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Controller-Augmented Hidden Markov Models: A Computational Framework for Constrained Sequential Inference

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arXiv:2606. 13850v1 Announce Type: cross Abstract: Hidden Markov models are foundational for sequential inference, but their Markovian assumption fails under pathwise constraints such as precedence requirements, visitation cardinalities, or monotonic state progression, which induce long-range dependencies that invalidate standard dynamic programming algorithms.

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Authors: Lekha Patel, Luis Damiano
Released: 2026-06-15 (24d ago)
Topic: Related to current project
Relation to my work: Read next because Controller-Augmented Hidden Markov Models: A Computational Framework for Constrained Sequential 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: code, under, eval, line, control, chain, trained, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2606.13850

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