A survey of Retrieval Augmented Generation (RAG) developments, including a four-axis taxonomy of efficiency, defense, interactivity, and reasoning. The survey examines contemporary RAG advancements, formalizing key components and reviewing methods for dense and sparse retrieval, fusion strategies, and evaluation practices.

Praxa is an evidence-bound harness for governed AI agent execution, providing explicit representations of proposal, authority, dispatch, external effect, and serving promotion through deterministic admission, brokered execution, read-back, reconciliation, and reviewed promotion. It includes four evidence lanes, but current evidence does not establish several key aspects, including adversarial security, production safety, and specialist superiority.

This paper introduces Commit-on-Evidence Memory (CoEM), a long-context reasoning method for large language models (LLMs) that preserves potentially useful input information and decides when to convert it into compact memory facts.

This paper presents an architectural mediation approach using the Model Context Protocol (MCP) to enable controlled interaction between large language model (LLM) agents and data space services, allowing for interoperable and standards-aligned integration of AI agents into data space ecosystems.