@@ -2,7 +2,8 @@ #![allow(non_snake_case)]
 use criterion::{criterion_group, criterion_main, Criterion};
 use shockwave_plus::ShockwavePlus;
 use shockwave_plus::R1CS;
-use tensor_pcs::Transcript;
+use tensor_pcs::rs_config::good_curves::secp256k1::secp256k1_good_curve;
+use tensor_pcs::{det_num_cols, Transcript};
 
 fn shockwave_plus_bench(c: &mut Criterion) {
     type F = halo2curves::secp256k1::Fp;
@@ -15,14 +16,23 @@ let (r1cs, witness) = R1CS::<F>::produce_synthetic_r1cs(num_vars, num_input);
 
         let mut group = c.benchmark_group(format!("ShockwavePlus num_cons: {}", r1cs.num_cons));
         let l = 319;
-        let num_rows = (((2f64 / l as f64).sqrt() * (num_vars as f64).sqrt()) as usize)
-            .next_power_of_two()
-            / 2;
-        let ShockwavePlus = ShockwavePlus::new(r1cs.clone(), l, num_rows);
+        let num_cols = det_num_cols(r1cs.z_len(), l);
+
+        let (good_curve, coset_offset) =
+            secp256k1_good_curve((num_cols as f64).log2() as usize + 1);
+
+        group.bench_function("config", |b| {
+            b.iter(|| {
+                ShockwavePlus::new(r1cs.clone(), l, good_curve, coset_offset);
+            })
+        });
+
+        let shockwave_plus = ShockwavePlus::new(r1cs.clone(), l, good_curve, coset_offset);
+
         group.bench_function("prove", |b| {
             b.iter(|| {
                 let mut transcript = Transcript::new(b"bench");
-                ShockwavePlus.prove(&witness, &r1cs.public_input, &mut transcript);
+                shockwave_plus.prove(&witness, &r1cs.public_input, &mut transcript);
             })
         });
     }

@@ -6,10 +6,10 @@
 use ark_std::{end_timer, start_timer};
 use serde::{Deserialize, Serialize};
 use sumcheck::{SCPhase1Proof, SCPhase2Proof, SumCheckPhase1, SumCheckPhase2};
+use tensor_pcs::{ecfft::GoodCurve, *};
 
 // Exports
 pub use r1cs::R1CS;
-pub use tensor_pcs::*;
 
 #[derive(Serialize, Deserialize)]
 pub struct PartialSpartanProof<F: FieldExt> {
@@ -30,21 +30,15 @@ pub C_eval_proof: TensorMLOpening<F>,
 }
 
 pub struct ShockwavePlus<F: FieldExt> {
-    pub r1cs: R1CS<F>,
-    pub pcs_witness: TensorMultilinearPCS<F>,
-    pub pcs_blinder: TensorMultilinearPCS<F>,
+    r1cs: R1CS<F>,
+    pcs: TensorMultilinearPCS<F>,
 }
 
 impl<F: FieldExt> ShockwavePlus<F> {
-    pub fn new(r1cs: R1CS<F>, l: usize, num_rows: usize) -> Self {
-        let num_cols = r1cs.num_vars / num_rows;
-
-        // Make sure that there are enough columns to run the l queries
-        assert!(num_cols > l);
-
+    pub fn new(r1cs: R1CS<F>, l: usize, good_curve: GoodCurve<F>, coset_offset: (F, F)) -> Self {
         let expansion_factor = 2;
 
-        let ecfft_config = rs_config::ecfft::gen_config(num_cols.next_power_of_two());
+        let ecfft_config = rs_config::ecfft::gen_config_form_curve(good_curve, coset_offset);
 
         let pcs_config = TensorRSMultilinearPCSConfig::<F> {
             expansion_factor,
@@ -52,31 +46,21 @@ domain_powers: None,
             fft_domain: None,
             ecfft_config: Some(ecfft_config),
             l,
-            num_entries: r1cs.num_vars,
-            num_rows,
         };
 
-        let pcs_witness = TensorMultilinearPCS::new(pcs_config);
+        let min_num_entries = r1cs.num_vars.next_power_of_two();
+        let min_num_cols = pcs_config.num_cols(min_num_entries);
 
-        let ecfft_config_blinder =
-            rs_config::ecfft::gen_config((r1cs.z_len() / num_rows).next_power_of_two());
-        let pcs_blinder_config = TensorRSMultilinearPCSConfig::<F> {
-            expansion_factor,
-            domain_powers: None,
-            fft_domain: None,
-            ecfft_config: Some(ecfft_config_blinder),
-            l,
-            num_entries: r1cs.z_len(),
-            num_rows,
-        };
+        let max_num_entries = r1cs.z_len().next_power_of_two();
+        let max_num_cols = pcs_config.num_cols(max_num_entries);
+        // Make sure that there are enough columns to run the l queries
+        assert!(min_num_cols > l);
+
+        assert_eq!(good_curve.k, (max_num_cols as f64).log2() as usize + 1);
 
-        let pcs_blinder = TensorMultilinearPCS::new(pcs_blinder_config);
+        let pcs = TensorMultilinearPCS::new(pcs_config);
 
