avoid abstract value types

remove basic value type ANY
remove concept of tensors with unknown dimensions
disallow value types with unbound indexed dimensions
remove predicates talking about abstract types
type of unknown values are now ERROR (was ANY)
require that overlapping indexed dimensions are of equal size
type unification now requires types to be equal ('if' expressions)
creating a tensor type without dimensions now gives a double type
make rank feature setup fail on invalid types (query/attribute)

36 files changed, 267 insertions, 869 deletions

diff --git a/document/src/tests/datatype/datatype_test.cpp b/document/src/tests/datatype/datatype_test.cpp
index ef41ee770a2..fff913a0f91 100644
--- a/document/src/tests/datatype/datatype_test.cpp
+++ b/document/src/tests/datatype/datatype_test.cpp
@@ -94,13 +94,6 @@ TEST_F("require that TensorDataType can check for assignable tensor type", Tenso
     EXPECT_TRUE(f.isAssignableType("tensor(x[2])"));
     EXPECT_FALSE(f.isAssignableType("tensor(x[3])"));
     EXPECT_FALSE(f.isAssignableType("tensor(y[2])"));
-    EXPECT_FALSE(f.isAssignableType("tensor(x[])"));
-    EXPECT_FALSE(f.isAssignableType("tensor(x{})"));
-    f.setup("tensor(x[])");
-    EXPECT_TRUE(f.isAssignableType("tensor(x[2])"));
-    EXPECT_TRUE(f.isAssignableType("tensor(x[3])"));
-    EXPECT_FALSE(f.isAssignableType("tensor(y[2])"));
-    EXPECT_FALSE(f.isAssignableType("tensor(x[])"));
     EXPECT_FALSE(f.isAssignableType("tensor(x{})"));
 }
 
diff --git a/eval/src/apps/tensor_conformance/generate.cpp b/eval/src/apps/tensor_conformance/generate.cpp
index f3a99f8a36c..47a37824c3a 100644
--- a/eval/src/apps/tensor_conformance/generate.cpp
+++ b/eval/src/apps/tensor_conformance/generate.cpp
@@ -104,10 +104,9 @@ void generate_join_expr(const vespalib::string &expr, const Sequence &seq, TestB
     std::vector<Layout> layouts = {
         {},                                    {},
         {x(5)},                                {x(5)},
-        {x(5)},                                {x(3)},
         {x(5)},                                {y(5)},
         {x(5)},                                {x(5),y(5)},
-        {x(3),y(5)},                           {x(4),y(4)},
+        {x(5),y(3)},                           {x(5),y(3)},
         {x(3),y(5)},                           {y(5),z(7)},
         {x({"a","b","c"})},                    {x({"a","b","c"})},
         {x({"a","b","c"})},                    {x({"a","b"})},
@@ -161,10 +160,6 @@ void generate_tensor_join(TestBuilder &dst) {
 void generate_dot_product(TestBuilder &dst) {
     dst.add("reduce(a*b,sum)", {{"a", spec(x(3), Seq({ 2, 3, 5 }))}, {"b", spec(x(3), Seq({ 7, 11, 13 }))}},
             spec((2 * 7) + (3 * 11) + (5 * 13)));
-    dst.add("reduce(a*b,sum)", {{"a", spec(x(2), Seq({ 2, 3 }))}, {"b", spec(x(3), Seq({ 7, 11, 13 }))}},
-            spec((2 * 7) + (3 * 11)));
-    dst.add("reduce(a*b,sum)", {{"a", spec(x(3), Seq({ 2, 3, 5 }))}, {"b", spec(x(2), Seq({ 7, 11 }))}},
-            spec((2 * 7) + (3 * 11)));
 }
 
