# Beyond "Hallucinations": A Framework for Stable Human-AI Reasoning

arXiv: https://arxiv.org/abs/2510.14665 · HTML: https://mlsanity.com/paper/2510.14665/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 20 of its 81 references that are in the map.
- First posted on arXiv 16 Oct 2025.

## Placed in

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

## Builds on (most cited first, 20 of 20)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [Discovering Latent Knowledge in Language Models Without Supervision](https://mlsanity.com/paper/2212.03827/) (2022)
- [A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models](https://mlsanity.com/paper/2401.01313/) (2024)
- [Why Language Models Hallucinate](https://mlsanity.com/paper/2509.04664/) (2025)
- [We're Afraid Language Models Aren't Modeling Ambiguity](https://mlsanity.com/paper/2304.14399/) (2023)
- [A Theory for Emergence of Complex Skills in Language Models](https://mlsanity.com/paper/2307.15936/) (2023)
- [The Butterfly Effect of Altering Prompts: How Small Changes and Jailbreaks Affect Large Language Model Performance](https://mlsanity.com/paper/2401.03729/) (2024)
- [Testing AI on language comprehension tasks reveals insensitivity to underlying meaning](https://mlsanity.com/paper/2302.12313/) (2023)
- [Conformity in Large Language Models](https://mlsanity.com/paper/2410.12428/) (2024)
- [How Decoding Strategies Affect the Verifiability of Generated Text](https://mlsanity.com/paper/1911.03587/) (2019)
- [The simulation of judgment in LLMs](https://mlsanity.com/paper/2502.04426/) (2025)
- [Machine learning and information theory concepts towards an AI Mathematician](https://mlsanity.com/paper/2403.04571/) (2024)
- [Prompting Science Report 2: The Decreasing Value of Chain of Thought in Prompting](https://mlsanity.com/paper/2506.07142/) (2025)
- [The Value of Disagreement in AI Design, Evaluation, and Alignment](https://mlsanity.com/paper/2505.07772/) (2025)
- [Prompting Science Report 1: Prompt Engineering is Complicated and Contingent](https://mlsanity.com/paper/2503.04818/) (2025)
- [Pitfalls of Evaluating Language Models with Open Benchmarks](https://mlsanity.com/paper/2507.00460/) (2025)
- [Conversational AI as a Catalyst for Informal Learning: An Empirical Large-Scale Study on LLM Use in Everyday Learning](https://mlsanity.com/paper/2506.11789/) (2025)

## Built on by (most cited first, 3 of 3)

- [The Missing Knowledge Layer in AI: A Framework for Stable Human-AI Reasoning](https://mlsanity.com/paper/2604.14881/) (2026)
- [Governing Reflective Human-AI Collaboration: A Framework for Epistemic Scaffolding and Traceable Reasoning](https://mlsanity.com/paper/2604.14898/) (2026)
- [From Consumption to Reflection: Designing Human-AI Relations for Stable Reasoning](https://mlsanity.com/paper/2606.11195/) (2026)

## Abstract (arXiv)

As large language models (LLMs) become integrated into everyday and high-stakes decision-making, they inherit the ambiguity and biases of human language. While they produce fluent and coherent outputs, they rely on statistical pattern prediction rather than grounded reasoning, creating a risk of outputs that are plausible but incorrect. This paper argues that these failures are not only technical but cognitive. LLMs reproduce associative patterns similar to intuitive human reasoning, amplifying systematic misinterpretations when combined with human users. To analyse this, we introduce the Rose-Frame, a cognitive-epistemological framework for diagnosing breakdowns in human-AI interaction. The framework identifies three recurrent traps: (i) map vs territory, distinguishing representations from reality; (ii) intuition vs reason, separating fast associative judgments from reflective reasoning; and (iii) conflict vs confirmation, examining whether ideas are critically tested or mutually reinforced. These mechanisms can compound into epistemic drift when human and model reasoning interact. We show how these failures emerge in practice and propose human-side interventions, including interpretive cues, reflective prompts, and structured disagreement, to stabilise reasoning. Rather than modifying models, the framework focuses on governing interaction. The central claim is that fluency can create an illusion of understanding. Aligning AI therefore requires not only technical improvements but structures that enable reflective and falsifiable human oversight.