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Beyond Post-hoc Explanation: Toward Glassbox AI via Probabilistic Mediation

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arXiv:2606. 07113v1 Announce Type: new Abstract: Large language models are rapidly becoming infrastructural components in high-stakes institutional settings, including public administration, legal reasoning, and healthcare, where opacity is not merely inconvenient but institutionally and legally untenable.

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Authors: Manuele Leonelli
Released: 2026-06-08 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Beyond Post-hoc Explanation: Toward Glassbox AI via Probabilistic Mediation 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: code, alignment, line, test, language, model. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2606.07113

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