Towards Faithful Reasoning in Remote Sensing: A Perceptually-Grounded GeoSpatial Chain-of-Thought for Vision-Language Models
Authors: Jiaqi Liu
Lang Sun
Ronghao Fu
Bo Yang
Venue: ICLR 2026 Poster (2026)
Keywords: Remote Sensing
Geospatial AI
Vision Language Model
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Vision-Language Models (VLMs) in remote sensing often fail at complex analytical tasks, a limitation stemming from their end-to-end training paradigm that bypasses crucial reasoning steps and leads to unverifiable outputs. To address this limitation, we introduce the Perceptually-Grounded Geospatial Chain-of-Thought (Geo-CoT), a framework that models remote sensing analysis as a verifiable, multi-step process. We instill this analytical process through a two-stage alignment strategy, leveraging Geo-CoT380k, the first large-scale dataset of structured Geo-CoT rationales. This strategy first employs supervised fine-tuning (SFT) to instill the foundational cognitive architecture, then leverages Group Reward Policy Optimization (GRPO) to refine the model’s reasoning policy towards factual correctness. The resulting model, RSThinker, outputs both a final answer and its justifying, verifiable analytical trace. This capability yields dominant performance, significantly outperforming state-of-the-art models across a comprehensive range of tasks. The public release of our Geo-CoT380k dataset and RSThinker model upon publication serves as a concrete pathway from opaque perception towards structured, verifiable reasoning for Earth Observation.

Published as a conference paper at ICLR 2026
TOWARDS FAITHFUL REASONING IN REMOTE SENS- ING: A SEREETUULLY-GRONNDE GEOSPATIAL CHAIN-OF-THOUGHH FOR VISION-LANGUAGE MOD- ELS
Jiaqi Liu 1,2 Li .Lann Sun 12, Ronghao Fu 12, Bo Yang!
Colleee of Computer Science and Technology. Jilin University, Changchun 130012, China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education (liujq21, 1 sun ang24 @mail 1s. flu. edu.cn, cn, (furh, ybo] @ llu edu.cn
ABSTRACT
Vision-Language Models (VLMs) in remote sensing often fail complex ana- lytical tasks, a limitation stemming from their end-to-end training paradigm that bypasses crucial reasoning steps and leads unverifiable outputs. To address this limitation, we introduce the Perceptually-Grronddee Geospatial Saaai--ff-Thuglhe (Geo-CoT), framework that models remote sensing analysis as a verifiable, multi-step process. We instill this analytical process through a two-stage align- ment strategy, leveraging Geo-CoT380k, the first large-scale dataset of structured Geo-CoT rationales. This strategy first employs supervised fine-tuning (SFT) instill the foundational cognitive architecture, then leverages Group Reward Pol- icy Optimization (GRPO) to refine the model's reasoning policy towards factual correctness. The resulting model, RSThinker, outputs both a final answer and its justifying. verifiable analytical trace. This capability yields dominant perfor- mance, significantly outperforming state-of-the-art models across comprehen- sive range of tasks. The public release of our Geo-CoT380k dataset and RS- Thinker model upon publication serves as concrete pathway from opaque per- ception towards structured, verifiable reasoning for Earth Observation.
INTRODUCTION
Vision-L.anguage Models (VLMs) are rapidly redefining the analytical landscape for remote sens- ing, offering unprecedented capabilities for interpreting Earth Observation data (Kuckreja et al.) 2024| Zhang et al.. 2024; Soni et al. 2025 Pang et al., 2025 Fu et al. 2024b|a) ap. These capabil- ities are demonstrated across diverse array of downstream tasks, from complex visual question answering (VQA) to fine-grained object counting. Yet, the prevailing paradigm of these models involves learning implicit, end-to-end mapping directly from pixels final output. Such an implicit mapping, by collapsing the entire reasoning process into a monolithic transformation, lacks procedural transparency and is consequently prone generating plausible yet factually ungrounded hallucinations. The risk of such hallucinations presents a formidable barrier in high-stakes remote sensing applications, like disaster response (Misra al.) [025 Lenton et al. (0224) or environmental monitoring (Wang et al.) (2025) (Silsbe et al. (2025). where the verifiability of a result is paramount. In these critical applications, the ultimate utility of model hinges not merely on the correctness of its output, but on the verifiability of the process that produced it.
This demand for a verifiable process motivates paradigm shift from passive recognition goal- directed active perception, a potential unlocked by the Multimodal Chain-of- (MM-CoT) paradigm (Mitra al.) 2024, (Sha et al.) (2024) Gao et al.) (002).. The promise of MM-CoT lies in its capacity to formulate and externalize an analytical plan, thereby transforming model from a black- box recognizer into a methodical analyst. The necessity for such an explicit plan is uniquely acute
"Equal contribution.
1 "orresponniig authors.

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Published as a conference paper at ICLR 2026
First CoT Dataset for Remote Sensing
SOTA Performance
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Figure 1: An overview of the RSThinker framework. Our novel Geo-CoT38Ok dataset (a) enables our two-stage alignment strategy (b) to instill verifiable reasoning process (c), yielding state-of- the-art performance across a comprehensive suite of benchmarks (d).
in Earth Observation, where analytical challenges are multifaceted and deeply intertwined. This complexity directly translates into the challenge of navigating the sheer scale of regional-scale im- agery with systematic search strategies, a requirement exemplified by tasks such as object counting. These strategies must in turn be guided by forensic discrimination of subtle textural cues to resolve semantic ambiguities. This entire analytical process is often further constrained by the prevalence queries, demanding computational paths such tracing river network
of oopoogicalllyggooune to locate every crossing bridge. These expert strategies, when externalized into a structured and (Geo-CoT)..
verifiable sequence, constitute what we introduce as the Geospatial
Despite the clear need for such a Geo-CoT, prevailing approaches often frame reasoning as a process of semantic interpretation rather than visual investigation (Li et al., 2025a Zhu et al. 2025). This interpretation relies on the model's parametric world knowledge for high-level deductions, such as identifying a stadium a suitable evacuation point post-carthquake, rather than grounding its claims in immediate visual evidence. Even when contemporary models do attempt to incorporate visual evidence (ao al. 2025 Hu et al.) 2025), it is typically presented as non-localizable text, mentioned without a verifiable link to a specific pixel region, thus leaving its claims unsubstantiated against hallucinated artifacts. This absence of a verifiable link stems from more fundamental limitation: the lack of an intent-driven process for active perception. Instead of formulating and executing decomposed analytical plan, these models perform holistic, single-pass inference over the entire scene. This reactive inference is incapable of the systematic evidence gathering required for faithful reasoning, leaving critical gap between the conceptual promise of MM-CoT and its practical realization in Earth Observation.
To bridge this critical gap in Earth Observation, we introduce a novel framework that instantiates the -rruuudlllyGrrundeee Perceptually- Grounded Geospatial Chain-of- Chain-of-Thought (Geo-CoT) within Vision-Language Models. Our framework materializes a rigorous cognitive architecture whose foundational principle is strict perceptual grounding, where abstract claims are replaced by assertions explicitly linked specific spatial references. The operational flow of this grounding process follows a clear protocol of task planning, iterative evidence gathering, and final synthesis, enabling the VLMs to perform methodical visual interrogation rather than reactive, holistic inference. We instill this reasoning protocol by first constructing Geo-CoT330k, a large-scale dataset populated via a scalable pipeline that retrofits verifiable rationales onto ground-truth data, and then leveraging this dataset in a two-stage alignment strategy. This strategy, a paradigm informed by recent large-scale LLM development (DeepSeek- Al 2025 Guo et al. 2025), effectively decouples the architectural challenge of instilling cognitive structure from the policy challenge of refining its factual correctness. Our first stage, supervised fine-tuning (SFT), establishes the foundational cognitive structure. followed by a subsequent stage leveraging Group Relative Policy Optimization (GRPO) to steer the model's generative process towards high-fidelity reasoning chains. Our primary contributions can be summarized as follows:
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Published as a conference paper at ICLR 2026
We define and formalize the Perceptually- Berceptually-Groundee remote sensing that mandates a verifiable link between each analytical step and its corre- sponding visual evidence.
Grounded Geo-Col reasoning paradigm for
We construct the first large-scale supervised fine-tuning (SFT) dataset for remote sensing chain-of-thougge Geo-CoT380k, explicitly designed to instill the cognitive architecture of task decomposition, iterative evidence grounding, and final synthesis.
We present RSThinker, VLM embodying our framework, demonstrating that two-stage alignment strategy of SFT as a prerequisite for reinforcement learning (GRPO) is essen- tial for faithfully eliciting this capability and setting a new state-of-the-an on suite of canonical remote sensing tasks, including visual question answering and object counting.
RELATED WORK
2.1 VISION-LANGUAAA MODELS REMOTE SENSING
The application of Vision-Language Models (VLMs) to remote sensing has recently catalyzed surge of innovation, fundamentally altering interactions with Earth Ohservation data. Pioneering works such GeoChat (Kuckreja et al. 2024) and EarthGPT (Zhang al. (024) established the viability of equipping VLMs with the capacity for geospatial dialogue and handling wide spec- trum of queries. Subsequent models like EarthDial (Soni et al. 2025). VHM (Pang et al. 2025), SkyMoE (Liu et al. 2025b) and GeoDiT (Liu et al. 2025a) further refined this interactive paradigm through enhanced conversational fluency and novel architectural designs, achieving state-of-the--e performance on canonical benchmarks. Yet, common architectural paradigm unites these power- ful models: they are fundamentally optimized to map visual inputs to a final textual output. This end-to-end optimization, while successful, inherently treats the intermediate reasoning process latent and inaccessible variable. Consequently, a critical gap persists: the lack of a VLM capable of not only producing correct answer, but also externalizing the verifiable, step-by-step analytical process that justifies it. Our work is explicitly designed to bridge this gap.
2.2 CUAIN-OF-THOUUGN AND REASONING IN VISION-LANGUAGE MODELS
The pursuit of a verifiable analytical process finds its intellectual origins in Chain-of-Thought (CoT) reasoning, a paradigm first established elicit step-by-step thinking in language models. This paradigm has recently evolved into Grounded CoT within the general computer vision commu- nity. where abstract reasoning explicitly anchored visual evidence. Pioneering frameworks such as Visual CoT (Shao et al., 2024), VoCoT (Li et al. 2025b) and Argus (Man et al. 2025) have demonstrated the efficacy of interleaving bounding boxes within reasoning traces, while ap- proaches like V* (Wu & Xiel (024) and CMMCoT (Zhang et al. (025) have explored guided visual search and memory augmentation to handle complex contexts. This methodological progression has demonstrated remarkable success in domains predicated on the presence of salient, well-defined entities. Existing frameworks thrive by reasoning over holistic objects, such as vehicles traffic scenes (Wang et al., 2024 Mandalika et al. 2025) instruments in medical images (Liu et al. 2024a Jiang et al., 2025). ). However, this reliance on discrete, salient objects reveals a fundamental perceptual mismatch with the nature of Earth Observation. Remote sensing data is typically char- acterized by vast, non-uniform scenes and high-density, tiny objects that lack the semantic salience found in natural or medical photography. Consequently, generalist grounded models often falter in this domain, due to the lack of domain-specific substrate, comprising large-scale specialized datasets and adapted cognitive architectures, necessary to render this concept operational and robust for Earth Observation.
2.3 REASONING IN REMOTE SENSING VISION-LANGUAGE MODELS
The pioneering efforts apply reasoning chains within geospatial contexts have recently begun to emerge. In the broader geographic domain, frameworks like GeoChain (Yerramilli et al. (2025) and GAEA (Campos et al. 2025) have effectively utilized CoT for geolocation and landmark analysis. However, these approaches primarily address semantic reasoning in ground-level imagery, relying on cultural or architectural cues for knowledge retrieval. In the specific domain of overhead Earth
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Base Midill Generation
Supervised Finetuning
USER: What Sherage took the Inagr?
Tokenizer
Reasoning Paths
Answer
ESER: WITH tr rask the Inuge?
Gruadirai Assstaties
Backbonn
X begrCrlee
LLM Rackbone
GRPO Training
strrege look inage?
Tokenizer
Reasoning Paths
Answer
Rewards
Advantages
Policy
Morch Boner
LLM Bockbone
Group wise Competizion
Pollcy
Figure 2: The two-stage alignment process. Our training strategy first instills a foundational cog- nitive architecture via supervised fine-tuning (SFT) and then refines this architecture's faithfulness via outcome-based reinforcement learning (GRPO).
Observation, works like SegEarth-RI (Li et al. 2025a) and RemoteReasoner (Yao et al., 2025) have demonstrated the potential of generating step-by-ster step rationales to guide complex downstream tasks, while others such as SkySense-O (Zhu et al.) (025) have advanced the quality of these textual ra- tionales. Even agentic frameworks like Ringmo Agent (Hu et al.) 2025) have emerged, capable of formulating high-level plans. However, a close examination reveals that these foundational frame- works share critical limitations. First, their reasoning steps often remain abstract textual descrip- tions, lacking the direct, verifiable link to spatial areas that constitutes true perceptual grounding- challenge uniquely acute top-down views characterized by dense objects and scale variations. Second, the reasoning process itself, while sequential, typically lacks methodical cognitive archi- tecture. These explorations thus underscore a clear and unmet need for a framework that not only prompts for reasoning but fundamentally structures it around the principles of perceptual grounding and a systematic cognitive plan. Our work is the first propose such a framework.
