EAGLE-360: Embodied Active Global-to-Local Exploration in $360^{\circ }$

Precise target localization in $360^{\circ }$ environments requires integrating global spatial context with fine-grained visual evidence while accounting for projection distortion and horizontal wrap-around geometry. For multimodal large language models (MLLMs), the challenge is not only to select informative views but also to derive geometrically consistent estimates of the target direction from visual observations. We present EAGLE-360, an Embodied Active Global-to-Local Exploration framework that leverages panoramic context to guide iterative, tool-augmented target localization. We introduce Spherical Atlas Rotary Position Embedding (AtlasRoPE) for local spherical positional modeling, alongside Spherically Stratified Replay (SSR) and Spherical Direction Regularization (SDR) for geometry-aware training. This training strategy increases exposure to underrepresented directions and incorporates continuous spherical supervision, explicitly accounting for seam-adjacent and high-latitude targets without adding inference-time modules. To support this framework, we construct a curated panoramic search dataset with multi-turn trajectories interleaving reasoning, projection actions, and visual observations. We train the model through supervised fine-tuning (SFT) followed by reinforcement learning based on Group Relative Policy Optimization (GRPO) to develop its multi-turn search capabilities. Experiments show that EAGLE-360-8b leads the main comparison on EAGLE-360 with 84.01% localization accuracy, together with strong transfer to H${}^{\ast }$Bench, achieving 75.00% object-search success under panoramic initialization without benchmark-specific training.