Why I Spent Years Building a 35,000 Prediction/s Forecasting Engine…

Why I Spent Years Building a 35,000 Prediction/s Forecasting Engine…

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I have spent most of my career working in high-frequency data environments where data moves continuously and at a large scale. In telecom networks, every cell, every subscriber interaction, every network function generates a time series, each one carrying relevant information about performance, behaviour, and risk. Yet for years, one assumption remained largely unchallenged: We cannot analyse everything. Not because it wasn’t valuable, but because it wasn’t practical. It has traditionally been too expensive, too computationally heavy, and too […]

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A Federated Many-to-One Hopfield model for associative Neural Networks

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arXiv:2603.19902v1 Announce Type: cross Abstract: Federated learning enables collaborative training without sharing raw data, but struggles under client heterogeneity and streaming distribution shifts, where drift and novel data can impair convergence and cause forgetting. We propose a federated associative-memory framework that learns shared archetypes in heterogeneous, continual settings, where client data are independent but not necessarily balanced. Each client encodes its experience as a low-rank Hebbian operator, sent to a central server for aggregation and factorization into global […]

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An effective Genetic Programming Hyper-Heuristic for Uncertain Agile Satellite Scheduling

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arXiv:2602.15070v1 Announce Type: new Abstract: This paper investigates a novel problem, namely the Uncertain Agile Earth Observation Satellite Scheduling Problem (UAEOSSP). Unlike the static AEOSSP, it takes into account a range of uncertain factors (e.g., task profit, resource consumption, and task visibility) in order to reflect the reality that the actual information is inherently unknown beforehand. An effective Genetic Programming Hyper-Heuristic (GPHH) is designed to automate the generation of scheduling policies. The evolved scheduling policies can be utilized […]

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MOSLD-Bench: Multilingual Open-Set Learning and Discovery Benchmark for Text Categorization

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Open-set learning and discovery (OSLD) is a challenging machine learning task in which samples from new (unknown) classes can appear at test time. It can be seen as a generalization of zero-shot learning, where the new classes are not known a priori, hence involving the active discovery of new classes. While zero-shot learning has been extensively studied in text classification, especially with the emergence of pre-trained language models, open-set learning and discovery is a comparatively new setup for […]

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Joint Interference Detection and Identification via Adversarial Multi-task Learning

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Precise interference detection and identification are crucial for enhancing the survivability of communication systems in non-cooperative wireless environments. While deep learning (DL) has advanced this field, existing single-task learning (STL) approaches neglect inherent task correlations. Furthermore, emerging multi-task learning (MTL) methods often lack a theoretical foundation for quantifying and modeling task relationships. To bridge this gap, we establish a theoretically grounded MTL framework for joint interference detection, modulation identification, and interference identification. First, we derive an upper bound […]

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Automatic Constraint Policy Optimization based on Continuous Constraint Interpolation Framework for Offline Reinforcement Learning

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Offline Reinforcement Learning (RL) relies on policy constraints to mitigate extrapolation error, where both the constraint form and constraint strength critically shape performance. However, most existing methods commit to a single constraint family: weighted behavior cloning, density regularization, or support constraints, without a unified principle that explains their connections or trade-offs. In this work, we propose Continuous Constraint Interpolation (CCI), a unified optimization framework in which these three constraint families arise as special cases along a common constraint […]

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NASA’s science budget won’t be a train wreck after all

NASA’s science budget won’t be a train wreck after all

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In June, the White House released a budget proposal for fiscal year 2026 that slashed funding for NASA’s science programs by nearly 50 percent. Then, in July, the Trump administration began telling the leaders of dozens of space science missions to prepare “closeout” plans for their spacecraft. Things looked pretty grim for a while, but then Congress stepped in. Congress, of course, sets the federal government’s budget. In many ways, Congress abdicated authority to the Trump administration last […]

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Runtime Reinforcement: Preventing “Instruction Decay” in Long Context Windows

Runtime Reinforcement: Preventing “Instruction Decay” in Long Context Windows

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Moving critical business logic from the System Prompt to a per-turn Injection Layer. Image Source: Google Gemini The “Floating Brain” Problem In our previous articles, we discussed how to give the agent knowledge (Graph), sight (Shape), and empathy (User Context). But even a perfect agent suffers from a fundamental flaw inherent to the Large Language Model architecture: it exists in a timeless void. An LLM has no internal clock. It does not know that “today” is Tuesday. Furthermore, as a conversation […]

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The life of a prescription at Amazon Pharmacy

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The life of a prescription at Amazon Pharmacy From pricing estimation and regulatory compliance to inventory management and chatbot assistants, machine learning models help Amazon Pharmacy customers stay healthy and save time and money. Conversational AI Alexandre Alves Anita Vila September 30, 01:32 PM October 02, 11:42 AM Pharmacies play a vital role in ensuring patients health, but the process of dispensing medications is far more complex than it may appear. At Amazon Pharmacy, we are using artificial […]

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Highly Efficient and Effective LLMs with Multi-Boolean Architectures

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arXiv:2505.22811v5 Announce Type: replace Abstract: Weight binarization has emerged as a promising strategy to reduce the complexity of large language models (LLMs). Existing approaches fall into post-training binarization, which is simple but causes severe performance loss, and training-aware methods, which depend on full-precision latent weights, adding complexity and limiting efficiency. We propose a novel framework that represents LLMs with multi-kernel Boolean parameters and, for the first time, enables direct finetuning LMMs in the Boolean domain, eliminating the need […]

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