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IBM announced a new modular cryogenic architecture to connect quantum processors, enabling scalability and future fault-tolerant quantum systems as part of their quantum roadmap.
This paper proposes CEAA, a modular cognitive architecture for embodied intelligent virtual agents that integrates high-level reasoning models with real-time embodied execution in interactive 3D environments.
Yohei Nakajima outlines a repo-centric modular architecture for agentic work, where the durable unit is a governed project repository rather than the model or conversation, with agents acting as replaceable execution components over persistent state.
Anthropic published a playbook proposing an AI Operating System architecture that coordinates memory, planning, tools, and evaluation, shifting from single-prompt chatbots to production-grade autonomous agents.
This paper introduces COOPA, a modular LLM agent architecture for operations research problems that combines iterative confidence-based modeling, element-level provenance, and multi-solver routing. Evaluated across eight LLM backbones and four baselines, COOPA achieves the best macro-average accuracy on six backbones and improves over the strongest baseline by up to 6.7 percentage points.
An essay arguing that the AI ecosystem is undergoing modularization similar to the PC revolution, with standardized interfaces like transformers, inference APIs, and agentic harnesses enabling specialization and rapid innovation, and that open-weights models are a direct economic consequence.
Decoupled Mixture-of-Experts (DMoE) proposes a modular architecture for parametric knowledge injection, decoupling experts and router from the base model to enable efficient auto-regressive inference and mitigate catastrophic forgetting.
This paper proposes PE-MHL, a Physics-Encoded Modular Hybrid Layer framework that incrementally refines a physics-based model with data-driven sub-models, providing theoretical convergence guarantees and outperforming monolithic networks on control benchmarks.
Proposes a modular reference architecture for embedded AI agent systems at the edge, decoupling on-device and cloud-augmented agents with a governance layer for safety and policy enforcement.
Introduces EARLY, an evolutionary framework for evolving multi-reservoir Echo State Networks that outperforms random search on temporal learning tasks and exhibits task-dependent structural differences.