# From Experience to Skill: Multi-Agent Generative Engine Optimization via Reusable Strategy Learning

arXiv: https://arxiv.org/abs/2604.19516 · HTML: https://mlsanity.com/paper/2604.19516/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 33 references that are in the map.
- First posted on arXiv 21 Apr 2026.

## Builds on (most cited first, 18 of 18)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://mlsanity.com/paper/2310.11511/) (2023)
- [Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate](https://mlsanity.com/paper/2305.19118/) (2023)
- [REPLUG: Retrieval-Augmented Black-Box Language Models](https://mlsanity.com/paper/2301.12652/) (2023)
- [RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs](https://mlsanity.com/paper/2407.02485/) (2024)
- [Limitations of the LLM-as-a-Judge Approach for Evaluating LLM Outputs in Expert Knowledge Tasks](https://mlsanity.com/paper/2410.20266/) (2024)
- [SE-Agent: Self-Evolution Trajectory Optimization in Multi-Step Reasoning with LLM-Based Agents](https://mlsanity.com/paper/2508.02085/) (2025)
- [Visual Document Understanding and Reasoning: A Multi-Agent Collaboration Framework with Agent-Wise Adaptive Test-Time Scaling](https://mlsanity.com/paper/2508.03404/) (2025)
- [AutoGeo: Automating Geometric Image Dataset Creation for Enhanced Geometry Understanding](https://mlsanity.com/paper/2409.09039/) (2024)
- [RealMem: Benchmarking LLMs in Real-World Memory-Driven Interaction](https://mlsanity.com/paper/2601.06966/) (2026)
- [MetaReflection: Learning Instructions for Language Agents using Past Reflections](https://mlsanity.com/paper/2405.13009/) (2024)
- [Concise Reasoning, Big Gains: Pruning Long Reasoning Trace with Difficulty-Aware Prompting](https://mlsanity.com/paper/2505.19716/) (2025)
- [R3Mem: Bridging Memory Retention and Retrieval via Reversible Compression](https://mlsanity.com/paper/2502.15957/) (2025)
- [From What to Why: A Multi-Agent System for Evidence-based Chemical Reaction Condition Reasoning](https://mlsanity.com/paper/2509.23768/) (2025)
- [LungNoduleAgent: A Collaborative Multi-Agent System for Precision Diagnosis of Lung Nodules](https://mlsanity.com/paper/2511.21042/) (2025)
- [CC-GSEO-Bench: A Content-Centric Benchmark for Measuring Source Influence in Generative Search Engines](https://mlsanity.com/paper/2509.05607/) (2025)
- [DeepThink: Aligning Language Models with Domain-Specific User Intents](https://mlsanity.com/paper/2502.05497/) (2025)
- [Zero-Forgetting CISS via Dual-Phase Cognitive Cascades](https://mlsanity.com/paper/2603.13874/) (2026)

## Built on by (most cited first, 4 of 4)

- [When Optimization Becomes Manipulation: Defending Generative Search against Malicious Generative Engine Optimization](https://mlsanity.com/paper/2609.02964/) (2026)
- [Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes](https://mlsanity.com/paper/2608.11390/) (2026)
- [Scoring Without the Engine: Validating a Deterministic, Manipulation-Resistant Content Score for Generative Engines, End to End](https://mlsanity.com/paper/2609.07559/) (2026)
- [Overtaught, Underlearned: Learner-Centered Distillation from Multi-Agent Discussion](https://mlsanity.com/paper/2610.16543/) (2026)

## Abstract (arXiv)

Generative engines (GEs) are reshaping information access by replacing ranked links with citation-grounded answers, yet current Generative Engine Optimization (GEO) methods optimize each instance in isolation, unable to accumulate or transfer effective strategies across tasks and engines. We reframe GEO as a strategy learning problem and propose MAGEO, a multi-agent framework in which coordinated planning, editing, and fidelity-aware evaluation serve as the execution layer, while validated editing patterns are progressively distilled into reusable, engine-specific optimization skills. To enable controlled assessment, we introduce a Twin Branch Evaluation Protocol for causal attribution of content edits and DSV-CF, a dual-axis metric that unifies semantic visibility with attribution accuracy. We further release MSME-GEO-Bench, a multi-scenario, multi-engine benchmark grounded in real-world queries. Experiments on three mainstream engines show that MAGEO substantially outperforms heuristic baselines in both visibility and citation fidelity, with ablations confirming that engine-specific preference modeling and strategy reuse are central to these gains, suggesting a scalable learning-driven paradigm for trustworthy GEO. Code is available at https://github.com/Wu-beining/MAGEO