# Kimchi

• This document specifies kimchi, a zero-knowledge proof system that’s a variant of PLONK.
• This document does not specify how circuits are created or executed, but only how to convert a circuit and its execution into a proof.

Table of content:

## Overview

There are three main algorithms to kimchi:

• Setup: takes a circuit and produces a prover index, and a verifier index.
• Proof creation: takes the prover index, and the execution trace of the circuit to produce a proof.
• Proof verification: takes the verifier index and a proof to verify.

As part of these algorithms, a number of tables are created (and then converted into polynomials) to create a proof.

### Tables used to describe a circuit

The following tables are created to describe the circuit:

Gates. A circuit is described by a series of gates, that we list in a table. The columns of the tables list the gates, while the rows are the length of the circuit. For each row, only a single gate can take a value while all other gates take the value .

01000000000
10100000000

Coefficients. The coefficient table has 15 columns, and is used to tweak the gates. Currently, only the Generic and the Poseidon gates use it (refer to their own sections to see how). All other gates set their values to .

row01234567891011121314
0///////////////

Wiring (or Permutation, or sigmas). For gates to take the outputs of other gates as inputs, we use a wiring table to wire registers together. To learn about registers, see the next section. It is defined at every row, but only for the first registers. Each cell specifies a (row, column) tuple that it should be wired to. Cells that are not connected to another cell are wired to themselves. Note that if three or more registers are wired together, they must form a cycle. For example, if register (0, 4) is wired to both registers (80, 6) and (90, 0) then you would have the following table:

row0123456
00,00,10,20,380,60,50,6
8080,080,180,280,380,480,590,0
900,490,190,290,390,490,590,6

The lookup feature is currently optional, as it can add some overhead to the protocol. In the case where you would want to use lookups, the following tables would be needed:

Lookup Tables. The different lookup tables that are used in the circuit. For example, the XOR lookup table:

lro
101
011
110
000

Lookup selectors. A lookup selector is used to perform a number of queries in different lookup tables. Any gate can advertise its use of a lookup selector (so a lookup selector can be associated to several gates), and on which rows they want to use them (current and/or next). In cases where a gate need to use lookups in its current row only, and is the only one performing a specific combination of queries, then its gate selector can be used in place of a lookup selector. As with gates, lookup selectors (including gates used as lookup selectors) are mutually exclusives (only one can be used on a given row).

We currently have two lookup selectors:

rowChaChaQueryChaChaFinalQuery
000
110

Where each apply 4 queries. A query is a table describing which lookup table it queries, and the linear combination of the witness to use in the query. For example, the following table describes a query into the XOR table made out of linear combinations of registers (checking that ):

table_idlro
XOR1, r01, r22, r1

### Tables produced during proof creation

The following tables are created by the prover at runtime:

Registers (or Witness). Registers are also defined at every row, and are split into two types: the IO registers from to usually contain input or output of the gates (note that a gate can output a value on the next row as well). I/O registers can be wired to each other (they’ll be forced to have the same value), no matter what row they’re on (for example, the register at row:0, col:4 can be wired to the register at row:80, col:6). The rest of the registers, through , are called advice registers as they can store values that useful only for the row’s active gate. Think of them as intermediary or temporary values needed in the computation when the prover executes a circuit.

row01234567891011121314
0///////////////

Wiring (Permutation) trace. You can think of the permutation trace as an extra register that is used to enforce the wiring specified in the wiring table. It is a single column that applies on all the rows as well, which the prover computes as part of a proof.

rowpt
0/

Queries trace. These are the actual values made by queries, calculated by the prover at runtime, and used to construct the proof.

Table trace. Represents the concatenation of all the lookup tables, combined into a single column at runtime by both the prover and the verifier.

Sorted trace. Represents the processed (see the lookup section) concatenation of the queries trace and the table trace. It is produced at runtime by the prover. The sorted trace is long enough that it is split in several columns.

Lookup (aggregation, or permutation) trace. This is a one column table that is similar to the wiring (permutation) trace we talked above. It is produced at runtime by the prover.

## Dependencies

To specify kimchi, we rely on a number of primitives that are specified outside of this specification. In this section we list these specifications, as well as the interfaces we make use of in this specification.

### Polynomial Commitments

Refer to the specification on polynomial commitments. We make use of the following functions from that specification:

• PolyCom.non_hiding_commit(poly) -> PolyCom::NonHidingCommitment
• PolyCom.commit(poly) -> PolyCom::HidingCommitment
• PolyCom.evaluation_proof(poly, commitment, point) -> EvaluationProof
• PolyCom.verify(commitment, point, evaluation, evaluation_proof) -> bool

### Poseidon hash function

Refer to the specification on Poseidon. We make use of the following functions from that specification:

• Poseidon.init(params) -> FqSponge
• Poseidon.update(field_elem)
• Poseidon.finalize() -> FieldElem

specify the following functions on top:

• Poseidon.produce_challenge() (TODO: uses the endomorphism)
• Poseidon.to_fr_sponge() -> state_of_fq_sponge_before_eval, FrSponge

With the current parameters:

### Pasta

Kimchi is made to work on cycles of curves, so the protocol switch between two fields Fq and Fr, where Fq represents the base field and Fr represents the scalar field.

See the Pasta curves specification.

## Constraints

Kimchi enforces the correct execution of a circuit by creating a number of constraints and combining them together. In this section, we describe all the constraints that make up the main polynomial once combined.

We define the following functions:

• combine_constraints(range_alpha, constraints), which takes a range of contiguous powers of alpha and a number of constraints. It returns the sum of all the constraints, where each constraint has been multiplied by a power of alpha. In other words it returns:

The different ranges of alpha are described as follows:

• gates. Offset starts at 0 and 21 powers of are used
• Permutation. Offset starts at 21 and 3 powers of are used

As gates are mutually exclusive (a single gate is used on each row), we can reuse the same range of powers of alpha across all the gates.

TODO: linearization

### Permutation

The permutation constraints are the following 4 constraints:

The two sides of the coin (with ):

and

the initialization of the accumulator:

and the accumulator’s final value:

You can read more about why it looks like that in this post.

The quotient contribution of the permutation is split into two parts and . They will be used by the prover.

and bnd:

The linearization:

where is computed as:

To compute the permutation aggregation polynomial, the prover interpolates the polynomial that has the following evaluations. The first evaluation represents the initial value of the accumulator: For , where is the size of the domain, evaluations are computed as:

with

and

If computed correctly, we should have .

Finally, randomize the last EVAL_POINTS evaluations and , in order to add zero-knowledge to the protocol.

