New AI Research Proposes Layer-Wise Information Scores for More Reliable LLM Question Answering

A new research paper proposes a conformal prediction framework for large language models that uses internal representations rather than output-level statistics to improve reliability in question-answering tasks. The method introduces Layer-Wise Information scores, which measure how conditioning on input reshapes predictive entropy across different model depths. These LI scores serve as nonconformity scores within a standard split conformal pipeline. The approach demonstrates improved validity-efficiency trade-offs compared to text-level methods across both closed-ended and open-domain QA benchmarks. The clearest performance gains occur under cross-domain shift conditions, where calibration-deployment mismatches typically weaken traditional uncertainty signals. The research addresses the growing need for reliability as LLMs are deployed in critical settings where output-level uncertainty metrics like token probabilities, entropy, and self-consistency can become brittle. The paper was announced on arXiv with identifier 2604.16217v1 and classified as a cross announcement type.