diff --git a/README.md b/README.md
index 117267d..140e106 100644
--- a/README.md
+++ b/README.md
@@ -160,7 +160,12 @@ conservative token threading in the compiler (see https://github.com/JuliaGPU/cu
 | Operation | Description |
 |-----------|-------------|
 | `reduce_sum(tile, axis)` | Sum along axis |
+| `reduce_mul(tile, axis)` | Product along axis |
 | `reduce_max(tile, axis)` | Maximum along axis |
+| `reduce_min(tile, axis)` | Minimum along axis |
+| `reduce_and(tile, axis)` | Bitwise AND along axis (integer) |
+| `reduce_or(tile, axis)` | Bitwise OR along axis (integer) |
+| `reduce_xor(tile, axis)` | Bitwise XOR along axis (integer) |
 
 ### Math
 | Operation | Description |
@@ -275,6 +280,11 @@ ct.permute(tile, (3, 1, 2))
 
 This applies to `bid`, `num_blocks`, `permute`, `reshape`, dimension arguments, etc.
 
+### axis convenience
+
+| `axis(i)` | Convert 1-based axis to 0-based (helper) |
+
+
 ### `Val`-like constants
 
 CuTile.jl uses `ct.Constant{T}` to encode compile-time constant values in the type domain, similar to how `Val` works. An explicit `[]` is needed to extract the value at runtime:
diff --git a/examples/reducekernel.jl b/examples/reducekernel.jl
new file mode 100644
index 0000000..f93c094
--- /dev/null
+++ b/examples/reducekernel.jl
@@ -0,0 +1,153 @@
+using Test
+using CUDA
+using cuTile
+import cuTile as ct
+
+# Kernel factory to properly capture element type and operation
+function makeReduceKernel(::Type{T}, op::Symbol) where {T}
+    reduceFunc = if op == :reduce_min
+        ct.reduce_min
+    elseif op == :reduce_max
+        ct.reduce_max
+    elseif op == :reduce_sum
+        ct.reduce_sum
+    elseif op == :reduce_xor
+        ct.reduce_xor
+    elseif op == :reduce_or
+        ct.reduce_or
+    elseif op == :reduce_and
+        ct.reduce_and
+    end
+
+    @inline function kernel(a::ct.TileArray{T,1}, b::ct.TileArray{T,1}, tileSz::ct.Constant{Int})
+        ct.store(b, ct.bid(1), reduceFunc(ct.load(a, ct.bid(1), (tileSz[],)), Val(1)))
+        return nothing
+    end
+    return kernel
+end
+
+# Test with UInt types
+@testset for elType in [UInt16, UInt32, UInt64]
+    @testset for op in [:reduce_min, :reduce_max, :reduce_sum, :reduce_xor, :reduce_or, :reduce_and]
+        sz = 32
+        N = 2^15
+
+        # Create kernel using factory
+        reduceKernel = try
+            makeReduceKernel(elType, op)
+        catch e
+            @test_broken false
+            rethrow()
+        end
+
+        # Create data and run kernel
+        a_gpu = CUDA.rand(elType, N)
+        b_gpu = CUDA.zeros(elType, cld(N, sz))
+        try
+            CUDA.@sync ct.launch(reduceKernel, cld(length(a_gpu), sz), a_gpu, b_gpu, ct.Constant(sz))
+        catch e
+            @test_broken false
+            rethrow()
+        end
+        res = Array(b_gpu)
+
+        # CPU computation
+        a_cpu = Array(a_gpu)
+        a_reshaped = reshape(a_cpu, sz, :)
+
+        if op == :reduce_min
+            cpu_result = minimum(a_reshaped, dims=1)[:]
+        elseif op == :reduce_max
+            cpu_result = maximum(a_reshaped, dims=1)[:]
+        elseif op == :reduce_sum
+            raw_sum = sum(a_reshaped, dims=1)[:]
+            cpu_result = raw_sum .& typemax(elType)
+        elseif op == :reduce_xor
+            cpu_result = mapslices(x -> reduce(⊻, x), a_reshaped, dims=1)[:]
+        elseif op == :reduce_or
+            cpu_result = mapslices(x -> reduce(|, x), a_reshaped, dims=1)[:]
+        elseif op == :reduce_and
+            cpu_result = mapslices(x -> reduce(&, x), a_reshaped, dims=1)[:]
+        end
+
+        @test cpu_result == res
+    end
+end
+
+# Test with signed Int types
+@testset for elType in [Int16, Int32, Int64]
+    @testset for op in [:reduce_min, :reduce_max, :reduce_sum, :reduce_xor, :reduce_or, :reduce_and]
+        sz = 32
+        N = 2^15
+
+        # Create kernel using factory
+        reduceKernel = try
+            makeReduceKernel(elType, op)
+        catch e
+            @test_broken false
+            rethrow()
+        end
+
+        # Create data and run kernel - use range to get negative values too
+        a_gpu = CuArray{elType}(rand(-1000:1000, N))
+        b_gpu = CUDA.zeros(elType, cld(N, sz))
+        try
+            CUDA.@sync ct.launch(reduceKernel, cld(length(a_gpu), sz), a_gpu, b_gpu, ct.Constant(sz))
+        catch e
+            @test_broken false
+            rethrow()
+        end
+        res = Array(b_gpu)
+
+        # CPU computation
+        a_cpu = Array(a_gpu)
+        a_reshaped = reshape(a_cpu, sz, :)
+
+        if op == :reduce_min
+            cpu_result = minimum(a_reshaped, dims=1)[:]
+        elseif op == :reduce_max
+            cpu_result = maximum(a_reshaped, dims=1)[:]
+        elseif op == :reduce_sum
+            cpu_result = sum(a_reshaped, dims=1)[:]
+        elseif op == :reduce_xor
+            cpu_result = mapslices(x -> reduce(⊻, x), a_reshaped, dims=1)[:]
+        elseif op == :reduce_or
+            cpu_result = mapslices(x -> reduce(|, x), a_reshaped, dims=1)[:]
+        elseif op == :reduce_and
+            cpu_result = mapslices(x -> reduce(&, x), a_reshaped, dims=1)[:]
+        end
+
+        @test cpu_result == res
+    end
+end
+
+# Test with Float types
+@testset for elType in [Float16, Float32, Float64]
+    @testset for op in [:reduce_min, :reduce_max, :reduce_sum]
+        sz = 32
+        N = 2^15
+
+        # Create kernel using factory
+        reduceKernel = makeReduceKernel(elType, op)
+
+        # Create data and run kernel
+        a_gpu = CUDA.rand(elType, N)
+        b_gpu = CUDA.zeros(elType, cld(N, sz))
+        CUDA.@sync ct.launch(reduceKernel, cld(length(a_gpu), sz), a_gpu, b_gpu, ct.Constant(sz))
+        res = Array(b_gpu)
+
+        # CPU computation
+        a_cpu = Array(a_gpu)
+        a_reshaped = reshape(a_cpu, sz, :)
+
+        if op == :reduce_min
+            cpu_result = minimum(a_reshaped, dims=1)[:]
+        elseif op == :reduce_max
+            cpu_result = maximum(a_reshaped, dims=1)[:]
+        elseif op == :reduce_sum
+            cpu_result = sum(a_reshaped, dims=1)[:]
+        end
+
+        @test isapprox(cpu_result, res)
+    end
+end
\ No newline at end of file
diff --git a/src/bytecode/encodings.jl b/src/bytecode/encodings.jl
index 9f06415..9d20820 100644
--- a/src/bytecode/encodings.jl
+++ b/src/bytecode/encodings.jl
@@ -1291,7 +1291,7 @@ function encode_ReduceOp!(body::Function, cb::CodeBuilder,
                           result_types::Vector{TypeId},
                           operands::Vector{Value},
                           dim::Int,
-                          identities::Vector{<:ReduceIdentity},
+                          identities::Vector{<:IdentityOp},
                           body_scalar_types::Vector{TypeId})
     encode_varint!(cb.buf, Opcode.ReduceOp)
 
