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arXiv AI By Tianyi Huang, Eric Liu, Jay Chong, Emily Yin, Wanqi Zhao

Provenance-Aware Belief Updating in Human-AI Feedback Loops

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The paper introduces Latent Quotient Evidence Training (LQET), a relational objective that uses report lineage to guide language models in updating beliefs based on evidence provenance rather than report frequency. Experiments on a binary benchmark show LQET reduces new‑evidence update RMSE by 41‑44% and log‑odds drift by 89‑91% compared to task‑only training, and improves performance in a closed‑loop simulation where model confidence controls report re‑inclusion. By grounding belief updates in supplied provenance, LQET enables dynamic human‑AI systems where influence follows new evidence instead of repeated mentions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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