SOURCE-LINKED INTELLIGENCE
MOAE: Multi-Objective Agent Evolution with Pareto-Preserving Search
As LLM-based agents continue to advance, their evaluation has become increasingly multifaceted: a capable agent must not only achieve high task completion accuracy but also perform well in interaction quality, safety, and efficiency, raising a central question: can these objectives be optimized simultaneously? Existing methods have considered multiple objectives, but many collapse heterogeneous measurements into a fixed scalar score. Such scalarization depends on metric normalization and preference weights and may discard candidates that represent useful deployment trade-offs. We introduce Mul
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-05T09:31:34.000Z
First collected: 2026-09-20T21:32:07.623Z. This is not the publication date.