diff --git a/src/engine/ReluConstraint.cpp b/src/engine/ReluConstraint.cpp
index e4b52b1d2..1f7b71f71 100644
--- a/src/engine/ReluConstraint.cpp
+++ b/src/engine/ReluConstraint.cpp
@@ -40,8 +40,6 @@ ReluConstraint::ReluConstraint( unsigned b, unsigned f )
     , _direction( PHASE_NOT_FIXED )
     , _haveEliminatedVariables( false )
     , _tighteningRow( NULL )
-    , _previousBias( 0.0 )
-    , _hasPreviousBias( false )
 {
 }
 
@@ -1042,18 +1040,7 @@ double ReluConstraint::computeBaBsr() const
     if ( !_networkLevelReasoner )
         throw MarabouError( MarabouError::NETWORK_LEVEL_REASONER_NOT_AVAILABLE );
 
-    double biasTerm;
-
-    if ( _hasPreviousBias )
-    {
-        biasTerm = _previousBias;
-    }
-    else
-    {
-        biasTerm = _networkLevelReasoner->getPreviousBias( this );
-        _hasPreviousBias = true;
-        _previousBias = biasTerm;
-    }
+    double biasTerm = _networkLevelReasoner->getPreviousBias( this );
 
     // get upper and lower bounds
     double ub = getUpperBound( _b );
diff --git a/src/engine/ReluConstraint.h b/src/engine/ReluConstraint.h
index 719448a45..5821813e3 100644
--- a/src/engine/ReluConstraint.h
+++ b/src/engine/ReluConstraint.h
@@ -305,12 +305,6 @@ class ReluConstraint : public PiecewiseLinearConstraint
      Assign a variable as an aux variable by the tableau, related to some existing aux variable.
     */
     void addTableauAuxVar( unsigned tableauAuxVar, unsigned constraintAuxVar ) override;
-
-    /*
-      cached biases for the source layers for ReLU neurons.
-    */
-    mutable double _previousBias;
-    mutable bool _hasPreviousBias;
 };
 
 #endif // __ReluConstraint_h__
diff --git a/src/nlr/NetworkLevelReasoner.cpp b/src/nlr/NetworkLevelReasoner.cpp
index 477c30e3d..8390a0190 100644
--- a/src/nlr/NetworkLevelReasoner.cpp
+++ b/src/nlr/NetworkLevelReasoner.cpp
@@ -596,69 +596,78 @@ void NetworkLevelReasoner::generateLinearExpressionForWeightedSumLayer(
 }
 
 /*
-    Get ReLU bias given a ReLU constraint
-    Used for BaBSR Heuristic
+    Initialize and fill ReLU Constraint to previous bias map
+    for BaBSR Heuristic
 */
-double NetworkLevelReasoner::getPreviousBias( const ReluConstraint *reluConstraint ) const
+void NetworkLevelReasoner::initializePreviousBiasMap()
 {
-    // Find the index of the reluConstraint in the _constraintsInTopologicalOrder list
-    unsigned constraintIndex = 0;
-    bool foundConstraint = false;
-
-    for ( const auto &constraint : _constraintsInTopologicalOrder )
-    {
-        if ( constraint == reluConstraint )
-        {
-            foundConstraint = true;
-            break;
-        }
-        ++constraintIndex;
-    }
+    // Clear the previous bias map
+    _previousBiases.clear();
 
-    // If the constraint was not found, throw an error
-    if ( !foundConstraint )
-    {
-        throw NLRError( NLRError::RELU_NOT_FOUND,
-                        "ReluConstraint not found in topological order." );
-    }
-
-    // Traverse through layers and find the corresponding ReLU layer
-    const NLR::Layer *reluLayer = nullptr;
+    // Track accumulated ReLU neurons across layers
     unsigned accumulatedNeurons = 0;
 
+    // Iterate through layers to find ReLU layers and their sources
     for ( const auto &layerPair : _layerIndexToLayer )
     {
         const NLR::Layer *layer = layerPair.second;
 
