# VerifyLLM: LLM-Based Pre-Execution Task Plan Verification for Robots

arXiv: https://arxiv.org/abs/2507.05118 · HTML: https://mlsanity.com/paper/2507.05118/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 38 references that are in the map.
- First posted on arXiv 7 Jul 2025.

## Builds on (most cited first, 23 of 23)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks](https://mlsanity.com/paper/2211.12588/) (2022)
- [Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents](https://mlsanity.com/paper/2201.07207/) (2022)
- [ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks](https://mlsanity.com/paper/1912.01734/) (2019)
- [LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models](https://mlsanity.com/paper/2212.04088/) (2022)
- [VirtualHome: Simulating Household Activities via Programs](https://mlsanity.com/paper/1806.07011/) (2018)
- [Can language models learn from explanations in context?](https://mlsanity.com/paper/2204.02329/) (2022)
- [Text2Motion: From Natural Language Instructions to Feasible Plans](https://mlsanity.com/paper/2303.12153/) (2023)
- [Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning](https://mlsanity.com/paper/2302.02662/) (2023)
- [Correcting Robot Plans with Natural Language Feedback](https://mlsanity.com/paper/2204.05186/) (2022)
- [LLatrieval: LLM-Verified Retrieval for Verifiable Generation](https://mlsanity.com/paper/2311.07838/) (2023)
- [Guiding Long-Horizon Task and Motion Planning with Vision Language Models](https://mlsanity.com/paper/2410.02193/) (2024)
- [RePLan: Robotic Replanning with Perception and Language Models](https://mlsanity.com/paper/2401.04157/) (2024)
- [Improving LLM Reasoning through Scaling Inference Computation with Collaborative Verification](https://mlsanity.com/paper/2410.05318/) (2024)
- [GRID: Scene-Graph-based Instruction-driven Robotic Task Planning](https://mlsanity.com/paper/2309.07726/) (2023)
- [SOTER: A Runtime Assurance Framework for Programming Safe Robotics Systems](https://mlsanity.com/paper/1808.07921/) (2018)
- [Grounding Classical Task Planners via Vision-Language Models](https://mlsanity.com/paper/2304.08587/) (2023)
- [Kanana: Compute-efficient Bilingual Language Models](https://mlsanity.com/paper/2502.18934/) (2025)
- [Extended Tree Search for Robot Task and Motion Planning](https://mlsanity.com/paper/2103.05456/) (2021)
- [Generalizing to New Domains by Mapping Natural Language to Lifted LTL](https://mlsanity.com/paper/2110.05603/) (2021)
- [Mind the Error! Detection and Localization of Instruction Errors in Vision-and-Language Navigation](https://mlsanity.com/paper/2403.10700/) (2024)

## Built on by (most cited first, 14 of 14)

- [ToolGate: Contract-Grounded and Verified Tool Execution for LLMs](https://mlsanity.com/paper/2601.04688/) (2026)
- [Spatial Traces: Enhancing VLA Models with Spatial-Temporal Understanding](https://mlsanity.com/paper/2508.09032/) (2025)
- [Verification-Aware Planning for Multi-Agent Systems](https://mlsanity.com/paper/2510.17109/) (2025)
- [Mind and Motion Aligned: A Joint Evaluation IsaacSim Benchmark for Task Planning and Low-Level Policies in Mobile Manipulation](https://mlsanity.com/paper/2508.15663/) (2025)
- [Plan Verification for LLM-Based Embodied Task Completion Agents](https://mlsanity.com/paper/2509.02761/) (2025)
- [LookPlanGraph: Embodied Instruction Following Method with VLM Graph Augmentation](https://mlsanity.com/paper/2512.21243/) (2025)
- [VASO: Formally Verifiable Self-Evolving Skills for Physical AI Agents](https://mlsanity.com/paper/2606.05395/) (2026)
- [Plover: Steering GUI Agents through Plan-Centric Interaction](https://mlsanity.com/paper/2607.15193/) (2026)
- [Cybo-Waiter: A Physical Agentic Framework for Humanoid Whole-Body Locomotion-Manipulation](https://mlsanity.com/paper/2603.10675/) (2026)
- [Closing the Loop in Humanoid VLA: Persistent 3D Object Tokens for Verifiable Loco-Manipulation](https://mlsanity.com/paper/2607.18016/) (2026)
- [Toward Safe LLM Agents: A Survey of Specification, Verification, and Enforcement](https://mlsanity.com/paper/2608.14590/) (2026)
- [Shaping Schema via Language Representation as the Next Frontier for LLM Intelligence Expanding](https://mlsanity.com/paper/2605.09271/) (2026)
- [HELP: Hierarchical Embodied Language Planner for Household Tasks](https://mlsanity.com/paper/2512.21723/) (2025)
- [Evidence-Gated Task and Motion Planning with Vision-Language Models](https://mlsanity.com/paper/2608.20084/) (2026)

## Abstract (arXiv)

In the field of robotics, researchers face a critical challenge in ensuring reliable and efficient task planning. Verifying high-level task plans before execution significantly reduces errors and enhance the overall performance of these systems. In this paper, we propose an architecture for automatically verifying high-level task plans before their execution in simulator or real-world environments. Leveraging Large Language Models (LLMs), our approach consists of two key steps: first, the conversion of natural language instructions into Linear Temporal Logic (LTL), followed by a comprehensive analysis of action sequences. The module uses the reasoning capabilities of the LLM to evaluate logical coherence and identify potential gaps in the plan. Rigorous testing on datasets of varying complexity demonstrates the broad applicability of the module to household tasks. We contribute to improving the reliability and efficiency of task planning and addresses the critical need for robust pre-execution verification in autonomous systems. The code is available at https://verifyllm.github.io.