🪐 SenseNova-MARS
Overview
While Vision-Language Models (VLMs) can solve complex tasks through agentic reasoning, their capabilities remain largely constrained to text-oriented chain-of-thought or isolated tool invocation. They fail
to exhibit the human-like proficiency required to seamlessly interleave dynamic tool manipulation with
continuous reasoning, particularly in knowledge-intensive and visually complex scenarios that demand
coordinated external tools such as search and image cropping. In this work, we introduce SenseNova-MARS,
a novel Multimodal Agentic Reasoning and Search framework that empowers VLMs with interleaved
visual reasoning and tool-use capabilities via reinforcement learning (RL). Specifically, SenseNova-MARS
dynamically integrates the image search, text search, and image crop tools to tackle fine-grained and
knowledge-intensive visual understanding challenges. In the RL stage, we propose the Batch-Normalized
Group Sequence Policy Optimization (BN-GSPO) algorithm to improve the training stability and advance
the model’s ability to invoke tools and reason effectively. To comprehensively evaluate the agentic VLMs
on complex visual tasks, we introduce the HR-MMSearch benchmark, the first search-oriented benchmark
composed of high-resolution images with knowledge-intensive and search-driven questions. Experiments
demonstrate that SenseNova-MARS achieves state-of-the-art performance on open-source search and
fine-grained image understanding benchmarks. Specifically, on search-oriented benchmarks, SenseNovaMARS-32B scores 74.3 on MMSearch and 54.4 on HR-MMSearch, surpassing proprietary models such
as Gemini-3-Pro and GPT-5.2. SenseNova-MARS represents a promising step toward agentic VLMs by
providing effective and robust tool-use capabilities.
Overall performance of SenseNova-MARS-32B compares to other models across six benchmarks. SenseNova-MARS-32B can surpass proprietary models such as Gemini-3-Pro and GPT-5.2 on the search-oriented benchmarks such as MMSearch and HR-MMSearch
SenseNova-MARS can tackle the challenging visual task by leveraging an integrated suite of text search, image search, and image crop tools within the reasoning process. This is a demo example.
Benchmark Performance
Search-oriented benchmarks
Type
Model
Average
MMSearch
HR-MMSearch
FVQA-test
InfoSeek
SimpleVQA
LiveVQA
MAT-Search
Direct Answer
Open-source
Qwen2.5-VL-7B-Instruct
27.70
7.60
0.58
26.28
31.95
47.88
19.63
60.00
Qwen3-VL-8B-Instruct
29.24
11.70
12.13
24.22
23.15
42.94
23.18
67.33
Qwen2.5-VL-32B-Instruct
32.01
11.70
3.93
30.50
36.65
48.57
21.40
71.33
Qwen3-VL-32B-Instruct
35.22
16.96
19.02
32.17
28.95
45.90
31.59
72.67
Proprietary
GPT-4o-mini
33.08
15.79
1.31
36.83
35.95
44.42
24.63
72.66
Gemini-2.5-Flash
40.87
21.64
7.54
43.78
44.10
55.48
31.57
82.00
GPT-4o
42.38
23.39
13.11
48.00
52.90
51.73
28.18
79.33
GPT-5
50.24
35.09
22.62
54.39
54.15
61.70
44.39
79.33
GPT-5.2
50.92
43.27
24.92
50.94
50.40
59.92
47.00
80.00
Gemini-3-Flash
53.68
57.31
21.97
56.50
54.85
63.57
38.90
82.67
Gemini-3-Pro
55.87
62.57
26.89
59.22
56.30
64.07
40.06
82.00
Agentic Model (zero-shot)
Open-source
Qwen2.5-VL-7B-Instruct
35.50
32.16
19.34
36.00
28.80
42.35
22.52
67.33
Qwen3-VL-8B-Instruct
51.52
47.37
27.87
53.61
46.15
62.29
39.37
84.00
Qwen2.5-VL-32B-Instruct
53.45
49.71
33.44
52.22
50.10
65.15
42.17
81.33
Qwen3-VL-32B-Instruct
53.82
49.12
34.43
54.28
49.85
64.17
42.87
82.00
Proprietary
GPT-4o-mini
45.65
38.60
26.23
50.00
42.35
50.84
31.54
80.00
GPT-4o
55.09
49.12
30.16
66.34
59.55
63.67
40.09
76.67
Gemini-2.5-Flash
58.05
59.06
40.00
61.72
53.70
68.81
47.75
75.33
GPT-5
60.12
52.63
38.36
62.61
55.95
70.58
56.02
84.67
Gemini-3-Flash
61.26
62.57
41.64
64.89
61.10
67.92
48.06
82.67
GPT-5.2
67.64
66.08
48.20
68.78
65.55
78.18
65.99
80.67
Gemini-3-Pro
69.06
74.27
48.52
72.61
66.45
75.91
59.69
86.00
Agentic Model
Open-source
Visual-ARFT
40.13
34.50
24.92
41.72
37.95
42.45
25.40
74.00
DeepMMSearch-R1
-
-
-
-
47.51
55.87
-
-
MMSearch-R1
52.49
53.80
20.33
58.40
55.10
57.40
48.40
74.00
DeepEyesV2
-
63.70
-
60.60
51.10
59.40
-
-
SenseNova-MARS-8B
64.20
67.84
41.64
67.11
61.70
70.19
56.22
84.67
SenseNova-MARS-32B
69.74
74.27
54.43
72.61
65.25
74.14
60.83
86.67
High-resolution Benchmarks
Model
V* Bench
HR-Bench 4K
HR-Bench 8K
MME RealWorld
Avg.
Direct Answer
Gemini-2.5-Pro
83.8
87.3
85.4
-
-
GPT-4o
67.5
65.0
59.6
62.8
63.7
LLaVA-onevison
75.4
63.0
59.8
57.4
63.9
Qwen2.5-VL-7B-Instruct
75.3
65.5
62.1
56.8
64.9
Qwen2.5-VL-32B-Instruct
80.6
69.3
63.6
59.1
68.2
Qwen3-VL-8B-Instruct
86.4
78.9
74.6
61.9
75.5
Agentic Model
SEAL
74.8
-
-
-
-
Qwen3-VL-32B-Instruct
91.1
84.6
81.6
-
-
Qwen3-VL-235B-A22B-Instruct
93.7
85.4
82.4
-
-
Monet
83.3
71.0
68.0
-
-
Pixel-Reasoner
84.3
72.6
66.1
64.4
71.9
DeepEyes
83.3
73.2
69.5
64.1
72.5
Thyme
82.2
77.0
72.0
64.8
74.0
DeepEyesV2
81.8
77.9
73.8
64.9
74.6
Mini-o3
88.2
77.5
73.3
65.5
76.1
Skywork-R1V4
88.0
82.8
79.8
71.4
80.5
SenseNova-MARS-8B
92.2
83.1
78.4
67.9
80.4
SenseNova-MARS-32B
94.2
90.2
86.6
72.7
85.9
Citation
@article{SenseNova-MARS,
title={SenseNova-MARS: Empowering Multimodal Agentic Reasoning and Search via Reinforcement Learning},
author={Yong Xien Chng and Tao Hu and Wenwen Tong and Xueheng Li and Jiandong Chen and Haojia Yu and Jiefan Lu and Hewei Guo and Hanming Deng and Chengjun Xie and Gao Huang and Dahua Lin and Lewei Lu},
journal={arXiv preprint arXiv:2512.24330},
year={2025}
}