diff --git a/src/bytecode/writer.jl b/src/bytecode/writer.jl
index eb87585..a6f34c1 100644
--- a/src/bytecode/writer.jl
+++ b/src/bytecode/writer.jl
@@ -234,30 +234,42 @@ end
 =============================================================================#
 
 """
-    ReduceIdentity
+    IdentityOp
 
-Abstract type for reduce identity attributes.
+Abstract type for binary operation identity attributes (reduce, scan, etc.).
 """
-abstract type ReduceIdentity end
+abstract type IdentityOp end
 
 """
-    FloatIdentity(value, type_id, dtype)
+    FloatIdentityOp(value, type_id, dtype)
 
-Float identity value for reduce operations.
+Float identity value for binary operations.
 """
-struct FloatIdentity <: ReduceIdentity
+struct FloatIdentityOp <: IdentityOp
     value::Float64
     type_id::TypeId
     dtype::Type  # Float16, Float32, Float64, etc.
 end
 
 """
-    encode_tagged_float!(cb, identity::FloatIdentity)
+    IntegerIdentityOp(value, type_id, dtype, signed)
+
+Integer identity value for binary operations.
+"""
+struct IntegerIdentityOp <: IdentityOp
+    value::UInt128  # Store as UInt128 to handle all unsigned values up to 64 bits
+    type_id::TypeId
+    dtype::Type   # Int8, Int16, Int32, Int64, UInt8, etc.
+    signed::Bool  # true for signed, false for unsigned
+end
+
+"""
+    encode_tagged_float!(cb, identity::FloatIdentityOp)
 
 Encode a tagged float attribute for reduce identity.
 Format: tag(Float=0x02) + typeid + ap_int(value_bits)
 """
-function encode_tagged_float!(cb::CodeBuilder, identity::FloatIdentity)
+function encode_tagged_float!(cb::CodeBuilder, identity::FloatIdentityOp)
     # Tag for Float attribute
     push!(cb.buf, 0x02)
     # Type ID
@@ -267,6 +279,59 @@ function encode_tagged_float!(cb::CodeBuilder, identity::FloatIdentity)
     encode_signed_varint!(cb.buf, bits)
 end
 
