diff --git a/web/src/engine/predictive-text/worker-thread/src/main/correction/distance-modeler.ts b/web/src/engine/predictive-text/worker-thread/src/main/correction/distance-modeler.ts
index 8eef460ee0f..a848e58098b 100644
--- a/web/src/engine/predictive-text/worker-thread/src/main/correction/distance-modeler.ts
+++ b/web/src/engine/predictive-text/worker-thread/src/main/correction/distance-modeler.ts
@@ -661,12 +661,16 @@ export async function *getBestMatches<
 
       let lowestCostSource = spaceQueue.dequeue();
       const newResult = lowestCostSource.handleNextNode();
-      spaceQueue.enqueue(lowestCostSource);
-      spaceQueue = new PriorityQueue(CORRECTION_QUEUE_COMPARATOR, spaceQueue.toArray());
 
       if(newResult.type == 'none') {
+        // Do not re-add the source if its searchspace is exhausted.
         return null;
-      } else if(newResult.type == 'complete') {
+      } else {
+        spaceQueue.enqueue(lowestCostSource);
+        spaceQueue = new PriorityQueue(CORRECTION_QUEUE_COMPARATOR, spaceQueue.toArray());
+      }
+
+      if(newResult.type == 'complete') {
         const mapping = newResult.mapping;
         return filter(mapping) ? mapping : null;
       }
@@ -683,7 +687,7 @@ export async function *getBestMatches<
     if(timer.timeSinceLastDefer > STANDARD_TIME_BETWEEN_DEFERS) {
       await timer.defer();
     }
-  } while(!timer.elapsed && spaceQueue.peek().currentCost < Number.POSITIVE_INFINITY);
+  } while(!timer.elapsed && spaceQueue.count > 0 && spaceQueue.peek().currentCost < Number.POSITIVE_INFINITY);
 
   return null;
 }
\ No newline at end of file
diff --git a/web/src/engine/predictive-text/worker-thread/src/main/predict-helpers.ts b/web/src/engine/predictive-text/worker-thread/src/main/predict-helpers.ts
index 171c3d675ff..8fcb2fa49ea 100644
--- a/web/src/engine/predictive-text/worker-thread/src/main/predict-helpers.ts
+++ b/web/src/engine/predictive-text/worker-thread/src/main/predict-helpers.ts
@@ -12,9 +12,9 @@ import { ContextState, determineContextSlideTransform } from './correction/conte
 import { ContextTransition } from './correction/context-transition.js';
 import { ExecutionTimer } from './correction/execution-timer.js';
 import ModelCompositor from './model-compositor.js';
-import { EDIT_DISTANCE_COST_SCALE, getBestTokenMatches } from './correction/distance-modeler.js';
+import { EDIT_DISTANCE_COST_SCALE, getBestMatches } from './correction/distance-modeler.js';
 import { TokenizationCorrector } from './correction/tokenization-corrector.js';
-import { TokenizationResultMapping } from './correction/tokenization-result-mapping.js';
+import { TokenizationResult, TokenizationResultMapping } from './correction/tokenization-result-mapping.js';
 
 const searchForProperty = defaultWordbreaker.searchForProperty;
 
@@ -671,30 +671,34 @@ export async function correctAndEnumerate(
   const tokenizations = transition.final.tokenizations;
   const searchModules = tokenizations.map(t => t.tail.searchModule);
 
+  const preppedTokenizationSearch = prepareTokenizationSearch(transition, tokenizations);
+
   // Only run the correction search when corrections are enabled.
   let rawPredictions: IntermediateTokenizedPrediction[] = [];
   let bestCorrectionCost: number;
-  for await(const match of getBestTokenMatches(searchModules, timer)) {
-    // Corrections obtained:  now to predict from them!
-    const tokenization = tokenizations.find(t => t.spaceId == match.spaceId);
-
-    // If our 'match' results in fully deleting the new token, reject it and try again.
-    if(match.matchSequence.length == 0 && match.inputSequence.length != 0) {
+  for await(const match of getBestMatches<TokenizationResult, TokenizationResultMapping, TokenizationCorrector>(preppedTokenizationSearch, timer)) {
+    const { totalEditCount, totalEditableCodepoints } = match.matchedResult;
+    // If our 'match' fully replaces the tokens, reject it and try again.
+    //
+    // If the known edit count matches the total length of editable text, reject
+    // the suggestion source. Q: for any token, or across ALL tokens? If "for
+    // any", we need to distinguish between penalization where corrections
+    // couldn't be found and where there's actual edit cost.  (2.5 edits does
+    // stand out a bit, but we should do something more robust.)
+    if(totalEditCount != 0 && totalEditableCodepoints == totalEditCount) { // TODO:  double-check approach!
       continue;
     }
 
-    // If our 'match' fully replaces the token, reject it and try again.
-    if(match.matchSequence.length != 0 && match.matchSequence.length == match.knownCost) {
+    // Perhaps make a return object, add a cumulative 'editCount' property?
+    // Or, we could just sum it up here.
+    if(totalEditCount > 0 && !searchModules.find(s => s.correctionsEnabled)) {
       continue;
     }
 
-    if(match.editCount > 0 && !searchModules.find(s => s.correctionsEnabled)) {
-      continue;
-    }
+    // Worth considering:  extend Traversal to allow direct prediction lookups?
+    // let traversal = match.finalTraversal;
 
-    const suggestionRange = determineSuggestionRange(transition.base.displayTokenization, tokenization)
-    const corrector = new TokenizationCorrector(tokenization, suggestionRange.tokensToPredict.length, () => true);
-    const predictions = buildAndMapPredictions(transition, new TokenizationResultMapping([match], corrector));
+    const predictions = buildAndMapPredictions(transition, match);
 
     // Only set 'best correction' cost when a correction ACTUALLY YIELDS predictions.
     if(predictions.length > 0 && bestCorrectionCost === undefined) {