When Can a Living Material Truly Learn? New Framework Puts ‘Smart’ Claims to the Test

Engineered living materials, or ELMs, are among the most striking products of modern convergence between materials science, synthetic biology, biofabrication, and systems engineering. These systems deliberately integrate living cells—bacteria, yeasts, mammalian cells, fungi, microbial consortia, or engineered biofilms—with material phases such as hydrogels, polymers, mineral matrices, cellulose networks, textiles, ceramics, implantable scaffolds, or electronic interfaces. The result is neither a conventional material nor a conventional biological culture, but a coupled living–material system in which cell state, scaffold chemistry, mechanics, interface geometry, environmental context, and external control may jointly determine behavior. The field has already produced remarkable demonstrations: light-patterned bacteria on textiles and ceramics, 3D-printed bacteria embedded in functional architectures, programmable Bacillus subtilis biofilms, therapeutic matrix-forming Escherichia coli systems, and biohybrid actuators that link living cells to robotic devices.

Yet as these systems grow more sophisticated, so does the language used to describe them. Materials are increasingly said to sense, respond, adapt, remember, learn, decide, or behave autonomously. Some of this vocabulary is scientifically legitimate. A bacterial genetic toggle switch can store a prior molecular input as a stable state. Microbial regulatory networks can show predictive responses to correlated environmental changes. Biofilms and cell-laden matrices can remodel material properties over time, and even purely abiotic smart materials can display hysteresis, shape memory, self-healing, and stimulus responsiveness. The problem, according to a new methodological framework published in Frontiers in Systems Biology, is not whether ELMs can exhibit genuinely history-dependent or adaptive functions. The problem is whether a specific claim is operationally defined, experimentally controlled, and attributed to the correct system level.

The framework, called ELM-CLAIM—short for Engineered Living Materials—Controls for Learning, Adaptation, Intelligence, and Memory—was developed by Jordi Vallverdú of the Department of Philosophy at the Universitat Autònoma de Barcelona. It is a claim-calibration and reporting framework designed to match adaptive vocabulary to measurable state variables, temporal criteria, attribution targets, and evidence classes. The author is explicit about what the framework does not do: it does not validate an experiment by itself, does not define cognition, and does not constitute a community standard. Instead, it provides operational language for authors, reviewers, and editors, specifying what is being claimed, which measurements define the claim, which controls are needed, which component or coupling relation is responsible, and which claims remain speculative.

Three recurring errors motivate the framework. Attribution errors occur when an observed behavior is credited to living cells although it arises from the scaffold, or credited to the scaffold although it requires active cellular metabolism, interface remodeling, or controller feedback. Scalar errors occur when molecular bistability, single-cell physiological change, population selection, tissue-scale morphology, and whole-material performance are all called memory without specifying the scale at which the state is encoded. Reproducibility errors occur when one laboratory reports learning using criteria that another would classify as habituation, sensitization, selection, maturation, damage, fatigue, or controller tuning. These risks are amplified in ELMs precisely because the systems are intentionally built: the investigator chooses the cell, scaffold, geometry, circuit, controller, and stimulus history, so a capacity may be designed into the material, emerge from living–material coupling, or merely appear at the output because an external algorithm performs the relevant adaptation.

At the heart of ELM-CLAIM is a six-level claim taxonomy that classifies specific behaviors rather than whole systems. Level 0, passive responsiveness, describes changes that are direct, immediate, reversible, and physically or chemically expected consequences of a stimulus, such as hydrogel swelling under pH change or thermal expansion, requiring no active cellular contribution. Level 1, active responsiveness, requires metabolically active living cells, active biointerface processes, or engineered biological regulation, but does not depend on prior stimulus history. Level 2, adaptation, introduces history dependence, subdivided into habituation, sensitization, and associative adaptation. Level 3, memory-like behavior, demands a persistent state change that outlasts the inducing stimulus, with subtypes covering short-term, long-term cellular, structural, and designed circuit memory. Level 4, learning-like behavior, requires acquisition, retention, specificity or transfer, and reversibility or extinction. Level 5, autonomous goal-directedness, is deliberately stringent: it requires an endogenously generated reference condition maintained by the living or coupled system under perturbation, not merely a return to an externally imposed set point.