 //-----------------------------------------------------------------------------
@@ -191,7 +186,7 @@ void generate_tensor_concat(TestBuilder &dst) {
             spec({x(4),y(2)}, Seq({1.0, 2.0, 3.0, 4.0, 5.0, 5.0, 6.0, 6.0})));
     dst.add("concat(a,b,x)", {{"a", spec(z(3), Seq({1.0, 2.0, 3.0}))}, {"b", spec(y(2), Seq({4.0, 5.0}))}},
             spec({x(2),y(2),z(3)}, Seq({1.0, 2.0, 3.0, 1.0, 2.0, 3.0, 4.0, 4.0, 4.0, 5.0, 5.0, 5.0})));
-    dst.add("concat(a,b,x)", {{"a", spec(y(3), Seq({1.0, 2.0, 3.0}))}, {"b", spec(y(2), Seq({4.0, 5.0}))}},
+    dst.add("concat(a,b,x)", {{"a", spec(y(2), Seq({1.0, 2.0}))}, {"b", spec(y(2), Seq({4.0, 5.0}))}},
             spec({x(2), y(2)}, Seq({1.0, 2.0, 4.0, 5.0})));
     dst.add("concat(concat(a,b,x),concat(c,d,x),y)", {{"a", spec(1.0)}, {"b", spec(2.0)}, {"c", spec(3.0)}, {"d", spec(4.0)}},
             spec({x(2), y(2)}, Seq({1.0, 3.0, 2.0, 4.0})));
diff --git a/eval/src/apps/tensor_conformance/test_spec.json b/eval/src/apps/tensor_conformance/test_spec.json
index 335c4bcb939..b5374e6033d 100644
--- a/eval/src/apps/tensor_conformance/test_spec.json
+++ b/eval/src/apps/tensor_conformance/test_spec.json
@@ -579,10 +579,9 @@
 {"expression":"map(a,f(a)((a+1)*2))","inputs":{"a":"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},"result":{"expect":"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}}
 {"expression":"a+b","inputs":{"a":"0x02003FB999999999999A","b":"0x02003FB999999999999A"},"result":{"expect":"0x02003FC999999999999A"}}
 {"expression":"a+b","inputs":{"a":"0x02010178053FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE0000000000000","b":"0x02010178053FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE0000000000000"},"result":{"expect":"0x02010178053FC999999999999A3FD999999999999A3FE33333333333333FE999999999999A3FF0000000000000"}}
-{"expression":"a+b","inputs":{"a":"0x02010178053FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE0000000000000","b":"0x02010178033FB999999999999A3FC999999999999A3FD3333333333333"},"result":{"expect":"0x02010178033FC999999999999A3FD999999999999A3FE3333333333333"}}
 {"expression":"a+b","inputs":{"a":"0x02010178053FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE0000000000000","b":"0x02010179053FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE0000000000000"},"result":{"expect":"0x02020178050179053FC999999999999A3FD33333333333343FD999999999999A3FE00000000000003FE33333333333333FD33333333333343FD999999999999A3FE00000000000003FE33333333333343FE66666666666663FD999999999999A3FE00000000000003FE33333333333333FE66666666666663FE999999999999A3FE00000000000003FE33333333333343FE66666666666663FE999999999999A3FECCCCCCCCCCCCD3FE33333333333333FE66666666666663FE999999999999A3FECCCCCCCCCCCCD3FF0000000000000"}}
 {"expression":"a+b","inputs":{"a":"0x02010178053FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE0000000000000","b":"0x02020178050179053FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE00000000000003FE33333333333333FE66666666666663FE999999999999A3FECCCCCCCCCCCCD3FF00000000000003FF199999999999A3FF33333333333333FF4CCCCCCCCCCCD3FF66666666666663FF80000000000003FF999999999999A3FFB3333333333333FFCCCCCCCCCCCCD3FFE66666666666640000000000000004000CCCCCCCCCCCD400199999999999A400266666666666640033333333333334004000000000000"},"result":{"expect":"0x02020178050179053FC999999999999A3FD33333333333343FD999999999999A3FE00000000000003FE33333333333333FE999999999999A3FECCCCCCCCCCCCC3FF00000000000003FF199999999999A3FF33333333333333FF66666666666673FF80000000000003FF999999999999A3FFB3333333333333FFCCCCCCCCCCCCD40000000000000004000CCCCCCCCCCCD400199999999999A400266666666666640033333333333334004CCCCCCCCCCCD400599999999999A400666666666666640073333333333334008000000000000"}}
-{"expression":"a+b","inputs":{"a":"0x02020178030179053FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE00000000000003FE33333333333333FE66666666666663FE999999999999A3FECCCCCCCCCCCCD3FF00000000000003FF199999999999A3FF33333333333333FF4CCCCCCCCCCCD3FF66666666666663FF8000000000000","b":"0x02020178040179043FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE00000000000003FE33333333333333FE66666666666663FE999999999999A3FECCCCCCCCCCCCD3FF00000000000003FF199999999999A3FF33333333333333FF4CCCCCCCCCCCD3FF66666666666663FF80000000000003FF999999999999A"},"result":{"expect":"0x02020178030179043FC999999999999A3FD999999999999A3FE33333333333333FE999999999999A3FF199999999999A3FF4CCCCCCCCCCCC3FF80000000000003FFB3333333333344000000000000000400199999999999A40033333333333344004CCCCCCCCCCCC"}}
+{"expression":"a+b","inputs":{"a":"0x02020178050179033FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE00000000000003FE33333333333333FE66666666666663FE999999999999A3FECCCCCCCCCCCCD3FF00000000000003FF199999999999A3FF33333333333333FF4CCCCCCCCCCCD3FF66666666666663FF8000000000000","b":"0x02020178050179033FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE00000000000003FE33333333333333FE66666666666663FE999999999999A3FECCCCCCCCCCCCD3FF00000000000003FF199999999999A3FF33333333333333FF4CCCCCCCCCCCD3FF66666666666663FF8000000000000"},"result":{"expect":"0x02020178050179033FC999999999999A3FD999999999999A3FE33333333333333FE999999999999A3FF00000000000003FF33333333333333FF66666666666663FF999999999999A3FFCCCCCCCCCCCCD4000000000000000400199999999999A40033333333333334004CCCCCCCCCCCD40066666666666664008000000000000"}}
 {"expression":"a+b","inputs":{"a":"0x02020178030179053FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE00000000000003FE33333333333333FE66666666666663FE999999999999A3FECCCCCCCCCCCCD3FF00000000000003FF199999999999A3FF33333333333333FF4CCCCCCCCCCCD3FF66666666666663FF8000000000000","b":"0x0202017905017A073FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE00000000000003FE33333333333333FE66666666666663FE999999999999A3FECCCCCCCCCCCCD3FF00000000000003FF199999999999A3FF33333333333333FF4CCCCCCCCCCCD3FF66666666666663FF80000000000003FF999999999999A3FFB3333333333333FFCCCCCCCCCCCCD3FFE66666666666640000000000000004000CCCCCCCCCCCD400199999999999A4002666666666666400333333333333340040000000000004004CCCCCCCCCCCD400599999999999A4006666666666666400733333333333340080000000000004008CCCCCCCCCCCD400999999999999A400A666666666666400B333333333333400C000000000000"},"result":{"expect":"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}}
 {"expression":"a+b","inputs":{"a":"0x010101780301613FB999999999999A01623FC999999999999A01633FD3333333333333","b":"0x010101780301613FB999999999999A01623FC999999999999A01633FD3333333333333"},"result":{"expect":"0x010101780301613FC999999999999A01623FD999999999999A01633FE3333333333333"}}
 {"expression":"a+b","inputs":{"a":"0x010101780301613FB999999999999A01623FC999999999999A01633FD3333333333333","b":"0x010101780201613FB999999999999A01623FC999999999999A"},"result":{"expect":"0x010101780201613FC999999999999A01623FD999999999999A"}}
@@ -594,10 +593,9 @@
 {"expression":"a+b","inputs":{"a":"0x03010178010179050301613FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE000000000000001623FE33333333333333FE66666666666663FE999999999999A3FECCCCCCCCCCCCD3FF000000000000001633FF199999999999A3FF33333333333333FF4CCCCCCCCCCCD3FF66666666666663FF8000000000000","b":"0x0301017A010179050401693FB999999999999A3FE00000000000003FECCCCCCCCCCCCD3FF4CCCCCCCCCCCD3FFB333333333333016A3FC999999999999A3FE33333333333333FF00000000000003FF66666666666663FFCCCCCCCCCCCCD016B3FD33333333333333FE66666666666663FF199999999999A3FF80000000000003FFE666666666666016C3FD999999999999A3FE999999999999A3FF33333333333333FF999999999999A4000000000000000"},"result":{"expect":"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}}
 {"expression":"join(a,b,f(a,b)(a+b))","inputs":{"a":"0x02003FB999999999999A","b":"0x02003FB999999999999A"},"result":{"expect":"0x02003FC999999999999A"}}
 {"expression":"join(a,b,f(a,b)(a+b))","inputs":{"a":"0x02010178053FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE0000000000000","b":"0x02010178053FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE0000000000000"},"result":{"expect":"0x02010178053FC999999999999A3FD999999999999A3FE33333333333333FE999999999999A3FF0000000000000"}}
-{"expression":"join(a,b,f(a,b)(a+b))","inputs":{"a":"0x02010178053FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE0000000000000","b":"0x02010178033FB999999999999A3FC999999999999A3FD3333333333333"},"result":{"expect":"0x02010178033FC999999999999A3FD999999999999A3FE3333333333333"}}
 {"expression":"join(a,b,f(a,b)(a+b))","inputs":{"a":"0x02010178053FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE0000000000000","b":"0x02010179053FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE0000000000000"},"result":{"expect":"0x02020178050179053FC999999999999A3FD33333333333343FD999999999999A3FE00000000000003FE33333333333333FD33333333333343FD999999999999A3FE00000000000003FE33333333333343FE66666666666663FD999999999999A3FE00000000000003FE33333333333333FE66666666666663FE999999999999A3FE00000000000003FE33333333333343FE66666666666663FE999999999999A3FECCCCCCCCCCCCD3FE33333333333333FE66666666666663FE999999999999A3FECCCCCCCCCCCCD3FF0000000000000"}}
 {"expression":"join(a,b,f(a,b)(a+b))","inputs":{"a":"0x02010178053FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE0000000000000","b":"0x02020178050179053FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE00000000000003FE33333333333333FE66666666666663FE999999999999A3FECCCCCCCCCCCCD3FF00000000000003FF199999999999A3FF33333333333333FF4CCCCCCCCCCCD3FF66666666666663FF80000000000003FF999999999999A3FFB3333333333333FFCCCCCCCCCCCCD3FFE66666666666640000000000000004000CCCCCCCCCCCD400199999999999A400266666666666640033333333333334004000000000000"},"result":{"expect":"0x02020178050179053FC999999999999A3FD33333333333343FD999999999999A3FE00000000000003FE33333333333333FE999999999999A3FECCCCCCCCCCCCC3FF00000000000003FF199999999999A3FF33333333333333FF66666666666673FF80000000000003FF999999999999A3FFB3333333333333FFCCCCCCCCCCCCD40000000000000004000CCCCCCCCCCCD400199999999999A400266666666666640033333333333334004CCCCCCCCCCCD400599999999999A400666666666666640073333333333334008000000000000"}}
-{"expression":"join(a,b,f(a,b)(a+b))","inputs":{"a":"0x02020178030179053FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE00000000000003FE33333333333333FE66666666666663FE999999999999A3FECCCCCCCCCCCCD3FF00000000000003FF199999999999A3FF33333333333333FF4CCCCCCCCCCCD3FF66666666666663FF8000000000000","b":"0x02020178040179043FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE00000000000003FE33333333333333FE66666666666663FE999999999999A3FECCCCCCCCCCCCD3FF00000000000003FF199999999999A3FF33333333333333FF4CCCCCCCCCCCD3FF66666666666663FF80000000000003FF999999999999A"},"result":{"expect":"0x02020178030179043FC999999999999A3FD999999999999A3FE33333333333333FE999999999999A3FF199999999999A3FF4CCCCCCCCCCCC3FF80000000000003FFB3333333333344000000000000000400199999999999A40033333333333344004CCCCCCCCCCCC"}}
+{"expression":"join(a,b,f(a,b)(a+b))","inputs":{"a":"0x02020178050179033FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE00000000000003FE33333333333333FE66666666666663FE999999999999A3FECCCCCCCCCCCCD3FF00000000000003FF199999999999A3FF33333333333333FF4CCCCCCCCCCCD3FF66666666666663FF8000000000000","b":"0x02020178050179033FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE00000000000003FE33333333333333FE66666666666663FE999999999999A3FECCCCCCCCCCCCD3FF00000000000003FF199999999999A3FF33333333333333FF4CCCCCCCCCCCD3FF66666666666663FF8000000000000"},"result":{"expect":"0x02020178050179033FC999999999999A3FD999999999999A3FE33333333333333FE999999999999A3FF00000000000003FF33333333333333FF66666666666663FF999999999999A3FFCCCCCCCCCCCCD4000000000000000400199999999999A40033333333333334004CCCCCCCCCCCD40066666666666664008000000000000"}}
 {"expression":"join(a,b,f(a,b)(a+b))","inputs":{"a":"0x02020178030179053FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE00000000000003FE33333333333333FE66666666666663FE999999999999A3FECCCCCCCCCCCCD3FF00000000000003FF199999999999A3FF33333333333333FF4CCCCCCCCCCCD3FF66666666666663FF8000000000000","b":"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},"result":{"expect":"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}}
 {"expression":"join(a,b,f(a,b)(a+b))","inputs":{"a":"0x010101780301613FB999999999999A01623FC999999999999A01633FD3333333333333","b":"0x010101780301613FB999999999999A01623FC999999999999A01633FD3333333333333"},"result":{"expect":"0x010101780301613FC999999999999A01623FD999999999999A01633FE3333333333333"}}
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@@ -609,10 +607,9 @@
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 {"expression":"join(a,b,f(a,b)(max(a,b)))","inputs":{"a":"0x010101780301613FB999999999999A01623FC999999999999A01633FD3333333333333","b":"0x010101780201613FB999999999999A01623FC999999999999A"},"result":{"expect":"0x010101780201613FB999999999999A01623FC999999999999A"}}
@@ -1209,10 +1167,9 @@
 {"expression":"join(a,b,f(a,b)(max(a,b)))","inputs":{"a":"0x03010178010179050301613FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE000000000000001623FE33333333333333FE66666666666663FE999999999999A3FECCCCCCCCCCCCD3FF000000000000001633FF199999999999A3FF33333333333333FF4CCCCCCCCCCCD3FF66666666666663FF8000000000000","b":"0x0301017A010179050401693FB999999999999A3FE00000000000003FECCCCCCCCCCCCD3FF4CCCCCCCCCCCD3FFB333333333333016A3FC999999999999A3FE33333333333333FF00000000000003FF66666666666663FFCCCCCCCCCCCCD016B3FD33333333333333FE66666666666663FF199999999999A3FF80000000000003FFE666666666666016C3FD999999999999A3FE999999999999A3FF33333333333333FF999999999999A4000000000000000"},"result":{"expect":"0x03020178017A010179050C016101693FB999999999999A3FE00000000000003FECCCCCCCCCCCCD3FF4CCCCCCCCCCCD3FFB3333333333330161016A3FC999999999999A3FE33333333333333FF00000000000003FF66666666666663FFCCCCCCCCCCCCD0161016B3FD33333333333333FE66666666666663FF199999999999A3FF80000000000003FFE6666666666660161016C3FD999999999999A3FE999999999999A3FF33333333333333FF999999999999A4000000000000000016201693FE33333333333333FE66666666666663FECCCCCCCCCCCCD3FF4CCCCCCCCCCCD3FFB3333333333330162016A3FE33333333333333FE66666666666663FF00000000000003FF66666666666663FFCCCCCCCCCCCCD0162016B3FE33333333333333FE66666666666663FF199999999999A3FF80000000000003FFE6666666666660162016C3FE33333333333333FE999999999999A3FF33333333333333FF999999999999A4000000000000000016301693FF199999999999A3FF33333333333333FF4CCCCCCCCCCCD3FF66666666666663FFB3333333333330163016A3FF199999999999A3FF33333333333333FF4CCCCCCCCCCCD3FF66666666666663FFCCCCCCCCCCCCD0163016B3FF199999999999A3FF33333333333333FF4CCCCCCCCCCCD3FF80000000000003FFE6666666666660163016C3FF199999999999A3FF33333333333333FF4CCCCCCCCCCCD3FF999999999999A4000000000000000"}}
 {"expression":"join(a,b,f(a,b)((a+b)/(a*b)))","inputs":{"a":"0x02003FB999999999999A","b":"0x02003FB999999999999A"},"result":{"expect":"0x02004033FFFFFFFFFFFF"}}
 {"expression":"join(a,b,f(a,b)((a+b)/(a*b)))","inputs":{"a":"0x02010178053FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE0000000000000","b":"0x02010178053FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE0000000000000"},"result":{"expect":"0x02010178054033FFFFFFFFFFFF4023FFFFFFFFFFFF401AAAAAAAAAAAAB4013FFFFFFFFFFFF4010000000000000"}}
-{"expression":"join(a,b,f(a,b)((a+b)/(a*b)))","inputs":{"a":"0x02010178053FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE0000000000000","b":"0x02010178033FB999999999999A3FC999999999999A3FD3333333333333"},"result":{"expect":"0x02010178034033FFFFFFFFFFFF4023FFFFFFFFFFFF401AAAAAAAAAAAAB"}}
 {"expression":"join(a,b,f(a,b)((a+b)/(a*b)))","inputs":{"a":"0x02010178053FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE0000000000000","b":"0x02010179053FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE0000000000000"},"result":{"expect":"0x02020178050179054033FFFFFFFFFFFF402E000000000000402AAAAAAAAAAAAB4028FFFFFFFFFFFF4027FFFFFFFFFFFF402E0000000000004023FFFFFFFFFFFF4020AAAAAAAAAAAB401E000000000000401BFFFFFFFFFFFF402AAAAAAAAAAAAB4020AAAAAAAAAAAB401AAAAAAAAAAAAB401755555555555540155555555555564028FFFFFFFFFFFF401E00000000000040175555555555554013FFFFFFFFFFFF40120000000000004027FFFFFFFFFFFF401BFFFFFFFFFFFF401555555555555640120000000000004010000000000000"}}
 {"expression":"join(a,b,f(a,b)((a+b)/(a*b)))","inputs":{"a":"0x02010178053FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE0000000000000","b":"0x02020178050179053FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE00000000000003FE33333333333333FE66666666666663FE999999999999A3FECCCCCCCCCCCCD3FF00000000000003FF199999999999A3FF33333333333333FF4CCCCCCCCCCCD3FF66666666666663FF80000000000003FF999999999999A3FFB3333333333333FFCCCCCCCCCCCCD3FFE66666666666640000000000000004000CCCCCCCCCCCD400199999999999A400266666666666640033333333333334004000000000000"},"result":{"expect":"0x02020178050179054033FFFFFFFFFFFF402E000000000000402AAAAAAAAAAAAB4028FFFFFFFFFFFF4027FFFFFFFFFFFF401AAAAAAAAAAAAB4019B6DB6DB6DB6E4018FFFFFFFFFFFF401871C71C71C71C4017FFFFFFFFFFFF4010F83E0F83E0F84010AAAAAAAAAAAB4010690690690691401030C30C30C30C40100000000000014008FFFFFFFFFFFF4008B4B4B4B4B4B5400871C71C71C71C400835E50D79435E4007FFFFFFFFFFFF4003CF3CF3CF3CF44003A2E8BA2E8BA340037A6F4DE9BD3840035555555555554003333333333333"}}
-{"expression":"join(a,b,f(a,b)((a+b)/(a*b)))","inputs":{"a":"0x02020178030179053FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE00000000000003FE33333333333333FE66666666666663FE999999999999A3FECCCCCCCCCCCCD3FF00000000000003FF199999999999A3FF33333333333333FF4CCCCCCCCCCCD3FF66666666666663FF8000000000000","b":"0x02020178040179043FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE00000000000003FE33333333333333FE66666666666663FE999999999999A3FECCCCCCCCCCCCD3FF00000000000003FF199999999999A3FF33333333333333FF4CCCCCCCCCCCD3FF66666666666663FF80000000000003FF999999999999A"},"result":{"expect":"0x02020178030179044033FFFFFFFFFFFF4023FFFFFFFFFFFF401AAAAAAAAAAAAB4013FFFFFFFFFFFF400D5555555555564008C30C30C30C3040056DB6DB6DB6DC4002E38E38E38E394000295FAD40A57E3FFD5555555555563FFADA67D508F3783FF8C30C30C30C30"}}
+{"expression":"join(a,b,f(a,b)((a+b)/(a*b)))","inputs":{"a":"0x02020178050179033FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE00000000000003FE33333333333333FE66666666666663FE999999999999A3FECCCCCCCCCCCCD3FF00000000000003FF199999999999A3FF33333333333333FF4CCCCCCCCCCCD3FF66666666666663FF8000000000000","b":"0x02020178050179033FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE00000000000003FE33333333333333FE66666666666663FE999999999999A3FECCCCCCCCCCCCD3FF00000000000003FF199999999999A3FF33333333333333FF4CCCCCCCCCCCD3FF66666666666663FF8000000000000"},"result":{"expect":"0x02020178050179034033FFFFFFFFFFFF4023FFFFFFFFFFFF401AAAAAAAAAAAAB4013FFFFFFFFFFFF4010000000000000400AAAAAAAAAAAAB4006DB6DB6DB6DB74003FFFFFFFFFFFF4001C71C71C71C7240000000000000003FFD1745D1745D173FFAAAAAAAAAAAAB3FF89D89D89D89D83FF6DB6DB6DB6DB73FF5555555555555"}}
 {"expression":"join(a,b,f(a,b)((a+b)/(a*b)))","inputs":{"a":"0x02020178030179053FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE00000000000003FE33333333333333FE66666666666663FE999999999999A3FECCCCCCCCCCCCD3FF00000000000003FF199999999999A3FF33333333333333FF4CCCCCCCCCCCD3FF66666666666663FF8000000000000","b":"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},"result":{"expect":"0x0203017803017905017A074033FFFFFFFFFFFF402E000000000000402AAAAAAAAAAAAB4028FFFFFFFFFFFF4027FFFFFFFFFFFF40275555555555554026DB6DB6DB6DB74018FFFFFFFFFFFF401871C71C71C71C4017FFFFFFFFFFFF4017A2E8BA2E8BA24017555555555555401713B13B13B13B4016DB6DB6DB6DB74010000000000001400FAAAAAAAAAAAC400F5F5F5F5F5F5F400F1C71C71C71C7400EE08FB823EE09400EAAAAAAAAAAAA400E79E79E79E79E4007A2E8BA2E8BA240077A6F4DE9BD3840075555555555554007333333333333400713B13B13B13B4006F684BDA12F684006DB6DB6DB6DB74002C234F72C234F4002AAAAAAAAAAAB400294A5294A5295400280000000000040026C9B26C9B26D40025A5A5A5A5A5A40024924924924924027555555555555401AAAAAAAAAAAAB40140000000000004010AAAAAAAAAAAB400D555555555556400AAAAAAAAAAAAB4008C30C30C30C3040056DB6DB6DB6DC400451451451451540036DB6DB6DB6DC4002B3884FCACE214002186186186186400195195195195240012492492492493FFEAAAAAAAAAAA93FFE0000000000003FFD6969696969693FFCE38E38E38E383FFC6BCA1AF286BD3FFBFFFFFFFFFFFF3FFB9E79E79E79E83FF90CEDE62433B73FF8BBFB0D9A96E13FF871C71C71C71C3FF82D82D82D82D83FF7EE7EE7EE7EE73FF7B425ED097B423FF77DF7DF7DF7DF3FF58469EE58469F3FF55555555555553FF5294A5294A5293FF50000000000003FF4D9364D9364D93FF4B4B4B4B4B4B53FF49249249249254025D1745D1745D24017A2E8BA2E8BA24010F83E0F83E0F8400B45D1745D1745400745D1745D174640049B26C9B26C9B4002B3884FCACE214000AAAAAAAAAAAB3FFF1C71C71C71C73FFD5555555555563FFBE0F83E0F83E03FFAAAAAAAAAAAAB3FF9A41A41A41A413FF8C30C30C30C303FF6F96F96F96F963FF64EC4EC4EC4ED3FF5B82E55B82E563FF53253253253253FF4BA8F07414BAA3FF44EC4EC4EC4EC3FF3ED3ED3ED3ED43FF2B3884FCACE213FF262957741314B3FF21861861861863FF1D41D41D41D423FF19519519519523FF15AC056B015AC3FF12492492492493FF02F149902F14A3FF00000000000003FEFA7E9FA7E9FA73FEF5555555555543FEF07C1F07C1F093FEEBEBEBEBEBEC03FEE79E79E79E79E"}}
 {"expression":"join(a,b,f(a,b)((a+b)/(a*b)))","inputs":{"a":"0x010101780301613FB999999999999A01623FC999999999999A01633FD3333333333333","b":"0x010101780301613FB999999999999A01623FC999999999999A01633FD3333333333333"},"result":{"expect":"0x010101780301614033FFFFFFFFFFFF01624023FFFFFFFFFFFF0163401AAAAAAAAAAAAB"}}
 {"expression":"join(a,b,f(a,b)((a+b)/(a*b)))","inputs":{"a":"0x010101780301613FB999999999999A01623FC999999999999A01633FD3333333333333","b":"0x010101780201613FB999999999999A01623FC999999999999A"},"result":{"expect":"0x010101780201614033FFFFFFFFFFFF01624023FFFFFFFFFFFF"}}
@@ -1223,8 +1180,6 @@
 {"expression":"join(a,b,f(a,b)((a+b)/(a*b)))","inputs":{"a":"0x030101790101780302036261723FC999999999999A3FD999999999999A3FE333333333333303666F6F3FB999999999999A3FD33333333333333FE0000000000000","b":"0x0301017901017A0702036261723FE999999999999A3FECCCCCCCCCCCCD3FF00000000000003FF199999999999A3FF33333333333333FF4CCCCCCCCCCCD3FF666666666666603666F6F3FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE00000000000003FE33333333333333FE6666666666666"},"result":{"expect":"0x0301017902017803017A0702036261724018FFFFFFFFFFFF401871C71C71C71C4017FFFFFFFFFFFF4017A2E8BA2E8BA24017555555555555401713B13B13B13B4016DB6DB6DB6DB7400E000000000000400CE38E38E38E38400BFFFFFFFFFFFF400B45D1745D1745400AAAAAAAAAAAAB400A2762762762774009B6DB6DB6DB6E4007555555555555400638E38E38E38E400555555555555640049B26C9B26C9B400400000000000040037CB7CB7CB7CB40030C30C30C30C303666F6F4033FFFFFFFFFFFF402E000000000000402AAAAAAAAAAAAB4028FFFFFFFFFFFF4027FFFFFFFFFFFF40275555555555554026DB6DB6DB6DB7402AAAAAAAAAAAAB4020AAAAAAAAAAAB401AAAAAAAAAAAAB40175555555555554015555555555556401400000000000040130C30C30C30C34027FFFFFFFFFFFF401BFFFFFFFFFFFF401555555555555640120000000000004010000000000000400D555555555556400B6DB6DB6DB6DC"}}
 {"expression":"join(a,b,f(a,b)((a+b)/(a*b)))","inputs":{"a":"0x03010178010179050301613FB999999999999A3FC999999999999A3FD33333333333333FD999999999999A3FE000000000000001623FE33333333333333FE66666666666663FE999999999999A3FECCCCCCCCCCCCD3FF000000000000001633FF199999999999A3FF33333333333333FF4CCCCCCCCCCCD3FF66666666666663FF8000000000000","b":"0x0301017A010179050401693FB999999999999A3FE00000000000003FECCCCCCCCCCCCD3FF4CCCCCCCCCCCD3FFB333333333333016A3FC999999999999A3FE33333333333333FF00000000000003FF66666666666663FFCCCCCCCCCCCCD016B3FD33333333333333FE66666666666663FF199999999999A3FF80000000000003FFE666666666666016C3FD999999999999A3FE999999999999A3FF33333333333333FF999999999999A4000000000000000"},"result":{"expect":"0x03020178017A010179050C016101694033FFFFFFFFFFFF401BFFFFFFFFFFFF4011C71C71C71C71400A2762762762774004B4B4B4B4B4B50161016A402E000000000000401AAAAAAAAAAAAB40115555555555564009B6DB6DB6DB6E400471C71C71C71C0161016B402AAAAAAAAAAAAB4019B6DB6DB6DB6E4010F83E0F83E0F84009555555555554400435E50D79435E0161016C4028FFFFFFFFFFFF4018FFFFFFFFFFFF4010AAAAAAAAAAAB4008FFFFFFFFFFFF4004000000000000016201694027555555555555400B6DB6DB6DB6DC4002E38E38E38E393FFE15E15E15E15E3FF969696969696A0162016A401AAAAAAAAAAAAB4008C30C30C30C3040020000000000003FFD34D34D34D34D3FF8E38E38E38E380162016B40140000000000004006DB6DB6DB6DB7400145D1745D17463FFC71C71C71C71C3FF86BCA1AF286BD0162016C4010AAAAAAAAAAAB40056DB6DB6DB6DC4000AAAAAAAAAAAB3FFBC71C71C71C713FF8000000000000016301694025D1745D1745D24006AAAAAAAAAAAB3FFE15E15E15E15E3FF7BC7BC7BC7BC93FF41414141414150163016A4017A2E8BA2E8BA240040000000000003FFC4EC4EC4EC4EB3FF6DB6DB6DB6DB73FF38E38E38E38E30163016B4010F83E0F83E0F840021861861861863FFADA67D508F3783FF61861861861873FF31674C59D31680163016C400B45D1745D17454000AAAAAAAAAAAB3FF9A41A41A41A413FF56DB6DB6DB6DC3FF2AAAAAAAAAAAB"}}
 {"expression":"reduce(a*b,sum)","inputs":{"a":"0x0201017803400000000000000040080000000000004014000000000000","b":"0x0201017803401C0000000000004026000000000000402A000000000000"},"result":{"expect":"0x0200405C000000000000"}}
-{"expression":"reduce(a*b,sum)","inputs":{"a":"0x020101780240000000000000004008000000000000","b":"0x0201017803401C0000000000004026000000000000402A000000000000"},"result":{"expect":"0x02004047800000000000"}}
-{"expression":"reduce(a*b,sum)","inputs":{"a":"0x0201017803400000000000000040080000000000004014000000000000","b":"0x0201017802401C0000000000004026000000000000"},"result":{"expect":"0x02004047800000000000"}}
 {"expression":"reduce(a*b,sum,x)","inputs":{"a":"0x02010178023FF00000000000004000000000000000","b":"0x020201780201790340080000000000004014000000000000401C0000000000004026000000000000402A0000000000004031000000000000"},"result":{"expect":"0x02010179034039000000000000403F0000000000004044800000000000"}}
 {"expression":"reduce(a*b,sum,y)","inputs":{"a":"0x02010179033FF000000000000040000000000000004008000000000000","b":"0x020201780201790340080000000000004014000000000000401C0000000000004026000000000000402A0000000000004031000000000000"},"result":{"expect":"0x020101780240410000000000004056000000000000"}}
 {"expression":"concat(a,b,x)","inputs":{"a":"0x02004024000000000000","b":"0x02004034000000000000"},"result":{"expect":"0x020101780240240000000000004034000000000000"}}
@@ -1234,7 +1189,7 @@
 {"expression":"concat(a,b,y)","inputs":{"a":"0x02020178020179023FF0000000000000400000000000000040080000000000004010000000000000","b":"0x020101790240140000000000004018000000000000"},"result":{"expect":"0x02020178020179043FF00000000000004000000000000000401400000000000040180000000000004008000000000000401000000000000040140000000000004018000000000000"}}
 {"expression":"concat(a,b,x)","inputs":{"a":"0x02020178020179023FF0000000000000400000000000000040080000000000004010000000000000","b":"0x020101780240140000000000004018000000000000"},"result":{"expect":"0x02020178040179023FF00000000000004000000000000000400800000000000040100000000000004014000000000000401400000000000040180000000000004018000000000000"}}
 {"expression":"concat(a,b,x)","inputs":{"a":"0x0201017A033FF000000000000040000000000000004008000000000000","b":"0x020101790240100000000000004014000000000000"},"result":{"expect":"0x0203017802017902017A033FF0000000000000400000000000000040080000000000003FF000000000000040000000000000004008000000000000401000000000000040100000000000004010000000000000401400000000000040140000000000004014000000000000"}}
-{"expression":"concat(a,b,x)","inputs":{"a":"0x02010179033FF000000000000040000000000000004008000000000000","b":"0x020101790240100000000000004014000000000000"},"result":{"expect":"0x02020178020179023FF0000000000000400000000000000040100000000000004014000000000000"}}
+{"expression":"concat(a,b,x)","inputs":{"a":"0x02010179023FF00000000000004000000000000000","b":"0x020101790240100000000000004014000000000000"},"result":{"expect":"0x02020178020179023FF0000000000000400000000000000040100000000000004014000000000000"}}
 {"expression":"concat(concat(a,b,x),concat(c,d,x),y)","inputs":{"a":"0x02003FF0000000000000","b":"0x02004000000000000000","c":"0x02004008000000000000","d":"0x02004010000000000000"},"result":{"expect":"0x02020178020179023FF0000000000000400800000000000040000000000000004010000000000000"}}
 {"expression":"rename(a,x,y)","inputs":{"a":"0x02010178053FF00000000000004000000000000000400800000000000040100000000000004014000000000000"},"result":{"expect":"0x02010179053FF00000000000004000000000000000400800000000000040100000000000004014000000000000"}}
 {"expression":"rename(a,y,x)","inputs":{"a":"0x0202017905017A053FF000000000000040000000000000004008000000000000401000000000000040140000000000004018000000000000401C00000000000040200000000000004022000000000000402400000000000040260000000000004028000000000000402A000000000000402C000000000000402E0000000000004030000000000000403100000000000040320000000000004033000000000000403400000000000040350000000000004036000000000000403700000000000040380000000000004039000000000000"},"result":{"expect":"0x0202017805017A053FF000000000000040000000000000004008000000000000401000000000000040140000000000004018000000000000401C00000000000040200000000000004022000000000000402400000000000040260000000000004028000000000000402A000000000000402C000000000000402E0000000000004030000000000000403100000000000040320000000000004033000000000000403400000000000040350000000000004036000000000000403700000000000040380000000000004039000000000000"}}
@@ -1245,4 +1200,4 @@
 {"expression":"tensor(x[10])(x+1)","inputs":{},"result":{"expect":"0x020101780A3FF000000000000040000000000000004008000000000000401000000000000040140000000000004018000000000000401C000000000000402000000000000040220000000000004024000000000000"}}
 {"expression":"tensor(x[5],y[4])(x*4+(y+1))","inputs":{},"result":{"expect":"0x02020178050179043FF000000000000040000000000000004008000000000000401000000000000040140000000000004018000000000000401C00000000000040200000000000004022000000000000402400000000000040260000000000004028000000000000402A000000000000402C000000000000402E00000000000040300000000000004031000000000000403200000000000040330000000000004034000000000000"}}
 {"expression":"tensor(x[5],y[4])(x==y)","inputs":{},"result":{"expect":"0x02020178050179043FF000000000000000000000000000000000000000000000000000000000000000000000000000003FF000000000000000000000000000000000000000000000000000000000000000000000000000003FF000000000000000000000000000000000000000000000000000000000000000000000000000003FF00000000000000000000000000000000000000000000000000000000000000000000000000000"}}
-{"num_tests":1247}
+{"num_tests":1202}
diff --git a/eval/src/tests/eval/node_types/node_types_test.cpp b/eval/src/tests/eval/node_types/node_types_test.cpp
index ce01d3f78c0..c18470887b2 100644
--- a/eval/src/tests/eval/node_types/node_types_test.cpp
+++ b/eval/src/tests/eval/node_types/node_types_test.cpp
@@ -74,47 +74,31 @@ TEST("require that leaf constants have appropriate type") {
 }
 