+"""
+    encode_tagged_int!(cb, identity::IntegerIdentityOp)
+
+Encode a tagged integer identity attribute.
+Format: tag(Int=0x01) + typeid + ap_int(value)
+"""
+function encode_tagged_int!(cb::CodeBuilder, identity::IntegerIdentityOp)
+    # Tag for Int attribute
+    push!(cb.buf, 0x01)
+    # Type ID
+    encode_typeid!(cb.buf, identity.type_id)
+    # Value: signed uses zigzag varint, unsigned uses plain varint
+    # Mask value to correct bit width and apply zigzag for signed types
+    masked_value = mask_to_width(identity.value, identity.dtype, identity.signed)
+    if identity.signed
+        encode_signed_varint!(cb.buf, masked_value)
+    else
+        encode_varint!(cb.buf, masked_value)
+    end
+end
+
+"""
+    mask_to_width(value, dtype, signed)
+
+Mask a UInt128 value to the correct bit width for the given type and apply zigzag if signed.
+For signed types, this masks first, then applies zigzag encoding.
+"""
+# Signed Int64: mask to 64 bits first, then zigzag encode
+mask_to_width(value::UInt128, ::Type{Int64}, signed::Bool) = 
+    let masked = UInt64(value & 0xFFFFFFFFFFFFFFFF)
+        UInt64((masked << 1) ⊻ (masked >>> 63))
+    end
+# Signed Int32: mask to 32 bits first, then zigzag encode
+mask_to_width(value::UInt128, ::Type{Int32}, signed::Bool) = 
+    let masked = UInt32(value & 0xFFFFFFFF)
+        UInt32((masked << 1) ⊻ (masked >>> 31))
+    end
+# Signed Int16: mask to 16 bits first, then zigzag encode
+mask_to_width(value::UInt128, ::Type{Int16}, signed::Bool) = 
+    let masked = UInt16(value & 0xFFFF)
+        UInt16((masked << 1) ⊻ (masked >>> 15))
+    end
+# Signed Int8: mask to 8 bits first, then zigzag encode
+mask_to_width(value::UInt128, ::Type{Int8}, signed::Bool) = 
+    let masked = UInt8(value & 0xFF)
+        UInt8((masked << 1) ⊻ (masked >>> 7))
+    end
+# Unsigned types: just mask to bit width, no zigzag
+mask_to_width(value::UInt128, ::Type{UInt64}, signed::Bool) = UInt64(value & 0xFFFFFFFFFFFFFFFF)
+mask_to_width(value::UInt128, ::Type{UInt32}, signed::Bool) = UInt32(value & 0xFFFFFFFF)
+mask_to_width(value::UInt128, ::Type{UInt16}, signed::Bool) = UInt16(value & 0xFFFF)
+mask_to_width(value::UInt128, ::Type{UInt8}, signed::Bool) = UInt8(value & 0xFF)
+
 """
     float_to_bits(value, dtype)
 
@@ -296,6 +361,7 @@ end
 Encode a signed integer as a variable-length integer.
 Uses zigzag encoding for signed values.
 """
+
 function encode_signed_varint!(buf::Vector{UInt8}, value::Union{UInt16, UInt32, UInt64, Int64})
     # For float bits, encode as unsigned varint
     encode_varint!(buf, UInt64(value))
@@ -304,15 +370,24 @@ end
 """
     encode_identity_array!(cb, identities)
 
-Encode an array of reduce identity attributes.
+Encode an array of binary operation identity attributes.
+Dispatches on identity type to encode correctly.
 """
-function encode_identity_array!(cb::CodeBuilder, identities::Vector{<:ReduceIdentity})
+function encode_identity_array!(cb::CodeBuilder, identities::Vector{<:IdentityOp})
     encode_varint!(cb.buf, length(identities))
     for identity in identities
-        encode_tagged_float!(cb, identity)
+        encode_identity!(cb, identity)
     end
 end
 
+"""
+    encode_identity!(cb, identity)
+
+Encode a single identity attribute, dispatching on type.
+"""
+encode_identity!(cb::CodeBuilder, identity::FloatIdentityOp) = encode_tagged_float!(cb, identity)
+encode_identity!(cb::CodeBuilder, identity::IntegerIdentityOp) = encode_tagged_int!(cb, identity)
+
 """
     BytecodeWriter
 
@@ -544,7 +619,7 @@ function finalize_function!(func_buf::Vector{UInt8}, cb::CodeBuilder,
 end
 
 #=============================================================================
- Optimization Hints 
+ Optimization Hints
 =============================================================================#
 
 """
diff --git a/src/compiler/intrinsics.jl b/src/compiler/intrinsics.jl
index 16c55da..e522141 100644
--- a/src/compiler/intrinsics.jl
+++ b/src/compiler/intrinsics.jl
@@ -8,6 +8,7 @@ using Base: compilerbarrier, donotdelete
 using ..cuTile: Tile, TileArray, Constant, TensorView, PartitionView
 using ..cuTile: Signedness, SignednessSigned, SignednessUnsigned
 using ..cuTile: ComparisonPredicate, CmpLessThan, CmpLessThanOrEqual, CmpGreaterThan, CmpGreaterThanOrEqual, CmpEqual, CmpNotEqual
+using ..cuTile: IdentityOp, FloatIdentityOp, IntegerIdentityOp
 
