# "PRISM" Is Not All You Need: Cross-Topic Name Collisions and Scientific Paper Retrieval

**Authors:** Jingjie Ning, Xueqi Li, Yibo Kong  
**Lab:** Carnegie Mellon · **Published:** 31 Oct 2026 · **Upvotes:** 4  
**Links:** [arXiv](https://arxiv.org/abs/2611.01362) · [PDF](https://arxiv.org/pdf/2611.01362) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2611.01362.html)

## Key numbers

- **4.33-fold**: Cross-topic name collision grows 4.33-fold from a pooled 2020-2022 reference to 2026
- **1.12-fold**: compared with 1.12-fold within topics
- **89.2%**: listwise language-model ranking reaches 89.2% using the query and candidate titles

## Abstract

Scientific names increasingly connect papers from different research topics, leaving readers to recover a paper's identity from context. We study this convergence in 564,861 AI-related arXiv records. Cross-topic name collision grows 4.33-fold from a pooled 2020-2022 reference to 2026, compared with 1.12-fold within topics. A second sentence encoder reproduces the pattern, and name-group resampling quantifies its uncertainty. Fixed word-frequency bands locate the main increase in an intermediate-frequency vocabulary shared by many research areas. We then measure the ambiguity that remains after supplying research clues. With the same three-term query construction, shared-name targets retain matched identification deficits of 25.7 percentage points under title-based BM25 and 6.9 points under title-plus-abstract E5. Within fixed same-name candidates, listwise language-model ranking reaches 89.2% using the query and candidate titles. Topic-specific comparisons show that context resolves more cross-topic ambiguity. The evaluation separates candidate competition, document fields, and contextual ranking through lexical, dense, and language-model baselines. Source-linked cases and a portable evaluation collection make the phenomenon inspectable and reusable for scientific retrieval and research assistants.

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