GLInt: Geometry-Matched Hard Negatives for Late-Interaction Retrieval

GLInt is a 149M-parameter late-interaction retriever developed by mining and training in the same MaxSim geometry used for its token-level retrieval scoring, reaching 57.43 mean nDCG@10 on BEIR-15 and 62.50 on BEIR-Decontaminated, narrowly surpassing the LateOn reference. The work finds that dense-retrieval mining thresholds do not transfer reliably to MaxSim because late-interaction scores are tightly compressed, so it replaces ratio-based positive-aware filtering with rank-relative filtering by an independent late-interaction judge. Multi-vector mining produced harder negatives than dense mining but also substantially more false negatives, making deep candidate pools, geometry-aware filtering, and dataset weighting by both usable volume and false-negative risk important for training. In knowledge distillation, score normalization and token-score aggregation also required adjustment to avoid nearly uniform targets, while expanding training from MS MARCO to a seven-source mixture yielded much larger gains than changing reranking teachers. Biomedical BiCA data was useful in supervised fine-tuning but harmful in fixed 32-candidate distillation lists because most slots required random padding. The report also concludes that approximate WARP retrieval can efficiently mine score-equivalent near-ties despite lower exact top-50 overlap, and that several post-training approaches, including query expansion, score reweighting, and self-derived transport distillation, reduced performance or collapsed retrieval.

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