diff --git a/kmath-core/src/commonMain/kotlin/scientifik/kmath/misc/AutoDiff.kt b/kmath-core/src/commonMain/kotlin/scientifik/kmath/misc/AutoDiff.kt
new file mode 100644
index 000000000..91256d114
--- /dev/null
+++ b/kmath-core/src/commonMain/kotlin/scientifik/kmath/misc/AutoDiff.kt
@@ -0,0 +1,233 @@
+package scientifik.kmath.misc
+
+import scientifik.kmath.linear.Point
+import scientifik.kmath.operations.ExtendedField
+import scientifik.kmath.operations.Field
+import scientifik.kmath.operations.sum
+import scientifik.kmath.structures.asBuffer
+
+/*
+ * Implementation of backward-mode automatic differentiation.
+ * Initial gist by Roman Elizarov: https://gist.github.com/elizarov/1ad3a8583e88cb6ea7a0ad09bb591d3d
+ */
+
+/**
+ * Differentiable variable with value and derivative of differentiation ([deriv]) result
+ * with respect to this variable.
+ */
+open class Variable<T : Any>(val value: T)
+
+class DerivationResult<T : Any>(
+    value: T,
+    val deriv: Map<Variable<T>, T>,
+    val context: ExtendedField<T>
+) : Variable<T>(value) {
+    fun deriv(variable: Variable<T>) = deriv[variable] ?: context.zero
+
+    /**
+     * compute divergence
+     */
+    fun div() = context.run { sum(deriv.values) }
+
+    /**
+     * Compute a gradient for variables in given order
+     */
+    fun grad(vararg variables: Variable<T>): Point<T> = if (variables.isEmpty()) {
+        error("Variable order is not provided for gradient construction")
+    } else {
+        variables.map(::deriv).asBuffer()
+    }
+}
+
+/**
+ * Runs differentiation and establishes [AutoDiffField] context inside the block of code.
+ *
+ * The partial derivatives are placed in argument `d` variable
+ *
+ * Example:
+ * ```
+ * val x = Variable(2) // define variable(s) and their values
+ * val y = deriv { sqr(x) + 5 * x + 3 } // write formulate in deriv context
+ * assertEquals(17.0, y.x) // the value of result (y)
+ * assertEquals(9.0, x.d)  // dy/dx
+ * ```
+ */
+fun <T : Any> ExtendedField<T>.deriv(body: AutoDiffField<T>.() -> Variable<T>): DerivationResult<T> =
+    AutoDiffContext<T>(this).run {
+        val result = body()
+        result.d = context.one// computing derivative w.r.t result
+        runBackwardPass()
+        DerivationResult(result.value, derivatives, this@deriv)
+    }
+
+
+abstract class AutoDiffField<T : Any> : Field<Variable<T>> {
+
+    abstract val context: ExtendedField<T>
+
+    /**
+     * Performs update of derivative after the rest of the formula in the back-pass.
+     *
+     * For example, implementation of `sin` function is:
+     *
+     * ```
+     * fun AD.sin(x: Variable): Variable = derive(Variable(sin(x.x)) { z -> // call derive with function result
+     *     x.d += z.d * cos(x.x) // update derivative using chain rule and derivative of the function
+     * }
+     * ```
+     */
+    abstract fun <R> derive(value: R, block: ExtendedField<T>.(R) -> Unit): R
+
+    /**
+     * A variable accessing inner state of derivatives.
+     * Use this function in inner builders to avoid creating additional derivative bindings
+     */
+    abstract var Variable<T>.d: T
+
+    abstract fun variable(value: T): Variable<T>
+
+    inline fun variable(block: ExtendedField<T>.() -> T) = variable(context.block())
+
+    // Overloads for Double constants
+
+    operator fun Number.plus(that: Variable<T>): Variable<T> =
+        derive(variable { this@plus.toDouble() * one + that.value }) { z ->
+            that.d += z.d
+        }
+
+    operator fun Variable<T>.plus(b: Number): Variable<T> = b.plus(this)
+
+    operator fun Number.minus(that: Variable<T>): Variable<T> =
+        derive(variable { this@minus.toDouble() * one - that.value }) { z ->
+            that.d -= z.d
+        }
+
+    operator fun Variable<T>.minus(that: Number): Variable<T> =
+        derive(variable { this@minus.value - one * that.toDouble() }) { z ->
+            this@minus.d += z.d
+        }
+}
+
+/**
+ * Automatic Differentiation context class.
