TECHNICAL NOTE · TECHNOLOGY
The integrated decision object
What changes when the model returns more than a prediction?
OCTOBER 2026
7 MIN READ
STANDARD DEEP MODEL
- INPUT
- MODEL
- PREDICTION
One output. Everything else is decided around it.
XPDEEP MODEL
INPUT
→MODEL
→ONE DECISION OBJECT · THE PREDICTION PLUS FOUR PRIMITIVES
- PREDICTION / ESTIMATE
- STRUCTURAL EXPLANATION
- ATTRIBUTED DOMAIN STATE
- MINIMAL FEASIBLE INTERVENTIONwhere applicable
- STRUCTURAL JUSTIFICATION
One decision object: the prediction plus four primitives, not five separate products.
Machines are starting to carry decisions with real consequences, adjusting a process, derating an asset, gating a perception output. Each of those decisions rests on what a learned model returns.
Most deep-learning systems return a prediction.
That may be a probability, a classification, a state estimate or a forecast.
For many applications, that is enough.
But when a learned model participates in a consequential machine decision, the surrounding system needs more than the output itself.
It needs to know:
- What drove this output?
- Is the model still operating inside conditions it learned?
- If the outcome needs to change, what feasible input change could alter it?
- Why does that intervention change the predicted outcome?
Xpdeep brings those questions together into one model-level decision object.
The important distinction is not simply that the model explains more.
It is that the output becomes structured enough to participate in a bounded operational decision.
01 THE OBJECT
The prediction plus four primitives. One decision object.
01 · STRUCTURAL EXPLANATION
What drove this output?
The learned model structure that produced the output: which input variables, with what weight, through which temporal dependencies.
Not reconstructed afterwards by a surrogate.
02 · ATTRIBUTED DOMAIN AWARENESS
Is the model still valid here?
A graded distance to the trained domain, with the responsible variables and temporal windows attributed. Includes novel-class detection where relevant.
Not a confidence score.
03 · CONSTRAINED MINIMAL INTERVENTION
What is a minimal feasible move?
A feasible input change, optimized for minimality, that improves the target outcome under stated operational constraints.
Not a recommendation. Not a global optimum.
04 · STRUCTURAL JUSTIFICATION
Why does this move change the outcome?
The learned structural dependencies that explain why the intervention changes the predicted outcome.
Not a narrative rationalization added afterwards.
The decision object brings together the prediction, structural explanation and attributed domain state. When useful and supported by the modality, a separate prescriptive inference adds a minimal feasible intervention and its structural justification.
The prediction is not traded away for this: on time-series data, accuracy is at least equivalent to standard deep learning and frequently superior.
The value is in the integration, not in any single primitive.
The primitives are not independent add-ons.
1
PREDICT + STRUCTURAL EXPLANATION
2
CHECK DOMAIN
3
WHEN AN INTERVENTION IS USEFUL: PRESCRIBE
4
JUSTIFY THE INTERVENTION
→
CUSTOMER-DEFINED RULES →ACT · HOLD · ESCALATE
The model first produces the prediction and its structural explanation.
The domain state establishes whether the current input remains inside learned conditions.
If an intervention is useful and the modality supports prescription, a separate inference computes a minimal feasible change under constraints.
The model then returns the structural justification of why that change alters the predicted outcome.
RUNTIME
Prediction and explanation travel together.
PRIMARY INFERENCE
SAME FORWARD PASS
- INPUT
- MODEL
- PREDICTION + STRUCTURAL EXPLANATION + DOMAIN STATE
OPTIONAL PRESCRIPTIVE INFERENCE
SEPARATE INFERENCE · RUN ONLY WHEN USEFUL
- CURRENT STATE + CONSTRAINTS
- MINIMAL FEASIBLE INTERVENTION + STRUCTURAL JUSTIFICATION
The structural explanation is produced with the prediction in the same forward pass. The domain state is read from the model’s latent space, with no second model. Prescription is a separate inference, invoked only when an intervention is useful.
NATIVE · COST AT INFERENCE
- Prediction and structural explanation share the same forward pass. The explanation adds no extra compute or latency. The explanation layer’s memory footprint must be budgeted.
RETROFIT · COST AT INFERENCE
- The added layer brings a small compute and memory overhead, measured on the target.
FIELDS AND TYPES
The exact structure of the decision object, its fields and its types are specified in the developer documentation. Inference APIs are available in C / C++ and Python, with outputs exportable in JSON and Parquet.
Open developer documentation ↗The explanation comes from the learned structure itself.
