The Master Chef's Kitchen

Imagine an Augmented Language Model (ALM) is a world-class chef. The core Language Model (like GPT-4) is the chef's brilliant mind—full of creativity, knowledge, and culinary intuition. But a chef's mind alone can't create a feast. They need a kitchen full of tools.

Asking a standard LLM a complex, real-world question is like asking the chef to make a soufflé without an oven. They can describe it perfectly but can't produce it. An ALM, our chef, can use tools: a Search Engine is their pantry for fresh facts, a Code Interpreter is a precision sous-vide machine for complex calculations, and other APIs are specialized knives.

The SEAL framework, then, is like a Michelin inspector. It doesn't just taste the final dish (the outcome); it goes into the kitchen to evaluate the chef's process. Did they use the right knife for the fish? Was the oven at the correct temperature? This process-based evaluation is the key to truly understanding how good these AI "chefs" are.

From Prompt to Action: The "Reason-Act" Cycle

An ALM follows a "Reason-Act" cycle. The LLM's 'brain' first thinks about the problem, then chooses and uses a tool, observes the result, and repeats until the task is complete. This section explains this fundamental process and how these tools are defined in code.

Code Snippet: Defining a Tool

For an ALM to use a tool, a developer must define it, typically as a function with a very clear description (the "docstring"). This docstring is crucial, as it's what the LLM reads to understand what the tool does and when to use it. Below is a Python example for a simple calculator tool, inspired by code from the Continual-Intelligence repository.

def calculator(expression: str) -> str:
    """
    A calculator that can evaluate mathematical expressions.
    Use this tool to compute numerical answers.

    Args:
        expression (str): The mathematical expression to evaluate. 
                          Example: "2 * (3 + 4)"
    Returns:
        str: The result of the calculation.
    """
    try:
        result = eval(expression)
        return str(result)
    except Exception as e:
        return f"Error: {e}"

The SEAL framework provides the "Michelin inspector's toolkit" for evaluating our AI chefs. Instead of one giant, complex test, it breaks down the evaluation into two core, manageable concepts: Skills and Scenarios. By testing these fundamental building blocks, we get a much clearer picture of what a model can and cannot do. Explore the cards below to see how they work.

The core idea to "chunk" is: SEAL evaluates ALMs by testing atomic Skills in isolation and then combining them in complex Scenarios to assess both process and outcome.

From "Recipe" to "Graph"

This is the core technical insight. A "Scenario" isn't just a prompt; it's a "white-box" test case. From a data science perspective, each scenario is structured as a Directed Acyclic Graph (DAG). Each node in the graph is a specific skill (a tool call), and the edges define the dependencies. This graph *is* the "correct recipe."

Why a DAG? Because it allows for a rigorous, scientific evaluation of the model's *process*. The model isn't just given a final exam; it's graded on every step of its homework.

Code Snippet: A Scenario as a DAG

This JSON-like snippet shows how the "AI Stock Analyst" scenario is defined. Notice the `depends_on` field, which creates the graph structure. The model's job is to replicate this logical flow.

{
  "scenario": "AI Stock Analyst",
  "steps": [
    { 
      "id": 1, 
      "skill": "Search", 
      "prompt": "Find recent news for $STOCK",
      "depends_on": [] 
    },
    { 
      "id": 2, 
      "skill": "Search", 
      "prompt": "Find financial data for $STOCK",
      "depends_on": [] 
    },
    { 
      "id": 3, 
      "skill": "Code", 
      "prompt": "Analyze sentiment of news (step 1) and financials (step 2)",
      "depends_on": [1, 2] 
    },
    { 
      "id": 4, 
      "skill": "Logic", 
      "prompt": "Synthesize analysis (step 3) into a recommendation",
      "depends_on": [3] 
    }
  ]
}

Process vs. Outcome Metrics

This graph structure allows for two types of scientific evaluation, which is what makes SEAL so powerful:

Here, you can explore the performance data from the SEAL evaluation. The chart below visualizes how leading models compare across different evaluation criteria. Use the buttons to switch between viewing overall performance, breaking it down by specific skills, or analyzing performance in complex scenarios. This interactive view allows you to see the nuanced strengths and weaknesses of each model, something a static report can't show.

While SEAL's process-based, DAG-driven approach is a significant scientific advancement, it's essential to understand its limitations and the broader challenges facing ALMs. A critical perspective helps us see the full picture.