 TEST("require that input parameters preserve their type") {
-    TEST_DO(verify("any", "any"));
     TEST_DO(verify("error", "error"));
     TEST_DO(verify("double", "double"));
-    TEST_DO(verify("tensor", "tensor"));
-    TEST_DO(verify("tensor(x{},y[10],z[])", "tensor(x{},y[10],z[])"));
+    TEST_DO(verify("tensor", "double"));
+    TEST_DO(verify("tensor(x{},y[10],z[5])", "tensor(x{},y[10],z[5])"));
 }
 
 TEST("require that if resolves to the appropriate type") {
     TEST_DO(verify("if(error,1,2)", "error"));
     TEST_DO(verify("if(1,error,2)", "error"));
     TEST_DO(verify("if(1,2,error)", "error"));
-    TEST_DO(verify("if(any,1,2)", "double"));
     TEST_DO(verify("if(double,1,2)", "double"));
-    TEST_DO(verify("if(tensor,1,2)", "double"));
-    TEST_DO(verify("if(double,tensor,tensor)", "tensor"));
-    TEST_DO(verify("if(double,any,any)", "any"));
-    TEST_DO(verify("if(double,tensor(a[2]),tensor(a[2]))", "tensor(a[2])"));
-    TEST_DO(verify("if(double,tensor(a[2]),tensor(a[3]))", "tensor(a[])"));
-    TEST_DO(verify("if(double,tensor(a[2]),tensor(a[]))", "tensor(a[])"));
-    TEST_DO(verify("if(double,tensor(a[2]),tensor(a{}))", "tensor"));
+    TEST_DO(verify("if(tensor(x[10]),1,2)", "double"));
     TEST_DO(verify("if(double,tensor(a{}),tensor(a{}))", "tensor(a{})"));
-    TEST_DO(verify("if(double,tensor(a{}),tensor(b{}))", "tensor"));
-    TEST_DO(verify("if(double,tensor(a{}),tensor)", "tensor"));
-    TEST_DO(verify("if(double,tensor,tensor(a{}))", "tensor"));
-    TEST_DO(verify("if(double,tensor,any)", "any"));
-    TEST_DO(verify("if(double,any,tensor)", "any"));
-    TEST_DO(verify("if(double,tensor,double)", "any"));
-    TEST_DO(verify("if(double,double,tensor)", "any"));
-    TEST_DO(verify("if(double,double,any)", "any"));
-    TEST_DO(verify("if(double,any,double)", "any"));
+    TEST_DO(verify("if(double,tensor(a[2]),tensor(a[2]))", "tensor(a[2])"));
+    TEST_DO(verify("if(double,tensor(a[2]),tensor(a[3]))", "error"));
+    TEST_DO(verify("if(double,tensor(a[2]),tensor(a{}))", "error"));
+    TEST_DO(verify("if(double,tensor(a{}),tensor(b{}))", "error"));
+    TEST_DO(verify("if(double,tensor(a{}),double)", "error"));
 }
 
 TEST("require that reduce resolves correct type") {
     TEST_DO(verify("reduce(error,sum)", "error"));
-    TEST_DO(verify("reduce(tensor,sum)", "double"));
     TEST_DO(verify("reduce(tensor(x{}),sum)", "double"));
     TEST_DO(verify("reduce(double,sum)", "double"));
-    TEST_DO(verify("reduce(any,sum)", "any"));
     TEST_DO(verify("reduce(error,sum,x)", "error"));
-    TEST_DO(verify("reduce(tensor,sum,x)", "any"));
-    TEST_DO(verify("reduce(any,sum,x)", "any"));
     TEST_DO(verify("reduce(double,sum,x)", "error"));
     TEST_DO(verify("reduce(tensor(x{},y{},z{}),sum,y)", "tensor(x{},z{})"));
     TEST_DO(verify("reduce(tensor(x{},y{},z{}),sum,x,z)", "tensor(y{})"));
@@ -125,18 +109,16 @@ TEST("require that reduce resolves correct type") {
 
 TEST("require that rename resolves correct type") {
     TEST_DO(verify("rename(error,x,y)", "error"));
-    TEST_DO(verify("rename(tensor,x,y)", "any"));
     TEST_DO(verify("rename(double,x,y)", "error"));
-    TEST_DO(verify("rename(any,x,y)", "any"));
-    TEST_DO(verify("rename(tensor(x{},y[],z[5]),a,b)", "error"));
-    TEST_DO(verify("rename(tensor(x{},y[],z[5]),x,y)", "error"));
-    TEST_DO(verify("rename(tensor(x{},y[],z[5]),x,x)", "tensor(x{},y[],z[5])"));
-    TEST_DO(verify("rename(tensor(x{},y[],z[5]),x,w)", "tensor(w{},y[],z[5])"));
-    TEST_DO(verify("rename(tensor(x{},y[],z[5]),y,w)", "tensor(x{},w[],z[5])"));
-    TEST_DO(verify("rename(tensor(x{},y[],z[5]),z,w)", "tensor(x{},y[],w[5])"));
-    TEST_DO(verify("rename(tensor(x{},y[],z[5]),(x,y,z),(z,y,x))", "tensor(z{},y[],x[5])"));
-    TEST_DO(verify("rename(tensor(x{},y[],z[5]),(x,z),(z,x))", "tensor(z{},y[],x[5])"));
-    TEST_DO(verify("rename(tensor(x{},y[],z[5]),(x,y,z),(a,b,c))", "tensor(a{},b[],c[5])"));
+    TEST_DO(verify("rename(tensor(x{},y[1],z[5]),a,b)", "error"));
+    TEST_DO(verify("rename(tensor(x{},y[1],z[5]),x,y)", "error"));
+    TEST_DO(verify("rename(tensor(x{},y[1],z[5]),x,x)", "tensor(x{},y[1],z[5])"));
+    TEST_DO(verify("rename(tensor(x{},y[1],z[5]),x,w)", "tensor(w{},y[1],z[5])"));
+    TEST_DO(verify("rename(tensor(x{},y[1],z[5]),y,w)", "tensor(x{},w[1],z[5])"));
+    TEST_DO(verify("rename(tensor(x{},y[1],z[5]),z,w)", "tensor(x{},y[1],w[5])"));
+    TEST_DO(verify("rename(tensor(x{},y[1],z[5]),(x,y,z),(z,y,x))", "tensor(z{},y[1],x[5])"));
+    TEST_DO(verify("rename(tensor(x{},y[1],z[5]),(x,z),(z,x))", "tensor(z{},y[1],x[5])"));
+    TEST_DO(verify("rename(tensor(x{},y[1],z[5]),(x,y,z),(a,b,c))", "tensor(a{},b[1],c[5])"));
 }
 
 vespalib::string strfmt(const char *pattern, const char *a) {
@@ -149,37 +131,24 @@ vespalib::string strfmt(const char *pattern, const char *a, const char *b) {
 
 void verify_op1(const char *pattern) {
     TEST_DO(verify(strfmt(pattern, "error"), "error"));
-    TEST_DO(verify(strfmt(pattern, "any"), "any"));
     TEST_DO(verify(strfmt(pattern, "double"), "double"));
-    TEST_DO(verify(strfmt(pattern, "tensor"), "tensor"));
-    TEST_DO(verify(strfmt(pattern, "tensor(x{},y[10],z[])"), "tensor(x{},y[10],z[])"));
+    TEST_DO(verify(strfmt(pattern, "tensor(x{},y[10],z[1])"), "tensor(x{},y[10],z[1])"));
 }
 
 void verify_op2(const char *pattern) {
     TEST_DO(verify(strfmt(pattern, "error", "error"), "error"));
-    TEST_DO(verify(strfmt(pattern, "any", "error"), "error"));
-    TEST_DO(verify(strfmt(pattern, "error", "any"), "error"));
     TEST_DO(verify(strfmt(pattern, "double", "error"), "error"));
     TEST_DO(verify(strfmt(pattern, "error", "double"), "error"));
-    TEST_DO(verify(strfmt(pattern, "tensor", "error"), "error"));
-    TEST_DO(verify(strfmt(pattern, "error", "tensor"), "error"));
-    TEST_DO(verify(strfmt(pattern, "any", "any"), "any"));
-    TEST_DO(verify(strfmt(pattern, "any", "double"), "any"));
-    TEST_DO(verify(strfmt(pattern, "double", "any"), "any"));
-    TEST_DO(verify(strfmt(pattern, "any", "tensor"), "any"));
-    TEST_DO(verify(strfmt(pattern, "tensor", "any"), "any"));
+    TEST_DO(verify(strfmt(pattern, "tensor(x{})", "error"), "error"));
+    TEST_DO(verify(strfmt(pattern, "error", "tensor(x{})"), "error"));
     TEST_DO(verify(strfmt(pattern, "double", "double"), "double"));
-    TEST_DO(verify(strfmt(pattern, "tensor", "double"), "tensor"));
-    TEST_DO(verify(strfmt(pattern, "double", "tensor"), "tensor"));
     TEST_DO(verify(strfmt(pattern, "tensor(x{})", "double"), "tensor(x{})"));
     TEST_DO(verify(strfmt(pattern, "double", "tensor(x{})"), "tensor(x{})"));
-    TEST_DO(verify(strfmt(pattern, "tensor", "tensor"), "any"));
     TEST_DO(verify(strfmt(pattern, "tensor(x{})", "tensor(x{})"), "tensor(x{})"));
     TEST_DO(verify(strfmt(pattern, "tensor(x{})", "tensor(y{})"), "tensor(x{},y{})"));
-    TEST_DO(verify(strfmt(pattern, "tensor(x[3])", "tensor(x[5])"), "tensor(x[3])"));
-    TEST_DO(verify(strfmt(pattern, "tensor(x[])", "tensor(x[5])"), "tensor(x[])"));
-    TEST_DO(verify(strfmt(pattern, "tensor(x[5])", "tensor(x[3])"), "tensor(x[3])"));
-    TEST_DO(verify(strfmt(pattern, "tensor(x[5])", "tensor(x[])"), "tensor(x[])"));
+    TEST_DO(verify(strfmt(pattern, "tensor(x[5])", "tensor(x[5])"), "tensor(x[5])"));
+    TEST_DO(verify(strfmt(pattern, "tensor(x[3])", "tensor(x[5])"), "error"));
+    TEST_DO(verify(strfmt(pattern, "tensor(x[5])", "tensor(x[3])"), "error"));
     TEST_DO(verify(strfmt(pattern, "tensor(x{})", "tensor(x[5])"), "error"));
 }
 
@@ -249,7 +218,8 @@ TEST("require that lambda tensor resolves correct type") {
 TEST("require that tensor concat resolves correct type") {
     TEST_DO(verify("concat(double,double,x)", "tensor(x[2])"));
     TEST_DO(verify("concat(tensor(x[2]),tensor(x[3]),x)", "tensor(x[5])"));
-    TEST_DO(verify("concat(tensor(x[2]),tensor(x[3]),y)", "tensor(x[2],y[2])"));
+    TEST_DO(verify("concat(tensor(x[2]),tensor(x[2]),y)", "tensor(x[2],y[2])"));
+    TEST_DO(verify("concat(tensor(x[2]),tensor(x[3]),y)", "error"));
     TEST_DO(verify("concat(tensor(x[2]),tensor(x{}),x)", "error"));
     TEST_DO(verify("concat(tensor(x[2]),tensor(y{}),x)", "tensor(x[3],y{})"));
 }
@@ -258,7 +228,7 @@ TEST("require that double only expressions can be detected") {
     Function plain_fun = Function::parse("1+2");
     Function complex_fun = Function::parse("reduce(a,sum)");
     NodeTypes plain_types(plain_fun, {});
-    NodeTypes complex_types(complex_fun, {ValueType::tensor_type({})});
+    NodeTypes complex_types(complex_fun, {ValueType::tensor_type({{"x"}})});
     EXPECT_TRUE(plain_types.get_type(plain_fun.root()).is_double());
     EXPECT_TRUE(complex_types.get_type(complex_fun.root()).is_double());
     EXPECT_TRUE(plain_types.all_types_are_double());
@@ -269,7 +239,7 @@ TEST("require that empty type repo works as expected") {
     NodeTypes types;
     Function function = Function::parse("1+2");
     EXPECT_FALSE(function.has_error());
-    EXPECT_TRUE(types.get_type(function.root()).is_any());
+    EXPECT_TRUE(types.get_type(function.root()).is_error());
     EXPECT_FALSE(types.all_types_are_double());
 }
 
diff --git a/eval/src/tests/eval/tensor_function/tensor_function_test.cpp b/eval/src/tests/eval/tensor_function/tensor_function_test.cpp
index 23ea1e8c13a..741b756e46f 100644
--- a/eval/src/tests/eval/tensor_function/tensor_function_test.cpp
+++ b/eval/src/tests/eval/tensor_function/tensor_function_test.cpp
@@ -261,11 +261,16 @@ TEST("require that if_node result is mutable only when both children produce mut
     const Node &cond = inject(DoubleValue::double_type(), 0, stash);
     const Node &a = inject(ValueType::from_spec("tensor(x[2])"), 0, stash);
     const Node &b = inject(ValueType::from_spec("tensor(x[3])"), 0, stash);
+    const Node &c = inject(ValueType::from_spec("tensor(x[5])"), 0, stash);
     const Node &tmp = concat(a, b, "x", stash); // will be mutable
-    const Node &if_con_con = if_node(cond, a, b, stash);
-    const Node &if_mut_con = if_node(cond, tmp, b, stash);
-    const Node &if_con_mut = if_node(cond, a, tmp, stash);
+    const Node &if_con_con = if_node(cond, c, c, stash);
+    const Node &if_mut_con = if_node(cond, tmp, c, stash);
+    const Node &if_con_mut = if_node(cond, c, tmp, stash);
     const Node &if_mut_mut = if_node(cond, tmp, tmp, stash);
+    EXPECT_EQUAL(if_con_con.result_type(), c.result_type());
+    EXPECT_EQUAL(if_con_mut.result_type(), c.result_type());
+    EXPECT_EQUAL(if_mut_con.result_type(), c.result_type());
+    EXPECT_EQUAL(if_mut_mut.result_type(), c.result_type());
     EXPECT_TRUE(!if_con_con.result_is_mutable());
     EXPECT_TRUE(!if_mut_con.result_is_mutable());
     EXPECT_TRUE(!if_con_mut.result_is_mutable());
@@ -277,21 +282,12 @@ TEST("require that if_node gets expected result type") {
     const Node &a = inject(DoubleValue::double_type(), 0, stash);
     const Node &b = inject(ValueType::from_spec("tensor(x[2])"), 0, stash);
     const Node &c = inject(ValueType::from_spec("tensor(x[3])"), 0, stash);
-    const Node &d = inject(ValueType::from_spec("tensor(x[])"), 0, stash);
-    const Node &e = inject(ValueType::from_spec("tensor(y[3])"), 0, stash);
-    const Node &f = inject(ValueType::from_spec("double"), 0, stash);
-    const Node &g = inject(ValueType::from_spec("error"), 0, stash);
+    const Node &d = inject(ValueType::from_spec("error"), 0, stash);
     const Node &if_same = if_node(a, b, b, stash);
-    const Node &if_similar = if_node(a, b, c, stash);
-    const Node &if_subtype = if_node(a, b, d, stash);
-    const Node &if_different = if_node(a, b, e, stash);
-    const Node &if_different_types = if_node(a, b, f, stash);
-    const Node &if_with_error = if_node(a, b, g, stash);
+    const Node &if_different = if_node(a, b, c, stash);
+    const Node &if_with_error = if_node(a, b, d, stash);
     EXPECT_EQUAL(if_same.result_type(), ValueType::from_spec("tensor(x[2])"));
-    EXPECT_EQUAL(if_similar.result_type(), ValueType::from_spec("tensor(x[])"));
-    EXPECT_EQUAL(if_subtype.result_type(), ValueType::from_spec("tensor(x[])"));
-    EXPECT_EQUAL(if_different.result_type(), ValueType::from_spec("tensor"));
-    EXPECT_EQUAL(if_different_types.result_type(), ValueType::from_spec("any"));
+    EXPECT_EQUAL(if_different.result_type(), ValueType::from_spec("error"));
     EXPECT_EQUAL(if_with_error.result_type(), ValueType::from_spec("error"));
 }
 
diff --git a/eval/src/tests/eval/value_type/value_type_test.cpp b/eval/src/tests/eval/value_type/value_type_test.cpp
index f7db7816fad..a755eac965f 100644
--- a/eval/src/tests/eval/value_type/value_type_test.cpp
+++ b/eval/src/tests/eval/value_type/value_type_test.cpp
@@ -10,12 +10,6 @@ using namespace vespalib::eval;
 
 const size_t npos = ValueType::Dimension::npos;
 
-TEST("require that ANY value type can be created") {
-    ValueType t = ValueType::any_type();
-    EXPECT_TRUE(t.type() == ValueType::Type::ANY);
-    EXPECT_EQUAL(t.dimensions().size(), 0u);
-}
-
 TEST("require that ERROR value type can be created") {
     ValueType t = ValueType::error_type();
     EXPECT_TRUE(t.type() == ValueType::Type::ERROR);
@@ -61,10 +55,9 @@ TEST("require that dimension names can be obtained") {
 
 TEST("require that dimension index can be obtained") {
     EXPECT_EQUAL(ValueType::error_type().dimension_index("x"), ValueType::Dimension::npos);
-    EXPECT_EQUAL(ValueType::any_type().dimension_index("x"), ValueType::Dimension::npos);
     EXPECT_EQUAL(ValueType::double_type().dimension_index("x"), ValueType::Dimension::npos);
     EXPECT_EQUAL(ValueType::tensor_type({}).dimension_index("x"), ValueType::Dimension::npos);
-    auto my_type = ValueType::tensor_type({{"y", 10}, {"x"}, {"z", 0}});
+    auto my_type = ValueType::tensor_type({{"y", 10}, {"x"}, {"z", 5}});
     EXPECT_EQUAL(my_type.dimension_index("x"), 0u);
     EXPECT_EQUAL(my_type.dimension_index("y"), 1u);
     EXPECT_EQUAL(my_type.dimension_index("z"), 2u);
@@ -72,29 +65,30 @@ TEST("require that dimension index can be obtained") {
 }
 
 void verify_equal(const ValueType &a, const ValueType &b) {
-    EXPECT_TRUE(a == b);
-    EXPECT_TRUE(b == a);
+    EXPECT_EQUAL(a, b);
+    EXPECT_EQUAL(b, a);
     EXPECT_FALSE(a != b);
     EXPECT_FALSE(b != a);
+    EXPECT_EQUAL(a, ValueType::either(a, b));
+    EXPECT_EQUAL(a, ValueType::either(b, a));
 }
-    
+
 void verify_not_equal(const ValueType &a, const ValueType &b) {
     EXPECT_TRUE(a != b);
     EXPECT_TRUE(b != a);
     EXPECT_FALSE(a == b);
     EXPECT_FALSE(b == a);
+    EXPECT_TRUE(ValueType::either(a, b).is_error());
+    EXPECT_TRUE(ValueType::either(b, a).is_error());
 }
 