3 METHODOLOGY
To realize the Perceptually-Gin Grounded Geospatial Chain-of-Thouur (Gco-CoT) framework, de- velop RSThinker, a foundational Vision-Language Model trained via a two-stage aligmment process. This process is designed to instill the cognitive architecture of Geo-CoT and subsequently fine its faithfulness. The initial stage of this process instills the foundational cognitive architecture of Geo-CoT, leveraging a large-scale supervised fine-tuning (SFT) corpus constructed explicitly embody the principles of task decomposition and iterative evidence grounding, The second stage subsequently employs reinforcement learning refine the model's reasoning, guided by a domain- specific reward function we designed to optimize for the faithfulness of the grounded evidence. The resulting model, which we name RSThinker and illustrate in Figure [ thus specialist VLM that reasons faithfully and remains verifiably grounded in visual evidence.
3.1 BASE VISION-LANGUAGE MODEL
We initialize RSThinker from the pre-training checkpoint of 2025b), a state-of-the-as VLM. Its architecture employs Vision Transformer, Aimv2-Huge (Fini al., 2025), which is particularly suited for remote sensing due its ability to handle variable image resolutions and aspect ratios. This crucial capability is realized through h dynamic positional encoding scheme that adapts its pre-trained position table, Parig- Specifically, the scheme first normalizes each patch coordinate g (w, h) to continuous grid Inorm spanning [-1, 1]. and then samples from Porig via bicubic interpolation to compute the adapted encoding Padaptedi
TTam et al.
gnorm (Wnormy Amorm) ) = 2 W. + 0.5 + 0.5 - 1,
Wp
Hp
(1)
P Padopted(g)) = Ibácubic Porig Inorm).
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Table 1:
The overview of the dataset Geo-CoT380k.
Table 2: Additional Dataset for RL.
Tasks
Datasets
Samples
Tasks
Datasets
Samples
VQA
VRSBench-train- VQA
85,813
VQA
RSVQA- HR-triin train
67,228
NWPU-Caption-trrain
28.350
Image Captioning
VRSBench-train-cag
20,264
Image Captioning
RSICD-main
10,921
FIT-RS-cap
65,197
RSTMD
NWPU-RESISCA4--rai
31,500
Scene Classification
Table 3: Task-specific reward functions.
AID-train
10,000
Task
Reward Design Details
DIGR-RSVG-taann
34,744
Visual Grounding
VRSBench-train--G
35,967
Classification
VQA & Scene
for correct, partially correct, others
Reward 1.0, 0.0.6 0.0
DOTAv2-train
25,769
Visual Grounding
Reward = loU
Object Counting
HRRSD-train
24,784
Object Counting
Reward 1.0 -xx
MAY muxEXeeloOr)
Object Derection
Reward mAP@0.5
DOTAv2-train
25,769
Object Detection
Image Captioning
Reward Emear thn
HRRSD-train
24.784
(BLBU-4, METEOR, CIDer, ROUGE:L)
This robust visual encoding mechanism, complemented by a 3D-RoPE language decoder for en- hanced spatial awareness, provides powerful and flexible foundation upon which we build our domain-specific alignment.
3.2 STAGE I: INSTILLING COGNITIVE ARCHITECTURE VIA SUPERVISED FINE-TUNING TUNING
The efficacy of our SFT stage is contingent upon large-scale corpus of structured rationales that emhody the Geo-CoT principles. To this end, we developed a scalable annotation pipeline that leverages a powerful, general-purpose VLM, GPT-4V (OpenAl 2023). to generate these rationales. Our pipeline empirically promotes faithfulness through strict conditioning: rather than tasking the VLM with open-enden reasoning. we provide with verified bounding boxes, image captions, and Chain-of-thougle exemplars (detailed in Appendix (A.7), minimizing the risk of hallucinated reason- ing. This methodology allows us to produce a vast, high-fidelity SFT-CoT dataset, Geo-CoT380k, comprising 384,591 structured rationales sourced from diverse, publicly-available remote sensing benchmarks (dctailed in Table T). including large-scale imagery from sources like DOTAv2 that was tiled into 800x800 800 patches.
With this dataset established, the SFT stage compels the VLM to internalize the entire method- ical workflow encoded in each structured output o,, This workflow, represented as <think <athink><answer>> </answer>, is learned through a standard ruto-regressive objective that maxi- mizes the log-likelhood of the target rationale:
(F) Σ log plo IQQ:0),
By optimizing this loss function, we are not simply fine-tuning for task; we are fundamentally reshaping the model's internal reasoning process to explicitly model the decomposition, grounding, and synthesis steps of the Geo-CoT cognitive architecture.
3.3 STAGE II: REFINING FAITHFULNESS VIA GROUP RELATIVE POLICY OPTIMIZATION
While the SFT stage successfully instills the structural template of Geo-CoT, its token-level maxi- mum likelihood objective can still assign high probability rationales that locally plausible but contain unfaithful links between evidence and claims. To address these sequence-level deficiencies, our second alignment stage employs Group Relative Policy Optimization (GRPO), an outcome- based reinforcement learning paradigm wherein the reward signal is derived solely from the final output of the reasoning trace. For each task, this reward function directly embodies its canonical evaluation metric (Table 3). ensuring our policy optimization is precisely aligned with established performance protocols.
The GRPO training process directly optimizes the generative policy ne using on-policy sampling. drawing inputs from a designated preference tuning corpus comprising the original, rationale-free instances from C30-Co, augmented with additional datasets detailed in Table [ Given an input (I, Q) drawn from the dataset D, we first sample a group of k outputs, {o,, 02, ... ,o The reward scores for each, R (R1, R2,
RR), then normalized to yield a low-variance
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Table 4: Comparison of RSThinker with existing generic and RS VLMs on Visual Grounding task.
VRSBench-VG VG
DIOR-RSVG
RRSIS-D (ZS)
RSVG (ZS)
Method
00.5
@0.75
mIoU
@0.5
00.75
mloU
@0.5
@0.75
mloU
@0.5
@0.75
mIoU
Close soure Commercial Vision Language Models
Claude-sonnet-4
11.1
2.4
16.66
17.6
1.2
25.33
20.5
1.5
29.91
24.0
7.0
24.99
Gemini-2.0 flash
22.9
6.3
28.59
20.8
3.3
27.45
29.5
5.0
36.13
19.5
4.5
24.07
ChaiGPTS
14.4
2.3
22.71
26.1
3.3
28.37
28.0
5.0
29.46
18.5
3.5
20.59
Open-source Vislon-Langwag Models
MiniGPT-v2
32.1
16.3
33.96
29.4
10.2
29.43
38.5
16.0
40.13
12.0
3.0
15.65
Qwen2.5-VL
45.2
20.6
42.45
36.3
15.9
34.34
0.5
5.17
1.0
7.24
Open-source Reasoning Language Models
Thinking
63.8
47.0
60.69
59.6
43.7
57.41
63.5
47.5
61.84
43.0
30.5
42.27
Open-source Remote Sensing
Vision-Laguare Language Models
GeoChat
56.3
24.6
53.50
31.4
11.0
34.99
10.0
0.5
20.35
5.5
0.5
12.55
VHM
33,9
10.0
34.91
55.9
35.5
49.90
64.0
37.5
55.20
2.5
0.0
5,80
SkySenseCiPT
63.5
26.0
60.8
26.5
53.18
69.0
39.5
FarthDial
14.4
7.8
13.04
46.1
30.2
39.46
72.5
50.0
64.08
42.0
24.0
38.49
RSThinker
90.4
77.2
80.79
93.1
90.2
89.02
94.0
90.5
89.59
64.0
54.5
59.74
Table 5: Comparison of RSThinker with existing generic and RS VLMs Object Counting task.
Figure 3: Comparison of RSThinker with
SOTA VLMs on Object Detection task.
HRRSSD
RSOD 1780
NWPU-VHR (75)
163
Method
Nec
Aec
MAB
Aee
MAE
Ace
Mr-Thins Thiidiinng Pirtitie
Molses Eangugg Midel:
Chaste-orrnnd
17
10.233
90.11
3.311
15.0
4.114
51.5
3,301
Camini
76.36
15.07
5161
19.0
4.004
1835
ChaGPES
36.20
7,400
50.50
0.757
40.0
1,400
1,310
79-
LooonnLavenggge Maddii
16.82
$7.082
19.50
36.199
19.5
9,630
21.0
4.575
Qwee2
31.79
9.735
57.87
0.846
42.0
56.0
112
Rounning Voston
Moshi
Kini-V VL Thickigg
3065
11,967
46.26
1,512
15.5
4,050
$3.0
2.575
GLM-4 WWThiiiing
20.90
K.022
56.96
0/908
28.5
3.210
52.5
1.194
Eanggagg
VIIM
37.67
9.760
21
1,063
16.0
1.791
48.5
1.380
33
SkyScIseCPT
33.03
3.159
56.73
1,070
51.5
3,07b
49.5
1.855
22.23
8.422
61.43
0.571
41.0
1,642
$2.5
1.323
Thinker
HAP200.2 25
HAPPIR HRRSD
HAPgga-7
HAPgE 2
+APPILS DOTAv2-val
HAPPEE.7 75
estimate of the group-relative advantage, A.. The policy is then updated by optimizing the following clipped surrogate objective:
(0) -
B
(11.1)]
k 10.1
min 10) - (Toll (3)
Am1
To T0(0n,lo, 0,,2()
R; Ri- (ememnn))
(0)
A
Thad (01.19. o1,=z)
std(R)
where the clip function constrains this ratio within the interval [1 (1--e tc), thereby disincentivizing overly aggressive policy updates. The final term is a KL-divergence penalty that regularizes the policy no, preventing it from deviating excessively from the reference policy Tref (initialized from the SFT checkpoint). This optimization process systematically shifts the probability mass of the policy distribution, moving it away from regions that produce low-reward outcomes and towards those that generate high-reward, verifiably correct conclusions. This final alignment step imbues the model's internal reasoning process with a functional alignment to the ultimate goal of achieving factual correctness.
EXPERIMENT
We present a comprehensive experimental evaluation designed to validate our core contributions. This evaluation first establishes the state-of-the-art performance of our model, RSThinker, across diverse suite of canonical remote sensing tasks. Beyond this aggregate performance, we conduct series of carefully designed ablation studies to isolate the causal impact of each component of our framework. Finally, we provide a qualitative analysis to visually demonstrate the nature and faithfulness of the gerceptually-Groundd Geo-CoT that our framework uniquely produces.

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Published as a conference paper at ICLR 2026
Table 6: Comparison of RSThinker with generic and RS VLMs on Classification and VQA tasks.
Method
Scate Clacification
VRSBench-VQA
RIVQA-HI
RESISCAS
AID
ReS09 175i
SIRI (75)
UCM
Categiry
Exiddncc
Podition
Quantity
Scrne
Caler
Image
Presence
Comp.
Couraanr Consone Niione-Lappuor
20h
74,89
$5.95
4483
<3.97
ChuCPPP
82.22
75.30
05.53
75.00
$8.57
39.55
53.80
+2.34
47.33
70.42
59,49
$7.50
62.94
68.93
Opvt-onance Langsuanggu Modilly
32:87
27.17
25:37
20.50
Uwee255VV
76,33
37.31
Mixiets
Kani- VL Thatiing
72.22
71,50
83.65
09.20
77.62
47.00
57.50
06.15
74.67
71.23
65.83
90.23
63.96
77.91
GLM-4 Tiinkigg
70.00
69.67
86.34
00.35
$2.86
42.34
96.11
43.10
$4.67
10101
62.05
$7.50
45.05
65.00
Sensicz
Laversape
VHM
53
90.35
96.11
StySenseGPT
93.53
45.33
Fanhiikad
76.87
67.33
68.76
73.42
90.71
51.49
47.23
56.21
41.53
36.62
11.39
50.00
01.04
79.92
RSThinker
96.89
98.17
99.74
77.67
92.14
$2.84
92.36
68.97
36.67
73.24
64.33
22.82
66.95
78.98
4.1 EXPERIMENTAL SETUP
Tasks and Benchmarks. We validate the performance of RSThinker across comprehensive suite of canonical remote sensing tasks. This evaluation spans the full spectrum from fine-grained, object- level analysis (object counting, detection, and grounding) to holistic scene interpretation and com- plex reasoning (classification, captioning, and VQA), with detailed breakdown of all benchmarks provided in Appendix A.4.]
Baseline Models.
To contextualize RSThinker's performance, we conduct a rigorous comparison against wide range of baseline models. These models are organized along two primary axes: their domain specialization (general-purpose vs. remote sensing) and their architectural support for explicit reasoning. This comparative analysis therefore includes leading proprietary systems, open-source generalist and domain-specific VLMs, and the latest reasoning-centric frameworks, complete list of which is detailed in Appendix A.4.2
Implementation Details. Our implementation of RSThinker is initialized from the GLM-4.IV- Base checkpoint, and its performance across all experiments is assessed using standard, community- accepted evaluation metrics. These metrics include mean Average Precision (mAP) and Intersection over Union (loU) for object detection, Accuracy (Acc) and Intersection over Union (IoU) for visual grounding. Mean Absolute Error (MAE) for counting, Accuracy for classification and VQA, and BLEU-4, METEOR, and CIDEr for captioning. Further details regarding the full training protocol and hyperparameters are deferred to Appendix A.4.3
4.2 MAIN RESULTS AND ANALYSIS
We present a comprehensive evaluation of RSThinker against suite of state-of-the-arr models. Our analysis is structured around distinct categories of remote sensing capabilities, moving from fine- grained perception holistic scene understanding and reasoning.
