decades, nation-state cyber operations resembled carefully orchestrated expeditions into the fortress-like domains of government agencies, diplomatic networks, and military systems. Today, that focus has shifted like tectonic plates beneath the digital landscape. Increasingly, private organizations find themselves drawn into the swirling […]

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Credit risk models are a critical decision-support tool for financial institutions, yet tightening data-protection rules (e.g., GDPR, CCPA) increasingly prohibit cross-border sharing of borrower data, even as these models benefit from cross-institution learning. Traditional default prediction suffers from two limitations: binary classification ignores default timing, treating early defaulters (high loss) equivalently to late defaulters (low loss), and centralized training violates emerging regulatory constraints. We propose a Federated Survival Learning framework with Bayesian Differential Privacy (FSL-BDP) that models time-to-default […]

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Demonstration selection is a practical bottleneck in in-context learning (ICL): under a tight prompt budget, accuracy can change substantially depending on which few-shot examples are included, yet selection must remain cheap enough to run per query over large candidate pools. We propose Meta-Sel, a lightweight supervised meta-learning approach for intent classification that learns a fast, interpretable scoring function for (candidate, query) pairs from labeled training data. Meta-Sel constructs a meta-dataset by sampling pairs from the training split and […]

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arXiv:2508.10630v2 Announce Type: replace-cross Abstract: A novel approximate Bayesian filter based on backward stochastic differential equations is introduced. It uses a nonlinear Feynman–Kac representation of the filtering problem and the approximation of an unnormalized filtering density using the well-known deep BSDE method and neural networks. The method is trained offline, which means that it can be applied online with new observations. A hybrid a priori-a posteriori error bound is proved under a parabolic H”ormander condition. The theoretical convergence […]

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Many large language model applications require conditioning on long contexts. Transformers typically support this by storing a large per-layer KV-cache of past activations, which incurs substantial memory overhead. A desirable alternative is ompressive memory: read a context once, store it in a compact state, and answer many queries from that state. We study this in a context removal setting, where the model must generate an answer without access to the original context at inference time. We introduce GradMem, […]

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arXiv:2604.01313v1 Announce Type: new Abstract: High-fidelity Monte Carlo simulations and complex inverse problems, such as mapping smeared experimental observations to ground-truth states, are computationally intensive yet essential for robust data analysis. Conditional Flow Matching (CFM) offers a mathematically robust approach to accelerating these tasks, but we demonstrate its standard training loss is fundamentally misleading. In rigorous physics applications, CFM loss plateaus prematurely, serving as an unreliable indicator of true convergence and physical fidelity. To investigate this disconnect, we […]

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