Advanced mathematical concepts are often taught in ways that prioritize formalism over intuition, leaving learners to piece together explanations from scattered sources like textbooks, lectures, and online forums. This gap is especially problematic in interdisciplinary fields like AI alignment, where researchers from diverse backgrounds need to quickly grasp complex math outside their training. Existing resources—such as Wikipedia or textbooks—are either too technical or lack the variety of perspectives needed for deep understanding.

A Multimodal, Community-Driven Learning Platform

One way to address this challenge is by creating a collaborative platform that aggregates multiple explanations for each mathematical concept, structured for optimal learning. Each concept page could start with intuitive, visual, or metaphorical explanations (e.g., "A Hilbert space is like an infinite-dimensional version of Euclidean space") before gradually introducing formalism. Interactive examples and "explanation paths" could cater to different backgrounds—such as physicists versus computer scientists. Community contributions, like annotations and peer-reviewed deep dives, could further enrich the content, alongside computational tools like embedded Jupyter notebooks.

Key features might include:

• Explanation voting: Users upvote the most helpful explanations, creating a confidence gradient for learners.
• Multimodal layers: Toggle between visualizations, symbolic proofs, and informal summaries.
• Concept maps: Dynamic graphs showing prerequisite relationships between topics.

Stakeholders and Execution

The platform could serve AI alignment researchers, graduate students, and self-learners who struggle with fragmented resources. Contributors—such as mathematicians and educators—might be incentivized by visibility and impact, while institutions could sponsor content for niche topics. An MVP could begin with a static wiki featuring manually curated "explanation stacks" for high-priority concepts, later evolving to include interactive elements and AI-assisted learning tools.

Compared to existing resources like Wikipedia or Wolfram MathWorld, this platform would prioritize pedagogy over mere reference, blending rigor with accessibility. By systematizing the "human" side of teaching math—something even advanced AI may struggle to replicate—it could become a foundational tool for interdisciplinary learning.