arXiv — Machine Learning · · 3 min read

DeFiFlowBench: Benchmarking and Improving Safe Executability in Natural-Language DeFi Workflow Synthesis

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Computer Science > Machine Learning

arXiv:2609.11504 (cs)
[Submitted on 10 Sep 2026]

Title:DeFiFlowBench: Benchmarking and Improving Safe Executability in Natural-Language DeFi Workflow Synthesis

View a PDF of the paper titled DeFiFlowBench: Benchmarking and Improving Safe Executability in Natural-Language DeFi Workflow Synthesis, by Abhinav Rajeev Kumar and 3 other authors
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Abstract:A structurally valid DeFi workflow can still authorize a costly trade. We introduce DeFiFlowBench, a benchmark of 207 team-authored prompts for natural-language DeFi workflow synthesis. It measures graph coverage, configuration completeness, and declared safety predicates, then tests supported trade configurations on a local EVM. Direct, constrained, and few-shot prompting produce 14-19 unsafe held-out executions per configuration under a fixed 5% price-impact cap. A slippage bound derived from a quote does not prevent the price impact of the order itself. We propose Koan-Safe, which combines a prompt-only intent parser, a replaceable generator, and structural repair with default safety parameters. On 75 held-out workflow prompts, its hybrid variant scores 0.67 on the static safety proxy, compared with 0.33 for the best baseline. Koan-Safe records no unsafe executions on the saved benchmark outputs. A matched-candidate ablation produces 14-17 unsafe executions when enforcement is disabled. Additional tests expose the limits of default injection: permissive existing thresholds can still authorize unsafe trades. A separately evaluated policy cap addresses this failure on a 36-case diagnostic grid. These results support explicit trade protections and execution-based evaluation, while distinguishing declared safety from a general guarantee.
Comments: Code and benchmark: this https URL
Subjects: Machine Learning (cs.LG); Software Engineering (cs.SE)
ACM classes: D.2.4; I.2.6; I.2.11
Cite as: arXiv:2609.11504 [cs.LG]
  (or arXiv:2609.11504v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2609.11504
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Abhinav Rajeev Kumar [view email]
[v1] Thu, 10 Sep 2026 13:10:32 UTC (52 KB)
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