-        Self {
-            r1cs,
-            pcs_witness,
-            pcs_blinder,
-        }
+        Self { r1cs, pcs }
     }
 
     pub fn prove(
@@ -91,7 +75,7 @@ let Z = R1CS::construct_z(r1cs_witness, r1cs_input);
 
         // Commit the witness polynomial
         let comm_witness_timer = start_timer!(|| "Commit witness");
-        let committed_witness = self.pcs_witness.commit(&witness_poly);
+        let committed_witness = self.pcs.commit(&witness_poly);
         let witness_comm = committed_witness.committed_tree.root;
         end_timer!(comm_witness_timer);
 
@@ -125,7 +109,8 @@ Cz_poly.clone(),
             tau.clone(),
             rx.clone(),
         );
-        let (sc_proof_1, (v_A, v_B, v_C)) = sc_phase_1.prove(&self.pcs_blinder, transcript);
+
+        let (sc_proof_1, (v_A, v_B, v_C)) = sc_phase_1.prove(&self.pcs, transcript);
         end_timer!(sc_phase_1_timer);
 
         transcript.append_fe(&v_A);
@@ -149,14 +134,14 @@ r.as_slice().try_into().unwrap(),
             ry.clone(),
         );
 
-        let sc_proof_2 = sc_phase_2.prove(&self.pcs_blinder, transcript);
+        let sc_proof_2 = sc_phase_2.prove(&self.pcs, transcript);
         end_timer!(sc_phase_2_timer);
 
         let z_open_timer = start_timer!(|| "Open witness poly");
         // Prove the evaluation of the polynomial Z(y) at ry
-        let z_eval_proof =
-            self.pcs_witness
-                .open(&committed_witness, &witness_poly, &ry[1..], transcript);
+        let z_eval_proof = self
+            .pcs
+            .open(&committed_witness, &witness_poly, &ry[1..], transcript);
         end_timer!(z_open_timer);
 
         // Prove the evaluation of the polynomials A(y), B(y), C(y) at ry
@@ -198,7 +183,7 @@ let rho = transcript.challenge_fe();
 
         let ex = SumCheckPhase1::verify_round_polys(&partial_proof.sc_proof_1, &rx, rho);
 
-        self.pcs_blinder.verify(
+        self.pcs.verify(
             &partial_proof.sc_proof_1.blinder_poly_eval_proof,
             transcript,
         );
@@ -234,7 +219,7 @@ (r_A * v_A + r_B * v_B + r_C * v_C) + rho_2 * partial_proof.sc_proof_2.blinder_poly_sum;
         let final_poly_eval =
             SumCheckPhase2::verify_round_polys(T_2, &partial_proof.sc_proof_2, &ry);
 
-        self.pcs_blinder.verify(
+        self.pcs.verify(
             &partial_proof.sc_proof_2.blinder_poly_eval_proof,
             transcript,
         );
@@ -247,8 +232,7 @@ let A_eval = A_mle.eval(&rx_ry);
         let B_eval = B_mle.eval(&rx_ry);
         let C_eval = C_mle.eval(&rx_ry);
 
-        self.pcs_witness
-            .verify(&partial_proof.z_eval_proof, transcript);
+        self.pcs.verify(&partial_proof.z_eval_proof, transcript);
 
         let witness_len = self.r1cs.num_vars.next_power_of_two();
         let input = (0..self.r1cs.num_input)
@@ -270,20 +254,25 @@ }
 
 #[cfg(test)]
 mod tests {
+    use tensor_pcs::rs_config::good_curves::secp256k1::secp256k1_good_curve;
+
     use super::*;
 
     #[test]
     fn test_shockwave_plus() {
         type F = halo2curves::secp256k1::Fp;
 
-        let num_vars = 2usize.pow(7);
+        let num_vars = 2usize.pow(6);
         let num_input = 3;
-        let l = 10;
+        let l = 2;
 
         let (r1cs, witness) = R1CS::<F>::produce_synthetic_r1cs(num_vars, num_input);
 
-        let num_rows = 4;
-        let ShockwavePlus = ShockwavePlus::new(r1cs.clone(), l, num_rows);
+        let num_cols = det_num_cols(r1cs.z_len(), l);
+
+        let k = (num_cols as f64).log2() as usize;
+        let (good_curve, coset_offset) = secp256k1_good_curve(k + 1);
+        let ShockwavePlus = ShockwavePlus::new(r1cs.clone(), l, good_curve, coset_offset);
         let mut prover_transcript = Transcript::new(b"bench");
         let (partial_proof, _) =
             ShockwavePlus.prove(&witness, &r1cs.public_input, &mut prover_transcript);

@@ -9,7 +9,7 @@ [dependencies]
 rand = "0.8.5"
 serde = { version = "1.0.152", features = ["derive"] }
 merlin = "3.0.0"
-ecfft = { git = "https://github.com/DanTehrani/ecfft" }
+ecfft = { git = "https://github.com/DanTehrani/ecfft", branch = "main" }
 tiny-keccak = { version = "2.0.2", features = ["keccak"] }
 halo2curves = "0.1.0"
 

@@ -15,11 +15,7 @@ let ml_poly = SparseMLPoly::new(evals, num_vars);
     ml_poly
 }
 
