This paper introduces a systems-level conceptual framing and a three-level taxonomy (intra-model, system-level, socio-technical) for uncertainty propagation in compound LLM applications, along with engineering insights and open challenges.
Learned hallucination detection in black-box LLMs using token-level entropy production rate
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LLM token rank-frequency distributions converge to a shared Mandelbrot distribution across models and domains, enabling a microsecond-scale statistical primitive for provenance verification and black-box anomaly triage.
A two-stage LLM explainer-verifier framework with iterative refeed improves faithfulness and accessibility of XAI explanations, as shown in experiments across five techniques and three LLM families, with EPR analysis indicating progressive stabilization.
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Uncertainty Propagation in LLM-Based Systems
This paper introduces a systems-level conceptual framing and a three-level taxonomy (intra-model, system-level, socio-technical) for uncertainty propagation in compound LLM applications, along with engineering insights and open challenges.
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The Surprising Universality of LLM Outputs: A Real-Time Verification Primitive
LLM token rank-frequency distributions converge to a shared Mandelbrot distribution across models and domains, enabling a microsecond-scale statistical primitive for provenance verification and black-box anomaly triage.
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A Two-Stage LLM Framework for Accessible and Verified XAI Explanations
A two-stage LLM explainer-verifier framework with iterative refeed improves faithfulness and accessibility of XAI explanations, as shown in experiments across five techniques and three LLM families, with EPR analysis indicating progressive stabilization.
- High-Entropy Tokens as Multimodal Failure Points in Vision-Language Models