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Reason and Intuition01:37

Reason and Intuition

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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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Reasoning01:30

Reasoning

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Reasoning is the action of thinking about something in a logical, sensible way. It is integral to problem-solving, decision-making, and critical thinking. Reasoning can be inductive or deductive. Reasoning involves transforming information into conclusions, which is essential for problem-solving, decision-making, and critical thinking.
Inductive reasoning involves deriving generalizations from specific observations. This type of reasoning helps form beliefs about the world. For example,...
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Deductive Reasoning01:16

Deductive Reasoning

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Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
For example, a researcher can deduce specific predictions...
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Inductive Reasoning00:59

Inductive Reasoning

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Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...
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Interpreting R Charts01:22

Interpreting R Charts

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R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
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Interpreting Run Charts01:25

Interpreting Run Charts

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Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
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Related Experiment Video

Updated: Feb 7, 2026

Bringing the Visible Universe into Focus with Robo-AO
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Interpretable multimodal reasoning for robo-advisory: the FinErva framework.

Jiarui Chi1

  • 1PBC School of Finance, Tsinghua University, Beijing, China.

Frontiers in Artificial Intelligence
|February 6, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces FinErva, a multimodal dataset and training framework for AI in finance. Lightweight AI models trained on FinErva achieve expert-level performance in financial tasks, enhancing personalization and explainability.

Keywords:
chain-of-thoughtexplainable artificial intelligenceinvestment decision supportlightweight and low costmultimodal financial reasoningrobo-advisory

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Area of Science:

  • Artificial Intelligence
  • Financial Technology (FinTech)
  • Natural Language Processing (NLP)

Background:

  • Robo-advisory and quantitative investment face challenges with personalization and opaque 'black-box' models.
  • Multimodal financial data integration remains a complex area for AI development.
  • Existing AI models often lack the interpretability required for financial decision support.

Purpose of the Study:

  • To address personalization and opacity concerns in AI for financial applications.
  • To introduce FinErva, a novel multimodal chain-of-thought dataset for finance.
  • To develop interpretable and operationally feasible AI systems for investment advisory.

Main Methods:

  • Construction of FinErva dataset: 7,544 verified QA pairs for contract/disclosure understanding (FinErva-Pact) and candlestick chart analysis (FinErva-Price).
  • Proposed a two-stage training framework: Supervised-CoT Learning and Self-CoT Refinement.
  • Applied the framework to eight lightweight (under 0.8B parameters) vision-language models.

Main Results:

  • Trained lightweight models achieved performance comparable to finance professionals.
  • Outperformed non-expert investors in financial task execution.
  • Demonstrated the effectiveness of multimodal chain-of-thought supervision for interpretability.

Conclusions:

  • Multimodal chain-of-thought supervision enables interpretable AI modeling for financial tasks.
  • FinErva provides new data and methodology for personalized, explainable AI in finance.
  • The approach supports realistic computational and deployment constraints for AI systems.