### Lookup

Lookups in kimchi allows you to check if a single value, or a series of values, are part of a table. The first case is useful to check for checking if a value belongs to a range (from 0 to 1,000, for example), whereas the second case is useful to check truth tables (for example, checking that three values can be found in the rows of an XOR table) or write and read from a memory vector (where one column is an index, and the other is the value stored at that index).

Similarly to the generic gate, each values taking part in a lookup can be scaled with a fixed field element.

The lookup functionality is an opt-in feature of kimchi that can be used by custom gates. From the user’s perspective, not using any gates that make use of lookups means that the feature will be disabled and there will be no overhead to the protocol.

For now, the Chacha gates are the only gates making use of lookups.

Refer to the lookup RFC for an overview of the lookup feature.

In this section, we describe the tables kimchi supports, as well as the different lookup selectors (and their associated queries)

#### The Lookup Tables

Kimchi currently supports a single lookup table:

/// The table ID associated with the XOR lookup table.
pub const XOR_TABLE_ID: i32 = 0;


XOR. The lookup table for 4-bit xor. Note that it is constructed so that (0, 0, 0) is the last position in the table.

This is because tables are extended to the full size of a column (essentially) by padding them with their final value. And, having the value (0, 0, 0) here means that when we commit to this table and use the dummy value in the lookup_sorted columns, those entries that have the dummy value of

will translate into a scalar multiplication by 0, which is free.

#### The Lookup Selectors

ChaChaSelector. Performs 4 queries to the XOR lookup table.

lro-lro-lro-lro
1, r31, r71, r11-1, r41, r81, r12-1, r51, r91, r13-1, r61, r101, r14

ChaChaFinalSelector. Performs 4 different queries to the XOR lookup table. (TODO: specify the layout)

#### Producing the sorted table as the prover

Because of our ZK-rows, we can’t do the trick in the plookup paper of wrapping around to enforce consistency between the sorted lookup columns.

Instead, we arrange the LookupSorted table into columns in a snake-shape.

Like so,

_   _
| | | | |
| | | | |
|_| |_| |


or, imagining the full sorted array is [ s0, ..., s8 ], like

s0 s4 s4 s8
s1 s3 s5 s7
s2 s2 s6 s6


So the direction (“increasing” or “decreasing” (relative to LookupTable) is

if i % 2 = 0 { Increasing } else { Decreasing }


Then, for each i < max_lookups_per_row, if i % 2 = 0, we enforce that the last element of LookupSorted(i) = last element of LookupSorted(i + 1), and if i % 2 = 1, we enforce that the first element of LookupSorted(i) = first element of LookupSorted(i + 1).

### Gates

A circuit is described as a series of gates. In this section we describe the different gates currently supported by kimchi, the constraints associated to them, and the way the register table, coefficient table, and permutation can be used in conjunction.

TODO: for each gate describe how to create it?

#### Double Generic Gate

The double generic gate contains two generic gates.

A generic gate is simply the 2-fan in gate specified in the vanilla PLONK protocol that allows us to do operations like:

• addition of two registers (into an output register)
• or multiplication of two registers
• equality of a register with a constant

More generally, the generic gate controls the coefficients in the equation:

The layout of the gate is the following:

01234567891011121314
l1r1o1l2r2o2

where l1, r1, and o1 (resp. l2, r2, o2) are the left, right, and output registers of the first (resp. second) generic gate.

The selectors are stored in the coefficient table as:

01234567891011121314
l1r1o1m1c1l2r2o2m2c2

with m1 (resp. m2) the mul selector for the first (resp. second) gate, and c1 (resp. c2) the constant selector for the first (resp. second) gate.

The constraints:

where the are the coefficients.

#### Poseidon

The poseidon gate encodes 5 rounds of the poseidon permutation. A state is represents by 3 field elements. For example, the first state is represented by (s0, s0, s0), and the next state, after permutation, is represented by (s1, s1, s1).

Below is how we store each state in the register table:

01234567891011121314
s0s0s0s4s4s4s1s1s1s2s2s2s3s3s3
s5s5s5

The last state is stored on the next row. This last state is either used:

• with another Poseidon gate on that next row, representing the next 5 rounds.
• or with a Zero gate, and a permutation to use the output elsewhere in the circuit.
• or with another gate expecting an input of 3 field elements in its first registers.

As some of the poseidon hash variants might not use rounds (for some ), the result of the 4-th round is stored directly after the initial state. This makes that state accessible to the permutation.

We define as the MDS matrix at row and column .

We define the S-box operation as for the SPONGE_BOX constant.

We store the 15 round constants required for the 5 rounds (3 per round) in the coefficient table:

01234567891011121314
r0r1r2r3r4r5r6r7r8r9r10r11r12r13r14

The initial state, stored in the first three registers, are not constrained. The following 4 states (of 3 field elements), including 1 in the next row, are constrained to represent the 5 rounds of permutation. Each of the associated 15 registers is associated to a constraint, calculated as:

first round:

second round:

third round:

fourth round:

fifth round:

where is the polynomial which points to the next row.

#### Chacha

There are four chacha constraint types, corresponding to the four lines in each quarter round.

a += b; d ^= a; d <<<= 16;
c += d; b ^= c; b <<<= 12;
a += b; d ^= a; d <<<= 8;
c += d; b ^= c; b <<<= 7;


or, written without mutation, (and where + is mod ),

a'  = a + b ; d' = (d ⊕ a') <<< 16;
c'  = c + d'; b' = (b ⊕ c') <<< 12;
a'' = a' + b'; d'' = (d' ⊕ a') <<< 8;
c'' = c' + d''; b'' = (c'' ⊕ b') <<< 7;


We lay each line as two rows.

Each line has the form

x += z; y ^= x; y <<<= k


or without mutation,

x' = x + z; y' = (y ⊕ x') <<< k


which we abbreviate as

L(x, x’, y, y’, z, k)

In general, such a line will be laid out as the two rows

01234567891011121314
xyz(y^x’)_0(y^x’)_1(y^x’)_2(y^x’)_3(x+z)_0(x+z)_1(x+z)_2(x+z)_3y_0y_1y_2y_3
x’y’(x+z)_8(y^x’)_4(y^x’)_5(y^x’)_6(y^x’)_7(x+z)_4(x+z)_5(x+z)_6(x+z)_7y_4y_5y_6y_7

where A_i indicates the i^th nybble (four-bit chunk) of the value A.

is special, since we know it is actually at most 1 bit (representing the overflow bit of x + z).

