AgenticGEO: A Self-Evolving Agentic System for Generative Engine Optimization

• URL: http://arxiv.org/abs/2603.20213v1
• Date: Mon, 02 Mar 2026 16:13:13 GMT
• Title: AgenticGEO: A Self-Evolving Agentic System for Generative Engine Optimization
• Abstract summary: 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.
• Score: 32.13915434333156
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative search engines represent a transition from traditional ranking-based retrieval to Large Language Model (LLM)-based synthesis, transforming optimization goals from ranking prominence towards content inclusion. Generative Engine Optimization (GEO), specifically, aims to maximize visibility and attribution in black-box summarized outputs by strategically manipulating source content. However, existing methods rely on static heuristics, single-prompt optimization, or engine preference rule distillation that is prone to overfitting. They cannot flexibly adapt to diverse content or the changing behaviors of generative engines. Moreover, effectively optimizing these strategies requires an impractical amount of interaction feedback from the engines. To address these challenges, we propose AgenticGEO, a self-evolving agentic framework formulating optimization as a content-conditioned control problem, which enhances intrinsic content quality to robustly adapt to the unpredictable behaviors of black-box engines. Unlike fixed-strategy methods, AgenticGEO employs a MAP-Elites archive to evolve diverse, compositional strategies. To mitigate interaction costs, we introduce a Co-Evolving Critic, a lightweight surrogate that approximates engine feedback for content-specific strategy selection and refinement, efficiently guiding both evolutionary search and inference-time planning. Through extensive in-domain and cross-domain experiments on two representative engines, AgenticGEO achieves state-of-the-art performance and demonstrates robust transferability, outperforming 14 baselines across 3 datasets. Our code and model are available at: https://github.com/AIcling/agentic_geo.

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)
• From Experience to Skill: Multi-Agent Generative Engine Optimization via Reusable Strategy Learning [18.351281487408176]
  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.
  arXiv  Detail & Related papers  (2026-04-21T14:39:24Z)
• Structural Feature Engineering for Generative Engine Optimization: How Content Structure Shapes Citation Behavior [3.433584969325024]
  We propose GEO-SFE, a systematic framework for structural feature engineering in generative engine optimization.<n>Our approach decomposes content structure into three hierarchical levels: macro-structure, meso-structure, and micro-structure.<n>We develop architecture-aware optimization strategies and predictive models that preserve semantic integrity while improving structural effectiveness.
  arXiv  Detail & Related papers  (2026-03-31T16:41:43Z)
• Task-free Adaptive Meta Black-box Optimization [55.461814601130044]
  We propose the Adaptive meta Black-box Optimization Model (ABOM), which performs online parameter adaptation using solely optimization data from the target task.<n>Unlike conventional metaBBO frameworks that decouple meta-training and optimization phases, ABOM introduces a closed-loop parameter learning mechanism, where parameterized evolutionary operators continuously self-update.<n>This paradigm shift enables zero-shot optimization: ABOM competitive performance on synthetic BBO benchmarks and realistic unmanned aerial vehicle path planning problems without any handcrafted training tasks.
  arXiv  Detail & Related papers  (2026-01-29T09:54:10Z)
• Evolving Interdependent Operators with Large Language Models for Multi-Objective Combinatorial Optimization [21.70371026963599]
  Multi-operator optimization in MOEAs is formulated as a Markov decision process.<n>E2OC consistently outperforms state-of-the-art AHD and other multi-heuristic co-design frameworks.
  arXiv  Detail & Related papers  (2026-01-25T16:31:07Z)
• SCOPE: Prompt Evolution for Enhancing Agent Effectiveness [53.75986399936395]
  Large Language Model (LLM) agents are increasingly deployed in environments that generate massive, dynamic contexts.<n>While agents have access to this context, their static prompts lack the mechanisms to manage it effectively.<n>We introduce textbfSCOPE (Self-evolving Context Optimization via Prompt Evolution)<n>We propose a Dual-Stream mechanism that balances tactical specificity (resolving immediate errors) with strategic generality (evolving long-term principles)
  arXiv  Detail & Related papers  (2025-12-17T12:25:05Z)
• Improving Multimodal Sentiment Analysis via Modality Optimization and Dynamic Primary Modality Selection [54.10252086842123]
  Multimodal Sentiment Analysis (MSA) aims to predict sentiment from language, acoustic, and visual data in videos.<n>This paper proposes a modality optimization and dynamic primary modality selection framework (MODS)<n>Experiments on four benchmark datasets demonstrate that MODS outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-11-09T11:13:32Z)
• Role-Augmented Intent-Driven Generative Search Engine Optimization [9.876307656819039]
  We propose a Role-Augmented Intent-Driven Generative Search Engine Optimization (G-SEO) method.<n>Our method models search intent through reflective refinement across diverse informational roles, enabling targeted content enhancement.<n> Experimental results demonstrate that search intent serves as an effective signal for guiding content optimization.
  arXiv  Detail & Related papers  (2025-08-15T02:08:55Z)
• 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)
• 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.