 TEST("require that value types can be compared") {
     TEST_DO(verify_equal(ValueType::error_type(), ValueType::error_type()));
-    TEST_DO(verify_not_equal(ValueType::error_type(), ValueType::any_type()));
     TEST_DO(verify_not_equal(ValueType::error_type(), ValueType::double_type()));
-    TEST_DO(verify_not_equal(ValueType::error_type(), ValueType::tensor_type({})));
-    TEST_DO(verify_equal(ValueType::any_type(), ValueType::any_type()));
-    TEST_DO(verify_not_equal(ValueType::any_type(), ValueType::double_type()));
-    TEST_DO(verify_not_equal(ValueType::any_type(), ValueType::tensor_type({})));
+    TEST_DO(verify_not_equal(ValueType::error_type(), ValueType::tensor_type({{"x"}})));
     TEST_DO(verify_equal(ValueType::double_type(), ValueType::double_type()));
-    TEST_DO(verify_not_equal(ValueType::double_type(), ValueType::tensor_type({})));
+    TEST_DO(verify_equal(ValueType::double_type(), ValueType::tensor_type({})));
+    TEST_DO(verify_not_equal(ValueType::double_type(), ValueType::tensor_type({{"x"}})));
     TEST_DO(verify_equal(ValueType::tensor_type({{"x"}, {"y"}}), ValueType::tensor_type({{"y"}, {"x"}})));
     TEST_DO(verify_not_equal(ValueType::tensor_type({{"x"}, {"y"}}), ValueType::tensor_type({{"x"}, {"y"}, {"z"}})));
     TEST_DO(verify_equal(ValueType::tensor_type({{"x", 10}, {"y", 20}}), ValueType::tensor_type({{"y", 20}, {"x", 10}})));
@@ -103,67 +97,55 @@ TEST("require that value types can be compared") {
 }
 
 void verify_predicates(const ValueType &type,
-                       bool expect_any, bool expect_error, bool expect_double, bool expect_tensor,
-                       bool expect_maybe_tensor, bool expect_abstract, bool expect_unknown_dimensions)
+                       bool expect_error, bool expect_double, bool expect_tensor,
+                       bool expect_sparse, bool expect_dense)
 {
-    EXPECT_EQUAL(type.is_any(), expect_any);
     EXPECT_EQUAL(type.is_error(), expect_error);
     EXPECT_EQUAL(type.is_double(), expect_double);
     EXPECT_EQUAL(type.is_tensor(), expect_tensor);
-    EXPECT_EQUAL(type.maybe_tensor(), expect_maybe_tensor);
-    EXPECT_EQUAL(type.is_abstract(), expect_abstract);
-    EXPECT_EQUAL(type.unknown_dimensions(), expect_unknown_dimensions);
+    EXPECT_EQUAL(type.is_sparse(), expect_sparse);
+    EXPECT_EQUAL(type.is_dense(), expect_dense);
 }
 
 TEST("require that type-related predicate functions work as expected") {
-    TEST_DO(verify_predicates(ValueType::any_type(),
-                              true, false, false, false,
-                              true, true, true));
-    TEST_DO(verify_predicates(ValueType::error_type(),
-                              false, true, false, false,
-                              false, false, false));
-    TEST_DO(verify_predicates(ValueType::double_type(),
-                              false, false, true, false,
-                              false, false, false));
-    TEST_DO(verify_predicates(ValueType::tensor_type({}),
-                              false, false, false, true,
-                              true, true, true));
-    TEST_DO(verify_predicates(ValueType::tensor_type({{"x"}}),
-                              false, false, false, true,
-                              true, false, false));
-    TEST_DO(verify_predicates(ValueType::tensor_type({{"x", 0}}),
-                              false, false, false, true,
-                              true, true, false));
+    TEST_DO(verify_predicates(ValueType::error_type(), true, false, false, false, false));
+    TEST_DO(verify_predicates(ValueType::double_type(), false, true, false, false, false));
+    TEST_DO(verify_predicates(ValueType::tensor_type({}), false, true, false, false, false));
+    TEST_DO(verify_predicates(ValueType::tensor_type({{"x"}}), false, false, true, true, false));
+    TEST_DO(verify_predicates(ValueType::tensor_type({{"x"},{"y"}}), false, false, true, true, false));
+    TEST_DO(verify_predicates(ValueType::tensor_type({{"x", 5}}), false, false, true, false, true));
+    TEST_DO(verify_predicates(ValueType::tensor_type({{"x", 5},{"y", 10}}), false, false, true, false, true));
+    TEST_DO(verify_predicates(ValueType::tensor_type({{"x", 5}, {"y"}}), false, false, true, false, false));
 }
 
 TEST("require that dimension predicates work as expected") {
-    ValueType type = ValueType::tensor_type({{"x"}, {"y", 10}, {"z", 0}});
-    ASSERT_EQUAL(3u, type.dimensions().size());
-    EXPECT_TRUE(type.dimensions()[0].is_mapped());
-    EXPECT_TRUE(!type.dimensions()[0].is_indexed());
-    EXPECT_TRUE(!type.dimensions()[0].is_bound());
-    EXPECT_TRUE(!type.dimensions()[1].is_mapped());
-    EXPECT_TRUE(type.dimensions()[1].is_indexed());
-    EXPECT_TRUE(type.dimensions()[1].is_bound());
-    EXPECT_TRUE(!type.dimensions()[2].is_mapped());
-    EXPECT_TRUE(type.dimensions()[2].is_indexed());
-    EXPECT_TRUE(!type.dimensions()[2].is_bound());
+    ValueType::Dimension x("x");
+    ValueType::Dimension y("y", 10);
+    ValueType::Dimension z("z", 0);
+    EXPECT_TRUE(x.is_mapped());
+    EXPECT_TRUE(!x.is_indexed());
+    EXPECT_TRUE(!x.is_bound());
+    EXPECT_TRUE(!y.is_mapped());
+    EXPECT_TRUE(y.is_indexed());
+    EXPECT_TRUE(y.is_bound());
+    EXPECT_TRUE(!z.is_mapped());
+    EXPECT_TRUE(z.is_indexed());
+    EXPECT_TRUE(!z.is_bound());
+}
+
+TEST("require that use of unbound dimensions result in error types") {
+    EXPECT_TRUE(ValueType::tensor_type({{"x", 0}}).is_error());
 }
 
 TEST("require that duplicate dimension names result in error types") {
     EXPECT_TRUE(ValueType::tensor_type({{"x"}, {"x"}}).is_error());
 }
 
-TEST("require that removing dimensions from non-abstract non-tensor types gives error type") {
+TEST("require that removing dimensions from non-tensor types gives error type") {
     EXPECT_TRUE(ValueType::error_type().reduce({"x"}).is_error());
     EXPECT_TRUE(ValueType::double_type().reduce({"x"}).is_error());
 }
 
-TEST("require that removing dimensions from abstract maybe-tensor types gives any type") {
-    EXPECT_TRUE(ValueType::any_type().reduce({"x"}).is_any());
-    EXPECT_TRUE(ValueType::tensor_type({}).reduce({"x"}).is_any());
-}
-
 TEST("require that dimensions can be removed from tensor value types") {
     ValueType type = ValueType::tensor_type({{"x", 10}, {"y", 20}, {"z", 30}});
     EXPECT_EQUAL(ValueType::tensor_type({{"y", 20}, {"z", 30}}), type.reduce({"x"}));
@@ -187,30 +169,22 @@ TEST("require that removing all dimensions gives double type") {
     EXPECT_EQUAL(ValueType::double_type(), type.reduce({"x", "y", "z"}));
 }
 
-TEST("require that dimensions can be combined for tensor value types") {
+TEST("require that dimensions can be combined for value types") {
     ValueType tensor_type_xy  = ValueType::tensor_type({{"x"}, {"y"}});
     ValueType tensor_type_yz  = ValueType::tensor_type({{"y"}, {"z"}});
     ValueType tensor_type_xyz = ValueType::tensor_type({{"x"}, {"y"}, {"z"}});
     ValueType tensor_type_y   = ValueType::tensor_type({{"y"}});
+    ValueType tensor_type_a10 = ValueType::tensor_type({{"a", 10}});
+    ValueType tensor_type_a10xyz = ValueType::tensor_type({{"a", 10}, {"x"}, {"y"}, {"z"}});
+    ValueType scalar = ValueType::double_type();
+    EXPECT_EQUAL(ValueType::join(scalar, scalar), scalar);
     EXPECT_EQUAL(ValueType::join(tensor_type_xy, tensor_type_yz), tensor_type_xyz);
     EXPECT_EQUAL(ValueType::join(tensor_type_yz, tensor_type_xy), tensor_type_xyz);
     EXPECT_EQUAL(ValueType::join(tensor_type_y, tensor_type_y), tensor_type_y);
-}
-
-TEST("require that indexed dimensions combine to the minimal dimension size") {
-    ValueType tensor_0 = ValueType::tensor_type({{"x", 0}});
-    ValueType tensor_10 = ValueType::tensor_type({{"x", 10}});
-    ValueType tensor_20 = ValueType::tensor_type({{"x", 20}});
-    EXPECT_EQUAL(ValueType::join(tensor_10, tensor_0), tensor_0);
-    EXPECT_EQUAL(ValueType::join(tensor_10, tensor_10), tensor_10);
-    EXPECT_EQUAL(ValueType::join(tensor_10, tensor_20), tensor_10);
-}
-
-void verify_combinable(const ValueType &a, const ValueType &b) {
-    EXPECT_TRUE(!ValueType::join(a, b).is_error());
-    EXPECT_TRUE(!ValueType::join(b, a).is_error());
-    EXPECT_TRUE(!ValueType::join(a, b).is_any());
-    EXPECT_TRUE(!ValueType::join(b, a).is_any());
+    EXPECT_EQUAL(ValueType::join(scalar, tensor_type_y), tensor_type_y);
+    EXPECT_EQUAL(ValueType::join(tensor_type_a10, tensor_type_a10), tensor_type_a10);
+    EXPECT_EQUAL(ValueType::join(tensor_type_a10, scalar), tensor_type_a10);
+    EXPECT_EQUAL(ValueType::join(tensor_type_xyz, tensor_type_a10), tensor_type_a10xyz);
 }
 
 void verify_not_combinable(const ValueType &a, const ValueType &b) {
@@ -218,70 +192,49 @@ void verify_not_combinable(const ValueType &a, const ValueType &b) {
     EXPECT_TRUE(ValueType::join(b, a).is_error());
 }
 
-void verify_maybe_combinable(const ValueType &a, const ValueType &b) {
-    EXPECT_TRUE(ValueType::join(a, b).is_any());
-    EXPECT_TRUE(ValueType::join(b, a).is_any());
-}
-
 TEST("require that mapped and indexed dimensions are not combinable") {
     verify_not_combinable(ValueType::tensor_type({{"x", 10}}), ValueType::tensor_type({{"x"}}));
 }
 
-TEST("require that dimension combining is only allowed (yes/no/maybe) for appropriate types") {
-    std::vector<ValueType> types = { ValueType::any_type(), ValueType::error_type(), ValueType::double_type(),
-                                     ValueType::tensor_type({}), ValueType::tensor_type({{"x"}}) };
-    for (size_t a = 0; a < types.size(); ++a) {
-        for (size_t b = a; b < types.size(); ++b) {
-            TEST_STATE(vespalib::make_string("a='%s', b='%s'", types[a].to_spec().c_str(), types[b].to_spec().c_str()).c_str());
-            if (types[a].is_error() || types[b].is_error()) {
-                verify_not_combinable(types[a], types[b]);
-            } else if (types[a].is_any() || types[b].is_any()) {
-                verify_maybe_combinable(types[a], types[b]);
-            } else if (types[a].is_double() || types[b].is_double()) {
-                verify_combinable(types[a], types[b]);
-            } else if (types[a].unknown_dimensions() || types[b].unknown_dimensions()) {
-                verify_maybe_combinable(types[a], types[b]);
-            } else {
-                verify_combinable(types[a], types[b]);
-            }
-        }
-    }
+TEST("require that indexed dimensions of different sizes are not combinable") {
+    verify_not_combinable(ValueType::tensor_type({{"x", 10}}), ValueType::tensor_type({{"x", 20}}));
+}
+
+TEST("require that error type combined with anything produces error type") {
+    verify_not_combinable(ValueType::error_type(), ValueType::error_type());
+    verify_not_combinable(ValueType::error_type(), ValueType::double_type());
+    verify_not_combinable(ValueType::error_type(), ValueType::tensor_type({{"x"}}));
+    verify_not_combinable(ValueType::error_type(), ValueType::tensor_type({{"x", 10}}));
 }
 
 TEST("require that value type can make spec") {
-    EXPECT_EQUAL("any", ValueType::any_type().to_spec());
     EXPECT_EQUAL("error", ValueType::error_type().to_spec());
     EXPECT_EQUAL("double", ValueType::double_type().to_spec());
-    EXPECT_EQUAL("tensor", ValueType::tensor_type({}).to_spec());
+    EXPECT_EQUAL("double", ValueType::tensor_type({}).to_spec());
     EXPECT_EQUAL("tensor(x{})", ValueType::tensor_type({{"x"}}).to_spec());
     EXPECT_EQUAL("tensor(y[10])", ValueType::tensor_type({{"y", 10}}).to_spec());
-    EXPECT_EQUAL("tensor(z[])", ValueType::tensor_type({{"z", 0}}).to_spec());
-    EXPECT_EQUAL("tensor(x{},y[10],z[])", ValueType::tensor_type({{"x"}, {"y", 10}, {"z", 0}}).to_spec());
+    EXPECT_EQUAL("tensor(x{},y[10],z[5])", ValueType::tensor_type({{"x"}, {"y", 10}, {"z", 5}}).to_spec());
 }
 
 TEST("require that value type spec can be parsed") {
-    EXPECT_EQUAL(ValueType::any_type(), ValueType::from_spec("any"));
     EXPECT_EQUAL(ValueType::double_type(), ValueType::from_spec("double"));
     EXPECT_EQUAL(ValueType::tensor_type({}), ValueType::from_spec("tensor"));
     EXPECT_EQUAL(ValueType::tensor_type({}), ValueType::from_spec("tensor()"));
     EXPECT_EQUAL(ValueType::tensor_type({{"x"}}), ValueType::from_spec("tensor(x{})"));
     EXPECT_EQUAL(ValueType::tensor_type({{"y", 10}}), ValueType::from_spec("tensor(y[10])"));
-    EXPECT_EQUAL(ValueType::tensor_type({{"z", 0}}), ValueType::from_spec("tensor(z[])"));
-    EXPECT_EQUAL(ValueType::tensor_type({{"x"}, {"y", 10}, {"z", 0}}), ValueType::from_spec("tensor(x{},y[10],z[])"));
+    EXPECT_EQUAL(ValueType::tensor_type({{"x"}, {"y", 10}, {"z", 5}}), ValueType::from_spec("tensor(x{},y[10],z[5])"));
     EXPECT_EQUAL(ValueType::tensor_type({{"y", 10}}), ValueType::from_spec("tensor<double>(y[10])"));
     EXPECT_EQUAL(ValueType::tensor_type({{"y", 10}}), ValueType::from_spec("tensor<float>(y[10])"));
 }
 
 TEST("require that value type spec can be parsed with extra whitespace") {
-    EXPECT_EQUAL(ValueType::any_type(), ValueType::from_spec(" any "));
     EXPECT_EQUAL(ValueType::double_type(), ValueType::from_spec(" double "));
     EXPECT_EQUAL(ValueType::tensor_type({}), ValueType::from_spec(" tensor "));
     EXPECT_EQUAL(ValueType::tensor_type({}), ValueType::from_spec(" tensor ( ) "));
     EXPECT_EQUAL(ValueType::tensor_type({{"x"}}), ValueType::from_spec(" tensor ( x { } ) "));
     EXPECT_EQUAL(ValueType::tensor_type({{"y", 10}}), ValueType::from_spec(" tensor ( y [ 10 ] ) "));
-    EXPECT_EQUAL(ValueType::tensor_type({{"z", 0}}), ValueType::from_spec(" tensor ( z [ ] ) "));
-    EXPECT_EQUAL(ValueType::tensor_type({{"x"}, {"y", 10}, {"z", 0}}),
-                 ValueType::from_spec(" tensor ( x { } , y [ 10 ] , z [ ] ) "));
+    EXPECT_EQUAL(ValueType::tensor_type({{"x"}, {"y", 10}, {"z", 5}}),
+                 ValueType::from_spec(" tensor ( x { } , y [ 10 ] , z [ 5 ] ) "));
     EXPECT_EQUAL(ValueType::tensor_type({{"y", 10}}), ValueType::from_spec(" tensor < double > ( y [ 10 ] ) "));
     EXPECT_EQUAL(ValueType::tensor_type({{"y", 10}}), ValueType::from_spec(" tensor < float > ( y [ 10 ] ) "));
 }
@@ -290,6 +243,7 @@ TEST("require that malformed value type spec is parsed as error") {
     EXPECT_TRUE(ValueType::from_spec("").is_error());
     EXPECT_TRUE(ValueType::from_spec("  ").is_error());
     EXPECT_TRUE(ValueType::from_spec("error").is_error());
+    EXPECT_TRUE(ValueType::from_spec("any").is_error());
     EXPECT_TRUE(ValueType::from_spec("tensor tensor").is_error());
     EXPECT_TRUE(ValueType::from_spec("tensor(x{10})").is_error());
     EXPECT_TRUE(ValueType::from_spec("tensor(x{},)").is_error());
@@ -304,6 +258,7 @@ TEST("require that malformed value type spec is parsed as error") {
     EXPECT_TRUE(ValueType::from_spec("tensor(x{},x{})").is_error());
     EXPECT_TRUE(ValueType::from_spec("tensor(x{},x[10])").is_error());
     EXPECT_TRUE(ValueType::from_spec("tensor(x{},x[])").is_error());
+    EXPECT_TRUE(ValueType::from_spec("tensor(z[])").is_error());
     EXPECT_TRUE(ValueType::from_spec("tensor<float16>(x[10])").is_error());
 }
 
@@ -327,15 +282,8 @@ ParseResult::ParseResult(const vespalib::string &spec_in)
 ParseResult::~ParseResult() { }
 
 TEST("require that we can parse a partial string into a type with the low-level API") {
-    ParseResult result("tensor(a[]) , ");
-    EXPECT_EQUAL(result.type, ValueType::tensor_type({{"a", 0}}));
-    ASSERT_TRUE(result.after_inside());
-    EXPECT_EQUAL(*result.after, ',');
-}
-
-TEST("require that we can parse an abstract tensor type from a partial string") {
-    ParseResult result("tensor , ");
-    EXPECT_EQUAL(result.type, ValueType::tensor_type({}));
+    ParseResult result("tensor(a[5]) , ");
+    EXPECT_EQUAL(result.type, ValueType::tensor_type({{"a", 5}}));
     ASSERT_TRUE(result.after_inside());
     EXPECT_EQUAL(*result.after, ',');
 }
@@ -349,55 +297,28 @@ TEST("require that 'error' is the valid representation of the error type") {
     EXPECT_TRUE(invalid.after == nullptr); // parse not ok
 }
 