-fn config_base<F: FieldExt>(ml_poly: &SparseMLPoly<F>) -> TensorRSMultilinearPCSConfig<F> {
-    let num_vars = ml_poly.num_vars;
-    let num_evals = 2usize.pow(num_vars as u32);
-    let num_rows = 2usize.pow((num_vars / 2) as u32);
-
+fn config_base<F: FieldExt>() -> TensorRSMultilinearPCSConfig<F> {
     let expansion_factor = 2;
 
     TensorRSMultilinearPCSConfig::<F> {
@@ -28,8 +24,6 @@ domain_powers: None,
         fft_domain: None,
         ecfft_config: None,
         l: 10,
-        num_entries: num_evals,
-        num_rows,
     }
 }
 
@@ -42,8 +36,10 @@ let open_at = (0..ml_poly.num_vars)
         .map(|i| F::from(i as u64))
         .collect::<Vec<F>>();
 
-    let mut config = config_base(&ml_poly);
-    config.fft_domain = Some(rs_config::smooth::gen_config::<F>(config.num_cols()));
+    let mut config = config_base();
+    config.fft_domain = Some(rs_config::smooth::gen_config::<F>(
+        config.num_cols(ml_poly.evals.len()),
+    ));
 
     let mut group = c.benchmark_group("pcs fft");
     group.bench_function("prove", |b| {
@@ -66,8 +62,10 @@ let open_at = (0..ml_poly.num_vars)
         .map(|i| F::from(i as u64))
         .collect::<Vec<F>>();
 
-    let mut config = config_base(&ml_poly);
-    config.ecfft_config = Some(rs_config::ecfft::gen_config::<F>(config.num_cols()));
+    let mut config = config_base();
+    config.ecfft_config = Some(rs_config::ecfft::gen_config::<F>(
+        config.num_cols(ml_poly.evals.len()),
+    ));
 
     let mut group = c.benchmark_group("pcs ecfft");
     group.bench_function("prove", |b| {
@@ -88,6 +86,6 @@
 criterion_group! {
     name = benches;
     config = set_duration();
-    targets = pcs_fft_bench, pcs_ecfft_bench
+    targets = pcs_ecfft_bench
 }
 criterion_main!(benches);

@@ -3,7 +3,7 @@ mod fft;
 mod polynomial;
 pub mod rs_config;
 mod tensor_code;
-mod tensor_pcs;
+mod tensor_rs_pcs;
 mod transcript;
 mod tree;
 mod utils;
@@ -14,7 +14,9 @@
 impl FieldExt for halo2curves::secp256k1::Fp {}
 impl FieldExt for halo2curves::pasta::Fp {}
 
+pub use ecfft;
 pub use polynomial::eq_poly::EqPoly;
 pub use polynomial::sparse_ml_poly::SparseMLPoly;
-pub use tensor_pcs::{TensorMLOpening, TensorMultilinearPCS, TensorRSMultilinearPCSConfig};
+pub use tensor_rs_pcs::{TensorMLOpening, TensorMultilinearPCS, TensorRSMultilinearPCSConfig};
 pub use transcript::{AppendToTranscript, Transcript};
+pub use utils::{det_num_cols, det_num_rows};

@@ -11,6 +11,10 @@ pub fn new(evals: Vec<(usize, F)>, num_vars: usize) -> Self {
         Self { evals, num_vars }
     }
 
+    pub fn num_entries(&self) -> usize {
+        2usize.pow(self.num_vars as u32)
+    }
+
     pub fn from_dense(dense_evals: Vec<F>) -> Self {
         let sparse_evals = dense_evals
             .iter()

@@ -1,5 +1,5 @@
 use crate::FieldExt;
-use ecfft::{prepare_domain, prepare_matrices, GoodCurve, Matrix2x2};
+use ecfft::{find_coset_offset, prepare_domain, prepare_matrices, GoodCurve, Matrix2x2};
 
 #[derive(Clone, Debug)]
 pub struct ECFFTConfig<F: FieldExt> {
@@ -8,6 +8,20 @@ pub matrices: Vec<Vec<Matrix2x2<F>>>,
     pub inverse_matrices: Vec<Vec<Matrix2x2<F>>>,
 }
 
+pub fn gen_config_form_curve<F: FieldExt>(
+    good_curve: GoodCurve<F>,
+    coset_offset: (F, F),
+) -> ECFFTConfig<F> {
+    let domain = prepare_domain(good_curve, coset_offset.0, coset_offset.1);
+    let (matrices, inverse_matrices) = prepare_matrices(&domain);
+
+    ECFFTConfig {
+        domain,
+        matrices,
+        inverse_matrices,
+    }
+}
+
 pub fn gen_config<F: FieldExt>(num_cols: usize) -> ECFFTConfig<F> {
     assert!(num_cols.is_power_of_two());
     let expansion_factor = 2;
@@ -15,8 +29,10 @@ let codeword_len = num_cols * expansion_factor;
 
     let k = (codeword_len as f64).log2() as usize;
 
