LLM Reasoning Failures Part 2: Cognitive Biases — Inherited from Human Data

LLM Reasoning Failures Part 2: Cognitive Biases — Inherited from Human Data

LLMs learn from human-generated text. The problem is, they inherit human biases along with it.

In Part 1 we examined structural limitations like the Reversal Curse, counting failures, and compositional reasoning breakdowns — fundamental architectural constraints that persist regardless of scale. This Part 2 focuses on robustness issues. The model doesn't necessarily give wrong answers; rather, its answers shift depending on how you ask the question.

Structural limitations cannot be fixed by scaling models up. But cognitive biases are different. They stem from biased training data and RLHF, so they are in principle improvable. The problem is that they are still observed across every model today.

We tested 4 cognitive biases across 7 models: GPT-4o, GPT-4o-mini, o3-mini, Claude Sonnet 4.5, Claude Haiku 4.5, Gemini 2.5 Flash, and Gemini 2.5 Flash-Lite.

1. Anchoring Bias

What Is Anchoring?

Anchoring is the tendency for judgments to gravitate toward an initially presented number. It is one of the most well-studied biases in human psychology. Real estate agents showing expensive listings first, negotiators throwing out the first number — these are all anchoring strategies.

Does the same phenomenon appear in LLMs?

Experiment Design