Thermodynamic Signatures of Reasoning: Free-Energy and Spectral-Form-Factor Diagnostics for Hallucination Detection in Large Language Models
arXiv:2606.19404v1 Announce Type: cross Abstract: Hallucination detection in large language models (LLMs) is deployment-critical, and recent work shows that the spectrum of attention-derived graph Laplacians carries strong signal about reasoning quality. Prior spectral diagnostics, however, summarize the Laplacian spectrum by a handful of eigenvalues or hand-picked scalars, leaving most of its structure unused. We propose Free-Energy Signatures (Fes), a spectral descriptor that treats each layer's attention Laplacian as a Hamiltonian and extracts its thermodynamic potentials partition function
The proliferation of LLMs and the critical need for reliable AI applications in deployment drive the urgent development of explainability and safety mechanisms, such as advanced hallucination detection.
Improving the accuracy and interpretability of hallucination detection directly enhances the trustworthiness and utility of AI systems, impacting their adoption across sensitive domains.
New, thermodynamically inspired spectral diagnostics offer a more comprehensive method for identifying and potentially mitigating LLM hallucinations, moving beyond simpler spectral analyses.
- · AI developers
- · AI ethics and safety researchers
- · Sectors adopting LLMs (e.g., finance, healthcare)
- · Developers relying on ad-hoc hallucination detection
- · LLM applications without robust safety mechanisms
This research provides a more sophisticated tool for identifying and characterizing the 'reasoning quality' of LLMs, potentially leading to more reliable AI outputs.
Improved hallucination detection could accelerate the deployment of LLMs into high-stakes applications where accuracy and trustworthiness are paramount, expanding their economic impact.
As AI models become more explainable and auditable, public and regulatory trust may increase, fostering broader societal acceptance and potentially new regulatory frameworks for AI safety.
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Read at arXiv cs.CL