-TEST("require that a sparse type must be a tensor with dimensions that all are mapped") {
-    EXPECT_TRUE(ValueType::from_spec("tensor(x{})").is_sparse());
-    EXPECT_TRUE(ValueType::from_spec("tensor(x{},y{})").is_sparse());
-    EXPECT_FALSE(ValueType::from_spec("tensor()").is_sparse());
-    EXPECT_FALSE(ValueType::from_spec("tensor(x[])").is_sparse());
-    EXPECT_FALSE(ValueType::from_spec("tensor(x{},y[])").is_sparse());
-    EXPECT_FALSE(ValueType::from_spec("double").is_sparse());
-    EXPECT_FALSE(ValueType::from_spec("any").is_sparse());
-    EXPECT_FALSE(ValueType::from_spec("error").is_sparse());
-}
-
-TEST("require that a dense type must be a tensor with dimensions that all are indexed") {
-    EXPECT_TRUE(ValueType::from_spec("tensor(x[])").is_dense());
-    EXPECT_TRUE(ValueType::from_spec("tensor(x[],y[])").is_dense());
-    EXPECT_FALSE(ValueType::from_spec("tensor()").is_dense());
-    EXPECT_FALSE(ValueType::from_spec("tensor(x{})").is_dense());
-    EXPECT_FALSE(ValueType::from_spec("tensor(x[],y{})").is_dense());
-    EXPECT_FALSE(ValueType::from_spec("double").is_dense());
-    EXPECT_FALSE(ValueType::from_spec("any").is_dense());
-    EXPECT_FALSE(ValueType::from_spec("error").is_dense());
-}
-
 TEST("require that tensor dimensions can be renamed") {
     EXPECT_EQUAL(ValueType::from_spec("tensor(x{})").rename({"x"}, {"y"}),
                  ValueType::from_spec("tensor(y{})"));
-    EXPECT_EQUAL(ValueType::from_spec("tensor(x{},y[])").rename({"x","y"}, {"y","x"}),
-                 ValueType::from_spec("tensor(y{},x[])"));
+    EXPECT_EQUAL(ValueType::from_spec("tensor(x{},y[5])").rename({"x","y"}, {"y","x"}),
+                 ValueType::from_spec("tensor(y{},x[5])"));
     EXPECT_EQUAL(ValueType::from_spec("tensor(x{})").rename({"x"}, {"x"}),
                  ValueType::from_spec("tensor(x{})"));
     EXPECT_EQUAL(ValueType::from_spec("tensor(x{})").rename({}, {}), ValueType::error_type());
     EXPECT_EQUAL(ValueType::double_type().rename({}, {}), ValueType::error_type());
     EXPECT_EQUAL(ValueType::from_spec("tensor(x{},y{})").rename({"x"}, {"y","z"}), ValueType::error_type());
     EXPECT_EQUAL(ValueType::from_spec("tensor(x{},y{})").rename({"x","y"}, {"z"}), ValueType::error_type());
-    EXPECT_EQUAL(ValueType::tensor_type({}).rename({"x"}, {"y"}), ValueType::any_type());
-    EXPECT_EQUAL(ValueType::any_type().rename({"x"}, {"y"}), ValueType::any_type());
     EXPECT_EQUAL(ValueType::double_type().rename({"a"}, {"b"}), ValueType::error_type());
     EXPECT_EQUAL(ValueType::error_type().rename({"a"}, {"b"}), ValueType::error_type());
 }
 
 TEST("require that types can be concatenated") {
     ValueType error    = ValueType::error_type();
-    ValueType any      = ValueType::any_type();
-    ValueType tensor   = ValueType::tensor_type({});
     ValueType scalar   = ValueType::double_type();
     ValueType vx_2     = ValueType::from_spec("tensor(x[2])");
     ValueType vx_m     = ValueType::from_spec("tensor(x{})");
     ValueType vx_3     = ValueType::from_spec("tensor(x[3])");
     ValueType vx_5     = ValueType::from_spec("tensor(x[5])");
-    ValueType vx_any   = ValueType::from_spec("tensor(x[])");
     ValueType vy_7     = ValueType::from_spec("tensor(y[7])");
     ValueType mxy_22   = ValueType::from_spec("tensor(x[2],y[2])");
     ValueType mxy_52   = ValueType::from_spec("tensor(x[5],y[2])");
@@ -407,30 +328,20 @@ TEST("require that types can be concatenated") {
 
     EXPECT_EQUAL(ValueType::concat(error,  vx_2,   "x"), error);
     EXPECT_EQUAL(ValueType::concat(vx_2,   error,  "x"), error);
-    EXPECT_EQUAL(ValueType::concat(error,  any,    "x"), error);
-    EXPECT_EQUAL(ValueType::concat(any,    error,  "x"), error);
     EXPECT_EQUAL(ValueType::concat(vx_m,   vx_2,   "x"), error);
     EXPECT_EQUAL(ValueType::concat(vx_2,   vx_m,   "x"), error);
     EXPECT_EQUAL(ValueType::concat(vx_m,   vx_m,   "x"), error);
     EXPECT_EQUAL(ValueType::concat(vx_m,   scalar, "x"), error);
     EXPECT_EQUAL(ValueType::concat(scalar, vx_m,   "x"), error);
+    EXPECT_EQUAL(ValueType::concat(vx_2,   vx_3,   "y"), error);
     EXPECT_EQUAL(ValueType::concat(vy_7,   vx_m,   "z"), cxyz_m72);
-    EXPECT_EQUAL(ValueType::concat(tensor, vx_2,   "x"), any);
-    EXPECT_EQUAL(ValueType::concat(vx_2,   tensor, "x"), any);
-    EXPECT_EQUAL(ValueType::concat(any,    vx_2,   "x"), any);
-    EXPECT_EQUAL(ValueType::concat(vx_2,   any,    "x"), any);
-    EXPECT_EQUAL(ValueType::concat(any,    tensor, "x"), any);
-    EXPECT_EQUAL(ValueType::concat(tensor, any,    "x"), any);
     EXPECT_EQUAL(ValueType::concat(scalar, scalar, "x"), vx_2);
     EXPECT_EQUAL(ValueType::concat(vx_2,   scalar, "x"), vx_3);
     EXPECT_EQUAL(ValueType::concat(scalar, vx_2,   "x"), vx_3);
     EXPECT_EQUAL(ValueType::concat(vx_2,   vx_3,   "x"), vx_5);
-    EXPECT_EQUAL(ValueType::concat(vx_2,   vx_any, "x"), vx_any);
-    EXPECT_EQUAL(ValueType::concat(vx_any, vx_2,   "x"), vx_any);
     EXPECT_EQUAL(ValueType::concat(scalar, vx_2,   "y"), mxy_22);
     EXPECT_EQUAL(ValueType::concat(vx_2, scalar,   "y"), mxy_22);
-    EXPECT_EQUAL(ValueType::concat(vx_2,   vx_3,   "y"), mxy_22);
-    EXPECT_EQUAL(ValueType::concat(vx_3,   vx_2,   "y"), mxy_22);
+    EXPECT_EQUAL(ValueType::concat(vx_2,   vx_2,   "y"), mxy_22);
     EXPECT_EQUAL(ValueType::concat(mxy_22, vx_3,   "x"), mxy_52);
     EXPECT_EQUAL(ValueType::concat(vx_3,   mxy_22, "x"), mxy_52);
     EXPECT_EQUAL(ValueType::concat(mxy_22, vy_7,   "y"), mxy_29);
@@ -438,34 +349,4 @@ TEST("require that types can be concatenated") {
     EXPECT_EQUAL(ValueType::concat(vx_5,   vy_7,   "z"), cxyz_572);
 }
 
-TEST("require that 'either' gives appropriate type") {
-    ValueType error    = ValueType::error_type();
-    ValueType any      = ValueType::any_type();
-    ValueType tensor   = ValueType::tensor_type({});
-    ValueType scalar   = ValueType::double_type();
-    ValueType vx_2     = ValueType::from_spec("tensor(x[2])");
-    ValueType vx_m     = ValueType::from_spec("tensor(x{})");
-    ValueType vx_3     = ValueType::from_spec("tensor(x[3])");
-    ValueType vx_any   = ValueType::from_spec("tensor(x[])");
-    ValueType vy_2     = ValueType::from_spec("tensor(y[2])");
-    ValueType mxy_22   = ValueType::from_spec("tensor(x[2],y[2])");
-    ValueType mxy_23   = ValueType::from_spec("tensor(x[2],y[3])");
-    ValueType mxy_32   = ValueType::from_spec("tensor(x[3],y[2])");
-    ValueType mxy_any2 = ValueType::from_spec("tensor(x[],y[2])");
-    ValueType mxy_2any = ValueType::from_spec("tensor(x[2],y[])");
-
-    EXPECT_EQUAL(ValueType::either(vx_2, error), error);
-    EXPECT_EQUAL(ValueType::either(error, vx_2), error);
-    EXPECT_EQUAL(ValueType::either(vx_2, vx_2), vx_2);
-    EXPECT_EQUAL(ValueType::either(vx_2, scalar), any);
-    EXPECT_EQUAL(ValueType::either(scalar, vx_2), any);
-    EXPECT_EQUAL(ValueType::either(vx_2, mxy_22), tensor);
-    EXPECT_EQUAL(ValueType::either(tensor, vx_2), tensor);
-    EXPECT_EQUAL(ValueType::either(vx_2, vy_2), tensor);
-    EXPECT_EQUAL(ValueType::either(vx_2, vx_m), tensor);
-    EXPECT_EQUAL(ValueType::either(vx_2, vx_3), vx_any);
-    EXPECT_EQUAL(ValueType::either(mxy_22, mxy_23), mxy_2any);
-    EXPECT_EQUAL(ValueType::either(mxy_32, mxy_22), mxy_any2);
-}
-
 TEST_MAIN() { TEST_RUN_ALL(); }
diff --git a/eval/src/tests/tensor/dense_add_dimension_optimizer/dense_add_dimension_optimizer_test.cpp b/eval/src/tests/tensor/dense_add_dimension_optimizer/dense_add_dimension_optimizer_test.cpp
index ce321b7c3c3..9b46fc3393a 100644
--- a/eval/src/tests/tensor/dense_add_dimension_optimizer/dense_add_dimension_optimizer_test.cpp
+++ b/eval/src/tests/tensor/dense_add_dimension_optimizer/dense_add_dimension_optimizer_test.cpp
@@ -27,7 +27,6 @@ EvalFixture::ParamRepo make_params() {
         .add("x5", spec({x(5)}, N()))
         .add("x5y1", spec({x(5),y(1)}, N()))
         .add("y1z1", spec({y(1),z(1)}, N()))
-        .add("x5_u", spec({x(5)}, N()), "tensor(x[])")
         .add("x_m", spec({x({"a"})}, N()));
 }
 EvalFixture::ParamRepo param_repo = make_params();
@@ -80,15 +79,11 @@ TEST("require that non-canonical dimension addition is not optimized") {
 }
 
 TEST("require that dimension addition with overlapping dimensions is not optimized") {
-    TEST_DO(verify_not_optimized("x5*tensor(x[1],y[1])(1)"));
-    TEST_DO(verify_not_optimized("tensor(x[1],y[1])(1)*x5"));
     TEST_DO(verify_not_optimized("x5y1*tensor(y[1],z[1])(1)"));
     TEST_DO(verify_not_optimized("tensor(y[1],z[1])(1)*x5y1"));
 }
 
 TEST("require that dimension addition with inappropriate dimensions is not optimized") {
-    TEST_DO(verify_not_optimized("x5_u*tensor(y[1])(1)"));
-    TEST_DO(verify_not_optimized("tensor(y[1])(1)*x5_u"));
     TEST_DO(verify_not_optimized("x_m*tensor(y[1])(1)"));
     TEST_DO(verify_not_optimized("tensor(y[1])(1)*x_m"));
 }
diff --git a/eval/src/tests/tensor/dense_dot_product_function/dense_dot_product_function_test.cpp b/eval/src/tests/tensor/dense_dot_product_function/dense_dot_product_function_test.cpp
index 60830e4abd7..fae5db75618 100644
--- a/eval/src/tests/tensor/dense_dot_product_function/dense_dot_product_function_test.cpp
+++ b/eval/src/tests/tensor/dense_dot_product_function/dense_dot_product_function_test.cpp
@@ -62,11 +62,6 @@ TEST("require that basic dot product with equal sizes is correct") {
     check_gen_with_result(2, 2, (3.0 * 5.0) + (4.0 * 6.0));
 }
 
-TEST("require that basic dot product with un-equal sizes is correct") {
-    check_gen_with_result(2, 3, (3.0 * 5.0) + (4.0 * 6.0));
-    check_gen_with_result(3, 2, (3.0 * 5.0) + (4.0 * 6.0));
-}
-
 //-----------------------------------------------------------------------------
 
 void assertDotProduct(size_t numCells) {
@@ -98,18 +93,6 @@ TEST("require that dot product with equal sizes is correct") {
     TEST_DO(assertDotProduct(1024 + 3));
 }
 
-TEST("require that dot product with un-equal sizes is correct") {
-    TEST_DO(assertDotProduct(8, 8 + 3));
-    TEST_DO(assertDotProduct(8 + 3, 8));
-    TEST_DO(assertDotProduct(16, 16 + 3));
-    TEST_DO(assertDotProduct(32, 32 + 3));
-    TEST_DO(assertDotProduct(64, 64 + 3));
-    TEST_DO(assertDotProduct(128, 128 + 3));
-    TEST_DO(assertDotProduct(256, 256 + 3));
-    TEST_DO(assertDotProduct(512, 512 + 3));
-    TEST_DO(assertDotProduct(1024, 1024 + 3));
-}
-
 //-----------------------------------------------------------------------------
 
 EvalFixture::ParamRepo make_params() {
@@ -120,14 +103,8 @@ EvalFixture::ParamRepo make_params() {
         .add("v04_y3", spec({y(3)}, MyVecSeq(10)))
         .add("v05_x5", spec({x(5)}, MyVecSeq(6.0)))
         .add("v06_x5", spec({x(5)}, MyVecSeq(7.0)))
-        .add("v07_x3_a", spec({x(3)}, MyVecSeq(8.0)), "any")
-        .add("v08_x3_u", spec({x(3)}, MyVecSeq(9.0)), "tensor(x[])")
-        .add("v09_x4_u", spec({x(4)}, MyVecSeq(3.0)), "tensor(x[])")
         .add("m01_x3y3", spec({x(3),y(3)}, MyVecSeq(1.0)))
-        .add("m02_x2y3", spec({x(2),y(3)}, MyVecSeq(2.0)))
-        .add("m03_x3y2", spec({x(3),y(2)}, MyVecSeq(3.0)))
-        .add("m04_xuy3", spec({x(3),y(3)}, MyVecSeq(4.0)), "tensor(x[],y[3])")
-        .add("m05_x3yu", spec({x(3),y(3)}, MyVecSeq(5.0)), "tensor(x[3],y[])");
+        .add("m02_x3y3", spec({x(3),y(3)}, MyVecSeq(2.0)));
 }
 EvalFixture::ParamRepo param_repo = make_params();
 
@@ -146,11 +123,6 @@ void assertNotOptimized(const vespalib::string &expr) {
     EXPECT_TRUE(info.empty());
 }
 
-TEST("require that dot product is not optimized for unknown types") {
-    TEST_DO(assertNotOptimized("reduce(v02_x3*v07_x3_a,sum)"));
-    TEST_DO(assertNotOptimized("reduce(v07_x3_a*v03_x3,sum)"));
-}
-
 TEST("require that dot product works with tensor function") {
     TEST_DO(assertOptimized("reduce(v05_x5*v06_x5,sum)"));
     TEST_DO(assertOptimized("reduce(v05_x5*v06_x5,sum,x)"));
@@ -162,18 +134,11 @@ TEST("require that dot product with compatible dimensions is optimized") {
     TEST_DO(assertOptimized("reduce(v01_x1*v01_x1,sum)"));
     TEST_DO(assertOptimized("reduce(v02_x3*v03_x3,sum)"));
     TEST_DO(assertOptimized("reduce(v05_x5*v06_x5,sum)"));
-
-    TEST_DO(assertOptimized("reduce(v02_x3*v06_x5,sum)"));
-    TEST_DO(assertOptimized("reduce(v05_x5*v03_x3,sum)"));
-    TEST_DO(assertOptimized("reduce(v08_x3_u*v05_x5,sum)"));
-    TEST_DO(assertOptimized("reduce(v05_x5*v08_x3_u,sum)"));
 }
 
 TEST("require that dot product with incompatible dimensions is NOT optimized") {
     TEST_DO(assertNotOptimized("reduce(v02_x3*v04_y3,sum)"));
     TEST_DO(assertNotOptimized("reduce(v04_y3*v02_x3,sum)"));
-    TEST_DO(assertNotOptimized("reduce(v08_x3_u*v04_y3,sum)"));
-    TEST_DO(assertNotOptimized("reduce(v04_y3*v08_x3_u,sum)"));
     TEST_DO(assertNotOptimized("reduce(v02_x3*m01_x3y3,sum)"));
     TEST_DO(assertNotOptimized("reduce(m01_x3y3*v02_x3,sum)"));
 }
@@ -188,11 +153,8 @@ TEST("require that expressions similar to dot product are not optimized") {
 }
 
 TEST("require that multi-dimensional dot product can be optimized") {
-    TEST_DO(assertOptimized("reduce(m01_x3y3*m02_x2y3,sum)"));
-    TEST_DO(assertOptimized("reduce(m02_x2y3*m01_x3y3,sum)"));
-    TEST_DO(assertOptimized("reduce(m01_x3y3*m04_xuy3,sum)"));
-    TEST_DO(assertOptimized("reduce(m04_xuy3*m01_x3y3,sum)"));
-    TEST_DO(assertOptimized("reduce(m04_xuy3*m04_xuy3,sum)"));
+    TEST_DO(assertOptimized("reduce(m01_x3y3*m02_x3y3,sum)"));
+    TEST_DO(assertOptimized("reduce(m02_x3y3*m01_x3y3,sum)"));
 }
 
 TEST("require that result must be double to trigger optimization") {
@@ -201,14 +163,6 @@ TEST("require that result must be double to trigger optimization") {
     TEST_DO(assertNotOptimized("reduce(m01_x3y3*m01_x3y3,sum,y)"));
 }
 
-TEST("require that additional dimensions must have matching size") {
-    TEST_DO(assertOptimized("reduce(m01_x3y3*m01_x3y3,sum)"));
-    TEST_DO(assertNotOptimized("reduce(m01_x3y3*m03_x3y2,sum)"));
-    TEST_DO(assertNotOptimized("reduce(m03_x3y2*m01_x3y3,sum)"));
-    TEST_DO(assertNotOptimized("reduce(m01_x3y3*m05_x3yu,sum)"));
-    TEST_DO(assertNotOptimized("reduce(m05_x3yu*m01_x3y3,sum)"));
-}
-
 void verify_compatible(const vespalib::string &a, const vespalib::string &b) {
     auto a_type = ValueType::from_spec(a);
     auto b_type = ValueType::from_spec(b);
@@ -231,13 +185,9 @@ TEST("require that type compatibility test is appropriate") {
     TEST_DO(verify_compatible("tensor(x[5])", "tensor(x[5])"));
     TEST_DO(verify_not_compatible("tensor(x[5])", "tensor(y[5])"));
     TEST_DO(verify_compatible("tensor(x[5])", "tensor(x[3])"));
-    TEST_DO(verify_compatible("tensor(x[])", "tensor(x[3])"));
     TEST_DO(verify_compatible("tensor(x[3],y[7],z[9])", "tensor(x[5],y[7],z[9])"));
-    TEST_DO(verify_compatible("tensor(x[3],y[7],z[9])", "tensor(x[],y[7],z[9])"));
     TEST_DO(verify_not_compatible("tensor(x[5],y[7],z[9])", "tensor(x[5],y[5],z[9])"));
-    TEST_DO(verify_not_compatible("tensor(x[5],y[],z[9])", "tensor(x[5],y[7],z[9])"));
     TEST_DO(verify_not_compatible("tensor(x[5],y[7],z[9])", "tensor(x[5],y[7],z[5])"));
-    TEST_DO(verify_not_compatible("tensor(x[5],y[7],z[])", "tensor(x[5],y[7],z[9])"));
 }
 
