3.1. Experiments Setup
3.1.1. General Scientific Reasoning Abilities
SGI-Bench [29]. SGI-Bench is a scientist-aligned benchmark for Scientific General Intelligence (SGI),
defined as the ability of an AI system to autonomously navigate the full scientific inquiry cycle of

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Deliberation, Conception, Action, and Perception. It operationalizes this goal with four task families
spanning 10 scientific disciplines and 1,000+ expert-curated samples: Scientific Deep Research, Idea
Generation, Dry/Wet Experiments, and Experimental Reasoning. Our results are reported on the
DeepResearch and IdeaGeneration subsets.
GAIA [25]. GAIA is a benchmark of real-world tasks that require coordinated abilities in reasoning,
multimodal understanding, web navigation, and tool use. We report results on its 165-question
validation set.
HLE [27]. Humanity’s Last Exam (HLE) is a large-scale multimodal benchmark of 2,500 expert-
written questions covering mathematics, humanities, and the natural sciences. It is designed to probe
frontier-level academic reasoning, where current LLMs still fall far short of human performance.
Frontier Science [28]. FrontierScience is a benchmark that evaluates whether AI systems can perform
expert-level scientific tasks, including study design, data interpretation, and hypothesis assessment.
Following the protocol in the original paper, our results are averaged over 20 runs on the Olympiad
subset and 30 runs on the Research subset using its standard evaluation set.
GPQA-diamond [26]. GPQA is a collection of 448 expert-written multiple-choice questions in biology,
chemistry, and physics, designed to test deep scientific reasoning rather than surface knowledge. We
use its 198-question GPQA-diamond subset for evaluation.


Table 2 | Performance comparison on SGI-bench. The best results are bolded and the second best
results are underlined.

    Method                                                Deep Research          Idea Generation
    Gemini-3-pro [63]                                         18.48                   39.68
    GPT-5 [64]                                                14.47                   55.40
    Claude-Sonnet-4.5 [65]                                    13.84                   43.20
    Qwen3-Max [66]                                            15.38                   39.83
    o4-mini [67]                                              11.95                   40.78
    InternAgent-1.5 (Gemini-3-pro+o4-mini)                    37.74                   58.11


Table 5 | Performance comparison on FrontierScience of olympiad and research tasks across bio, chem,
and phy domains. The best results are bolded and the second best results are underlined.
                                           Olympiad (avg N=20)                              Research (avg N=30)
 Method                           Bio        Chem          Phy           All       Bio       Chem        Phy         All
 o4-mini [67]                  47.00±14.90 65.00±6.40   53.40±4.50   57.40±3.30 9.67±5.47 8.17±4.37 0.83±2.27     6.20±2.54
 InternS1-235B [78]            17.00±12.69 52.88±4.05   50.40±3.88   48.05±2.84 4.50±4.35 11.00±3.74 2.67±3.35    6.06±2.30
 Mirothinker-v1.5-30B-A3B [70]  22.86±4.52 69.64±7.49   54.86±3.18   57.57±3.66 8.17±6.39 8.50±6.21 5.83±4.10     7.50±3.77
 DeepSeek-V3.2-Thinking [79]    26.50±7.26 72.25±3.25   66.30±2.63   64.70±2.41 2.50±3.10 16.33±4.64 1.40±2.70    6.84±1.88
 Qwen3-235B-A22B-Thinking [66] 24.00±9.17 61.13±6.05    57.10±4.79   55.40±3.68 10.17±5.08 10.00±6.32 1.58±2.41   7.34±3.37
 Qwen3-30B-A3B-Thinking [66]    13.50±9.10 47.25±4.47   42.70±3.65   41.60±2.94 1.50±2.93 2.00±3.32 0.70±1.79     1.41±1.52
 Our Method
 InternAgent-1.5              46.00±8.00 85.50±3.67 76.80±2.99 77.20±3.06 10.33±4.64 22.00±6.00 3.67±2.87 12.00±2.49