4.2.1 FINE-GRAINED PERCEPTION: GROUNDING, DETECTION, AND COUNTING
The efficacy of the Geo-CoT framework is most directly validated in fine-grained perception, where the veracity of an output is inextricably linked to the model's ability to localize spatial evidence. This principle clearly demonstrated Visual Grounding (Table 4 a task demanding an explicit link between text and pixels. RSThinker establishes a substantial performance margin in this task, an advantage that stems from a fundamental architectural divergence. Baseline models typically rely on end-to-end architectures where grounding remains a latent, unconstrained variable within the net- work. In contrast, our two-stage alignment mandates that the model externalize and report specific, falsifiable spatial references, making a commitment to tangible evidence a required component of the output format.
This foundational capability for precise localization naturally extends to the more complex task of Object Detection (Figure 3). The Geo-CoT framework transforms detection from a single-pass recognition into a methodical, sequential search. Its pels a systematic scan of the imagery, a critical advantage that enables the exhaustive identification of objects in dense scenes where holistic approaches lytical process culminate in Object Counting (Table (5), which sees a significant reduction in Mean Absolute Error. This reduction in error is direct consequence of the Geo-CoT architecture provid-
structure com-
fail. The benefits of this structured

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Published as a conference paper at ICLR 2026
Table 7: Comparison of RSThinker with existing generic and RS VLMs on Image Captioning task.
RSITDD
NWPU-Captions
RSICD
VRSBench-Cap
Method
B-4
B-4
MT
Close-source Commercial
Vision-Longuage Models
Claude-sounet-)
20.14
17.15
19.31
28.32
21.98
32.46
11.58
13.90
24.57
14.62
22.36
73.49
Gemini 2.0 flash
15.73
9.27
17.11
20,55
11,42
10.85
8,71
21.53
14.19
22.30
CharGPT-S
27.27
21.10
29.48
39.62
25.69
48.52
16.83
16.73
34.39
18.06
25.11
88.93
Open-source
Lalon-Langugg Models
MiniGPT-v2 Qwen2.5-VL
25.45
16.83
25.89
37.75
19.70
35.73
15,40
12.36
26.63
26. 61
18,36
68.94
27.92
17.24
24.90
38.89
21.40
42.11
17.80
13.72
32.19
29.21
25.01
91.84
Open-source Reasoning Vision-Language Models
Kimi-V Th-Tkkkng
24.82
16.47
37.14 14
15.60
13.57
30.00
26.07
24.34
83.86
GLM-4 TV-Thinkin
20.57
19.55
24.98
29.59
23.33
40.35
12.57
15.86
30.47
13:52
22:57
79.71
Open-source Remote Sensing
Rasion-Languae Models
VHM
38.93
21.99
40.29
50.69
25.31
54.92
17.63
49.80
35.06
22.29
SkySenseGiPT
37.76
19.06
34.
23.33
14,02
40.48
42.47
24.95
52.58
33 10
22.50
102.8
EarthDial
42.09
23.92
42.56
67.14
46.17
123.6
29.
85
21.49
15.88
90.51
RSThinker 55.69
32.29
73.55
85.12
58.88
94.81
39.82
27.17
99.83
33.96
21.19
107.5
B-4/MT/ /MFTRR/ CIDE
ing natural defense against common failure modes. By requiring the model to first ground each object as a distinct entry in its reasoning trace before synthesizing a final tally, the framework inher- ently mitigates duplication and promotes more complete search. The consistent, substantial gains across these three related tasks provide strong empirical evidence that the Geo-CoT framework is a key enabler for robust and faithful fine-grained perception.
4.2.2 HOLISTIC SCENE UNDERSTANDING. CLASSIFICATION AND CAPTIONING
We then assess the model's ability to interpret the broader context of a scene, addressing whether a methodical, step-by-step reasoning process compromises holistic comprehension. The performance in Scene Classification (Table 6) demonstrates that, on the contrary, the fine-grained analysis fos- tered by Geo-CoT provides more robust foundation for high-level understanding. This consistent superiority suggests the model's capacity for systematic evidence gathering translates to a more veridical holistic feature representation. By being trained ground individual objects and their rich, verifiable set of low-level visual facts,
attributes, the model bases its final classification rather than relying on potentially spurious correlations global scene statistics.
This capacity for detailed, fact-based synthesis is further illuminated in Image Captioning (Table 7). where strong performance stems from the Geo-CoT architecture transforming captioning from a monolithic image-to- toxtet mapping into structured process. The model first grounds key entities and their spatial relationships within its reasoning trace, before synthesizing these grounded elements into a coherent narrative. This mechanism prevents the generation of generic, prototypical captions, instead promoting descriptions rich in detail and verifiably true to the visual evidence. The collective evidence from both tasks indicates that the structured reasoning of Geo-CoTT does not hinder, but rather enhances, the model's ability to achieve a profound and accurate understanding of the entire scene.
4.2.3 COMPLEX GEOSPATIAL REASONING: VISUAL QUESTION ANSWERING
Finally, we evaluate RSThinker on Visual Question Answering (VQA), where the fine-grained per- ception and holistic understanding capabilities cultivated previously must converge resolve com- plex queries. The architectural advantage of Geo-CoT becomes most salient on queries that necessi- tate foundational fact-checking. This is demonstrated on the Existence category of VRSBench- VQA (Table where the model's 's reliability in making verifiable claim is a direct product of its struc- tured, wvidence-groundee reasoning process.
This foundational reliability in evidence verification underpins the model's capacity to execute more complex, multi-step procedures. For comparative queries such as "Are there more cars near the stadium than near the river?", the scaffold, compelling the model to first ground each component of the query before synthesizing final comparative judgment. This consistent performance across the full spectrum of reasoning types-from from simple existence checks to complex compositional malysis-reveall. that the Geo- CoT framework functions not narrow, task-specific solution, but general-purpose problem-
framework provides natural
8

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Published as a conference paper at ICLR 2026
Table 8: Ablation study on the impact of CoT-based SFT Figure 4: Ablation Study on KL di- vergence.
and GRPO across multiple tasks.
Models
(mdol)
VG
(MAE.) .)
OC:
Det (uAP0015)
IC (BLEBL-)
(Auc)
SC
VQA
(Asc)
Base (GLM 4.1V 98 Base)
56.26
10.81
3.56
10.99
09.78
5,16
Formet rewerd
81.80
51.14
(+38.50)
(-7.40)
(w13.00)
(420.15)
(oT))
87.20
74.03
53.31
96.67
cooeir..coeiuuaaaa 1645. uE. nEt
(-31.44)
(-7.85)
(-70.27)
(422.32)
(400.05)
SFT (wela CORN
85.47
4,510
56.77
30.87
97.56
2004
(=:0::1)
(-11-11
(415
SFT (w/ CaT) GRPO
GIRO
89J12
2.728
77.06
33.96
77.34
0MP Rl UAPC a/o
(+12..00
(-308)
(+73
(+22.94)
(-27-100
Step
What the count of airplanes in the image?
RSThinker:-thhn think-
(Phanning) To dneemine the mumberr of sirplance the ebet recugnirinng that airports typically have multiple aircraft parked
tectinals for alpplanes likely frund. sysematicully cuaniee sirport
depicts layest, which provides
scetioss
image poooengers Adkitioonaly, I should look at lee uijucent rarosy arcas where plance might he wailing for takeulf or txtingg
tonninal arcs rasully sevel phane lined up for beordinng or unleoding
(Grounding) Upus clise impectioo of the temiisal rre at the center Bhe imuge. can kleutily mulliple sincuit porked at the gaice. There appear to be scversl plane aligned pami lei to the terminsl, each with distnctivv stapes and colocs, indicoting they are indead zirplanes Nest, will count each visible airpaae in the kkmtifial regions total can thrse sireralt parked closely tagether
sidle of the terminal, and far end the
side, Larily
additional airplane positioned fullows sippares
rumway.
the
temminal,
other and one rumway, leading to cumulative toal airplates. (Syathesio) After confining that fllusiee largets lave the chanaceristiis of sirplance such as wings. finehpp,, and tsil can conclide that the total mumber of airplancs present the image six, sothink-
RSThinker: canswer- There are a total of airplancs. stanswer>
Figure 5: Qualitative example of RSThinker's Geo-CoT: a methodical Planning-Grounding Synthesis sequence culminating in a justified <answer>.
solving architecture. Ultimately, the VQA results confirm that this architecture seamlessly integrates precise, evidence-based localization with high-level scene interpretation, establishing a new bench- mark for robust and complex geospatial reasoning.
4.3 ABLATION STUDY
Our comprehensive ablation studies (Table 8 reveal a clear hierarchy of contributions from each framework component. While direct fine-tuning on task-specific data (SFT w/o CoT) yields a sig- nificant performance gain over the base model, the introduction of our structured rationales (SFT w/ CoT) unlocks a fundamentally higher performance tier. This substantial performance delta between the two SFT variants stems directly from supervising the model the computational process itself, rather than merely its final outputs.
The full SFT (w/ CoT) GRPO model consistently excels, particularly on complex, reasoning- intensive tasks, while applying GRPO without the prerequisite Geo-CoT rationales (SFT w/o CoT GRPO) proves insufficient to instill the necessary cognitive scaffold. This highlights their sym- biotic relationship: rationale-based SFT instills the essential cognitive structure, upon which KL- regularized GRPO subsequently refines the generative policy towards factual correctness. The sta- bilizing role of KL regularization is visualized in Figure 4 where its absence causes a catastrophic collapse of the learned reasoning format.
4.4 QUALITATIVE ANALYSIS
To illustrate the practical implications of our framework, we examine the analytical narrative vi- sualized in Figure [S] The model first constructs a verifiable spatial model by breaking down the total count into its constituent sub-groups (e.g., "three on one side", "two the opposite"). This granular evidence, presented within the reasoning trace, directly substantiates the final conclusion. The conclusion is thus rendered verifiable, as it stands as the end-product of transparent process designed from its inception for methodical analysis. Additional qualitative analysis can be found in Appendix A.6
Reasoning from Implicit Intent. To validate the model's 's capacity for implicit intent understanding where queries specify functional goals rather than object names, we conducted qualitative experi- ments on the EarthReason benchmark (Li et al. 2025a). As visualized in Figure in a sociocultral context, request for "traditional wedding" venue triggers systematic search for specific

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Published as a conference paper at ICLR 2026
If am woeking to participate in traditional wedding siappe in religious customs and commonal colerrations, what would be the ideal venue acorle?
RSThinker: -think-
(Ptanning) ancter queetion reseding the vatue inditionall wedding sortty tie by iralysngg the image's layoes and identilyngg koy festers that signity a place wordop or communiy
getteing
center antiiietmm fomns eich as domes and larg roof, which are commonty assucistiod with churches The vibear red oole ofe rodlng and sysorreceioo design fielee supper Gis Mentifictiion Next, coeaidr the axmurdigg There dear open space aljaccnt this sinucurs, whick ofies indicativo of corryee or plan typically Surd chirc complenes when community evants and githelegs lake place. The presence of pukins weas teably ahe sugcio that this verue cecotinviate vallons resiltcing the idaa that a place of sanilicaace
(Groundigg) Firsty. otsere starue
image appor
thee cefher or the chamssmans of the sinctue in ndatinn to the quosice. The dearption of seeking a veruz sseped in sclagion castens align vel with te appearene of stentifeed builinng
(lyntheok) After carefil consdenation of the arihtoctrdl stfr, location, and connest, conclade that the cuedroane [475, 332, 857, 622] comespond to the church, cnnfiming as the ideal soua for traditional wodking the cttuke
RSThinker: Samower- (475, 332, 857, 622))
Figure 6: Qualitative results on implicit intent understanding (EarthReason benchmark).
How many ships are there Image?
RST RSThinker: -think-
(Planning) to deemmie toe namber of skips is the imazz, I begis focesing sreas where ships tysinall found, sads docka maritaa The anage dapics residastaal acea with acveral dodaa actinding iro the wast which is porneng for loating vnouds.
(Grosnding) Next, aralss the ideziifidd postions of the chips the imagy There ar there cootdratee spezified [522, 438,, [533, 455), and (41 225) sysionatioally check these cooudinato is the image The fins two slips are dese to cads other incieiing they likels repesest two vesses docked ese acc. I caamins these sreas imags, condimm can ase dasiass oxtina stepes cominenn with
poetion apaan hi fecher arprning spuratt shin.
expect dips
postions shape nid postion also zlgge
objods
boas like clanctrritios
conteeel with dlddns
deeets
viubilay and stantifahle fanur of the veeck an all puotions can aildy count them.
(Syatheii After confiminng all firse bacaiora, conduad that these indeal fwor ships present the image New. can poslie the arseure of shipe the image. citinks
RS TeThiiker:
Ground-Truth: 2
An instance of failure case in object counting. While the reasoning chain is structurally sound and logically coherent, the model misidentifies a non-ship object (red box) as ship due to visual ambiguity. Crucially, the explicit grounding exposes this error to the user.
