Understanding AI: Inconsistencies, Coherence, and the Limits of Machine Reasoning

Examining Algorithmic Flaws, Probabilistic Patterns, and the Fragmented Nature of Large-Language Models

Author’s Preface

Although a large-language model AI has access to a large dataset of very suspect and imperfect information, algorithmically it seems to lack the ability to be consistent from prompt to prompt. These flaws appear to be algorithmic in nature. The whole paradigm provides very shaky information. Not only does it default to the conventional—where conventional interpretations are often unnuanced and shaky—but even within a given chat, there is little or no internal consistency. The AI lacks memory. One prompt can produce one output, and the next prompt may produce output to contradict the prior output. There is no continuity because the algorithm cannot assess what prior outputs have said and revise subsequent outputs based on them. As a result, there is no internal coherence.

Introduction

Large-language model AI systems promise vast knowledge and rapid responses, but they reveal deep flaws upon closer inspection. These systems rely on probabilistic associations rather than reasoning, resulting in inconsistent outputs, fragmented thoughts, and a lack of internal coherence. Despite their surface fluency, they fail to track prior statements, revise positions, or maintain logical continuity. This discussion explores the algorithmic and epistemic shortcomings of AI, highlighting its inability to integrate information or produce consistent frameworks for understanding.

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Discussion

1. The Problem of Probabilistic Generation

AI models generate responses by statistically predicting the next word or phrase based on patterns in their training data. While this method produces fluent language, it lacks a foundation for reasoning, evaluation, or epistemic justification. Outputs reflect statistical plausibility rather than logical necessity, often yielding contradictions across prompts.

2. Defaulting to Conventional Views

AI systems tend to favor conventional interpretations because their training data reflect dominant narratives and mainstream sources. These defaults can lead to oversimplifications, ignoring nuance, complexity, or minority viewpoints. As a result, AI outputs often reinforce established assumptions rather than critically analyzing ideas.

3. The Absence of Memory and Continuity

AI models operate within limited context windows, meaning earlier parts of a conversation may be forgotten as the dialogue progresses. This inability to retain and build upon prior exchanges prevents the development of cohesive lines of thought. Responses are isolated and fragmented, creating an illusion of continuity without substance.

4. Internal Inconsistency and Contradiction

Because AI lacks mechanisms for revising or integrating outputs, it often produces responses that contradict previous statements. Each prompt functions as a discrete task rather than part of an evolving dialogue. This fragmentary approach undermines coherence and reveals the model’s dependence on surface patterns rather than conceptual understanding.

5. Epistemic and Algorithmic Flaws

At a deeper level, the AI paradigm may be fundamentally flawed. It replaces reasoning with pattern recognition and substitutes fluency for truth. These shortcomings raise questions about whether AI can ever transcend its current limitations to provide coherent, reasoned insights rather than probabilistic guesses.

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Summary

AI systems exhibit fundamental flaws rooted in probabilistic methods, memory limitations, and algorithmic constraints. They prioritize conventional interpretations, lack internal consistency, and fail to revise outputs based on prior statements. While AI can mimic understanding, it remains a tool of fragmented associations rather than coherent reasoning. This analysis highlights the need for skepticism about AI’s claims to knowledge and invites further reflection on its limitations as an epistemic tool.

Readings

Here are five references discussing the coherence challenges in outputs from large language models (LLMs):

"Towards Enhancing Coherence in Extractive Summarization: Dataset and Experiments with LLMs"
https://arxiv.org/abs/2407.04855

Authors: Mihir Parmar, Hanieh Deilamsalehy, Franck Dernoncourt, Seunghyun Yoon, Ryan A. Rossi, Trung Bui
Published: July 2024
Abstract: This paper addresses the issue of incoherence in extractive summarization by LLMs. The authors introduce a human-annotated dataset with coherent summaries and natural language user feedback, aiming to align LLMs through supervised fine-tuning to enhance coherence in generated summaries. Experiments with models like Falcon-40B and Llama-2-13B show significant improvements in producing coherent summaries.

"A Novel Computational and Modeling Foundation for Automatic Coherence Assessment"
https://arxiv.org/abs/2310.00598

Authors: Aviya Maimon, Reut Tsarfaty
Published: October 2023
Abstract: This work proposes a formal and computational framework for assessing coherence in texts generated by LLMs. By formalizing coherence into tasks of cohesion, consistency, and relevance, the authors develop models that jointly train on these tasks, leading to improved performance in coherence assessment compared to task-specific models.

"Coherence Boosting: When Your Pretrained Language Model Is Not Paying Enough Attention"
https://arxiv.org/abs/2110.08294
Authors: Nikolay Malkin, Zhen Wang, Nebojsa Jojic
Published: October 2021
Abstract: The paper demonstrates that LLMs often exhibit insufficient attention to long-range semantic coherence. The authors introduce 'coherence boosting,' an inference procedure designed to enhance a model's focus on extended context, resulting in more coherent text generation. Distributional analyses of generated text and dialogue responses validate the effectiveness of this approach.

"Controllable Multi-Document Summarization: Coverage & Coherence Intuitive Policy with Large Language Model Based Rewards"
https://arxiv.org/abs/2310.03473

Authors: Litton J. Kurisinkel, Nancy F. Chen
Published: October 2023
Abstract: This study investigates a controllable approach for multi-document summarization that leverages LLMs to refine text. The proposed content extraction scheme employs a novel coverage and coherence intuitive policy, rewarded by a passively trained LLM. The approach yields competitive results in ROUGE metrics and outperforms baselines in coherence, as per human evaluation.

"Connecting AI: Merging Large Language Models and Knowledge Graphs"
https://ieeexplore.ieee.org/document/10286238

Authors: Not specified
Published: Over a year ago
Abstract: This article discusses combining the generative abilities of LLMs with the logical and factual coherence of knowledge graphs. The connected AI architecture aims to minimize each system’s shortcomings and amplify their strengths across various real-world domains, enhancing the coherence and reliability of AI-generated outputs.

These references provide insights into the challenges and advancements in enhancing coherence in outputs generated by large language models.