diff --git a/.jules/bolt.md b/.jules/bolt.md
new file mode 100644
index 0000000..e7a488f
--- /dev/null
+++ b/.jules/bolt.md
@@ -0,0 +1,7 @@
+## 2026-01-31 - [O(1) Pattern Matching vs O(N) Wildcards]
+**Learning:** Using a general `Any()` wildcard pattern in a rewrite pass that only targets specific operations is a performance anti-pattern. It forces the optimizer to call the rewriter for EVERY node in the graph, resulting in O(N) complexity. By registering specific `Op()` patterns, the optimizer can use its internal indexing for O(1) matching.
+**Action:** Always prefer specific `Op()` patterns over `Any()` wildcards when the set of target operations is known. Refactor `PatternRewritePass` to support multiple specific patterns if a pass targets multiple op types.
+
+## 2026-01-31 - [Const Node Shape Discovery]
+**Learning:** TensorFlow `Const` nodes often store their shape information within the `tensor` proto of the `value` attribute, rather than a top-level `shape` attribute or `_output_shapes`. Standard shape extraction utilities must explicitly check the `tensor_shape` field within the `value` attribute's tensor to avoid losing shape information for constants.
+**Action:** Ensure `get_node_shape` utilities handle the `Const` value tensor as a fallback for shape discovery.
diff --git a/core.py b/core.py
index 25d8232..ebabb00 100644
--- a/core.py
+++ b/core.py
@@ -321,6 +321,12 @@ def get_node_shape(self, node_or_name):
         if "shape" in node.attr:
             return [dim.size for dim in node.attr["shape"].shape.dim]
 
+        # Handle Const nodes by reading shape from the value attribute's tensor
+        if node.op == "Const" and "value" in node.attr:
+            tensor = node.attr["value"].tensor
+            if tensor.HasField("tensor_shape"):
+                return [dim.size for dim in tensor.tensor_shape.dim]
+
         return None
 
     def get_node_rank(self, node_or_name):
@@ -1516,12 +1522,35 @@ class PatternRewritePass(BasePass):
     for the actual pattern matching. Iterates until convergence (no more matches).
     """
 
-    def __init__(self, pattern, rewriter, name=None, optimizer_alias=None):
+    def __init__(self, patterns=None, rewriter=None, name=None, optimizer_alias=None, **kwargs):
+        """
+        Initialize a pattern rewrite pass.
+
+        Args:
+            patterns: A single Pattern or a list of Patterns to match. (Also accepts 'pattern' for compat)
+            rewriter: The rewriter function to execute on matches.
+            name: Human-readable pass name.
+            optimizer_alias: Short alias for node naming.
+        """
+        # Support legacy 'pattern' keyword argument
+        if patterns is None:
+            patterns = kwargs.pop("pattern", None)
+
+        if patterns is None:
+            raise ValueError("At least one pattern must be provided to PatternRewritePass")
+
         # Use iterative mode - run until convergence
         super().__init__(name, optimizer_alias, iterative=True, max_iterations=100)
-        self.pattern = pattern
+        if not isinstance(patterns, list):
+            patterns = [patterns]
+        self.patterns = patterns
         self.rewriter = trace_transformation(rewriter)
 
+    @property
+    def pattern(self):
+        """Property for backward compatibility (returns the first pattern)."""
+        return self.patterns[0] if self.patterns else None
+
     def transform_once(
         self,
         optimizer: GraphOptimizer,
@@ -1534,9 +1563,10 @@ def transform_once(
         Returns:
             int: Number of changes made
         """
-        # Register the pattern (clear first to avoid duplicates)
+        # Register all patterns (clear first to avoid duplicates)
         optimizer.clear_transformations()
-        optimizer.add_transformation(self.pattern, self.rewriter)
+        for pattern in self.patterns:
+            optimizer.add_transformation(pattern, self.rewriter)
 
