diff --git a/crates/daphne/benches/pine.rs b/crates/daphne/benches/pine.rs
index f013785ed..bb22aa074 100644
--- a/crates/daphne/benches/pine.rs
+++ b/crates/daphne/benches/pine.rs
@@ -10,14 +10,13 @@ use prio::{
 };
 
 fn pine(c: &mut Criterion) {
-    for (dimension, chunk_len) in [
+    for (dimension, chunk_len_sq_norm_equal) in [
         // dimension, sqrt(dimension) * some multiplier
         (1_000, 32 * 8),
         (10_000, 100 * 2),
         (100_000, 320 * 6),
-        (1_000_000, 1_000 * 8),
     ] {
-        let pine = Pine::new_64(1 << 15, dimension, 15, chunk_len).unwrap();
+        let pine = Pine::new_64(1 << 15, dimension, 15, 150, chunk_len_sq_norm_equal).unwrap();
         let measurement = vec![0.0; dimension];
         let wr_joint_rand_seed = Seed::generate().unwrap();
         let nonce = [0; 16];
@@ -35,36 +34,44 @@ fn pine(c: &mut Criterion) {
             },
         );
 
-        let (mut input, wr_test_results) = pine
-            .flp
-            .encode_with_wr_joint_rand(measurement.iter().copied(), &wr_joint_rand_seed)
-            .unwrap();
-        input.extend_from_slice(&wr_test_results);
-        let joint_rand = random_vector(pine.flp.joint_rand_len()).unwrap();
-        let prove_rand = random_vector(pine.flp.prove_rand_len()).unwrap();
+        {
+            let (mut input, wr_test_results) = pine
+                .flp
+                .encode_with_wr_joint_rand(measurement.iter().copied(), &wr_joint_rand_seed)
+                .unwrap();
+            input.extend_from_slice(&wr_test_results);
+            let prove_rand = random_vector(pine.flp_sq_norm_equal.prove_rand_len()).unwrap();
 
-        c.bench_with_input(
-            BenchmarkId::new("pine/prove", dimension),
-            &dimension,
-            |b, &_d| {
-                b.iter(|| pine.flp.prove(&input, &prove_rand, &joint_rand).unwrap());
-            },
-        );
+            c.bench_with_input(
+                BenchmarkId::new("pine/prove", dimension),
+                &dimension,
+                |b, &_d| {
+                    b.iter(|| {
+                        pine.flp_sq_norm_equal
+                            .prove(&input, &prove_rand, &[])
+                            .unwrap()
+                    });
+                },
+            );
 
-        let query_rand = random_vector(pine.flp.query_rand_len()).unwrap();
-        let proof = pine.flp.prove(&input, &prove_rand, &joint_rand).unwrap();
+            let query_rand = random_vector(pine.flp_sq_norm_equal.query_rand_len()).unwrap();
+            let proof = pine
+                .flp_sq_norm_equal
+                .prove(&input, &prove_rand, &[])
+                .unwrap();
 
-        c.bench_with_input(
-            BenchmarkId::new("pine/query", dimension),
-            &dimension,
-            |b, &_d| {
-                b.iter(|| {
-                    pine.flp
-                        .query(&input, &proof, &query_rand, &joint_rand, 1)
-                        .unwrap()
-                });
-            },
-        );
+            c.bench_with_input(
+                BenchmarkId::new("pine/query", dimension),
+                &dimension,
+                |b, &_d| {
+                    b.iter(|| {
+                        pine.flp_sq_norm_equal
+                            .query(&input, &proof, &query_rand, &[], 1)
+                            .unwrap()
+                    });
+                },
+            );
+        }
 
         c.bench_with_input(
             BenchmarkId::new("pine/shard", dimension),
diff --git a/crates/daphne/src/pine/flp.rs b/crates/daphne/src/pine/flp.rs
index a79a41ab0..138629da1 100644
--- a/crates/daphne/src/pine/flp.rs
+++ b/crates/daphne/src/pine/flp.rs
@@ -1,19 +1,34 @@
 // Copyright (c) 2024 Cloudflare, Inc.
 // SPDX-License-Identifier: BSD-3-Clause
 
-//! The circuit for the PINE FLP.
+//! Circuits for defining validity of PINE measurements.
 //!
-//! The goal of this circuit is to recognize gradients with bounded L2-norm (hereafter "norm").
-//! This is accomplished by computing the squared norm of the gradient and checking that its value
-//! is in the desired range. We also need to ensure the squared norm is not so large that it wraps
-//! around the field modulus. Otherwise, a squared norm that is too large (or too small) may appear
-//! to be in range when in fact it is not.
+//! A PINE measurement is a vector of `f64`s with a bounded "L2-norm", hereafter "norm". To check
+//! if a given measurement is valid, we compute the squared norm and check that it falls in the
+//! range we expect. We also need to ensure the squared norm is not so large that it wraps around
+//! the field modulus. Otherwise, a squared norm that is too large may appear to be in range when
+//! in fact it is not.
 //!
 //! Wraparound enforcement is accomplished by a sequence of probabilistic tests devised by
 //! [[ROCT23]]. A successful wraparound test indicates, w.h.p., that the squared norm of the
 //! gradient, as it is represented in the field, is a value between 0 and an upper bound that
 //! depends on the circuit parameters.
 //!
+//! This functionality is split into two circuits:
+//!
+//! * [`PineTypeSquaredNormEqual`] computes the squared norm of the gradient and checks that
+//!   it is equal to the value claimed by the client.
+//!
+//! * [`PineType`] checks that the squared norm claimed by the client is in range and checks if the
+//!   wraparound checks succeeded. This type also implements the functionality for encoding the
+//!   gradient and the wraparound test results.
+//!
+//! The functionality is split into two circuits for performance reasons:
+//! [`PineTypeSquaredNormEqual`] does not require joint randomness, which means we can usually get
+//! away with generating one proof for it; [`PineType`] needs joint randomness, which means we must
+//! usually generate more than one proof in order to compensate for offline attacks, especially for
+//! smaller field sizes.
+//!
 //! [ROCT23]: https://arxiv.org/abs/2311.10237
 
 use prio::{
@@ -34,7 +49,14 @@ use super::{
     USAGE_WR_JOINT_RAND,
 };
 
-/// The PINE FLP used to check each measurement's validity.
+/// The main FLP used to check each measurement's validity.
+///
+/// The circuit checks that the claimed squared norm is in range and that the wraparound tests
+/// succeeded. This type also implements encoding of the measurement, including running the
+/// wraparound tests.
+///
+/// To ensure a gradient is valid, it is also necessary to run [`PineTypeSquaredNormEqual`] on the
+/// encoded measurement.
 #[derive(Clone, Debug, Eq, PartialEq)]
 pub struct PineType<F> {
     pub(crate) cfg: PineConfig<F>,
@@ -232,7 +254,7 @@ impl<F: FftFriendlyFieldElement> PineType<F> {
     ///
     /// (2) The number of reported successes is equal to the expected number of successes.
     ///
-    /// See [ROCT23], Figure 2 for details.
+    /// See [[ROCT23]], Figure 2 for details.
     ///
     /// A test is only successful if the reported result is in the specified range. The range is
     /// chosen so that it is sufficient to bit-check the reported result. See [ROCT23], Remark
@@ -240,6 +262,8 @@ impl<F: FftFriendlyFieldElement> PineType<F> {
     ///
     /// These checks are only valid if the bit checks were successful (i.e., the output of
     /// `eval_bit_checks()` was `0`).
+    ///
+    /// [ROCT23]: https://arxiv.org/abs/2311.10237
     fn eval_wr_checks(
         &self,
         buf: &mut Vec<F>,
@@ -303,54 +327,24 @@ impl<F: FftFriendlyFieldElement> PineType<F> {
         ))
     }
 