-    let good_curve = GoodCurve::find_k(k);
-    let domain = prepare_domain(good_curve);
+    let good_curve = GoodCurve::<F>::find_k(k);
+    let (coset_offset_x, coset_offset_y) =
+        find_coset_offset(good_curve.a, good_curve.B_sqrt.square());
+    let domain = prepare_domain(good_curve, coset_offset_x, coset_offset_y);
     let (matrices, inverse_matrices) = prepare_matrices(&domain);
 
     ECFFTConfig {

@@ -0,0 +1,1 @@
+pub mod secp256k1;

@@ -0,0 +1,456 @@
+use ecfft::GoodCurve;
+
+type Fp = halo2curves::secp256k1::Fp;
+
+const CURVE_4_A: Fp = Fp::from_raw([
+    1924362692430828527,
+    180888387886949819,
+    14444912836850558493,
+    2716763698990320170,
+]);
+
+const CURVE_4_B_SQRT: Fp = Fp::from_raw([
+    10596826214460559417,
+    9041891995856355984,
+    392200829566232436,
+    5616829616257048236,
+]);
+
+const CURVE_4_GX: Fp = Fp::from_raw([
+    3060553808241114122,
+    4367422627483541323,
+    1326591990371471461,
+    1051615568340430255,
+]);
+
+const CURVE_4_GY: Fp = Fp::from_raw([
+    1576479964359531032,
+    10706990284747222844,
+    2069836301523772900,
+    11540652371418823164,
+]);
+
+const CURVE_4_CX: Fp = Fp::from_raw([
+    1394469693679244729,
+    3481743377114570646,
+    685293755929734561,
+    9752242693766949385,
+]);
+
+const CURVE_4_CY: Fp = Fp::from_raw([
+    11112828892610998404,
+    11816693849252775007,
+    3142482327686601672,
+    2138128838908646944,
+]);
+
+const CURVE_5_A: Fp = Fp::from_raw([
+    18402892062958705657,
+    10955586493449255806,
+    274692491874833279,
+    3521647190010012104,
+]);
+
+const CURVE_5_B_SQRT: Fp = Fp::from_raw([
+    13277815540701934041,
+    10000316683343802069,
+    13748514902267845339,
+    5043980866827216326,
+]);
+
+const CURVE_5_GX: Fp = Fp::from_raw([
+    8681597433860724212,
+    16010850653546434744,
+    1655308633092053542,
+    13482638234089226570,
+]);
+
+const CURVE_5_GY: Fp = Fp::from_raw([
+    695535688134352662,
+    12810977243071276429,
+    6639318313449462421,
+    9854099205183828948,
+]);
+
+const CURVE_5_CX: Fp = Fp::from_raw([
+    7244846058583153822,
+    15236867482366246868,
+    7610066066648153412,
+    8717324474930230203,
+]);
+
+const CURVE_5_CY: Fp = Fp::from_raw([
+    15955524643521385563,
+    14108119042026331605,
+    8376852394104379031,
+    5145942493709290957,
+]);
+
+const CURVE_6_A: Fp = Fp::from_raw([
+    11754870036548954207,
+    1758746815041297131,
+    5040922207106606105,
+    6156268686419792864,
+]);
+
+const CURVE_6_B_SQRT: Fp = Fp::from_raw([
+    16551703456907310471,
+    7307795367003411231,
+    9107551177630293136,
+    3643865576794489637,
+]);
+
+const CURVE_6_GX: Fp = Fp::from_raw([
+    3057786712414561431,
+    3924976238282577064,
+    1535938406046208114,
+    4471499328874959330,
+]);
+
+const CURVE_6_GY: Fp = Fp::from_raw([
+    7420330572426678478,
+    11093910355894798679,
+    8046171174582240023,
+    16159208434053522767,
+]);
+
+const CURVE_6_CX: Fp = Fp::from_raw([
+    15232142892107882662,
+    2997925312254061635,
+    875684261157844424,
+    8054980201271915862,
+]);
+
+const CURVE_6_CY: Fp = Fp::from_raw([
+    5573271252396838460,
+    7659129927758801858,
+    11224891608690076565,
+    8114225763096549468,
+]);
+
+const CURVE_7_A: Fp = Fp::from_raw([
+    15267998901538414419,
+    17985868627099147199,
+    5570198032670981398,
+    7365202425498739811,
+]);
+
+const CURVE_7_B_SQRT: Fp = Fp::from_raw([
+    13238569970078865336,
+    1859729155619525190,
+    2289004025597154627,
+    16424324367845100069,
+]);
+
+const CURVE_7_GX: Fp = Fp::from_raw([
+    13067803014914932854,
+    8460655374139991694,
+    17522879348989963876,
+    2592776320942502074,
+]);
+
+const CURVE_7_GY: Fp = Fp::from_raw([
+    17581244616257969879,
+    13563260062750024799,
+    17836667944921387338,
+    5158385585024810784,
+]);
+
+const CURVE_7_CX: Fp = Fp::from_raw([
+    17627362889681060942,
+    10449394617197091758,
+    11211669951719111062,
+    18402164978442722259,
+]);
+
+const CURVE_7_CY: Fp = Fp::from_raw([
+    3635904622687808257,
+    12660024001564793695,
+    2997578841449112866,
+    7489869964282615463,
+]);
+
+const CURVE_8_A: Fp = Fp::from_raw([
+    16479441517948017563,
+    12244661565122532810,
+    16423402461885171455,
+    15804938408404708752,
+]);
+
+const CURVE_8_B_SQRT: Fp = Fp::from_raw([
+    4114471407724985276,
+    6429895848762172356,
+    9060307719139806083,
+    1606308100763345976,
+]);
+
+const CURVE_8_GX: Fp = Fp::from_raw([
+    18005553174453754936,
+    7879246565041753863,
+    15708703128473390087,
+    12948592289805182905,
+]);
+
+const CURVE_8_GY: Fp = Fp::from_raw([
+    2637815016833021192,
+    5625620963822185667,
+    15498097759340857613,
+    2802364189360038003,
+]);
+
+const CURVE_8_CX: Fp = Fp::from_raw([
+    12514982531648064548,
+    7254771947927897203,
+    6879061275311364813,
+    4385541459413917142,
+]);
+
+const CURVE_8_CY: Fp = Fp::from_raw([
+    13726278170638118925,
+    10016993418218833106,
+    13091102901943378213,
+    8612533232618193985,
+]);
+
+const CURVE_9_A: Fp = Fp::from_raw([
+    2821731813563793393,
+    3977895281010832865,
+    8603743292399951036,
+    4645234720790204102,
+]);
+
+const CURVE_9_B_SQRT: Fp = Fp::from_raw([
+    15890535715675950137,
+    7339610358409226035,
+    12609222160720627891,
+    12499110658591842997,
+]);
+
+const CURVE_9_GX: Fp = Fp::from_raw([
+    6103380741459351369,
+    14746101125474414882,
+    12417547802268400852,
+    7335532149994146446,
+]);
+
+const CURVE_9_GY: Fp = Fp::from_raw([
+    4181331351064768648,
+    16489913493464340135,
