Google's NEW Dual-System AI - TALK & REASON Agents

Introduction to Dual Agent Architecture:

Google Deep Mind introduces a dual agent architecture consisting of a Talker agent for fast, intuitive communication and a Reasoner agent for slower, deliberative reasoning.
Both agents share a common memory where the Reasoner uploads insights for the Talker to use in conversations with humans.
The architecture uses a chain of sord prompting for reasoning traces and task-specific actions, and incorporates self-reflection to improve causal reasoning.

Talker Agent (System 1):

Handles fast, intuitive, real-time conversations with humans.
Generates natural language responses and retrieves the latest belief states from common memory.
Employs an in-context learned LLM (Language Learning Model) conditioned for coherence and empathy.

Reasoner Agent (System 2):

Manages slow, deliberative, logical causal reasoning tasks.
Performs multi-step reasoning and planning, and can connect to external tools or API calls for information retrieval.
Updates and maintains the structured belief state about the user and the environment.

Interaction Between Agents:

Operate asynchronously with the Talker using the most recent belief state while the Reasoner updates beliefs and generates complex planning.
Both agents interact through a shared memory system, with the Reasoner updating the belief state for the Talker to access.

Agent Environment and Decision Process:

The environment is formulated as a partially observable Markov decision process (POMDP).
Agents can take actions, select tools, and update beliefs to create a plan for solving problems.
Belief states are updated through Bayesian inference, reflecting uncertainty about the true state of the world.

Action Space and Memory Integration:

The action space includes reasoning traces (sords), tool usage, belief updates, and conversational responses.
Belief states are structured objects (e.g., JSON) that store the agent’s understanding of user goals and preferences.
The Talker pulls relevant information from memory to inform conversational responses.

Reinforcement Learning and In-Context Learning:

The agents employ a reinforcement learning approach to balance the Talker’s fast responses with the Reasoner’s thorough reasoning.
In-context learning is used to provide the agents with a context window that includes user input, current belief state, interaction history, and instructions.

Future Developments:

Performance can be improved by adding multiple specialized Reasoners.
The Talker could automatically probe the Reasoner when complex reasoning is required.
The system evolves to handle varying complexity levels in human queries.

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