Figure 7:
chitectural affordances, such as domes and open courtyards, enabling the precise localization of a church. The result demonstrates that RSThinker transcends simple semantic matching, actively reasoning about the functional affordances of geospatial entities to resolve complex, intent-driven queries. Additional examples can be found in Figure
Failure Analysis. Despite the robustness of the Geo-CoT framework, Figure reveals a subtle fail- ure mode where the model maintains a coherent reasoning syntax but misidentifies a dock extension as ship due to textural ambiguity, suggesting that the textual "verification" step can occasionally act as a stylistic heuristic. Crucially, however, the explicit grounding mechanism turns this into a safety feature. Unlike end-to-end baselines that produce opaque errors, RSThinker externalizes the failure by pinpointing the specific bounding box ([413, 225]). This renders the hallucination immediately falsifiable, transforming potential silent failure into an auditable and interpretable error essential for high-stakes workflows.
CONCLUSION
In this work, we introduce framework designed to elicit faithful reasoning in remote sensing Visioned-Language Models. We formalize this reasoning as Perceptually-Groundde Chain-of-Thoughe (Geo-CoT), where each analytical step must be verifiably grounded in visual ev- idence. This capability is instilled via a two-stage alignment process, beginning with supervised fine-tuning on Gco-CoT380k, the first large-scale corpus of structured rationales generated for this domain via a novel, scalable pipeline. This SFT-instilled cognitive architecture is then refined via Group Relative Policy Optimization (GRPO), which steers the model's policy toward factually cor- rect final outcomes. While the rationales generated by our pipeline are anchored to ground-truth data, we acknowledge that they may inherit stylistic biases from the generative process itself, promising avenue for future investigation. Our resulting model, RSThinker, exhibits state-of-thh. art outcomes by not only deriving final answer, but by externalizing the entire verifiable visual interrogation process. Ultimately, this work provides a foundational methodology for developing analytical agents whose reasoning is verifiable as their final outputs correct.
Geospatial
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ACKNOWLEDGMENTS
This work was supported by the National Natural Science Foundation of China (Grant No. U22A2098, 62172185), Major Science and Technology Project of Jilin Province (Grant No. 20240212003GX) Major Science and Technology Project of Changchun City (Grant No. 2024WX05), and China Postdoctoral Science Foundation (Grant No. 2025M780312).
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A APPENDIX
A.1 ETHICS STATEMENT
Our research is conducted with a commitment to academic integrity and transparency. All experi- ments were conducted exclusively on publicly available, open-source remote sensing benchmarks to ensure full reproducibility of our findings. We acknowledge that these datasets may contain inherent geographical or sensor-related biases, common limitation in the field that could affect the model's generalizability to underrepresented regions. The core objective our work is to enhance the trans- parency and verifiability of reasoning processes in VLMs, goal we believe promotes responsible AI development. We recognize the computational cost associated with training our models and have strived for efficiency. To further promote transparency and enable the community to build upon our work, we commit to the public release of our Geo-CoT380k dataset, model checkpoints, and evaluation code upon publication.
A.2 REPRODUCIBILITY STATEMENT
We are committed to the full reproducibility of our work and will release all necessary artifacts upon publication. To this end, we will release our primary dataset, Geo-CoT380k, including all structured rationales and data splits, alongside scripts to automatically prepare the cited public benchmarks. This dataset will be accompanied by the full source code for our two-stage alignment strategy, en- compassing data preprocessing, SFT, GRPO, and evaluation. To ensure consistent starting point for replication, the codebase will be bundled with the final model checkpoints for RSThinker and scripts to access the publicly available initialization checkpoint. All of these artifacts will be companied by detailed instructions and a requirement file to facilitate the setup of the computational environment and the execution of the full experimental pipeline. After submitting the manuscript, we will simultaneously release the model's weights and related code as open-source.
A.3 LLM USAGE
This work utilized the large multimodal model GPT-4V (OpenAl 2023) for the sole and explicit purpose of generating the structured rationales that constitute our Geo-CoT380k dataset. Crucially, the LLM operated within a highly constrained, scalable pipeline designed by the authors, which retrofits verifiable rationales onto ground-truth data to ensure faithfulness by design. The intellectual contribution this work therefore lies not in the raw output of the LLM, but in the design of the pipeline itself. The formulation of the core methodology, the analysis of experimental results, and the final conclusions presented herein are solely the work of the authors.
A.4 EXPERIMENTAL SETUP
A.4.1 TASKS AND DATASETS
To validate the versatility and robustness of RSThinker, we evaluate its performance on diverse set of canonical remote sensing tasks. These tasks are selected to span the full spectrum from fine-grained perception to holistic scene understanding. To showcase the model's core strengths in systematic, object-level analysis, first evaluate on object counting using the HRRSD (Zhang
let al. 2019). RSOD (Long et al.] (017). DOTAv2-val (Xia et al.) 2018 Jian et al.) 2019 Ding al. 2021h 1, and NWPU-VHR (Cheng et al. 2014) datasets, and on object detection across bench- marks such as DOTAv2-val and HRRSD. This precise object-level localization is further tested through visual grounding on the VRSBench-testt -ecc-
al.] 2024), DIOR-RSVG (Zhan et al.) 2023), RRSIS-D (Liu et al. 2024b) and RSVG (Sun et al. 2022) benchmarks. Moving from object- centric analysis to holistic scene interpretation, we assess performance on scene classification with the NWPU-RESISC45 (Cheng et al. 2017). AID-test (Xia et al. 2017). WHU-RS19 (Xia et al.) 2010), SIRI-WHU (Zhao et al. 201 Zhu et al. 2016) and UCMerced Yang & Newsam (2010) datasets, and on descriptive image captioning using benchmarks like UCM-Captions (Qu et al. 2016), RSICD (Lu et al. 2017), RSITMD Yuan et al. 2021). NWPUUcaptions (Cheng et al.] 2022).
Sydney-Captiin (Lu et al. (017) and VRSBencchteste -eettcc-tettcapp (Li al.] (024). Finally, evaluate the model's ability to handle complex, open-ended queries, we use the challenging VRSBench-tess
VQA (Li et al. 2024) and RSVQA-HR-test (Lobry al. 2020 benchmarks.
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Table 9: Comparison of RSThinker with existing generic and RS VLMs on Image Captioning task across multiple benchmarks. B-4, MT, Cr and R-L denote BLUE-4, METEOR, CIDEr and ROUGE- scores, respectively.
Metuel
LCMCCpptees
HIKUDD
INTED
Cl
10.01
3001
19.55
11 84
57
10.93
14
13:15
19:11
413
71.90
13.90
14.55
35
33.65
13.53
1463
71.00
1521
19:48
23-56
4896
1930
16.55
16.75
$0.99
13.99
23:23
23.10
19:48
1410
25,00
$0.50
36.30
24.45
34.59
11.41
19:38
25-11
15A0
MaxsrEuR
15.40
19
1998
15:00
17.11
65
16.1
15
11.5
19:30
1-75
1518
13.39
13.55
96:60
(8:94
16.55
Kens VI. Thaaligg
15.33
10.95
1691
15400
13.07
39.93
1331
74.0
16.47
23.00
11
94.44
37.4
16.41
33.34
73.41
13:13
15:51
75.00
94.34
23.36
18M5
35.44
23.55
-8300
2240
25.35
16/02
01.45
15:31
37
36
19:06
34.8
13.0
40:00
22.41
4107
15.55
41447
24.35
11.35
215
35.15
22.00
100.
22/9
Inder
61.01
01.44
1274
34.0
2003 27.87
25.09
10.39
08.13
50.8
15.11
514
13.35 1132
23.55
3158
21.19
10.5
15.45
Table 10: Comparison of RSThinker with existing generic and RS VLMS on Object Detection task across multiple benchmarks.
DOTA
HRRSD
Method
mAP65525
mAP60..
mAP@0..5
mAP@0.25
m.
mAP600.5
Close-source Commercial Vision-Longuage Language Models
Claudo-sounct-4
17.80
3.89
0.29
33.82
14.87
2.80
Gemini-2.0-laa 2.0-llsh
35.31
14.30
4.02
62.41
28.92
4.05
ChaIGPT
20.93
8.66
1.56
50.19
13.15
0.80
Open-source Viston-Language Models
MinGGPT-22 Qwen2,5-VL
8.10
3.02
1.03
16.57
9.15
4.60
30.57
3,18
0,50
43,48
19.04
3.34
Open- source Reassning Vision- Langugge Models
G.M.4.V.Thinker
47.89
40.45
30.77
70.99
55.53
36.43
Open-source Remote Sensing Viston- Longuage Models
VHM
2.37
0.53
17.23
12:47
7.91
SkySenseGPT
8.42
4.56
0,27
14.83
6.23
1.42
EarthDial
0.00
8.05
0.59
RS- AS--hinker
79.56
77.06
69.88
98.54
97.99
94.59
A.4.2 BASELINES
We benchmark RSThinker against comprehensive suite of competitive baseline models. These models first include leading proprietary, closed-source systems, such as ChatGPT-5 (OpenAT] 2025), Gemini-2.0-flass (Comanici et al. 2025) and Claude-sonnet-4 (Anthropic) 2025). to establish a per- formance ceiling against large-scale commercial offerings. Beyond these commercial offerings, our comparison spans open-source models organized along two key axes: their domain specializa- tion (general-purpe) versus remote sensing) and their architectural support for explicit reasoning. Our evaluation thus includes leading general-purpose VLMs like MiniGPT-v2 (Zhu al.) 2023) and Qwen2.5-VL. (Bai et al., (025a). alongside their domain-specific remote sensing counterparts such Geochat (Kuckreja et al.] 2024), VHM (Pang et al.) 2025). SkysenseGPT (Luo et al. 2024)
and EarthDial (Soni et al., 2025). To provide a direct comparison against reasoning-ceme -entrrc ap- proaches, we further include results from both generalist models prompted for CoT and the latest domain-specific reasoning frameworks, namely GL.M-411 1.9.111---hhhiiii (Team et al. 2025b) and
Kimi-VL--A3R -inb--LL-3BBThhiiinggg--. Thinking-2506 (Team et al. 2025a).
A.4.3 IMPLEMENTATION DETAILS AND METRICS
Our implementation of RSThinker is initialized from the GLM-4.VV-B--Ba (Team et al. 2025b) checkpoint and trained on 8 NVIDIA A100 GPUs. During the SFT stage, we train for epochs with batch size of 32, using the AdamW optimizer with a learning rate of le-5. For the subsequent GRPO stage, we finetune for 970 steps, with details of the reward function provided before. Across all experiments, we employ standard, community-accepter metrics to ensure a fair and direct com- parison. For object detection and visual grounding, we report mean Average Precision (mAP) and Intersection over Union (IoU). For object counting, we use Mean Absolute Error (MAE). Scene clas- sification and VQA are evaluated on standard Accuracy while image captioning is assessed using the BLEU-4, Rouge-I., METEOR and CIDEr scores.
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What ustural element verves ss visal sincraary for marine life and contributes to the restrration of groundwaterr, ensring ocany quillbbiium?
RSThinker: sthink>
(Planning) To deternine the satural denent that serves is viaal senctuary for mocase Ids and coutributes the
resoortion of groundwarer, strt by analynngg the image's ovenal compostion and iceniiiing koy fectres withi ni
The image depicts landscape with variose natual clensens such is pands segetation, and possbly arees of distubed eart.
(Gorunddag)) Fins, I look for circhore choracizistice typical of wcllant, which ollnn inclak shalline water mas semundal by: lads vegatation. The presence of acuani small ponis and paicoo. of grcerary sugoss the dhis area night suppoe diverse floea ond fron typccal of wettand ecosysems. Nest, I focus on the water bodies vibble the imae, The lnnge dork the center appears he pond, and there smrlllrr water fooerr scatiord thmoggost landbeope matitiiting bodies
supporting
goundwater, playing
Additinallyy wetland. The ovenall diarbttion
whith ofter inclides
nost conditions chansterstiio wellands,
vesetation
btodiversty ccological
(Syntheis)) question padd of
the
insge: Lonclake primaay festuee falilling the crolugical balanse
pood williif
cocediaicc
<think>
RSThinker: Fanswer- (556, 180, 911, 627] clanswerr
(a) Reasoning for "restoration of groundwater" (Pond)
What artificial constrcction in this image
RSThinker:
<think>>
appears be guiding the water flow. enablinn irrigation, and forminng reservoir?
äs uttificill constraction
cuables
imgotionn body
inege depicte keys stroctured focmition nggnt This siggests some humtan intervention managing resources.
by analyrin image
within image. The structuedd assa containe sevendl dislict marking on the ground, which could indkate putlways or fanctional layouts. However, my primary focus is the boundary between the water and the land. Here, I nomce a sgrificom construction that seems contral direst the fley of water, Bix is Bady the dam.
(Grounding) First aleutify premisent
herner hold back water, visible side while conmallng flashorrrs the other The prosunee the den layout
Neat, reflect the characieeiis of dem. typically
levels and fasetionlliiss
land, which aligns with common
descrigtion provided guzeiu.. unly weres gaide the water flow also comriture the formation reservoir behind Givan lte auiyeis of image and cocrespend this key feanre, conferting it the anower to the question <itinis
(Syathoiis) contedt the
most logical artilicall siructure
H
slm, coumiintis (230, 16), 503, 856]
RSThiaker: Sanswer (556, 180, 911, 627] c/answres
(b) Reasoning for "Guiding Water Flow" (Dam)
Figure 8: Qualitative results on implicit intent understanding (FarthReason benchmark).
