Application ideas

Explore trading, experimentation, monitoring, routing, spatial decisions, and control through concrete inputs, results, feedback, and retained state.

Start with a result your application needs to improve through experience: a prediction, a decision, a supported explanation, or a control routine. Adapt-1 retains what it learns so later calls can use observations, outcomes, and corrections from earlier work.

These are possible integrations built from documented learning patterns. Each example identifies the inputs, the returned result, and what changes with experience; it is a starting point for your own data and evaluation.

Applications to explore

Your application connects the data source, defines permitted operations, and uses or executes the result. Parsing, sensing, actuator mapping, and outcome measurement belong to that integration.

For independent image questions and grounded pointing, Spatial Vision provides a stateless route. The spatial Domain example above is a separate learning workflow with its own structured observations and feedback.

Follow an application through the loop

Trading research

A trading research system can use two different learning relationships. Choose the one that matches the result you need.

Workflow	Available before the result	Adapt-1 result	What returns afterward
Transition estimation	Current numerical market signals and relevant state	An estimate of the next observable value or change	The transition that actually occurred
Entry and position decisions	Market context, current position, and permitted choices	Support for taking or skipping an entry, or holding, reducing, or closing a position	The executed choice, position events, exit reason, and attributed outcome

For transition estimation, query before the next value is observed, then ingest the completed transition. The retained predictor can change as new records arrive. Use transition learning or Transition Discovery. An external portfolio rule can consume that prediction; its trades do not establish that Adapt-1 learned the rule.

For policy learning, preserve the returned decision and its pre-action context. Record the action actually taken, any override, and subsequent position and exit events. Return the measured outcome to the correct decision. Use contextual feedback for an attributable choice, or sequential learning when entry and position actions affect later state and a later outcome should revise them.

Keep portfolio limits, permissions, order handling, and execution in the surrounding application. In a paper or shadow workflow, distinguish simulated fills from real execution and include the chosen cost assumptions in the outcome. Check later time periods with learning frozen before interpreting a change as transferable behavior.

Scientific experimentation

A laboratory or process-development tool can choose among permitted trials, observe what happened, and carry that experience into the next choice.

1. Describe the trial context. Supply the material, batch, instrument state, or other pre-trial conditions, along with permitted settings.
2. Request the useful result. Ask for a predicted output, a trial choice, or a structured explanation. These use different learning relationships.
3. Run and measure. Record the intervention actually performed and its measured yield, error, duration, or other declared outcome.
4. Retain the change. Update the corresponding predictor, choice policy, or hypothesis support, then inspect the next result.

A prediction target describes what occurred; a reward describes how useful the chosen trial was. Keep those meanings explicit when one experiment supplies both. For mechanism discovery, use intervention records to test competing explanations and reserve additional measurements for checking their predictions.

The learning relationships guide covers experiment selection and hidden-process discovery. Learning schedules explains when to retain a shared history and when a newly drawn hidden process needs its own learning scope.

Interactive control

Choose when Adapt-1 receives a new observation and what it is allowed to return.

Interaction	Returned result	Feedback relationship	Start here
Choose a game action or macro from the current state	One permitted discrete choice	Direct feedback when that choice has a sufficient attributable outcome	Learn from an outcome
Choose actions whose effects accumulate toward a later goal	One state-dependent choice at each decision boundary	Ordered transitions and delayed or terminal outcomes	Sequential learning
Learn coordinated continuous controls	A numeric sequence committed for an attempt or execution window	Actual controls, measured states, and the attempt or window outcome	Machina execution patterns
Adjust an acquired routine to current conditions	Bounded corrections to a retained base	Candidate comparisons during learning; unchanged learned state during frozen use	Contextual refinement

For example, a robot interface can accept position, orientation, and gripper commands, while fixed inverse kinematics and motor controllers execute them. Adapt-1 acquires which commands to use and how to coordinate them. Positioning or aiming tasks can use different control widths and the same measured-outcome loop.

Current continuous-control evidence comes from simulation with task-specific policies. Physical equipment needs its own evaluation. The configuration guide defines the observation, goal, action, and timing contract.

Service request routing

A support integration can start with a smaller loop: observe request and queue conditions, select a permitted route, record the queue actually used, then return resolution, reassignment, or delay as the defined outcome. Retained feedback can revise later route choices under related conditions.

Use the Domain guide for the task contract and the feedback guide for attribution.

Choose the integration pattern

What should improve with experience?	Interface to start with
Predictions of an observed value or transition	Transition learning tutorial
Choices among available operations	Contextual policy feedback
Available evidence and explicit constraints	Continuity across calls
Support for competing explanations	Domain structure and explanations
Useful predictive fields, combinations, and temporal relationships	Structure Discovery
Earlier decisions with later consequences	Sequential learning
Numeric control sequences and contextual corrections	Machina configuration

One product can combine compatible relationships. Keep the targets, feedback meanings, and state scopes explicit. Choose a workflow compares the request and result contracts.

Check the proposed value

Choose one useful result and a comparison you already understand: a fixed route, an existing predictor, or an unchanged routine. Collect the observations and outcomes needed by that relationship, then check whether retained state changes later results on separate cases.

For a frozen evaluation, stop learning writes and verify that the applicable learner state stays unchanged. Inspect support, abstention, and the operation actually executed alongside the outcome.

Verify adaptation

Connect a learning update to a later result and the operation actually used.

Choose a learning schedule

Decide when experience is collected, when it is reused, and when learning can continue.