+    7051826832725726336,
+    887431923330984487,
+]);
+
+const CURVE_9_CX: Fp = Fp::from_raw([
+    9670988472649099633,
+    15261760137634294840,
+    2288914830631271678,
+    6241984859397428357,
+]);
+
+const CURVE_9_CY: Fp = Fp::from_raw([
+    3996701096097868069,
+    16808707541849580191,
+    2008740307070264540,
+    10234541905633632584,
+]);
+
+const CURVE_10_A: Fp = Fp::from_raw([
+    13443933661892288238,
+    6366097774645666914,
+    12539700524489124232,
+    2960403700358460234,
+]);
+
+const CURVE_10_B_SQRT: Fp = Fp::from_raw([
+    11179334656770650694,
+    12204828351656968056,
+    17469374953427230415,
+    2698602761568343027,
+]);
+
+const CURVE_10_GX: Fp = Fp::from_raw([
+    4752429915723436981,
+    6658961595441054005,
+    943316193080952835,
+    10509103062531384873,
+]);
+
+const CURVE_10_GY: Fp = Fp::from_raw([
+    7405820527339739030,
+    1149755149636620515,
+    12315441721581649311,
+    9740641083146831387,
+]);
+
+const CURVE_10_CX: Fp = Fp::from_raw([
+    12133915190353440166,
+    12735241419273571667,
+    984598181344074714,
+    4945074058633718103,
+]);
+
+const CURVE_10_CY: Fp = Fp::from_raw([
+    7787944055361603336,
+    16188630343349344241,
+    1798488611520969499,
+    15905180573830923441,
+]);
+
+const CURVE_11_A: Fp = Fp::from_raw([
+    44690967250983077,
+    13024355091469571869,
+    2426866618505792061,
+    5439410159441159777,
+]);
+
+const CURVE_11_B_SQRT: Fp = Fp::from_raw([
+    2482839174035592440,
+    13977599229562359858,
+    9165253311652858048,
+    11796280965050311461,
+]);
+
+const CURVE_11_GX: Fp = Fp::from_raw([
+    3785100838262116535,
+    14366163517008314631,
+    6520093107874784461,
+    1432940500835404998,
+]);
+
+const CURVE_11_GY: Fp = Fp::from_raw([
+    15446934078954168044,
+    13724149936204307181,
+    291296515805666972,
+    17295416299404581082,
+]);
+
+const CURVE_11_CX: Fp = Fp::from_raw([
+    3987179730389290606,
+    12765099312359453542,
+    14085665078244679772,
+    1158541383839945849,
+]);
+
+const CURVE_11_CY: Fp = Fp::from_raw([
+    2812404283588715887,
+    10748530967036022352,
+    15279323815639380689,
+    7472866256744067949,
+]);
+
+pub fn secp256k1_good_curve(k: usize) -> (GoodCurve<Fp>, (Fp, Fp)) {
+    if k == 4 {
+        (
+            GoodCurve::new(CURVE_4_A, CURVE_4_B_SQRT, CURVE_4_GX, CURVE_4_GY, k),
+            (CURVE_4_CX, CURVE_4_CY),
+        )
+    } else if k == 5 {
+        (
+            GoodCurve::new(CURVE_5_A, CURVE_5_B_SQRT, CURVE_5_GX, CURVE_5_GY, k),
+            (CURVE_5_CX, CURVE_5_CY),
+        )
+    } else if k == 6 {
+        (
+            GoodCurve::new(CURVE_6_A, CURVE_6_B_SQRT, CURVE_6_GX, CURVE_6_GY, k),
+            (CURVE_6_CX, CURVE_6_CY),
+        )
+    } else if k == 7 {
+        (
+            GoodCurve::new(CURVE_7_A, CURVE_7_B_SQRT, CURVE_7_GX, CURVE_7_GY, k),
+            (CURVE_7_CX, CURVE_7_CY),
+        )
+    } else if k == 8 {
+        (
+            GoodCurve::new(CURVE_8_A, CURVE_8_B_SQRT, CURVE_8_GX, CURVE_8_GY, k),
+            (CURVE_8_CX, CURVE_8_CY),
+        )
+    } else if k == 9 {
+        (
+            GoodCurve::new(CURVE_9_A, CURVE_9_B_SQRT, CURVE_9_GX, CURVE_9_GY, k),
+            (CURVE_9_CX, CURVE_9_CY),
+        )
+    } else if k == 10 {
+        (
+            GoodCurve::new(CURVE_10_A, CURVE_10_B_SQRT, CURVE_10_GX, CURVE_10_GY, k),
+            (CURVE_10_CX, CURVE_10_CY),
+        )
+    } else if k == 11 {
+        (
+            GoodCurve::new(CURVE_11_A, CURVE_11_B_SQRT, CURVE_11_GX, CURVE_11_GY, k),
+            (CURVE_11_CX, CURVE_11_CY),
+        )
+    } else {
+        panic!("k must be between 4 and 11")
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use ecfft::{find_coset_offset, GoodCurve};
+
+    type F = halo2curves::secp256k1::Fp;
+
+    fn to_limbs(x: F) -> [u64; 4] {
+        let bytes = x.to_bytes();
+        let mut limbs = [0u64; 4];
+
+        for i in 0..4 {
+            let mut limb_i = 0;
+            for j in 0..8 {
+                limb_i += (bytes[8 * i + j] as u64) << (8 * j);
+            }
+            limbs[i] = limb_i;
+        }
+        limbs
+    }
+
+    #[test]
+    fn find_curves() {
+        // We expect the tensor-IOP to use a square matrix for now,
+        // so we only need to find curves with the square of the number
+        // of evaluations
+        for k in 4..12 {
+            let curve = GoodCurve::<F>::find_k(k);
+            let (coset_offset_x, coset_offset_y) =
+                find_coset_offset(curve.a, curve.B_sqrt.square());
+            println!(
+                "const CURVE_{}_A: Fp = Fp::from_raw(
+                {:?},
+                );
+
+                const CURVE_{}_B_SQRT: Fp = Fp::from_raw(
+                    {:?},
+                    );
+
+                const CURVE_{}_GX: Fp = Fp::from_raw(
+                    {:?},
+                    );
+
+                const CURVE_{}_GY: Fp = Fp::from_raw(
+                {:?}
+                );
+
+                const CURVE_{}_CX: Fp = Fp::from_raw(
+                    {:?},
+                    );
+
+                const CURVE_{}_CY: Fp = Fp::from_raw(
+                {:?},
+                );
+                ",
+                k,
+                to_limbs(curve.a),
+                k,
+                to_limbs(curve.B_sqrt),
+                k,
+                to_limbs(curve.gx),
+                k,
+                to_limbs(curve.gy),
+                k,
+                to_limbs(coset_offset_x),
+                k,
+                to_limbs(coset_offset_y),
+            );
+        }
+    }
+}