+ */
+private class AutoDiffContext<T : Any>(override val context: ExtendedField<T>) : AutoDiffField<T>() {
+
+    // this stack contains pairs of blocks and values to apply them to
+    private var stack = arrayOfNulls<Any?>(8)
+    private var sp = 0
+
+    internal val derivatives = HashMap<Variable<T>, T>()
+
+
+    /**
+     * A variable coupled with its derivative. For internal use only
+     */
+    inner class VariableWithDeriv(x: T, var d: T = context.zero) : Variable<T>(x)
+
+
+    override fun variable(value: T): Variable<T> =
+        VariableWithDeriv(value)
+
+    override var Variable<T>.d: T
+        get() = (this as? VariableWithDeriv)?.d ?: derivatives[this] ?: context.zero
+        set(value) {
+            if (this is VariableWithDeriv) {
+                d = value
+            } else {
+                derivatives[this] = value
+            }
+        }
+
+    @Suppress("UNCHECKED_CAST")
+    override fun <R> derive(value: R, block: ExtendedField<T>.(R) -> Unit): R {
+        // save block to stack for backward pass
+        if (sp >= stack.size) stack = stack.copyOf(stack.size * 2)
+        stack[sp++] = block
+        stack[sp++] = value
+        return value
+    }
+
+    @Suppress("UNCHECKED_CAST")
+    fun runBackwardPass() {
+        while (sp > 0) {
+            val value = stack[--sp]
+            val block = stack[--sp] as ExtendedField<T>.(Any?) -> Unit
+            context.block(value)
+        }
+    }
+
+    // Basic math (+, -, *, /)
+
+
+    override fun add(a: Variable<T>, b: Variable<T>): Variable<T> =
+        derive(variable { a.value + b.value }) { z ->
+            a.d += z.d
+            b.d += z.d
+        }
+
+    override fun multiply(a: Variable<T>, b: Variable<T>): Variable<T> =
+        derive(variable { a.value * b.value }) { z ->
+            a.d += z.d * b.value
+            b.d += z.d * a.value
+        }
+
+    override fun divide(a: Variable<T>, b: Variable<T>): Variable<T> =
+        derive(variable { a.value / b.value }) { z ->
+            a.d += z.d / b.value
+            b.d -= z.d * a.value / (b.value * b.value)
+        }
+
+    override fun multiply(a: Variable<T>, k: Number): Variable<T> =
+        derive(variable { k.toDouble() * a.value }) { z ->
+            a.d += z.d * k.toDouble()
+        }
+
+    override val zero: Variable<T> get() = Variable(context.zero)
+    override val one: Variable<T> get() = Variable(context.one)
+}
+
+// Extensions for differentiation of various basic mathematical functions
+
+// x ^ 2
+fun <T : Any> AutoDiffField<T>.sqr(x: Variable<T>): Variable<T> = derive(variable { x.value * x.value }) { z ->
+    x.d += z.d * 2 * x.value
+}
+
+// x ^ 1/2
+fun <T : Any> AutoDiffField<T>.sqrt(x: Variable<T>): Variable<T> = derive(variable { sqrt(x.value) }) { z ->
+    x.d += z.d * 0.5 / z.value
+}
+
+// x ^ y (const)
+fun <T : Any> AutoDiffField<T>.pow(x: Variable<T>, y: Double): Variable<T> =
+    derive(variable { power(x.value, y) }) { z ->
+        x.d += z.d * y * power(x.value, y - 1)
+    }
+
+fun <T : Any> AutoDiffField<T>.pow(x: Variable<T>, y: Int): Variable<T> = pow(x, y.toDouble())
+
+// exp(x)
+fun <T : Any> AutoDiffField<T>.exp(x: Variable<T>): Variable<T> = derive(variable { exp(x.value) }) { z ->
+    x.d += z.d * z.value
+}
+
+// ln(x)
+fun <T : Any> AutoDiffField<T>.ln(x: Variable<T>): Variable<T> = derive(
+    variable { ln(x.value) }
+) { z ->
+    x.d += z.d / x.value
+}
+
+// x ^ y (any)
+fun <T : Any> AutoDiffField<T>.pow(x: Variable<T>, y: Variable<T>): Variable<T> = exp(y * ln(x))
+
+// sin(x)
+fun <T : Any> AutoDiffField<T>.sin(x: Variable<T>): Variable<T> = derive(variable { sin(x.value) }) { z ->
+    x.d += z.d * cos(x.value)
+}
+
+// cos(x)