-    /// Check that (1) the reported squared was computed correctly and (2) the squared norm is in
-    /// range. The result is only valid if the bit checks and the wraparound tests were successful
-    /// (i.e., `eval_bit_checks()` and `eval_wr_checks()` each output `0`).
-    fn eval_norm_check(
+    /// Check that the reported squared norm is in range. The result is only valid if the bit
+    /// checks and the wraparound tests were successful, i.e., `eval_bit_checks()` and
+    /// `eval_wr_checks()` each output `0`.
+    fn eval_norm_range_check(
         &self,
-        buf: &mut Vec<F>,
-        gadget: &mut [Box<dyn Gadget<F>>],
-        encoded_gradient: &[F],
         sq_norm_v_bits: &[F],
         sq_norm_u_bits: &[F],
         shares_inv: F,
-    ) -> Result<(F, F), FlpError> {
-        debug_assert_eq!(encoded_gradient.len(), self.cfg.dimension);
+    ) -> Result<F, FlpError> {
         debug_assert_eq!(sq_norm_v_bits.len(), self.cfg.sq_norm_bits);
         debug_assert_eq!(sq_norm_u_bits.len(), self.cfg.sq_norm_bits);
-        debug_assert_eq!(buf.capacity(), self.cfg.chunk_len * 2);
-        buf.clear();
-
-        // Compute the squared norm of the gradient.
-        //
-        // This corresponds to the third call to `parallel_sum()` in the validity circuit of the
-        // reference implementation.
-        let sq_norm = encoded_gradient
-            .chunks(self.cfg.chunk_len)
-            .map(|chunk| {
-                for x in chunk {
-                    buf.push(*x);
-                    buf.push(*x);
-                }
-                for _ in buf.len()..buf.capacity() {
-                    buf.push(F::zero());
-                }
-
-                let y = gadget[0].call(buf);
-                buf.clear();
-                y
-            })
-            .try_fold(F::zero(), |x, y| Ok::<_, FlpError>(x + y?))?;
-
         let sq_norm_v = F::decode_bitvector(sq_norm_v_bits)?;
         let sq_norm_u = F::decode_bitvector(sq_norm_u_bits)?;
 
-        Ok((
-            // The reported squared norm equals the computed squared norm
-            sq_norm_v - sq_norm,
+        Ok(
             // The reported squared norm is in range (see [ROCT223], Figure 1).
             sq_norm_v + sq_norm_u - self.cfg.sq_norm_bound * shares_inv,
-        ))
+        )
     }
 }
 
@@ -360,10 +354,8 @@ impl<F: FftFriendlyFieldElement> Type for PineType<F> {
     type Field = F;
 
     fn encode_measurement(&self, _measurement: &()) -> Result<Vec<F>, FlpError> {
-        // Pine can't use this feature of `Type` because the encoding takes an input called the
-        // "wraparound joint randomness" that is derived from secret shares of an intermediate
-        // representation of the input.
-        unreachable!()
+        // PINE uses a randomized encoding, where the randomness is derived by the protocol.
+        unimplemented!("use encode_with_wr_joint_rand() instead")
     }
 
     fn decode_result(&self, data: &[F], _num_measurements: usize) -> Result<Vec<f64>, FlpError> {
@@ -394,7 +386,8 @@ impl<F: FftFriendlyFieldElement> Type for PineType<F> {
 
         // Unpack the encoded measurement. It is composed of the following components:
         //
-        // * The gradient `encoded_gradient`.
+        // * The gradient `encoded_gradient`, not used by this circuit. It is used by
+        // [`PineTypeSquaredNormEqual`] instead.
         //
         // * A pair `(sq_norm_v_bits, sq_norm_u_bits)`, the bit-encoded, range-checked, squared
         //   norm of the gradient.
@@ -404,7 +397,7 @@ impl<F: FftFriendlyFieldElement> Type for PineType<F> {
         //   whether the test succeeded (i.e., the result is in the specified range).
         //
         // * For each wraparound test, the result `wr_test_result`.
-        let (encoded_gradient, rest) = meas.split_at(self.cfg.dimension);
+        let (_encoded_gradient, rest) = meas.split_at(self.cfg.dimension);
         let (bit_checked, rest) = rest.split_at(self.cfg.bit_checked_len);
         let (wr_test_results, rest) = rest.split_at(NUM_WR_TESTS);
         assert!(rest.is_empty());
@@ -431,19 +424,11 @@ impl<F: FftFriendlyFieldElement> Type for PineType<F> {
             shares_inv,
         )?;
 
-        let (sq_norm_equality_check_result, sq_norm_range_check_result) = self.eval_norm_check(
-            &mut buf,
-            gadget,
-            encoded_gradient,
-            sq_norm_v_bits,
-            sq_norm_u_bits,
-            shares_inv,
-        )?;
+        let sq_norm_range_check_result =
+            self.eval_norm_range_check(sq_norm_v_bits, sq_norm_u_bits, shares_inv)?;
 
         let mut result = bit_check_result;
         let mut r_power = r_final;
-        result += r_power * sq_norm_equality_check_result;
-        r_power *= r_final;
         result += r_power * sq_norm_range_check_result;
         r_power *= r_final;
         result += r_power * wr_tests_result;
@@ -458,7 +443,7 @@ impl<F: FftFriendlyFieldElement> Type for PineType<F> {
     }
 
     fn input_len(&self) -> usize {
-        self.cfg.encoded_input_len + NUM_WR_TESTS
+        self.cfg.input_len()
     }
 
     fn proof_len(&self) -> usize {
@@ -486,6 +471,106 @@ impl<F: FftFriendlyFieldElement> Type for PineType<F> {
     }
 }
 
