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

🤖 Content-adaptive GEO in one click: multi-turn rewriting with minimal feedback.

🧩 What: Optimize document visibility & attribution in black‑box generative search engines (Generative Engine Optimization, GEO). 🔧 How: Model GEO as content‑conditioned problem, then train a MAP‑Elites strategy archive + co‑evolving critic for rewriting strategy selection. 🚀 Why it matters: GEO often relies on an assumed globally-optimal fixed prompt; AgenticGEO learns a content-adaptive strategy selection policy, optmizing with fewer GE calls.

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✨ Highlights

• Content‑conditioned GEO under non‑stationary black‑box engines.
• Quality‑diversity strategy memory (MAP‑Elites) for adaptive strategy selection.
• Co‑evolving critic as a surrogate evaluator & inference‑time planner.
• Low feedback regime: strong performance retained with limited GE calls (see paper).

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🧭 Overview

AgenticGEO consists of three stages:

1. Offline Critic Alignment: warm‑start a lightweight critic using offline preference pairs.
2. Online Strategy–Critic Co‑Evolution: co‑evolve a MAP‑Elites archive and continuously recalibrate the critic with limited GE feedback.
3. Inference‑time Multi‑turn Rewriting: critic‑guided planning selects a content‑adaptive sequence of strategies.

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📏 Metrics

We use GEO‑Bench impression metrics:

• Attributed Word Count (word)
• Position‑Weighted Citation Order (pos)
• Overall (combination of word & pos)

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🚀 Quickstart

1) Install dependencies

pip install -r requirements.txt

2) Prepare the Critic base model (local path)

src/run_geo.py loads a base model for architecture/tokenizer (e.g. Qwen/Qwen2.5-1.5B-Instruct). You can download it into base_model/ using the provided script:

python base_model/download_base_model.py

3) Preload dataset sources into cache (recommended)

src/run_geo.py reads sources from src/global_cache.json. Preloading avoids missing sources at runtime.

• GEO-Bench (GEO-optim/GEO):

python src/preload_cache_from_geobench.py

• MSdata:

python src/preload_cache_from_msdata.py

4) Configure the LLM endpoint (OpenAI-compatible)

The repo reads OpenAI-compatible settings from config.ini (or environment variables).

• Using a local OpenAI-compatible server (e.g. vLLM / llama.cpp server / any compatible service):

  • Set in config.ini:
    • USE_LOCAL_LLM = True
    • LOCAL_LLM_BASE = http://localhost:8000/v1
    • LOCAL_LLM_MODEL = <your served model name>

• Using OpenAI or a hosted compatible provider:

  • Set USE_LOCAL_LLM = False and provide OPENAI_API_KEY / OPENAI_API_BASE in config.ini
  • You can also override via environment variables (recommended for CI/servers)

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5) Run evaluation

Required environment variables (minimal)

# Dataset type and split
$env:DATASET_TYPE  = "geobench"   # geobench | msdata | ecommerce
$env:DATASET_SPLIT = "test"       # train | test | val

# Base model path (for Critic structure/tokenizer)
$env:EVOLVED_BASE_MODEL = "E:\AICling\agentic_geo\base_model"

Critic & strategies (auto-loaded from evolved/ by default)

No extra weights are required by default; src/run_geo.py will auto-load:

• Strategies: evolved/archive/strategies.json
• Critic LoRA: evolved/critic/lora_adapter/
• Critic value head: evolved/critic/value_head.bin

If you want to override any of them, set these optional environment variables (paths can be absolute or relative to the project root):

# Optional: override strategies file
# $env:EVOLVED_STRATEGIES = "E:\path\to\strategies.json"

# Optional: override value head
# $env:EVOLVED_VALUE_HEAD = "E:\path\to\value_head.bin"

# Optional: override LoRA adapter
# $env:EVOLVED_LORA_ADAPTER = "E:\path\to\lora_adapter"

# Optional: override backbone weights (e.g. pytorch_model.bin)
# $env:EVOLVED_PRETRAINED_BACKBONE = "E:\path\to\pytorch_model.bin"

Concurrency & cache (optional)

$env:USE_CONCURRENT = "True"
$env:MAX_WORKERS    = "10"

# Optional: cache file location (default: src/global_cache.json)
# $env:GLOBAL_CACHE_FILE = "E:\AICling\agentic_geo\src\global_cache.json"

Run

python src/run_geo.py

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Output

The script prints the final output path. Results are saved under src/results/ with a filename like:

• geo_results_{model}_{split}.json

Where {model} comes from:

• LOCAL_LLM_MODEL in config.ini (when USE_LOCAL_LLM=True), or
• env var MODEL_NAME (when USE_LOCAL_LLM=False)

🧪 Reproducibility (paper setting)

• Critic backbone: Qwen2.5‑1.5B
• Evolver: Qwen2.5‑7B‑Instruct
• Rewriter tool model: Qwen2.5‑32B‑Instruct
• Downstream GEs: Qwen2.5‑32B‑Instruct / Llama‑3.3‑70B‑Instruct
• Fine‑tuning: LoRA, 2 epochs
• Inference: select top‑25 strategies, up to 3 rewrite steps

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🪪 License

This project is released under the MIT License. See LICENSE.

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🙏 Acknowledgements

We thank the GEO‑Bench authors and the open‑source LLM ecosystem.