MOAE: Multi-Objective Agent Evolution with Pareto-Preserving Search
Quick summary
arXiv:2609.05992v1 Announce Type: new Abstract: 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 represen
Key takeaways
- arXiv:2609.05992v1 Announce Type: new Abstract: 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 represen
Why it matters
This development is a reminder to test misuse and data-leak scenarios alongside speed and quality. Trust should come from testable controls and clear failure reporting, not protection claims alone.
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