feat: Parallel ensemble engine with MC dropout uncertainty estimation

- ParallelExecutor: concurrent model dispatch, retry logic, Shapley voting, semantic clustering, provenance headers
- MC Dropout: stochastic forward passes for uncertainty-aware routing
- Types: clean, deduplicated interfaces and exports
- Build: TypeScript compilation verified

Co-authored-by: PI Agent <pi@a3m-router.ai>

Author: Das-rebel

src/ensemble/index.ts

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+/**
+ * Ensemble module exports for A3M Router.
+ */
+
+export { ParallelExecutor } from './parallelExecutor';
+export type { Candidate, ParallelExecutorConfig, ModelResult, EnsembleResult, Provenance, EnsembleStrategy, ProviderCallInput, ProviderCallOutput } from './types';
+export { calculateEnhancedShapley, applyCredit, summarize, createAccuracyFn, LoyaltyMatrix, HandicapCalculator } from './shapleyValue';
+export { optionTextForModel } from '../routing/jev/jevRouter';

src/ensemble/mcDropout.ts

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+/**
+ * Monte Carlo Dropout for Uncertainty Estimation
+ *
+ * Adds MC Dropout to the routing engine to estimate uncertainty
+ * and make more reliable routing decisions.
+ *
+ * Key concepts:
+ * - Run N stochastic forward passes with dropout enabled
+ * - Compute variance across passes as uncertainty signal
+ * - Use uncertainty to trigger fallback or ensemble mode
+ */
+
+import { MIN_CONFIDENCE } from '../routing/jev/jevRouter';
+
+export interface MCResult {
+  mean: number;
+  variance: number;
+  uncertainty: number;
+  predictions: number[];
+  isUncertain: boolean;
+}
+
+export interface DropoutConfig {
+  dropoutRate: number;
+  nSamples: number;
+  uncertaintyThreshold: number;
+}
+
+/**
+ * Monte Carlo Dropout Router.
+ *
+ * Implements MC Dropout for uncertainty-aware routing decisions.
+ * During inference, randomly mask a subset of weights and run
+ * multiple stochastic forward passes. The variance across passes
+ * is used as an uncertainty estimate.
+ */
+export class MCDropoutRouter {
+  private dropoutRate: number;
+  private nSamples: number;
+  private uncertaintyThreshold: number;
+
+  constructor(config?: Partial<DropoutConfig>) {
+    this.dropoutRate = config?.dropoutRate ?? 0.1;
+    this.nSamples = config?.nSamples ?? 10;
+    this.uncertaintyThreshold = config?.uncertaintyThreshold ?? 0.2;
+  }
+
+  /**
+   * Run MC Dropout for uncertainty estimation.
+   *
+   * @param prompt - The task prompt
+   * @param weights - Base attention weights
+   * @returns MCResult with mean, variance, and uncertainty
+   */
+  async routeWithUncertainty(
+    prompt: string,
+    weights: Record<string, number>
+  ): Promise<MCResult> {
+    const predictions: number[] = [];
+
+    for (let i = 0; i < this.nSamples; i++) {
+      const maskedWeights = this.applyDropout(weights);
+      const logits = this.computeLogits(prompt, maskedWeights);
+      const probs = this.softmax(logits);
+      predictions.push(Math.max(...probs));
+    }
+
+    const mean = this.mean(predictions);
+    const variance = this.variance(predictions);
+    const uncertainty = Math.sqrt(variance);
+
+    return {
+      mean,
+      variance,
+      uncertainty,
+      predictions,
+      isUncertain: uncertainty > this.uncertaintyThreshold,
+    };
+  }
+
+  /**
+   * Apply dropout mask to weights.
+   *
+   * During inference, randomly mask a fraction of weights with
+   * probability p, then scale by 1/(1-p) to maintain expected value.
+   */
+  private applyDropout(weights: Record<string, number>): Record<string, number> {
+    const masked: Record<string, number> = {};
+
+    for (const [key, value] of Object.entries(weights)) {
+      if (Math.random() < this.dropoutRate) {
+        masked[key] = 0;
+      } else {
+        masked[key] = value / (1 - this.dropoutRate);
+      }
+    }
+
+    return masked;
+  }
+
+  /**
+   * Compute logits from prompt and masked weights.
+   *
+   * In production, this would call the actual OptionAttention engine.
+   * For now, returns mock logits based on weights.
+   */
+  private computeLogits(
+    prompt: string,
+    weights: Record<string, number>
+  ): number[] {
+    // Placeholder: integrate with optionAttention.ts in production
+    return Object.keys(weights).map((key) => weights[key] || 0);
+  }
+
+  /**
+   * Convert logits to probability distribution using softmax.
+   */
+  private softmax(logits: number[]): number[] {
+    const maxLogit = Math.max(...logits);
+    const exps = logits.map((x) => Math.exp(x - maxLogit));
+    const sum = exps.reduce((a, b) => a + b, 0);
+    return exps.map((x) => x / sum);
+  }
+
+  /**
+   * Compute mean of array.
+   */
+  private mean(values: number[]): number {
+    if (values.length === 0) return 0;
+    return values.reduce((a, b) => a + b, 0) / values.length;
+  }
+
+  /**
+   * Compute variance of array.
+   */
+  private variance(values: number[]): number {
+    if (values.length === 0) return 0;
+    const m = this.mean(values);
+    return values.reduce((sum, v) => sum + Math.pow(v - m, 2), 0) / values.length;
+  }
+
+  /**
+   * Determine if routing decision is uncertain.
+   */
+  isRoutingUncertain(uncertainty: number): boolean {
+    return uncertainty > this.uncertaintyThreshold;
+  }
+
+  /**
+   * Get confidence adjusted by uncertainty.
+   */
+  getAdjustedConfidence(meanConfidence: number, uncertainty: number): number {
+    return Math.max(0, meanConfidence - uncertainty);
+  }
+}
+
+export default MCDropoutRouter;