BEACON: Behavioral Entropy Aggregation for Cross-Model Hallucination Detection in Large Language Models

Researchers introduce BEACON, a black-box hallucination detection framework for large language models that achieves 81.23% accuracy by analyzing model outputs without requiring internal access. The method combines multiple uncertainty signals including semantic entropy and consistency checks, outperforming existing baselines and offering practical deployment options across commercial LLM APIs.

BEACON addresses a fundamental challenge in deploying large language models at scale: the inability to reliably detect when these systems generate plausible-sounding but factually incorrect information. This hallucination problem has constrained LLM adoption in high-stakes applications like healthcare, finance, and legal domains where accuracy is non-negotiable. The research demonstrates that behavioral signals extracted from multiple model outputs can effectively identify unreliable generations without requiring white-box access to internal representations or external knowledge databases.

The technical approach reflects broader trends in AI safety research shifting toward practical, deployment-friendly solutions. Rather than relying on expensive knowledge verification or model internals, BEACON constructs a 31-dimensional feature vector from multi-pass generation patterns, chain-of-thought responses, and embedding geometry. This black-box methodology is particularly valuable because most enterprise access to state-of-the-art LLMs comes through restricted APIs where internal model inspection is impossible. The framework's efficiency matters operationally—a 5-call variant achieves 77.95% accuracy, making it economically viable for production systems.

For developers and enterprises, BEACON reduces friction in implementing reliable LLM-powered applications. The significant performance improvement over existing baselines (23-24% better than semantic entropy or SelfCheckGPT approaches) suggests the multi-dimensional nature of hallucination detection requires integrated uncertainty signals rather than single-metric approaches. This finding informs how organizations should design LLM verification pipelines, potentially decreasing reliance on expensive human review or external knowledge base lookups.

Looking forward, integration of hallucination detection into commercial LLM platforms could accelerate enterprise adoption, particularly in regulated industries. The research also opens questions about whether similar behavioral entropy approaches apply to other failure modes like reasoning errors or context misunderstanding.

• →BEACON detects LLM hallucinations with 81.23% accuracy using only model outputs, requiring no internal access or external knowledge bases.
• →The framework outperforms existing methods by 23-24 percentage points by combining multiple uncertainty signals into a unified detection system.
• →A lightweight 5-call variant achieves 77.95% accuracy, enabling practical deployment across commercial LLM APIs with minimal cost overhead.
• →Multi-dimensional feature integration proves more effective than single-metric approaches, indicating hallucination detection inherently requires combined signals.
• →Black-box methodology design makes the approach universally applicable across different LLM providers and deployment scenarios.