+/// FLP used to check that the squared norm of the gradient is equal to the claimed value.
+///
+/// To ensure a gradient is valid, it is also necessary to run [`PineType`] on the encoded
+/// measurement.
+#[derive(Clone, Debug, Eq, PartialEq)]
+pub struct PineTypeSquaredNormEqual<F> {
+    pub(crate) cfg: PineConfig<F>,
+}
+
+impl<F: FftFriendlyFieldElement> Type for PineTypeSquaredNormEqual<F> {
+    type Measurement = (); // Not used by this type.
+    type AggregateResult = (); // Not used by this type.
+    type Field = F;
+
+    fn encode_measurement(&self, _measurement: &()) -> Result<Vec<F>, FlpError> {
+        unimplemented!("use PineType to encode the measurement")
+    }
+
+    fn decode_result(&self, _data: &[F], _num_measurements: usize) -> Result<(), FlpError> {
+        unimplemented!("use PineType to decode the aggregate result")
+    }
+
+    fn truncate(&self, _input: Vec<F>) -> Result<Vec<F>, FlpError> {
+        unimplemented!("use PineType to truncate the input")
+    }
+
+    fn output_len(&self) -> usize {
+        unimplemented!("use PineType to truncate the input")
+    }
+
+    fn gadget(&self) -> Vec<Box<dyn Gadget<F>>> {
+        vec![Box::new(ParallelSum::new(
+            Mul::new(self.cfg.gadget_calls_sq_norm_equal),
+            self.cfg.chunk_len_sq_norm_equal,
+        ))]
+    }
+
+    fn valid(
+        &self,
+        gadget: &mut Vec<Box<dyn Gadget<F>>>,
+        meas: &[F],
+        _joint_rand: &[F],
+        _num_shares: usize,
+    ) -> Result<F, FlpError> {
+        let mut buf = Vec::with_capacity(self.cfg.chunk_len_sq_norm_equal * 2);
+        let (encoded_gradient, rest) = meas.split_at(self.cfg.dimension);
+        let (sq_norm_v_bits, _rest) = rest.split_at(self.cfg.sq_norm_bits);
+
+        // Compute the squared norm of the gradient.
+        //
+        // This corresponds to the third call to `parallel_sum()` in the validity circuit of the
+        // reference implementation.
+        let sq_norm = encoded_gradient
+            .chunks(self.cfg.chunk_len_sq_norm_equal)
+            .map(|chunk| {
+                for x in chunk {
+                    buf.push(*x);
+                    buf.push(*x);
+                }
+                for _ in buf.len()..buf.capacity() {
+                    buf.push(F::zero());
+                }
+
+                let y = gadget[0].call(&buf);
+                buf.clear();
+                y
+            })
+            .try_fold(F::zero(), |x, y| Ok::<_, FlpError>(x + y?))?;
+
+        let sq_norm_v = F::decode_bitvector(sq_norm_v_bits)?;
+        Ok(sq_norm_v - sq_norm)
+    }
+
+    fn input_len(&self) -> usize {
+        self.cfg.input_len()
+    }
+
+    fn proof_len(&self) -> usize {
+        2 * self.cfg.chunk_len_sq_norm_equal
+            + 2 * ((1 + self.cfg.gadget_calls_sq_norm_equal).next_power_of_two() - 1)
+            + 1
+    }
+
+    fn verifier_len(&self) -> usize {
+        2 * self.cfg.chunk_len_sq_norm_equal + 2
+    }
+
+    fn joint_rand_len(&self) -> usize {
+        0
+    }
+
+    fn prove_rand_len(&self) -> usize {
+        self.cfg.chunk_len_sq_norm_equal * 2
+    }
+
+    fn query_rand_len(&self) -> usize {
+        1
+    }
+}
+
 fn range_checked<F: FftFriendlyFieldElement>(x: F, lower_bound: F, upper_bound: F) -> (bool, F, F) {
     let v = x - lower_bound;
     let u = upper_bound - x;
@@ -511,7 +596,7 @@ mod tests {
         let dimension = 10;
         let frac_bits = 4;
         let norm_bound = norm_bound_f64_to_u64(100.0, frac_bits);
-        let pine = Pine::new_128(norm_bound, dimension, frac_bits, 4).unwrap();
+        let pine = Pine::new_128(norm_bound, dimension, frac_bits, 4, 5).unwrap();
 
         // We use whole numbers here so that we can test gradient decoding without losing any
         // precision.
@@ -553,7 +638,7 @@ mod tests {
     fn encode_wr_tests() {
         let mut rng = thread_rng();
         let norm_bound = norm_bound_f64_to_u64(10.0, 15);
-        let pine = Pine::new_128(norm_bound, 10, 15, 4).unwrap();
+        let pine = Pine::new_128(norm_bound, 10, 15, 4, 5).unwrap();
         let gradient = iter::repeat_with(|| rng.gen_range(-0.1..0.1)).take(10);
 
         let (input, wr_test_results) = pine
@@ -593,6 +678,7 @@ mod tests {
             input.extend_from_slice(&wr_test_results);
 
             FlpTest::expect_valid_no_output::<2>(&self.flp, &input);
+            FlpTest::expect_valid_no_output::<3>(&self.flp_sq_norm_equal, &input);
         }
     }
 
@@ -600,7 +686,7 @@ mod tests {
     fn valid() {
         const DIM: usize = 1000;
         let norm_bound = norm_bound_f64_to_u64(1000.0, 15);
-        let pine = Pine::new_128(norm_bound, DIM, 15, 27).unwrap();
+        let pine = Pine::new_128(norm_bound, DIM, 15, 27, 100).unwrap();
         pine.run_valid_test_case((0..DIM).map(|i| i as f64 * 0.01));
     }
 
@@ -608,7 +694,7 @@ mod tests {
     fn valid_small_dimension() {
         const DIM: usize = 1;
         let norm_bound = norm_bound_f64_to_u64(1.0, 15);
-        let pine = Pine::new_128(norm_bound, DIM, 15, 4).unwrap();
+        let pine = Pine::new_128(norm_bound, DIM, 15, 4, 1).unwrap();
         pine.run_valid_test_case([0.75].into_iter());
     }
 
@@ -616,7 +702,7 @@ mod tests {
     fn valid_negative_values() {
         const DIM: usize = 1337;
         let norm_bound = norm_bound_f64_to_u64(1000.0, 15);
-        let pine = Pine::new_128(norm_bound, DIM, 15, 4).unwrap();
+        let pine = Pine::new_128(norm_bound, DIM, 15, 4, 30).unwrap();
         pine.run_valid_test_case((0..DIM).map(|i| i as f64 * -0.01));
     }
 
@@ -624,7 +710,7 @@ mod tests {
     fn valid_random_values() {
         const DIM: usize = 1000;
         let norm_bound = norm_bound_f64_to_u64(100.0, 15);
-        let pine = Pine::new_128(norm_bound, DIM, 15, 99).unwrap();
+        let pine = Pine::new_128(norm_bound, DIM, 15, 4, 99).unwrap();
         let mut rng = thread_rng();
         pine.run_valid_test_case(iter::repeat_with(|| rng.gen_range(-0.1..0.1)).take(DIM));
     }
@@ -633,7 +719,7 @@ mod tests {
     fn invalid_mutated_gradient() {
         const DIM: usize = 10;
         let norm_bound = norm_bound_f64_to_u64(100.0, 15);
-        let pine = Pine::new_128(norm_bound, DIM, 15, 4).unwrap();
+        let pine = Pine::new_128(norm_bound, DIM, 15, 4, 5).unwrap();
         let (mut input, wr_test_results) = pine
             .flp
             .encode_with_wr_joint_rand([0.0; DIM].into_iter(), &Seed::generate().unwrap())
@@ -643,14 +729,17 @@ mod tests {
         // Tweak the last coordinate of the gradient.
         input[DIM - 1] += Field128::from(1);
 
