Multi-Agent System Boosts Accuracy of Procedural Graph Extraction from Text

Global: Multi-Agent System Boosts Accuracy of Procedural Graph Extraction from Text

A team of computer scientists has introduced a new multi‑agent framework designed to extract procedural workflows as graphs from natural‑language descriptions, addressing longstanding challenges of structural validity and logical coherence. The approach, detailed in a recent arXiv preprint, formulates the extraction task as an iterative reasoning process that incorporates targeted feedback to refine both the shape of the graph and its semantic alignment with the source text.

Framework Overview

The proposed system, referred to as model{}, operates through three distinct stages. First, a graph builder agent generates an initial procedural graph based on the input description. Second, a simulation agent examines the draft for structural defects, providing natural‑language explanations of any inconsistencies. Third, a semantic agent evaluates the logical flow, ensuring that the graph’s ordering matches the linguistic cues present in the original passage.

Graph Extraction Phase

During the initial extraction, the graph builder leverages large language model capabilities to map sequential actions and decision points into nodes and edges. This phase establishes a baseline representation that can later be refined, rather than attempting to produce a perfect graph in a single pass.

Structural Feedback Phase

The simulation agent acts as a diagnostic tool, identifying issues such as missing connections, cycles, or orphaned nodes. By articulating these problems in plain language, the agent creates actionable feedback that is re‑introduced into the prompting of the graph builder, allowing the system to correct structural errors without modifying model parameters.

Logical Feedback Phase

In the final stage, the semantic agent aligns the graph’s logical order with the narrative flow of the source text. It highlights mismatches where, for example, a prerequisite step appears after a dependent action, and suggests adjustments that preserve the intended procedural logic.

Experimental Results

According to the arXiv abstract, experiments demonstrate that model{} achieves substantial improvements in both structural correctness and logical consistency when compared with strong baseline methods. The authors attribute these gains to the modular feedback loops, which enable focused refinement of distinct error categories without additional supervision.

Implications and Future Work

The modular design of the framework suggests broader applicability to other domains that require reliable transformation of unstructured text into structured representations, such as automated instruction generation or workflow automation. The authors indicate plans to explore scaling the approach to larger corpora and integrating domain‑specific knowledge bases to further enhance accuracy.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.