 //-----------------------------------------------------------------------------
diff --git a/eval/src/tests/tensor/dense_fast_rename_optimizer/dense_fast_rename_optimizer_test.cpp b/eval/src/tests/tensor/dense_fast_rename_optimizer/dense_fast_rename_optimizer_test.cpp
index c892ed02808..10b4c622a0a 100644
--- a/eval/src/tests/tensor/dense_fast_rename_optimizer/dense_fast_rename_optimizer_test.cpp
+++ b/eval/src/tests/tensor/dense_fast_rename_optimizer/dense_fast_rename_optimizer_test.cpp
@@ -25,7 +25,6 @@ const TensorEngine &prod_engine = DefaultTensorEngine::ref();
 EvalFixture::ParamRepo make_params() {
     return EvalFixture::ParamRepo()
         .add("x5", spec({x(5)}, N()))
-        .add("x5_u", spec({x(5)}, N()), "tensor(x[])")
         .add("x_m", spec({x({"a", "b", "c"})}, N()))
         .add("x5y3", spec({x(5),y(3)}, N()));
 }
@@ -64,10 +63,6 @@ TEST("require that transposing dense renames are not optimized") {
     TEST_DO(verify_not_optimized("rename(x5y3,(y,x),(a,b))"));
 }
 
-TEST("require that abstract dense renames are not optimized") {
-    TEST_DO(verify_not_optimized("rename(x5_u,x,y)"));
-}
-
 TEST("require that non-dense renames are not optimized") {
     TEST_DO(verify_not_optimized("rename(x_m,x,y)"));
 }
diff --git a/eval/src/tests/tensor/dense_inplace_join_function/dense_inplace_join_function_test.cpp b/eval/src/tests/tensor/dense_inplace_join_function/dense_inplace_join_function_test.cpp
index 3a5b27965d0..7ee603e1763 100644
--- a/eval/src/tests/tensor/dense_inplace_join_function/dense_inplace_join_function_test.cpp
+++ b/eval/src/tests/tensor/dense_inplace_join_function/dense_inplace_join_function_test.cpp
@@ -45,10 +45,8 @@ EvalFixture::ParamRepo make_params() {
         .add_mutable("mut_x5_A", spec({x(5)}, seq))
         .add_mutable("mut_x5_B", spec({x(5)}, seq))
         .add_mutable("mut_x5_C", spec({x(5)}, seq))
-        .add_mutable("mut_x4", spec({x(4)}, seq))
         .add_mutable("mut_x5y3_A", spec({x(5),y(3)}, seq))
         .add_mutable("mut_x5y3_B", spec({x(5),y(3)}, seq))
-        .add_mutable("mut_x5_unbound", spec({x(5)}, seq), "tensor(x[])")
         .add_mutable("mut_x_sparse", spec({x({"a", "b", "c"})}, seq));
 }
 EvalFixture::ParamRepo param_repo = make_params();
@@ -112,8 +110,6 @@ TEST("require that join(tensor,scalar) operations are not optimized") {
 }
 
 TEST("require that join with different tensor shapes are not optimized") {
-    TEST_DO(verify_not_optimized("mut_x5_A-mut_x4"));
-    TEST_DO(verify_not_optimized("mut_x4-mut_x5_A"));
     TEST_DO(verify_not_optimized("mut_x5_A*mut_x5y3_B"));
 }
 
@@ -124,12 +120,6 @@ TEST("require that inplace join operations can be chained") {
     TEST_DO(verify_p2_optimized("con_x5_A-(con_x5_B-mut_x5_C)", 2));
 }
 
-TEST("require that abstract tensors are not optimized") {
-    TEST_DO(verify_not_optimized("mut_x5_unbound+mut_x5_A"));
-    TEST_DO(verify_not_optimized("mut_x5_A+mut_x5_unbound"));
-    TEST_DO(verify_not_optimized("mut_x5_unbound+mut_x5_unbound"));
-}
-
 TEST("require that non-mutable tensors are not optimized") {
     TEST_DO(verify_not_optimized("con_x5_A+con_x5_B"));
 }
diff --git a/eval/src/tests/tensor/dense_inplace_map_function/dense_inplace_map_function_test.cpp b/eval/src/tests/tensor/dense_inplace_map_function/dense_inplace_map_function_test.cpp
index 77af747a066..a17b7e02eb8 100644
--- a/eval/src/tests/tensor/dense_inplace_map_function/dense_inplace_map_function_test.cpp
+++ b/eval/src/tests/tensor/dense_inplace_map_function/dense_inplace_map_function_test.cpp
@@ -27,7 +27,6 @@ EvalFixture::ParamRepo make_params() {
         .add_mutable("_d", spec(5.0))
         .add_mutable("_x5", spec({x(5)}, N()))
         .add_mutable("_x5y3", spec({x(5),y(3)}, N()))
-        .add_mutable("_x5_u", spec({x(5)}, N()), "tensor(x[])")
         .add_mutable("_x_m", spec({x({"a", "b", "c"})}, N()));
 }
 EvalFixture::ParamRepo param_repo = make_params();
@@ -60,10 +59,6 @@ TEST("require that inplace map operations can be chained") {
     TEST_DO(verify_optimized("map(map(_x5,f(x)(x+10)),f(x)(x-5))", 2));
 }
 
-TEST("require that abstract tensors are not optimized") {
-    TEST_DO(verify_not_optimized("map(_x5_u,f(x)(x+10))"));
-}
-
 TEST("require that non-mutable tensors are not optimized") {
     TEST_DO(verify_not_optimized("map(x5,f(x)(x+10))"));
 }
diff --git a/eval/src/tests/tensor/dense_remove_dimension_optimizer/dense_remove_dimension_optimizer_test.cpp b/eval/src/tests/tensor/dense_remove_dimension_optimizer/dense_remove_dimension_optimizer_test.cpp
index b87393c2980..ac451d10b50 100644
--- a/eval/src/tests/tensor/dense_remove_dimension_optimizer/dense_remove_dimension_optimizer_test.cpp
+++ b/eval/src/tests/tensor/dense_remove_dimension_optimizer/dense_remove_dimension_optimizer_test.cpp
@@ -26,7 +26,6 @@ EvalFixture::ParamRepo make_params() {
     return EvalFixture::ParamRepo()
         .add("x1y5z1", spec({x(1),y(5),z(1)}, N()))
         .add("x1y1z1", spec({x(1),y(1),z(1)}, N()))
-        .add("x1y5z1_u", spec({x(1),y(5),z(1)}, N()), "tensor(x[1],y[5],z[])")
         .add("x1y5z_m", spec({x(1),y(5),z({"a"})}, N()));
 }
 EvalFixture::ParamRepo param_repo = make_params();
@@ -74,8 +73,6 @@ TEST("require that full reduce is not optimized") {
 }
 
 TEST("require that inappropriate tensor types cannot be optimized") {
-    TEST_DO(verify_not_optimized("reduce(x1y5z1_u,sum,x)"));
-    TEST_DO(verify_not_optimized("reduce(x1y5z1_u,sum,z)"));
     TEST_DO(verify_not_optimized("reduce(x1y5z_m,sum,x)"));
     TEST_DO(verify_not_optimized("reduce(x1y5z_m,sum,z)"));
 }
diff --git a/eval/src/tests/tensor/dense_xw_product_function/dense_xw_product_function_test.cpp b/eval/src/tests/tensor/dense_xw_product_function/dense_xw_product_function_test.cpp
index f18e72b0d07..b55e223ab07 100644
--- a/eval/src/tests/tensor/dense_xw_product_function/dense_xw_product_function_test.cpp
+++ b/eval/src/tests/tensor/dense_xw_product_function/dense_xw_product_function_test.cpp
@@ -49,12 +49,7 @@ EvalFixture::ParamRepo make_params() {
         .add("x8y5", spec({x(8),y(5)}, MyMatSeq()))
         .add("y5z8", spec({y(5),z(8)}, MyMatSeq()))
         .add("x5y16", spec({x(5),y(16)}, MyMatSeq()))
-        .add("y16z5", spec({y(16),z(5)}, MyMatSeq()))
-        .add("a_y3", spec({y(3)}, MyVecSeq()), "any")
-        .add("y3_u", spec({y(3)}, MyVecSeq()), "tensor(y[])")
-        .add("a_x2y3", spec({x(2),y(3)}, MyMatSeq()), "any")
-        .add("x2_uy3", spec({x(2),y(3)}, MyMatSeq()), "tensor(x[],y[3])")
-        .add("x2y3_u", spec({x(2),y(3)}, MyMatSeq()), "tensor(x[2],y[])");
+        .add("y16z5", spec({y(16),z(5)}, MyMatSeq()));
 }
 EvalFixture::ParamRepo param_repo = make_params();
 
@@ -91,14 +86,6 @@ TEST("require that xw product gives same results as reference join/reduce") {
     TEST_DO(verify_optimized("reduce(y16*y16z5,sum,y)", 16, 5, false));
 }
 
-TEST("require that xw product is not optimized for abstract types") {
-    TEST_DO(verify_not_optimized("reduce(a_y3*x2y3,sum)"));
-    TEST_DO(verify_not_optimized("reduce(y3*a_x2y3,sum)"));
-    TEST_DO(verify_not_optimized("reduce(y3_u*x2y3,sum)"));
-    TEST_DO(verify_not_optimized("reduce(y3*x2_uy3,sum)"));
-    TEST_DO(verify_not_optimized("reduce(y3*x2y3_u,sum)"));
-}
-
 TEST("require that various variants of xw product can be optimized") {
     TEST_DO(verify_optimized("reduce(y3*x2y3,sum,y)", 3, 2, true));
     TEST_DO(verify_optimized("reduce(x2y3*y3,sum,y)", 3, 2, true));
@@ -118,8 +105,6 @@ TEST("require that expressions similar to xw product are not optimized") {
 }
 
 TEST("require that xw products with incompatible dimensions are not optimized") {
-    TEST_DO(verify_not_optimized("reduce(y3*x1y1,sum,y)"));
-    TEST_DO(verify_not_optimized("reduce(y3*x8y5,sum,y)"));
     TEST_DO(verify_not_optimized("reduce(y3*x2z3,sum,y)"));
     TEST_DO(verify_not_optimized("reduce(y3*x2z3,sum,z)"));
 }
diff --git a/eval/src/tests/tensor/tensor_mapper/tensor_mapper_test.cpp b/eval/src/tests/tensor/tensor_mapper/tensor_mapper_test.cpp
index 124295a500d..60b17930f0b 100644
--- a/eval/src/tests/tensor/tensor_mapper/tensor_mapper_test.cpp
+++ b/eval/src/tests/tensor/tensor_mapper/tensor_mapper_test.cpp
@@ -96,48 +96,6 @@ TEST("require that sparse tensors can be mapped to dense type") {
                    .add({{"x",1},{"y",2}}, 0)));
 }
 
-TEST("require that sparse tensors can be mapped to abstract dense type") {
-    TEST_DO(verify(TensorSpec("tensor(x{},y{})")
-                   .add({{"x","0"},{"y","0"}}, 1)
-                   .add({{"x","1"},{"y","0"}}, 3)
-                   .add({{"x","0"},{"y","1"}}, 5)
-                   .add({{"x","10"},{"y","1"}}, 7),
-                   "tensor(x[2],y[])",
-                   TensorSpec("tensor(x[2],y[2])")
-                   .add({{"x",0},{"y",0}}, 1)
-                   .add({{"x",0},{"y",1}}, 5)
-                   .add({{"x",1},{"y",0}}, 3)
-                   .add({{"x",1},{"y",1}}, 0)));
-
-    TEST_DO(verify(TensorSpec("tensor(x{},y{})")
-                   .add({{"x","0"},{"y","0"}}, 1)
-                   .add({{"x","1"},{"y","0"}}, 3)
-                   .add({{"x","0"},{"y","1"}}, 5)
-                   .add({{"x","2"},{"y","0"}}, 7),
-                   "tensor(x[],y[])",
-                   TensorSpec("tensor(x[3],y[2])")
-                   .add({{"x",0},{"y",0}}, 1)
-                   .add({{"x",0},{"y",1}}, 5)
-                   .add({{"x",1},{"y",0}}, 3)
-                   .add({{"x",1},{"y",1}}, 0)
-                   .add({{"x",2},{"y",0}}, 7)
-                   .add({{"x",2},{"y",1}}, 0)));
-
-    TEST_DO(verify(TensorSpec("tensor(x{},y{})")
-                   .add({{"x","0"},{"y","0"}}, 1)
-                   .add({{"x","1"},{"y","0"}}, 3)
-                   .add({{"x","0"},{"y","1"}}, 5)
-                   .add({{"x","10"},{"y","3"}}, 7),
-                   "tensor(x[],y[3])",
-                   TensorSpec("tensor(x[2],y[3])")
-                   .add({{"x",0},{"y",0}}, 1)
-                   .add({{"x",0},{"y",1}}, 5)
-                   .add({{"x",0},{"y",2}}, 0)
-                   .add({{"x",1},{"y",0}}, 3)
-                   .add({{"x",1},{"y",1}}, 0)
-                   .add({{"x",1},{"y",2}}, 0)));
-}
-
 TEST("require that dense tensors can be mapped to sparse type") {
     TEST_DO(verify(TensorSpec("tensor(x[2],y[2])")
                    .add({{"x",0},{"y",0}}, 1)
@@ -168,7 +126,7 @@ TEST("require that mixed tensors can be mapped to dense type") {
                    .add({{"x",0},{"y","1"}}, 3)
                    .add({{"x",1},{"y","0"}}, 5)
                    .add({{"x",1},{"y","1"}}, 7),
-                   "tensor(y[])",
+                   "tensor(y[2])",
                    TensorSpec("tensor(y[2])")
                    .add({{"y",0}}, 6)
                    .add({{"y",1}}, 10)));
@@ -180,7 +138,7 @@ TEST("require that mixed tensors can be mapped to mixed type") {
                    .add({{"x",0},{"y","1"}}, 3)
                    .add({{"x",1},{"y","0"}}, 5)
                    .add({{"x",1},{"y","1"}}, 7),
-                   "tensor(x{},y[])",
+                   "tensor(x{},y[2])",
                    TensorSpec("tensor(x{},y[2])")
                    .add({{"x","0"},{"y",0}}, 1)
                    .add({{"x","0"},{"y",1}}, 3)
@@ -194,7 +152,7 @@ TEST("require that dense tensors can be mapped to mixed type") {
                    .add({{"x",0},{"y",1}}, 3)
                    .add({{"x",1},{"y",0}}, 5)
                    .add({{"x",1},{"y",1}}, 7),
-                   "tensor(x{},y[])",
+                   "tensor(x{},y[2])",
                    TensorSpec("tensor(x{},y[2])")
                    .add({{"x","0"},{"y",0}}, 1)
                    .add({{"x","0"},{"y",1}}, 3)
@@ -208,7 +166,7 @@ TEST("require that sparse tensors can be mapped to mixed type") {
                    .add({{"x","0"},{"y","1"}}, 3)
                    .add({{"x","1"},{"y","0"}}, 5)
                    .add({{"x","1"},{"y","1"}}, 7),
-                   "tensor(x[],y{})",
+                   "tensor(x[2],y{})",
                    TensorSpec("tensor(x[2],y{})")
                    .add({{"x",0},{"y","0"}}, 1)
                    .add({{"x",0},{"y","1"}}, 3)
@@ -225,14 +183,14 @@ TEST("require that missing dimensions are added appropriately") {
 
     TEST_DO(verify(TensorSpec("tensor(x[1])")
                    .add({{"x",0}}, 42),
-                   "tensor(x[1],y[],z[2])",
+                   "tensor(x[1],y[1],z[2])",
                    TensorSpec("tensor(x[1],y[1],z[2])")
                    .add({{"x",0},{"y",0},{"z",0}}, 42)
                    .add({{"x",0},{"y",0},{"z",1}}, 0)));
 
     TEST_DO(verify(TensorSpec("tensor(a{})")
                    .add({{"a","foo"}}, 42),
-                   "tensor(a{},b[],c{},d[2])",
+                   "tensor(a{},b[1],c{},d[2])",
                    TensorSpec("tensor(a{},b[1],c{},d[2])")
                    .add({{"a","foo"},{"b",0},{"c",""},{"d",0}}, 42)
                    .add({{"a","foo"},{"b",0},{"c",""},{"d",1}}, 0)));
diff --git a/eval/src/vespa/eval/eval/node_types.cpp b/eval/src/vespa/eval/eval/node_types.cpp
index c85c81b3752..94e69aadf55 100644
--- a/eval/src/vespa/eval/eval/node_types.cpp
+++ b/eval/src/vespa/eval/eval/node_types.cpp
@@ -183,7 +183,7 @@ TypeResolver::~TypeResolver() {}
 } // namespace vespalib::eval::nodes
 
 NodeTypes::NodeTypes()
-    : _not_found(ValueType::any_type()),
+    : _not_found(ValueType::error_type()),
       _type_map()
 {
 }
diff --git a/eval/src/vespa/eval/eval/node_types.h b/eval/src/vespa/eval/eval/node_types.h
index 0b049598f4c..a9ddf371c31 100644
--- a/eval/src/vespa/eval/eval/node_types.h
+++ b/eval/src/vespa/eval/eval/node_types.h
@@ -16,7 +16,7 @@ class Function;
  * calculations for a single function. The constructor performs type
  * resolution for each node in the AST based on the type of all
  * function parameters. The default constructor creates an empty type
- * repo representing an unknown number of unknown values.
+ * repo where all lookups will result in error types.
  **/
 class NodeTypes
 {
diff --git a/eval/src/vespa/eval/eval/simple_tensor.cpp b/eval/src/vespa/eval/eval/simple_tensor.cpp
index 1bce8983666..36c18183c8a 100644
--- a/eval/src/vespa/eval/eval/simple_tensor.cpp
+++ b/eval/src/vespa/eval/eval/simple_tensor.cpp
@@ -24,7 +24,6 @@ constexpr uint32_t FLOAT_CELL_TYPE = 1;
 
 void assert_type(const ValueType &type) {
     (void) type;
-    assert(!type.is_abstract());
     assert(type.is_double() || type.is_tensor());
 }
 
diff --git a/eval/src/vespa/eval/eval/test/eval_fixture.cpp b/eval/src/vespa/eval/eval/test/eval_fixture.cpp
index bd4dcc37e3c..321b472a3fa 100644
--- a/eval/src/vespa/eval/eval/test/eval_fixture.cpp
+++ b/eval/src/vespa/eval/eval/test/eval_fixture.cpp
@@ -68,7 +68,6 @@ std::vector<Value::UP> make_params(const TensorEngine &engine, const Function &f
         auto pos = param_repo.map.find(function.param_name(i));
         ASSERT_TRUE(pos != param_repo.map.end());
         result.push_back(engine.from_spec(pos->second.value));
-        ASSERT_TRUE(!result.back()->type().is_abstract());
     }
     return result;
 }
diff --git a/eval/src/vespa/eval/eval/test/tensor_conformance.cpp b/eval/src/vespa/eval/eval/test/tensor_conformance.cpp
index 7060bb76544..8cbc7507592 100644
--- a/eval/src/vespa/eval/eval/test/tensor_conformance.cpp
+++ b/eval/src/vespa/eval/eval/test/tensor_conformance.cpp
@@ -597,14 +597,6 @@ struct TestContext {
                               spec({x(1),y(1)}, Seq({ 3 })),
                               spec({x(1),y(1)}, Seq({ 5 }))));
         TEST_DO(test_apply_op(eval,
-                              spec(x(1), Seq({ op(3, 0) })),
-                              spec(x(1), Seq({ 3 })),
-                              spec(x(2), Seq({ 0, 7 }))));
-        TEST_DO(test_apply_op(eval,
-                              spec(x(1), Seq({ op(0, 5) })),
-                              spec(x(2), Seq({ 0, 3 })),
-                              spec(x(1), Seq({ 5 }))));
-        TEST_DO(test_apply_op(eval,
                               spec({x(2),y(2),z(2)},
                                    Seq({        op(1,  7), op(1, 11),
                                                 op(2, 13), op(2, 17),
@@ -623,11 +615,9 @@ struct TestContext {
         std::vector<Layout> layouts = {
             {},                                    {},
             {x(5)},                                {x(5)},
-            {x(5)},                                {x(3)},
             {x(5)},                                {y(5)},
             {x(5)},                                {x(5),y(5)},
             {y(3)},                                {x(2),z(3)},
-            {x(3),y(5)},                           {x(4),y(4)},
             {x(3),y(5)},                           {y(5),z(7)},
             {x({"a","b","c"})},                    {x({"a","b","c"})},
             {x({"a","b","c"})},                    {x({"a","b"})},
@@ -696,12 +686,6 @@ struct TestContext {
         TEST_DO(test_dot_product(((2 * 7) + (3 * 11) + (5 * 13)),
                                  spec(x(3), Seq({ 2, 3, 5 })),
                                  spec(x(3), Seq({ 7, 11, 13 }))));
-        TEST_DO(test_dot_product(((2 * 7) + (3 * 11)),
-                                 spec(x(2), Seq({ 2, 3 })),
-                                 spec(x(3), Seq({ 7, 11, 13 }))));
-        TEST_DO(test_dot_product(((2 * 7) + (3 * 11)),
-                                 spec(x(3), Seq({ 2, 3, 5 })),
-                                 spec(x(2), Seq({ 7, 11 }))));
     }
 
