🧠 Can AI Really Think? The Truth About LLMs.

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In today’s Sunday Special:

Read Time: 7 minutes

📜THE PRELUDE

Consider this simple logic puzzle: “Jared has two brothers and two sisters. How many siblings does his sister Jenny have?”

If you said “four,” you’re right! Most of us solve this type of question instantly without a second thought. But conversational chatbots struggle with logic puzzles like this one.

What’s causing them to struggle? The problem lies in their limited ability to reason. While conversational chatbots are great at generating human-like text, they don’t truly understand the logic behind what they’re generating.

So, what exactly is reasoning? How do LLMs work? How well do they reason? And can LRMs do any better?

💭WHAT IS REASONING?

Philosophers divide Reasoning into three categories: Deductive, Inductive, and Abductive.

During the 4th century BC, ancient Greek philosopher Aristotle conceived of Deductive Reasoning and Inductive Reasoning in the Organon, a collection of six works on logical analysis.

During the late 19th century, American mathematician Charles Sanders Peirce defined a new logical process known as Abductive Reasoning.

Here’s what makes each type of Reasoning distinct:

We often combine different forms of Reasoning to solve everyday problems. For example, scientists use Abductive Reasoning to generate hypotheses that explain observations. Then, they employ Deductive Reasoning to derive testable experiments from those hypotheses. Next, they rely on Inductive Reasoning to generalize results from those testable experiments into broader theories.

So, where do LLMs fail in the landscape of Reasoning?

💬CAN LLMs REASON?

An LLM is a sophisticated autocomplete machine trained on the entire Internet.

To train an LLM, developers essentially show it millions of sentences with the last word covered up (i.e., “The fat cat sat on the {BLANK}.”) and have it guess what comes next.

Each time the LLM guesses wrong, it adjusts thousands of Weights, which are numerical values that help it decide which words or patterns are most important for making better guesses in the future.

In simple terms, Weights control how tens of thousands of words relate to each other within an LLM. These relationships help form the Neural Network (NN): a highly interdependent framework that processes all the words using two methods:

While LLMs excel at generating human-like text, their ability to Reason is fundamentally different from ours.

Here’s how they perform across the three distinct types of Reasoning:

Let’s revisit the simple logic puzzle: “Jared has two brothers and two sisters. How many siblings does his sister Jenny have?”

When OpenAI’s GPT-4o (“o” for “omni”) is asked to solve this logic puzzle, it falls short.

GPT-4o (“o” for “omni”) mistakenly included Jared as one of the two brothers. We intuitively know that Jared’s two brothers exclude him.

Fortunately, developers recently introduced LRMs to overcome this limitation, and they’re powering all of today’s most advanced AI models.

🤖CAN LRMs REASON?

LRMs are built to plan and reason, not just generate human-like text. They achieve this by utilizing Test-Time Compute (TTC), which allocates more computing power during AI Inference: everything that happens after you enter your prompt.

TTC leverages CoT and RL:

Together, CoT and RL enable LRMs to mimic certain components of Abductive Reasoning by deconstructing complex problems into manageable sub-problems and reinforcing multiple potential solutions that are logically sound.

This process is what allows OpenAI’s OpenAI o4-mini, Anthropic’s Claude Opus 4, and Google’s Gemini 2.5 Pro to mimic a human’s decision-making capabilities and problem-solving capacity.

When you ask OpenAI o4-mini to solve the same simple logic puzzle, it passes with flying colors.

🔑KEY TAKEAWAY

LLMs are great at recognizing patterns, but they often fall short of understanding the logic behind those patterns. LRMs, which leverage TTC to deploy CoT and RL, allow advanced AI models to reason more like humans.

This matters because it brings us closer to confidently using advanced AI models in critical fields like law, finance, or medicine, where hallucinations can have serious consequences for people’s fundamental rights, safety, or health.

📒FINAL NOTE

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