Procedural Graphs: Self-Evolving Execution Structures for LLM Agents
A research paper titled "Procedural Graphs: Self-Evolving Execution Structures for LLM Agents" was published on arXiv.org on September 8, 2026. Authored by Yuxing Lu, this 36-page document, including references and appendices, explores Artificial Intelligence, Computation and Language, and Multiagent Systems. It is identified as arXiv:2609.09153 [cs.AI] and is available in its first version (v1).
Time & source
- Published
- 09/09, 17:13 UTC+0
- Ingested
- 09/09, 21:00 UTC+0
- Source type
- Research
- Tier
- Community
- Source status
- Healthy
Tier is a per-source editorial setting, not a per-item score.
View PDF HTML (experimental)
Abstract: 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. As trajectories lengthen, agents can lose track of their objectives, invoke tools out of order, and repeat unproductive actions. We introduce the Procedural Graph: just as 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 decision step, the framework localizes the agent's active node, and a guidance model translates the surrounding subgraph into step-level situational guidance that biases the solver's next action without dictating it. The graph is self-evolving: an LLM refiner contrasts failed trajectories with successful ones and edits the graph's topology and attributes, committing edits that preserve or improve held-out validation performance while retaining rejected ones to discourage repetition. Starting from a minimal skeleton, the loop builds graphs that match or surpass hand-designed ones. It can also repair a flawed expert prior. Across multiple datasets, task types, and LLMs, the Procedural Graph delivers consistent gains over memory-based baselines, and self-evolution further improves performance without manual engineering.
Comments: 36 pages including references and appendices, 6 figures, 11 tables
Subjects:
Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA)
Cite as: arXiv:2609.09153 [cs.AI]
(or arXiv:2609.09153v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.09153
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yuxing Lu [view email] [v1] Tue, 8 Sep 2026 17:59:41 UTC (4,896 KB)