     //-------------------------------------------------------------------------
@@ -729,7 +713,7 @@ struct TestContext {
                             spec({x(4),y(2)}, Seq({1.0, 2.0, 3.0, 4.0, 5.0, 5.0, 6.0, 6.0}))));
         TEST_DO(test_concat(spec(z(3), Seq({1.0, 2.0, 3.0})), spec(y(2), Seq({4.0, 5.0})), "x",
                             spec({x(2),y(2),z(3)}, Seq({1.0, 2.0, 3.0, 1.0, 2.0, 3.0, 4.0, 4.0, 4.0, 5.0, 5.0, 5.0}))));
-        TEST_DO(test_concat(spec(y(3), Seq({1.0, 2.0, 3.0})), spec(y(2), Seq({4.0, 5.0})), "x",
+        TEST_DO(test_concat(spec(y(2), Seq({1.0, 2.0})), spec(y(2), Seq({4.0, 5.0})), "x",
                             spec({x(2), y(2)}, Seq({1.0, 2.0, 4.0, 5.0}))));
     }
 
diff --git a/eval/src/vespa/eval/eval/value_type.cpp b/eval/src/vespa/eval/eval/value_type.cpp
index 1d49fe494b7..269f17b71c5 100644
--- a/eval/src/vespa/eval/eval/value_type.cpp
+++ b/eval/src/vespa/eval/eval/value_type.cpp
@@ -20,11 +20,6 @@ size_t my_dimension_index(const std::vector<Dimension> &list, const vespalib::st
     return ValueType::Dimension::npos;
 }
 
-Dimension *find_dimension(std::vector<Dimension> &list, const vespalib::string &name) {
-    size_t idx = my_dimension_index(list, name);
-    return (idx != ValueType::Dimension::npos) ? &list[idx] : nullptr;
-}
-
 const Dimension *find_dimension(const std::vector<Dimension> &list, const vespalib::string &name) {
     size_t idx = my_dimension_index(list, name);
     return (idx != ValueType::Dimension::npos) ? &list[idx] : nullptr;
@@ -35,50 +30,71 @@ void sort_dimensions(DimensionList &dimensions) {
               [](const auto &a, const auto &b){ return (a.name < b.name); });
 }
 
-bool has_duplicates(const DimensionList &dimensions) {
-    for (size_t i = 1; i < dimensions.size(); ++i) {
-        if (dimensions[i - 1].name == dimensions[i].name) {
-            return true;
+bool verify_dimensions(const DimensionList &dimensions) {
+    for (size_t i = 0; i < dimensions.size(); ++i) {
+        if (dimensions[i].size == 0) {
+            return false;
+        }
+        if ((i > 0) && (dimensions[i - 1].name == dimensions[i].name)) {
+            return false;
         }
     }
-    return false;
+    return true;
 }
 
-struct DimensionResult {
+struct MyJoin {
     bool mismatch;
     DimensionList dimensions;
-    DimensionResult() : mismatch(false), dimensions() {}
+    vespalib::string concat_dim;
+    MyJoin(const DimensionList &lhs, const DimensionList &rhs)
+        : mismatch(false), dimensions(), concat_dim() { my_join(lhs, rhs); }
+    MyJoin(const DimensionList &lhs, const DimensionList &rhs, vespalib::string concat_dim_in)
+        : mismatch(false), dimensions(), concat_dim(concat_dim_in) { my_join(lhs, rhs); }
+    ~MyJoin();
+private:
     void add(const Dimension &a) {
-        dimensions.push_back(a);
+        if (a.name == concat_dim) {
+            if (a.is_indexed()) {
+                dimensions.emplace_back(a.name, a.size + 1);
+            } else {
+                mismatch = true;
+            }
+        } else {
+            dimensions.push_back(a);
+        }
     }
     void unify(const Dimension &a, const Dimension &b) {
-        if (a.is_mapped() == b.is_mapped()) {
-            add(Dimension(a.name, std::min(a.size, b.size)));
+        if (a.name == concat_dim) {
+            if (a.is_indexed() && b.is_indexed()) {
+                dimensions.emplace_back(a.name, a.size + b.size);
+            } else {
+                mismatch = true;
+            }
+        } else if (a == b) {
+            add(a);
         } else {
             mismatch = true;
         }
     }
-};
-
-DimensionResult my_join(const DimensionList &lhs, const DimensionList &rhs) {
-    DimensionResult result;
-    auto pos = rhs.begin();
-    auto end = rhs.end();
-    for (const Dimension &dim: lhs) {
-        while ((pos != end) && (pos->name < dim.name)) {
-            result.add(*pos++);
+    void my_join(const DimensionList &lhs, const DimensionList &rhs) {
+        auto pos = rhs.begin();
+        auto end = rhs.end();
+        for (const Dimension &dim: lhs) {
+            while ((pos != end) && (pos->name < dim.name)) {
+                add(*pos++);
+            }
+            if ((pos != end) && (pos->name == dim.name)) {
+                unify(dim, *pos++);
+            } else {
+                add(dim);
+            }
         }
-        if ((pos != end) && (pos->name == dim.name)) {
-            result.unify(dim, *pos++);
-        } else {
-            result.add(dim);
+        while (pos != end) {
+            add(*pos++);
         }
     }
-    while (pos != end) {
-        result.add(*pos++);
-    }
-    return result;
-}
+};
+MyJoin::~MyJoin() = default;
 
 struct Renamer {
     const std::vector<vespalib::string> &from;
@@ -104,10 +120,11 @@ struct Renamer {
 constexpr ValueType::Dimension::size_type ValueType::Dimension::npos;
 
 ValueType::~ValueType() = default;
+
 bool
 ValueType::is_sparse() const
 {
-    if (!is_tensor() || dimensions().empty()) {
+    if (dimensions().empty()) {
         return false;
     }
     for (const auto &dim : dimensions()) {
@@ -121,7 +138,7 @@ ValueType::is_sparse() const
 bool
 ValueType::is_dense() const
 {
-    if (!is_tensor() || dimensions().empty()) {
+    if (dimensions().empty()) {
         return false;
     }
     for (const auto &dim : dimensions()) {
@@ -150,14 +167,10 @@ ValueType::dimension_names() const
 ValueType
 ValueType::reduce(const std::vector<vespalib::string> &dimensions_in) const
 {
-    if (is_error() || is_any()) {
-        return *this;
+    if (is_error()) {
+        return error_type();
     } else if (dimensions_in.empty()) {
         return double_type();
-    } else if (!is_tensor()) {
-        return error_type();
-    } else if (_dimensions.empty()) {
-        return any_type();
     }
     size_t removed = 0;
     std::vector<Dimension> result;
@@ -171,9 +184,6 @@ ValueType::reduce(const std::vector<vespalib::string> &dimensions_in) const
     if (removed != dimensions_in.size()) {
         return error_type();
     }
-    if (result.empty()) {
-        return double_type();
-    }
     return tensor_type(std::move(result));
 }
 
@@ -181,12 +191,9 @@ ValueType
 ValueType::rename(const std::vector<vespalib::string> &from,
                   const std::vector<vespalib::string> &to) const
 {
-    if (!maybe_tensor() || from.empty() || (from.size() != to.size())) {
+    if (from.empty() || (from.size() != to.size())) {
         return error_type();
     }
-    if (unknown_dimensions()) {
-        return any_type();
-    }
     Renamer renamer(from, to);
     std::vector<Dimension> dim_list;
     for (const auto &dim: _dimensions) {
@@ -201,8 +208,11 @@ ValueType::rename(const std::vector<vespalib::string> &from,
 ValueType
 ValueType::tensor_type(std::vector<Dimension> dimensions_in)
 {
+    if (dimensions_in.empty()) {
+        return double_type();
+    }
     sort_dimensions(dimensions_in);
-    if (has_duplicates(dimensions_in)) {
+    if (!verify_dimensions(dimensions_in)) {
         return error_type();
     }
     return ValueType(Type::TENSOR, std::move(dimensions_in));
@@ -229,10 +239,8 @@ ValueType::join(const ValueType &lhs, const ValueType &rhs)
         return rhs;
     } else if (rhs.is_double()) {
         return lhs;
-    } else if (lhs.unknown_dimensions() || rhs.unknown_dimensions()) {
-        return any_type();
     }
-    DimensionResult result = my_join(lhs._dimensions, rhs._dimensions);
+    MyJoin result(lhs._dimensions, rhs._dimensions);
     if (result.mismatch) {
         return error_type();
     }
@@ -244,22 +252,12 @@ ValueType::concat(const ValueType &lhs, const ValueType &rhs, const vespalib::st
 {
     if (lhs.is_error() || rhs.is_error()) {
         return error_type();
-    } else if (lhs.unknown_dimensions() || rhs.unknown_dimensions()) {
-        return any_type();
     }
-    DimensionResult result = my_join(lhs._dimensions, rhs._dimensions);
-    auto lhs_dim = find_dimension(lhs.dimensions(), dimension);
-    auto rhs_dim = find_dimension(rhs.dimensions(), dimension);
-    auto res_dim = find_dimension(result.dimensions, dimension);
-    if (result.mismatch || (res_dim && res_dim->is_mapped())) {
+    MyJoin result(lhs._dimensions, rhs._dimensions, dimension);
+    if (result.mismatch) {
         return error_type();
     }
-    if (res_dim) {
-        if (res_dim->is_bound()) {
-            res_dim->size = (lhs_dim ? lhs_dim->size : 1)
-                            + (rhs_dim ? rhs_dim->size : 1);
-        }
-    } else {
+    if (!find_dimension(result.dimensions, dimension)) {
         result.dimensions.emplace_back(dimension, 2);
     }
     return tensor_type(std::move(result.dimensions));
@@ -268,35 +266,10 @@ ValueType::concat(const ValueType &lhs, const ValueType &rhs, const vespalib::st
 ValueType
 ValueType::either(const ValueType &one, const ValueType &other)
 {
-    if (one.is_error() || other.is_error()) {
+    if (one != other) {
         return error_type();
     }
-    if (one == other) {
-        return one;
-    }
-    if (!one.is_tensor() || !other.is_tensor()) {
-        return any_type();
-    }
-    if (one.dimensions().size() != other.dimensions().size()) {
-        return tensor_type({});
-    }
-    std::vector<Dimension> dims;
-    for (size_t i = 0; i < one.dimensions().size(); ++i) {
-        const Dimension &a = one.dimensions()[i];
-        const Dimension &b = other.dimensions()[i];
-        if (a.name != b.name) {
-            return tensor_type({});
-        }
-        if (a.is_mapped() != b.is_mapped()) {
-            return tensor_type({});
-        }
-        if (a.size == b.size) {
-            dims.push_back(a);
-        } else {
-            dims.emplace_back(a.name, 0);
-        }
-    }
-    return tensor_type(std::move(dims));
+    return one;
 }
 
 std::ostream &
diff --git a/eval/src/vespa/eval/eval/value_type.h b/eval/src/vespa/eval/eval/value_type.h
index 9d95d91ae15..6e30a5c0a47 100644
--- a/eval/src/vespa/eval/eval/value_type.h
+++ b/eval/src/vespa/eval/eval/value_type.h
@@ -15,7 +15,7 @@ namespace vespalib::eval {
 class ValueType
 {
 public:
-    enum class Type { ANY, ERROR, DOUBLE, TENSOR };
+    enum class Type { ERROR, DOUBLE, TENSOR };
     struct Dimension {
         using size_type = uint32_t;
         static constexpr size_type npos = -1;
@@ -51,7 +51,6 @@ public:
     ValueType &operator=(const ValueType &) = default;
     ~ValueType();
     Type type() const { return _type; }
-    bool is_any() const { return (_type == Type::ANY); }
     bool is_error() const { return (_type == Type::ERROR); }
     bool is_double() const { return (_type == Type::DOUBLE); }
     bool is_tensor() const { return (_type == Type::TENSOR); }
@@ -60,16 +59,6 @@ public:
     const std::vector<Dimension> &dimensions() const { return _dimensions; }
     size_t dimension_index(const vespalib::string &name) const;
     std::vector<vespalib::string> dimension_names() const;
-    bool maybe_tensor() const { return (is_any() || is_tensor()); }
-    bool unknown_dimensions() const { return (maybe_tensor() && _dimensions.empty()); }
-    bool is_abstract() const {
-        for (const auto &dimension: _dimensions) {
-            if (dimension.is_indexed() && !dimension.is_bound()) {
-                return true;
-            }
-        }
-        return (is_any() || (is_tensor() && (dimensions().empty())));
-    }
     bool operator==(const ValueType &rhs) const {
         return ((_type == rhs._type) && (_dimensions == rhs._dimensions));
     }
@@ -79,8 +68,7 @@ public:
     ValueType rename(const std::vector<vespalib::string> &from,
                      const std::vector<vespalib::string> &to) const;
 
-    static ValueType any_type() { return ValueType(Type::ANY); }
-    static ValueType error_type() { return ValueType(Type::ERROR); };
+    static ValueType error_type() { return ValueType(Type::ERROR); }
     static ValueType double_type() { return ValueType(Type::DOUBLE); }
     static ValueType tensor_type(std::vector<Dimension> dimensions_in);
     static ValueType from_spec(const vespalib::string &spec);
diff --git a/eval/src/vespa/eval/eval/value_type_spec.cpp b/eval/src/vespa/eval/eval/value_type_spec.cpp
index 6076988ad50..737943f902e 100644
--- a/eval/src/vespa/eval/eval/value_type_spec.cpp
+++ b/eval/src/vespa/eval/eval/value_type_spec.cpp
@@ -167,9 +167,7 @@ parse_spec(const char *pos_in, const char *end_in, const char *&pos_out)
 {
     ParseContext ctx(pos_in, end_in, pos_out);
     vespalib::string type_name = parse_ident(ctx);
-    if (type_name == "any") {
-        return ValueType::any_type();
-    } else if (type_name == "error") {
+    if (type_name == "error") {
         return ValueType::error_type();
     } else if (type_name == "double") {
         return ValueType::double_type();
@@ -206,9 +204,6 @@ to_spec(const ValueType &type)
     asciistream os;
     size_t cnt = 0;
     switch (type.type()) {
-    case ValueType::Type::ANY:
-        os << "any";
-        break;
     case ValueType::Type::ERROR:
         os << "error";
         break;
diff --git a/eval/src/vespa/eval/tensor/dense/dense_add_dimension_optimizer.cpp b/eval/src/vespa/eval/tensor/dense/dense_add_dimension_optimizer.cpp
index cd874186e46..f55566fe199 100644
--- a/eval/src/vespa/eval/tensor/dense/dense_add_dimension_optimizer.cpp
+++ b/eval/src/vespa/eval/tensor/dense/dense_add_dimension_optimizer.cpp
@@ -20,7 +20,7 @@ using namespace eval::operation;
 namespace {
 
 bool is_concrete_dense_tensor(const ValueType &type) {
-    return (type.is_dense() && !type.is_abstract());
+    return type.is_dense();
 }
 
 bool not_overlapping(const ValueType &a, const ValueType &b) {
diff --git a/eval/src/vespa/eval/tensor/dense/dense_fast_rename_optimizer.cpp b/eval/src/vespa/eval/tensor/dense/dense_fast_rename_optimizer.cpp
index 4bc2458c29d..b32a1efa234 100644
--- a/eval/src/vespa/eval/tensor/dense/dense_fast_rename_optimizer.cpp
+++ b/eval/src/vespa/eval/tensor/dense/dense_fast_rename_optimizer.cpp
@@ -22,8 +22,8 @@ bool is_concrete_dense_stable_rename(const ValueType &from_type, const ValueType
                                      const std::vector<vespalib::string> &from,
                                      const std::vector<vespalib::string> &to)
 {
-    if (!from_type.is_dense() || from_type.is_abstract() ||
-        !to_type.is_dense() || to_type.is_abstract() ||
+    if (!from_type.is_dense() ||
+        !to_type.is_dense() ||
         (from.size() != to.size()))
     {
         return false;
diff --git a/eval/src/vespa/eval/tensor/dense/dense_inplace_join_function.cpp b/eval/src/vespa/eval/tensor/dense/dense_inplace_join_function.cpp
index b3ed588a74a..4828808683a 100644
--- a/eval/src/vespa/eval/tensor/dense/dense_inplace_join_function.cpp
+++ b/eval/src/vespa/eval/tensor/dense/dense_inplace_join_function.cpp
@@ -41,7 +41,7 @@ void my_inplace_join_op(eval::InterpretedFunction::State &state, uint64_t param)
 }
 
 bool sameShapeConcreteDenseTensors(const ValueType &a, const ValueType &b) {
-    return (a.is_dense() && !a.is_abstract() && (a == b));
+    return (a.is_dense() && (a == b));
 }
 
 } // namespace vespalib::tensor::<unnamed>
diff --git a/eval/src/vespa/eval/tensor/dense/dense_inplace_map_function.cpp b/eval/src/vespa/eval/tensor/dense/dense_inplace_map_function.cpp
index a0aba25f342..bac1e336292 100644
--- a/eval/src/vespa/eval/tensor/dense/dense_inplace_map_function.cpp
+++ b/eval/src/vespa/eval/tensor/dense/dense_inplace_map_function.cpp
@@ -30,7 +30,7 @@ void my_inplace_map_op(eval::InterpretedFunction::State &state, uint64_t param)
 }
 
 bool isConcreteDenseTensor(const ValueType &type) {
-    return (type.is_dense() && !type.is_abstract());
+    return type.is_dense();
 }
 
 } // namespace vespalib::tensor::<unnamed>
diff --git a/eval/src/vespa/eval/tensor/dense/dense_remove_dimension_optimizer.cpp b/eval/src/vespa/eval/tensor/dense/dense_remove_dimension_optimizer.cpp
index 302c81080dd..d6716e8ad1a 100644
--- a/eval/src/vespa/eval/tensor/dense/dense_remove_dimension_optimizer.cpp
+++ b/eval/src/vespa/eval/tensor/dense/dense_remove_dimension_optimizer.cpp
@@ -15,7 +15,7 @@ using namespace eval::tensor_function;
 namespace {
 
 bool is_concrete_dense_tensor(const ValueType &type) {
-    return (type.is_dense() && !type.is_abstract());
+    return type.is_dense();
 }
 
 bool is_ident_aggr(Aggr aggr) {
diff --git a/eval/src/vespa/eval/tensor/dense/dense_xw_product_function.cpp b/eval/src/vespa/eval/tensor/dense/dense_xw_product_function.cpp
index c7cb16f37fd..660e5e3e0b7 100644
--- a/eval/src/vespa/eval/tensor/dense/dense_xw_product_function.cpp
+++ b/eval/src/vespa/eval/tensor/dense/dense_xw_product_function.cpp
@@ -76,7 +76,7 @@ void my_xw_product_op(eval::InterpretedFunction::State &state, uint64_t param) {
 }
 
 bool isConcreteDenseTensor(const ValueType &type, size_t d) {
-    return (type.is_dense() && (type.dimensions().size() == d) && !type.is_abstract());
+    return (type.is_dense() && (type.dimensions().size() == d));
 }
 
 bool isDenseXWProduct(const ValueType &res, const ValueType &vec, const ValueType &mat) {
diff --git a/eval/src/vespa/eval/tensor/tensor_mapper.cpp b/eval/src/vespa/eval/tensor/tensor_mapper.cpp
index dbf1965d441..e6358675cea 100644
--- a/eval/src/vespa/eval/tensor/tensor_mapper.cpp
+++ b/eval/src/vespa/eval/tensor/tensor_mapper.cpp
@@ -102,111 +102,6 @@ SparseTensorMapper<TensorT>::map(const Tensor &tensor,
 
