# Submitted and Diagnostic Analysis of Full-Text Temporal Retrieval for LongEval-Sci

arXiv: https://arxiv.org/abs/2607.04088 · HTML: https://mlsanity.com/paper/2607.04088/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 5 Jul 2026.

## Builds on (most cited first, 5 of 5)

- [Incremental IVF Index Maintenance for Streaming Vector Search](https://mlsanity.com/paper/2411.00970/) (2024)
- [Academic information retrieval using citation clusters: In-depth evaluation based on systematic reviews](https://mlsanity.com/paper/2207.03299/) (2022)
- [Efficient Immediate-Access Dynamic Indexing](https://mlsanity.com/paper/2211.06030/) (2022)
- [LongEval at CLEF 2025: Longitudinal Evaluation of IR Model Performance](https://mlsanity.com/paper/2503.08541/) (2025)
- [Fast, Incremental Inverted Indexing in Main Memory for Web-Scale Collections](https://mlsanity.com/paper/1305.0699/) (2013)

## Abstract (arXiv)

LongEval-Sci evaluates scientific retrieval under collection change, where a system should be effective on the current corpus and remain usable as documents accumulate over time. This paper reports both official Task 1 results and development diagnostics for LongEval-Sci 2026. We compare the official PyTerrier BM25 and Qwen3 dense baselines with full-text BM25, additive and router variants, temporal full-text retrieval, temporal+citation retrieval, RM3 query expansion, cross-encoder reranking, and reciprocal rank fusion (RRF). In the official DCTR evaluation, the temporalized full-text runs are our strongest submissions: FT BM25+temporal and FT BM25+temporal+citation obtain the best ARP on all three snapshots (0.285, 0.267, and 0.180 nDCG@10) and reduce snapshot-3 relative change from 0.481 for the BM25 pivot to 0.368. Citation features match the temporal-only variant but do not provide a measurable additional gain in the official summary. Our internal snapshot-1 diagnostics show a complementary pattern: full-text BM25 is the strongest single development retriever (DCTR nDCG@10 = 0.3302, MAP = 0.2853), RRF gives the best deep recall (Recall@1000 = 0.9667), and some uncalibrated overlays can sharply degrade top-rank quality. We therefore conclude that full-text retrieval is the strongest foundation, temporal integration can improve official longitudinal effectiveness when applied to that foundation, and citation evidence still requires cleaner ablation and calibration. Beyond ranking, we also report a qualitative weekly IR-system update-monitoring analysis based on ingestion velocity and stale-coverage drift.