Forecasting-Modulated Discovery Traversal

Mechanism

The forecasting engine described in Chapter 4 of the cognition patent emits a prediction structure consisting of a set of candidate regions in the semantic index, each with an associated forecast score in [0, 1]. The discovery subsystem maintains an active frontier of anchors awaiting expansion. At each traversal step, the next anchor selected for expansion is drawn from the frontier under a selection function that combines the anchor's intrinsic priority with the forecast score of the region containing the anchor.

The selection function is a policy-defined combination, by default a convex blend p_select = (1 - lambda) * p_intrinsic + lambda * p_forecast, where lambda is the forecasting weight. The blend produces a shaped traversal: when lambda is zero, the frontier behaves identically to the unforecast baseline; when lambda is positive, anchors in predicted regions are selected sooner; when lambda is one, the traversal follows the forecast strictly. The blending weight is bounded by the prior, which is the central structural constraint of the mechanism.

The bounded prior limits the cumulative influence of the forecast on the traversal. Specifically, lambda is itself a function of the traversal's accumulated forecast-following: as the traversal explores more anchors that the forecast predicted, the marginal weight of subsequent forecast scores is reduced. The prior is implemented as a decay term lambda_eff = lambda_0 * exp(-k * f), where f is the count of forecast-following steps already taken and k is the decay rate. The decay ensures that the forecast cannot maintain unbounded influence over an arbitrarily long traversal.

When in-traversal evidence diverges from the forecast, the mechanism rebalances. If anchors in predicted regions consistently produce low per-step confidence, the forecast is treated as miscalibrated and lambda_eff is further suppressed. The rebalancing is governed by a divergence statistic computed from the per-step confidence values relative to the forecast score, exposed as a structured field in the cognitive frame and audited in the lineage. Rebalancing is monotone: once suppressed, lambda_eff cannot be re-raised within a single traversal except through a policy-defined reset event.

Operating Parameters

The base forecasting weight lambda_0 is constrained to [0, 0.7] at policy load, with values above 0.7 rejected on the ground that they would permit the forecast to dominate intrinsic frontier priority and structurally enable bias-locking. Default lambda_0 is 0.4 in general-purpose deployments, biased lower in safety-critical domains and biased higher in latency-sensitive deployments where forecast-following accelerates time-to-result.

The decay rate k is constrained to [0.01, 0.5] and governs how quickly the forecast's influence decays as forecast-following steps accumulate. A small k preserves forecast influence over long traversals; a large k forces the traversal to revert to intrinsic priority after a small number of forecast-following steps. The decay rate is selected per domain based on the expected traversal length and the calibration of the forecasting engine.

The divergence threshold for rebalancing is parameterized as a per-step confidence floor relative to the forecast score: when the running mean of c_i for forecast-followed anchors falls below the forecast score by more than the threshold, lambda_eff is suppressed. The threshold is constrained to [0.05, 0.4]. The reset event that re-raises lambda_eff is a policy-defined boundary, typically the start of a new traversal or the receipt of an updated forecast envelope from the forecasting engine.

The forecasting engine itself produces forecast scores at a configurable cadence. The cadence may be every step, every k steps, or only at frontier-level inflection points. Each forecast emission is content-addressed and recorded in the lineage, so any traversal can be replayed under either the forecasts in force at the time or under counterfactual forecast streams. This enables A/B evaluation of forecasting models by replay rather than by re-execution of the underlying traversal.

Frontier expansion limits, anchor budget caps, and forecast-region size limits are all parameters of the mechanism. Forecast regions exceeding the size cap are split into sub-regions with independent scores to prevent a single overbroad forecast from absorbing the entire frontier weight. The size cap is constrained to ensure that the bounded prior is computable in time linear in the region size, preserving the determinism guarantee.

Alternative Embodiments

The canonical embodiment uses a convex blend with exponential decay and confidence-driven rebalancing. Several alternative embodiments are within scope. A first alternative replaces the convex blend with a Thompson-sampling-style stochastic selection, where the next anchor is drawn with probability proportional to a Bayesian-updated posterior over forecast accuracy. This embodiment is preferred when the forecasting engine's calibration is itself uncertain and exploration value is high.

A second alternative uses a hard top-k forecast restriction, where only anchors in the top-k forecast regions are admitted to the frontier at all, with the bounded prior enforced by reducing k as forecast-following steps accumulate. This embodiment is preferred in latency-bounded deployments where the cost of expanding unpredicted anchors is prohibitive, and the bounded prior ensures that the traversal can still escape into broader regions when the forecast proves miscalibrated.

