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 Multi-Objective Agent Evolution (MOAE), which organizes iterative in-context refinement as a Pareto-...
| Authors | Hengle Jiang, Qijun Cai, Ziying Luo et al. |
|---|---|
| Primary category | cs.AI |
| Published | 5 September 2026 |
| Last updated at source | 5 September 2026 |
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