The proposed Koan-Safe, which combines a prompt-only intent parser, a replaceable generator, and structural repair with default safety parameters, is proposed, which supports explicit trade protections and execution-based evaluation, while distinguishing declared safety from a general guarantee.
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.
Large language models (LLMs) are increasingly used in software pipelines, raising concerns about harmful behaviors in security-critical domains. Existing safety evaluations predominantly probe models with single prompts or short interactions, and therefore do not capture how safety behaves under multi-step workflows wh...
Lu Yan, Zhuo Zhang, Xiang-Zhe Xu et al.· Proceedings of the ACM on So...· 0 citations
EvoMap results show that verified execution experience can be retained and shared as a reusable external resource, enabling models to improve long-workflow completion without repeatedly paying the full cost of experience discovery.
Xiao Zhang, Qu-Meng Sun, Jiahao Li et al.· 0 citations
SemVerBench is introduced, the first benchmark of LLM version-constraint resolution semantics across three ecosystems (npm, PEP 440, Cargo), and six frontier models are evaluated: Opus significantly outperforms all other models, and Sonnet outperforms the OpenAI models (McNemar).
SWE-Flux is introduced, a repository-level benchmark for dynamic execution reasoning containing 480 execution-grounded instances across 12 real Python repositories, with gold answers automatically harvested from instrumented test executions rather than written manually or judged by LLMs.
Hamed Taherkhani, Mohammad Abdollahi, Melika Sepidband et al.· 0 citations
This work introduces PonyEval, a SWE-bench-style benchmark of 291 real GitHub issue-pull-request pairs from 15 Pony repositories that defines a matched evaluation with mini-SWE-agent 2.4.6 for GPT-5.6-sol, DeepSeek-V4-Pro, GLM-5.2, MiniMax-M3, and Kimi-K3, followed by strict patch application, compilation, and hidden-t...
Bang Xie, Hao Liu, Zhen-Yu Shi et al.· 0 citations
Guardrails for tool-using LLM agents are usually application-specific rules, which makes multi-step, data-dependent safety policies hard to specify, audit and reuse. As a declarative alternative, we evaluate metric first-order temporal logic (MFOTL), replaying the recorded trajectories that AgentDojo, STAC and R-Judge...
Nikolaos Kekatos, Stylianos Basagiannis, Marinelio Chintri et al.· Proceedings of the 2026 8th...· 2 citations
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduOct 14, 2026
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
MIT News · Artificial Intelligence· news.mit.eduOct 13, 2026
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
Microsoft Research Blog· microsoft.comOct 12, 2026
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.