So the first line L(a, a', d, d', b, 8) for example becomes the two rows

01234567891011121314
a’d’(a+b)_8(d^a’)_4(d^a’)_5(d^a’)_6(d^a’)_7(a+b)_4(a+b)_5(a+b)_6(a+b)_7d_4d_5d_6d_7

along with the equations

• (booleanity check)

The rotates the nybbles left by 4, which means bit-rotating by as desired.

The final line is a bit more complicated as we have to rotate by 7, which is not a multiple of 4. We accomplish this as follows.

Let’s say we want to rotate the nybbles left by 7. First we’ll rotate left by 4 to get

Rename these as

We now want to left-rotate each by 3.

Let be the low bit of . Then, the low 3 bits of are .

The result will thus be

or re-writing in terms of our original nybbles ,

For neatness, letting , the first 2 rows for the final line will be:

01234567891011121314
xyz(y^x’)_0(y^x’)_1(y^x’)_2(y^x’)_3(x+z)_0(x+z)_1(x+z)_2(x+z)_3y_0y_1y_2y_3
x’_(x+z)_8(y^x’)_4(y^x’)_5(y^x’)_6(y^x’)_7(x+z)_4(x+z)_5(x+z)_6(x+z)_7y_4y_5y_6y_7

but then we also need to perform the bit-rotate by 1.

For this we’ll add an additional 2 rows. It’s probably possible to do it with just 1, but I think we’d have to change our plookup setup somehow, or maybe expand the number of columns, or allow access to the previous row.

Let be the low bit of the nybble n. The 2 rows will be

01234567891011121314
y’(y^x’)_0(y^x’)_1(y^x’)_2(y^x’)_3lo((y^x’)_0)lo((y^x’)_1)lo((y^x’)_2)lo((y^x’)_3)
_(y^x’)_4(y^x’)_5(y^x’)_6(y^x’)_7lo((y^x’)_4)lo((y^x’)_5)lo((y^x’)_6)lo((y^x’)_7)

On each of them we’ll do the plookups

((cols[1] - cols[5])/2, (cols[1] - cols[5])/2, 0) in XOR
((cols[2] - cols[6])/2, (cols[2] - cols[6])/2, 0) in XOR
((cols[3] - cols[7])/2, (cols[3] - cols[7])/2, 0) in XOR
((cols[4] - cols[8])/2, (cols[4] - cols[8])/2, 0) in XOR


which checks that is a nybble, which guarantees that the low bit is computed correctly.

There is no need to check nybbleness of (y^x’)_i because those will be constrained to be equal to the copies of those values from previous rows, which have already been constrained for nybbleness (by the lookup in the XOR table).

And we’ll check that y’ is the sum of the shifted nybbles.

The layout is

012345678910
x1y1x2y2x3y3infsame_xsinf_zx21_inv

where

• (x1, y1), (x2, y2) are the inputs and (x3, y3) the output.
• inf is a boolean that is true iff the result (x3, y3) is the point at infinity.

The rest of the values are inaccessible from the permutation argument, but

• same_x is a boolean that is true iff x1 == x2.

The following constraints are generated:

constraint 1:

constraint 2:

constraint 3:

constraint 4:

constraint 5:

constraint 6:

constraint 7:

#### Endo Scalar

We give constraints for the endomul scalar computation.

Each row corresponds to 8 iterations of the inner loop in “Algorithm 2” on page 29 of the Halo paper.

The state of the algorithm that’s updated across iterations of the loop is (a, b). It’s clear from that description of the algorithm that an iteration of the loop can be written as

(a, b, i) ->
( 2 * a + c_func(r_{2 * i}, r_{2 * i + 1}),
2 * b + d_func(r_{2 * i}, r_{2 * i + 1}) )


for some functions c_func and d_func. If one works out what these functions are on every input (thinking of a two-bit input as a number in ), one finds they are given by

c_func(x), defined by

• c_func(0) = 0
• c_func(1) = 0
• c_func(2) = -1
• c_func(3) = 1

d_func(x), defined by

• d_func(0) = -1
• d_func(1) = 1
• d_func(2) = 0
• d_func(3) = 0

One can then interpolate to find polynomials that implement these functions on .

You can use sage, as

R = PolynomialRing(QQ, 'x')
c_func = R.lagrange_polynomial([(0, 0), (1, 0), (2, -1), (3, 1)])
d_func = R.lagrange_polynomial([(0, -1), (1, 1), (2, 0), (3, 0)])


Then, c_func is given by

2/3 * x^3 - 5/2 * x^2 + 11/6 * x


and d_func is given by

2/3 * x^3 - 7/2 * x^2 + 29/6 * x - 1 <=> c_func + (-x^2 + 3x - 1)


We lay it out the witness as

01234567891011121314Type
n0n8a0b0a8b8x0x1x2x3x4x5x6x7ENDO

where each xi is a two-bit “crumb”.

We also use a polynomial to check that each xi is indeed in , which can be done by checking that each is a root of the polyunomial below:

crumb(x)
= x (x - 1) (x - 2) (x - 3)
= x^4 - 6*x^3 + 11*x^2 - 6*x
= x *(x^3 - 6*x^2 + 11*x - 6)


Each iteration performs the following computations

• Update :
• Update :
• Update :

Then, after the 8 iterations, we compute expected values of the above operations as:

• expected_n8 := 2 * ( 2 * ( 2 * ( 2 * ( 2 * ( 2 * ( 2 * (2 * n0 + x0) + x1 ) + x2 ) + x3 ) + x4 ) + x5 ) + x6 ) + x7
• expected_a8 := 2 * ( 2 * ( 2 * ( 2 * ( 2 * ( 2 * ( 2 * (2 * a0 + c0) + c1 ) + c2 ) + c3 ) + c4 ) + c5 ) + c6 ) + c7
• expected_b8 := 2 * ( 2 * ( 2 * ( 2 * ( 2 * ( 2 * ( 2 * (2 * b0 + d0) + d1 ) + d2 ) + d3 ) + d4 ) + d5 ) + d6 ) + d7

Putting together all of the above, these are the 11 constraints for this gate

• Checking values after the 8 iterations:
• Constrain :  0 = expected_n8 - n8
• Constrain :  0 = expected_a8 - a8
• Constrain :  0 = expected_b8 - b8
• Checking the crumbs, meaning each is indeed in the range :
• Constrain : 0 = x0 * ( x0^3 - 6 * x0^2 + 11 * x0 - 6 )
• Constrain : 0 = x1 * ( x1^3 - 6 * x1^2 + 11 * x1 - 6 )
• Constrain : 0 = x2 * ( x2^3 - 6 * x2^2 + 11 * x2 - 6 )
• Constrain : 0 = x3 * ( x3^3 - 6 * x3^2 + 11 * x3 - 6 )
• Constrain : 0 = x4 * ( x4^3 - 6 * x4^2 + 11 * x4 - 6 )
• Constrain : 0 = x5 * ( x5^3 - 6 * x5^2 + 11 * x5 - 6 )
• Constrain : 0 = x6 * ( x6^3 - 6 * x6^2 + 11 * x6 - 6 )
• Constrain : 0 = x7 * ( x7^3 - 6 * x7^2 + 11 * x7 - 6 )

#### Endo Scalar Multiplication

We implement custom gate constraints for short Weierstrass curve endomorphism optimised variable base scalar multiplication.

