@inproceedings{miao-etal-2026-grounding,
title = "Grounding What Matters: Evidence-Aware Credit Assignment for Remote {S}ensing Reasoning",
author = "Miao, Ziqi and
Xie, Zhanxu and
Han, Liangzhe and
Xiong, Yuan and
Qian, Chen and
Li, Lijun and
Lv, Weifeng and
Sun, Leilei",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2269/",
pages = "51598--51627",
ISBN = "979-8-89176-459-0",
abstract = "Remote sensing has emerged as an important domain for multimodal large language models (MLLMs), where reliable understanding requires locating small, dense, and spatially subtle visual evidence. However, our diagnostic analysis shows that current MLLMs often fail because their reasoning is not grounded to the correct visual evidence, leading to wrong-region attention or missed task-relevant objects. To address this issue, we introduce RsEvi-8B, an evidence-aware remote sensing MLLM that reasons with parseable bounding-box-grounded evidence. To enable this capability, we construct RsEvi-248K, a large-scale supervised dataset spanning VQA, visual grounding, object detection, object counting, and scene classification. The dataset provides verified bounding-box-grounded reasoning traces, where each intermediate visual claim is aligned with supporting evidence regions and filtered for both answer correctness and evidence faithfulness. To improve evidence-faithful reasoning, we further propose the evidence-aware GRPO, which uses an evidence verifier to regulate learning on generated evidence spans while preserving task-level outcome optimization. Extensive experiments across five remote-sensing task families show that RsEvi-8B improves the dataset-balanced average score by 17.15 points over Qwen3-VL-8B-Instruct, establishing explicit visual grounding as a key ingredient for reliable remote-sensing reasoning."
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<abstract>Remote sensing has emerged as an important domain for multimodal large language models (MLLMs), where reliable understanding requires locating small, dense, and spatially subtle visual evidence. However, our diagnostic analysis shows that current MLLMs often fail because their reasoning is not grounded to the correct visual evidence, leading to wrong-region attention or missed task-relevant objects. To address this issue, we introduce RsEvi-8B, an evidence-aware remote sensing MLLM that reasons with parseable bounding-box-grounded evidence. To enable this capability, we construct RsEvi-248K, a large-scale supervised dataset spanning VQA, visual grounding, object detection, object counting, and scene classification. The dataset provides verified bounding-box-grounded reasoning traces, where each intermediate visual claim is aligned with supporting evidence regions and filtered for both answer correctness and evidence faithfulness. To improve evidence-faithful reasoning, we further propose the evidence-aware GRPO, which uses an evidence verifier to regulate learning on generated evidence spans while preserving task-level outcome optimization. Extensive experiments across five remote-sensing task families show that RsEvi-8B improves the dataset-balanced average score by 17.15 points over Qwen3-VL-8B-Instruct, establishing explicit visual grounding as a key ingredient for reliable remote-sensing reasoning.</abstract>
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%0 Conference Proceedings
%T Grounding What Matters: Evidence-Aware Credit Assignment for Remote Sensing Reasoning
%A Miao, Ziqi
%A Xie, Zhanxu
%A Han, Liangzhe
%A Xiong, Yuan
%A Qian, Chen
%A Li, Lijun
%A Lv, Weifeng
%A Sun, Leilei
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F miao-etal-2026-grounding
%X Remote sensing has emerged as an important domain for multimodal large language models (MLLMs), where reliable understanding requires locating small, dense, and spatially subtle visual evidence. However, our diagnostic analysis shows that current MLLMs often fail because their reasoning is not grounded to the correct visual evidence, leading to wrong-region attention or missed task-relevant objects. To address this issue, we introduce RsEvi-8B, an evidence-aware remote sensing MLLM that reasons with parseable bounding-box-grounded evidence. To enable this capability, we construct RsEvi-248K, a large-scale supervised dataset spanning VQA, visual grounding, object detection, object counting, and scene classification. The dataset provides verified bounding-box-grounded reasoning traces, where each intermediate visual claim is aligned with supporting evidence regions and filtered for both answer correctness and evidence faithfulness. To improve evidence-faithful reasoning, we further propose the evidence-aware GRPO, which uses an evidence verifier to regulate learning on generated evidence spans while preserving task-level outcome optimization. Extensive experiments across five remote-sensing task families show that RsEvi-8B improves the dataset-balanced average score by 17.15 points over Qwen3-VL-8B-Instruct, establishing explicit visual grounding as a key ingredient for reliable remote-sensing reasoning.
%U https://aclanthology.org/2026.emnlp-main.2269/
%P 51598-51627
Markdown (Informal)
[Grounding What Matters: Evidence-Aware Credit Assignment for Remote Sensing Reasoning](https://aclanthology.org/2026.emnlp-main.2269/) (Miao et al., EMNLP 2026)
ACL
- Ziqi Miao, Zhanxu Xie, Liangzhe Han, Yuan Xiong, Chen Qian, Lijun Li, Weifeng Lv, and Leilei Sun. 2026. Grounding What Matters: Evidence-Aware Credit Assignment for Remote Sensing Reasoning. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 51598–51627, Budapest, Hungary. Association for Computational Linguistics.