 //-----------------------------------------------------------------------------
 
-class TensorTypeMapper : public TensorVisitor
-{
-    ValueType _type;
-    std::vector<ValueType::Dimension> _dimensions;
-
-    bool addressOK(const TensorAddress &address);
-    void expandUnboundDimensions(const TensorAddress &address);
-
-    virtual void visit(const TensorAddress &address, double value) override;
-
-    TensorTypeMapper(const ValueType &type);
-    ~TensorTypeMapper();
-
-    ValueType build();
-public:
-    static ValueType map(const Tensor &tensor, const ValueType &type);
-};
-
-bool
-TensorTypeMapper::addressOK(const TensorAddress &address)
-{
-    TensorAddressElementIterator<TensorAddress> addressIterator(address);
-    auto dimIterator = _dimensions.begin();
-    for (const auto &dimension : _type.dimensions()) {
-        if (addressIterator.skipToDimension(dimension.name)) {
-            if (dimension.is_indexed()) {
-                uint32_t label = DenseTensorAddressMapper::mapLabelToNumber(addressIterator.label());
-                if (label == DenseTensorAddressMapper::BAD_LABEL ||
-                    (dimension.is_bound() && label >= dimIterator->size)) {
-                    return false;
-                }
-            }
-            addressIterator.next();
-        }
-        ++dimIterator;
-    }
-    assert(dimIterator == _dimensions.end());
-    return true;
-}
-
-
-void
-TensorTypeMapper::expandUnboundDimensions(const TensorAddress &address)
-{
-    TensorAddressElementIterator<TensorAddress> addressIterator(address);
-    auto dimIterator = _dimensions.begin();
-    for (const auto &dimension : _type.dimensions()) {
-        if (addressIterator.skipToDimension(dimension.name)) {
-            if (dimension.is_indexed()) {
-                uint32_t label = DenseTensorAddressMapper::mapLabelToNumber(addressIterator.label());
-                if (label != DenseTensorAddressMapper::BAD_LABEL &&
-                    !dimension.is_bound() &&
-                    label >= dimIterator->size) {
-                    dimIterator->size = label + 1;
-                }
-            }
-            addressIterator.next();
-        }
-        ++dimIterator;
-    }
-    assert(dimIterator == _dimensions.end());
-}
-
-void
-TensorTypeMapper::visit(const TensorAddress &address, double value)
-{
-    (void) value;
-    if (addressOK(address)) {
-        expandUnboundDimensions(address);
-    }
-}
-
-TensorTypeMapper::TensorTypeMapper(const ValueType &type)
-    : _type(type),
-      _dimensions(type.dimensions())
-{
-    for (auto &dimension : _dimensions) {
-        if (dimension.is_indexed()) {
-            if (!dimension.is_bound()) {
-                dimension.size = 1;
-            }
-        }
-    }
-}
-
-TensorTypeMapper::~TensorTypeMapper()
-{
-}
-
-ValueType
-TensorTypeMapper::build()
-{
-    return ValueType::tensor_type(std::move(_dimensions));
-}
-
-ValueType
-TensorTypeMapper::map(const Tensor &tensor, const ValueType &type)
-{
-    TensorTypeMapper mapper(type);
-    tensor.accept(mapper);
-    return mapper.build();
-}
-
-//-----------------------------------------------------------------------------
-
 class DenseTensorMapper : public TensorVisitor
 {
     ValueType _type;
@@ -259,9 +154,7 @@ DenseTensorMapper::visit(const TensorAddress &address, double value)
 std::unique_ptr<Tensor>
 DenseTensorMapper::map(const Tensor &tensor, const ValueType &type)
 {
-    DenseTensorMapper mapper(type.is_abstract() ?
-                             TensorTypeMapper::map(tensor, type) :
-                             type);
+    DenseTensorMapper mapper(type);
     tensor.accept(mapper);
     return mapper.build();
 }
@@ -322,9 +215,7 @@ WrappedTensorMapper::visit(const TensorAddress &address, double value)
 std::unique_ptr<Tensor>
 WrappedTensorMapper::map(const Tensor &tensor, const ValueType &type)
 {
-    WrappedTensorMapper mapper(type.is_abstract() ?
-                               TensorTypeMapper::map(tensor, type) :
-                               type);
+    WrappedTensorMapper mapper(type);
     tensor.accept(mapper);
     return mapper.build();
 }
diff --git a/searchlib/src/tests/attribute/tensorattribute/tensorattribute_test.cpp b/searchlib/src/tests/attribute/tensorattribute/tensorattribute_test.cpp
index bd814b0ad32..cbbaa518b16 100644
--- a/searchlib/src/tests/attribute/tensorattribute/tensorattribute_test.cpp
+++ b/searchlib/src/tests/attribute/tensorattribute/tensorattribute_test.cpp
@@ -40,9 +40,6 @@ static bool operator==(const Tensor &lhs, const Tensor &rhs)
 
 vespalib::string sparseSpec("tensor(x{},y{})");
 vespalib::string denseSpec("tensor(x[2],y[3])");
-vespalib::string denseAbstractSpec_xy("tensor(x[],y[])");
-vespalib::string denseAbstractSpec_x("tensor(x[2],y[])");
-vespalib::string denseAbstractSpec_y("tensor(x[],y[3])");
 
 struct Fixture
 {
@@ -307,7 +304,7 @@ Fixture::testSaveLoad()
 void
 Fixture::testCompaction()
 {
-    if (_useDenseTensorAttribute && _denseTensors && !_cfg.tensorType().is_abstract()) {
+    if (_useDenseTensorAttribute && _denseTensors) {
         LOG(info, "Skipping compaction test for tensor '%s' which is using free-lists", _cfg.tensorType().to_spec().c_str());
         return;
     }
@@ -411,34 +408,4 @@ TEST("Test dense tensors with dense tensor attribute")
     testAll([]() { return std::make_shared<Fixture>(denseSpec, true); });
 }
 
-TEST("Test dense tensors with generic tensor attribute with unbound x and y dims")
-{
-    testAll([]() { return std::make_shared<Fixture>(denseAbstractSpec_xy); });
-}
-
-TEST("Test dense tensors with dense tensor attribute with unbound x and y dims")
-{
-    testAll([]() { return std::make_shared<Fixture>(denseAbstractSpec_xy, true); });
-}
-
-TEST("Test dense tensors with generic tensor attribute with unbound x dim")
-{
-    testAll([]() { return std::make_shared<Fixture>(denseAbstractSpec_x); });
-}
-
-TEST("Test dense tensors with dense tensor attribute with unbound x dim")
-{
-    testAll([]() { return std::make_shared<Fixture>(denseAbstractSpec_x, true); });
-}
-
-TEST("Test dense tensors with generic tensor attribute with unbound y dim")
-{
-    testAll([]() { return std::make_shared<Fixture>(denseAbstractSpec_y); });
-}
-
-TEST("Test dense tensors with dense tensor attribute with unbound y dim")
-{
-    testAll([]() { return std::make_shared<Fixture>(denseAbstractSpec_y, true); });
-}
-
 TEST_MAIN() { TEST_RUN_ALL(); vespalib::unlink("test.dat"); }
diff --git a/searchlib/src/tests/features/ranking_expression/ranking_expression_test.cpp b/searchlib/src/tests/features/ranking_expression/ranking_expression_test.cpp
index 2419f450950..c7c3447a4cc 100644
--- a/searchlib/src/tests/features/ranking_expression/ranking_expression_test.cpp
+++ b/searchlib/src/tests/features/ranking_expression/ranking_expression_test.cpp
@@ -54,7 +54,7 @@ ExpressionReplacer::SP make_replacer() {
     auto replacer = std::make_shared<ListExpressionReplacer>();
     replacer->add(std::make_unique<NullExpressionReplacer>());
     replacer->add(std::make_unique<DummyReplacer>("foo", FeatureType::number()));
-    replacer->add(std::make_unique<DummyReplacer>("bar", FeatureType::object(ValueType::from_spec("tensor(x[])"))));
+    replacer->add(std::make_unique<DummyReplacer>("bar", FeatureType::object(ValueType::from_spec("tensor(x[5])"))));
     return replacer;
 }
 
@@ -124,15 +124,6 @@ TEST("require that ranking expression can resolve to concrete complex type") {
                                FeatureType::object(ValueType::from_spec("tensor(x{},y{},z{})"))));
 }
 
-TEST("require that ranking expression can resolve to abstract complex type") {
-    TEST_DO(verify_output_type({{"a", "tensor"}}, "a*b", FeatureType::object(ValueType::from_spec("tensor"))));
-}
-
-TEST("require that ranking expression can resolve to 'any' type") {
-    TEST_DO(verify_output_type({{"a", "tensor(x{},y{})"}, {"b", "tensor"}}, "a*b",
-                               FeatureType::object(ValueType::from_spec("any"))));
-}
-
 TEST("require that setup fails for incompatible types") {
     TEST_DO(verify_setup_fail({{"a", "tensor(x{},y{})"}, {"b", "tensor(y[10],z{})"}}, "a*b"));
 }
@@ -150,7 +141,7 @@ TEST("require that replaced expressions override result type") {
     TEST_DO(verify_output_type({{"b", "tensor(z{})"}}, "foo*b*c",
                                FeatureType::number()));
     TEST_DO(verify_output_type({{"b", "tensor(z{})"}}, "a*b*bar",
-                               FeatureType::object(ValueType::from_spec("tensor(x[])"))));
+                               FeatureType::object(ValueType::from_spec("tensor(x[5])"))));
     TEST_DO(verify_output_type({{"b", "tensor(z{})"}}, "foo*b*bar",
                                FeatureType::number()));
 }
diff --git a/searchlib/src/tests/tensor/dense_tensor_store/dense_tensor_store_test.cpp b/searchlib/src/tests/tensor/dense_tensor_store/dense_tensor_store_test.cpp
index 9f47dede46a..1d80fec720a 100644
--- a/searchlib/src/tests/tensor/dense_tensor_store/dense_tensor_store_test.cpp
+++ b/searchlib/src/tests/tensor/dense_tensor_store/dense_tensor_store_test.cpp
@@ -60,34 +60,6 @@ TEST_F("require that we can store 1d bound tensor", Fixture("tensor(x[3])"))
                                        add({{"x", 2}}, 5));
 }
 
-TEST_F("require that we can store 1d un-bound tensor", Fixture("tensor(x[])"))
-{
-    f.assertSetAndGetTensor(TensorSpec("tensor(x[3])").
-                                       add({{"x", 0}}, 2).
-                                       add({{"x", 1}}, 3).
-                                       add({{"x", 2}}, 5));
-}
-
-TEST_F("require that un-bound dimension is concrete in returned 2d tensor", Fixture("tensor(x[3],y[])"))
-{
-    f.assertSetAndGetTensor(TensorSpec("tensor(x[3],y[2])").
-                                       add({{"x", 0}, {"y", 0}}, 2).
-                                       add({{"x", 0}, {"y", 1}}, 3).
-                                       add({{"x", 1}, {"y", 0}}, 5).
-                                       add({{"x", 1}, {"y", 1}}, 7).
-                                       add({{"x", 2}, {"y", 0}}, 11).
-                                       add({{"x", 2}, {"y", 1}}, 13));
-}
-
-TEST_F("require that un-bound dimensions are concrete in returned 3d tensor", Fixture("tensor(x[],y[2],z[])"))
-{
-    f.assertSetAndGetTensor(TensorSpec("tensor(x[1],y[2],z[2])").
-                                       add({{"x", 0}, {"y", 0}, {"z", 0}}, 2).
-                                       add({{"x", 0}, {"y", 0}, {"z", 1}}, 3).
-                                       add({{"x", 0}, {"y", 1}, {"z", 0}}, 5).
-                                       add({{"x", 0}, {"y", 1}, {"z", 1}}, 7));
-}
-
 TEST_F("require that correct empty tensor is returned for 1d bound tensor", Fixture("tensor(x[3])"))
 {
     f.assertEmptyTensor(TensorSpec("tensor(x[3])").
@@ -96,21 +68,6 @@ TEST_F("require that correct empty tensor is returned for 1d bound tensor", Fixt
                                    add({{"x", 2}}, 0));
 }
 
-TEST_F("require that empty 2d tensor has size 1 in un-bound dimension", Fixture("tensor(x[3],y[])"))
-{
-    f.assertEmptyTensor(TensorSpec("tensor(x[3],y[1])").
-                                   add({{"x", 0}, {"y", 0}}, 0).
-                                   add({{"x", 1}, {"y", 0}}, 0).
-                                   add({{"x", 2}, {"y", 0}}, 0));
-}
-
-TEST_F("require that empty 3d tensor has size 1 in un-bound dimensions", Fixture("tensor(x[],y[2],z[])"))
-{
-    f.assertEmptyTensor(TensorSpec("tensor(x[1],y[2],z[1])").
-                                   add({{"x", 0}, {"y", 0}, {"z", 0}}, 0).
-                                   add({{"x", 0}, {"y", 1}, {"z", 0}}, 0));
-}
-
 void
 assertArraySize(const vespalib::string &tensorType, uint32_t expArraySize) {
     Fixture f(tensorType);
@@ -122,13 +79,7 @@ TEST("require that array size is calculated correctly")
     TEST_DO(assertArraySize("tensor(x[1])", 32));
     TEST_DO(assertArraySize("tensor(x[10])", 96));
     TEST_DO(assertArraySize("tensor(x[3])", 32));
-    TEST_DO(assertArraySize("tensor(x[3],y[])", 32));
-    TEST_DO(assertArraySize("tensor(x[3],y[],z[])", 32));
-    TEST_DO(assertArraySize("tensor(x[3],y[],z[],z2[])", 64));
     TEST_DO(assertArraySize("tensor(x[10],y[10])", 800));
-    TEST_DO(assertArraySize("tensor(x[])", 32));
-    TEST_DO(assertArraySize("tensor(x[],x2[],x3[],x4[],x5[],x6[])", 32));
-    TEST_DO(assertArraySize("tensor(x[],x2[],x3[],x4[],x5[],x6[],x7[])", 64));
 }
 
 TEST_MAIN() { TEST_RUN_ALL(); }
diff --git a/searchlib/src/vespa/searchlib/features/attributefeature.cpp b/searchlib/src/vespa/searchlib/features/attributefeature.cpp
index b3ebd0f3822..56d02ce6d4e 100644
--- a/searchlib/src/vespa/searchlib/features/attributefeature.cpp
+++ b/searchlib/src/vespa/searchlib/features/attributefeature.cpp
@@ -295,10 +295,13 @@ AttributeBlueprint::setup(const search::fef::IIndexEnvironment & env,
     vespalib::string attrType = type::Attribute::lookup(env.getProperties(), _attrName);
     if (!attrType.empty()) {
         _tensorType = ValueType::from_spec(attrType);
+        if (_tensorType.is_error()) {
+            LOG(error, "%s: invalid type: '%s'", getName().c_str(), attrType.c_str());
+        }
     }
-    FeatureType output_type = _tensorType.is_tensor()
-                              ? FeatureType::object(_tensorType)
-                              : FeatureType::number();
+    FeatureType output_type = _tensorType.is_double()
+                              ? FeatureType::number()
+                              : FeatureType::object(_tensorType);
     describeOutput("value", "The value of a single value attribute, "
                    "the value at the given index of an array attribute, "
                    "the given key of a weighted set attribute, or"
@@ -309,7 +312,7 @@ AttributeBlueprint::setup(const search::fef::IIndexEnvironment & env,
         describeOutput("count", "Returns the number of elements in this array or weighted set attribute.");
     }
     env.hintAttributeAccess(_attrName);
-    return true;
+    return !_tensorType.is_error();
 }
 
 search::fef::Blueprint::UP
diff --git a/searchlib/src/vespa/searchlib/features/constant_feature.cpp b/searchlib/src/vespa/searchlib/features/constant_feature.cpp
index 4d76512ab00..ced9d95fb33 100644
--- a/searchlib/src/vespa/searchlib/features/constant_feature.cpp
+++ b/searchlib/src/vespa/searchlib/features/constant_feature.cpp
@@ -63,8 +63,10 @@ ConstantBlueprint::setup(const IIndexEnvironment &env,
 {
     _key = params[0].getValue();
     _value = env.getConstantValue(_key);
-    if (!_value || _value->type().is_error()) {
+    if (!_value) {
         LOG(error, "Constant '%s' not found", _key.c_str());
+    } else if (_value->type().is_error()) {
+        LOG(error, "Constant '%s' has invalid type", _key.c_str());
     }
     FeatureType output_type = _value ?
                               FeatureType::object(_value->type()) :
diff --git a/searchlib/src/vespa/searchlib/features/queryfeature.cpp b/searchlib/src/vespa/searchlib/features/queryfeature.cpp
index eb7eb427283..b9041901ced 100644
--- a/searchlib/src/vespa/searchlib/features/queryfeature.cpp
+++ b/searchlib/src/vespa/searchlib/features/queryfeature.cpp
@@ -98,12 +98,15 @@ QueryBlueprint::setup(const IIndexEnvironment &env, const ParameterList &params)
     vespalib::string queryFeatureType = type::QueryFeature::lookup(env.getProperties(), _key);
     if (!queryFeatureType.empty()) {
         _valueType = ValueType::from_spec(queryFeatureType);
+        if (_valueType.is_error()) {
+            LOG(error, "%s: invalid type: '%s'", getName().c_str(), queryFeatureType.c_str());
+        }
     }
-    FeatureType output_type = _valueType.is_tensor()
-                              ? FeatureType::object(_valueType)
-                              : FeatureType::number();
+    FeatureType output_type = _valueType.is_double()
+                              ? FeatureType::number()
+                              : FeatureType::object(_valueType);
     describeOutput("out", "The value looked up in query properties using the given key.", output_type);
-    return true;
+    return !_valueType.is_error();
 }
 
 namespace {
diff --git a/searchlib/src/vespa/searchlib/features/rankingexpressionfeature.cpp b/searchlib/src/vespa/searchlib/features/rankingexpressionfeature.cpp
index 72865d042e7..b2c8c64d55a 100644
--- a/searchlib/src/vespa/searchlib/features/rankingexpressionfeature.cpp
+++ b/searchlib/src/vespa/searchlib/features/rankingexpressionfeature.cpp
@@ -239,9 +239,6 @@ RankingExpressionBlueprint::setup(const fef::IIndexEnvironment &env,
         LOG(error, "rank expression contains type errors: %s\n", script.c_str());
         return false;
     }
-    if (root_type.is_any()) {
-        LOG(warning, "rank expression could produce run-time type errors: %s\n", script.c_str());
-    }
     auto compile_issues = CompiledFunction::detect_issues(rank_function);
     auto interpret_issues = InterpretedFunction::detect_issues(rank_function);
     if (do_compile && compile_issues && !interpret_issues) {