Procedural Graphs: Self-Evolving Execution Structures for LLM Agents
Procedural Graph framework stores procedural knowledge as triplets and self-evolves via LLM refinement, beating memory-based baselines across datasets, tasks, and LLMs.
The Procedural Graph organizes procedural knowledge into (procedure, relation, procedure) triplets; at each decision step the framework localizes the agent's active node and a guidance model translates the surrounding subgraph into step-level guidance that biases the solver's next action. An LLM refiner contrasts failed with successful trajectories and edits the graph's topology and attributes, retaining rejected edits to discourage repetition. Starting from a minimal skeleton, the loop builds graphs that match or surpass hand-designed ones and can repair flawed expert priors, delivering consistent gains over memory-based baselines across multiple datasets, task types, and LLMs.
- Stores procedural knowledge as (procedure, relation, procedure) triplets, analogous to knowledge graphs.
- Guidance model converts active-node subgraphs into step-level action guidance.
- LLM refiner edits graph topology by contrasting failed and successful trajectories.
- Outperforms memory-based baselines across datasets, task types, and LLMs.
- Can repair flawed expert priors without manual engineering.
From the original article
1 min read at huggingface.co
Large language models are increasingly deployed as agents that plan over long horizons and act through external tools. Most agents select actions through unconstrained generation over an accumulating history, leaving implicit the procedural knowledge of what to do, in what order, and under which conditions.
By Yuxing Lu, Yicheng Chen, Shanchan Wu · huggingface.co
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