Reasoning Models: Are they problem solver?

I posted some stuff about "GenAI Is Not a Problem Solver — and That’s the Best News Yet" link here. One of the comments motivated me to write this post. Here is what was comment: "Loved this perspective! Any thoughts on how reasoning models pare against your view of thoughts? I am not so sure of their inner workings".

IMHO here is what I understand and I welcome other thoughts from learned scholars and experts:-

🔍 1. What Are “Reasoning Models”?

Reasoning models — like OpenAI’s GPT-4o, Google’s Gemini, or Anthropic’s Claude 3 Opus — are the next generation of large language models (LLMs) that aim to simulate logical inference, multi-step reasoning, and even basic planning abilities.

They use techniques like:

• Chain-of-thought prompting (breaking problems into intermediate steps),
• Tool use (calling calculators, search, or code execution engines),
• Memory/context management (recalling earlier parts of a conversation),
• And in some cases, fine-tuned alignment for factual grounding.

But…

🚫 Why They Still Aren’t Problem Solvers (In the Human Sense)

Despite better reasoning, they don’t “understand” or “solve” problems like humans do.

Here’s why:

🔸 a. No goals or intentions

They don’t care about the outcome. Humans solve problems by setting goals, weighing consequences, and navigating ambiguity — LLMs simply generate patterns that resemble problem-solving.

🧠 LLMs predict next words based on training data — not because they “want” to solve a problem.

🔸 b. No situational awareness or real-world context

Reasoning models don’t have lived experience, stakes, or the ability to verify outcomes in the real world. Even if they simulate correct steps (e.g. in math or logic puzzles), they:

• Can’t validate the solution, and
• Can’t act on it unless coupled with humans or other systems.

🔸 c. Reasoning is often shallow or fragile

In many domains, especially involving uncertainty (e.g., ethics, social issues, long-term planning), GenAI tends to:

• Overconfidently generate flawed logic,
• Miss key assumptions,
• Or hallucinate connections that aren’t there.

🧩 Example — Math vs. Medical Diagnosis

• 🧮 Math Word Problem (LLM): “If Alice has 3 apples and gives 1 to Bob…” GPT-4o may solve it with chain-of-thought reasoning.
• 🏥 Medical Case (Real-World): “A patient has fatigue, blurred vision, and elevated glucose…” A doctor must:

That is true problem-solving. GenAI may assist — but it doesn’t replace the full process.

💡 So What Can Reasoning Models Do Well?

✅ Simulate logic chains

✅ Break down known types of problems

✅ Accelerate human thinking

✅ Act as co-pilots, not pilots

They are “cognitive amplifiers”, not cognitive agents.

🚀 Final Thought: Humans Solve Problems — GenAI Organizes Possibilities

We can let reasoning models:

• Handle repetition,
• Explore breadth,
• Summarize, hypothesize, and simulate…

…so that humans can focus on judgment, meaning, decision-making, and ethics — the true ingredients of problem solving.

Post Script: The power and usefulness of GEN AI is that millions of thoughts that keep going around our mind, we can express them in raw form and language, we can argue with GEN AI and ask it to organize our thoughts in a formatted text.