TriLens: Per-Layer Logit-Lens Entropy for White-Box Hallucination Detection

TriLens is a novel white-box detection method that identifies hallucinations in language models by tracking entropy changes across internal computational layers. Rather than examining only final outputs, the technique monitors uncertainty signals from multi-head attention, feed-forward networks, and residual streams using logit lens analysis, creating a compact 3L-dimensional trajectory that reveals how model confidence settles during inference.

TriLens addresses a critical vulnerability in large language models: the difficulty of detecting when outputs are factually incorrect despite appearing confident. The research recognizes that hallucinations often leave detectable traces in internal model computations—disagreement between attention heads, divergent feed-forward pathways, or unstable confidence signals across layers. This insight shifts hallucination detection from post-hoc verification to real-time internal monitoring.

The approach builds on the logit lens framework, which interprets hidden states by projecting them through the model's output vocabulary at each layer. TriLens extends this by extracting only entropy measurements from three distinct computational pathways at every transformer layer, avoiding the storage overhead of full hidden states or the computational cost of generating multiple candidate outputs. This efficiency is significant for production deployment where latency and memory constraints matter.

For the AI systems industry, accurate hallucination detection directly impacts trustworthiness and liability exposure. Enterprise applications in legal, medical, and financial domains require confidence that incorrect outputs can be flagged before reaching end users. The method's demonstrated effectiveness across multiple instruction-tuned models and question-answering benchmarks suggests broad applicability rather than narrow specialization.

The complementary nature of module-wise entropy trajectories—attention versus feed-forward disagreement patterns—indicates that hallucination mechanisms involve complex internal dynamics rather than single-point failures. This understanding could inform architecture design and training procedures. Future work likely involves integrating TriLens into inference pipelines and testing against adversarial prompts designed to trigger hallucinations deliberately.

• →TriLens detects hallucinations by monitoring entropy changes in attention, feed-forward, and residual stream modules across all layers without storing full hidden states.
• →The method provides a compact 3L-dimensional signal that tracks how model certainty forms during inference, revealing internal disagreement patterns invisible to final-layer analysis alone.
• →White-box hallucination detection enables real-time filtering before outputs reach users, reducing liability and trustworthiness risks in production LLM systems.
• →The three module-wise entropy trajectories provide complementary evidence, suggesting hallucinations involve complex internal mechanisms rather than single computational failures.
• →Efficient design with minimal memory overhead makes the approach practical for deployment in resource-constrained environments compared to alternative detection methods.