+fun <T : Any> AutoDiffField<T>.cos(x: Variable<T>): Variable<T> = derive(variable { cos(x.value) }) { z ->
+    x.d -= z.d * sin(x.value)
+}
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/scientifik/kmath/operations/AutoDiff.kt b/kmath-core/src/commonMain/kotlin/scientifik/kmath/operations/AutoDiff.kt
deleted file mode 100644
index 9714630c1..000000000
--- a/kmath-core/src/commonMain/kotlin/scientifik/kmath/operations/AutoDiff.kt
+++ /dev/null
@@ -1,218 +0,0 @@
-package scientifik.kmath.operations
-
-import scientifik.kmath.linear.Point
-import scientifik.kmath.structures.asBuffer
-import kotlin.math.pow
-import kotlin.math.sqrt
-
-/*
- * Implementation of backward-mode automatic differentiation.
- * Initial gist by Roman Elizarov: https://gist.github.com/elizarov/1ad3a8583e88cb6ea7a0ad09bb591d3d
- */
-
-/**
- * Differentiable variable with value and derivative of differentiation ([deriv]) result
- * with respect to this variable.
- */
-open class Variable(val value: Double) {
-    constructor(x: Number) : this(x.toDouble())
-}
-
-class DerivationResult(value: Double, val deriv: Map<Variable, Double>) : Variable(value) {
-    fun deriv(variable: Variable) = deriv[variable] ?: 0.0
-
-    /**
-     * compute divergence
-     */
-    fun div() = deriv.values.sum()
-
-    /**
-     * Compute a gradient for variables in given order
-     */
-    fun grad(vararg variables: Variable): Point<Double> = if (variables.isEmpty()) {
-        error("Variable order is not provided for gradient construction")
-    } else {
-        variables.map(::deriv).toDoubleArray().asBuffer()
-    }
-}
-
-/**
- * Runs differentiation and establishes [AutoDiffField] context inside the block of code.
- *
- * The partial derivatives are placed in argument `d` variable
- *
- * Example:
- * ```
- * val x = Variable(2) // define variable(s) and their values
- * val y = deriv { sqr(x) + 5 * x + 3 } // write formulate in deriv context
- * assertEquals(17.0, y.x) // the value of result (y)
- * assertEquals(9.0, x.d)  // dy/dx
- * ```
- */
-fun deriv(body: AutoDiffField.() -> Variable): DerivationResult =
-    AutoDiffContext().run {
-        val result = body()
-        result.d = 1.0 // computing derivative w.r.t result
-        runBackwardPass()
-        DerivationResult(result.value, derivatives)
-    }
-
-
-abstract class AutoDiffField : Field<Variable> {
-    /**
-     * Performs update of derivative after the rest of the formula in the back-pass.
-     *
-     * For example, implementation of `sin` function is:
-     *
-     * ```
-     * fun AD.sin(x: Variable): Variable = derive(Variable(sin(x.x)) { z -> // call derive with function result
-     *     x.d += z.d * cos(x.x) // update derivative using chain rule and derivative of the function
-     * }
-     * ```
-     */
-    abstract fun <R> derive(value: R, block: (R) -> Unit): R
-
-    /**
-     * A variable accessing inner state of derivatives.
-     * Use this function in inner builders to avoid creating additional derivative bindings
-     */
-    abstract var Variable.d: Double
-
-    abstract fun variable(value: Double): Variable
-
-    // Overloads for Double constants
-
-    operator fun Number.plus(that: Variable): Variable = derive(variable(this.toDouble() + that.value)) { z ->
-        that.d += z.d
-    }
-
-    operator fun Variable.plus(b: Number): Variable = b.plus(this)
-
-    operator fun Number.minus(that: Variable): Variable = derive(variable(this.toDouble() - that.value)) { z ->
-        that.d -= z.d
-    }
-
-    operator fun Variable.minus(that: Number): Variable = derive(variable(this.value - that.toDouble())) { z ->
-        this.d += z.d
-    }
-}
-
-/**
- * Automatic Differentiation context class.