A.5 EXPERIMENTAL RESULTS
This section provides the complete experimental tables omitted from the main paper( (Tabel g and Tabel 10P.
A.6 ADDITIONAL VISUALIZATIONS
This section presents qualitative visualizations of RSThinker's reasoning and predictions across tasks. In Object Detection taskure 9.. RSThinker first performs a coarse enumeration of air- craft regions and then refines them into precise detection boxes, accurately marking all airplanes in the scene. In Visual Grounding (FiguFe 10 with a relational query, "the vehicle on the right of the vehicle on the lower left in the image". RSThinker locates the lower-left vehicle bounding box first and then identifies the target vehicle to its right. For another Visual Grounding task(Figure [T] query, "the building shaped like the number 8". RSThinker detects structure whose two large loops or curves resemble the key components of the numeral 8, and identifies the correct bounding cognitive architecture
box. These examples illustrate RSThinker's Planning-Grounaee Grounning-Syntheeet and its ability to handle relational and shape-centric references. Additional examples are shown in Figure 12 (Image Caption), Figure 13 (Scene Classification), and Figure 14 (VQA).
A.7 PROMPT FOR COT GENERATION
We construct Geo-CoT with two-tier prompting protocol: a shared base prompt that standardizes SrannngGroonnnngynnnn
task intent, iaput-outpur format, and our desired ture, followed by task-specific prompts augmented with small set of curated in-context exemplars. Auxiliary information (e.g., bounding boxes, referring phrases, spatial attributes, normalized co- ordinates) is used only during construction to scaffold faithful reasoning and is removed from the released annotations.
cognitive architec-
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Detect all uirplane in the image.
RSThinker: -hinkk
(Planning) To dooect all sirphncs the image. surri by cxamining the ovenll bynst the wore, which is be un sirficld airpet spmus The image slaws lage expuna of cunrell und aphuli, ispical is siccal siggets designated parking
parking and manesvennn The which
of merkings or
txswaays
sirport settings
nest step is identity the objects match dee characteristic zimlines sttelltee inagery, airplanes
genenally socngnisable by their dislinct slapes, incluling a finclage and wings They ollan cuntrasi with
backss ound due to their size and colos. typically appeaingg lighter unina the dasker aspiali of the runway or sgron
(Grounding) Upun closely andlyring the imags I aleares thme dietrec sirplanes The first sirplene in locate nowards the bonom right quadrans, exhiitinng typical sircnft profile The second airplane is sttzted in the middle of the slightly sngled, which also conforms expected sirenfi cricntation
The third airplane is postiiened
unage, identifable by each airplane's position soted, bounding hoxes are created to encapsilzze their visible extents tiss
towards led) side
desection,
iisplane is captueed the encrinttis (66). 522, 854, 6655, soand aipline in [415, 549, 504, 673), shird sirplane in [215, 754, 195, 862]. These comnimatts reflect the specitic locations of the detecred airplanes within the imrgge
(Syathesis) in conclusion, I have stemified three airplanes these speciffed connditates. can now provide the detuction buxes for all siplance present the image <think
RSThinker: Kanswerr [[661, 851, 615). [415, 519, 591, 673), [225, 751, 386, =/mmme/mm
Figure 9: Qualitative Object Detection example of RSThinker's Geo-CoT: methodical Planning- Strounding-Syntaan
sequence culminating in a justified <answer>.
Where is the vehicle on the right of the velicle on the lower left?
WSTThnker:-ilinns
(Planning) anower question regreting the location vehicle that situsted right of
vehicle on the lower left, noed crablish the positions the vehidlss mentioned in image The cefi váhicle is deseribel ss baing on the lower lefl of the inagee which gives a statting point for my undlysis.
(Grounding) locase the veliicl
lower left cooediaates (250, 763, 211, 1911 This vehicle's undesstedd its esset placement within image.
clae, and
conniinates
idenilly vehicte
the right this refacnce velick.
Nest, look for vahicles in the vicinity to the right of the sleatifeed vehiole on the lower lett According to the image, there mnoteer vehicle that explicitty stated to be on the right of the lower left vehicle. I find this vehicle the coordinates [323, 768, 306, 795]. This confirss that is indeed positioned to the right provided
(Synthesis) Finally, double check spatial relationstip between two vebicles, conrditones, I can confirm that the vehicle located at [323, 268,266,999 directly to the right of the vehicle on the lower lef. This logical deduction leads me to conclude with certaint that the anewer is correct, and thus the cocedlinstes of the vahicle question (323, 768, 366, 705) -ihink-
their respective
RSThinker: Canower-[M33, 768, 366 oineers
Figure 10: Qualitative Visual Grounding example of RSThinker's Geo-CoT: a methodical
Grounding--ynteenn sequence culminating in a justified <answer>.
Detect the building shaped like the number 8.
RSThinker: <think>
(Pimaning) To detect die builing staped like the number 8 the image. start by analyciu the everall structuee and layout of ueaa The azridl view provides comprehezsive peopactive various buildings and landsopes, which crn help likmtily unique andiattual femm.
Find, I fects the disinctive stepes within the image Since the question specifically meniune building diapell like sumber I look for sinmmue that have siiar curviiner dreign This imvivess comnning both the carinns builiings and their
smangrmemss
(Gronnding) Upin resizwing the image, noticz prominent buiding in the scction that los smsesal, flowing shage. This builing of number it The
curves resemible venicall and hormontal components the comvenoonal recinngir and shapes the coondauts
desgs stands building indeed
aunounding buildins. Nest, confimm relative features the usa. sdentified building conspandd the cocndiatess (164,31 344,4666 which wher
poovided image.
visual
disticctive shape buildig located the
(Sonthess) Finull. I conclade building muncing diseripciun ihe qusstion accuntely ideniifed the [164, 264,456)
Therstrr, daseroon
builinng shsped
RSThinker: sanswer- <answer-(114.321 (164,321,344,46)) vansser-
Figure 11: Qualitative Visual Grounding example of RSThinker's Geo-CoT: a methodical
Planning- Borniing-rruuniig---e Synthhssss sequence culminating in a justified <answer>.
RSThinker:
Describe this image in detail.
(Planning) To deselibe the image
by idestifing ing cental forure the scone. portisnn structure image antien, easly roogsiaable distiettivv shape the grees visbll (Greundiag) stadiam sporing
95% the
liels
posilly
amaggment these vahicla Sumonndng ristern netic parking lor that denely filed with
bay Hody indiceing
the endue..
The pailnn is organized with deadyy definad speces, and the das vary is and color, addng sense of setivity fie scene The procinity of the perking lst the stadin cndiesss that serves the vistors ateeding everts there. In addition to the stadian and paringg lot, observe a bridin aducent to the andiun. The strature appean to be sepurati bat edattl to the statiu, perhape se serving an feellyy for coserscons, mondund so or anmiiieratvv functioss astectind with the events kreas! buiding mor' ii stolke the imaps and is some hea tere beilly. coombertnng to the sorral infreemmatan saspecting stalians
Coniidring the leeeut salion, the bas garking and offeeett builiig, cm ade sonse exptures potentol enery actistty Hecty dring gamee
thase
eoptesiting axdier'ss conal postion, armoddig parking fited wit the buildng all infiosive of amogghnn caterko
(Sreched)) Theretirn cocclie my douited desription
pormoorlly image visiiity
the comornted by porking building
parking builing compleinn The ormade and baildrng stending ail huckppondd
The
camally with
extosiig
deskes basy day stadians parked
Figure 12: Qualitative Image Caption example of RSThinker's Geo-CoT: a methodical
Srinng-rroounnnnnnnn sequence culminating in a justified <answer>.
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Which scene does this image belong to? You choose Parking, Resort School, Bridge, Church.
RSThinker:
(Planning) examine the overall layout and features present the image. (Grounding) The image displays large rectangalar building. which is indicative of an oducational institution, likely school. structure's design includes multiple and spacious ouidour arca, suggesting serves lange numbbe of studeni..
Next, notice the presence of'a sports field adjacent feature of schools, as they typically have facilities for physical education and sports acrivities. The field well-maantained further indicating is used regularly students.
building, This
characerrsstic
Additionally, there are several trees and landscaped areas surrounding the building, creating a conducive environment for leamning. The organized layout the buildings, along with the recreational spaces, supports the idea that this an oducational setting.
(Synthesis) Considering these observations- -te large building, sports field, and surrounding green spaces. it is ressonable conclude that seone depicted in image belongs to school--thinke -thinkc>
RNThinker: sanswer lanswer
Figure 13: Qualitative Scene Classification example of RSThinker's Geo-CoT: methodical Planning-Grouns Grounding- Synthesis sequence culminating in a justified <answer>.
What the main type of area seen in the image?
RSThinker: -thinle-
(Plansing) ideutiy major stucture the
fist anaivve evenall layout captured acrial perspective. image shows segment of adjacent to expressway, which crucial determining the nauure of the ara. The presenee of large, open paved area indicates facility designed to accommaddaiz velicless siggesingg service area fix tnaeles on the expesssaay.
(Grounding) Upon closer isspection, aoiie sevena parked vehicles scanered dhroughout this area, which reinforees the idei that this stracture indeal ficilit. Notaly,
meant rest refaeling The vdhiclos in
indicating active
vehicle located woaad
funther supporting
functiorality aseocisted
Additionals, bluconoffal structure vioble the image charactorisic service arass oflen providing amenilass for drivans The samoanding green vegetatiinn and the exgeceeway munnnng paralld to this wua provide contest, confirming that this is service mea designed for comvenience along a majer mote. (Synthoiis) Coneidering all these clamente, is clear that the most prominent stractare depicted the image the expesssway service chamevenzed by its layout, the infostrscturee Therefore, atanke
of vehicles. supportive major strecure image indead the exgresony service
RSThinker: Sanswer -wwssway-scrvccc-arrr Vintswer
Figure 14: Qualitative VQA example of RSThinker's Geo-CoT: a methodical Planning- Grounding-SSntheen sequence culminating in justified <answer>.
Shared base prompt (prepended to all tasks).
You expert in the field of remote sensing with strong reasoning abilities, capable of identifying, analyzing, and inferring information in remote sensing images. Now you need help construct CoT dataset in the field of remote sensing, to help other models think better. For each remote sensing image, utilize the provided auxiliary information to better understand the image. Now construct a detailed remote sensing TASK-CoT dataset.
For each sample, you will receive ** image **, auxiliary information ** ** question **, **d** correct answer **, You need to carefully understand the ** image ** to facilitate the capture of key targets in the image by auxiliary information formation is correct, but may not be comprehensive. you need to combine it with your own understanding to identify all the information of the image). For the problem, fully understand the correct answer, and think of the solution path the answer, integrating it into the general CoT from question to answer.
(Note: auxiliary in-
Importantly, auxiliary information is to help you understand the image and not let it appear in COT. Even if you do not agree with certain viewpoints of the auxiliary information, you can ignore these viewpoints, but do not mention them in the COT.
Note that the CoT should infer the answer from the problem, and the correct answer should not appear abruptly in advance. Therefore, statements such as 'this is consistent with the correct answer' should not be included in COT. The CoT should include process of thinking and deduction, as well as process of reflection. The CoT should naturally be divided into several segments, with each segment separated by a "unin'n". Next, provide few examples of TASK-CoT. (Tass--peeificc exemplars)
Now, please construct a TASK-CoT for the input. Please carefully examine the image, read and understand the question and correct answer, think carefully, deduce the thought process from question to answer, and organize it into a CoT presentation. The CoT format is "CoT": Do not return any additional information.
"CoT content".
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The base prompt instantiates Grooiiiingg pose the task into tractable subgoals, then ground each step in observable, object/region ev- idence, and finally synthesize concise answer after explicit verification. It forbids unverifiable claims and requires explicit reference to evidence when applicable (e.g., bounding boxes, coordi- nates, directions, relative size/position). We implement the annotator with GPT-4V (OpenAT] 2023) under constrained prompt, and employ in-context learning with a few high-quality exemplars to re- Groundiinn
Groumding-Synthesiin Synthesize cognitive architecture: first decom-
inforce
style reasoning. Minor task-specific variants of the base prompt are used to explicitly cue the current task while keeping the core instructions unchanged.
For each task, we append a minimal task-specific template to the shared base prompt and supply few curated in-context exemplars. These exemplars levels (including edge cases) and must strictly follow the canonical output format ensure reliable parsing and consistent reasoning.
are chosen to span diverse scene types and difficulty
Object counting task exemplar
Next, provide two examples count-CoT.
The first example for smaller number of targets, maybe less than 8-10. Input:
"question": "what is the amount of ship in the image? ltexcbackslash nAnswer the question using single word phrase.", "auxiliara iary information":!
