The paper critiques current AI agents, distinguishing superficial agentic systems from truly agentive ones, outlines five fundamental shortcomings, and proposes the Goal‑Identity‑Configurator (GIC) architecture—illustrated with the PocketOS incident—to achieve genuine autonomy, safety, and auditability.
The article dissects Hermes’s autonomous learning loop, detailing how immutable facts are stored in long‑term memory, reusable methods become skills, session history is searchable, and a background review process periodically consolidates knowledge while a pre‑compression rescue safeguards key information.
The article analyzes a groundbreaking AI research blueprint proposed by Yann LeCun, Emmanuel Dupoux, and Jitendra Malik, outlining three interacting systems—observation, action, and meta‑control—to enable machines to learn autonomously like infants, while highlighting technical and ethical challenges.
SEAgent introduces a self‑evolving framework that enables a GUI agent to master unfamiliar software through autonomous exploration and experience learning, leveraging a curriculum generator, a world‑state model, and GRPO‑based reinforcement with adversarial imitation, achieving state‑of‑the‑art performance on OSWorld.