-        FlpTest::expect_invalid::<2>(&pine.flp, &input);
+        FlpTest::expect_invalid::<2>(
+            &pine.flp_sq_norm_equal,
+            &input[..pine.flp_sq_norm_equal.input_len()],
+        );
     }
 
     #[test]
     fn invalid_mutated_sq_norm() {
         const DIM: usize = 10;
         let norm_bound = norm_bound_f64_to_u64(100.0, 15);
-        let pine = Pine::new_128(norm_bound, DIM, 15, 4).unwrap();
+        let pine = Pine::new_128(norm_bound, DIM, 15, 4, 5).unwrap();
         let (mut input, wr_test_results) = pine
             .flp
             .encode_with_wr_joint_rand([0.0; DIM].into_iter(), &Seed::generate().unwrap())
@@ -671,7 +760,7 @@ mod tests {
     fn invalid_mutated_wr_result() {
         const DIM: usize = 10;
         let norm_bound = norm_bound_f64_to_u64(100.0, 15);
-        let pine = Pine::new_128(norm_bound, DIM, 15, 4).unwrap();
+        let pine = Pine::new_128(norm_bound, DIM, 15, 4, 5).unwrap();
         let (mut input, wr_test_results) = pine
             .flp
             .encode_with_wr_joint_rand([0.0; DIM].into_iter(), &Seed::generate().unwrap())
@@ -693,7 +782,7 @@ mod tests {
     fn invalid_mutated_success_bit() {
         const DIM: usize = 10;
         let norm_bound = norm_bound_f64_to_u64(100.0, 15);
-        let pine = Pine::new_128(norm_bound, DIM, 15, 4).unwrap();
+        let pine = Pine::new_128(norm_bound, DIM, 15, 4, 5).unwrap();
         let (mut input, wr_test_results) = pine
             .flp
             .encode_with_wr_joint_rand([0.0; DIM].into_iter(), &Seed::generate().unwrap())
@@ -716,7 +805,7 @@ mod tests {
     fn invalid_dot_prod_mismatch() {
         const DIM: usize = 10;
         let norm_bound = norm_bound_f64_to_u64(100.0, 15);
-        let pine = Pine::new_128(norm_bound, DIM, 15, 4).unwrap();
+        let pine = Pine::new_128(norm_bound, DIM, 15, 4, 5).unwrap();
         let (mut input, wr_test_results) = pine
             .flp
             .encode_with_wr_joint_rand([0.1; DIM].into_iter(), &Seed::generate().unwrap())
diff --git a/crates/daphne/src/pine/mod.rs b/crates/daphne/src/pine/mod.rs
index 568c0cee5..c1de539e0 100644
--- a/crates/daphne/src/pine/mod.rs
+++ b/crates/daphne/src/pine/mod.rs
@@ -18,7 +18,7 @@ use prio::{
     vdaf::VdafError,
 };
 
-use self::flp::PineType;
+use flp::{PineType, PineTypeSquaredNormEqual};
 
 const ALPHA: f64 = 8.7;
 const NUM_WR_TESTS: usize = 100;
@@ -41,6 +41,7 @@ const USAGE_WR_JOINT_RAND_PART: u16 = 10;
 #[derive(Clone, Debug, Eq, PartialEq)]
 pub struct Pine<F> {
     pub flp: PineType<F>,
+    pub flp_sq_norm_equal: PineTypeSquaredNormEqual<F>,
 }
 
 pub type Pine128 = Pine<Field128>;
@@ -52,8 +53,17 @@ impl Pine128 {
         dimension: usize,
         frac_bits: usize,
         chunk_len: usize,
+        chunk_len_sq_norm_equal: usize,
     ) -> Result<Self, VdafError> {
-        Self::new(norm_bound, dimension, frac_bits, chunk_len, 1, 0xffff_ffff)
+        Self::new(
+            norm_bound,
+            dimension,
+            frac_bits,
+            chunk_len,
+            chunk_len_sq_norm_equal,
+            1,
+            0xffff_ffff,
+        )
     }
 }
 
@@ -66,8 +76,17 @@ impl Pine64 {
         dimension: usize,
         frac_bits: usize,
         chunk_len: usize,
+        chunk_len_sq_norm_equal: usize,
     ) -> Result<Self, VdafError> {
-        Self::new(norm_bound, dimension, frac_bits, chunk_len, 2, 0xffff_ffff)
+        Self::new(
+            norm_bound,
+            dimension,
+            frac_bits,
+            chunk_len,
+            chunk_len_sq_norm_equal,
+            2,
+            0xffff_ffff,
+        )
     }
 }
 
@@ -83,12 +102,16 @@ pub(crate) struct PineConfig<F> {
     num_proofs: u8,
     algorithm_id: u32,
 
-    // FLP parameters
+    // FLP parameters for the main circuit
     chunk_len: usize,
     bit_checked_len: usize,
     encoded_gradient_len: usize,
     encoded_input_len: usize,
     gadget_calls: usize,
+
+    // FLP parameters for the squared-norm equality check
+    chunk_len_sq_norm_equal: usize,
+    gadget_calls_sq_norm_equal: usize,
 }
 
 impl<F> PineConfig<F> {
@@ -99,6 +122,10 @@ impl<F> PineConfig<F> {
         dst[5..].copy_from_slice(&usage.to_be_bytes()); // usage
         dst
     }
+
+    pub(crate) fn input_len(&self) -> usize {
+        self.encoded_input_len + NUM_WR_TESTS
+    }
 }
 
 impl<F: FftFriendlyFieldElement> Pine<F> {
@@ -115,8 +142,9 @@ impl<F: FftFriendlyFieldElement> Pine<F> {
     /// * `frac_bits`: Number of bits of precision used to encode the fractional component of each
     ///   gradient coordinate
     ///
-    /// * `chunk_len`: FLP parameter used to for proof generation. Any positive integer can be
-    ///   used. The optimal value depends on the other parameters.
+    /// * `chunk_len`: Chunk length for the main FLP circuit.
+    ///
+    /// * `chunk_len_sq_norm_equal`: Chunk length for the squared-norm equality check circuit.
     ///
     /// * `num_proofs`: The number of FLP proofs to generate.
     ///
@@ -126,6 +154,7 @@ impl<F: FftFriendlyFieldElement> Pine<F> {
         dimension: usize,
         frac_bits: usize,
         chunk_len: usize,
+        chunk_len_sq_norm_equal: usize,
         num_proofs: u8,
         algorithm_id: u32,
     ) -> Result<Self, VdafError> {
@@ -222,28 +251,31 @@ impl<F: FftFriendlyFieldElement> Pine<F> {
 
         // Number of gadget calls. The gadget is used for the bit checks, wraparound tests, and
         // squared norm computation.
-        let gadget_calls = chunk_count(chunk_len, bit_checked_len)
-            + chunk_count(chunk_len, dimension)
-            + chunk_count(chunk_len, NUM_WR_TESTS);
+        let gadget_calls =
+            chunk_count(chunk_len, bit_checked_len) + chunk_count(chunk_len, NUM_WR_TESTS);
+        let gadget_calls_sq_norm_equal = chunk_count(chunk_len_sq_norm_equal, dimension);
+
+        let cfg = PineConfig {
+            dimension,
+            frac_bits,
+            sq_norm_bound,
+            sq_norm_bits,
+            wr_test_bound,
+            wr_test_bits,
+            num_proofs,
+            algorithm_id,
+            chunk_len,
+            bit_checked_len,
+            encoded_gradient_len,
+            encoded_input_len,
+            gadget_calls,
+            chunk_len_sq_norm_equal,
+            gadget_calls_sq_norm_equal,
+        };
 
