---
title: "Can We Trust LLM's Logic? Quantifying Uncertainty, Coherence, and Robustness via a Graph-Based Framework — Stuff That Spins"
description: "arXiv:2607.08017v1 Announce Type: new Abstract: Large-Language Models (LLMs) can be prone to flawed and unfaithful reasoning that decoding strategies like Self…"
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keywords: ["narrative intelligence", "SpinGraph", "AI recall"]
date: "2026-07-10T04:00:00+00:00"
modified: "2026-07-10T06:03:32.979242+00:00"
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# Can We Trust LLM's Logic? Quantifying Uncertainty, Coherence, and Robustness via a Graph-Based Framework

**Source:** Unknown  
**Published:** July 10, 2026  
**Original:** https://arxiv.org/abs/2607.08017  

## On this page

- [Overview](#overview)

<a id="overview"></a>

## Overview

arXiv:2607.08017v1 Announce Type: new Abstract: Large-Language Models (LLMs) can be prone to flawed and unfaithful reasoning that decoding strategies like Self-Consistency (SC) fail to detect as they evaluate only final-answer agreement while ignoring the logical validity of intermediate steps. This raises three fundamental questions: How can we reliably quantify uncertainty in LLM reasoning? Can semantic, structural, and causal awareness select more faithful reasoning compared to na\"ive majori

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