@@ -1,3 +1,4 @@
 pub mod ecfft;
+pub mod good_curves;
 pub mod naive;
 pub mod smooth;

@@ -9,7 +9,7 @@ use crate::polynomial::eq_poly::EqPoly;
 use crate::polynomial::sparse_ml_poly::SparseMLPoly;
 use crate::tensor_code::TensorCode;
 use crate::transcript::Transcript;
-use crate::utils::{dot_prod, hash_all, rlc_rows, sample_indices};
+use crate::utils::{det_num_cols, det_num_rows, dot_prod, hash_all, rlc_rows, sample_indices};
 
 use super::tensor_code::CommittedTensorCode;
 
@@ -20,17 +20,15 @@ pub domain_powers: Option<Vec<Vec<F>>>,
     pub fft_domain: Option<Vec<F>>,
     pub ecfft_config: Option<ECFFTConfig<F>>,
     pub l: usize,
-    pub num_entries: usize,
-    pub num_rows: usize,
 }
 
 impl<F: FieldExt> TensorRSMultilinearPCSConfig<F> {
-    pub fn num_cols(&self) -> usize {
-        self.num_entries / self.num_rows()
+    pub fn num_cols(&self, num_entries: usize) -> usize {
+        det_num_cols(num_entries, self.l)
     }
 
-    pub fn num_rows(&self) -> usize {
-        self.num_rows
+    pub fn num_rows(&self, num_entries: usize) -> usize {
+        det_num_rows(num_entries, self.l)
     }
 }
 
