🚀 Latent Program Networks (LPN) for Tool Execution Strategies

This README explains how a Latent Program Networks (LPN)-inspired approach can generate tool execution strategies for multi-tool tasks in an agent workflow. This method offers advantages over relying solely on Large Language Models (LLMs) for tool strategy via prompting. A toy Python script demonstrates the concept in action.

🤖 What is LPN?

Latent Program Networks (LPNs), originally designed for abstract reasoning (e.g., the ARC benchmark), embed "rules" or "completions" into a searchable latent space. Here, we adapt LPNs to embed tool configurations and execution order, enabling smooth coordination of multiple tools (e.g., generate_summary and create_pie_chart) for tasks like summarization and data visualization.

🔑 Key Features

Embeds Configurations: Encodes tool parameters (e.g., length=170 for generate_summary, data={'Report': 41} for create_pie_chart) as latent vectors.
Blends Tools: Combines configurations into a single vector (e.g., [0.17, 0.09, 0.19]), ensuring coherent execution.
Search-Based Optimization: Uses examples to refine execution strategies without modifying tool code.

📊 Comparing LPN with LLM-Based Strategies

🧠 LLM Tool Execution Strategy

Process:

Prompt an LLM: "Summarize a report and chart topics with generate_summary and create_pie_chart."

The LLM outputs step-by-step tool calls:

1. Call `generate_summary(length=200)`
2. Extract topics
3. Call `create_pie_chart(data=topics)`

Pros: ✔ Simple setup—uses an off-the-shelf LLM.
✔ Flexible—adapts via natural language reasoning.

Cons: ❌ Error-prone—Misorders tools or picks incorrect configurations.
❌ Inconsistent—Output varies across prompts (e.g., length=200 vs. 300).
❌ Overhead—Requires multiple reasoning steps for complex tool interactions.

🔬 LPN-Inspired Approach

Process:

Pre-train a latent space with tool config examples.
RAG retrieves a blended config vector.
Search refines it to optimize for the task.
Decode into ordered tool calls (e.g., generate_summary(length=170) → create_pie_chart(data={'Report': 41})).

Pros: ✔ Accurate—Search optimization reduces execution errors.
✔ Consistent—Deterministic outputs for the same inputs.
✔ Efficient—Faster than iterative LLM prompting.
✔ Multi-Tool Coordination—Blends configs across tools seamlessly.

Cons: ❌ Complex setup—Requires training a latent space + RAG integration.
❌ Less flexible—Relies on pre-trained patterns rather than ad-hoc reasoning.

⚖ When to Use LPN vs. LLM

✅ When LPN Wins

Multi-Tool Tasks: Ensures correct tool order and config alignment.
Dynamic Needs: Adjusts configs based on input length (e.g., 10,000-word report vs. 1,000-word article).
Error Reduction: Prevents LLM missteps in complex workflows.

✅ When LLM Wins

Simple Tasks: Single-tool config tweaks (e.g., chunk_size=800).
No Training Data: If no prior examples exist, LLM’s zero-shot reasoning is more practical.

🔍 Key Insights from Our Exploration

LPN Embeds Configs, Not Code: The tool code remains unchanged; LPN only optimizes configurations.
Blending Happens in Latent Space: Configuration vectors guide execution rather than raw tool commands.
Order is Ensured via Decoding: LPN itself doesn’t enforce order; decoding logic ensures proper sequencing.
RAG + Search: RAG retrieves an approximate config; search refines it.
Optimization Over Retrieval: Unlike static RAG matches, LPN refines execution plans dynamically.

📝 Python Script Explanation

The included script (script.py) demonstrates how LPN retrieves, refines, and decodes tool configurations to execute generate_summary and create_pie_chart seamlessly.

By using LPN for tool execution, we achieve greater accuracy, consistency, and efficiency in multi-tool workflows while reducing LLM-based errors. 🚀

grahama1970/LPN_for_Tool_Execution_and_Config_Strategy.md