arXiv cs.CL· Obada Kraishan·· 10 天前AI 评分30
大语言模型推理预算升级下的认知偏差：长路通向同一答案
The Long Road to the Same Answer: Cognitive Bias Under Escalating Reasoning Budgets in Large Language Models
AI 导读
一项针对四个模型家族的剂量-反应研究显示，推理模型并不比同系列非推理版本偏差更小，各家族点估计甚至偏向相反方向，但项目级汇总对比不显著（Δ=+0.031，p=.157）。
正文 · 原文
Abstract:Reasoning models allocate extra computation at inference time and present their answers as the product of deliberate thought. If this deliberation works the way dual-process accounts of human cognition suggest, longer thinking should weaken the classic decision biases that fast, intuitive judgment produces. Using 30 vignettes covering six biases (anchoring, framing, loss aversion, escalation of commitment, availability, confirmation) from an established benchmark, we run a dose-response study across four model families, pairing each reasoning model with a matched non-reasoning sibling and requesting thinking ceilings of 0, 1,024, 4,096, and 8,192 tokens, for 12,350 API calls. Because a requested ceiling is not the same as realized deliberation, we use the reasoning tokens each call consumed as the dose. First, reasoning models are not less biased than their siblings; the point estimate leans the other way in every family, but the item-level pooled contrast is not reliable (Delta = +0.031, t(29) = 1.45, p = .157). Second, bias magnitude does not reliably fall as realized deliberation grows: no slope is significantly negative, and where anything moves it is the signed score drifting further from the human direction. Third, anchoring is the only bias in the human direction (d = 1.89). Four of the other five lean the opposite way in all seven models; with five items per bias, that reversal is reliable for framing and directional for escalation of commitment, confirmation, and loss aversion, while availability is absent. A one-line instruction to restate the anchor before answering lowered anchoring on all five anchoring items, which no amount of additional thinking did, although the effect does not reach significance (p = .057). The results argue against treating test-time reasoning as a rationality guarantee and for auditing deployed models bias by bias.
| 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: this https URL |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.10049 [cs.CL] |
| (or arXiv:2610.10049v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.10049 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Obada Kraishan [view email]
[v1]
Wed, 7 Oct 2026 13:23:51 UTC (74 KB)
来源：arXiv cs.CL · arxiv.org