@@ -51,6 +49,7 @@ pub test_u_prime: Vec<F>,
     pub test_r_prime: Vec<F>,
     pub eval_r_prime: Vec<F>,
     pub eval_u_prime: Vec<F>,
+    pub poly_num_vars: usize,
 }
 
 impl<F: FieldExt> TensorMultilinearPCS<F> {
@@ -61,7 +60,10 @@
     pub fn commit(&self, poly: &SparseMLPoly<F>) -> CommittedTensorCode<F> {
         // Merkle commit to the evaluations of the polynomial
         let tensor_code = self.encode_zk(poly);
-        let tree = tensor_code.commit(self.config.num_cols(), self.config.num_rows());
+        let tree = tensor_code.commit(
+            self.config.num_cols(poly.num_entries()),
+            self.config.num_rows(poly.num_entries()),
+        );
         tree
     }
 
@@ -72,10 +74,16 @@ poly: &SparseMLPoly<F>,
         point: &[F],
         transcript: &mut Transcript<F>,
     ) -> TensorMLOpening<F> {
-        let num_cols = self.config.num_cols();
-        let num_rows = self.config.num_rows();
+        let num_cols = self.config.num_cols(poly.num_entries());
+        let num_rows = self.config.num_rows(poly.num_entries());
         debug_assert_eq!(poly.num_vars, point.len());
 
+        let mut padded_evals = poly.evals.clone();
+        padded_evals.resize(
+            num_cols * num_rows,
+            (2usize.pow(poly.num_vars as u32), F::ZERO),
+        );
+
         // ########################################
         // Testing phase
         // Prove the consistency between the random linear combination of the evaluation tensor (u_prime)
@@ -87,7 +95,7 @@ let r_u = transcript.challenge_vec(num_rows);
 
         let u = (0..num_rows)
             .map(|i| {
-                poly.evals[(i * num_cols)..((i + 1) * num_cols)]
+                padded_evals[(i * num_cols)..((i + 1) * num_cols)]
                     .iter()
                     .map(|entry| entry.1)
                     .collect::<Vec<F>>()
@@ -143,14 +151,16 @@ eval_u_prime,
             base_opening: BaseOpening {
                 hashes: u_hat_comm.committed_tree.column_roots.clone(),
             },
+            poly_num_vars: poly.num_vars,
         }
     }
 }
 
 impl<F: FieldExt> TensorMultilinearPCS<F> {
     pub fn verify(&self, opening: &TensorMLOpening<F>, transcript: &mut Transcript<F>) {
-        let num_rows = self.config.num_rows();
-        let num_cols = self.config.num_cols();
+        let poly_num_entries = 2usize.pow(opening.poly_num_vars as u32);
+        let num_rows = self.config.num_rows(poly_num_entries);
+        let num_cols = self.config.num_cols(poly_num_entries);
 
         // Verify the base opening
         let base_opening = &opening.base_opening;
@@ -249,7 +259,13 @@ randomized_codeword
     }
 
     fn rs_encode(&self, message: &[F]) -> Vec<F> {
+        let mut padded_message = message.to_vec();
+        padded_message.resize(message.len().next_power_of_two(), F::ZERO);
+        let codeword_len = padded_message.len() * self.config.expansion_factor;
+
+        let codeword_len_log2 = (codeword_len as f64).log2() as usize;
         let codeword = if self.config.fft_domain.is_some() {
+            // TODO: Resize the domain according to the message length
             let fft_domain = self.config.fft_domain.as_ref().unwrap();
             let mut padded_coeffs = message.clone().to_vec();
             padded_coeffs.resize(fft_domain.len(), F::ZERO);
@@ -257,13 +273,27 @@ let codeword = fft(&padded_coeffs, &fft_domain);
 
             codeword
         } else if self.config.ecfft_config.is_some() {
-            let ecfft_config = self.config.ecfft_config.as_ref().unwrap();
+            let mut ecfft_config = self.config.ecfft_config.clone().unwrap();
+
+            // Resize the domain to the correct size
+            let config_domain_size = ecfft_config.domain.len();
+
+            assert!(config_domain_size >= codeword_len_log2 - 1);
+            ecfft_config.domain =
+                ecfft_config.domain[(config_domain_size - (codeword_len_log2 - 1))..].to_vec();
+            ecfft_config.matrices =
+                ecfft_config.matrices[(config_domain_size - (codeword_len_log2 - 1))..].to_vec();
+            ecfft_config.inverse_matrices = ecfft_config.inverse_matrices
+                [(config_domain_size - (codeword_len_log2 - 1))..]
+                .to_vec();
+
             assert_eq!(
-                message.len() * self.config.expansion_factor,
+                padded_message.len() * self.config.expansion_factor,
                 ecfft_config.domain[0].len()
             );
+
             let extended_evals = extend(
-                message,
+                &padded_message,
                 &ecfft_config.domain,
                 &ecfft_config.matrices,
                 &ecfft_config.inverse_matrices,
@@ -273,6 +303,7 @@
             let codeword = [message.to_vec(), extended_evals].concat();
             codeword
         } else {
+            // TODO: Resize the domain according to the message length
             let domain_powers = self.config.domain_powers.as_ref().unwrap();
             assert_eq!(message.len(), domain_powers[0].len());
             assert_eq!(
@@ -311,12 +342,20 @@ u_hat_openings
     }
 