 end
 
diff --git a/src/compiler/intrinsics/core.jl b/src/compiler/intrinsics/core.jl
index 7fb2530..53d35af 100644
--- a/src/compiler/intrinsics/core.jl
+++ b/src/compiler/intrinsics/core.jl
@@ -512,7 +512,7 @@ end
     Sum reduction along 0-indexed axis.
     Compiled to cuda_tile.reduce with ADD.
     """
-    @noinline function reduce_sum(tile::Tile{T, S}, ::Val{axis}) where {T <: AbstractFloat, S, axis}
+    @noinline function reduce_sum(tile::Tile{T, S}, ::Val{axis}) where {T, S, axis}
         reduced_shape = ntuple(i -> S[i < axis + 1 ? i : i + 1], length(S) - 1)
         Tile{T, reduced_shape}()
     end
@@ -523,7 +523,65 @@ end
     Maximum reduction along 0-indexed axis.
     Compiled to cuda_tile.reduce with MAX.
     """
-    @noinline function reduce_max(tile::Tile{T, S}, ::Val{axis}) where {T <: AbstractFloat, S, axis}
+    @noinline function reduce_max(tile::Tile{T, S}, ::Val{axis}) where {T, S, axis}
+        reduced_shape = ntuple(i -> S[i < axis + 1 ? i : i + 1], length(S) - 1)
+        Tile{T, reduced_shape}()
+    end
+
+    """
+        reduce_mul(tile, axis_val)
+
+    Product reduction along 0-indexed axis.
+    Compiled to cuda_tile.reduce with MUL.
+    """
+    @noinline function reduce_mul(tile::Tile{T, S}, ::Val{axis}) where {T, S, axis}
+        reduced_shape = ntuple(i -> S[i < axis + 1 ? i : i + 1], length(S) - 1)
+        Tile{T, reduced_shape}()
+    end
+
+    """
+        reduce_min(tile, axis_val)
+
+    Minimum reduction along 0-indexed axis.
+    Compiled to cuda_tile.reduce with MIN.
+    """
+    @noinline function reduce_min(tile::Tile{T, S}, ::Val{axis}) where {T, S, axis}
+        reduced_shape = ntuple(i -> S[i < axis + 1 ? i : i + 1], length(S) - 1)
+        Tile{T, reduced_shape}()
+    end
+
+    """
+        reduce_and(tile, axis_val)
+
+    Bitwise AND reduction along 0-indexed axis.
+    Compiled to cuda_tile.reduce with AND.
+    Integer types only.
+    """
+    @noinline function reduce_and(tile::Tile{T, S}, ::Val{axis}) where {T <: Integer, S, axis}
+        reduced_shape = ntuple(i -> S[i < axis + 1 ? i : i + 1], length(S) - 1)
+        Tile{T, reduced_shape}()
+    end
+
+    """
+        reduce_or(tile, axis_val)
+
+    Bitwise OR reduction along 0-indexed axis.
+    Compiled to cuda_tile.reduce with OR.
+    Integer types only.
+    """
+    @noinline function reduce_or(tile::Tile{T, S}, ::Val{axis}) where {T <: Integer, S, axis}
+        reduced_shape = ntuple(i -> S[i < axis + 1 ? i : i + 1], length(S) - 1)
+        Tile{T, reduced_shape}()
+    end
+
+    """
+        reduce_xor(tile, axis_val)
+
+    Bitwise XOR reduction along 0-indexed axis.
+    Compiled to cuda_tile.reduce with XOR.
+    Integer types only.
+    """
+    @noinline function reduce_xor(tile::Tile{T, S}, ::Val{axis}) where {T <: Integer, S, axis}
         reduced_shape = ntuple(i -> S[i < axis + 1 ? i : i + 1], length(S) - 1)
         Tile{T, reduced_shape}()
     end
@@ -534,6 +592,22 @@ end
 function emit_intrinsic!(ctx::CGCtx, ::typeof(Intrinsics.reduce_max), args)
     emit_reduce!(ctx, args, :max)
 end
+function emit_intrinsic!(ctx::CGCtx, ::typeof(Intrinsics.reduce_mul), args)
+    emit_reduce!(ctx, args, :mul)
+end
+function emit_intrinsic!(ctx::CGCtx, ::typeof(Intrinsics.reduce_min), args)
+    emit_reduce!(ctx, args, :min)
+end
+function emit_intrinsic!(ctx::CGCtx, ::typeof(Intrinsics.reduce_and), args)
+    emit_reduce!(ctx, args, :and)
+end
+function emit_intrinsic!(ctx::CGCtx, ::typeof(Intrinsics.reduce_or), args)
+    emit_reduce!(ctx, args, :or)
+end
+function emit_intrinsic!(ctx::CGCtx, ::typeof(Intrinsics.reduce_xor), args)
+    emit_reduce!(ctx, args, :xor)
+end
+
 function emit_reduce!(ctx::CGCtx, args, reduce_fn::Symbol)
     cb = ctx.cb
     tt = ctx.tt
@@ -558,32 +632,127 @@ function emit_reduce!(ctx::CGCtx, args, reduce_fn::Symbol)
 
     # Output tile type
     output_tile_type = tile_type!(tt, dtype, output_shape)
-
     # Scalar type for reduction body (0D tile)
     scalar_tile_type = tile_type!(tt, dtype, Int[])
 
-    # Create identity value - use simple dtype (f32), not tile type
-    identity_val = reduce_fn == :add ? -0.0 : (reduce_fn == :max ? -Inf : 0.0)
-    identity = FloatIdentity(identity_val, dtype, elem_type)
+    # Create identity value via dispatch on reduction function and element type
+    identity = operation_identity(Val(reduce_fn), dtype, elem_type)
 
     # Emit ReduceOp
     results = encode_ReduceOp!(cb, [output_tile_type], [input_tv.v], axis, [identity], [scalar_tile_type]) do block_args
         acc, elem = block_args[1], block_args[2]
 