The taxonomy is organized along two dimensions: temporal dependence, meaning whether behavior depends only on current stimulus conditions or also on prior history, and active system participation, meaning whether the behavior can be explained by passive physical chemistry or requires living metabolism, gene regulation, biointerface activity, morphology, controller feedback, or coupled-system organization. Crucially, the levels are evidential thresholds for specific behaviors, not fixed ontological ranks of materials. A single ELM may support an L1 claim for one output, an L3 claim for another, and no adaptive claim for a third. A rigorously supported L1 claim may be more scientifically valuable than an under-controlled L4 claim, and the framework explicitly warns against reading the ladder as a hierarchy of system quality.

To connect claims with evidence, ELM-CLAIM provides an evidence-tiered claim-to-control matrix using three tiers: Essential, meaning normally required for the claim, with the claim lowered or marked provisional if absent; Recommended, meaning strongly strengthening but replaceable by a justified alternative; and Alternative or system-specific, appropriate in some ELM classes when authors explain why the selected option is adequate. Because ELMs are experimentally diverse—a genetic knockout may be feasible in a tractable microbial system but impossible in a primary mammalian-cell hydrogel—the matrix specifies evidential functions rather than a universal list of assays. A three-step proportional substitution rule allows laboratories that cannot perform an ideal perturbation to state why it is infeasible, use an orthogonal control or model-based perturbation addressing the same alternative explanation, and report the residual uncertainty created by the substitution.

Attribution logic forms another pillar of the framework. The Principle of Minimal Attribution holds that an observed behavior should be attributed to the simplest component or coupling relation sufficient to explain it, with higher-level attributions earned by ruling out lower-level alternatives. The framework defines a component ontology spanning living cells, scaffold, biointerface, morphology, environmental gradients, algorithm or controller, and the coupled system itself. For component-level attribution, authors must evaluate necessity, sufficiency, and specificity. ELM-device hybrids create a special risk: the living material may function merely as a sensor, actuator, or substrate while adaptation occurs entirely in software. A learning-like claim about the ELM is not supported if only the controller changes its parameters; in that case the correct statement is that the ELM-device system exhibits controller-mediated adaptive performance. Genetic circuit confounds and population-level selection confounds receive parallel treatment, with explicit requirements to distinguish circuit memory in individual cells, population-level memory, material-level memory, and emergent memory arising from cell–material coupling.

The framework also imposes model-transparency requirements and a reviewer-oriented reporting checklist. When adaptive claims depend on a mathematical or computational model, the model’s role must be declared as descriptive, predictive, or mechanistic, because these roles are not interchangeable: a good descriptive fit does not by itself establish the mechanism required for a memory-like or learning-like claim. Quantitative models should report equations, state variables, parameters, calibration and evaluation data, identifiability assessment, and sensitivity analysis, with deposition in standard formats such as SBML and COMBINE archives where possible. The reporting checklist covers claim levels, operational definitions, vocabulary audits, system components, living and scaffold states, controller status, level-specific controls, attribution statements, replication, data and code availability, and FAIR-compliant sharing, with a machine-readable version supplied for reuse.

To stress-test interpretability, the article applies ELM-CLAIM retrospectively to representative published cases, from shape-memory polymers as an abiotic boundary case to engineered biofilms, printed bacterial materials, the classic E. coli genetic toggle switch, predictive microbial regulation, optogenetic muscle-powered biohybrid machines, and closed-loop neural cultures in a simulated task environment. The exercise is explicitly illustrative rather than a statistical validation, and the author emphasizes that it should not be read as an assessment of the quality of the cited studies. Four lessons emerge: abiotic smart materials can show real shape memory without supporting living memory claims; many impressive ELM systems are strong L1 systems that should not be labeled memory-like or learning-like without temporal controls; designed circuit memory is valid but should be named as such; and closed-loop biohybrid systems require controller audits because learning-like behavior may reside in software, living tissue, or coupled interaction. The central message is simple: do not forbid adaptive vocabulary, but make it earn its evidential status. Community testing, inter-reviewer reliability assessment, and prospective experimental use remain the necessary next steps before ELM-CLAIM can mature from an author-proposed framework into a community-refined standard.

Subject of Research: A claim-calibration framework for attributing adaptive, memory-like, learning-like, and goal-directed behaviors in engineered living materials

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

Article References: ELM-CLAIM: a claim-calibration framework for adaptive, memory-like, learning-like, and goal-directed claims in engineered living materials. (n.d.). https://doi.org/10.3389/fsysb.2026.1905585

Image Credits: AI Generated

DOI: 10.3389/fsysb.2026.1905585

Keywords: engineered living materials, ELM-CLAIM, claim calibration, synthetic biology, systems biology, memory-like behavior, learning-like behavior, biohybrid systems, attribution, experimental controls, adaptive materials, reproducibility