Given a finite field of order , if the order is not a multiple of 2 nor 3, then an elliptic curve over in short Weierstrass form is represented by the set of points that satisfy the following equation with and : If and are two points in the curve , the goal of this operation is to perform the operation efficiently as .

S = (P + (b ? T : −T)) + P

The same algorithm can be used to perform other scalar multiplications, meaning it is not restricted to the case , but it can be used for any arbitrary . This is done by decomposing the scalar into its binary representation. Moreover, for every step, there will be a one-bit constraint meant to differentiate between addition and subtraction for the operation :

In particular, the constraints of this gate take care of 4 bits of the scalar within a single EVBSM row. When the scalar is longer (which will usually be the case), multiple EVBSM rows will be concatenated.

Row01234567891011121314Type
ixTyTØØxPyPnxRyRs1s3b1b2b3b4EVBSM
i+1==xSySn’xR’yR’s1’s3’b1’b2’b3’b4’EVBSM

The layout of this gate (and the next row) allows for this chained behavior where the output point of the current row gets accumulated as one of the inputs of the following row, becoming in the next constraints. Similarly, the scalar is decomposed into binary form and ( respectively) will store the current accumulated value and the next one for the check.

For readability, we define the following variables for the constraints:

• endo EndoCoefficient
• xq1 endo
• xq2 endo
• yq1
• yq2

These are the 11 constraints that correspond to each EVBSM gate, which take care of 4 bits of the scalar within a single EVBSM row:

• First block:
• (xq1 - xp) * s1 = yq1 - yp
• (2 * xp – s1^2 + xq1) * ((xp – xr) * s1 + yr + yp) = (xp – xr) * 2 * yp
• (yr + yp)^2 = (xp – xr)^2 * (s1^2 – xq1 + xr)
• Second block:
• (xq2 - xr) * s3 = yq2 - yr
• (2*xr – s3^2 + xq2) * ((xr – xs) * s3 + ys + yr) = (xr – xs) * 2 * yr
• (ys + yr)^2 = (xr – xs)^2 * (s3^2 – xq2 + xs)
• Booleanity checks:
• Bit flag : 0 = b1 * (b1 - 1)
• Bit flag : 0 = b2 * (b2 - 1)
• Bit flag : 0 = b3 * (b3 - 1)
• Bit flag : 0 = b4 * (b4 - 1)
• Binary decomposition:
• Accumulated scalar: n_next = 16 * n + 8 * b1 + 4 * b2 + 2 * b3 + b4

The constraints above are derived from the following EC Affine arithmetic equations:

• (1) =>
• (2&3) =>
• (2) =>
• <=>
• (3) =>
• <=>
• (4) =>
• (5&6) =>
• (5) =>
• <=>
• (6) =>
• <=>

Defining and as

Gives the following equations when substituting the values of and :

1. (xq1 - xp) * s1 = (2 * b1 - 1) * yt - yp
2. (2 * xp – s1^2 + xq1) * ((xp – xr) * s1 + yr + yp) = (xp – xr) * 2 * yp
3. (yr + yp)^2 = (xp – xr)^2 * (s1^2 – xq1 + xr)
1. (xq2 - xr) * s3 = (2 * b2 - 1) * yt - yr
2. (2 * xr – s3^2 + xq2) * ((xr – xs) * s3 + ys + yr) = (xr – xs) * 2 * yr
3. (ys + yr)^2 = (xr – xs)^2 * (s3^2 – xq2 + xs)

#### Scalar Multiplication

We implement custom Plonk constraints for short Weierstrass curve variable base scalar multiplication.

Given a finite field of order , if the order is not a multiple of 2 nor 3, then an elliptic curve over in short Weierstrass form is represented by the set of points that satisfy the following equation with and : If and are two points in the curve , the algorithm we represent here computes the operation (point doubling and point addition) as .

Point has nothing to do with the order of the field .

The original algorithm that is being used can be found in the Section 3.1 of https://arxiv.org/pdf/math/0208038.pdf, which can perform the above operation using 1 multiplication, 2 squarings and 2 divisions (one more squaring) if ), thanks to the fact that computing the -coordinate of the intermediate addition is not required. This is more efficient to the standard algorithm that requires 1 more multiplication, 3 squarings in total and 2 divisions.

Moreover, this algorithm can be applied not only to the operation , but any other scalar multiplication . This can be done by expressing the scalar in biwise form and performing a double-and-add approach. Nonetheless, this requires conditionals to differentiate from . For that reason, we will implement the following pseudocode from https://github.com/zcash/zcash/issues/3924 (where instead, they give a variant of the above efficient algorithm for Montgomery curves ).

Acc := [2]T
for i = n-1 ... 0:
Q := (r_i == 1) ? T : -T
Acc := Acc + (Q + Acc)
return (d_0 == 0) ? Q - P : Q


The layout of the witness requires 2 rows. The i-th row will be a VBSM gate whereas the next row will be a ZERO gate.

Row01234567891011121314Type
ixTyTx0y0nn’x1y1x2y2x3y3x4y4VBSM
i+1x5y5b0b1b2b3b4s0s1s2s3s4ZERO

The gate constraints take care of 5 bits of the scalar multiplication. Each single bit consists of 4 constraints. There is one additional constraint imposed on the final number. Thus, the VarBaseMul gate argument requires 21 constraints.

For every bit, there will be one constraint meant to differentiate between addition and subtraction for the operation :

S = (P + (b ? T : −T)) + P

• If the bit is positive, the sign should be a subtraction
• If the bit is negative, the sign should be an addition

Then, paraphrasing the above, we will represent this behavior as:

S = (P - (2 * b - 1) * T ) + P

Let us call Input the point with coordinates (xI, yI) and Target is the point being added with coordinates (xT, yT). Then Output will be the point with coordinates (xO, yO) resulting from O = ( I ± T ) + I

Do not confuse our Output point (xO, yO) with the point at infinity that is normally represented as .

