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A review of adaptive human-AI collaboration literature through a closed-loop lens and integrates three taxonomies into MCAL, a dual-timescale Multimodal Co-Adaptation Loop reference model.
ARISMA treats AI as an inspected, benchmarked, logged, and reversible assistant rather than an autonomous reviewer, built around one governing principle: every consequential scientific decision must remain human-interpretable, human-auditable, and human-accountable.
Three building blocks toward a complete, privacy-preserving browser agent, a verified repair instrument pairing a trilingual seeded-violation benchmark with an audit-inject-verify loop, and a dual-condition protocol that measures harm as carefully as benefit are contributed.
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