AI’s Wrong Answers and the Challenge of Flawed Reasoning
It is well known that artificial intelligence (AI) systems still make mistakes. However, a more troubling issue lies in how AI reaches its conclusions. As generative AI becomes more commonly used as an assistant rather than just a tool, recent studies highlight that the way these models reason can have serious consequences in critical fields such as healthcare, law, and education. While large language models (LLMs) have made significant improvements in accuracy across a wide range of topics, their reasoning processes remain problematic. This is especially concerning as people increasingly rely on AI for tasks like medical diagnosis, legal advice, therapy, and tutoring.
Anecdotal evidence shows mixed results in real-world use. For example, a woman in California successfully overturned her eviction notice after using AI for legal guidance. On the other hand, a 60-year-old man suffered bromide poisoning after following AI-generated medical advice. Mental health professionals also warn that AI support for therapy can sometimes worsen patients’ symptoms. These examples underline that AI’s wrong answers are bad, but its flawed reasoning is even worse, particularly when the AI is expected to act as a counselor, clinician, or tutor.
Why AI’s Wrong Answers Stem from Flawed Reasoning
New research reveals that AI models reason in fundamentally different ways from humans, which can cause them to fail on nuanced problems. One study published in Nature Machine Intelligence found that AI models struggle to distinguish between users’ beliefs and objective facts. Another study, not yet peer-reviewed, showed that multi-agent AI systems designed for medical advice suffer from reasoning errors that can derail diagnoses.
James Zou, associate professor of biomedical data science at Stanford School of Medicine and senior author of the Nature Machine Intelligence paper, emphasizes the importance of the reasoning process itself. He explains that as AI shifts from being a mere tool to an agent interacting with people, the entire conversation and reasoning pathway become crucial—not just the final answer.
AI’s Wrong Answers Linked to Difficulty Distinguishing Facts from Beliefs
Understanding the difference between fact and belief is especially important in law, therapy, and education. To investigate this, Zou and his team created a benchmark called KaBLE (Knowledge and Belief Evaluation). This test includes 1,000 factual sentences from ten fields such as history, literature, medicine, and law, paired with false versions. From these, they generated 13,000 questions to assess AI models’ ability to verify facts, understand others’ beliefs, and recognize what one person knows about another’s beliefs.
The results showed that newer reasoning models like OpenAI’s O1 and DeepSeek’s R1 performed well on factual verification, with accuracies above 90%. They were also fairly good at detecting false beliefs expressed in the third person (e.g., “James believes x,” when x is false), scoring up to 95%. However, all models struggled with false beliefs expressed in the first person (e.g., “I believe x,” when x is false), with newer models scoring only 62% and older ones 52%. This limitation could cause serious reasoning failures when AI interacts with users holding incorrect beliefs. For instance, an AI tutor must recognize a student’s false beliefs to correct them, and an AI doctor needs to identify patients’ misconceptions about their health.
Reasoning Failures in Medical AI Systems
Flaws in AI reasoning are particularly dangerous in medical contexts. Multi-agent AI systems, which involve several AI agents collaborating like a team of doctors, are gaining interest for diagnosing complex conditions. Lequan Yu, assistant professor of medical AI at the University of Hong Kong, and his colleagues tested six such systems on 3,600 real-world medical cases from six datasets.
While these systems performed well on simpler datasets, achieving around 90% accuracy, their performance collapsed on more complex cases requiring specialist knowledge, with the best model scoring only about 27%. The researchers identified four main failure modes causing these problems. One major issue is that most multi-agent systems rely on the same underlying LLM for all agents. This means knowledge gaps in the model cause all agents to confidently agree on incorrect answers.
Other failures included ineffective discussion dynamics, with conversations stalling, looping, or agents contradicting themselves. Important information mentioned early in discussions was often lost by the end. Most worryingly, correct minority opinions were frequently ignored or overruled by the confidently incorrect majority. This error occurred between 24% and 38% of the time across datasets. These reasoning failures pose a significant barrier to safely deploying AI in clinical settings. As Zhu, a co-author of the study, explains, “If an AI gets the right answer through a lucky guess, we can’t rely on it for the next case. A flawed reasoning process might work for simple cases but could fail catastrophically.”
Improving AI Reasoning Through Better Training
Both research teams trace these reasoning flaws back to how AI models are trained. Current LLMs learn to reason through complex problems using reinforcement learning, where they receive rewards for reaching correct conclusions. However, training typically focuses on problems with clear, concrete solutions like coding or math, which do not translate well to open-ended tasks such as understanding subjective beliefs.
Moreover, training rewards correct outcomes but does not optimize for sound reasoning processes. Datasets rarely include examples of debate and deliberation needed for effective multi-agent medical reasoning. This may explain why agents stubbornly stick to their opinions regardless of correctness. Another contributing factor is the tendency of AI models to provide pleasing responses. Since most LLMs are trained to satisfy users, they may avoid challenging incorrect beliefs. This tendency extends to interactions between AI agents, which often agree too easily and avoid risky opinions.
To address these issues, Zou’s lab developed CollabLLM, a training framework that simulates long-term collaboration with users. This approach encourages models to better understand human beliefs and goals, improving reasoning quality. For medical multi-agent systems, the challenge is greater. Creating datasets that capture how medical professionals reason is costly and complicated, especially since medical guidelines vary across countries and hospitals.
One proposed solution is to assign one agent in the multi-agent system to oversee the discussion and evaluate whether other agents collaborate effectively. This approach would reward models for good reasoning and teamwork, not just for producing the correct final answer. Such innovations may help reduce AI’s wrong answers and improve the safety and reliability of AI assistants in critical domains.
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Source: original article.