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From Explicit Elements to Implicit Intent: A Predefined Library for Auditable Behavioral Inference

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arXiv:2606. 11207v1 Announce Type: new Abstract: We present SemantiClean, a modular framework for extracting structured semantic signals from e-commerce session data and driving pluggable inference targets including purchase intent, customer segmentation, and product affinity through a shared element library.

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Authors: Liu hung ming
Released: 2026-06-11 (28d ago)
Topic: Related to current project
Relation to my work: Read next because From Explicit Elements to Implicit Intent: A Predefined Library for Auditable Behavioral Inference overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, under, line, rate, implement, control, full, model. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2606.11207

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