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PreprintsTechnical ReportNot peer reviewed

Position Bias in Long-Context Retrieval: A Replication Study

Version 1 · 19 August 2026 · 10.xxxx/ctm.pp.2026.0003

Several studies report that language models use information in the middle of a long context less reliably than information at the start or end. We replicate these findings across eleven models and four context lengths using a controlled retrieval task. The effect is reproducible but smaller than originally reported, and largely disappears when the task format is held fixed across lengths. We release the harness and the full per-position results.

Abstract PDF 2,210 7401 cited

PreprintsShort CommunicationNot peer reviewed

Calibrated Uncertainty in Retrieval-Augmented Generation

Amélie Roussel, Tomás Ibarra

Version 2 (updated) · 5 June 2026 · 10.xxxx/ctm.pp.2026.0001

Retrieval-augmented generation grounds answers in retrieved documents, yet models remain over-confident when the retrieved evidence is irrelevant or contradictory. We propose a lightweight confidence estimator that combines the generator's token probabilities with a retrieval-agreement score, and calibrate it with temperature scaling on a small validation set. On three question-answering datasets the estimator lowers expected calibration error from 0.19 to 0.06 and enables selective answering: abstaining on the 20% least confident questions raises accuracy from 71% to 84%. The method needs no model retraining.

Abstract PDF 2,718 9043 cited