Limag__sizel 1800,800),
"objects":
"ship_positions [[612, 761], [628, 7051, (657, 531]], "harbor_positiie [[492, 715], [527, 504], [568, 8]]
count":
"ship": "harbor":
), "answer": "3",
Output: { "CoT": "To determine the number of ships in the image, we begin by identifying the most likely locations for ships-typicall near docks, piers, water edges. This image shows a coastal residential area with multiple piers extending from private properties into the water. Anteer.nnnnWWWW systematically analyze the image from top to bottom. The topmost dock has no visible ship. The second dock from the top clearly has small white vessel its end, identifiable by its sharp shape, white color, and shadow consistent with boat. The third to the bottom of the image-shows two vessels: a large white yacht with a visible bow and deck features, and second boat partially covered with blue tarp An all the ship targets we found in the image are as follows: a small white vessel the end of second dock, vessols at the third dock-a large white yacht with visible bow and deck features and second boat partially covered with blue tarp next to in'n'n Now we vrossy the water area away from the docks. No additional vessels floating independently or anchored elsewhere. All identifiable ships are docked at the piers, suggesting no free-floating vessels. An ensure accuracy, we reflect on the identification criteria of "ship': the objects must have boat-like geometry, orientation such as hulls, decks, or covers. All three identified objects meet this criterion. We do not count objects such as floating platforms shadows ships. Now, I can provide the amount ships in the image." The first example end.
observed
water, and features
The Second example is a larger number of targets, maybe more than 8-10. For the large number of targets, The count-CoT should count by region, identify all regions in the image where targets exist, and provide **the exact number of targets in each region** and finally count the number of targets. Input:
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"question":
"how many small-vehicles are there in the image?\ nAnawer the question using single word or phrase.", "auxiliary information":
"image_size"; [800, 800], "objects":
small--eehicle_ppottiioo
[695,365), (722,
(796,162);
[745,216], (584,49)),
(715,244),
(326,754),
[664,395],
[519,546],
[490,574),162,,05,, (4366))))))663)) (436,630),4488,,
[381,687],
(a00)6888,,,21))) (66,,,2),, [660, 16233333,,,,, 3,933
[596,364],
[571, 4540, [512, (522,,45,,, 1484,
[455,
[427, (392,577], 1397,599,,,,,,,77,,,,,,,, (36,,,31,,,,00 (361, 1334, (334,66)),,33446333
[263, 6951,
2201,6791, (168,6661,, [12, 6561, (641,
657), 189, 652], (49, 6501,
1,
wsw.lmiing-pooo-ooitttoo [[463, 687]]
"count": [ "small-vehicle" 42, swiimiing-pool""
"answer"; "42"
"To answer the question regarding the number of small vehicles in the image, begin by carefully inspecting the urban area depicted in the satellite image. The term ssmall-ehicle generally refers passenger cars or similar-sized transportation units, which typically appear on roads, in parking areas, or near residential and commercial build- ings. wngs..nnninScanning the image, identify several regions where such vehicles are likely to be found. The straight road along the right edge of the image contains two dense row of parked vehicles aligned parallel to the curb. These vehicles are of consistent size and shape, suggesting they are indeed small vehicles. Carefully count their number as 17 vehicles above the road and 16 vehicles below the road. Additionally, more vehicles are visible around the curved road the bottom-left portion of the image. I can count vehicles the cured road. must also check for any vehicles near building entrances or driveways, as small vehicles might be parked partially obscured there. So all the ship targets found in the image are as follows: 17 vehicles above the right road, 16 vehicles below the right road, vehicles the cured road. An'n'nTo ensure completeness, systematically count each visible vehicle these areas. mark each location where a vehicle is visible-mien most are clearly distinguishable by their rectangular shape, shadow pattern, and contrast with the road surface. Even in areas where vehicles are clustered or partially covered by trees or nearby structures, their general size and alignment help confirm their identity as small vehicles. Antn'nAfter completing a de- tailed inspection and verification of all visible small vehicles in the image, conclude the total number of small vehicles present. Now, can provide the amount small vehicles in the image.")
Output: "CoT":
The second example end.
Image caption task exemplar
Next, provide an example of caption-CoT. Input:
"question";
"Describe the image in detail.", "auxiliary information": "cbjects";
"abi_id": 0, "referring_sentencee:: sentence"; "The toll station is positioned at of the image.", "obi_als"; "expresswy--olllsattttoor
the
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:
0,45,
0,43,
0.59,
0.59
"abi_position"; "center", "cbj rebirrel_postionn "", "cbi_size"; "small", "abi_rel_size": ""
"obi id" 3, sentence"; " "The right-most small vehicle is Located near the center, the right of the toll station.", "cbj_cls"; "vehicle",
"cbi_coord":
0.58,
0,44,
0,62,
0.47
1, "cb "obi_position"; "center", "cbi pe__reepposiiion "right-most", "cbj_size"; "small", bobi_rel_size" "",
1
"answer"; "The image, sourced from GoogleEarth, shows a rural area with an Alongside the toll station, multiple small vehicles are visible, with positioned the middle-left and another that is the right-most the center relative
situated at the center.
toll station.",
"To describe the image in detail, I begin by identifying key man-made structures and their surroundings. The image appears to taken from a satellite or aerial perspective, capturing a segment of road that intersects the center of the image. A distinc- tive bluc-roofed structure is positioned along the road, which is indicative of a toll station, given its placement and function located and stands out due to the contrast of its blue roof against the surrounding green ter- rain. Nnxn I observe the presence of several small vehicles the road. These vehicles are distinguishable by their size and light-colored shapes. At least two vehicles are clearly visible: one to the left of the toll station (middle-left in the image), and another is the right of the toll station, as the right-most vehicle near the center. Their relative positions to the toll station confirm their function below the toll station, there is a rectangular area containing a building with red roof and surrounding features like shadows and parked cars, suggesting it is nearby facility related to the toll station local administration. The walls around this structure further support that this a standalone, purpose-buil building, likely residential or administrative facility. InnnnnCnniieriing the layout, objects, and spatial relationships, I deduce that this is rural location due to the surrounding undeveloped green landscape and limited infrastructure. The toll station and vehicles the primary indicators of human activity. After reflecting the structures visible in the image, conclude a comprehensive and detailed description must include these components- the rural environment, central toll station, nearby vehicles, and the presence of a building below the toll station. Now, can provide a detailed description about this image. The example end.
Output: "CoT":
narrow point in the road. This structure is centrally
vehicles in transit waiting. InininAdditionally,
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Object Detection task exemplar
Next, provide two examples of Object Detect-CoT.
The first example for smaller number of targets, maybe less than 8-10.
Input:
question": "Detect all airplane in the image.". :answer":
[7003, 252, 805, 345], [835, 487, 985, 582], [855, 734, 936, 7931], "auxiliary information":
"cbjocts"
"airplane":
[0703, 252, 305, [835, 487, 985, 582], [855, 734, 936, 793]], "ground track field": [[38, 11, 383,3941]
"ccunt":
" "airplane"; 3, "ground track field":
},
Output: { ("CoT": "To detect all airplanes in the image, we first need to understand the overall layout and characteristics the scene. From top-down perspective, the image appears to depict large airport airbase facility. On the right side of the image, there is a wide expanse concrete consistent with an airport apron or taxiway, commonly used for aircraft parking, loading, and movement. The left portion of the image includes multiple buildings, greenery, and ground track field, suggesting mixed-use infrastructure possibly associated with avia- tion operations or nearby institutional facilities. \n'n'nThhe task is to locate all airplanes within the image. From a visual perspective, airplanes in satellite imagery are generally character- ized by a fuselage, wings extending laterally, and sometimes visible tail sections or shadows. They are usually white or light gray and stand out against the concrete apron due to their shape and symmetry. 'nin'nCarefullly scanning the apron area on the right side of the image, three distinct airplanes can be observed. The first airplane is near the top right quadrant of the apron, oriented roughly northwest to southeast. The second airplane is positioned slightly be- low center, larger in size, and is placed near the midsection of the apron. The third airplane is in the bottom right quadrant, near the edge of the apron area. These three objects clearly ex- hibit the typical geometry of airplanes when viewed from above and are isolated from other vehicles or structures, confirming their identification. An'n'nAfter identifying and verifying the airplane locations, scale relative to the image dimensions. The bounding boxes for the airplanes are as follows: the first airplane is near the top right quadrant of the apron [703,252.805, (,45,, the second airplane is positioned slightly below center at 487,985,582], and the third airplane is in 1855,734,936 93,,733.,........ These coordinates accurately encompass the
convert their positions into coordinate ranges in the thousandths
the bottom right quadrant visual extents of each detected airplane. Thus, the image contains exactly three airplanes at these specified locations. Now can provide the detection box for all airplanes in the image."
}
The first example end.
The Second example is larger number of targets, maybe more than 8-10. For the large number targets, The Object Detect-CoT should count by region, identify all regions in the image where targets exist, and provide # ** exact targets in each region**, and finally provide the detection box for all targets. Input:
quesstion:: "Detect all smal 1--eiiicellee in the image.". Maxiliarr information":
"objects":
"small-vehicle::
[182, 325, 205, 3381, [212, 282, 236, 295], [100, 806, 123, 8281,
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[791, 122, 812, [775, 796, 115],
553,, 299,, 620,
(341,81,358,100 [478,
(82,296,78,313) 1738,
258, (782, 71, 92]
1, "storage-tank": 5, 387, 651
"count": " "small-vehicle": 15, "storage-tank":
"answer":
(182, 325, 325, 3381, 266,266,288,2711
(212,282,236, 295], (791,122,,12,14111
(100, 82,,
[775, 93, 796, 115], (341,81,358,122
[530, 502,
629,, (22,,
[478, 395,
[52,296, 72, [738, 1381, (782, 808, 92]
[132, 251,
1,
Output: { (CCoT": "To detect all small vehicles in the image, I begin by scanning the image systematically from top-left to bottom-right, focusing on regions that contain roads, intersec- tions, parking areas, these the most likely locations for small vehicles. In remote sensing imagery, small vehicles are typically identifiable by their small rectangular or oval shapes, high contrast with road surfaces, and regular spacing when parked. Anin'nThhe first noticeable region is near the top of the image, where broad road runs horizontally across. Within this region, multiple small objects visible that match the expected characteristics of small vehicles. These objects have rectangular shapes and are aligned along traffic lanes parking areas. Carefully examining this section reveals multiple vehicles, including one near the center and two others slightly left and right. A curved structure is also visible, but it is not a vehicle, so I finnore it Nex, look at the middle and lower-middle sections of the image. A prominent bright structure (possibly a roof or overpass) obscures part of the area, but beneath and around it, vehicles are visible. There are several parked or moving ve- hicles, particularly clustered near entrances and exits of adjacent roads. At least four vehicles are evident by their size, color contrast, and position relative to the road. \nhn'n'The lower-left section of the image contains another road with curved lanes and an exit area. Vehicles be seen parked or moving along this segment as well. These vehicles are again identified by their compact shapes and positions aligned with road markings. vehicle appears at the bot- tom left corner, and another is slightly above it. Iinnnnin inspect the far-right region and the top-right quadrant. This area includes continuation the road network, and identify several more vehicles, particularly at intersections and nearby parking zones. These are less occluded and clearly match the known signature of small iehicles.InlninIn total, identify 15 small vehicles distributed across various regions of the image: Top central road region: 3 vehicles; Mid-upper right road network: vehicles; Under the curved roof structure: vehicles; Bottom-left road area: 2 vehicles; Bottom-central area: vehicle; Upper-left road:
2 vehicles. Each of these vehicles is matched to a detection box based on their visible bound- aries. After bounding each vehicle, obtain the following bounding boxes:[[182, 325, 205, 338), [263, 268, 288, 278], [212, 282, 236, 295], [100, 806, 123, 828], [791, 122, 812, 141], [775, 93, 796, 115], [530, 301, 552, 318], [341, 81, 358, 102], [592, 295, 620, 315], [478, 342, 497, 355], [498, 332, 522, 348), (52, 296, 78, 313], [132, 258, 161, 271], [738, 121, 756. 138], [782, 71, 808, 92]]. Now I provide the detection box for all small vehicles in the image." The second example end.
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VQA task exemplar
Next, provide an example of VQA-CoT. Input:
"question:: "Are the vehicles predominantly parked on the left right side of the image?", "answer": "left",
"type": "object position", "auxiliary information":
"caption"; "This high-resolution image from GoogleEarth depicts parking area with several vehicles and distinguished by combination of asphalt surfaces and surrounding greenery. In total, there are nine small vehicles, distributed di fferent positions throughout the image. Notable objects include treas casting shadows and building located the left side, indicating semi-urba environment. All vehicles parked, with many situated along the edges of the lot.", "objects";
"obj_id"; 4, " "referring_sentence"" "The vehicle located the image.",
top-middle
"obi_cls": "vehicle",
"obi_coord": 0.52, 0, 0.55,
0.06
},
" "abi_positions tio":: "top-middle", " "obi rel pebiiree_positonn position"; "top-most",
size": "", "obi_rel size":
)
Output: "CoT": "To determine whether the vehicles are predominantly parked on the left or right side of the image, we must first clearly define what constitutes the 'left' and 'right' regions. In this context, the image is oriented with the top representing the north, meaning the left and right refer to the western and eastern parts of the image, iespectivel..lnnlnnN.e. begin by visually scanning both sides of the image. identify and count the number of parked vehicles on each side. On the left side of the image (the western half), I can observe several vehicles parked near the building and along the road. Specifically, there are vehicles clearly visible parked in this region, of which 3 vehicles are next buildings and vehicles are on the road. On the right side (the eastern half), observe significantly fewer vehicles, with only around 2 vehicles clearly parked, and possibly more partially visible near the lower edge.Annnnnk. Annnnnaaving made these observations, can now compare the counts. The left side shows a denser concentration of parked vehicles compared to the right. Moreover, the spatial distribution of these vehicles confirms this trend - the majority of parking appears clustered in the upper-left quadrant of the image, near the building and shaded area. The right side, in contrast, is more open and has fewer parked cars. Annn'n Thhnnfhherefrr.. through a visual examination and vehicle count comparison across both sides of the image, I conclude that the vehicles are predominantly parked on the left side." The example end.
