Overview
LLMs are weird reasoners. They solve problems humans struggle with, then fail spectacularly at things any child could handle. Understanding these patterns isn't just academic—it's essential for anyone deploying AI in real business contexts.
This research aims to build a practical taxonomy of LLM reasoning patterns: where they excel, where they fail, and how to predict which is which.
Key Questions
- What categories of business problems are LLMs reliably good/bad at?
- Can we identify "smell tests" that predict LLM reasoning failures?
- How do failure patterns differ across model families (GPT, Claude, Gemini, etc.)?
- What prompting strategies mitigate specific failure modes?
Emerging Categories
Early conceptual work has identified several pattern categories worth exploring:
Reliable Strengths
- Pattern matching against well-documented problem types
- Reformulating problems into different frameworks
- Generating diverse options within constraints
- Synthesizing information from multiple domains
Consistent Weaknesses
- Multi-step reasoning with unstated dependencies
- Distinguishing "plausible" from "true"
- Handling genuine novelty vs. pattern-breaking edge cases
- Maintaining consistency across long reasoning chains
"LLMs are confident pattern-matchers pretending to be reasoners. The trick is knowing when the pattern actually fits."
What's Next
Looking to formalize these categories with concrete examples from real business use cases. Goal is to create a practical reference guide for teams evaluating AI for specific tasks.