# Think and Answer ME: Benchmarking and Exploring Multi-Entity Reasoning Grounding in Remote Sensing

Source: [Think and Answer ME: Benchmarking and Exploring Multi-Entity Reasoning Grounding in Remote Sensing](<https://www.sota2.com/research/paper/69b78e690636e5bf4afbdac8>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-03-24T05:00:03.763Z

Authors: Shuchang Lyu, Haiquan Wen, Guangliang Cheng, Meng Li, Zheng Zhou, You Zhou, Dingding Yao, Zhenwei Shi

Published: 2026-03-13T08:47:19.000Z

## Abstract

Recent advances in reasoning language models and reinforcement learning with verifiable rewards have significantly enhanced multi-step reasoning capabilities. This progress motivates the extension of reasoning paradigms to remote sensing visual grounding task. However, existing remote sensing grounding methods remain largely confined to perception-level matching and single-entity formulations, limiting the role of explicit reasoning and inter-entity modeling. To address this challenge, we introduce a new benchmark dataset for Multi-Entity Reasoning Grounding in Remote Sensing (ME-RSRG). Based on ME-RSRG, we reformulate remote sensing grounding as a multi-entity reasoning task and propose an Entity-Aware Reasoning (EAR) framework built upon visual-linguistic foundation models. EAR generates structured reasoning traces and subject-object grounding outputs. It adopts supervised fine-tuning for cold-start initialization and is further optimized via entity-aware reward-driven Group Relative Policy Optimization (GRPO). Extensive experiments on ME-RSRG demonstrate the challenges of multi-entity reasoning and verify the effectiveness of our proposed EAR framework. Our dataset, code, and models will be available at https://github.com/CV-ShuchangLyu/ME-RSRG.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Multi-entity reasoning grounding on ME-RSRG (test)](<https://www.sota2.com/research/sota/multi-entity-reasoning-grounding-on-me-rsrg-test.md>): Multi-entity reasoning grounding; ME-RSRG (test); Subject Accuracy (IoU=0.5): 44.4.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2603.12788>)

[PDF](<https://arxiv.org/pdf/2603.12788.pdf>)