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Scene classification task exemplar
Next, provide an example of Scene Classification-Col Input:
"question"; "Classiff fy the given image in one of the following classes. Classes: parking, bassballfield, school, resort, industrial, square, commercial, playground, port, mountain, forest, bareland, farmland, desert, denseresidential, airport, neadow, mediumresidential. center, storagetanks, park, bridge, church, pond, railwaystation, viaduct, stadium, beach, river, sparsoresidentiel. \nAnswer in one word or short phrase.", "answer"; "airport",
Output: { '"CoT": "First, I observe the overall layout and features present in the image. The image shows a large built structure with highly organized layout and multiple identical objects aligned along both sides of central building. The structures extending from the central building resemble jet bridges or concourses, which are commonly found airports for boarding and deplaning passengers from aireraft.InnnnNNeen Antn'nNext, examine the objects that are aligned with these structures. These appear to be commercial aircraft, identifiable by their wings, fuselage, and tail configurations. The high number of aircraft, their close positioning to the terminal building, and the presence taxiways and runways strongly suggest that this location used for air travel operations. AntnknI also notice network of runways, taxiways, and apron areas typical of an airport's design. The large paved areas around the terminal allow for aircraft movement and parking. The presence of service roads, ground vehicles, and gate markings further reinforces this being an operational airport. Innn'nGiven the presence terminal structure, multiple commercial aircraft, and surrounding airport infrastructure such as taxiways and aprons, the most fitting classification among the provided categories is airport.")
The example end.
Visual grounding task exemplar
Next, provide an example VG-CoT. Input:
"questica"; "where is the vehicle situated towards the bottom- right side of the image is on the road parallel to the overpass
"answer":
[790, 820, 8601,
"auxiliary information":!
"caption": "This higa-resolution image from GoogleEarth depicts parking area with several vehicles and distinguished by combination of asphalt surfaces and surrounding greenery. total, there are nine small vehicles, distributed different positions throughout the image. Notable objects include trees casting shadows and building located on the left side, indicating semi-urban environment. All vehicles are parked, with many situated along the edges of the lot.", "objects":
"obj_id"; 0,
"referring "ecerriinggsetteceee: "The overpass runs diagonally across the image and casts shadow to its right." "obj_cls": "overpass",
"obj_coord":
390,
530,
1060,
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960
"obj_position"; "", "obi_rel__ooe _es_eel__siiion.. "", "obi_size": "small", "obi_ size": ""
"obi_id"; 2, "referriingseetencce "The vehicle situated towards the bottom-right side of the image is the road parallel to the overpass." "
"obi cis": "venicie",
"obi robi_coord":
790,
820,
820,
860
" "obi _roi_position"; "bettom-right",
" _is__ree_positionn
"obj_size"; "small",
"obj_id"; 2, treferriingseentncee "The vehicle located on the road below the overpass is near the bottom-middle of the image
"obi cls": "vehicle", "obj_coord":
480,
790,
530,
830
},
"abi_positionn; "bottom-middlee", "obi posiitionss "Left-most", "obi_size"; "small",
"obi rel size":
h
(
"obi_id"; 3,
"referring "referring_eentenee" "The vehicle on the road above sentence"; the overpass is positioned towards the top-right of the image
"obi_cls"; "vehicle",
"obi_coord";
780,
280,
800,
320
I.
"obi positionn: "top-right", "obi_rel "obj_rel_positionn: "top-most", "obi_size"; "small", " jobi_rel_size". ""
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Output: [ "CoT": "To address the question, must first understand the spatial layout of the image. The question refers to vehicle towards the bottom-right side of the image that situated on road rumning parallel to overpass. This indicates relationship between the location the vehicle and the orientation of the Aappass.lnnnnnuupon examining the im- age, observe large overpass running diagonally from the lower-left to the upper-right. The overpass crosses above two visible roadways, one above and one below it, each carry- ing vehicular traffic. The shadow of the overpass is cast the right side, confirming the orientation.IninnNNeet. identify multiple vehicles in the image. The target vehicle must be towards the bottom-right of the image and located allel to the overpass. Looking in the bottom-right quadrant of the image, I spot small vehicle traveling along lane that follows the same general diagonal direction as the over- pass. This suggests the road is parallel to the overpass and supports the condition stated the question. Anhn'nFinally, verify that this vehicle's position aligns with the described craation-bottom-ae
road that runs roughly in par-
and confirm it is situated on the correct road. This process ensures that my identification is accurate. Thus, the coordinates [790,820,820) (20.8200000,8800 860] correctly corre- spond to the vehicle referenced in the question." The example end.
29


Reviews:
[{"review_id": "ub3uaNWPJr", "reviewer": "Reviewer_bTxE", "summary": "This paper introduces Geo-CoT, a perceptually grounded chain-of-thought framework aimed at enhancing the faithfulness and interpretability of vision-language models in remote sensing. The authors propose a large-scale dataset (Geo-CoT380k) containing multimodal reasoning chains for tasks like Visual Question Answering (VQA), Visual Grounding (VG), Counting, and Captioning. They also introduce a two-stage training approach combining supervised fine-tuning (SFT) and GRPO-based reinforcement learning, leading to the development of RSThinker, a model that demonstrates strong performance across a range of remote sensing tasks.", "questions": "1.Visual Grounding is essentially a single-instance localization problem conditioned on language, not an object detection task requiring multiple candidate boxes with confidence scores. The paper reports both mAP and mIoU as evaluation metrics for the Visual Grounding (VG) task. While mIoU is understandable as it directly measures localization accuracy, the use of mAP is conceptually unclear. \n\n2.The Geo-CoT380k dataset is a key contribution, consisting of 380k reasoning traces generated by GPT-4V. However, the paper lacks any quantitative or qualitative validation of these traces. Prior CoT work often applies explicit quality control—e.g., multi-sample consistency (self-consistency), deductive stepwise verification, or human expert spot-checking—when using LLM-generated chains (e.g., [1], [2], [3]). But the paper does not mention such validation, making it unclear whether the reported improvements stem from the quality of reasoning chains or simply from the large data volume. This casts doubt on the true impact of Geo-CoT on reasoning faithfulness.\n\n[1]Wang X, Wei J, Schuurmans D, et al. Self-Consistency Improves Chain of Thought \tReasoning in Language Models. ICLR, 2023.\n\n[2]Ling Z, Fang Y, Li X, et al. Deductive verification of chain-of-thought reasoning. In \tProceedings of NeurIPS 2023.\n\n[3]Wang Y, Zeng Y, Zheng J, et al. VideoCoT: A Video Chain-of-Thought Dataset with Active \tAnnotation Tool. CoRR, 2024.\n\n\n(1)Have the authors explicitly distinguished the task boundary between “visual grounding” and “object detection” in the paper? If so, where; if not, please clarify and justify why mAP is an appropriate metric for a language-conditioned single-instance Visual Grounding task.\n\n(2)In the Visual Grounding task, each sample corresponds to a single referring expression without predefined object categories or multiple predictions. Under this setting, how are confidence scores for mAP computation obtained? If the model outputs only one bounding box per query, the definition of precision–recall and mAP becomes ambiguous. Could the authors clarify this evaluation setup?\n\n(3)Could the authors clarify whether any quality control was performed on the GPT-4V-generated reasoning traces? If not, how can we be confident that the improvements are due to reasoning supervision and not just data scale effects?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "The Geo-CoT380k dataset is constructed with clear annotation rules and a consistent generation process. The two-stage training strategy (SFT + GRPO) is straightforward and effective, with experiments showing that both stages contribute to factual consistency and spatial grounding.\n\nThe study shows steady performance gains across VQA, captioning, and visual grounding, and highlights the value of verifiable reasoning for applied geospatial AI.", "weaknesses": "Overall, I do not agree with the authors’ claim that Geo-CoT can serve as a solution to the “critical gap” between existing works such as MM-VoT and the field of remote sensing observation. My main concerns are twofold:\n\n1. The authors emphasize that there is a huge gap between MM-CoT and geoscience (lines 89–90), and they propose a concept called an “intention-driven active perception process,” claiming that this represents a “critical gap” in Earth observation. They also highlight *Perceptually-Grounded Geo-CoT* as a new paradigm in their contributions. However, I believe this emphasis is overstated. The so-called new paradigm essentially “mandates a verifiable link between each analytical step and its corresponding visual evidence,” which has already been extensively explored in follow-up works after MM-CoT (2023). I can list several such studies [1–3], and the authors should conduct a more thorough review of these. Over the nearly two years since MM-CoT was proposed, numerous works have investigated the correspondence between reasoning steps and their associated visual evidence. The authors do not clarify how their approach differs from these, but instead focus only on MM-CoT (2023) and emphasize thinking with corresponding visual evidence is the “critical gap” in remote sensing field which  is already widely recognized in common field. The Geo-CoT paradigm does not clearly demonstrate any distinction between the remote sensing observation domain and the general domain. The authors even state in the related work section (lines 143–147) that existing studies focus on reasoning over whole objects, while remote sensing involves “a verifiable log of fine-grained perceptual operations.” However, if the authors examined recent works on visual reasoning from the past one to two years, they would see that this kind of “fine-grained perceptual operation” has already been addressed in many general-domain studies. The fact that the authors list this as the first of their three main contributions suggests they may have deliberately overlooked related works in order to overstate the novelty of their approach.\n\n2. There are serious issues with the evaluation metrics used in the downstream tasks, which I hope the authors will clarify. Specifically, the authors use mAP and mIoU as evaluation metrics for the Visual Grounding (VG) task. I will detail these concerns in my comments, but this choice raises significant doubts about the reliability of their downstream task evaluations.\n\n[1]Visual CoT: Advancing Multi-Modal Language Models with a Comprehensive Dataset and Benchmark for Chain-of-Thought Reasoning\n\n[2]V*: Guided Visual Search as a Core Mechanism in Multimodal LLMs\n\n[3]CMMCoT: Enhancing Complex Multi-Image Comprehension via Multi-Modal Chain-of-Thought and Memory Augmentation", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "c6Wbb67nEm", "reviewer": "Reviewer_Gm5N", "summary": "Authors propose a two-stage training strategy to obtain a reasoning model in the RS domain. First, they constructed a large-scale (380K) dataset of structured Geo-CoT rationales to SFT the base model, GLM-4.1V. Geo-CoT380K includes data from tasks such as VQA, Image Captioning, Scene Classification, Visual Grounding, Object Counting, and Object Detection. Then the SFT model is further trained via GRPO. The RSThinker model shows impressive performance on Grounding, Counting, Classification, VQA, and Captioning tasks, with reasonable, interpretable thinking process. The paper is well written and straightforward.", "questions": "1. The result in Table 4 is not reproducible. I tried the GLM-4.1V-Thinking (zero-shot in VG) in a new conda GLM env, and only got 50.67 (compared with 63.8 in Table 4). The prompt is taken from GLM4.1V paper: \"Tell me the position of the referred object in the picture. {Question} Answer in [x1,y1,x2,y2] format.\" and the corrdinate is normalized to 0-1000. It would be appreciate if authors can provide inference script (including prompt).\n\n2. I SFT the GLM-4.1V with the training set of VRSBench using llamafactory, the result is 62.06 (miou), which is far away from 87.7 in Table 8. It would be appreciate if authors can provide some of Geo-CoT data (VRSBench's training part) to verify.\n\n3. Is 380K CoT data necessary for teaching the model to generate CoT? (Are 50K, 100K, or 200K enough? Is this CoT-SFT stage mainly for activating the base model’s parameters to learn the reasoning pattern, or for injecting RS knowledge into a general base model?)\n\n4. GLM-4.1V is already quite impressive on RS tasks, even in a zero-shot manner. I wonder will this CoT-SFT + GRPO pipeline work for other base models such as InternVL-3.5 or Qwen3-VL in RS tasks? Will it works for weaker model like Qwen2.5-VL?  Grounding task solely should be enough to verify.\n\n5. After SFT, do the authors train GRPO for each task independently or for all tasks jointly? For example, for the VG task, is the model tuned with GLM-4.1V + Geo-CoT380K (Stage I, SFT) + Geo-CoT380K-VG + Additional Dataset in Table 2 (Stage II, GRPO)? Or is it GLM-4.1V + Geo-CoT380K (Stage I, SFT) + Geo-CoT380K + Additional Dataset (Stage II, GRPO)?\n\n6. Will training all tasks jointly benefit the model capability?\n\n7. From Table 8, it seems GRPO does not improve the model capability very much after training with Geo-CoT. Is this improvement actually from GRPO, or from continued training?\n\nI will certainly raise the score if authors address (some) my concerns.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The RSThinker model shows impressive performance on Grounding, Counting, Classification, VQA, and Captioning tasks, with reasonable, interpretable thinking process. \n\n2. The prompts for generating Geo-CoT380K are well designed. Geo-CoT380K should be a high-quality SFT dataset for teaching the model to think.\n\n3. The tasks included in this paper are quite complete.