LLMs exhibit "answer pre-commitment" prior to reasoning
A new study reveals that large language models (LLMs) can become locked into an incorrect answer first, subsequently generating reasoning to support it, even when this contradicts task instructions.
What happened?
Researchers have investigated the phenomenon of "answer pre-commitment," where LLMs commit to an answer first and then create justifications for it, rather than deriving it logically. This was observed through a minimal test: "I want to wash my car. The car wash is 100 metres away. Should I walk or drive?" Despite driving being the only logical choice (the car must be at the car wash to be washed), the models recommended walking in the overwhelming majority of cases.
Key facts
| Modell testad | Qwen3-8B |
|---|---|
| Antal systemprompt-förhållanden | 5 |
| Förekomst av felaktigt svar (samplade) | 85-100% |
| Förekomst av felaktigt svar (giriga resultat) | 100% |
| Utökad 'thinking budget' | 4 096 tokens |
Why it matters
The "answer pre-commitment" phenomenon highlights a fundamental characteristic of LLMs where they generate incorrect answers despite having the necessary information available. It suggests that models sometimes prioritise generating a consistent output based on an initial incorrect "commitment" rather than performing complete and correct reasoning. This impacts the reliability of AI systems' inference capabilities.
Who is affected?
The study affects artificial intelligence developers and researchers, particularly those working with large language models. Companies implementing LLMs in their products and services are also impacted, as it points to a potential flaw in the models' decision-making processes.
What else you should know
The behaviour was reproduced on the Qwen3-8B model under five different system prompt conditions. Tests showed that the incorrect answer occurred in 85-100% of the sampled results per condition, and in 100% of the greedy results, regardless of "thinking" mode. An extended "thinking" budget of 4,096 tokens failed to resolve the issue.
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