ELM-CLAIM: a claim-calibration framework for adaptive, memory-like, learning-like, and goal-directed claims in engineered living materials

Engineered Living Materials (ELMs) combine living cells with matrices, interfaces, environmental fields, and sometimes electronic or computational controllers. Their multicomponent architecture creates an attribution problem: the same observed output may reflect passive material physics, active cellular regulation, interface or morphological change, environmental gradients, controller adaptation, or coupled-system dynamics. This article proposes ELM-CLAIM (Engineered Living Materials-Controls for Learning, Adaptation, Intelligence, and Memory), an author-proposed claim-calibration and reporting framework for matching adaptive vocabulary to measurable state variables, temporal criteria, attribution targets, and evidence classes. The framework classifies specific behaviors from passive responsiveness (L0) through active responsiveness (L1), adaptation (L2), memory-like behavior (L3), learning-like behavior (L4), and autonomous goal-directedness (L5). It also provides an evidence-tiered claim-to-control matrix, proportional substitution rules for experimentally intractable controls, component- and controller-level attribution logic, model-transparency requirements, and a reviewer-oriented reporting checklist. Essential entries denote evidential functions rather than a single mandatory assay, allowing justified orthogonal controls while requiring claims to be lowered when major alternatives remain unresolved. Published cases are used illustratively to stress-test interpretability rather than to validate the framework statistically. ELM-CLAIM calibrates language; it does not by itself validate an experiment, define cognition, or constitute a community standard. Community testing, inter-reviewer reliability assessment, and prospective experimental use remain necessary next steps.