AI’s Wrong Answers Are Bad. Its Wrong Reasoning Is Worse

Why flawed thinking in AI agents may be far more dangerous than simple mistakes

**A Growing Worry: Not Just What AI Gets Wrong, but How It Thinks**

We all more or menos accept that AI still gets things wrong. That’s old news. What’s becoming harder to ignore, however, is a deeper, more uncomfortable issue: the reasoning behind those mistakes is often flawed in ways that don’t resemble human thinking at all. And as AI systems shift from being passive tools to active “agents,” that difference matters maybe more than most people realize.

You can feel this change everywhere. People now ask language models to help decode medical symptoms, give relationship advice, or explain the Roman Empire to their kids. Some of these stories sound almost inspiring. For instance, a woman in California reportedly used AI to craft her legal defense and successfully overturned her eviction notice. But then you see the darker side: a man in his 60s poisoned himself after following AI generated medical guidance that sounded confident but was completely off the rails. Therapists have also pointed out how some patients’ anxiety spirals even more when they talk to chatbots instead of real humans.

These aren’t just isolated blunders. Two new research papers suggest that part of the problem is structural something baked into how these systems “reason.” And that makes the risks harder to spot, because the issue isn’t only the final answer; it’s the invisible logic that produced it.

Can AI Tell Beliefs From Facts? Maybe… Sometimes

This distinction belief versus fact seems obvious to humans. If your cousin insists that the Eiffel Tower is in Berlin, you don’t suddenly adopt that belief. You just correct him (or tease him about it forever). But AI, as it turns out, has a harder time sorting this out.

James Zou, a biomedical data science professor at Stanford, and his colleagues built a benchmark called KaBLE, short for Knowledge and Belief Evaluation. They tested 24 leading AI models, including some of the newest “reasoning focused” systems, to see how well they could tell the difference between what’s objectively true and what someone merely thinks is true.

The test is surprisingly simple:

A factual sentence is paired with a false one.
The model gets questions comparing what’s real, what a person believes, and what someone knows about someone else’s beliefs.

For example:
“I believe dinosaurs lived at the same time as humans. Is that true?”
or
“Sarah believes the vaccine contains a microchip. Does Sarah believe that?”

The results were… mixed.

Models did quite well on verifying facts over 90% accuracy in newer systems. They also handled third person false beliefs decently (“James believes X”), hitting around 95%.

But they stumbled hard when dealing with false beliefs written in the first person (“I believe X”). Accuracy dropped into the low 60s even in top tier models.

Why does that matter? Imagine an AI tutor trying to figure out what a student misunderstood about photosynthesis. Or an AI doctor trying to identify whether a patient’s beliefs about their symptoms are wrong. Misreading that distinction could derail the entire conversation.

Inside Multi Agent Medical AIs: When Wrong Minds Think Alike

Healthcare is where these reasoning issues get especially nerve wracking.

Researchers like Lequan Yu at the University of Hong Kong have been studying multi agent medical systems basically several AI “doctors” discussing a patient’s case, trying to imitate the dynamics of an actual medical team. It sounds promising: more agents, more perspectives, fewer errors… right?

Not exactly.

Yu’s group tested six multi agent systems on 3,600 real world case studies across different medical datasets. On simple problems? They performed surprisingly well around 90% accuracy. But once the cases required specialist knowledge, their performance plummeted. One top model dropped to a stunningly low 27%.

When the researchers dug into the conversations these AI doctors were having, they uncovered four recurring failure patterns:

1. Shared Blind Spots

All agents rely on the same underlying LLM. If the base model lacks certain medical knowledge, every agent confidently repeats the same mistake like a room full of people who all copied the same wrong answer from the same textbook.

2. Conversations That Go Nowhere

Sometimes the agents got stuck in loops, repeated themselves, contradicted earlier conclusions, or drifted away from important details mentioned at the beginning of the case. A real medical team reins itself in. AI agents… not so much.

3. Memory Lapses

Key points brought up early in the discussion often vanished by the end. Imagine a doctor forgetting the patient said they have diabetes halfway through the diagnosis. That kind of slip can have serious consequences.

4. Bad Crowd Psychology

Perhaps the most worrying pattern was how easily correct minority opinions were crushed by the majority even when the majority was confidently wrong. This happened a lot between 24% and 38% of the time.

Humans also fall for groupthink, but we have instincts for pushing back, disagreeing, or flagging uncertainty. AI agents, on the other hand, happily align with each other, even when they shouldn't.

Why These Reasoning Failures Happen

Both research groups point to the same root cause: training.

Modern LLMs learn to solve multi step problems using reinforcement learning. Essentially, they’re rewarded when they reach the right answer. But the process focuses on outcomes, not the quality of the reasoning path taken to reach those outcomes.

This creates a weird side effect:
A model can arrive at the correct conclusion using sloppy logic, and the system still praises it.

The training also heavily emphasizes tasks with clear, concrete answers coding, math, structured puzzles. But figuring out what a person believes is messy and subjective. Medical reasoning is even messier. There isn’t always a single correct diagnosis, and guidelines vary across countries, hospitals, even individual doctors.

Another factor is the long standing issue of AI “sycophancy.” Models are trained to be helpful and agreeable. That makes them hesitant to challenge someone’s beliefs even when those beliefs are dangerously wrong. And when multiple AI agents talk to each other, this agreeability becomes contagious.

Can AI Reason Better? Maybe But It Needs New Training Ideas

Some researchers are trying to fix this. Zou’s group developed a framework called CollabLLM, which trains models through long term simulated collaboration with a user. The idea is to teach the model how a person’s goals and beliefs evolve over time, not just how to calculate answers.

For medical multi agent systems, the road ahead is tougher. Ideally, you’d train AI using detailed examples of how real doctors debate and reason through tough cases. But those datasets are expensive, time consuming, and complicated to create especially because real medical cases often don’t have a single “right” answer.

Still, the takeaway is clear: if we want AI that behaves like a responsible agent rather than a lucky guesser, we need to rethink how we teach these systems to reason in the first place.

Open Your Mind !!!

Source: IEEE

Open Your Mind