While Vision-Language Models (VLMs) are set to transform robotic navigation, existing methods often underutilize their reasoning capabilities. To unlock the full potential of VLMs in robotics, we shift their role from passive observers to active strategists in the navigation process. Our framework outsources high-level planning to a VLM, which leverages its contextual understanding to guide a frontier-based exploration agent. This intelligent guidance is achieved through a trio of techniques: structured chain-of-thought prompting that elicits logical, step-by-step reasoning; dynamic inclusion of the agent's recent action history to prevent getting stuck in loops; and a novel capability that enables the VLM to interpret top-down obstacle maps alongside first-person views, thereby enhancing spatial awareness. When tested on challenging benchmarks like HM3D, Gibson, and MP3D, this method produces exceptionally direct and logical trajectories, marking a substantial improvement in navigation efficiency over existing approaches and charting a path toward more capable embodied agents.

The MIT Stephen A. Schwarzman College of Computing (SCC) will reorient the Institute to bring the power of computing and artificial intelligence to all fields at MIT, and to allow the future of computing and AI to be shaped by all MIT disciplines. To support ongoing planning for the new college, Dean Melissa Nobles invited faculty from all 14 of MIT's humanistic disciplines in the School of Humanities, Arts, and Social Sciences to respond to two questions: As Nobles says in her foreword to the series, "Together, the following responses to these two questions offer something of a guidebook to the myriad, productive ways that technical, humanistic, and scientific fields can join forces at MIT, and elsewhere, to further human and planetary well-being." The following excerpts highlight faculty responses, with links to full commentaries. The excerpts are sequenced by fields in the following order: the humanities, arts, and social sciences. "The advent of artificial intelligence presents our species with an historic opportunity -- disguised as an existential challenge: Can we stay human in the age of AI? In fact, can we grow in humanity, can we shape a more humane, more just, and sustainable world? With a sense of promise and urgency, we are embarked at MIT on an accelerated effort to more fully integrate the technical and humanistic forms of discovery in our curriculum and research, and in our habits of mind and action."

The MIT Stephen A. Schwarzman College of Computing (SCC) will reorient the Institute to bring the power of computing and artificial intelligence to all fields at MIT, and to allow the future of computing and AI to be shaped by all MIT disciplines. To support ongoing planning for the new college, Dean Melissa Nobles invited faculty from all 14 of MIT's humanistic disciplines in the School of Humanities, Arts, and Social Sciences to respond to two questions: As Nobles says in her foreword to the series, "Together, the following responses to these two questions offer something of a guidebook to the myriad, productive ways that technical, humanistic, and scientific fields can join forces at MIT, and elsewhere, to further human and planetary well-being." The following excerpts highlight faculty responses, with links to full commentaries. The excerpts are sequenced by fields in the following order: the humanities, arts, and social sciences. "The advent of artificial intelligence presents our species with an historic opportunity -- disguised as an existential challenge: Can we stay human in the age of AI? In fact, can we grow in humanity, can we shape a more humane, more just, and sustainable world? With a sense of promise and urgency, we are embarked at MIT on an accelerated effort to more fully integrate the technical and humanistic forms of discovery in our curriculum and research, and in our habits of mind and action."

The MIT Stephen A. Schwarzman College of Computing (SCC) will reorient the Institute to bring the power of computing and artificial intelligence to all fields at MIT, and to allow the future of computing and AI to be shaped by all MIT disciplines. To support ongoing planning for the new college, Dean Melissa Nobles invited faculty from all 14 of MIT's humanistic disciplines in the School of Humanities, Arts, and Social Sciences to respond to two questions: As Nobles says in her foreword to the series, "Together, the following responses to these two questions offer something of a guidebook to the myriad, productive ways that technical, humanistic, and scientific fields can join forces at MIT, and elsewhere, to further human and planetary well-being." The following excerpts highlight faculty responses, with links to full commentaries. The excerpts are sequenced by fields in the following order: the humanities, arts, and social sciences. "The advent of artificial intelligence presents our species with an historic opportunity -- disguised as an existential challenge: Can we stay human in the age of AI? In fact, can we grow in humanity, can we shape a more humane, more just, and sustainable world? With a sense of promise and urgency, we are embarked at MIT on an accelerated effort to more fully integrate the technical and humanistic forms of discovery in our curriculum and research, and in our habits of mind and action."

In the context of tree-search stochastic planning algorithms where a generative model is available, we consider on-line planning algorithms building trees in order to recommend an action. We investigate the question of avoiding re-planning in subsequent decision steps by directly using sub-trees as action recommender. Firstly, we propose a method for open loop control via a new algorithm taking the decision of re-planning or not at each time step based on an analysis of the statistics of the sub-tree. Secondly, we show that the probability of selecting a suboptimal action at any depth of the tree can be upper bounded and converges towards zero. Moreover, this upper bound decays in a logarithmic way between subsequent depths. This leads to a distinction between node-wise optimality and state-wise optimality. Finally, we empirically demonstrate that our method achieves a compromise between loss of performance and computational gain.