diff --git a/src/problems/optimization_problems.jl b/src/problems/optimization_problems.jl
index 6d6033ea9..cd3d4436b 100644
--- a/src/problems/optimization_problems.jl
+++ b/src/problems/optimization_problems.jl
@@ -131,8 +131,7 @@ function OptimizationProblem(
     OptimizationProblem{isinplace(f)}(f, args...; kwargs...)
 end
 function OptimizationProblem(f, args...; kwargs...)
-    isinplace(f, 2, has_two_dispatches = false)
-    OptimizationProblem{true}(OptimizationFunction{true}(f), args...; kwargs...)
+    OptimizationProblem(OptimizationFunction(f), args...; kwargs...)
 end
 
 function OptimizationFunction(
diff --git a/src/scimlfunctions.jl b/src/scimlfunctions.jl
index 1b61bd491..9704ccbce 100644
--- a/src/scimlfunctions.jl
+++ b/src/scimlfunctions.jl
@@ -948,7 +948,7 @@ dt: the time step
 
 ```julia
 ImplicitDiscreteFunction{iip,specialize}(f;
-                                analytic = __has_analytic(f) ? f.analytic : nothing, 
+                                analytic = __has_analytic(f) ? f.analytic : nothing,
                                 resid_prototype = __has_resid_prototype(f) ? f.resid_prototype : nothing)
 ```
 
@@ -2107,7 +2107,7 @@ A representation of a ODE function `f` with inputs, defined by:
 ```math
 \frac{dx}{dt} = f(x, u, p, t)
 ```
-where `x` are the states of the system and `u` are the inputs (which may represent 
+where `x` are the states of the system and `u` are the inputs (which may represent
 different things in different contexts, such as control variables in optimal control).
 
