# Humans Disengage, Reasoning Models Persist: Separating Difficulty Registration from Deliberation Allocation

arXiv: https://arxiv.org/abs/2606.26502 · HTML: https://mlsanity.com/paper/2606.26502/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning.
- Builds on 8 of its 38 references that are in the map.
- First posted on arXiv 25 Jun 2026.

## Placed in

- LLM reasoning / [Test-time compute and efficient thinking](https://mlsanity.com/lr/test-time-compute-and-efficient-thinking/)

## Builds on (most cited first, 8 of 8)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters](https://mlsanity.com/paper/2408.03314/) (2024)
- [Using cognitive psychology to understand GPT-3](https://mlsanity.com/paper/2206.14576/) (2022)
- [Beyond Semantics: The Unreasonable Effectiveness of Reasonless Intermediate Tokens](https://mlsanity.com/paper/2505.13775/) (2025)
- [Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!](https://mlsanity.com/paper/2504.09762/) (2025)
- [Local Coherence or Global Validity? Investigating RLVR Traces in Math Domains](https://mlsanity.com/paper/2510.18176/) (2025)

## Abstract (arXiv)

Large reasoning models (LRMs) tend to produce longer reasoning traces on problems that also take humans longer. This correspondence leaves open how the systems distribute further work on those problems. We distinguish *difficulty registration*, sensitivity to differences in problem difficulty, from *deliberation allocation*, the distribution of further work once difficulty is encountered. We examine both in item-matched data from three reasoning tasks. In visual abstraction (H-ARC), model trace length follows the human ordering of problems by duration. After item identity is controlled, successful human attempts last longer than failed attempts, while failed LRM attempts have longer traces than successful ones in the pooled model analysis. The estimated slopes follow the same pattern in intuitive reasoning (INTUIT). In relational reasoning (Cortes), successful attempts are longer in separate human and model analyses, while a joint fit on shared items finds a human-LRM difference. Longer human attempts include more grid actions. At comparable lengths, failed LRM traces contain more hedging on H-ARC and more repetition on Cortes. A resource-rational account relates these patterns to what further work is expected to achieve and how that progress is valued. The results identify a difference in the allocation of continued work that cross-problem duration alignment alone leaves undetected.