arXiv AI
Jul 10

Can We Trust LLM's Logic? Quantifying Uncertainty, Coherence, and Robustness via a Graph-Based Framework

arXiv:2607. 08017v1 Announce Type: cross 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.

By Riccardo Revalor, Jalees Rehman, Debjit Pal
arXiv AI
Sep 3

Spectral Initialization and Scheduled Graph Smoothness for Uncertain Knowledge Graph Completion

The paper introduces QUEST, a method for uncertain knowledge graph completion that adds no trainable parameters to the standard pipeline. QUEST first initializes entity embeddings using the smallest non‑trivial eigenvectors of the confidence‑weighted graph Laplacian, thereby preserving community and hub structure before training. It then applies an unbiased mini‑batch Dirichlet energy regularizer to enforce early‑stage structural consistency, leading to improved confidence and link prediction on most metric‑dataset pairs and eliminating instability spikes on dense graphs.

By Md Abrar Jahin, Taufikur Rahman Fuad, Jay Pujara, Craig A. Knoblock
arXiv AI
Sep 7

GUT: Quantifying and Optimizing the Reasoning Uncertainty of LLMs via Graph Complexity

The paper introduces GUT, a method that uses directed acyclic graphs to represent all possible reasoning branches of Large Language Models (LLMs). It comprises two modules: GUT-Q, which quantifies reasoning uncertainty by approximating graph complexity, and GUT-O, which reduces uncertainty through reinforcement learning that rewards lower uncertainty. Experiments on four LLMs across five datasets demonstrate GUT’s effectiveness in measuring and mitigating reasoning uncertainty.

By Shuang Liang, Xin-Yu Hu, Xiang-Jun Ou, Shao-Qun Zhang