Google researchers just introduced a Procedural Graph based framework for LLM agents, which consiste
Dr. Ashish Bamania · LinkedIn · source
Google researchers just introduced a Procedural Graph based framework for LLM agents, which consistently improves their capabilities over memory-based methods.
Current agents often perform poorly on long-horizon, multi-step trajectories. They lose track of their objectives, invoke tools out of order, and repeat unproductive actions. This is what Procedural Graphs solve.
Similar to how a Knowledge graph organizes factual knowledge into (entity, relation, entity) triplets for “what-is” questions, a Procedural Graph organizes procedural knowledge into (procedure, relation, procedure) triplets for “what-to-do” questions.
At each step of execution, the framework locates the agent’s position on the graph and uses the surrounding subgraph to generate situational guidance. This guidance then influences the agent’s next move. The graph is also dynamically updated to improve based on the agent’s failed and successful runs.
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