         # Run one pattern matching iteration
         new_graph_def, changes = optimizer.match_patterns_once(
diff --git a/transforms/scalar/algebraic_simplify.py b/transforms/scalar/algebraic_simplify.py
index 76c94af..518ffd7 100644
--- a/transforms/scalar/algebraic_simplify.py
+++ b/transforms/scalar/algebraic_simplify.py
@@ -87,9 +87,15 @@ class AlgebraicSimplifyPass(PatternRewritePass):
     """
 
     def __init__(self):
-        # We'll handle multiple patterns manually in _rewrite
-        pattern = Any(alias="op")  # fallback, we check inside
-        super().__init__(pattern, self._rewrite, name="AlgebraicSimplify")
+        # Specific operations supported by this pass
+        supported_ops = [
+            "Add", "Sub", "Mul", "Div", "Neg", "LogicalNot", "Abs", "Square", "Sqrt", "Pow",
+            "Equal", "NotEqual", "Less", "Greater", "LessEqual", "GreaterEqual",
+            "LogicalAnd", "LogicalOr", "Select", "Identity"
+        ]
+        # Register specific Op patterns instead of a catch-all Any() for O(1) matching
+        patterns = [Op(op, alias="op") for op in supported_ops]
+        super().__init__(patterns, self._rewrite, name="AlgebraicSimplify")
 
     def _rewrite(self, match, optimizer):
         node = match.matched_nodes["op"]
@@ -127,20 +133,9 @@ def _is_const(node_name, value):
             # Check if all elements are equal to the target value
             return np.all(np.equal(val, value))
 
-        # Helper to get shape of a node
+        # Use improved core.py utility for shape discovery
         def _get_shape(node_name):
-            node = _get_node(node_name)
-            if node is None:
-                return None
-            # Check for shape attribute (Placeholder, etc.)
-            if "shape" in node.attr:
-                return [d.size for d in node.attr["shape"].shape.dim]
-            # Check for Const value shape
-            if node.op == "Const" and "value" in node.attr:
-                tensor = node.attr["value"].tensor
-                if tensor.HasField("tensor_shape"):
-                    return [d.size for d in tensor.tensor_shape.dim]
-            return None
+            return optimizer.get_node_shape(node_name)
 
         # Helper to check if a node is definitely scalar
         def _is_scalar(node_name):
diff --git a/transforms/scalar/constant_fold.py b/transforms/scalar/constant_fold.py
index 187cff4..513f145 100644
--- a/transforms/scalar/constant_fold.py
+++ b/transforms/scalar/constant_fold.py
@@ -58,9 +58,18 @@ class ConstantFoldPass(PatternRewritePass):
     """
 
     def __init__(self):
-        # Matches any operation with all inputs as Const
-        pattern = Any(alias="op")
-        super().__init__(pattern, self._rewrite_constant_op, name="ConstantFold")
+        # Specific operations supported for constant folding
+        foldable_ops = [
+            "Add", "Mul", "Sub", "Div", "Neg", "Equal", "NotEqual", "Less", "Greater",
+            "LessEqual", "GreaterEqual", "LogicalAnd", "LogicalOr", "LogicalNot",
+            "BitwiseAnd", "BitwiseOr", "BitwiseXor", "Abs", "Exp", "Expm1", "Log",
+            "Log1p", "Sqrt", "Pow", "Rsqrt", "Square", "Sin", "Cos", "Tan", "Asin",
+            "Acos", "Atan", "Atan2", "Floor", "Ceil", "Round", "Sign",
+            "Reshape", "Transpose", "ConcatV2", "Select", "Cast"
+        ]
+        # Register specific Op patterns instead of a catch-all Any() for O(1) matching
+        patterns = [Op(op, alias="op") for op in foldable_ops]
+        super().__init__(patterns, self._rewrite_constant_op, name="ConstantFold")
 
     def _is_all_const(self, inputs, optimizer):
         """Check if all inputs are Const nodes.