# LanG 2.0 preprint reports security detection gains with lower energy use

_Published Monday, November 2, 2026 at 4:05 AM EST · Science, Security · Latest · Tier 2 — Notable_

A LanG 2.0 preprint reports that small local AI models improved security investigations while using less energy than Claude Opus 5.5 in its tests. Campaign correlation precision rose from 18.75% to 51.81% against the system's predecessor, while rule acceptance increased from at most 60.00% to 98.33%.

The system links evidence to entities, requires human approval for rule changes and keeps a ledger designed to expose tampering. Tests covered eight flow datasets from five networks, real XDR alerts and multistage campaigns. On unseen networks, it reported 73.42% F1, a measure combining detection precision and recall, for at least 650 times less energy than Claude Opus 5.5. These results concern the tested workloads.

## Sources

- [cs.CR updates on arXiv.org](https://arxiv.org/abs/2610.41072)

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Canonical: https://techandbusiness.org/newswire/Z6CKfppGNvKy69LMyEvabt
Published: 2026-11-02T09:05:53.553Z
Story chronology: 2026-11-02T05:00:00.000Z
Retrieved: 2026-11-02T11:30:00.898Z
Publisher: Tech & Business (techandbusiness.org)