VARIABLE 03
T−18 → T−06
LOCALIZED WINDOW
MORPHOLOGY
DEPENDENCY
On temporal data, Xpdeep does not stop at ranking variables.
It can localize the time windows and signal morphology that carried the model output.
This lets engineers inspect the learned decision at signal resolution.
POTENTIAL ENGINEERING USES
MODEL DEBUGGING
ARTEFACT LOCALIZATION
MEASUREMENT CORRECTION
FAULT ATTRIBUTION SUPPORT
The model localizes the learned driver. The engineer identifies the physical cause.
Validity is not the same as confidence.
INSIDE KNOWN CONDITIONS
APPROACHING DOMAIN EDGE
OUTSIDE KNOWN CONDITIONS
ATTRIBUTED TO:
VARIABLE
TIME WINDOW
PATTERN
Xpdeep reports domain validity separately from predictive confidence.
The output is graded rather than binary.
When the current state departs from the trained domain, the responsible variables and temporal windows are attributed.
Where relevant, novel-class detection identifies an output that belongs to no class the model learned.
Domain awareness is necessary for bounded delegation. It is not sufficient for system safety.
Prescription answers a different question: what can change now?
CURRENT STATE
TARGET OUTCOME
OPERATIONAL CONSTRAINTS
FEASIBLE REGION
MINIMAL FEASIBLE INTERVENTION
The intervention is an input change produced under stated constraints and optimized for minimality while improving the target outcome.
Minimal means minimal feasible under the stated constraints and optimization procedure.
A proposed change is not enough. The model must show why it changes the outcome.
- INPUT CHANGE
- LEARNED STRUCTURAL DEPENDENCY
- PREDICTED OUTCOME CHANGE
The structural justification identifies the learned dependencies that connect the intervention to the changed prediction.
This makes the intervention inspectable.
Structural justification is not automatically physical causality.
OPERATION
The decision object turns a model output into a bounded decision input.
CONVENTIONAL FLOW
- MODEL
- PREDICTION
- HUMAN INTERPRETATION
- DECISION
XPDEEP FLOW
- MODEL
- DECISION OBJECT
- CUSTOMER-DEFINED EXECUTION RULES
- ACT / HOLD / ESCALATE
The surrounding system stays in control: its rules, not the decision object, authorize ACT, HOLD or ESCALATE.
Xpdeep closes the model-constrained part of the loop. It does not replace system-level control.
The production object exists in both modes. What changes is how far the model lifecycle can go.
RETROFIT
Existing PyTorch predictive model remains frozen.
Production can return:
- prediction
- structural explanation
- domain state
- prescription and justification
No targeted structural editing. No controlled structural correction.
NATIVE
Prediction and explanation are learned jointly. Production returns the same decision object.
Additionally enables:
- input reduction
- KPI alignment
- targeted local editing
- controlled evolution
Retrofit is structural, not ante-hoc. Native is the mode that closes all three loops.
The object depends on the modality.
TIME SERIES / TABULAR
Production chain can include:
- prediction
- structural explanation
- attributed domain state
- minimal feasible intervention, where supported
- structural justification of the intervention
Prescription is available on temporal and tabular stages where supported.
IMAGE / VIDEO
Current offering includes:
- structural explanation
- attributed domain state
- design-loop diagnosis
✕ NO PRESCRIPTION AT IMAGE-LEVEL DETECTION
The value is not “more explanation”.
DESIGN
- Engineers can see what the model learned and where it needs correction.
PRODUCTION
- The system receives richer decision inputs than a prediction alone.
BOUNDING
- Authority can depend on whether the model remains inside known conditions.
EVOLUTION
- Operational evidence can support targeted correction in Native mode.
The decision object is the bridge between model understanding and bounded machine authority.
SCIENTIFIC BOUNDARIES
What the decision object does not imply.
STRUCTURAL, NOT AUTOMATICALLY CAUSAL
- Learned dependencies do not automatically establish physical causality.
MINIMAL FEASIBLE, NOT UNIQUE GLOBAL OPTIMUM
- Prescription is produced under stated constraints and the optimization procedure.
DOMAIN STATE IS NOT CONFIDENCE
- Reported separately from predictive confidence.
DOMAIN AWARENESS IS NOT SYSTEM SAFETY
- It is necessary for bounded delegation, not sufficient for system safety.
A prediction answers: “What does the model think?”
The decision object also answers:
- WHY?
- ARE WE STILL INSIDE KNOWN CONDITIONS?
- WHAT CAN CHANGE?
- WHY WOULD THAT CHANGE THE OUTCOME?
That is what changes a model output from information into a bounded decision input.