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Adapt-1 forms and revises representations, rules, predictive models, and policies during operation, and carries the resulting state into later work. Its internal learning process selects learners and validates and calibrates their outputs. The API can return structured results directly without a language model. The application exposes observations, available operations, measurable outcomes, and the task boundary. A Domain declares the task contract for this interaction. The State gateway carries evidence and corrections across calls through its own record lifecycle.

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Form useful task structure

With Discovery, inputs, field combinations, temporal context, and action or intervention bindings can begin unresolved. Adapt-1 forms an executable learner view from interaction and retains it as revisable state. Compatible earlier events can then be reconsidered through the formed view. Structural learning induces rules from observations. Later evidence can specialize, generalize, replace, or retire them. Recurring combinations of active rules can form recognizable latent states, allowing their learned transition history to be reused when the same rule configuration returns. Discovery covers structure formation.

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Reason with retained evidence

Relational Hopfield Memory combines similarity with associative recovery. Typed relations and hyperedges preserve the joint context of observations. Executable conditions and hypotheses support comparison of explanations and inference of consequences. Hypothesis results expose their supporting evidence and counterevidence. Adaptive reads can strengthen associations. Consolidation stabilizes selected records, and corrections revise their evidence. Unused associations lose influence. Later queries use this evolving state to recover context and produce supported results. See Continuity across calls and Explain a result.

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Learn predictions and decisions

Transition learning estimates observable consequences. Contextual policies learn which choices work in a given situation, and sequential credit allows later outcomes to revise earlier state-changing choices. Adapt-1 updates the applicable state as observations and attributed outcomes arrive. Where trainable models are enabled, it also trains candidates from accumulated evidence, validates them on later observations, and installs accepted models while operation continues. See Learning relationships for the supported evidence and feedback patterns, or Learn from an outcome to connect execution to later decisions.

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Acquire and revise procedures

In Machina, Adapt-1 operates with continuous control sequences. It acquires commands through execution and measured outcomes, compares attempts, tests command removal and ordering, and learns contextual corrections to an acquired base. The resulting procedure becomes reusable learned state. During frozen use, current observations can yield different controls through unchanged learned parameters. See Machina and Retained use. Learning schedules explains how to configure acquisition and later use, including runs that keep the learner state fixed.
Temporal Context Projection (TCP) makes bounded episode history available when the current observation alone is insufficient.Counterfactual Utility Plasticity (CUP) learns which registered prediction sources and combinations add useful predictive information, then revises their influence on later predictions as evidence changes.