-        if reduce_fn == :add
-            res = encode_AddFOp!(cb, scalar_tile_type, acc, elem)
-        elseif reduce_fn == :max
-            res = encode_MaxFOp!(cb, scalar_tile_type, acc, elem)
-        else
-            error("Unsupported reduction function: $reduce_fn")
-        end
-
+        res = encode_reduce_body(cb, scalar_tile_type, acc, elem, Val(reduce_fn), elem_type)
         encode_YieldOp!(cb, [res])
     end
 
     CGVal(results[1], output_tile_type, Tile{elem_type, Tuple(output_shape)}, output_shape)
 end
 
+#=============================================================================
+ Reduce Identity Values via Dispatch
+=============================================================================#
+
+"""
+    is_signed(::Type{T}) -> Bool
+
+Return true if type T is signed, false for unsigned types.
+"""
+is_signed(::Type{T}) where T <: Integer = T <: Integer && !(T <: Unsigned)
+is_signed(::Type{T}) where T <: AbstractFloat = false
+
+"""
+    operation_identity(fn, dtype, elem_type) -> IdentityOp
+
+Return the identity value for a binary operation (reduce, scan, etc.).
+Identity must satisfy: identity ⊕ x = x for the operation.
+"""
+
+"""
+    to_uint128(value, dtype)
+
+Convert an integer value to UInt128 for storage in IntegerIdentityOp.
+For signed types, this returns the two's complement bit representation.
+"""
+# Unsigned types: directly convert
+to_uint128(value::UInt64) = UInt128(value)
+to_uint128(value::UInt32) = UInt128(value)
+to_uint128(value::UInt16) = UInt128(value)
+to_uint128(value::UInt8) = UInt128(value)
+# Signed types: reinterpret as unsigned first, then convert
+to_uint128(value::Int64) = UInt128(reinterpret(UInt64, value))
+to_uint128(value::Int32) = UInt128(reinterpret(UInt32, value))
+to_uint128(value::Int16) = UInt128(reinterpret(UInt16, value))
+to_uint128(value::Int8) = UInt128(reinterpret(UInt8, value))
+
+# Addition identity: 0 + x = x
+operation_identity(::Val{:add}, dtype, ::Type{T}) where T <: AbstractFloat =
+    FloatIdentityOp(zero(T), dtype, T)
+operation_identity(::Val{:add}, dtype, ::Type{T}) where T <: Integer =
+    IntegerIdentityOp(to_uint128(zero(T)), dtype, T, is_signed(T))
+
+# Maximum identity: max(typemin(T), x) = x
+operation_identity(::Val{:max}, dtype, ::Type{T}) where T <: AbstractFloat =
+    FloatIdentityOp(typemin(T), dtype, T)
+operation_identity(::Val{:max}, dtype, ::Type{T}) where T <: Integer =
+    IntegerIdentityOp(to_uint128(typemin(T)), dtype, T, is_signed(T))
+
+# Multiplication identity: 1 * x = x
+operation_identity(::Val{:mul}, dtype, ::Type{T}) where T <: AbstractFloat =
+    FloatIdentityOp(one(T), dtype, T)
+operation_identity(::Val{:mul}, dtype, ::Type{T}) where T <: Integer =
+    IntegerIdentityOp(to_uint128(one(T)), dtype, T, is_signed(T))
+
+# Minimum identity: min(typemax(T), x) = x
+operation_identity(::Val{:min}, dtype, ::Type{T}) where T <: AbstractFloat =
+    FloatIdentityOp(typemax(T), dtype, T)
+operation_identity(::Val{:min}, dtype, ::Type{T}) where T <: Integer =
+    IntegerIdentityOp(to_uint128(typemax(T)), dtype, T, is_signed(T))
+
+# AND identity: all bits set (x & identity == x)
+# For signed: -one(T) has all bits set in two's complement
+# For unsigned: typemax(T) has all bits set
+operation_identity(::Val{:and}, dtype, ::Type{T}) where T <: Integer =
+    IntegerIdentityOp(to_uint128(is_signed(T) ? -one(T) : typemax(T)), dtype, T, is_signed(T))
+
+# OR identity: 0 | x = x
+operation_identity(::Val{:or}, dtype, ::Type{T}) where T <: Integer =
+    IntegerIdentityOp(to_uint128(zero(T)), dtype, T, is_signed(T))
+
+# XOR identity: 0 ⊕ x = x
+operation_identity(::Val{:xor}, dtype, ::Type{T}) where T <: Integer =
+    IntegerIdentityOp(to_uint128(zero(T)), dtype, T, is_signed(T))
+
+#=============================================================================
+ Reduce Body Operations - dispatch on Val{fn} and elem_type
+=============================================================================#
+
+encode_reduce_body(cb, type, acc, elem, ::Val{:add}, ::Type{T}) where T <: AbstractFloat =
+    encode_AddFOp!(cb, type, acc, elem)
+encode_reduce_body(cb, type, acc, elem, ::Val{:max}, ::Type{T}) where T <: AbstractFloat =
+    encode_MaxFOp!(cb, type, acc, elem)
+encode_reduce_body(cb, type, acc, elem, ::Val{:mul}, ::Type{T}) where T <: AbstractFloat =
+    encode_MulFOp!(cb, type, acc, elem)
+encode_reduce_body(cb, type, acc, elem, ::Val{:min}, ::Type{T}) where T <: AbstractFloat =
+    encode_MinFOp!(cb, type, acc, elem)
+encode_reduce_body(cb, type, acc, elem, ::Val{:add}, ::Type{T}) where T <: Integer =
+    encode_AddIOp!(cb, type, acc, elem)
+encode_reduce_body(cb, type, acc, elem, ::Val{:max}, ::Type{T}) where T <: Integer =
+    encode_MaxIOp!(cb, type, acc, elem; signedness=is_signed(T) ? SignednessSigned : SignednessUnsigned)
+encode_reduce_body(cb, type, acc, elem, ::Val{:mul}, ::Type{T}) where T <: Integer =
+    encode_MulIOp!(cb, type, acc, elem)
+encode_reduce_body(cb, type, acc, elem, ::Val{:min}, ::Type{T}) where T <: Integer =
+    encode_MinIOp!(cb, type, acc, elem; signedness=is_signed(T) ? SignednessSigned : SignednessUnsigned)
+
+
+# less likely commutative/associative ops can be reduced too for whatever reason.
+# eg: and, or, xor.
+encode_reduce_body(cb, type, acc, elem, ::Val{:and}, ::Type{T}) where T <: Integer =
+    encode_AndIOp!(cb, type, acc, elem)
+encode_reduce_body(cb, type, acc, elem, ::Val{:or}, ::Type{T}) where T <: Integer =
+    encode_OrIOp!(cb, type, acc, elem)
+encode_reduce_body(cb, type, acc, elem, ::Val{:xor}, ::Type{T}) where T <: Integer =
+    encode_XOrIOp!(cb, type, acc, elem)
 
