Implicit Minor Detection from Multi-Turn Dialogue in High-Risk AI Interactions: A Constrained Self-Evolving LLM Harness

Key points

This paper formulates the task of estimating whether the current speaker is likely a minor from weak cues across multiple turns, without strong identity signals such as accounts or faces.
The authors propose a constrained framework of a fixed LLM, a versioned Harness, and a Maker-Checker protocol, where the Harness controls when inference is invoked, how evidence is represented, and how outputs are validated.
Every decisive claim must rest on a short quotation or near-verbatim span locatable in the current dialogue, and mentions of other people or quoted material cannot support a claim about the current speaker.
Harness evolution promotes a candidate only when it passes admission constraints on validation data and otherwise retains the incumbent, preventing regressions that damage previously correct cases.
On a Chinese benchmark of 8,328 multi-turn dialogues, the method improves average precision by 7.60 points and minor-class F1 by 6.37 points over direct inference with the same LLM family.
In a 50-participant challenge study, human mean accuracy was 72.6% but fell to 54.8% on Boundary items, showing that difficult cases remain disputed even among humans.

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