Polaris: A Godel Agent Framework for Small Language Models through Experience-Abstracted Policy Repair
arXiv:2603.23129v3 Announce Type: replace Abstract: G\"odel agent realize recursive self-improvement: an agent inspects its own policy and traces and then modifies that policy in a tested loop. We introduce Polaris, a G\"odel agent for compact models that performs policy repair via experience abstraction, turning failures into policy updates through a structured cycle of analysis, strategy formation, abstraction, and minimal code pat ch repair with conservative checks. Unlike response level self correction or parameter tuning, Polaris makes policy level changes with small, auditable patches th
The development of more sophisticated AI agent architectures is a natural progression as researchers push for greater autonomy and self-correction in AI models.
This development moves beyond simple response-level correction toward policy-level self-improvement, suggesting a path to more robust and adaptable AI agents.
AI agents can now engage in more fundamental self-repair and improvement of their underlying policies, rather than just their immediate outputs.
- · AI agent developers
- · Organizations deploying AI for complex tasks
- · Small Language Model (SLM) applications
- · Fixed-policy AI systems
- · Labor relying on repetitive, rule-based white-collar tasks
AI agents become more capable of autonomously learning from failures and adapting their operational logic.
This could lead to a significant reduction in human oversight required for maintaining and improving AI system performance in defined domains.
Increased reliability and autonomy could accelerate the adoption of AI agents across a wider range of critical applications, including those with higher stakes.
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