Asking like Socrates: Socrates helps VLMs understand remote sensing images
Recent multimodal reasoning models, inspired by DeepSeek-R1, have significantly advanced vision-language systems. However, in remote sensing (RS) tasks, we observe widespread pseudo reasoning: models narrate the process of reasoning rather than genuinely reason toward the correct answer based on visual evidence. We attribute this to the Glance Effect, where a single, coarse perception of large-scale RS imagery results in incomplete understanding and reasoning based on linguistic self-consistency instead of visual evidence. To address this, we propose RS-EoT (Remote Sensing Evidence-of-Thought), a language-driven, iterative visual evidence-seeking paradigm. To instill this paradigm, we propose SocraticAgent, a self-play multi-agent system that synthesizes reasoning traces via alternating cycles of reasoning and visual inspection. To enhance and generalize these patterns, we propose a two-stage progressive RL strategy: first, RL on fine-grained Grounding tasks to enhance RS-EoT capabilities, followed by RL on RS VQA to generalize to broader understanding scenarios. Experiments show RS-EoT achieves state-of-the-art performance on multiple RS VQA and grounding benchmarks. Analyses reveal clear iterative cycles of reasoning and evidence seeking, confirming RS-EoT mitigates the Glance Effect and enables genuine evidence-grounded reasoning. Our code, data, and models are available at https://geox-lab.github.io/Asking_like_Socrates
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Run Shao
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2025-12-02T08:49:46.409Z
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RunShao
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Ziyu Li
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Zhaoyang Zhang
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2026-03-05T09:58:30.265Z
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Zhaoyang Zhang
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Linrui Xu
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Xinran He
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Hongyuan Yuan
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Bolei He
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2026-02-06T18:57:26.613Z
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stoneustc
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Yongxing Dai
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Yiming Yan
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Yijun Chen
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Wang Guo
692d1fbc4397b1ec214f68de
Haifeng Li
Asking like Socrates: Socrates helps VLMs understand remote sensing images
Recent multimodal reasoning models, inspired by DeepSeek-R1, have significantly advanced vision-language systems. However, in remote sensing (RS) tasks, we observe widespread pseudo reasoning: models narrate the process of reasoning rather than genuinely reason toward the correct answer based on visual evidence. We attribute this to the Glance Effect, where a single, coarse perception of large-scale RS imagery results in incomplete understanding and reasoning based on linguistic self-consistency instead of visual evidence. To address this, we propose RS-EoT (Remote Sensing Evidence-of-Thought), a language-driven, iterative visual evidence-seeking paradigm. To instill this paradigm, we propose SocraticAgent, a self-play multi-agent system that synthesizes reasoning traces via alternating cycles of reasoning and visual inspection. To enhance and generalize these patterns, we propose a two-stage progressive RL strategy: first, RL on fine-grained Grounding tasks to enhance RS-EoT capabilities, followed by RL on RS VQA to generalize to broader understanding scenarios. Experiments show RS-EoT achieves state-of-the-art performance on multiple RS VQA and grounding benchmarks. Analyses reveal clear iterative cycles of reasoning and evidence seeking, confirming RS-EoT mitigates the Glance Effect and enables genuine evidence-grounded reasoning. Our code, data, and models are available at https://geox-lab.github.io/Asking_like_Socrates
RS-EoT, a language-driven iterative visual evidence-seeking paradigm, addresses pseudo reasoning in remote sensing tasks by using a self-play multi-agent system and a two-stage RL strategy, achieving state-of-the-art performance on RS VQA benchmarks.
multimodal reasoning DeepSeek-R1 remote sensing pseudo reasoning Glance Effect RS-EoT language-driven iterative visual evidence-seeking SocraticAgent self-play multi-agent reasoning traces visual inspection progressive RL Grounding tasks RS VQA evidence-grounded reasoning