The Long Road to the Same Answer: Cognitive Bias Under Escalating Reasoning Budgets in Large Language Models

cs.CL

Submitted: 2026-10-07

Updated: 2026-10-07

Comments: Accepted at the 2026 IEEE 8th International Conference on Cognitive Machine Intelligence (IEEE CogMI 2026). 8 pages, 3 figures, 5 tables. Code and data: https://github.com/obadaKraishan/anchored-minds

Code: https://github.com/obadaKraishan/anchored-minds

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Sources

• OpenAI o1 System Card
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
• Qwen3 Technical Report
• Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
• s1: Simple test-time scaling
• A Comprehensive Evaluation of Cognitive Biases in LLMs
• The Bias is in the Details: An Assessment of Cognitive Bias in LLMs
• Reasoning Models Don't Always Say What They Think
• The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity

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