 # cuda_tile.reshape
 @eval Intrinsics begin
diff --git a/src/language/operations.jl b/src/language/operations.jl
index 463a358..4e1de57 100644
--- a/src/language/operations.jl
+++ b/src/language/operations.jl
@@ -10,7 +10,7 @@
  Load/Store
 =============================================================================#
 
-public bid, num_blocks, num_tiles, load, store, gather, scatter
+public bid, num_blocks, num_tiles, axis, load, store, gather, scatter
 
 """
 Padding mode for load operations.
@@ -59,6 +59,24 @@ Axis is 1-indexed. Equivalent to cld(arr.sizes[axis], shape[axis]).
     Intrinsics.get_index_space_shape(pv, axis - One())  # convert to 0-indexed
 end
 
+"""
+    axis(i::Integer) -> Val{i-1}
+
+Return a compile-time axis selector for tile operations.
+Axis indices are 1-based (axis(1) = first dimension, axis(2) = second, etc.).
+Internally converts to 0-based for Tile IR.
+
+Use this instead of raw `Val` for self-documenting code.
+
+# Examples
+```julia
+ct.cumsum(tile, ct.axis(1))   # Scan along first axis
+ct.cumsum(tile, ct.axis(2))   # Scan along second axis
+ct.scan(tile, ct.axis(1), :add)
+```
+"""
+@inline axis(i::Integer) = Val(i - One())
+
 """
     load(arr::TileArray, index, shape; padding_mode=PaddingMode.Undetermined, latency=nothing, allow_tma=true) -> Tile
 
@@ -515,7 +533,7 @@ result = ct.astype(acc, ct.TFloat32)  # Convert to TF32 for tensor cores
  Reduction
 =============================================================================#
 
-public reduce_sum, reduce_max
+public reduce_sum, reduce_max, reduce_mul, reduce_min, reduce_and, reduce_or, reduce_xor
 
 """
     reduce_sum(tile::Tile{T, S}, axis::Integer) -> Tile{T, reduced_shape}
@@ -523,16 +541,18 @@ public reduce_sum, reduce_max
 Sum reduction along the specified axis (1-indexed).
 Returns a tile with the specified dimension removed.
 
+Supports any numeric type (Float16, Float32, Float64, and integer types).
+
 # Example
 ```julia
 # For a (128, 64) tile, reducing along axis 2:
 sums = ct.reduce_sum(tile, 2)  # Returns (128,) tile
 ```
 """
-@inline function reduce_sum(tile::Tile{T, S}, axis::Integer) where {T <: AbstractFloat, S}
+@inline function reduce_sum(tile::Tile{T, S}, axis::Integer) where {T <: Number, S}
     Intrinsics.reduce_sum(tile, Val(axis - 1))
 end
-@inline function reduce_sum(tile::Tile{T, S}, ::Val{axis}) where {T <: AbstractFloat, S, axis}
+@inline function reduce_sum(tile::Tile{T, S}, ::Val{axis}) where {T <: Number, S, axis}
     Intrinsics.reduce_sum(tile, Val(axis - 1))
 end
 
@@ -540,19 +560,113 @@ end
     reduce_max(tile::Tile{T, S}, axis::Integer) -> Tile{T, reduced_shape}
 
 Maximum reduction along the specified axis (1-indexed).
+Supports any numeric type (Float16, Float32, Float64, and integer types).
 
