LLM Safety Reasoning With Actor-Critic Trajectory Feedback
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Solution Overview
Problem
Existing autonomous agents powered by Large Language Models (LLMs) lack a proactive mechanism to learn from past experiences, leading to repeated unsafe actions and inadequate safety reasoning, and existing safety monitors are reactive rather than proactive, failing to improve the agent's intrinsic safety reasoning.
Innovation Solution
A multi-agent framework utilizing a first LLM as an Actor to generate proposed actions and a second LLM as a Critic for real-time corrective feedback, combined with a contrastive learning database of past experiences, to enhance safety reasoning and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If a monitor-based architecture is used to detect and intervene in unsafe operations, then harmful factors are reduced, but the system remains reactive rather than proactive and does not improve the agent's intrinsic safety reasoning
Solution Approach 1:
The patent implements a safety guidance module that proactively provides safety reasoning before the agent executes actions. Instead of waiting for unsafe operations to occur and then intervening (reactive monitoring), the system预先 provides safety guidance and reasoning to the agent, enabling it to make safer decisions in advance. This transforms the safety mechanism from reactive to proactive, addressing the core limitation of existing monitor-based architectures.
Solution Approach 2:
The patent incorporates a feedback mechanism where the safety guidance module continuously monitors the agent's reasoning process and provides corrective feedback when unsafe patterns are detected. This feedback loop enables the agent to learn from its mistakes and improve its intrinsic safety reasoning over time, rather than simply being stopped by external monitors after making errors.
2Productivity
If reasoning with actions is combined to enhance performance on complex tasks, then productivity is improved, but the agent lacks a reflective mechanism for learning from past experiences
Solution Approach 1:
The patent implements a feedback mechanism where the safety guidance module analyzes the agent's past actions and reasoning processes, identifies errors and unsafe patterns, and provides corrective feedback. This enables the agent to learn from its experiences and improve its safety reasoning over time, transforming it from a system that merely executes tasks to one that continuously learns and improves.
Solution Approach 2:
The patent enables the agent to self-reflect on its own reasoning processes and actions through the safety guidance module. The agent can independently analyze its past behavior, identify safety issues, and adjust its future actions accordingly, reducing reliance on external monitoring and enabling continuous self-improvement.
3Object-generated harmful factors
If existing safety monitors intervene to prevent harm, then object-generated harmful factors are reduced, but the agent's intrinsic safety reasoning is not improved and the same mistakes are repeated
Solution Approach 1:
The patent implements a feedback mechanism where the safety guidance module continuously analyzes the agent's reasoning and actions, provides corrective feedback on unsafe patterns, and enables the agent to learn from its mistakes. This transforms external intervention into internal learning, allowing the agent to develop intrinsic safety reasoning and avoid repeating the same errors.
Solution Approach 2:
The patent enables the agent to self-correct unsafe behaviors through the safety guidance module's feedback mechanism. Instead of relying solely on external monitors to stop harmful actions, the agent learns to independently identify and correct its own safety issues, developing intrinsic safety reasoning capabilities.
Data Source
AI summary
A method for controlling an artificial intelligence (AI) device can include receiving a user query, retrieving safe and unsafe trajectories from a trajectory history database, providing the user query, the at least one safe trajectory, and the at least one unsafe trajectory to an actor agent configured as a first large language model, generating, by the actor agent, a proposed action and a thought process for performing a step related to the task based on the at least one safe trajectory and the at least one unsafe trajectory. Also, the method can further include providing the proposed action and the thought process to a critic agent configured as a second large language model, generating, by the critic agent, a feedback critique of the proposed action, and executing, by the actor agent, a final action in an environment based on the feedback critique from the second large language model.