Reasoning Frameworks
Reasoning frameworks provide a structured approach for AI agents to process information, make decisions, and generate responses. They act as a blueprint for the agent's cognitive architecture, guiding its thought process and actions.
Below are a few popular and notable reasoning frameworks:
ReAct (Reason and Act): ReAct is a prompting framework that combines reasoning and action steps, enabling language models to interact with the environment and generate reasoning traces in natural language. It encourages the model to explicitly state its thoughts, actions, and observations, leading to more transparent and interpretable behavior. For instance, an agent using ReAct might think, "I need to find a flight from Austin to Zurich," then take the action of searching a flight database, observe the results, and continue reasoning based on the findings.
Chain-of-Thought (CoT): CoT enhances problem-solving by prompting the model to break down complex tasks into a series of smaller, more manageable steps. This step-by-step reasoning process helps the model arrive at more accurate and logical conclusions.3 It's like providing a model with a set of guiding questions or prompts to lead it through a logical thought process.
Apart from ReAct and CoT, other notable reasoning frameworks used in AI agents are:
Tree-of-Thoughts (ToT): ToT expands on CoT by exploring multiple reasoning possibilities at each step, creating a tree-like structure of potential solutions. It allows for a more comprehensive evaluation of different thought paths.
Reflexion: This framework equips agents with dynamic memory and self-reflection capabilities to improve reasoning skills. It helps agents identify and correct mistakes by analyzing their past actions and planning inefficiencies.
References / Sources:
Google Whitepaper, 'Agents' - September 2024
