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

• URL: http://arxiv.org/abs/2604.19516v1
• Date: Tue, 21 Apr 2026 14:39:24 GMT
• Title: From Experience to Skill: Multi-Agent Generative Engine Optimization via Reusable Strategy Learning
• Abstract summary: Generative engines (GEs) are reshaping information access by replacing ranked links with citation-grounded answers.<n>We propose MAGEO, a multi-agent framework in which coordinated planning, editing, and fidelity-aware evaluation serve as the execution layer.<n>Experiments on three mainstream engines show MAGEO substantially outperforms baselines in both visibility and citation fidelity.
• Score: 18.351281487408176
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

Related papers

• Agent2UCB: Agentic System for Generative Engine Optimization [39.812101274332385]
  Agent2UCB autonomously improves content visibility through customized, feedback-driven optimization.<n>For each content item, the system evaluates nine GEO strategies, identifies the most effective method, and accelerates selection.<n>The system also provides a lightweight, text-only SEO readiness evaluation covering readability, topical coverage, and EEAT-style credibility.
  arXiv  Detail & Related papers  (2026-08-29T05:36:06Z)
• AgenticGEO: A Self-Evolving Agentic System for Generative Engine Optimization [32.13915434333156]
  AgenticGEO is a self-evolving agentic framework formulating optimization as a content-conditioned control problem.<n>AgenticGEO achieves state-of-the-art performance and demonstrates robust transferability, outperforming 14 baselines across 3 datasets.
  arXiv  Detail & Related papers  (2026-03-02T16:13:13Z)
• CARD: Towards Conditional Design of Multi-agent Topological Structures [83.18278008173746]
  CARD (Conditional Agentic Graph Designer) is a conditional graph-generation framework that instantiates AMACP, a protocol for adaptive multi-agent communication.<n> CARD produces communication structures that are both effective and resilient to shifts in model capability or resource availability.
  arXiv  Detail & Related papers  (2026-03-01T13:02:36Z)
• GEO-Bench-2: From Performance to Capability, Rethinking Evaluation in Geospatial AI [52.13138825802668]
  GeoFMs are transforming Earth Observation, but evaluation lacks standardized protocols.<n> GEO-Bench-2 addresses this with a comprehensive framework spanning classification, segmentation, regression, object detection, and instance segmentation.<n>Code, data, and leaderboard for GEO-Bench-2 are publicly released under a permissive license.
  arXiv  Detail & Related papers  (2025-11-19T17:45:02Z)
• What Generative Search Engines Like and How to Optimize Web Content Cooperatively [29.736392688493808]
  Generative Engines, such as Google AI overview and ChatGPT, provide significantly enhanced user experiences.<n>Their rapid adoption also drives the needs of Generative Engine Optimization (GEO), as content providers are eager to gain more traction from them.<n>We introduce AutoGEO, a framework to automatically learn generative engine preferences when using retrieved contents for response generation.
  arXiv  Detail & Related papers  (2025-10-13T14:10:26Z)
• Sample-Efficient Online Learning in LM Agents via Hindsight Trajectory Rewriting [92.57796055887995]
  We introduce ECHO, a prompting framework that adapts hindsight experience replay from reinforcement learning for language model agents.<n> ECHO generates optimized trajectories for alternative goals that could have been achieved during failed attempts.<n>We evaluate ECHO on stateful versions of XMiniGrid, a text-based navigation and planning benchmark, and PeopleJoinQA, a collaborative information-gathering enterprise simulation.
  arXiv  Detail & Related papers  (2025-10-11T18:11:09Z)
• AutoMLGen: Navigating Fine-Grained Optimization for Coding Agents [27.864519204078004]
  Large language models (LLMs) have shown impressive performance in general programming tasks.<n>We introduce AutoMLGen, an LLM-based coding agent that integrates a domain knowledge base for high-quality prior guidance.<n>We show that AutoMLGen achieves state-of-the-art performance in numerous dimensions, such as the average medal rate and the valid submission rate.
  arXiv  Detail & Related papers  (2025-10-09T17:45:05Z)
• Dynamic Context-oriented Decomposition for Task-aware Low-rank Adaptation with Less Forgetting and Faster Convergence [131.41894248194995]
  We propose context-oriented decomposition adaptation (CorDA), a novel method that initializes adapters in a task-aware manner.<n>Thanks to the task awareness, our method enables two optional adaptation modes, knowledge-preserved mode (KPM) and instruction-previewed mode (IPM)
  arXiv  Detail & Related papers  (2025-06-16T07:55:14Z)
• Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement Learning [88.55095746156428]
  Retrieval-augmented generation (RAG) is widely utilized to incorporate external knowledge into large language models.<n>A standard RAG pipeline consists of several components, such as query rewriting, document retrieval, document filtering, and answer generation.<n>We propose treating the complex RAG pipeline with multiple components as a multi-agent cooperative task, in which each component can be regarded as an RL agent.
  arXiv  Detail & Related papers  (2025-01-25T14:24:50Z)
• GEO: Generative Engine Optimization [50.45232692363787]
  We formalize the unified framework of generative engines (GEs) GEs use large language models (LLMs) to gather and summarize information to answer user queries. Generative Engines typically satisfy queries by synthesizing information from multiple sources and summarizing them. We introduce Generative Engine Optimization (GEO), the first novel paradigm to aid content creators in improving their content visibility in generative engine responses.
  arXiv  Detail & Related papers  (2023-11-16T10:06:09Z)
• Optimization-Inspired Learning with Architecture Augmentations and Control Mechanisms for Low-Level Vision [74.9260745577362]
  This paper proposes a unified optimization-inspired learning framework to aggregate Generative, Discriminative, and Corrective (GDC) principles. We construct three propagative modules to effectively solve the optimization models with flexible combinations. Experiments across varied low-level vision tasks validate the efficacy and adaptability of GDC.
  arXiv  Detail & Related papers  (2020-12-10T03:24:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.