Large language models (LLMs) equipped with retrieval--the Retrieval-Augmented Generation (RAG) paradigm--should combine their parametric knowledge with external evidence, yet in practice they often hallucinate, over-trust noisy snippets, or ignore vital context. We introduce TCR (Transparent Conflict Resolution), a plug-and-play framework that makes this decision process observable and controllable. TCR (i) disentangles semantic match and factual consistency via dual contrastive encoders, (ii) estimates self-answerability to gauge confidence in internal memory, and (iii) feeds the three scalar signals to the generator through a lightweight soft-prompt with SNR-based weighting. Across seven benchmarks TCR improves conflict detection (+5-18 F1), raises knowledge-gap recovery by +21.4 pp and cuts misleading-context overrides by -29.3 pp, while adding only 0.3% parameters. The signals align with human judgements and expose temporal decision patterns.
@article{arxiv.2601.06842,
title = {Seeing through the Conflict: Transparent Knowledge Conflict Handling in Retrieval-Augmented Generation},
author = {Hua Ye and Siyuan Chen and Ziqi Zhong and Canran Xiao and Haoliang Zhang and Yuhan Wu and Fei Shen},
journal= {arXiv preprint arXiv:2601.06842},
year = {2026}
}