In each step of the algorithm, we consider the following elliptic curves affine arithmetic equations:

For readability, we define the following 3 variables in such a way that can be expressed as u / t:

• rx
• t rx
• u t

Next, for each bit in the algorithm, we create the following 4 constraints that derive from the above:

• Booleanity check on the bit : 0 = b * b - b
• Constrain : (xI - xT) * s1 = yI – (2b - 1) * yT
• Constrain Output -coordinate and : 0 = u^2 - t^2 * (xO - xT + s1^2)
• Constrain Output -coordinate and : 0 = (yO + yI) * t - (xI - xO) * u

When applied to the 5 bits, the value of the Target point (xT, yT) is maintained, whereas the values for the Input and Output points form the chain:

[(x0, y0) -> (x1, y1) -> (x2, y2) -> (x3, y3) -> (x4, y4) -> (x5, y5)]

Similarly, 5 different s0..s4 are required, just like the 5 bits b0..b4.

Finally, the additional constraint makes sure that the scalar is being correctly expressed into its binary form (using the double-and-add decomposition) as: This equation is translated as the constraint:

• Binary decomposition: 0 = n' - (b4 + 2 * (b3 + 2 * (b2 + 2 * (b1 + 2 * (b0 + 2*n)))))

## Setup

In this section we specify the setup that goes into creating two indexes from a circuit:

The circuit creation part is not specified in this document. It might be specified in a separate document, or we might want to specify how to create the circuit description tables.

As such, the transformation of a circuit into these two indexes can be seen as a compilation step. Note that the prover still needs access to the original circuit to create proofs, as they need to execute it to create the witness (register table).

### Common Index

In this section we describe data that both the prover and the verifier index share.

URS (Uniform Reference String) The URS is a set of parameters that is generated once, and shared between the prover and the verifier. It is used for polynomial commitments, so refer to the poly-commitment specification for more details.

Kimchi currently generates the URS based on the circuit, and attach it to the index. So each circuit can potentially be accompanied with a different URS. On the other hand, Mina reuses the same URS for multiple circuits (see zkapps for more details).

Domain. A domain large enough to contain the circuit and the zero-knowledge rows (used to provide zero-knowledge to the protocol). Specifically, the smallest subgroup in our field that has order greater or equal to n + ZK_ROWS, with n is the number of gates in the circuit. TODO: what if the domain is larger than the URS?

Note that in this specification we always assume that the first element of a domain is .

Shifts. As part of the permutation, we need to create PERMUTS shifts. To do that, the following logic is followed (in pseudo code): (TODO: move shift creation within the permutation section?)

shifts[0] = 1 # first shift is identity

for i in 0..7: # generate 7 shifts
i = 7
shift, i = sample(domain, i)
while shifts.contains(shift) do:
shift, i = sample(domain, i)
shift[i] = shift

def sample(domain, i):
i += 1
shift = Field(Blake2b512(to_be_bytes(i)))
i += 1
shift = Field(Blake2b512(to_be_bytes(i)))
return shift, i


Public. This variable simply contains the number of public inputs. (TODO: actually, it’s not contained in the verifier index)

The compilation steps to create the common index are as follow:

1. If the circuit is less than 2 gates, abort.
2. Create a domain for the circuit. That is, compute the smallest subgroup of the field that has order greater or equal to n + ZK_ROWS elements.
3. Pad the circuit: add zero gates to reach the domain size.
4. sample the PERMUTS shifts.

### Lookup Index

If lookup is used, the following values are added to the common index:

LookupSelectors. The list of lookup selectors used. In practice, this tells you which lookup tables are used.

TableIds. This is a list of table ids used by the Lookup gate.

MaxJointSize. This is the maximum number of columns appearing in the lookup tables used by the lookup selectors. For example, the XOR lookup has 3 columns.

To create the index, follow these steps:

1. If no lookup is used in the circuit, do not create a lookup index

2. Get the lookup selectors and lookup tables (TODO: how?)

3. Concatenate runtime lookup tables with the ones used by gates

4. Get the highest number of columns max_table_width that a lookup table can have.

5. Create the concatenated table of all the fixed lookup tables. It will be of height the size of the domain, and of width the maximum width of any of the lookup tables. In addition, create an additional column to store all the tables’ table IDs.

For example, if you have a table with ID 0

123
567
000

and another table with ID 1

89

the concatenated table in a domain of size 5 looks like this:

123
567
000
890
000

with the table id vector:

table id
0
0
0
1
0

To do this, for each table:

• Update the corresponding entries in a table id vector (of size the domain as well) with the table ID of the table.
• Copy the entries from the table to new rows in the corresponding columns of the concatenated table.
• Fill in any unused columns with 0 (to match the dummy value)
6. Pad the end of the concatened table with the dummy value.

7. Pad the end of the table id vector with 0s.

8. pre-compute polynomial and evaluation form for the look up tables

9. pre-compute polynomial and evaluation form for the table IDs, only if a table with an ID different from zero was used.

### Prover Index

Both the prover and the verifier index, besides the common parts described above, are made out of pre-computations which can be used to speed up the protocol. These pre-computations are optimizations, in the context of normal proofs, but they are necessary for recursion.

pub struct ProverIndex<G: CommitmentCurve> {
/// constraints system polynomials
#[serde(bound = "ConstraintSystem<ScalarField<G>>: Serialize + DeserializeOwned")]
pub cs: ConstraintSystem<ScalarField<G>>,

/// The symbolic linearization of our circuit, which can compile to concrete types once certain values are learned in the protocol.
#[serde(skip)]
pub linearization: Linearization<Vec<PolishToken<ScalarField<G>>>>,

/// The mapping between powers of alpha and constraints
#[serde(skip)]
pub powers_of_alpha: Alphas<ScalarField<G>>,

/// polynomial commitment keys
#[serde(skip)]
pub srs: Arc<SRS<G>>,

/// maximal size of polynomial section
pub max_poly_size: usize,

/// maximal size of the quotient polynomial according to the supported constraints
pub max_quot_size: usize,

/// random oracle argument parameters
#[serde(skip)]
pub fq_sponge_params: ArithmeticSpongeParams<BaseField<G>>,
}


### Verifier Index

Same as the prover index, we have a number of pre-computations as part of the verifier index.