- */
-private class AutoDiffContext : AutoDiffField() {
-
-    // this stack contains pairs of blocks and values to apply them to
-    private var stack = arrayOfNulls<Any?>(8)
-    private var sp = 0
-
-    internal val derivatives = HashMap<Variable, Double>()
-
-
-    /**
-     * A variable coupled with its derivative. For internal use only
-     */
-    class VariableWithDeriv(x: Double, var d: Double = 0.0) : Variable(x)
-
-
-    override fun variable(value: Double): Variable = VariableWithDeriv(value)
-
-    override var Variable.d: Double
-        get() = (this as? VariableWithDeriv)?.d ?: derivatives[this] ?: 0.0
-        set(value) {
-            if (this is VariableWithDeriv) {
-                d = value
-            } else {
-                derivatives[this] = value
-            }
-        }
-
-    @Suppress("UNCHECKED_CAST")
-    override fun <R> derive(value: R, block: (R) -> Unit): R {
-        // save block to stack for backward pass
-        if (sp >= stack.size) stack = stack.copyOf(stack.size * 2)
-        stack[sp++] = block
-        stack[sp++] = value
-        return value
-    }
-
-    @Suppress("UNCHECKED_CAST")
-    fun runBackwardPass() {
-        while (sp > 0) {
-            val value = stack[--sp]
-            val block = stack[--sp] as (Any?) -> Unit
-            block(value)
-        }
-    }
-
-    // Basic math (+, -, *, /)
-
-
-    override fun add(a: Variable, b: Variable): Variable =
-        derive(variable(a.value + b.value)) { z ->
-            a.d += z.d
-            b.d += z.d
-        }
-
-    override fun multiply(a: Variable, b: Variable): Variable =
-        derive(variable(a.value * b.value)) { z ->
-            a.d += z.d * b.value
-            b.d += z.d * a.value
-        }
-
-    override fun divide(a: Variable, b: Variable): Variable =
-        derive(Variable(a.value / b.value)) { z ->
-            a.d += z.d / b.value
-            b.d -= z.d * a.value / (b.value * b.value)
-        }
-
-    override fun multiply(a: Variable, k: Number): Variable =
-        derive(variable(k.toDouble() * a.value)) { z ->
-            a.d += z.d * k.toDouble()
-        }
-
-    override val zero: Variable get() = Variable(0.0)
-    override val one: Variable get() = Variable(1.0)
-}
-
-// Extensions for differentiation of various basic mathematical functions
-
-// x ^ 2
-fun AutoDiffField.sqr(x: Variable): Variable = derive(variable(x.value * x.value)) { z ->
-    x.d += z.d * 2 * x.value
-}
-
-// x ^ 1/2
-fun AutoDiffField.sqrt(x: Variable): Variable = derive(variable(sqrt(x.value))) { z ->
-    x.d += z.d * 0.5 / z.value
-}
-
-// x ^ y (const)
-fun AutoDiffField.pow(x: Variable, y: Double): Variable = derive(variable(x.value.pow(y))) { z ->
-    x.d += z.d * y * x.value.pow(y - 1)
-}
-
-fun AutoDiffField.pow(x: Variable, y: Int): Variable = pow(x, y.toDouble())
-
-// exp(x)
-fun AutoDiffField.exp(x: Variable): Variable = derive(variable(kotlin.math.exp(x.value))) { z ->
-    x.d += z.d * z.value
-}
-
-// ln(x)
-fun AutoDiffField.ln(x: Variable): Variable = derive(Variable(kotlin.math.ln(x.value))) { z ->
-    x.d += z.d / x.value
-}
-
-// x ^ y (any)
-fun AutoDiffField.pow(x: Variable, y: Variable): Variable = exp(y * ln(x))
-
-// sin(x)
-fun AutoDiffField.sin(x: Variable): Variable = derive(variable(kotlin.math.sin(x.value))) { z ->
-    x.d += z.d * kotlin.math.cos(x.value)
-}
-
-// cos(x)
