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Every day, media use is growing at an unimaginable rate and generating huge amounts of data. Facebook generates nearly 5 petabytes of data per day; Netflix, around 2 petabytes per week; and Google, about 20 petabytes every day. Business decisions depend on analytics, and analytics requires processed data every single day. So how do we process all this data? Traditional ETL tools can’t keep up when data volumes reach terabytes—and they’re too slow. That’s why the data world […]

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:::info Astounding Stories of Super-Science October 2022, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. THE MURDER OF ROGER ACKROYD – MRS. ACKROYD Astounding Stories of Super-Science October 2022: THE MURDER OF ROGER ACKROYD – MRS. ACKROYD By Agatha Christie ::: After the evening talk I have just chronicled, the affair seemed to me to enter on a different phase. The whole thing can be divided into […]

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Direct preference optimization (DPO) is an effective technique to train language models to generate preferred over dispreferred responses. However, this binary "winner-takes-all" approach is suboptimal for vision-language models whose response quality is highly dependent on visual content. In particular, a response may still be faithful to the visual inputs even if it is less preferable than an alternative. The standard Bradley-Terry DPO formulation lacks this nuance, upweighting winning responses without sufficient regard for whether the "losing" response still […]

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As AI systems become agentic and interact directly with enterprise tools through Model Context Protocol (MCP), gateway-based security models may no longer be sufficient. Policy enforcement must move closer to where capability execution occurs. I was tempted to start by saying that AI operations and AI interactions must be secure, even from local threats. But that is obvious. Instead, it is worth looking at a familiar story from the history of enterprise security. The Rise and Fall of […]

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In reinforcement learning, an agent interacts sequentially with an environment to maximize a reward, receiving only partial, probabilistic feedback. This creates a fundamental exploration-exploitation trade-off: the agent must explore to learn the hidden dynamics while exploiting this knowledge to maximize its target objective. While extensively studied classically, applying this framework to quantum systems requires dealing with hidden quantum states that evolve via unknown dynamics. We formalize this problem via a framework where the environment maintains a hidden quantum […]

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I didn’t add per‑category OOD thresholds because it was academically elegant. I added them because my baseline runs were telling me the same story over and over: some prompt categories were systematically getting mis-gated by a single global uncertainty threshold. When that happens, you don’t just waste compute—you route the wrong jobs into the wrong generation strategy, and your downstream scoring starts making “confident” decisions on top of the wrong substrate. Phase 2 calibration in this codebase is […]

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Modern multimodal systems deployed in industrial and safety-critical environments must remain reliable under partial sensor failures, signal degradation, or cross-modal inconsistencies. This work introduces a mathematically grounded framework for fault-tolerant multimodal representation learning that unifies self-supervised anomaly detection and error correction within a single architecture. Building upon a theoretical analysis of perturbation propagation, we derive Lipschitz- and Jacobian-based criteria that determine whether a neural operator amplifies or attenuates localized faults. Guided by this theory, we propose a two-stage […]

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Agriculture is increasingly challenged by climate change, soil degradation, and resource depletion, and hence requires advanced data-driven crop classification and recommendation solutions. This work presents an explainable ensemble learning paradigm that fuses optimized feature pyramids, deep networks, self-attention mechanisms, and residual networks for bolstering crop suitability predictions based on soil characteristics (e.g., pH, nitrogen, potassium) and climatic conditions (e.g., temperature, rainfall). With a dataset comprising 3,867 instances and 29 features from the Ethiopian Agricultural Transformation Agency and NASA, […]

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Linearized string representations serve as the foundation of scalable autoregressive molecular generation; however, they introduce a fundamental modality mismatch where a single molecular graph maps to multiple distinct sequences. This ambiguity leads to textit{trajectory divergence}, where the latent representations of structurally equivalent partial graphs drift apart due to differences in linearization history. To resolve this without abandoning the efficient string formulation, we propose Structure-Invariant Generative Molecular Alignment (SIGMA). Rather than altering the linear representation, SIGMA enables the model […]

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arXiv:2603.19808v2 Announce Type: replace-cross Abstract: Population-based learning paradigms, including evolutionary strategies, Population-Based Training (PBT), and recent model-merging methods, combine fast within-model optimisation with slower population-level adaptation. Despite their empirical success, a general mathematical description of the resulting collective training dynamics remains incomplete. We introduce a theoretical framework for neural network training based on two-time-scale population dynamics. We model a population of neural networks as an interacting agent system in which network parameters evolve through fast noisy gradient updates […]

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