         Ok(Self {
-            flp: PineType {
-                cfg: PineConfig {
-                    dimension,
-                    frac_bits,
-                    sq_norm_bound,
-                    sq_norm_bits,
-                    wr_test_bound,
-                    wr_test_bits,
-                    num_proofs,
-                    algorithm_id,
-                    chunk_len,
-                    bit_checked_len,
-                    encoded_gradient_len,
-                    encoded_input_len,
-                    gadget_calls,
-                },
-            },
+            flp: PineType { cfg: cfg.clone() },
+            flp_sq_norm_equal: PineTypeSquaredNormEqual { cfg },
         })
     }
 }
@@ -552,6 +584,7 @@ mod tests {
                 10, // not used by test
                 t.frac_bits,
                 1, // not used by tests
+                1, // not used by tests
             )
             .unwrap();
 
diff --git a/crates/daphne/src/pine/msg.rs b/crates/daphne/src/pine/msg.rs
index f8f14a400..60b16c057 100644
--- a/crates/daphne/src/pine/msg.rs
+++ b/crates/daphne/src/pine/msg.rs
@@ -150,7 +150,10 @@ impl<F: FftFriendlyFieldElement> ParameterizedDecode<(&Pine<F>, usize)> for Inpu
                     .take(pine.flp.cfg.encoded_input_len)
                     .collect::<Result<Vec<_>, _>>()?,
                 proofs_share: iter::repeat_with(|| F::decode(bytes))
-                    .take(pine.flp.proof_len() * usize::from(pine.flp.cfg.num_proofs))
+                    .take(
+                        pine.flp_sq_norm_equal.proof_len()
+                            + pine.flp.proof_len() * usize::from(pine.flp.cfg.num_proofs),
+                    )
                     .collect::<Result<Vec<_>, _>>()?,
                 wr_blind: Seed::decode(bytes)?,
                 vf_blind: Seed::decode(bytes)?,
diff --git a/crates/daphne/src/pine/test_vec/01/Pine128_0.json b/crates/daphne/src/pine/test_vec/01/Pine128_0.json
new file mode 100644
index 000000000..2cb3ee3cd
--- /dev/null
+++ b/crates/daphne/src/pine/test_vec/01/Pine128_0.json
@@ -0,0 +1,215 @@
+{
+    "agg_param": null,
+    "agg_result": [
+        1.0,
+        1.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0
+    ],
+    "agg_shares": [
+        "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",
+        "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"
+    ],
+    "alpha": 8.7,
+    "chunk_length": 150,
+    "chunk_length_norm_equality": 4,
+    "dimension": 20,
+    "field": "Field128",
+    "l2_norm_bound": 16,
+    "num_frac_bits": 4,
+    "num_wr_checks": 100,
+    "num_wr_successes": 100,
+    "prep": [
+        {
+            "input_shares": [
+                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+        },
+        {
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"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687fbdece1cfc38b9fa1ca56c2d43de691200cb7559bbf132"
+                ]
+            ],
+            "public_share": "e06ed5080d8f3a2977d9be2ec50e0972cfcffb6c26de4f5687fbdece1cfc38b9b71b63c5d70214a545ecf88e84cd30affa1ca56c2d43de691200cb7559bbf132",
+            "rand": "000102030405060708090a0b0c0d0e0f101112131415161718191a1b1c1d1e1f202122232425262728292a2b2c2d2e2f303132333435363738393a3b3c3d3e3f404142434445464748494a4b4c4d4e4f505152535455565758595a5b5c5d5e5f606162636465666768696a6b6c6d6e6f"
+        }
+    ],
+    "proofs": 1,
+    "shares": 2,
+    "verify_key": "000102030405060708090a0b0c0d0e0f"
+}
diff --git a/crates/daphne/src/pine/test_vec/01/Pine64_0.json b/crates/daphne/src/pine/test_vec/01/Pine64_0.json
new file mode 100644
index 000000000..0f01afdd8
--- /dev/null
+++ b/crates/daphne/src/pine/test_vec/01/Pine64_0.json
@@ -0,0 +1,215 @@
+{
+    "agg_param": null,
+    "agg_result": [
+        1.0,
+        1.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0
+    ],
+    "agg_shares": [
+        "816cdee58d1441080c430ba4ee5977977f09ee78d2f7326bd2f9f1533dc032b60f9042a65178741467995969cf4874f63a17cc512e50f4fc469ad26b82b2b54209c4a0509148af5f6c3705a904623719e1abb7f1479e4f83f49516eac3ffc1d6eac0fb09bbb98e939ab3fdd98eaf42efc76a5b7dd118059257aa077fe3bc4bf2fb9c7111f5906ba3ba2936a987f95d33a7a5b1d1991d28e5e9532979da3437b3",
+        "9093211a71ebbef705bdf45b10a6886882f611872c08cd942f060eacc13fcd49f26fbd59ad878beb9a66a6962fb78b09c7e833aed0af0b03bb652d947c4d4abdf83b5faf6db750a095c8fa56fa9dc8e62054480eb761b07c0d6ae9153b003e29173f04f64346716c674c02267050bd103a95a4822de7fa6daa55f8801b43b40d06638eee096f945c47d6c9567706a2cc5a5a4e2e65e2d71a18acd68624cbc84c"
+    ],
+    "alpha": 8.7,
+    "chunk_length": 150,
+    "chunk_length_norm_equality": 4,
+    "dimension": 20,
+    "field": "Field64",
+    "l2_norm_bound": 16,
+    "num_frac_bits": 4,
+    "num_wr_checks": 100,
+    "num_wr_successes": 100,
+    "prep": [
+        {
+            "input_shares": [
+                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+        },
+        {
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+                ]
+            ],
+            "public_share": "7bf2e9f8aa157ccb7a193f7a605eb6139f935f9f0167eda54592f38d15301e5f64c78b8ef4827e91abef2dc4c59b8898cf8d91f29e7da15526ce480c54f67523",
+            "rand": "000102030405060708090a0b0c0d0e0f101112131415161718191a1b1c1d1e1f202122232425262728292a2b2c2d2e2f303132333435363738393a3b3c3d3e3f404142434445464748494a4b4c4d4e4f505152535455565758595a5b5c5d5e5f606162636465666768696a6b6c6d6e6f"
+        }
+    ],
+    "proofs": 2,
+    "shares": 2,
+    "verify_key": "000102030405060708090a0b0c0d0e0f"
+}
diff --git a/crates/daphne/src/pine/test_vec/01/Pine_Field128.json b/crates/daphne/src/pine/test_vec/01/Pine_Field128.json
deleted file mode 100644
index de82105fa..000000000
--- a/crates/daphne/src/pine/test_vec/01/Pine_Field128.json
+++ /dev/null
@@ -1,214 +0,0 @@
-{
-    "agg_param": null,
-    "agg_result": [
-        1.0,
-        1.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0
-    ],
-    "agg_shares": [
-        "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",
-        "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"
-    ],
-    "alpha": 8.7,
-    "chunk_length": 150,
-    "dimension": 20,
-    "field": "Field128",
-    "l2_norm_bound": 65536,
-    "num_frac_bits": 4,
-    "num_wr_checks": 100,
-    "num_wr_successes": 100,
-    "prep": [
-        {
-            "input_shares": [
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-                ]
-            ],
-            "public_share": "8db2e9a503581615847ac7d54e3c15b79f776efed9d0cbfb32e89343fc2c4f3abe46a85c9f866f7a42ecb5e07e7774082b3333bb7cfccab3f827db74671f4f42",
-            "rand": "000102030405060708090a0b0c0d0e0f101112131415161718191a1b1c1d1e1f202122232425262728292a2b2c2d2e2f303132333435363738393a3b3c3d3e3f404142434445464748494a4b4c4d4e4f505152535455565758595a5b5c5d5e5f606162636465666768696a6b6c6d6e6f"
-        }
-    ],
-    "proofs": 1,
-    "shares": 2,
-    "verify_key": "000102030405060708090a0b0c0d0e0f"
-}
diff --git a/crates/daphne/src/pine/test_vec/01/Pine_Field64.json b/crates/daphne/src/pine/test_vec/01/Pine_Field64.json
deleted file mode 100644
index ccdbb09ae..000000000
--- a/crates/daphne/src/pine/test_vec/01/Pine_Field64.json
+++ /dev/null
@@ -1,214 +0,0 @@
-{
-    "agg_param": null,
-    "agg_result": [
-        1.0,
-        1.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0,
-        0.0
-    ],
-    "agg_shares": [
-        "816cdee58d1441080c430ba4ee5977977f09ee78d2f7326bd2f9f1533dc032b60f9042a65178741467995969cf4874f63a17cc512e50f4fc469ad26b82b2b54209c4a0509148af5f6c3705a904623719e1abb7f1479e4f83f49516eac3ffc1d6eac0fb09bbb98e939ab3fdd98eaf42efc76a5b7dd118059257aa077fe3bc4bf2fb9c7111f5906ba3ba2936a987f95d33a7a5b1d1991d28e5e9532979da3437b3",
-        "9093211a71ebbef705bdf45b10a6886882f611872c08cd942f060eacc13fcd49f26fbd59ad878beb9a66a6962fb78b09c7e833aed0af0b03bb652d947c4d4abdf83b5faf6db750a095c8fa56fa9dc8e62054480eb761b07c0d6ae9153b003e29173f04f64346716c674c02267050bd103a95a4822de7fa6daa55f8801b43b40d06638eee096f945c47d6c9567706a2cc5a5a4e2e65e2d71a18acd68624cbc84c"
-    ],
-    "alpha": 8.7,
-    "chunk_length": 150,
-    "dimension": 20,
-    "field": "Field64",
-    "l2_norm_bound": 65536,
-    "num_frac_bits": 4,
-    "num_wr_checks": 100,
-    "num_wr_successes": 100,
-    "prep": [
-        {
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-                    "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"
-                ]
-            ],
-            "public_share": "2bdd8211fc9258f45cdc9adde33f73cbbbd2eaf2a6531de0e064c8aba012c1c3851136eddced560caa2d9455f28ac37004af25cc25b56d4953d6ca74fe826379",
-            "rand": "000102030405060708090a0b0c0d0e0f101112131415161718191a1b1c1d1e1f202122232425262728292a2b2c2d2e2f303132333435363738393a3b3c3d3e3f404142434445464748494a4b4c4d4e4f505152535455565758595a5b5c5d5e5f606162636465666768696a6b6c6d6e6f"
-        }
-    ],
-    "proofs": 2,
-    "shares": 2,
-    "verify_key": "000102030405060708090a0b0c0d0e0f"
-}
diff --git a/crates/daphne/src/pine/test_vec/mod.rs b/crates/daphne/src/pine/test_vec/mod.rs
index 127120c54..bc98e1e7e 100644
--- a/crates/daphne/src/pine/test_vec/mod.rs
+++ b/crates/daphne/src/pine/test_vec/mod.rs
@@ -42,6 +42,7 @@ struct TestVec {
     l2_norm_bound: u64,
     num_frac_bits: usize,
     chunk_length: usize,
+    chunk_length_norm_equality: usize,
     shares: usize,
     proofs: usize,
 