-fun AutoDiffField.cos(x: Variable): Variable = derive(variable(kotlin.math.cos(x.value))) { z ->
-    x.d -= z.d * kotlin.math.sin(x.value)
-}
\ No newline at end of file
diff --git a/kmath-core/src/commonMain/kotlin/scientifik/kmath/operations/NumberAlgebra.kt b/kmath-core/src/commonMain/kotlin/scientifik/kmath/operations/NumberAlgebra.kt
index 83fe3a4d9..a8c7f99fa 100644
--- a/kmath-core/src/commonMain/kotlin/scientifik/kmath/operations/NumberAlgebra.kt
+++ b/kmath-core/src/commonMain/kotlin/scientifik/kmath/operations/NumberAlgebra.kt
@@ -33,7 +33,7 @@ inline class Real(val value: Double) : FieldElement<Double, Real, RealField> {
  * A field for double without boxing. Does not produce appropriate field element
  */
 @Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
-object RealField : Field<Double>, ExtendedFieldOperations<Double>, Norm<Double, Double> {
+object RealField : ExtendedField<Double>, Norm<Double, Double> {
     override val zero: Double = 0.0
     override inline fun add(a: Double, b: Double) = a + b
     override inline fun multiply(a: Double, b: Double) = a * b
@@ -64,7 +64,7 @@ object RealField : Field<Double>, ExtendedFieldOperations<Double>, Norm<Double,
 }
 
 @Suppress("EXTENSION_SHADOWED_BY_MEMBER", "OVERRIDE_BY_INLINE", "NOTHING_TO_INLINE")
-object FloatField : Field<Float>, ExtendedFieldOperations<Float>, Norm<Float, Float> {
+object FloatField : ExtendedField<Float>, Norm<Float, Float> {
     override val zero: Float = 0f
     override inline fun add(a: Float, b: Float) = a + b
     override inline fun multiply(a: Float, b: Float) = a * b
diff --git a/kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/Buffers.kt b/kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/Buffers.kt
index 6aaa2da8f..a38f09c8d 100644
--- a/kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/Buffers.kt
+++ b/kmath-core/src/commonMain/kotlin/scientifik/kmath/structures/Buffers.kt
@@ -123,7 +123,7 @@ inline class ListBuffer<T>(val list: List<T>) : Buffer<T> {
     override fun iterator(): Iterator<T> = list.iterator()
 }
 
-fun <T> List<T>.asBuffer() = ListBuffer(this)
+fun <T> List<T>.asBuffer() = ListBuffer<T>(this)
 
 @Suppress("FunctionName")
 inline fun <T> ListBuffer(size: Int, init: (Int) -> T) = List(size, init).asBuffer()
diff --git a/kmath-core/src/commonTest/kotlin/scientifik/kmath/operations/AutoDiffTest.kt b/kmath-core/src/commonTest/kotlin/scientifik/kmath/misc/AutoDiffTest.kt
similarity index 95%
rename from kmath-core/src/commonTest/kotlin/scientifik/kmath/operations/AutoDiffTest.kt
rename to kmath-core/src/commonTest/kotlin/scientifik/kmath/misc/AutoDiffTest.kt
index e4fad888e..9a2597737 100644
--- a/kmath-core/src/commonTest/kotlin/scientifik/kmath/operations/AutoDiffTest.kt
+++ b/kmath-core/src/commonTest/kotlin/scientifik/kmath/misc/AutoDiffTest.kt
@@ -1,5 +1,6 @@
-package scientifik.kmath.operations
+package scientifik.kmath.misc
 
+import scientifik.kmath.operations.RealField
 import scientifik.kmath.structures.asBuffer
 import kotlin.math.PI
 import kotlin.test.Test
@@ -7,6 +8,11 @@ import kotlin.test.assertEquals
 import kotlin.test.assertTrue
 
 class AutoDiffTest {
+
+    fun Variable(int: Int) = Variable(int.toDouble())
+
+    fun deriv(body: AutoDiffField<Double>.() -> Variable<Double>) = RealField.deriv(body)
+
     @Test
     fun testPlusX2() {
         val x = Variable(3) // diff w.r.t this x at 3