Accepted to SEA @ NeurIPS 2025
Authors: Myan Vu, Harrish Ayyanar, Pang Jiang, Anwiketh Reddy
Current automated agent design frameworks produce either static workflows that lack adaptability or per-query optimizers that prevent the accumulation of deep, agent-level task expertise. We propose creating stateful teams of specialist agents that accumulate knowledge over time and can be reconfigured for novel tasks entirely without human intervention. We introduce ASpec, a framework that autonomously discovers specialist archetypes via evolutionary search and then cultivates their expertise through experience. ASpec further introduces a lightweight hierarchical control policy called retain-then-escalate, which governs when to leverage the established agent system versus when to adapt its structure. Through comprehensive experiments, our approach demonstrates significant performance gains on expert-level scientific benchmarks like GPQA while matching state-of-the-art on broader domain tasks.