 # Example
 ```julia
 maxes = ct.reduce_max(tile, 2)  # Max along axis 2
 ```
 """
-@inline function reduce_max(tile::Tile{T, S}, axis::Integer) where {T <: AbstractFloat, S}
+@inline function reduce_max(tile::Tile{T, S}, axis::Integer) where {T <: Number, S}
     Intrinsics.reduce_max(tile, Val(axis - 1))
 end
-@inline function reduce_max(tile::Tile{T, S}, ::Val{axis}) where {T <: AbstractFloat, S, axis}
+@inline function reduce_max(tile::Tile{T, S}, ::Val{axis}) where {T <: Number, S, axis}
     Intrinsics.reduce_max(tile, Val(axis - 1))
 end
 
+"""
+    reduce_mul(tile::Tile{T, S}, axis::Integer) -> Tile{T, reduced_shape}
+
+Product reduction along the specified axis (1-indexed).
+Returns a tile with the specified dimension removed.
+
+# Example
+```julia
+# For a (128, 64) tile, reducing along axis 2:
+products = ct.reduce_mul(tile, 2)  # Returns (128,) tile
+```
+"""
+@inline function reduce_mul(tile::Tile{T, S}, axis::Integer) where {T <: Number, S}
+    Intrinsics.reduce_mul(tile, Val(axis - 1))
+end
+@inline function reduce_mul(tile::Tile{T, S}, ::Val{axis}) where {T <: Number, S, axis}
+    Intrinsics.reduce_mul(tile, Val(axis - 1))
+end
+
+"""
+    reduce_min(tile::Tile{T, S}, axis::Integer) -> Tile{T, reduced_shape}
+
+Minimum reduction along the specified axis (1-indexed).
+
+# Example
+```julia
+mins = ct.reduce_min(tile, 2)  # Min along axis 2
+```
+"""
+@inline function reduce_min(tile::Tile{T, S}, axis::Integer) where {T <: Number, S}
+    Intrinsics.reduce_min(tile, Val(axis - 1))
+end
+@inline function reduce_min(tile::Tile{T, S}, ::Val{axis}) where {T <: Number, S, axis}
+    Intrinsics.reduce_min(tile, Val(axis - 1))
+end
+
+"""
+    reduce_and(tile::Tile{T, S}, axis::Integer) -> Tile{T, reduced_shape}
+
+Bitwise AND reduction along the specified axis (1-indexed).
+Integer types only.
+
+# Example
+```julia
+# For an Int32 tile, reducing along axis 2:
+result = ct.reduce_and(tile, 2)  # Returns (128,) tile of Int32
+```
+"""
+@inline function reduce_and(tile::Tile{T, S}, axis::Integer) where {T <: Integer, S}
+    Intrinsics.reduce_and(tile, Val(axis - 1))
+end
+@inline function reduce_and(tile::Tile{T, S}, ::Val{axis}) where {T <: Integer, S, axis}
+    Intrinsics.reduce_and(tile, Val(axis - 1))
+end
+
+"""
+    reduce_or(tile::Tile{T, S}, axis::Integer) -> Tile{T, reduced_shape}
+
+Bitwise OR reduction along the specified axis (1-indexed).
+Integer types only.
+
+# Example
+```julia
+# For an Int32 tile, reducing along axis 2:
+result = ct.reduce_or(tile, 2)  # Returns (128,) tile of Int32
+```
+"""
+@inline function reduce_or(tile::Tile{T, S}, axis::Integer) where {T <: Integer, S}
+    Intrinsics.reduce_or(tile, Val(axis - 1))
+end
+@inline function reduce_or(tile::Tile{T, S}, ::Val{axis}) where {T <: Integer, S, axis}
+    Intrinsics.reduce_or(tile, Val(axis - 1))
+end
+
+"""
+    reduce_xor(tile::Tile{T, S}, axis::Integer) -> Tile{T, reduced_shape}
+
+Bitwise XOR reduction along the specified axis (1-indexed).
+Integer types only.
+
+# Example
+```julia
+# For an Int32 tile, reducing along axis 2:
+result = ct.reduce_xor(tile, 2)  # Returns (128,) tile of Int32
+```
+"""
+@inline function reduce_xor(tile::Tile{T, S}, axis::Integer) where {T <: Integer, S}
+    Intrinsics.reduce_xor(tile, Val(axis - 1))
+end
+@inline function reduce_xor(tile::Tile{T, S}, ::Val{axis}) where {T <: Integer, S, axis}
+    Intrinsics.reduce_xor(tile, Val(axis - 1))
+end
+
 #=============================================================================
  Matrix multiplication
 =============================================================================#
@@ -628,4 +742,3 @@ br = ct.extract(tile, (2, 2), (4, 4))  # Bottom-right (rows 5-8, cols 5-8)
     Intrinsics.extract(tile, Val(map(i -> i - 1, index)), Val(shape))
 @inline extract(tile::Tile{T}, ::Val{Index}, ::Val{Shape}) where {T, Index, Shape} =
     Intrinsics.extract(tile, Val(map(i -> i - 1, Index)), Val(Shape))
-
diff --git a/test/reduce_ops.jl b/test/reduce_ops.jl
new file mode 100644
index 0000000..4250904
--- /dev/null
+++ b/test/reduce_ops.jl
@@ -0,0 +1,154 @@
+
+using cuTile
+import cuTile as ct
+using CUDA
+using Test
+
+# Kernel factory to properly capture element type and operation
+function makeReduceKernel(::Type{T}, op::Symbol) where {T}
+    reduceFunc = if op == :reduce_min
+        ct.reduce_min
+    elseif op == :reduce_max
+        ct.reduce_max
+    elseif op == :reduce_sum
+        ct.reduce_sum
+    elseif op == :reduce_xor
+        ct.reduce_xor
+    elseif op == :reduce_or
+        ct.reduce_or
+    elseif op == :reduce_and
+        ct.reduce_and
+    end
+
+    @inline function kernel(a::ct.TileArray{T,1}, b::ct.TileArray{T,1}, tileSz::ct.Constant{Int})