     fn encode_zk(&self, poly: &SparseMLPoly<F>) -> TensorCode<F> {
-        let num_rows = self.config.num_rows();
-        let num_cols = self.config.num_cols();
+        let num_rows = self.config.num_rows(poly.num_entries());
+        let num_cols = self.config.num_cols(poly.num_entries());
+
+        // Pad the sparse evaluations with zeros
+        let mut evals = poly.evals.clone();
+        evals.resize(
+            num_cols * num_rows,
+            (2usize.pow(poly.num_vars as u32), F::ZERO),
+        );
+        debug_assert_eq!(evals.len(), num_cols * num_rows);
 
         let codewords = (0..num_rows)
             .map(|i| {
-                poly.evals[i * num_cols..(i + 1) * num_cols]
+                evals[i * num_cols..(i + 1) * num_cols]
                     .iter()
                     .map(|entry| entry.1)
                     .collect::<Vec<F>>()
@@ -330,10 +369,12 @@ }
 
 #[cfg(test)]
 mod tests {
+    use ::ecfft::find_coset_offset;
+
     use super::*;
-    use crate::rs_config::{ecfft, naive, smooth};
+    use crate::rs_config::{ecfft, good_curves::secp256k1::secp256k1_good_curve, naive, smooth};
 
-    const TEST_NUM_VARS: usize = 10;
+    const TEST_NUM_VARS: usize = 8;
     const TEST_L: usize = 10;
 
     fn test_poly<F: FieldExt>() -> SparseMLPoly<F> {
@@ -364,10 +405,6 @@ pcs.verify(&opening, &mut verifier_transcript);
     }
 
     fn config_base<F: FieldExt>(ml_poly: &SparseMLPoly<F>) -> TensorRSMultilinearPCSConfig<F> {
-        let num_vars = ml_poly.num_vars;
-        let num_evals = 2usize.pow(num_vars as u32);
-        let num_rows = 2usize.pow((num_vars / 2) as u32);
-
         let expansion_factor = 2;
 
         TensorRSMultilinearPCSConfig::<F> {
@@ -376,8 +413,6 @@ domain_powers: None,
             fft_domain: None,
             ecfft_config: None,
             l: TEST_L,
-            num_entries: num_evals,
-            num_rows,
         }
     }
 
@@ -387,7 +422,7 @@ type F = halo2curves::pasta::Fp;
         // FFT config
         let ml_poly = test_poly();
         let mut config = config_base(&ml_poly);
-        config.fft_domain = Some(smooth::gen_config(config.num_cols()));
+        config.fft_domain = Some(smooth::gen_config(config.num_cols(ml_poly.num_entries())));
 
         // Test FFT PCS
         let tensor_pcs_fft = TensorMultilinearPCS::<F>::new(config);
@@ -400,7 +435,12 @@ type F = halo2curves::secp256k1::Fp;
         let ml_poly = test_poly();
 
         let mut config = config_base(&ml_poly);
-        config.ecfft_config = Some(ecfft::gen_config(config.num_cols()));
+
+        let num_cols = config.num_cols(ml_poly.num_entries());
+        let k = ((num_cols * config.expansion_factor).next_power_of_two() as f64).log2() as usize;
+
+        let (curve, coset_offset) = secp256k1_good_curve(k);
+        config.ecfft_config = Some(ecfft::gen_config_form_curve(curve, coset_offset));
 
         // Test FFT PCS
         let tensor_pcs_ecf = TensorMultilinearPCS::<F>::new(config);
@@ -415,7 +455,7 @@ let ml_poly = test_poly();
 
         // Naive config
         let mut config = config_base(&ml_poly);
-        config.domain_powers = Some(naive::gen_config(config.num_cols()));
+        config.domain_powers = Some(naive::gen_config(config.num_cols(ml_poly.num_entries())));
 
         // Test FFT PCS
         let tensor_pcs_naive = TensorMultilinearPCS::<F>::new(config);

@@ -12,7 +12,6 @@
 impl<F: FieldExt> CommittedMerkleTree<F> {
     pub fn from_leaves(leaves: Vec<F>, num_cols: usize) -> Self {
         let n = leaves.len();
-        debug_assert!(n.is_power_of_two());
         let num_rows = n / num_cols;
         assert!(num_rows & 1 == 0); // Number of rows must be even
 

@@ -75,6 +75,21 @@
     indices
 }
 
+pub fn det_num_cols(num_entries: usize, l: usize) -> usize {
+    let num_entries_sqrt = (num_entries as f64).sqrt() as usize;
+    // The number of columns must be a power of two
+    // to tensor-query the polynomial evaluation
+    let num_cols = std::cmp::max(num_entries_sqrt, l).next_power_of_two();
+    num_cols
+}
+
+pub fn det_num_rows(num_entries: usize, l: usize) -> usize {
+    // The number of rows must be a power of two
+    // to tensor-query the polynomial evaluation
+    let num_rows = (num_entries / det_num_cols(num_entries, l)).next_power_of_two();
+    num_rows
+}
+
 #[cfg(test)]
 mod tests {
     use super::*;