# MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention

arXiv: https://arxiv.org/abs/2606.01063 · HTML: https://mlsanity.com/paper/2606.01063/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 31 of its 42 references that are in the map.
- First posted on arXiv 31 May 2026.

## Builds on (most cited first, 30 of 31)

- [Qwen3-VL Technical Report](https://mlsanity.com/paper/2511.21631/) (2025)
- [InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency](https://mlsanity.com/paper/2508.18265/) (2025)
- [Video-R1: Reinforcing Video Reasoning in MLLMs](https://mlsanity.com/paper/2503.21776/) (2025)
- [VirtualHome: Simulating Household Activities via Programs](https://mlsanity.com/paper/1806.07011/) (2018)
- [ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation](https://mlsanity.com/paper/2007.04954/) (2020)
- [Understanding Social Reasoning in Language Models with Language Models](https://mlsanity.com/paper/2306.15448/) (2023)
- [FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactions](https://mlsanity.com/paper/2310.15421/) (2023)
- [MMToM-QA: Multimodal Theory of Mind Question Answering](https://mlsanity.com/paper/2401.08743/) (2024)
- [ToMBench: Benchmarking Theory of Mind in Large Language Models](https://mlsanity.com/paper/2402.15052/) (2024)
- [HI-TOM: A Benchmark for Evaluating Higher-Order Theory of Mind Reasoning in Large Language Models](https://mlsanity.com/paper/2310.16755/) (2023)
- [OneThinker: All-in-one Reasoning Model for Image and Video](https://mlsanity.com/paper/2512.03043/) (2025)
- [OpenToM: A Comprehensive Benchmark for Evaluating Theory-of-Mind Reasoning Capabilities of Large Language Models](https://mlsanity.com/paper/2402.06044/) (2024)
- [MuMA-ToM: Multi-modal Multi-Agent Theory of Mind](https://mlsanity.com/paper/2408.12574/) (2024)
- [Explore Theory of Mind: Program-guided adversarial data generation for theory of mind reasoning](https://mlsanity.com/paper/2412.12175/) (2024)
- [RoboClaw: An Agentic Framework for Scalable Long-Horizon Robotic Tasks](https://mlsanity.com/paper/2603.11558/) (2026)
- [RoboOS: A Hierarchical Embodied Framework for Cross-Embodiment and Multi-Agent Collaboration](https://mlsanity.com/paper/2505.03673/) (2025)
- [VideoAuto-R1: Video Auto Reasoning via Thinking Once, Answering Twice](https://mlsanity.com/paper/2601.05175/) (2026)
- [SIV-Bench: A Video Benchmark for Social Interaction Understanding and Reasoning](https://mlsanity.com/paper/2506.05425/) (2025)
- [SoMi-ToM: Evaluating Multi-Perspective Theory of Mind in Embodied Social Interactions](https://mlsanity.com/paper/2506.23046/) (2025)
- [MOMENTS: A Comprehensive Multimodal Benchmark for Theory of Mind](https://mlsanity.com/paper/2507.04415/) (2025)
- [AEROS: A Single-Agent Operating Architecture with Embodied Capability Modules](https://mlsanity.com/paper/2604.07039/) (2026)
- [ABot-Claw: A Foundation for Persistent, Cooperative, and Self-Evolving Robotic Agents](https://mlsanity.com/paper/2604.10096/) (2026)
- [BDIQA: A New Dataset for Video Question Answering to Explore Cognitive Reasoning through Theory of Mind](https://mlsanity.com/paper/2402.07402/) (2024)
- [CHARTOM: A Visual Theory-of-Mind Benchmark for LLMs on Misleading Charts](https://mlsanity.com/paper/2408.14419/) (2024)
- [From Black Boxes to Transparent Minds: Evaluating and Enhancing the Theory of Mind in Multimodal Large Language Models](https://mlsanity.com/paper/2506.14224/) (2025)
- [MindPower: Enabling Theory-of-Mind Reasoning in VLM-based Embodied Agents](https://mlsanity.com/paper/2511.23055/) (2025)
- [StreamingClaw Technical Report](https://mlsanity.com/paper/2603.22120/) (2026)
- [ROSClaw: An OpenClaw ROS 2 Framework for Agentic Robot Control and Interaction](https://mlsanity.com/paper/2603.26997/) (2026)
- [EnactToM: An Evolving Benchmark for Functional Theory of Mind in Embodied Agents](https://mlsanity.com/paper/2605.09826/) (2026)
- [Modeling the Mental World for Embodied AI: A Comprehensive Review](https://mlsanity.com/paper/2601.02378/) (2025)

## Abstract (arXiv)

Theory-of-Mind (ToM) reasoning enables embodied agents to understand human beliefs, goals, and intentions, but existing benchmarks mainly evaluate this ability through offline question answering or scenario-level action prediction. MindPower advances embodied ToM by introducing robot-centric reasoning from perception to action; however, it does not evaluate whether an agent can continuously interact with a changing environment and intervene only when assistance is needed. Building on MindPower, we introduce the MindHelper Challenge, which extends embodied ToM evaluation to real-time closed-loop precision intervention. An agent must continuously observe the environment, maintain actor-specific beliefs, identify when a human requires assistance, generate executable actions, and remain silent when intervention is unnecessary. We further propose MindClaw, a simple yet effective Claw-style framework that integrates an actor-specific Belief Table, embodied cognitive skills, and a Trigger-based cognitive dispatcher. Experiments show that MindClaw achieves 36.63% precise intervention rate and 14.36% task accuracy, substantially outperforming direct VLM baselines, whose corresponding results remain below 12.05% and 3.80%.