#[serde_as]
#[derive(Serialize, Deserialize)]
pub struct LookupVerifierIndex<G: CommitmentCurve> {
pub lookup_used: LookupsUsed,
#[serde(bound = "PolyComm<G>: Serialize + DeserializeOwned")]
pub lookup_table: Vec<PolyComm<G>>,
#[serde(bound = "PolyComm<G>: Serialize + DeserializeOwned")]
pub lookup_selectors: Vec<PolyComm<G>>,

/// Table IDs for the lookup values.
/// This may be None if all lookups originate from table 0.
#[serde(bound = "PolyComm<G>: Serialize + DeserializeOwned")]
pub table_ids: Option<PolyComm<G>>,

/// The maximum joint size of any joint lookup in a constraint in kinds. This can be computed from kinds.
pub max_joint_size: u32,

/// An optional selector polynomial for runtime tables
#[serde(bound = "PolyComm<G>: Serialize + DeserializeOwned")]
pub runtime_tables_selector: Option<PolyComm<G>>,
}

#[serde_as]
#[derive(Serialize, Deserialize)]
pub struct VerifierIndex<G: CommitmentCurve> {
/// evaluation domain
#[serde_as(as = "o1_utils::serialization::SerdeAs")]
pub domain: D<ScalarField<G>>,
/// maximal size of polynomial section
pub max_poly_size: usize,
/// maximal size of the quotient polynomial according to the supported constraints
pub max_quot_size: usize,
/// polynomial commitment keys
#[serde(skip)]
pub srs: Arc<SRS<G>>,

// index polynomial commitments
/// permutation commitment array
#[serde(bound = "PolyComm<G>: Serialize + DeserializeOwned")]
pub sigma_comm: [PolyComm<G>; PERMUTS],
/// coefficient commitment array
#[serde(bound = "PolyComm<G>: Serialize + DeserializeOwned")]
pub coefficients_comm: [PolyComm<G>; COLUMNS],
/// coefficient commitment array
#[serde(bound = "PolyComm<G>: Serialize + DeserializeOwned")]
pub generic_comm: PolyComm<G>,

// poseidon polynomial commitments
/// poseidon constraint selector polynomial commitment
#[serde(bound = "PolyComm<G>: Serialize + DeserializeOwned")]
pub psm_comm: PolyComm<G>,

// ECC arithmetic polynomial commitments
/// EC addition selector polynomial commitment
#[serde(bound = "PolyComm<G>: Serialize + DeserializeOwned")]
/// EC variable base scalar multiplication selector polynomial commitment
#[serde(bound = "PolyComm<G>: Serialize + DeserializeOwned")]
pub mul_comm: PolyComm<G>,
/// endoscalar multiplication selector polynomial commitment
#[serde(bound = "PolyComm<G>: Serialize + DeserializeOwned")]
pub emul_comm: PolyComm<G>,
/// endoscalar multiplication scalar computation selector polynomial commitment
#[serde(bound = "PolyComm<G>: Serialize + DeserializeOwned")]
pub endomul_scalar_comm: PolyComm<G>,

/// Chacha polynomial commitments
#[serde(bound = "PolyComm<G>: Serialize + DeserializeOwned")]
pub chacha_comm: Option<[PolyComm<G>; 4]>,

// Range check gates polynomial commitments
#[serde(bound = "Vec<PolyComm<G>>: Serialize + DeserializeOwned")]
pub range_check_comm: Vec<PolyComm<G>>,

/// wire coordinate shifts
#[serde_as(as = "[o1_utils::serialization::SerdeAs; PERMUTS]")]
pub shift: [ScalarField<G>; PERMUTS],
/// zero-knowledge polynomial
#[serde(skip)]
pub zkpm: DensePolynomial<ScalarField<G>>,
// TODO(mimoo): isn't this redundant with domain.d1.group_gen ?
/// domain offset for zero-knowledge
#[serde(skip)]
pub w: ScalarField<G>,
/// endoscalar coefficient
#[serde(skip)]
pub endo: ScalarField<G>,

#[serde(bound = "PolyComm<G>: Serialize + DeserializeOwned")]
pub lookup_index: Option<LookupVerifierIndex<G>>,

#[serde(skip)]
pub linearization: Linearization<Vec<PolishToken<ScalarField<G>>>>,
/// The mapping between powers of alpha and constraints
#[serde(skip)]
pub powers_of_alpha: Alphas<ScalarField<G>>,

// random oracle argument parameters
#[serde(skip)]
pub fr_sponge_params: ArithmeticSpongeParams<ScalarField<G>>,
#[serde(skip)]
pub fq_sponge_params: ArithmeticSpongeParams<BaseField<G>>,
}


## Proof Construction & Verification

Originally, kimchi is based on an interactive protocol that was transformed into a non-interactive one using the Fiat-Shamir transform. For this reason, it can be useful to visualize the high-level interactive protocol before the transformation:

sequenceDiagram
participant Prover
participant Verifier

Note over Prover,Verifier: Prover produces commitments to secret polynomials

Prover->>Verifier: public input & witness commitment

Verifier->>Prover: beta & gamma
Prover->>Verifier: permutation commitment

opt lookup
Prover->>Verifier: sorted
Prover->>Verifier: aggreg
end

Note over Prover,Verifier: Prover produces commitment to quotient polynomial

Verifier->>Prover: alpha
Prover->>Verifier: quotient commitment

Note over Prover,Verifier: Verifier produces an evaluation point

Verifier->>Prover: zeta

Note over Prover,Verifier: Prover provides helper evaluations

Prover->>Verifier: the generic selector gen(zeta) & gen(zeta * omega)
Prover->>Verifier: the poseidon selector pos(zeta) & pos(zeta * omega)
Prover->>Verifier: negated public input p(zeta) & p(zeta * omega)

Note over Prover,Verifier: Prover provides needed evaluations for the linearization

Note over Verifier: change of verifier (change of sponge)

Prover->>Verifier: permutation poly z(zeta) & z(zeta * omega)
Prover->>Verifier: the 15 registers w_i(zeta) & w_i(zeta * omega)
Prover->>Verifier: the 6 sigmas s_i(zeta) & s_i(zeta * omega)

Prover->>Verifier: ft(zeta * omega)

opt lookup
Prover->>Verifier: sorted(zeta) & sorted(zeta * omega)
Prover->>Verifier: aggreg(zeta) & aggreg(zeta * omega)
Prover->>Verifier: table(zeta) & table(zeta * omega)
end

Note over Prover,Verifier: Batch verification of evaluation proofs

Verifier->>Prover: u, v

Note over Verifier: change of verifier (change of sponge)

Prover->>Verifier: aggregated evaluation proof (involves more interaction)


The Fiat-Shamir transform simulates the verifier messages via a hash function that hashes the transcript of the protocol so far before outputing verifier messages. You can find these operations under the proof creation and proof verification algorithms as absorption and squeezing of values with the sponge.

