arXiv:2601.03509v2 Announce Type: replace Abstract: We study continual skill acquisition in open-ended embodied environments where an agent must construct, refine, and reuse an expanding library of executable skills. We introduce the Programmatic Skill Network (PSN), a framework in which skills are executable symbolic programs forming a compositional network that evolves through experience. PSN defines three core mechanisms instantiated via large language models: (1)~\opreflect for structured fault localization over skill compositions, (2)~progressive optimization with maturity-aware update ga
The accelerating capabilities of large language models are enabling new approaches to continual skill acquisition and autonomous agent development.
This research introduces a novel framework for AI agents to continually learn and refine skills, pushing towards more generalized and adaptive intelligence in complex environments.
AI systems could move beyond fixed repertoires to dynamically construct, refine, and reuse executable skills, leading to more robust and versatile autonomous agents.
- · AI research institutions
- · Robotics companies
- · Generative AI platforms
- · Software developers
- · Companies reliant on rigid, task-specific automation
- · Legacy AI solutions
AI agents will exhibit improved adaptability and efficiency in open-ended tasks.
The development of highly skilled, continually learning agents could accelerate automation across various industries.
This progression may lead to foundational shifts in human-computer interaction and the nature of work itself.
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Read at arXiv cs.AI