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.
Figures & tables
Figure 1. Characterization of the GEO result on GEO-Bench instances. y -axis reports maximum performance among 9 rewriting strategies, and x -axis reports performance variance among strategies. (i) Optimization success varies greatly by strategy and content. (ii) Existing strategies fail to optimize nearly half of instances (points in gray and red areas), indicating static strategy pool is not enough and needs evolving. Details can be found in Appendix A.1.1 .
Figure 2. GEO v.s. AgenticGEO. Static GEO methods apply fixed rewriting heuristics, whereas AgenticGEO maintains an evolving strategy archive and a critic to adaptively retrieve high-scoring strategies for iterative rewriting.
Figure 3. Overview of the AgenticGEO framework with two-stage training. Offline Alignment warm-starts a surrogate critic using offline preference pairs from the initial archive M0 , calibrating it for fast, content-conditioned strategy scoring. Online Co-Evolution then interacts with the black-box Generative Engine (GE) to iteratively, simultaneously evolve a MAP-Elites quality-diversity archive and the critic. Parent strategies are mutated by a learned evolver trained with sibling-aware AWR, the critic screens candidates to reduce GE calls, and newly collected GE feedback is stored in a replay buffer to continually recalibrate the critic and update the archive through a value-novelty gate. At inference time, the evolved archive and critic enable agentic multi-turn rewriting by selecting and executing a content-adaptive plan of strategies.
Table 1. Structure of the evolving strategy representation. Each dimension is mutated independently. It can be rendered into a compact summary for critic scoring and a full strategy prompt for rewriting (see Appendix A.1.5 ).
Methods
GEO-Bench
Qwen2.5-32B-Instruct
Llama3.3-70B-Instruct
word
pos
overall
word
pos
overall
No optimization
20.05
20.26
20.21
19.19
19.33
19.20
Keyword Stuffing
20.73
20.86
20.69
19.99
20.16
20.02
Unique Words
17.59
17.94
17.78
16.78
16.66
16.56
Easy-To-Understand
20.10
20.19
20.05
18.72
18.93
18.85
Table 2. Overall Performance on the in-domain setting. Average results on 5 independent runs are reported. ∗ indicates the statistically significant improvements over the best baseline, with p -value smaller than 0.001 .
Qwen2.5-32B-Instruct
Llama3.3-70B-Instruct
Methods
MS MARCO
E-Commerce
MS MARCO
E-Commerce
word
pos
overall
word
pos
overall
word
pos
overall
word
pos
overall
No optimization
19.99
20.15
20.05
18.30
18.09
18.01
19.45
19.64
19.67
19.70
19.45
19.68
Keyword Stuffing
23.26
22.63
22.75
18.94
18.52
18.65
22.02
21.77
21.96
20.24
19.96
20.16
Unique Words
17.95
18.24
18.23
18.12
18.09
18.01
18.94
18.88
18.76
19.16
19.04
19.15
Easy-To-Understand
20.48
20.46
20.46
20.46
20.45
20.29
19.75
19.84
19.84
20.28
19.91
20.18
Table 3. Overall Performance on the cross-domain setting. Average results on 5 independent runs are reported. ∗ indicates the statistically significant improvements over the best baseline, with p -value smaller than 0.001 .
Figure 4. Ablation study of AgenticGEO on three datasets. We compare AgenticGEO with four variants: (a) an offline-only critic trained without online co-evolution, (b) removing the evolved strategy archive, (c) replacing the critic with random rewrite planning and the evolver directly generates the strategy, (d) maintaining the archive by performance only without diversity.
Figure 5. Hyper-parameter sensitivity analysis on GEO-Bench. We report overall impression score when varying the multi-turn rewriting steps (left) and the archive size (right).
Figure 9
Figure 7. Semantic consistency and optimization effectiveness. Semantic Similarity is measured by BERTScore-F1 ( Zhang et al., 2019 ) with roberta-large ( Liu et al., 2019 ) , computed between the original content and the rewritten version.
Appendix figures & tables3 assets
Supplementary material from the paper’s appendix.
Appendix
Dataset
#Queries
#Docs
Avg. Content Tokens
GEO-Bench
1000
5,000
980.61
MS-Marco
1000
5,000
91.91
E-commerce
416
2,180
1,459.69
Appendix
Table 5. Dataset statistics after preprocessing. Following the GEO-Bench protocol, each query is paired with 5 documents.
Module
Param.
Description
Value
Offline Critic Alignment
Loss weight
λ
Weight in Ltotal=Lpair+λLreg
0.2
Warm-up steps
Sfreeze
Epochs with frozen backbone before unfreezing
1
Online Co-Evolution (Selection & Budget)
Iterations
T
Total online evolution iterations
100
Exploit size
Ktop
Top- Ktop selected by critic
4
Appendix
Table 6. Key hyperparameters of AgenticGEO (values left blank).
Figure 8. Qualitative case studies of AgenticGEO. For each query-content pair, we show the original content, the optimized rewrite, and the activated strategy sequence selected by critic-guided planning.