Problem
Research questions and friction points this paper is trying to address.
quantitative syntax
research logic
workflow reification
reproducibility
computational linguistics
Reifying Research Logic: AI-Assisted Workflow Construction and Incremental Refinement for Quantitative Syntax
🤖 AI Summary
Quantitative syntactic research often suffers from poor auditability, limited shareability, and hindered iteration due to computational logic being embedded implicitly within scripts. This work proposes QLWF, a platform that leverages an AI-assisted five-stage pipeline to transform natural language research descriptions into explicit, executable workflows. Its key innovation lies in integrating visual logic restructuring with deterministic execution semantics, enabling incremental iteration driven by localized modifications without requiring full workflow regeneration. Built upon large language models, QLWF ensures reproducibility through a fixed node library and a dedicated execution engine, while an incremental refinement mechanism reduces restructuring overhead. Experimental results demonstrate that on the 64-task QL-Bench, the platform generates workflows that are 100% structurally valid and 98.4% semantically reasonable; in a 12-task lifecycle evaluation, incremental revisions achieved a 100% success rate with only one-third of the token consumption required for full regeneration.
📝 Abstract
Quantitative language research often depends on long chains of computational steps, yet the logic connecting those steps usually remains buried in scripts. This makes analyses harder to inspect, share, and revise than they need to be. Focusing on quantitative syntax, we present QLWF, a visual workflow platform that turns natural-language research descriptions into executable workflows through an AI assisted five-stage pipeline. In this setting, reification makes the research logic visible as a workflow, while formalization gives that workflow deterministic execution semantics. The language model is used only during construction. Execution is handled by a fixed node library and engine, which keeps the resulting workflows reproducible. QLWF also supports incremental refinement, so saved workflows can be revised by changing only the parts that need to change rather than being rebuilt from scratch. To evaluate the approach, we build a 64-task benchmark called QL-Bench from the quantitative-syntax literature. Across three runs, QLWF produces structurally valid and executable workflows for every task and reaches a mean output-plausibility rate of 98.4%, well above the prompt-based baselines. On a separate 12-task lifecycle benchmark, this refinement process succeeds in every case and uses roughly one-third of the tokens required by full regeneration. The paper also releases the node library, benchmark, workflow templates, and platform as reusable resources for quantitative-syntax research.
Innovation
Methods, ideas, or system contributions that make the work stand out.
workflow reification
AI-assisted construction
incremental refinement
reproducible execution
quantitative syntax
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