Can We Trust LLM's Logic? Quantifying Uncertainty, Coherence, and Robustness via a Graph-Based Framework
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 majority voting? and How robust is reasoning topology under adversarial conditions? To address these quest
The rapid deployment and increasing reliance on large language models necessitate robust methods for evaluating their reasoning fidelity, especially as autonomous AI systems become more prevalent.
Quantifying LLM uncertainty and improving logical validity is critical for developing trustworthy AI, which impacts adoption and regulatory frameworks across industries.
This research introduces a graph-based framework to assess LLM reasoning beyond just final answer agreement, potentially leading to more reliable AI outputs and better debugging tools.
- · AI safety researchers
- · Enterprises deploying LLMs
- · AI developers
- · AI ethics organizations
- · Developers of unverified LLM applications
- · Uncritical LLM deployments
Improved methods for evaluating LLM reasoning will lead to more robust and less 'hallucinatory' AI applications.
Increased trust in LLM outputs could accelerate adoption in high-stakes domains like finance, law, and medicine.
These evaluation frameworks may become standard components of AI development pipelines and regulatory compliance, shaping the future of AI certification.
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Read at arXiv cs.CL