arXiv AI
MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
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arXiv AI
Jun 2
MindClaw: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
arXiv:2606. 01063v1 Announce Type: new Abstract: Theory of Mind (ToM) enables an agent to reason about another actor's beliefs, goals, and intentions, which is essential for human-centered embodied assistance.
arXiv AI
Jun 2
MindZero: Learning Online Mental Reasoning With Zero Annotations
arXiv:2606. 00240v1 Announce Type: new Abstract: Effective real-world assistance requires AI agents with robust Theory of Mind (ToM): inferring human mental states from their behavior.
arXiv Computer Vision
Oct 2
Guide, Think, Act: Interactive Embodied Reasoning in Vision-Language-Action Models
The paper introduces GTA‑VLA, an interactive Vision‑Language‑Action framework that lets users guide robot policies with explicit visual cues such as affordance points, boxes, and traces. Unlike traditional direct sense‑to‑act models, GTA‑VLA incorporates a spatial‑visual Chain‑of‑Thought that blends human guidance with internal task planning, and couples this reasoning module with a lightweight reactive action head for efficient execution. Experiments on the SimplerEnv WidowX benchmark show a state‑of‑the‑art 81.2 % success rate, and the framework significantly improves task success under out‑of‑domain visual shifts and spatial ambiguities, demonstrating the benefit of interactive reasoning for failure recovery in embodied control.
arXiv AI
Oct 15
ParallelWorld: Test-Time Scaling for Agentic Embodied Reasoning
arXiv:2608.22971v2 Announce Type: replace Abstract: Embodied AI has emerged as a key paradigm for autonomous systems operating in complex physical environments. While end-to-end models have establish...
arXiv Machine Learning
Jul 30
Think Short, Defer Smart, Act, and Repeat: Calibrated Reasoning and Uncertainty-Aware Deferral for Edge LLM Agents
arXiv:2607. 26865v1 Announce Type: cross Abstract: LLM agents following the ReAct paradigm are promising enablers of complex multi-step tasks, including multi-hop question answering, code generation, and control of physical AI systems.
arXiv Machine Learning
Aug 27
Infer Human's Intentions Before Following Natural Language Instructions
The paper introduces FISER, a framework that explicitly infers human goals and intentions before planning actions for AI agents to follow natural language instructions in collaborative embodied tasks. It employs Transformer-based models and is evaluated on the HandMeThat benchmark, outperforming end-to-end approaches and strong baselines such as Chain of Thought prompting. FISER achieves state‑of‑the‑art performance on this embodied social reasoning task.