# EAGLE-360: Embodied Active Global-to-Local Exploration in 360^circ

arXiv: https://arxiv.org/abs/2607.02479 · HTML: https://mlsanity.com/paper/2607.02479/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 28 of its 41 references that are in the map.
- First posted on arXiv 2 Jul 2026.

## Placed in

- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)

## Builds on (most cited first, 28 of 28)

- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [HybridFlow: A Flexible and Efficient RLHF Framework](https://mlsanity.com/paper/2409.19256/) (2024)
- [InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency](https://mlsanity.com/paper/2508.18265/) (2025)
- [Matterport3D: Learning from RGB-D Data in Indoor Environments](https://mlsanity.com/paper/1709.06158/) (2017)
- [Spherical CNNs](https://mlsanity.com/paper/1801.10130/) (2018)
- [Joint 2D-3D-Semantic Data for Indoor Scene Understanding](https://mlsanity.com/paper/1702.01105/) (2017)
- [REVERIE: Remote Embodied Visual Referring Expression in Real Indoor Environments](https://mlsanity.com/paper/1904.10151/) (2019)
- [Towards Learning a Generalist Model for Embodied Navigation](https://mlsanity.com/paper/2312.02010/) (2023)
- [Habitat-Web: Learning Embodied Object-Search Strategies from Human Demonstrations at Scale](https://mlsanity.com/paper/2204.03514/) (2022)
- [HorizonNet: Learning Room Layout with 1D Representation and Pano Stretch Data Augmentation](https://mlsanity.com/paper/1901.03861/) (2019)
- [HoHoNet: 360 Indoor Holistic Understanding with Latent Horizontal Features](https://mlsanity.com/paper/2011.11498/) (2020)
- [GOAT-Bench: A Benchmark for Multi-Modal Lifelong Navigation](https://mlsanity.com/paper/2404.06609/) (2024)
- [Pano-AVQA: Grounded Audio-Visual Question Answering on 360^circ Videos](https://mlsanity.com/paper/2110.05122/) (2021)
- [HM3D-OVON: A Dataset and Benchmark for Open-Vocabulary Object Goal Navigation](https://mlsanity.com/paper/2409.14296/) (2024)
- [Embodied-Reasoner: Synergizing Visual Search, Reasoning, and Action for Embodied Interactive Tasks](https://mlsanity.com/paper/2503.21696/) (2025)
- [360-Indoor: Towards Learning Real-World Objects in 360deg Indoor Equirectangular Images](https://mlsanity.com/paper/1910.01712/) (2019)
- [Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning?](https://mlsanity.com/paper/2505.11907/) (2025)
- [PanoSwin: a Pano-style Swin Transformer for Panorama Understanding](https://mlsanity.com/paper/2308.14726/) (2023)
- [SphereUFormer: A U-Shaped Transformer for Spherical 360 Perception](https://mlsanity.com/paper/2412.06968/) (2024)
- [Towards Omnidirectional Reasoning with 360-R1: A Dataset, Benchmark, and GRPO-based Method](https://mlsanity.com/paper/2505.14197/) (2025)
- [ODI-Bench: Can MLLMs Understand Immersive Omnidirectional Environments?](https://mlsanity.com/paper/2510.11549/) (2025)
- [Dense360: Dense Understanding from Omnidirectional Panoramas](https://mlsanity.com/paper/2506.14471/) (2025)
- [PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction](https://mlsanity.com/paper/2507.21960/) (2025)
- [PanoEnv: Exploring 3D Spatial Intelligence in Panoramic Environments with Reinforcement Learning](https://mlsanity.com/paper/2602.21992/) (2026)
- [360deg Image Perception with MLLMs: A Comprehensive Benchmark and a Training-Free Method](https://mlsanity.com/paper/2603.16179/) (2026)
- [ReasonNavi: Human-Inspired Global Map Reasoning for Zero-Shot Embodied Navigation](https://mlsanity.com/paper/2602.15864/) (2026)

## Built on by (most cited first, 1 of 1)

- [SCCM: Spherically Consistent Coarse Matching for ERP Dense Feature Correspondence](https://mlsanity.com/paper/2609.36545/) (2026)

## Abstract (arXiv)

While Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in standard visual understanding, adapting them for active visual search in 360^circ panoramic environments exposes fundamental limitations. Specifically, standard MLLMs struggle to effectively model inherent panoramic properties, such as severe polar distortion and continuous cylindrical topologies, which significantly degrades target detection accuracy. Consequently, existing panoramic search methods attempt to compensate by relying heavily on fragmented local viewpoints. Burdened by rigid initialization and a lack of global panoramic priors, these approaches suffer from myopic, inefficient exploration and struggle with robust error recovery when targets are out of view. To overcome these challenges, we propose EAGLE-360, a novel Embodied Active Global-to-Local Exploration framework. Rather than performing exhaustive local searches, EAGLE-360 leverages global priors to establish an initial holistic perspective, iteratively reasoning and progressively narrowing the search space. Architecturally, we adapt RoPE Rolling, a coordinate-shifting positional encoding mechanism, to seamlessly model the continuous topologies of panoramas. To facilitate this paradigm, we construct the large-scale EAGLE-360 dataset, comprising 14,000+ 4K panoramas and 70,000+ rounds of high-quality VQA dialogues. By employing a training pipeline that integrates Supervised Fine-Tuning (SFT) with Group Relative Policy Optimization (GRPO), we effectively elicit complex spatial reasoning and tool-calling capabilities. Extensive experiments demonstrate that EAGLE-360 establishes a new state-of-the-art for 360^circ visual search, achieving nearly an 8-fold increase in accuracy over the base model while significantly enhancing exploration efficiency.