-        // Only process ReLU layers
         if ( layer->getLayerType() == Layer::RELU )
         {
-            unsigned layerSize = layer->getSize(); // Cache size of the current layer
+            // Get source layer info
+            const auto &sourceLayers = layer->getSourceLayers();
+            unsigned sourceLayerIndex = sourceLayers.begin()->first;
+            const NLR::Layer *sourceLayer = getLayer( sourceLayerIndex );
+
+            // Match ReLU constraints to their source layer biases
+            unsigned layerSize = layer->getSize();
+
+            // Iterate through constraints
+            auto constraintIterator = _constraintsInTopologicalOrder.begin();
+            for ( unsigned currentIndex = 0;
+                  currentIndex < accumulatedNeurons &&
+                  constraintIterator != _constraintsInTopologicalOrder.end();
+                  ++currentIndex, ++constraintIterator )
+            {
+            }
 
-            // Check if the constraint belongs to this layer (based on neuron counts)
-            if ( constraintIndex < accumulatedNeurons + layerSize )
+            // Now at correct starting position
+            for ( unsigned i = 0;
+                  i < layerSize && constraintIterator != _constraintsInTopologicalOrder.end();
+                  ++i, ++constraintIterator )
             {
-                // This is the ReLU layer corresponding to the constraint
-                reluLayer = layer;
-                break;
+                if ( auto reluConstraint =
+                         dynamic_cast<const ReluConstraint *>( *constraintIterator ) )
+                {
+                    // Store bias in map
+                    _previousBiases[reluConstraint] = sourceLayer->getBias( i );
+                }
             }
-            accumulatedNeurons += layerSize; // Update only if it's a ReLU layer
+
+            accumulatedNeurons += layerSize;
         }
     }
+}
 
-    // If we couldn't find the ReLU layer, throw an error
-    if ( !reluLayer )
-        throw NLRError( NLRError::LAYER_TYPE_NOT_SUPPORTED, "Expected a ReLU layer." );
-
-    // Find the source layer feeding into this ReLU layer
-    const auto &sourceLayers = reluLayer->getSourceLayers();
-    unsigned sourceLayerIndex = sourceLayers.begin()->first; // Cache source layer index
+/*
+    Get previous layer bias of a ReLU neuron
+    for BaBSR Heuristic
+*/
+double NetworkLevelReasoner::getPreviousBias( const ReluConstraint *reluConstraint ) const
+{
+    // Initialize map if empty
+    if ( _previousBiases.empty() )
+    {
+        const_cast<NetworkLevelReasoner *>( this )->initializePreviousBiasMap();
+    }
 
-    const NLR::Layer *sourceLayer = getLayer( sourceLayerIndex );
+    // Look up pre-computed bias
+    if ( !_previousBiases.exists( reluConstraint ) )
+    {
+        throw NLRError( NLRError::RELU_NOT_FOUND, "ReluConstraint not found in bias map." );
+    }
 
-    // Retrieve the bias for the corresponding neuron in the source layer
-    unsigned reluNeuron = constraintIndex - accumulatedNeurons;
-    return sourceLayer->getBias( reluNeuron );
+    return _previousBiases[reluConstraint];
 }
 
 unsigned
diff --git a/src/nlr/NetworkLevelReasoner.h b/src/nlr/NetworkLevelReasoner.h
index cc5d005d9..2660795be 100644
--- a/src/nlr/NetworkLevelReasoner.h
+++ b/src/nlr/NetworkLevelReasoner.h
@@ -172,6 +172,10 @@ class NetworkLevelReasoner : public LayerOwner
     */
     void generateQuery( Query &query );
 
+    /*
+      Given a ReLU Constraint, get the previous layer bias
+      for the BaBSR Heuristic
+    */
     double getPreviousBias( const ReluConstraint *reluConstraint ) const;
 
     /*
@@ -238,6 +242,13 @@ class NetworkLevelReasoner : public LayerOwner
                              unsigned outputDimension );
     void reduceLayerIndex( unsigned layer, unsigned startIndex );
 
+    /*
+      Store previous biases for each ReLU neuron in a map for getPreviousBias()
+      and BaBSR heuristic
+    */
+    Map<const ReluConstraint *, double> _previousBiases;
+    void initializePreviousBiasMap();
+
     /*
       If the NLR is manipulated manually in order to generate a new
       input query, this method can be used to assign variable indices