# AI reasoning effort predicts human decision time in content moderation

arXiv: https://arxiv.org/abs/2508.20262 · HTML: https://mlsanity.com/paper/2508.20262/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 7 of its 42 references that are in the map.
- First posted on arXiv 27 Aug 2025.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 7 of 7)

- [Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters](https://mlsanity.com/paper/2408.03314/) (2024)
- [Measuring Faithfulness in Chain-of-Thought Reasoning](https://mlsanity.com/paper/2307.13702/) (2023)
- [Reasoning Models Don't Always Say What They Think](https://mlsanity.com/paper/2505.05410/) (2025)
- [Deliberative Alignment: Reasoning Enables Safer Language Models](https://mlsanity.com/paper/2412.16339/) (2024)
- [The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity](https://mlsanity.com/paper/2506.06941/) (2025)
- [Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety](https://mlsanity.com/paper/2507.11473/) (2025)
- [Implicit Bias-Like Patterns in Reasoning Models](https://mlsanity.com/paper/2503.11572/) (2025)

## Abstract (arXiv)

Large language models can now generate intermediate reasoning steps before producing answers, improving performance on difficult problems by interactively developing solutions. This study uses a content moderation task to examine parallels between human decision times and model reasoning effort, measured using the length of the chain-of-thought (CoT). Across three frontier models, CoT length consistently predicts human decision time. Moreover, humans took longer and models produced longer CoTs when important variables were held constant, suggesting similar sensitivity to task difficulty. Analyses of the CoT content shows that models reference various contextual factors more frequently when making such decisions. These findings show parallels between human and AI reasoning on practical tasks and underscore the potential of reasoning traces for enhancing interpretability and decision-making.