A third alternative integrates the forecasting weight with the discovery-traversal confidence vector, so that lambda_eff is itself a function of the running aggregate C: high-aggregate traversals trust the forecast more, low-aggregate traversals trust it less. A fourth alternative permits multiple concurrent forecasts from independent forecasting engines, blended through a meta-aggregator and capped at a system-wide maximum total weight. A fifth alternative supports negative forecasts (regions predicted to be irrelevant) that subtract from the selection score; the bounded prior applies symmetrically to suppression and amplification.

Composition With Adjacent Mechanisms

Forecasting-modulated discovery traversal composes with the forecasting engine, the discovery-traversal confidence mechanism, the canonical-fields layer, and the lineage subsystem. The forecasting engine supplies the forecast envelope; without forecasts, lambda_eff is structurally zero and the frontier behaves as the unforecast baseline. The discovery-traversal confidence mechanism supplies the per-step c_i values consumed by the divergence statistic; without per-step confidence, the rebalancing rule cannot be evaluated.

The canonical-fields layer carries the forecast envelope, the divergence statistic, and the running lambda_eff as structured fields in the cognitive frame. The composition ensures that any auditor can replay the traversal from canonical inputs and recover the same selection sequence. The lineage subsystem records every selection event, every divergence evaluation, and every lambda_eff update, providing complete provenance.

The mechanism composes with the gating subsystem indirectly: gating policies may key on whether a result was forecast-followed or unforecast-discovered, exposed as a structured field on each result. This permits policies that admit forecast-discovered results only when the forecast was well-calibrated for the traversal, as measured by the divergence statistic at the time of admission.

Prior-Art Distinction

Conventional priority-queue traversal in graph search uses a fixed heuristic to order frontier expansion; the heuristic is not modulated by an external forecasting engine and has no bounded prior to prevent the heuristic from dominating the traversal. A* and its variants admit admissible heuristics but provide no structural mechanism for bounding heuristic influence over long paths or for rebalancing when in-path evidence contradicts the heuristic. The mechanism disclosed here differs in that the bounded prior, the decay term, and the confidence-driven rebalancing are explicit parameters of the policy reference.

Reinforcement-learning-driven exploration policies adapt selection over time but typically require multiple episodes of training and do not produce a single traversal that is auditable through canonical lineage. Bandit-style frontier expansion balances exploration and exploitation but does not admit an external forecaster as a first-class input bounded by a prior. The mechanism disclosed here is structurally distinct in that the forecast is consumed as an envelope rather than learned implicitly, and the bounded prior is enforced at policy load rather than emerging from training dynamics.

Predictive prefetching systems in retrieval and caching predict future accesses and warm caches accordingly, but they do not shape the traversal itself, and they provide no mechanism for preserving traversal completeness under forecaster miscalibration. The mechanism disclosed here differs in that the forecast directly shapes the selection function and the bounded prior structurally guarantees that miscalibrated forecasts cannot lock the traversal into a single hypothesis.

Disclosure Scope

This disclosure covers the structural mechanism by which forecast outputs shape the discovery traversal frontier, the bounded prior that limits cumulative forecast influence, the confidence-driven rebalancing rule, and the parameterization through a versioned policy reference. The disclosure includes the canonical convex-blend-with-exponential-decay embodiment and the enumerated alternative embodiments. The disclosure extends to compositions with the forecasting engine, the discovery-traversal confidence mechanism, the canonical-fields layer, and the lineage subsystem.

The disclosure does not cover the specific forecasting model used to produce forecast scores, the underlying retrieval substrate, or the user-facing presentation of forecast-influenced results. Implementations using any forecasting model that emits scores in [0, 1] and any retrieval substrate that supports priority-driven frontier expansion are within scope. The disclosure is forecaster-independent so that the structural guarantees survive evolution of the forecasting model.

The disclosure further covers the structural completeness guarantee: under the bounded prior, no forecast (however confidently asserted) can permanently exclude a region of the index from eventual exploration if the active query and the in-traversal evidence warrant it. Embodiments in which the bounded prior is implemented through exponential decay, polynomial decay, hard step-count caps, or hybrid schedules are within scope, as are embodiments in which the rebalancing rule is triggered by alternative divergence statistics such as Kullback-Leibler divergence between the forecast distribution and the empirical confidence distribution. The choice of decay schedule and divergence statistic does not affect the structural completeness guarantee, provided the prior remains bounded and the rebalancing remains monotone within a traversal.

Equivalents are intended to be covered. Any selection function that combines an intrinsic priority with a forecast-derived priority under a bounded prior is structurally equivalent for the purposes of this disclosure. Any rebalancing rule that suppresses forecast influence in response to evidence of forecaster miscalibration is within scope, regardless of the specific statistic used to detect miscalibration. The mechanism is defined by the bounded-prior property and the rebalancing-monotonicity property rather than by any particular blending formula or decay function.