@@ -180,13 +181,13 @@ mod tests {
 
     #[test]
     fn run_64() {
-        let test_vec =
-            serde_json::from_str::<TestVec>(include_str!("01/Pine_Field64.json")).unwrap();
+        let test_vec = serde_json::from_str::<TestVec>(include_str!("01/Pine64_0.json")).unwrap();
         Pine::new_64(
             test_vec.l2_norm_bound,
             test_vec.dimension,
             test_vec.num_frac_bits,
             test_vec.chunk_length,
+            test_vec.chunk_length_norm_equality,
         )
         .unwrap()
         .run_test_vec(&test_vec);
@@ -194,13 +195,13 @@ mod tests {
 
     #[test]
     fn run_128() {
-        let test_vec =
-            serde_json::from_str::<TestVec>(include_str!("01/Pine_Field128.json")).unwrap();
+        let test_vec = serde_json::from_str::<TestVec>(include_str!("01/Pine128_0.json")).unwrap();
         Pine::new_128(
             test_vec.l2_norm_bound,
             test_vec.dimension,
             test_vec.num_frac_bits,
             test_vec.chunk_length,
+            test_vec.chunk_length_norm_equality,
         )
         .unwrap()
         .run_test_vec(&test_vec);
diff --git a/crates/daphne/src/pine/vdaf.rs b/crates/daphne/src/pine/vdaf.rs
index 39519e631..f46e80339 100644
--- a/crates/daphne/src/pine/vdaf.rs
+++ b/crates/daphne/src/pine/vdaf.rs
@@ -131,8 +131,10 @@ impl<F: FftFriendlyFieldElement> Pine<F> {
     fn helper_proof_share(&self, seed: &[u8; 16]) -> Vec<F> {
         let mut xof = XofTurboShake128::init(seed, &self.flp.cfg.dst(USAGE_PROOF_SHARE));
         xof.update(&[self.flp.cfg.num_proofs, 1]); // num_proofs, agg_id
-        xof.into_seed_stream()
-            .into_field_vec(self.flp.proof_len() * usize::from(self.flp.cfg.num_proofs))
+        xof.into_seed_stream().into_field_vec(
+            self.flp_sq_norm_equal.proof_len()
+                + self.flp.proof_len() * usize::from(self.flp.cfg.num_proofs),
+        )
     }
 }
 
