While current Large Language Models (LLMs) like ChatGPT and Claude have revolutionized technology, they face a fundamental limitation: they are essentially advanced pattern matchers, predicting the next word rather than truly understanding content. A new generation of AI, known as Large Reasoning Models (LRMs), is emerging to bridge this gap, moving from mere prediction to actual critical thinking.

The Limitation of Traditional LLMs

Current AI models operate as "super smart autocomplete." They rely on attention mechanisms and rotary position encoding, which treat word positions as fixed distances rather than a coherent narrative path. This architecture causes them to struggle with tracking state changes over time—such as following variables in code or tracking an object’s status throughout a story. Consequently, while they excel at speed chess-style pattern recognition, they fail at complex logical deductions.

How Reasoning Models Differ

Large Reasoning Models (LRMs), such as OpenAI's O3, Google's Gemini 2.5, and the open-source Deep Seek R1, utilize "Test Time Compute." Unlike traditional models that output answers instantly, LRMs engage in "Chain of Thought" reasoning. They pause to generate long streams of internal logic, allowing them to:

• Plan multiple steps ahead (like a chess grandmaster).
• Backtrack when errors are detected.
• Consider various strategies before finalizing an answer.

Innovations like MIT's "path attention" further enhance this by treating word relationships as adaptive journeys rather than fixed points.

The Cost of Intelligence

This shift comes with significant economic and performance trade-offs. Reasoning models are computationally expensive—sometimes costing up to $200 per task compared to mere cents for standard LLMs. However, for high-stakes fields like medicine or law, the jump in accuracy (e.g., from 50% to 95%) makes this cost justifiable. We are moving toward a hybrid ecosystem where cheap models handle everyday tasks, and expensive reasoning models handle complex analysis.

Future Outlook and Challenges

Despite the hype, challenges remain. New benchmarks show even top models scoring poorly (around 8%) on genuine reasoning tests, and latency issues make them difficult to integrate into real-time apps. Furthermore, the industry is hitting a "scaling wall," where simply adding more data and power is yielding diminishing returns. The future lies in novel approaches, such as combining deep learning (gut instinct) with program synthesis (hard logic), and potentially leveraging hybrid quantum-classical computing to break through current performance ceilings.

Mentoring question

Considering that reasoning models offer higher accuracy but at significantly higher latency and cost, which specific high-stakes processes in your organization would actually justify this investment compared to using standard, faster language models?

Source: https://youtube.com/watch?v=xndQSydmgSc&is=c7A54ykVfXq2dBfX