### Proof Structure

A proof consists of the following data structures:

#[derive(Clone)]
pub struct LookupEvaluations<Field> {
/// sorted lookup table polynomial
pub sorted: Vec<Field>,
/// lookup aggregation polynomial
pub aggreg: Field,
// TODO: May be possible to optimize this away?
/// lookup table polynomial
pub table: Field,

/// Optionally, a runtime table polynomial.
pub runtime: Option<Field>,
}

// TODO: this should really be vectors here, perhaps create another type for chunked evaluations?
#[derive(Clone)]
pub struct ProofEvaluations<Field> {
/// witness polynomials
pub w: [Field; COLUMNS],
/// permutation polynomial
pub z: Field,
/// permutation polynomials
/// (PERMUTS-1 evaluations because the last permutation is only used in commitment form)
pub s: [Field; PERMUTS - 1],
/// lookup-related evaluations
pub lookup: Option<LookupEvaluations<Field>>,
/// evaluation of the generic selector polynomial
pub generic_selector: Field,
/// evaluation of the poseidon selector polynomial
pub poseidon_selector: Field,
}

/// Commitments linked to the lookup feature
#[derive(Clone)]
pub struct LookupCommitments<G: AffineCurve> {
pub sorted: Vec<PolyComm<G>>,
pub aggreg: PolyComm<G>,

/// Optional commitment to concatenated runtime tables
pub runtime: Option<PolyComm<G>>,
}

/// All the commitments that the prover creates as part of the proof.
#[derive(Clone)]
pub struct ProverCommitments<G: AffineCurve> {
/// The commitments to the witness (execution trace)
pub w_comm: [PolyComm<G>; COLUMNS],
/// The commitment to the permutation polynomial
pub z_comm: PolyComm<G>,
/// The commitment to the quotient polynomial
pub t_comm: PolyComm<G>,
/// Commitments related to the lookup argument
pub lookup: Option<LookupCommitments<G>>,
}

/// The proof that the prover creates from a [ProverIndex](super::prover_index::ProverIndex) and a witness.
#[derive(Clone)]
pub struct ProverProof<G: AffineCurve> {
/// All the polynomial commitments required in the proof
pub commitments: ProverCommitments<G>,

/// batched commitment opening proof
pub proof: OpeningProof<G>,

/// Two evaluations over a number of committed polynomials
// TODO(mimoo): that really should be a type Evals { z: PE, zw: PE }
pub evals: [ProofEvaluations<Vec<ScalarField<G>>>; 2],

/// Required evaluation for [Maller's optimization](https://o1-labs.github.io/mina-book/crypto/plonk/maller_15.html#the-evaluation-of-l)
pub ft_eval1: ScalarField<G>,

/// The public input
pub public: Vec<ScalarField<G>>,

/// The challenges underlying the optional polynomials folded into the proof
pub prev_challenges: Vec<(Vec<ScalarField<G>>, PolyComm<G>)>,
}


The following sections specify how a prover creates a proof, and how a verifier validates a number of proofs.

### Proof Creation

To create a proof, the prover expects:

• A prover index, containing a representation of the circuit (and optionaly pre-computed values to be used in the proof creation).
• The (filled) registers table, representing parts of the execution trace of the circuit.

The public input is expected to be passed in the first Public rows of the registers table.

The following constants are set:

• EVAL_POINTS = 2. This is the number of points that the prover has to evaluate their polynomials at. ( and where will be deterministically generated.)
• ZK_ROWS = 3. This is the number of rows that will be randomized to provide zero-knowledgeness. Note that it only needs to be greater or equal to the number of evaluations (2) in the protocol. Yet, it contains one extra row to take into account the last constraint (final value of the permutation accumulator). (TODO: treat the final constraint separately so that ZK_ROWS = 2)

The prover then follows the following steps to create the proof:

1. Ensure we have room in the witness for the zero-knowledge rows. We currently expect the witness not to be of the same length as the domain, but instead be of the length of the (smaller) circuit. If we cannot add ZK_ROWS rows to the columns of the witness before reaching the size of the domain, abort.

2. Pad the witness columns with Zero gates to make them the same length as the domain. Then, randomize the last ZK_ROWS of each columns.

3. Setup the Fq-Sponge.

4. Compute the negated public input polynomial as the polynomial that evaluates to for the first public_input_size values of the domain, and for the rest.

5. Commit (non-hiding) to the negated public input polynomial.

6. Absorb the commitment to the public polynomial with the Fq-Sponge.

Note: unlike the original PLONK protocol, the prover also provides evaluations of the public polynomial to help the verifier circuit. This is why we need to absorb the commitment to the public polynomial at this point.

7. Commit to the witness columns by creating COLUMNS hidding commitments.

Note: since the witness is in evaluation form, we can use the commit_evaluation optimization.

8. Absorb the witness commitments with the Fq-Sponge.

9. Compute the witness polynomials by interpolating each COLUMNS of the witness. TODO: why not do this first, and then commit? Why commit from evaluation directly?

10. If using lookup:

• If queries involve a lookup table with multiple columns then squeeze the Fq-Sponge to obtain the joint combiner challenge , otherwise set the joint combiner challenge to .
• Derive the scalar joint combiner from using the endomorphism (TOOD: specify)
• If multiple lookup tables are involved, set the table_id_combiner as the with the maximum width of any used table. Essentially, this is to add a last column of table ids to the concatenated lookup tables.
• Compute the dummy lookup value as the combination of the last entry of the XOR table (so (0, 0, 0)). Warning: This assumes that we always use the XOR table when using lookups.
• Compute the lookup table values as the combination of the lookup table entries.
• Compute the sorted evaluations.
• Randomize the last EVALS rows in each of the sorted polynomials in order to add zero-knowledge to the protocol.
• Commit each of the sorted polynomials.
• Absorb each commitments to the sorted polynomials.
11. Sample with the Fq-Sponge.

12. Sample with the Fq-Sponge.

13. If using lookup:

• Compute the lookup aggregation polynomial.
• Commit to the aggregation polynomial.
• Absorb the commitment to the aggregation polynomial with the Fq-Sponge.
14. Compute the permutation aggregation polynomial .