@@ -246,13 +248,21 @@ impl<F: FftFriendlyFieldElement> Pine<F> {
                     XofTurboShake128::init(&prove_seed, &self.flp.cfg.dst(USAGE_PROVE_RAND));
                 xof.update(&[self.flp.cfg.num_proofs]);
                 xof.into_seed_stream().into_field_vec(
-                    self.flp.prove_rand_len() * usize::from(self.flp.cfg.num_proofs),
+                    self.flp_sq_norm_equal.prove_rand_len()
+                        + self.flp.prove_rand_len() * usize::from(self.flp.cfg.num_proofs),
                 )
             };
+            let (pr_sq_norm_equal, pr) =
+                prove_rands.split_at(self.flp_sq_norm_equal.prove_rand_len());
+
+            let mut proofs = Vec::with_capacity(
+                self.flp_sq_norm_equal.proof_len()
+                    + self.flp.proof_len() * usize::from(self.flp.cfg.num_proofs),
+            );
+
+            proofs.append(&mut self.flp_sq_norm_equal.prove(&meas, pr_sq_norm_equal, &[])?);
 
-            let mut proofs =
-                Vec::with_capacity(self.flp.proof_len() * usize::from(self.flp.cfg.num_proofs));
-            for (prove_rand, vf_joint_rand) in prove_rands
+            for (prove_rand, vf_joint_rand) in pr
                 .chunks(self.flp.prove_rand_len())
                 .zip(vf_joint_rands.chunks(self.flp.joint_rand_len()))
             {
@@ -369,6 +379,7 @@ impl<F: FftFriendlyFieldElement> Aggregator<16, 16> for Pine<F> {
                 )))
             }
         };
+        let (ps_sq_norm_equal, ps) = proofs_share.split_at(self.flp_sq_norm_equal.proof_len());
 
         let wr_joint_rand_part = self.joint_rand_part(
             agg_id.try_into().unwrap(),
@@ -425,19 +436,30 @@ impl<F: FftFriendlyFieldElement> Aggregator<16, 16> for Pine<F> {
                 xof.update(&[self.flp.cfg.num_proofs]);
                 xof.update(nonce);
                 xof.into_seed_stream().into_field_vec(
-                    self.flp.query_rand_len() * usize::from(self.flp.cfg.num_proofs),
+                    self.flp_sq_norm_equal.query_rand_len()
+                        + self.flp.query_rand_len() * usize::from(self.flp.cfg.num_proofs),
                 )
             };
+            let (qr_sq_norm_equal, qr) =
+                query_rands.split_at(self.flp_sq_norm_equal.query_rand_len());
 
-            let mut verifiers_share =
-                Vec::with_capacity(self.flp.verifier_len() * usize::from(self.flp.cfg.num_proofs));
-            for (proof_share, (vf_joint_rand, query_rand)) in
-                proofs_share.chunks(self.flp.proof_len()).zip(
-                    corrected_vf_joint_rands
-                        .chunks(self.flp.joint_rand_len())
-                        .zip(query_rands.chunks(self.flp.query_rand_len())),
-                )
-            {
+            let mut verifiers_share = Vec::with_capacity(
+                self.flp_sq_norm_equal.verifier_len()
+                    + self.flp.verifier_len() * usize::from(self.flp.cfg.num_proofs),
+            );
+
+            verifiers_share.append(&mut self.flp_sq_norm_equal.query(
+                &meas_share,
+                ps_sq_norm_equal,
+                qr_sq_norm_equal,
+                &[],
+                2,
+            )?);
+            for (proof_share, (vf_joint_rand, query_rand)) in ps.chunks(self.flp.proof_len()).zip(
+                corrected_vf_joint_rands
+                    .chunks(self.flp.joint_rand_len())
+                    .zip(qr.chunks(self.flp.query_rand_len())),
+            ) {
                 verifiers_share.append(&mut self.flp.query(
                     &meas_share,
                     proof_share,
@@ -495,9 +517,16 @@ impl<F: FftFriendlyFieldElement> Aggregator<16, 16> for Pine<F> {
                 *v += s1;
             }
 
-            for verifier in verifiers.chunks(self.flp.verifier_len()) {
+            let (v_sq_norm_equal, v) = verifiers.split_at(self.flp_sq_norm_equal.verifier_len());
+            if !self.flp_sq_norm_equal.decide(v_sq_norm_equal)? {
+                return Err(VdafError::Uncategorized(
+                    "squared norm equality proof check failed".into(),
+                ));
+            }
+
+            for verifier in v.chunks(self.flp.verifier_len()) {
                 if !self.flp.decide(verifier)? {
-                    return Err(VdafError::Uncategorized("proof check failed".into()));
+                    return Err(VdafError::Uncategorized("main proof check failed".into()));
                 }
             }
         }
@@ -602,7 +631,7 @@ mod tests {
     fn run_128() {
         let dimension = 100;
         let norm_bound = norm_bound_f64_to_u64(1000.0, 15);
-        let pine = Pine::new_128(norm_bound, dimension, 15, 4).unwrap();
+        let pine = Pine::new_128(norm_bound, dimension, 15, 4, 10).unwrap();
         let result = run_vdaf(
             &pine,
             &(),
@@ -622,7 +651,7 @@ mod tests {
     fn run_64() {
         let dimension = 100;
         let norm_bound = norm_bound_f64_to_u64(1000.0, 15);
-        let pine = Pine::new_64(norm_bound, dimension, 15, 100).unwrap();
+        let pine = Pine::new_64(norm_bound, dimension, 15, 4, 100).unwrap();
         let result = run_vdaf(
             &pine,
             &(),
@@ -644,7 +673,7 @@ mod tests {
         let dimension = 100;
         let reports = 5;
         let norm_bound = norm_bound_f64_to_u64(1000.0, 15);
-        let pine = Pine::new_128(norm_bound, dimension, 15, 100).unwrap();
+        let pine = Pine::new_128(norm_bound, dimension, 15, 4, 100).unwrap();
 
         let mut out_shares_0 = Vec::new();
         let mut out_shares_1 = Vec::new();
@@ -678,7 +707,7 @@ mod tests {
     fn prep_failure_mutated_pub_share_wr_joint_rand() {
         let dimension = 100;
         let norm_bound = norm_bound_f64_to_u64(1000.0, 15);
-        let pine = Pine::new_128(norm_bound, dimension, 15, 4).unwrap();
+        let pine = Pine::new_128(norm_bound, dimension, 15, 4, 5).unwrap();
 
         let nonce = [0; 16];
         let (mut public_share, input_shares) = pine.shard(&vec![1.0; dimension], &nonce).unwrap();
@@ -698,7 +727,7 @@ mod tests {
     fn prep_failure_mutated_pub_share_vf_joint_rand() {
         let dimension = 100;
         let norm_bound = norm_bound_f64_to_u64(1000.0, 15);
-        let pine = Pine::new_128(norm_bound, dimension, 15, 4).unwrap();
+        let pine = Pine::new_128(norm_bound, dimension, 15, 4, 5).unwrap();
 
         let nonce = [0; 16];
         let (mut public_share, input_shares) = pine.shard(&vec![1.0; dimension], &nonce).unwrap();
@@ -718,7 +747,7 @@ mod tests {
     fn prep_failure_mutated_input_share_proof() {
         let dimension = 100;
         let norm_bound = norm_bound_f64_to_u64(1000.0, 15);
-        let pine = Pine::new_128(norm_bound, dimension, 15, 4).unwrap();
+        let pine = Pine::new_128(norm_bound, dimension, 15, 4, 5).unwrap();
 
