AI Research Analysis
Unlocking Scientific Discovery: Beyond End-to-End LLMs
Our research delves into the capabilities and limitations of Multimodal Large Language Models (MLLMs) in complex scientific problem-solving. We introduce a novel evaluation framework that dissects tasks into subcomponents, revealing where current models excel and fall short, especially with high-resolution visual data.
Executive Impact: Bridging the Gap in Scientific AI
This analysis provides critical insights for leaders deploying AI in scientific research. Understanding the granular performance of MLLMs helps in designing more robust, reliable, and precise AI solutions, moving beyond black-box end-to-end approaches to integrate external tools and agentic strategies for higher accuracy in complex scientific workflows.
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Accuracy with Scripting
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Accuracy without Scripting (avg. value)
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RMSE for Pixel Coordinates (Base Model)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Scripting as a Critical Enabler
96% Accuracy with scriptingFor high-resolution scientific tasks, models achieved 96% accuracy when allowed to generate and use Python scripts. Without scripting, accuracy plummeted to 2% for average value tasks, highlighting its necessity.
Decomposed Scientific QA Process
Our framework breaks down complex scientific questions into manageable subtasks for fine-grained evaluation, ensuring a systematic approach to problem-solving.
Parse City Name & Coords
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Map Coords to Pixel Pos
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Retrieve Intensity Value
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Integrate for Final Answer
| Subtask | Gemini 2.5 Pro | Qwen2.5-VL-32B |
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| City to Lat/Lon |
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| Lat/Lon to Pixel |
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| Intensity Value |
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Precision Requirement in Science
50% Relative Error Bound (unacceptable)The models exhibited extremely low accuracy for intensity value estimation, even at a 50% relative error bound. This highlights the critical need for higher precision in scientific applications, where hallucinations are less acceptable.
The Agentic AI Paradigm Shift
Our findings suggest that future scientific AI systems may require agent-based or multi-model approaches. By decomposing tasks and delegating to specialized tools or models, we can overcome current limitations and achieve reliable performance on complex scientific tasks. This shift moves away from monolithic LLMs towards more modular, precise, and robust AI workflows.
Quantify Your AI Impact
Estimate the potential annual savings and reclaimed hours by implementing AI-driven scientific workflows in your organization.
Annual Savings
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Hours Reclaimed
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Your AI Implementation Roadmap
A phased approach to integrating advanced AI into your scientific workflows for optimal results and seamless adoption.
Phase 1: Discovery & Assessment
Identify key scientific challenges, data modalities, and define success metrics. Conduct a pilot project to assess feasibility and gather initial insights.
Phase 2: Tooling & Integration
Implement necessary external tools and agentic frameworks. Integrate MLLMs with existing scientific data pipelines and visualization platforms.
Phase 3: Fine-tuning & Optimization
Adapt models to domain-specific data and tasks using supervised fine-tuning. Continuously monitor performance and iterate on prompt engineering and tool orchestration.
Phase 4: Scalable Deployment
Deploy the integrated AI system across relevant research groups. Establish monitoring and feedback loops for ongoing improvement and expansion.
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