# ProFocus: Proactive Perception and Focused Reasoning in Vision-and-Language Navigation

arXiv: https://arxiv.org/abs/2603.05530 · HTML: https://mlsanity.com/paper/2603.05530/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 14 of its 57 references that are in the map.
- First posted on arXiv 1 Mar 2026.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)

## Builds on (most cited first, 14 of 14)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [DeepSeek-V3 Technical Report](https://mlsanity.com/paper/2412.19437/) (2024)
- [GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning](https://mlsanity.com/paper/2507.01006/) (2025)
- [Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models](https://mlsanity.com/paper/2501.09686/) (2025)
- [Embodied Navigation Foundation Model](https://mlsanity.com/paper/2509.12129/) (2025)
- [Nav-R1: Reasoning and Navigation in Embodied Scenes](https://mlsanity.com/paper/2509.10884/) (2025)
- [Reinforcement Learning Meets Large Language Models: A Survey of Advancements and Applications Across the LLM Lifecycle](https://mlsanity.com/paper/2509.16679/) (2025)
- [CorrectNav: Self-Correction Flywheel Empowers Vision-Language-Action Navigation Model](https://mlsanity.com/paper/2508.10416/) (2025)
- [Cross from Left to Right Brain: Adaptive Text Dreamer for Vision-and-Language Navigation](https://mlsanity.com/paper/2505.20897/) (2025)
- [SAIL-Embedding Technical Report: Omni-modal Embedding Foundation Model](https://mlsanity.com/paper/2510.12709/) (2025)
- [Improving Multimodal Sentiment Analysis via Modality Optimization and Dynamic Primary Modality Selection](https://mlsanity.com/paper/2511.06328/) (2025)
- [EvolveNav: Empowering LLM-Based Vision-Language Navigation via Self-Improving Embodied Reasoning](https://mlsanity.com/paper/2506.01551/) (2025)
- [MSNav: Zero-Shot Vision-and-Language Navigation with Dynamic Memory and LLM Spatial Reasoning](https://mlsanity.com/paper/2508.16654/) (2025)

## Built on by (most cited first, 4 of 4)

- [World Models as Group Actions](https://mlsanity.com/paper/2605.24578/) (2026)
- [Rank-Consistent Set Reasoning for Co-Salient Object Detection](https://mlsanity.com/paper/2609.13706/) (2026)
- [Fysiverse-3D-Vision Technical Report: Generating Executable 3D Worlds from Images through Unified Spatial Reasoning](https://mlsanity.com/paper/2609.25741/) (2026)
- [Robust Vision-Language Navigation with Awareness of Instruction-Execution Progress](https://mlsanity.com/paper/2610.13104/) (2026)

## Abstract (arXiv)

Vision-and-Language Navigation (VLN) requires agents to accurately perceive complex visual environments and reason over navigation instructions and histories. However, existing methods passively process redundant visual inputs and treat all historical contexts indiscriminately, resulting in inefficient perception and unfocused reasoning. To address these challenges, we propose ProFocus, a training-free progressive framework that unifies underlineProactive Perception and underlineFocused Reasoning through collaboration between large language models (LLMs) and vision-language models (VLMs). For proactive perception, ProFocus transforms panoramic observations into structured ego-centric semantic maps, enabling the orchestration agent to identify missing visual information needed for reliable decision-making, and to generate targeted visual queries with corresponding focus regions that guide the perception agent to acquire the required observations. For focused reasoning, we propose Branch-Diverse Monte Carlo Tree Search (BD-MCTS) to identify top-k high-value waypoints from extensive historical candidates. The decision agent focuses reasoning on the historical contexts associated with these waypoints, rather than considering all historical waypoints equally. Extensive experiments validate the effectiveness of ProFocus, achieving state-of-the-art performance among zero-shot methods on R2R and REVERIE benchmarks.