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This paper introduces scaling participation, a new paradigm for building modular AI systems through contributions from diverse stakeholders, where small models collaborate to outperform monolithic LLMs by up to 15.4% across various tasks, demonstrating emergent capabilities and improved diversity benefits.
Researchers from Amazon AGI introduce Cartridges at Scale (CAS), a training framework that distills document collections into modular, reusable KV caches, enabling scalable multi-cartridge learning over collections exceeding one million tokens. CAS improves over monolithic cartridge baselines by 10–31 points and matches or exceeds conventional RAG accuracy while consuming 3–4× fewer prompt tokens.
Proposes PDRNN, a modular hybrid AI-assisted pedestrian dead reckoning system that combines a recurrent neural network with separate ML models for orientation, velocity, and distance estimation, with optional radio-based stabilization. Experiments on dynamic sports movement data show superior accuracy and precision compared to classic and ML-based methods.