Model-Agnostic Semantic Discovery

Mechanism

Model-agnostic discovery separates the traversal architecture from the inference engines that evaluate content at each anchor through a structured interface contract. The discovery object specifies what inference capabilities it needs (semantic similarity scoring within a domain, entailment classification over a schema, summarization at a stated abstraction level, structured extraction against a named schema) rather than which specific model is to be invoked. Anchors expose inference services that advertise their capability surfaces through the same contract, and the matching layer at each anchor selects an inference resource whose advertised capabilities meet or exceed the discovery object's requirements at the moment of traversal. The selection is local to the anchor, so different anchors visited by the same discovery object may resolve the same capability requirement through different underlying models without affecting the object's traversal logic.

The contract is the load-bearing element. Each capability is defined by a type signature (input shape, output shape, semantic constraints on the output, and any structured-output validation rules) and a quality dimension (calibration of confidence values, stability across paraphrase, agreement with a reference rubric on a published evaluation set). A model is admissible at an anchor for a given capability if and only if its outputs conform to the type signature and its measured quality on the evaluation set meets the threshold the anchor advertises. The discovery object never queries the model directly; it queries the capability, and the anchor's matching layer arranges for an admissible model to produce the response.

Outputs are returned in the contract's structured form rather than the model's native form. A discovery object that consumes entailment classifications consumes them as a fixed enumeration with associated calibrated probabilities, regardless of whether the underlying model is a transformer trained on natural-language inference, a hybrid retrieval-and-classify pipeline, a domain-specific specialist model, or a future model class not yet enumerated. The translation from native model output to contract output is the responsibility of an adapter associated with the model registration, not the discovery object.

Operating Parameters

Each capability declared by a discovery object carries operating parameters that govern its matching and execution. A required-quality threshold specifies the minimum measured quality the matching model must possess; the anchor refuses the capability rather than degrading to an unqualified model. A latency budget bounds the time the anchor may spend on the inference; models exceeding the budget are excluded from selection at that anchor even if their quality is otherwise sufficient. A determinism declaration specifies whether the capability tolerates stochastic outputs or requires deterministic outputs reproducible from the same inputs; non-deterministic models are excluded for capability invocations marked deterministic. A cost ceiling expressed in the index's accounting units bounds the resources that may be consumed for a single capability invocation, and a cumulative cost budget bounds the resources consumed by the entire traversal across all anchors visited.

Each model registered at an anchor carries operating parameters of its own. A capability surface enumerates the contract capabilities the model can serve, with per-capability quality measurements, latency distributions, and cost figures. A residency declaration specifies where the model executes (locally on the anchor, in a regional inference fleet, or via a remote API) and any data-residency constraints that follow from execution location. A version declaration records the model identifier and weight version, and an availability schedule records the periods during which the model is admissible for selection. A fallback ordering specifies which other registered models the matching layer should consult if the preferred model is unavailable or exceeds operating parameters.

The matching layer evaluates the intersection of the discovery object's parameters and each registered model's parameters at every traversal step, producing either a selected model or an unmet-capability signal that the discovery object handles according to its own continuation policy. Continuation policies range from skip-and-continue (the anchor is treated as silent for that capability) to retry-elsewhere (the discovery object reroutes to another anchor offering the capability) to fail (the traversal halts and reports the unmet capability).

Alternative Embodiments

The mechanism admits embodiments differentiated by deployment context. In a federated embodiment, anchors maintained by independent organizations expose their own model fleets through the shared contract, and a discovery object traverses across organizational boundaries with capability matching resolved at each anchor under the local organization's policy; the discovery object obtains comparable structured outputs from heterogeneous infrastructure without negotiating model identity. In a regulatory embodiment, anchors in jurisdictions with model-residency or model-approval constraints register only models that satisfy the local regulation, and the matching layer enforces the constraint architecturally so that a discovery object cannot inadvertently invoke a non-compliant model.

A cost-tiered embodiment registers multiple models at each anchor differentiated by cost-quality tradeoff, and the matching layer selects the lowest-cost admissible model for each capability invocation, producing an index whose operating cost adapts to the precision demanded by each traversal. A research-evaluation embodiment registers competing models against the same capability surface and routes a sampled fraction of capability invocations to each, accumulating measured-quality data that feeds back into the registration parameters. A migration embodiment runs an incumbent model and a candidate model in parallel during a transition window, with the matching layer comparing outputs and promoting the candidate only when measured agreement on the contract surface meets a published threshold.

Composition

Model-agnostic discovery composes with the broader semantic discovery framework along three seams. First, it composes with traversal logic: the discovery object's traversal is expressed against contract capabilities, so the same traversal program runs unchanged across anchors served by different models. Second, it composes with confidence and provenance: every capability output carries a provenance record naming the model, version, and quality evidence under which it was produced, and downstream consumers can apply model-specific weighting if their policy requires it. Third, it composes with caching: the contract form makes outputs comparable across model identities, so a cached result produced by one model can satisfy a later request that would otherwise have invoked a different model, provided the cached output satisfies the requesting capability's quality and freshness parameters.

Compositional behavior with capability evolution is also load-bearing. As the contract grows to admit new capabilities, models registered before the new capabilities appear continue to serve their existing surfaces without modification, and new models registered after the additions can serve both legacy and new capabilities. The discovery object's traversal logic gates its use of new capabilities behind feature checks against the matching layer, so a single discovery object can run across anchors with mixed capability availability, exploiting new capabilities where present and falling back to legacy capabilities where not.

Prior-Art Distinction

Conventional retrieval and discovery systems either embed a specific model into the architecture (locking the system to that model's lifecycle) or expose a low-level inference API that callers must adapt to per model (pushing model heterogeneity into application code). Multi-model orchestration frameworks generally treat models as substitutable at the call-site level but do not impose a structured contract that makes outputs comparable across models, so substitution remains a manual integration task. Embedding-based retrieval approaches achieve a form of model independence through vector-space substitution but lose access to richer semantic operations such as entailment, structured extraction, and rubric-graded summarization. The disclosed mechanism imposes a capability contract above the model layer, requires structured-output conformance at registration, and makes substitution an operational concern resolved at traversal time. The discovery object expresses requirements in capability terms; the anchor resolves them against whatever models satisfy the contract; substitution is invisible to the traversal program.

Disclosure Scope

The cross-model-portability primitive, the capability contract that mediates between discovery objects and inference engines, the registration and matching parameters governing model selection at each anchor, and the compositional seams with traversal, confidence, provenance, caching, and capability evolution are disclosed in U.S. Provisional Application No. 64/049,409. The provisional records the contract as the load-bearing element that decouples discovery traversal from any specific model identity, and treats substitution, heterogeneity, and evolution as operational concerns resolved at traversal time rather than architectural ruptures requiring system redesign.

This disclosure covers the cross-model-portability primitive for semantic discovery, including the capability contract that mediates between discovery objects and inference engines, the registration parameters for models advertised at anchors, the matching layer that selects an admissible model at each traversal step, the embodiments enumerated above, and the compositional seams with traversal, confidence, provenance, caching, and capability evolution. The scope extends to inference engine classes not enumerated whose registration produces capability surfaces conformant with the contract, to matching policies not described whose behavior reduces to the parameter intersection above, and to compositional uses with downstream systems that consume contract-form outputs as part of their own logic.