15. Commit (hidding) to the permutation aggregation polynomial .

16. Absorb the permutation aggregation polynomial with the Fq-Sponge.

17. Sample with the Fq-Sponge.

18. Derive from using the endomorphism (TODO: details)

19. TODO: instantiate alpha?

20. Compute the quotient polynomial (the in ). The quotient polynomial is computed by adding all these polynomials together:

• the combined constraints for all the gates
• the combined constraints for the permutation
• TODO: lookup
• the negated public polynomial and by then dividing the resulting polynomial with the vanishing polynomial . TODO: specify the split of the permutation polynomial into perm and bnd?
21. commit (hiding) to the quotient polynomial TODO: specify the dummies

22. Absorb the the commitment of the quotient polynomial with the Fq-Sponge.

23. Sample with the Fq-Sponge.

24. Derive from using the endomorphism (TODO: specify)

25. If lookup is used, evaluate the following polynomials at and :

• the aggregation polynomial
• the sorted polynomials
• the table polynonial
26. Chunk evaluate the following polynomials at both and :

• lookup (TODO)
• generic selector
• poseidon selector

By “chunk evaluate” we mean that the evaluation of each polynomial can potentially be a vector of values. This is because the index’s max_poly_size parameter dictates the maximum size of a polynomial in the protocol. If a polynomial exceeds this size, it must be split into several polynomials like so:

And the evaluation of such a polynomial is the following list for :

TODO: do we want to specify more on that? It seems unecessary except for the t polynomial (or if for some reason someone sets that to a low value)

27. Evaluate the same polynomials without chunking them (so that each polynomial should correspond to a single value this time).

28. Compute the ft polynomial. This is to implement Maller’s optimization.

29. construct the blinding part of the ft polynomial commitment see https://o1-labs.github.io/mina-book/crypto/plonk/maller_15.html#evaluation-proof-and-blinding-factors

30. Evaluate the ft polynomial at only.

31. Setup the Fr-Sponge

32. Squeeze the Fq-sponge and absorb the result with the Fr-Sponge.

33. Evaluate the negated public polynomial (if present) at and .

34. Absorb all the polynomial evaluations in and :

• the public polynomial
• z
• generic selector
• poseidon selector
• the 15 register/witness
• 6 sigmas evaluations (the last one is not evaluated)
35. Absorb the unique evaluation of ft: .

36. Sample with the Fr-Sponge

37. Derive from using the endomorphism (TODO: specify)

38. Sample with the Fr-Sponge

39. Derive from using the endomorphism (TODO: specify)

40. Create a list of all polynomials that will require evaluations (and evaluation proofs) in the protocol. First, include the previous challenges, in case we are in a recursive prover.

41. Then, include:

• the negated public polynomial
• the ft polynomial
• the permutation aggregation polynomial z polynomial
• the generic selector
• the poseidon selector
• the 15 registers/witness columns
• the 6 sigmas
• optionally, the runtime table
42. Create an aggregated evaluation proof for all of these polynomials at and using and .

### Proof Verification

TODO: we talk about batch verification, but is there an actual batch operation? It seems like we’re just verifying an aggregated opening proof

We define two helper algorithms below, used in the batch verification of proofs.

#### Fiat-Shamir argument

We run the following algorithm:

1. Setup the Fq-Sponge.

2. Absorb the commitment of the public input polynomial with the Fq-Sponge.

3. Absorb the commitments to the registers / witness columns with the Fq-Sponge.

4. If lookup is used:

• If it involves queries to a multiple-column lookup table, then squeeze the Fq-Sponge to obtain the joint combiner challenge , otherwise set the joint combiner challenge to .
• Derive the scalar joint combiner challenge from using the endomorphism. (TODO: specify endomorphism)
• absorb the commitments to the sorted polynomials.
5. Sample with the Fq-Sponge.

6. Sample with the Fq-Sponge.

7. If using lookup, absorb the commitment to the aggregation lookup polynomial.

8. Absorb the commitment to the permutation trace with the Fq-Sponge.

9. Sample with the Fq-Sponge.

10. Derive from using the endomorphism (TODO: details).

11. Enforce that the length of the commitment is of size PERMUTS.

12. Absorb the commitment to the quotient polynomial into the argument.

13. Sample with the Fq-Sponge.

14. Derive from using the endomorphism (TODO: specify).

15. Setup the Fr-Sponge.

16. Squeeze the Fq-sponge and absorb the result with the Fr-Sponge.

17. Evaluate the negated public polynomial (if present) at and .

NOTE: this works only in the case when the poly segment size is not smaller than that of the domain.

18. Absorb all the polynomial evaluations in and :

• the public polynomial
• z
• generic selector
• poseidon selector
• the 15 register/witness
• 6 sigmas evaluations (the last one is not evaluated)
19. Absorb the unique evaluation of ft: .

20. Sample with the Fr-Sponge.

21. Derive from using the endomorphism (TODO: specify).

22. Sample with the Fr-Sponge.

23. Derive from using the endomorphism (TODO: specify).

24. Create a list of all polynomials that have an evaluation proof.

25. Compute the evaluation of .

#### Partial verification

For every proof we want to verify, we defer the proof opening to the very end. This allows us to potentially batch verify a number of partially verified proofs. Essentially, this steps verifies that .

1. Commit to the negated public input polynomial.
2. Run the Fiat-Shamir argument.
3. Combine the chunked polynomials’ evaluations (TODO: most likely only the quotient polynomial is chunked) with the right powers of and .
4. Compute the commitment to the linearized polynomial . To do this, add the constraints of all of the gates, of the permutation, and optionally of the lookup. (See the separate sections in the constraints section.) Any polynomial should be replaced by its associated commitment, contained in the verifier index or in the proof, unless a polynomial has its evaluation provided by the proof in which case the evaluation should be used in place of the commitment.
5. Compute the (chuncked) commitment of (see Maller’s optimization).
6. List the polynomial commitments, and their associated evaluations, that are associated to the aggregated evaluation proof in the proof:
• recursion
• public input commitment
• ft commitment (chunks of it)
• permutation commitment
• index commitments that use the coefficients
• witness commitments
• sigma commitments
• lookup commitments

#### Batch verification of proofs

Below, we define the steps to verify a number of proofs (each associated to a verifier index). You can, of course, use it to verify a single proof.

1. If there’s no proof to verify, the proof validates trivially.
2. Ensure that all the proof’s verifier index have a URS of the same length. (TODO: do they have to be the same URS though? should we check for that?)
3. Validate each proof separately following the partial verification steps.
4. Use the PolyCom.verify to verify the partially evaluated proofs.

## Optimizations

• commit_evaluation: TODO

TODO