LLM Reasoning Failures Part 2: Cognitive Biases — Inherited from Human Data
Anchoring, Order Bias, Sycophancy, Confirmation Bias — cognitive biases from RLHF and training data, tested across 7 models.
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
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Paper of the Week #3 — Half the FLOPs Is Not Half the Time
One integer halves a fine-grained MoE's expert compute (arXiv 2609.04575) and its Table 5 replicates on one A100 to within a point. The paper never reports time, so I measured it: nothing in HF transformers, nothing at batch 1 in the vLLM you run today, 1.35x at batch 8. Plus the OLMoE control and the iso-cost harness control promised in issue #2.
The paper stopped at half the experts. A quarter loses four points instead of thirty, and a model trained without renormalization tells you why.
Two cells arXiv:2609.04575 never ran: k₁=2 and k₁=3 on Qwen3.6-35B-A3B, and the whole k₂ trick on OLMoE, which was trained without renormalization. The second one is a clean test of the paper's mechanism.
One integer halves MoE expert compute. We measured the speed the paper didn't: free at batch 8, not at batch 1.
We reproduced Table 5 of arXiv:2609.04575 on one A100 and measured the throughput the paper leaves out: nothing in HF transformers, nothing at batch 1 in stock vLLM, 1.35× at batch 8.