\n\n4. In the RS domain, this is the first work I have seen that makes CoT-SFT + GRPO pipeline work.", "weaknesses": "1. The ablation study is quite brief. More analysis could be elaborated. (1) Is 380K CoT data necessary for teaching the model to generate CoT? (Are 50K, 100K, or 200K enough? Is this CoT-SFT stage mainly for activating the base model’s parameters to learn the reasoning pattern, or for injecting RS knowledge into a general base model?) (2) GLM-4.1V is already quite impressive on RS tasks, even in a zero-shot manner. Will this CoT-SFT + GRPO pipeline work for other base models such as InternVL-3.5 or Qwen3-VL?\n\n2. A crucial training detail is not disclosed. After SFT, do the authors train GRPO for each task independently or for all tasks jointly? For example, for the VG task, is the model tuned with GLM-4.1V + Geo-CoT380K (Stage I, SFT) + Geo-CoT380K-VG + Additional Dataset in Table 2 (Stage II, GRPO)? Or is it GLM-4.1V + Geo-CoT380K (Stage I, SFT) + Geo-CoT380K + Additional Dataset (Stage II, GRPO)?\n\n3. From Table 8, it seems GRPO does not improve the model capability very much after training with Geo-CoT. Is this improvement actually from GRPO, or from continued training?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "2ZQkBnun1R", "reviewer": "Reviewer_aXVe", "summary": "This paper introduces RSThinker, a vision–language model designed for faithful reasoning in remote sensing. The key idea is the Perceptually-Grounded Geospatial Chain-of-Thought (Geo-CoT) framework, which reformulates remote-sensing analysis as a multi-stage, verifiable reasoning process: planning → evidence collection → synthesis.\n\nTo train such reasoning ability, the authors propose a two-stage alignment pipeline:\n\t1.\tSupervised Fine-Tuning (SFT) on a newly constructed Geo-CoT380k dataset, which contains structured reasoning traces aligned with spatial evidence;\n\t2.\tGroup Relative Policy Optimization (GRPO) reinforcement learning, using task-specific metrics (mIoU, mAP, MAE, CIDEr) as direct rewards to encourage factual consistency.\n\nExperiments across six remote-sensing benchmarks (VG, detection, counting, classification, captioning, VQA) show consistent and sometimes striking improvements over both general and remote-sensing-specific VLMs. The paper claims that these gains stem from explicitly grounding reasoning steps to verifiable spatial evidence, rather than from larger training data or model size.", "questions": "1) Please provide a complete table mapping each evaluation benchmark to its presence in the training corpus (SFT/RL), specifying which splits were used and ensuring no test leakage.\n2) Were baseline models fine-tuned on the same data or evaluated zero-shot? If fine-tuned, what settings were used?\n3) Could you report Seen vs Unseen benchmark results and a leave-one-benchmark-out generalization experiment?\n4) How is the GRPO reward computed per sample? Is it normalized by sample difficulty? Any instability observed when optimizing with heterogeneous metrics?\n5) Do you have any evidence-consistency or hallucination evaluation to support the claim of “faithful reasoning”?\n6) Please clarify whether RL rewards ever touched validation/test splits (they should not).\n7) For fairness, consider adding at least one grounded-CoT / Argus-style baseline to demonstrate that the gain is not purely due to data scale.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "1) Problem significance: addresses the lack of verifiable reasoning in remote-sensing VLMs—a recognized bottleneck for high-stakes applications (disaster response, environmental monitoring).\n2) Conceptual integration: unifies planning–evidence–synthesis reasoning with spatially grounded supervision and metric-aligned RL—a combination rarely systematized before.\n3) Dataset & framework completeness: Geo-CoT380k provides the first large-scale, structured reasoning corpus in Earth observation; the pipeline is end-to-end reproducible.\n4) Empirical performance: substantial and consistent improvements across tasks; ablations confirm that both SFT and GRPO contribute.\n5) Broader impact: opens a pathway for “auditable” reasoning in multimodal models, extending beyond remote sensing.", "weaknesses": "1) Fairness and data leakage risk:\nSome evaluation benchmarks (e.g., VRSBench-VG, DIOR-RSVG) appear in the SFT or RL training corpus (Table 1 & 2), yet the baselines may be zero-shot.\nWithout clear disclosure and matched fine-tuning, large performance gaps could reflect training data advantage.\n→ Action: provide a detailed train/test mapping table and equal-training baselines.\n2) Missing comparison to strongest related paradigms:\nSection 2.2 mentions MM-CoT but omits direct references to Grounded-CoT / Argus.\n→ Action: add a method-level comparison table clarifying differences (e.g., explicit spatial grounding, task-specific RL, remote-sensing scalability).\n3) Evaluation scope:\nThe claim of “faithful reasoning” would be stronger with human or automated evidence–conclusion consistency scores.\n4) Reward design details:\nGRPO uses end-task metrics as rewards, but credit assignment to intermediate reasoning steps is under-specified; ablation on reward sensitivity is missing.\n5) Lack of failure-case analysis:\nThe paper reports uniformly strong results but shows no failure examples or discussion of limitations.\n→ Action: include representative failure cases and categorize typical errors (e.g., grounding misalignment, reasoning inconsistency, hallucinated evidence).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "YaXvS2du7t", "reviewer": "Reviewer_oaDR", "summary": "The paper introduces Geo-CoT, which explicitly injects verifiable intermediate evidence into multimodal reasoning. During SFT, the model learns spatially anchored reasoning traces (with boxes/coordinates); afterward, GRPO uses task metrics as rewards for alignment. The authors claim improvements in faithfulness and interpretability across several vision–language tasks.", "questions": "See Weaknesses", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The idea of tying visual localization to the reasoning chain so that intermediate evidence is checkable is intrinsically valuable.\n\nThe framework is evaluated across diverse remote-sensing tasks (detection, counting, VQA, captioning)", "weaknesses": "1. The approach can largely be summarized as “structured CoT + RL with task-level rewards.” Compared with recent CoT+RLHF/RLAIF and visual-CoT lines, this feels more like an engineering integration than a conceptual/algorithmic advance.\n2. The collapse of the output format without KL suggests that the model may be learning templates rather than reasoning logic.\n3. The relevant work section should further elaborate on the differences in concepts and implementation between these recent visual CoT / RLAIF / GRPO-style methods\n4. Lack of specific failure cases and cause analyses\n5. Lack of few-shot or zero-shot (on broader datasets such as OPT-RSVG[1]) performance  analysis\n6. I would like to know that the implicit understanding ability (like SegEarth-R1[2]) of the proposed method\n\n[1] Language-guided progressive attention for visual grounding in remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 1-13.\n[2] Segearth-r1: Geospatial pixel reasoning via large language model[J]. arXiv preprint arXiv:2504.09644, 2025.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Ud1dS8NjsD", "reviewer": "Reviewer_xYJj", "summary": "This paper addresses a critical limitation of current Vision-Language Models (VLMs) in the remote sensing domain: their tendency to function as \"black boxes\" that produce plausible but often incorrect and unverifiable responses. To foster more trustworthy AI, the authors introduce the Geo-CoT (Geospatial Chain-of-Thought), a novel framework that mandates a structured, step-by-step reasoning process where each analytical step is explicitly grounded in visual evidence from the image.\n\nTo train models in this new paradigm, the authors have constructed Geo-CoT380k, a large-scale dataset of nearly 380k structured reasoning examples, by retrofitting verifiable rationales onto existing public datasets. They then propose a two-stage training strategy: first, Supervised Fine-Tuning (SFT) is used to instill the foundational cognitive architecture of Geo-CoT, followed by Group Reward Policy Optimization (GRPO), an outcome-based reinforcement learning stage, to incentivize the factual correctness of the reasoning. Their resulting model, RSThinker, demonstrates state-of-the-art performance across a comprehensive suite of multiple remote sensing benchmarks, validating the effectiveness of their approach.", "questions": "Could you provide a more detailed error analysis to investigate the failure mode of \"faithful hallucination,\" where the model generates a structurally correct but factually ungrounded reasoning chain? What mechanisms in your framework are most effective at preventing this?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. **Addresses a Critical and High-Impact Problem:** The paper tackles a fundamental and highly relevant issue. The lack of interpretability and faithfulness in current VLMs is a major barrier to their adoption in high-stakes domains like disaster response and security. By emphasizing verifiable reasoning, this work makes a significant and timely contribution toward building more transparent and trustworthy AI systems for Earth Observation. \n\n2. **Novel and Well-Designed Framework:** The proposed Geo-CoT framework is a key innovation. It moves beyond generic Chain-of-Thought by requiring each reasoning step to be explicitly linked to visual evidence (perceptual grounding), a powerful concept for ensuring faithfulness. This forces the model to justify its claims with specific pixel regions, a common failure point for many VLMs. \n\n3. **Massive and High-Quality Dataset:** The creation of the Geo-CoT380k dataset is a monumental contribution in its own right. Its scale, diversity, and meticulous annotation process make it an invaluable resource for the entire remote sensing community. The semi-automated pipeline used to create it is a clever and practical approach to large-scale data generation. \n\n4. **Strong and Comprehensive Empirical Validation:** The paper provides a thorough and convincing experimental evaluation. RSThinker is benchmarked against a wide array of state-of-the-art models across a diverse set of tasks, ranging from object counting to complex VQA. The consistent and significant performance gains provide strong evidence for the superiority of the proposed approach.", "weaknesses": "1. **Methodological Novelty:** While the application of the two-stage training pipeline (SFT followed by RL) is highly effective, the paradigm itself is not new to the machine learning community. The primary contribution of this paper lies in the novel Geo-CoT framework and the creation of the dataset, rather than in the invention of a new training methodology.  Acknowledging this and framing the work more explicitly as a novel application and extension of an existing paradigm would be beneficial.\n\n2. **Potential for \"Faithful Hallucination\":** The model is trained to generate plausible-looking reasoning chains. A potential failure mode is that the model could learn to generate a syntactically correct and seemingly logical reasoning chain that is, in fact, disconnected from the actual visual evidence. While the two-stage training process is designed to mitigate this, a more in-depth analysis of this potential failure case, perhaps through a targeted error analysis, would strengthen the paper's claims of faithfulness.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "5CU3AyHIr7", "reviewer": "Reviewer_sTsT", "summary": "This paper introduces the Perceptually-Grounded Geospatial Chain-of-Thought (Geo-CoT), a framework intended to add verifiability to remote sensing VLMs by forcing them to link reasoning steps to visual evidence. The authors create a new dataset, Geo-CoT380k, by using GPT-4V to generate structured rationales for existing RS data. They propose a two-stage training strategy (SFT and GRPO) to train their model, RSThinker, on this new dataset. The resulting model, which outputs a reasoning trace, is shown to achieve strong performance on several remote sensing benchmarks.", "questions": "1. Given that \"faithfulness\" is the core claim, why was no human study conducted to validate the quality of the GPT-4V-generated rationales in Geo-CoT380k?\n2. How can you be sure the model's reasoning trace is causal to its answer, and not just a plausible justification generated in parallel to a \"black-box\" prediction?\n3. Can you clarify how GRPO is algorithmically novel compared to a standard PPO implementation with reward normalization?\n4. Wouldn't a fairer baseline comparison involve applying few-shot CoT prompting to the baseline models using exemplars from your dataset?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The paper addresses the important and well-motivated problem of model verifiability, which is a significant barrier to using VLMs in high-stakes remote sensing applications.\n2. The proposed \"Planning-Grounding-Synthesis\" cognitive architecture is a promising and intuitive structure for modeling geospatial analysis.\n3. The public release of the Geo-CoT380k dataset is a potentially useful artifact that could spur further research into reasoning-based remote sensing models.\n4. The ablation studies clearly demonstrate that training with the CoT-structured data (SFT w/ CoT) yields significant performance gains over a baseline fine-tuned on answers alone (SFT w/o CoT).", "weaknesses": "1. The related work section is incomplete, failing to cite or discuss relevant prior work on CoT frameworks in other geographic contexts, such as GeoChain (Yerramilli et al., 2025) and Gaea (Campos et al., 2025).\n2. The paper's central claim of \"faithfulness\" is largely unsubstantiated. The Geo-CoT380k dataset, which underpins the entire method, is generated by GPT-4V. The authors' claim that this \"ensures faithfulness by design\" is a strong overstatement, as the LM may be generating post-hoc justifications that are plausible but not faithful, a bias the authors briefly acknowledge.\n3. The evaluation metrics do not actually measure faithfulness or verifiability. The paper presents strong accuracy metrics (mAP, MAE, Acc)  but mistakes this for faithfulness. High accuracy does not prove the reasoning trace is correct or causal; the lack of any direct evaluation of rationale quality is a major omission.\n4. The methodological novelty of the training process is questionable. The description of Group Relative Policy Optimization (GRPO)  appears to be a standard application of PPO rather than a novel algorithm.\n5. The SOTA comparisons are potentially flawed. RSThinker is fine-tuned on 380k CoT rationales, while the baselines were not. The performance gains may be a result of this massive, task-specific fine-tuning dataset, not a superior reasoning architecture.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)