# The Plausibility Trap: Using Probabilistic Engines for Deterministic Tasks

arXiv: https://arxiv.org/abs/2601.15130 · HTML: https://mlsanity.com/paper/2601.15130/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 21 Jan 2026.

## Builds on (most cited first, 2 of 2)

- [Language Models Learn to Mislead Humans via RLHF](https://mlsanity.com/paper/2409.12822/) (2024)
- [Evaluating Large Language Models in Scientific Discovery](https://mlsanity.com/paper/2512.15567/) (2025)

## Abstract (arXiv)

The ubiquity of Large Language Models (LLMs) is driving a paradigm shift where user convenience supersedes computational efficiency. This article defines the "Plausibility Trap": a phenomenon where individuals with access to Artificial Intelligence (AI) models deploy expensive probabilistic engines for simple deterministic tasks-such as Optical Character Recognition (OCR) or basic verification-resulting in significant resource waste. Through micro-benchmarks and case studies on OCR and fact-checking, we quantify the "efficiency tax"-demonstrating a ~6.5x latency penalty-and the risks of algorithmic sycophancy. To counter this, we introduce Tool Selection Engineering and the Deterministic-Probabilistic Decision Matrix, a framework to help developers determine when to use Generative AI and, crucially, when to avoid it. We argue for a curriculum shift, emphasizing that true digital literacy relies not only in knowing how to use Generative AI, but also on knowing when not to use it.