 Includes all of its related functions, such as the Jacobian of `f`, its gradient
@@ -2134,7 +2134,7 @@ ODEInputFunction{iip, specialize}(f;
     sys = __has_sys(f) ? f.sys : nothing)
 ```
 
-`f` should be given as `f(x_out,x,u,p,t)` or `out = f(x,u,p,t)`. 
+`f` should be given as `f(x_out,x,u,p,t)` or `out = f(x,u,p,t)`.
 See the section on `iip` for more details on in-place vs out-of-place handling.
 
 - `mass_matrix`: the mass matrix `M` represented in the BVP function. Can be used
@@ -4199,7 +4199,10 @@ IntervalNonlinearFunction(f::IntervalNonlinearFunction; kwargs...) = f
 struct NoAD <: AbstractADType end
 
 (f::OptimizationFunction)(args...) = f.f(args...)
-OptimizationFunction(args...; kwargs...) = OptimizationFunction{true}(args...; kwargs...)
+function OptimizationFunction(f, args...; kwargs...)
+    isinplace(f, 2, outofplace_param_number=2)
+    OptimizationFunction{true}(f, args...; kwargs...)
+end
 
 function OptimizationFunction{iip}(f, adtype::AbstractADType = NoAD();
         grad = nothing, fg = nothing, hess = nothing, hv = nothing, fgh = nothing,
@@ -4251,8 +4254,9 @@ end
 (f::MultiObjectiveOptimizationFunction)(args...) = f.f(args...)
 
 # Convenience constructor
-function MultiObjectiveOptimizationFunction(args...; kwargs...)
-    MultiObjectiveOptimizationFunction{true}(args...; kwargs...)
+function MultiObjectiveOptimizationFunction(f, args...; kwargs...)
+    isinplace(f, 3)
+    MultiObjectiveOptimizationFunction{true}(f, args...; kwargs...)
 end
 
 # Constructor with keyword arguments
@@ -4339,7 +4343,8 @@ function BVPFunction{iip, specialize, twopoint}(f, bc;
         if iip_f
             jac = update_coefficients! #(J,u,p,t)
         else
-            jac = (u, p, t) -> update_coefficients!(deepcopy(jac_prototype), u, p, t)
+            jac_prototype_copy = deepcopy(jac_prototype)
+            jac = (u, p, t) -> update_coefficients!(jac_prototype_copy, u, p, t)
         end
     end
 
@@ -4347,7 +4352,8 @@ function BVPFunction{iip, specialize, twopoint}(f, bc;
         if iip_bc
             bcjac = update_coefficients! #(J,u,p,t)
         else
-            bcjac = (u, p, t) -> update_coefficients!(deepcopy(bcjac_prototype), u, p, t)
+            bcjac_prototype_copy = deepcopy(bcjac_prototype)
+            bcjac = (u, p, t) -> update_coefficients!(bcjac_prototype_copy, u, p, t)
         end
     end
 
@@ -4512,7 +4518,8 @@ function DynamicalBVPFunction{iip, specialize, twopoint}(f, bc;
         if iip_f
             jac = update_coefficients! #(J,u,p,t)
         else
-            jac = (u, p, t) -> update_coefficients!(deepcopy(jac_prototype), u, p, t)
+            jac_prototype_copy = deepcopy(jac_prototype)
+            jac = (u, p, t) -> update_coefficients!(jac_prototype_copy, u, p, t)
         end
     end
 
@@ -4520,7 +4527,8 @@ function DynamicalBVPFunction{iip, specialize, twopoint}(f, bc;
         if iip_bc
             bcjac = update_coefficients! #(J,u,p,t)
         else
-            bcjac = (u, p, t) -> update_coefficients!(deepcopy(bcjac_prototype), u, p, t)
+            bcjac_prototype_copy = deepcopy(jac_prototype)
+            bcjac = (u, p, t) -> update_coefficients!(bcjac_prototype_copy, u, p, t)
         end
     end
 
diff --git a/test/aqua.jl b/test/aqua.jl
index d3bdc5240..0989ee391 100644
--- a/test/aqua.jl
+++ b/test/aqua.jl
@@ -29,7 +29,7 @@ end
     # for method_ambiguity in ambs
     #     @show method_ambiguity
     # end
-    @warn "Number of method ambiguities: $(length(ambs))"
+    !isempty(ambs) &&@warn "Number of method ambiguities: $(length(ambs))"
     @test length(ambs) ≤ 8
 end
 
diff --git a/test/function_building_error_messages.jl b/test/function_building_error_messages.jl
index ee7ee4af7..931283ebe 100644
--- a/test/function_building_error_messages.jl
+++ b/test/function_building_error_messages.jl
@@ -54,8 +54,8 @@ ofboth(u, p, t) = u
 ofboth(du, u, p, t) = du .= u
 
 ODEFunction(ofboth)
-ODEFunction{true}(ofboth)
-ODEFunction{false}(ofboth)
+@inferred ODEFunction{true}(ofboth)
+@inferred ODEFunction{false}(ofboth)
 
 jac(u, t) = [1.0]
 @test_throws SciMLBase.TooFewArgumentsError ODEFunction(fiip, jac = jac)
@@ -428,8 +428,8 @@ nfboth(u, p) = u
 nfboth(du, u, p) = du .= u
 
 NonlinearFunction(nfboth)
-NonlinearFunction{true}(nfboth)
-NonlinearFunction{false}(nfboth)
+@inferred NonlinearFunction{true}(nfboth)
+@inferred NonlinearFunction{false}(nfboth)
 
 njac(u) = [1.0]
 @test_throws SciMLBase.TooFewArgumentsError NonlinearFunction(nfiip, jac = njac)
@@ -520,8 +520,8 @@ bcfboth(u, p, t) = u
 bcfboth(du, u, p, t) = du .= u
 
 BVPFunction(bfboth, bcfboth)
-BVPFunction{true}(bfboth, bcfboth)
-BVPFunction{false}(bfboth, bcfboth)
+@inferred BVPFunction{true}(bfboth, bcfboth)
+@inferred BVPFunction{false}(bfboth, bcfboth)
 
 bjac(u, t) = [1.0]
 bcjac(u, t) = [1.0]
@@ -663,8 +663,8 @@ dbcfboth(du, u, p, t) = u
 dbcfboth(res, du, u, p, t) = res .= du .- u
 
 DynamicalBVPFunction(dbfboth, dbcfboth)
-DynamicalBVPFunction{true}(dbfboth, dbcfboth)
-DynamicalBVPFunction{false}(dbfboth, dbcfboth)
+@inferred DynamicalBVPFunction{true}(dbfboth, dbcfboth)
+@inferred DynamicalBVPFunction{false}(dbfboth, dbcfboth)
 
 dbjac(du, u, t) = [1.0]
 dbcjac(du, u, t) = [1.0]