@inproceedings{ranaldi-2026-evolving,
    title = "Evolving Agents",
    author = "Ranaldi, Leonardo",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.754/",
    pages = "16561--16569",
    ISBN = "979-8-89176-390-6",
    abstract = "AI agents struggle to operate within open and dynamic environments because they lack a fundamental capacity: the autonomous generation of abstractions. Current models remain static entities, incapable of compressing the infinite complexity of the real world into generalisable concepts once their training phase has concluded.We introduce EVA (Evolving Agents), a novel paradigm for autonomous learning driven by pseudo-symbolic abstraction. EVA introduces a meta-control system that dynamically orchestrates observation and active interaction to distil on-the-fly abstract representations of states, actions, and goals. By disentangling contextual noise from pure logical reasoning, these pseudo-symbolic abstractions allow the agent to construct a highly robust internal curriculum.EVA leverages these self-generated abstractions to form an internal curriculum. This continuous compression of raw sensorimotor experience into reusable concepts allows the agent to independently guide its own exploration, planning, and error correction. Structured upon a bi-level evolutionary-developmental (Evo/Devo) framework, EVA demonstrates how the dynamic refinement of abstractions enables rapid adaptation to unforeseen scenarios. This approach resolves the domain mismatch problem and lays the groundwork for truly autonomous, continuously evolving AI models."
}