# MOAE: Multi-Objective Agent Evolution with Pareto-Preserving Search

arXiv: https://arxiv.org/abs/2609.05992 · HTML: https://mlsanity.com/paper/2609.05992/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 16 of its 25 references that are in the map.
- First posted on arXiv 5 Sep 2026.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 16 of 16)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://mlsanity.com/paper/2307.16789/) (2023)
- [Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution](https://mlsanity.com/paper/2309.16797/) (2023)
- [TravelPlanner: A Benchmark for Real-World Planning with Language Agents](https://mlsanity.com/paper/2402.01622/) (2024)
- [AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents](https://mlsanity.com/paper/2401.13178/) (2024)
- [Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs](https://mlsanity.com/paper/2406.11695/) (2024)
- [EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms](https://mlsanity.com/paper/2406.14228/) (2024)
- [Panacea: Pareto Alignment via Preference Adaptation for LLMs](https://mlsanity.com/paper/2402.02030/) (2024)
- [Bi-Factorial Preference Optimization: Balancing Safety-Helpfulness in Language Models](https://mlsanity.com/paper/2408.15313/) (2024)
- [SkillMOO: Multi-Objective Optimization of Agent Skills for Software Engineering](https://mlsanity.com/paper/2604.09297/) (2026)
- [AgentBreeder: Mitigating the AI Safety Risks of Multi-Agent Scaffolds via Self-Improvement](https://mlsanity.com/paper/2502.00757/) (2025)
- [Faster and Better LLMs via Latency-Aware Test-Time Scaling](https://mlsanity.com/paper/2505.19634/) (2025)
- [From Long to Lean: Performance-aware and Adaptive Chain-of-Thought Compression via Multi-round Refinement](https://mlsanity.com/paper/2509.22144/) (2025)
- [Towards Pareto-Optimal Tool-Integrated Agents with Pareto Ranking Policy Optimization](https://mlsanity.com/paper/2606.16111/) (2026)
- [MOCHA: Multi-Objective Chebyshev Annealing for Agent Skill Optimization](https://mlsanity.com/paper/2605.19330/) (2026)

## Abstract (arXiv)

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-preserving evolutionary search over complete agent rollouts. Given a limited rollout budget, MOAE maintains an empirical archive of non-dominated candidates, uses objective-specific diagnostics to guide offspring generation, and applies constraint-aware selection only at deployment. This separates candidate preservation during search from the preference used to return a final solution. The procedure requires no parameter updates and allows each objective to be replaced by any measurable property, which we instantiate as task performance, trajectory quality, and safety. Experiments on TravelPlanner and AgentDojo show that MOAE consistently improves task performance and trajectory quality while maintaining strong safety under matched rollout budgets. Search-behavior analysis further shows that Pareto preservation expands the attainable objective region and increases the frequency of joint improvement. These results demonstrate the potential of Pareto-preserving in-context evolution for optimizing multiple agent properties without committing to a fixed scalarization during search.