arXiv:2606.02372v1 Announce Type: cross Abstract: Equipping language agents with world models enables them to anticipate environment dynamics and evaluate candidate actions before execution. However, existing textual world models are typically fixed after training, preventing them from adapting to the on-policy state-action distributions induced by an evolving agent. Meanwhile, agent-improvement methods often rely on external rewards or verifiers, limiting their applicability in realistic interactive environments. In this paper, we propose COMAP, a novel framework that co-evolves textual world
The rapid advancement in Large Language Models (LLMs) has created a pressing need for agents that can autonomously adapt and learn from their environment rather than operating with fixed initial configurations.
This development is crucial for enabling AI systems to operate more effectively in complex, dynamic, and real-world environments, moving beyond static programming to continuous learning and self-improvement.
AI agents can now co-evolve their internal understanding of the world (world models) and their decision-making rules (policies), allowing for more robust and adaptive autonomous behavior without constant human intervention or external reward signals.
- · AI agents developers
- · Robotics
- · Generative AI
- · Autonomous systems
- · Fixed-policy AI systems
- · AI requiring extensive human oversight
AI agents will exhibit significantly improved performance and autonomy in interactive tasks.
The proliferation of more capable AI agents will accelerate automation across various industries.
This capability could lead to more sophisticated and potentially emergent AI behaviors, raising new challenges in control and ethics.
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