+        ct.store(b, ct.bid(1), reduceFunc(ct.load(a, ct.bid(1), (tileSz[],)), Val(1)))
+        return nothing
+    end
+    return kernel
+end
+
+# Test with UInt types
+@testset for elType in [UInt16, UInt32, UInt64]
+    @testset for op in [:reduce_min, :reduce_max, :reduce_sum, :reduce_xor, :reduce_or, :reduce_and]
+        sz = 32
+        N = 2^15
+
+        # Create kernel using factory
+        reduceKernel = try
+            makeReduceKernel(elType, op)
+        catch e
+            @test_broken false
+            rethrow()
+        end
+
+        # Create data and run kernel
+        a_gpu = CUDA.rand(elType, N)
+        b_gpu = CUDA.zeros(elType, cld(N, sz))
+        try
+            CUDA.@sync ct.launch(reduceKernel, cld(length(a_gpu), sz), a_gpu, b_gpu, ct.Constant(sz))
+        catch e
+            @test_broken false
+            rethrow()
+        end
+        res = Array(b_gpu)
+
+        # CPU computation
+        a_cpu = Array(a_gpu)
+        a_reshaped = reshape(a_cpu, sz, :)
+
+        if op == :reduce_min
+            cpu_result = minimum(a_reshaped, dims=1)[:]
+        elseif op == :reduce_max
+            cpu_result = maximum(a_reshaped, dims=1)[:]
+        elseif op == :reduce_sum
+            raw_sum = sum(a_reshaped, dims=1)[:]
+            cpu_result = raw_sum .& typemax(elType)
+        elseif op == :reduce_xor
+            cpu_result = mapslices(x -> reduce(⊻, x), a_reshaped, dims=1)[:]
+        elseif op == :reduce_or
+            cpu_result = mapslices(x -> reduce(|, x), a_reshaped, dims=1)[:]
+        elseif op == :reduce_and
+            cpu_result = mapslices(x -> reduce(&, x), a_reshaped, dims=1)[:]
+        end
+
+        @test cpu_result == res
+    end
+end
+
+# Test with signed Int types
+@testset for elType in [Int16, Int32, Int64]
+    @testset for op in [:reduce_min, :reduce_max, :reduce_sum, :reduce_xor, :reduce_or, :reduce_and]
+        sz = 32
+        N = 2^15
+
+        # Create kernel using factory
+        reduceKernel = try
+            makeReduceKernel(elType, op)
+        catch e
+            @test_broken false
+            rethrow()
+        end
+
+        # Create data and run kernel - use range to get negative values too
+        a_gpu = CuArray{elType}(rand(-1000:1000, N))
+        b_gpu = CUDA.zeros(elType, cld(N, sz))
+        try
+            CUDA.@sync ct.launch(reduceKernel, cld(length(a_gpu), sz), a_gpu, b_gpu, ct.Constant(sz))
+        catch e
+            @test_broken false
+            rethrow()
+        end
+        res = Array(b_gpu)
+
+        # CPU computation
+        a_cpu = Array(a_gpu)
+        a_reshaped = reshape(a_cpu, sz, :)
+
+        if op == :reduce_min
+            cpu_result = minimum(a_reshaped, dims=1)[:]
+        elseif op == :reduce_max
+            cpu_result = maximum(a_reshaped, dims=1)[:]
+        elseif op == :reduce_sum
+            cpu_result = sum(a_reshaped, dims=1)[:]
+        elseif op == :reduce_xor
+            cpu_result = mapslices(x -> reduce(⊻, x), a_reshaped, dims=1)[:]
+        elseif op == :reduce_or
+            cpu_result = mapslices(x -> reduce(|, x), a_reshaped, dims=1)[:]
+        elseif op == :reduce_and
+            cpu_result = mapslices(x -> reduce(&, x), a_reshaped, dims=1)[:]
+        end
+
+        @test cpu_result == res
+    end
+end
+
+# Test with Float types
+@testset for elType in [Float16, Float32, Float64]
+    @testset for op in [:reduce_min, :reduce_max, :reduce_sum]
+        sz = 32
+        N = 2^15
+
+        # Create kernel using factory
+        reduceKernel = makeReduceKernel(elType, op)
+
+        # Create data and run kernel
+        a_gpu = CUDA.rand(elType, N)
+        b_gpu = CUDA.zeros(elType, cld(N, sz))
+        CUDA.@sync ct.launch(reduceKernel, cld(length(a_gpu), sz), a_gpu, b_gpu, ct.Constant(sz))
+        res = Array(b_gpu)
+
+        # CPU computation
+        a_cpu = Array(a_gpu)
+        a_reshaped = reshape(a_cpu, sz, :)
+
+        if op == :reduce_min
+            cpu_result = minimum(a_reshaped, dims=1)[:]
+        elseif op == :reduce_max
+            cpu_result = maximum(a_reshaped, dims=1)[:]
+        elseif op == :reduce_sum
+            cpu_result = sum(a_reshaped, dims=1)[:]
+        end
+
+        @test isapprox(cpu_result, res)
+    end
+end
diff --git a/test/runtests.jl b/test/runtests.jl
index 6b9aed9..a52163a 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -45,7 +45,7 @@ if filter_tests!(testsuite, args)
 
     cuda_functional = CUDA.functional()
     filter!(testsuite) do (test, _)
-        if in(test, ["execution"]) || startswith(test, "examples/")
+        if in(test, ["execution", "reduce_ops"]) || startswith(test, "examples/")
             return cuda_functional
         else
             return true