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arXiv · 2604.09982

Reproduction Beyond Benchmarks: ConstBERT and ColBERT-v2 Across Backends and Query Distributions

Utshab Kumar Ghosh·Ashish David·Shubham Chatterjee
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Abstract

Reproducibility must validate architectural robustness, not just numerical accuracy. We evaluate ColBERT-v2 and ConstBERT across five dimensions, finding that while ConstBERT reproduces within 0.05% MRR@10 on MS-MARCO, both models show a drop of 86-97% on long, narrative queries (TREC ToT 2025). Ablations prove this failure is architectural: performance plateaus at 20 words because the MaxSim operator's uniform token weighting cannot distinguish signal from filler noise. Furthermore, undocumented backend parameters create an 8-point gap due to ConstBERT's sparse centroid coverage, and fine-tuning with 3x more data actually degrades performance by up to 29%. We conclude that architectural constraints in multi-vector retrieval cannot be overcome by adaptation alone. Code: https://github.com/utshabkg/multi-vector-reproducibility.

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Utshab Kumar Ghosh, Ashish David, Shubham Chatterjee. 2026-04-11. Reproduction Beyond Benchmarks: ConstBERT and ColBERT-v2 Across Backends and Query Distributions. https://doi.org/10.1145/3805712.3808561

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