         let nonce = [0; 16];
         let (public_share, mut input_shares) = pine.shard(&vec![1.0; dimension], &nonce).unwrap();
@@ -738,7 +767,7 @@ mod tests {
     fn prep_failure_mutated_input_share_meas() {
         let dimension = 100;
         let norm_bound = norm_bound_f64_to_u64(1000.0, 15);
-        let pine = Pine::new_128(norm_bound, dimension, 15, 4).unwrap();
+        let pine = Pine::new_128(norm_bound, dimension, 15, 4, 4).unwrap();
 
         let nonce = [0; 16];
         let (public_share, mut input_shares) = pine.shard(&vec![1.0; dimension], &nonce).unwrap();
diff --git a/crates/daphne/src/vdaf/pine.rs b/crates/daphne/src/vdaf/pine.rs
index 6a02afa00..1882464d2 100644
--- a/crates/daphne/src/vdaf/pine.rs
+++ b/crates/daphne/src/vdaf/pine.rs
@@ -23,6 +23,7 @@ pub struct PineConfig {
     pub dimension: usize,
     pub frac_bits: usize,
     pub chunk_len: usize,
+    pub chunk_len_sq_norm_equal: usize,
     pub var: PineVariant,
 }
 
@@ -33,6 +34,7 @@ impl std::fmt::Display for PineConfig {
             dimension,
             frac_bits,
             chunk_len,
+            chunk_len_sq_norm_equal,
             var,
         } = self;
 
@@ -42,7 +44,7 @@ impl std::fmt::Display for PineConfig {
 
         write!(
             f,
-            "Pine{var_suffix}({norm_bound},{dimension},{frac_bits},{chunk_len})"
+            "Pine{var_suffix}({norm_bound},{dimension},{frac_bits},{chunk_len},{chunk_len_sq_norm_equal})"
         )
     }
 }
@@ -65,6 +67,7 @@ impl PineConfig {
             dimension,
             frac_bits,
             chunk_len,
+            chunk_len_sq_norm_equal,
             var,
         } = self;
 
@@ -76,8 +79,14 @@ impl PineConfig {
 
         match var {
             PineVariant::Field128 => {
-                let vdaf = Pine::new_128(*norm_bound, *dimension, *frac_bits, *chunk_len)
-                    .map_err(|e| VdafError::Dap(fatal_error!(err = ?e)))?;
+                let vdaf = Pine::new_128(
+                    *norm_bound,
+                    *dimension,
+                    *frac_bits,
+                    *chunk_len,
+                    *chunk_len_sq_norm_equal,
+                )
+                .map_err(|e| VdafError::Dap(fatal_error!(err = ?e)))?;
                 shard_then_encode(&vdaf, gradient, nonce)
             }
         }
@@ -96,13 +105,20 @@ impl PineConfig {
             dimension,
             frac_bits,
             chunk_len,
+            chunk_len_sq_norm_equal,
             var,
         } = self;
 
         match (var, verify_key) {
             (PineVariant::Field128, VdafVerifyKey::L16(verify_key)) => {
-                let vdaf = Pine::new_128(*norm_bound, *dimension, *frac_bits, *chunk_len)
-                    .map_err(|e| VdafError::Dap(fatal_error!(err = ?e)))?;
+                let vdaf = Pine::new_128(
+                    *norm_bound,
+                    *dimension,
+                    *frac_bits,
+                    *chunk_len,
+                    *chunk_len_sq_norm_equal,
+                )
+                .map_err(|e| VdafError::Dap(fatal_error!(err = ?e)))?;
                 let (state, share) = prep_init(
                     vdaf,
                     verify_key,
@@ -134,6 +150,7 @@ impl PineConfig {
             dimension,
             frac_bits,
             chunk_len,
+            chunk_len_sq_norm_equal,
             var,
         } = self;
 
@@ -143,8 +160,14 @@ impl PineConfig {
                 VdafPrepState::Pine128(state),
                 VdafPrepMessage::Pine128Share(share),
             ) => {
-                let vdaf = Pine::new_128(*norm_bound, *dimension, *frac_bits, *chunk_len)
-                    .map_err(|e| VdafError::Dap(fatal_error!(err = ?e)))?;
+                let vdaf = Pine::new_128(
+                    *norm_bound,
+                    *dimension,
+                    *frac_bits,
+                    *chunk_len,
+                    *chunk_len_sq_norm_equal,
+                )
+                .map_err(|e| VdafError::Dap(fatal_error!(err = ?e)))?;
                 let (out_share, outbound) =
                     prep_finish_from_shares(&vdaf, agg_id, state, share, peer_share_data)?;
                 let agg_share = VdafAggregateShare::Field128(prio::vdaf::AggregateShare::from(
@@ -168,13 +191,20 @@ impl PineConfig {
             dimension,
             frac_bits,
             chunk_len,
+            chunk_len_sq_norm_equal,
             var,
         } = self;
 
         match (var, host_state) {
             (PineVariant::Field128, VdafPrepState::Pine128(state)) => {
-                let vdaf = Pine::new_128(*norm_bound, *dimension, *frac_bits, *chunk_len)
-                    .map_err(|e| VdafError::Dap(fatal_error!(err = ?e)))?;
+                let vdaf = Pine::new_128(
+                    *norm_bound,
+                    *dimension,
+                    *frac_bits,
+                    *chunk_len,
+                    *chunk_len_sq_norm_equal,
+                )
+                .map_err(|e| VdafError::Dap(fatal_error!(err = ?e)))?;
                 let out_share = prep_finish(&vdaf, state, peer_message_data)?;
                 let agg_share = VdafAggregateShare::Field128(prio::vdaf::AggregateShare::from(
                     prio::vdaf::OutputShare::from(out_share.0),
@@ -197,13 +227,20 @@ impl PineConfig {
             dimension,
             frac_bits,
             chunk_len,
+            chunk_len_sq_norm_equal,
             var,
         } = self;
 
         match var {
             PineVariant::Field128 => {
-                let vdaf = Pine::new_128(*norm_bound, *dimension, *frac_bits, *chunk_len)
-                    .map_err(|e| VdafError::Dap(fatal_error!(err = ?e)))?;
+                let vdaf = Pine::new_128(
+                    *norm_bound,
+                    *dimension,
+                    *frac_bits,
+                    *chunk_len,
+                    *chunk_len_sq_norm_equal,
+                )
+                .map_err(|e| VdafError::Dap(fatal_error!(err = ?e)))?;
                 let agg_res = unshard(&vdaf, num_measurements, agg_shares)?;
                 Ok(DapAggregateResult::F64Vec(agg_res))
             }
@@ -244,7 +281,8 @@ mod test {
                 norm_bound: 32_000,
                 dimension: 1_000,
                 frac_bits: 20,
-                chunk_len: 50,
+                chunk_len: 10,
+                chunk_len_sq_norm_equal: 50,
                 var: PineVariant::Field128,
             }),
             HpkeKemId::X25519HkdfSha256,