diff --git a/.coveragerc b/.coveragerc
new file mode 100644
index 0000000..72c83c2
--- /dev/null
+++ b/.coveragerc
@@ -0,0 +1,15 @@
+[run]
+source = toploc
+omit =
+    toploc/__init__.py
+    toploc/C/csrc/__init__.py
+
+[report]
+exclude_lines =
+    pragma: no cover
+    def __repr__
+    raise NotImplementedError
+    if __name__ == .__main__.:
+    pass
+    raise ImportError 
+    
\ No newline at end of file
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 49d39b1..c3334c6 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -65,6 +65,11 @@ Run single test:
 uv run pytest tests/test_utils.py::test_get_fp32_parts
 ```
 
+Run coverage:
+```bash
+uv run pytest --cov=toploc --cov-report=term-missing --cov-report=html
+```
+
 ## Code Quality
 
 Install pre-commit hooks:
diff --git a/README.md b/README.md
index 97b8f5c..7ae0920 100644
--- a/README.md
+++ b/README.md
@@ -10,15 +10,15 @@ The feature set includes:
 
 For code used by experiments in our paper, check out: https://github.com/PrimeIntellect-ai/toploc-experiments
 
-### Installation
+## Installation
 
 ```bash
 pip install -U toploc
 ```
 
-### Usage
+## Usage
 
-#### Build proofs from activations:
+### Build proofs from activations:
 As bytes (more compact when stored in binary formats):
 ```python
 import torch
@@ -53,38 +53,30 @@ Activation shapes: [torch.Size([1, 5, 16]), torch.Size([1, 16]), torch.Size([1,
 Proofs: ['/9kbQitnukt1bQ==', '/9nLuJrxhiVUoA==', '/9m0aNrm5KtBtg==', '/9mAZNZYMOKvcw==', '/9nSBGTqkZH21w==']
 ```
 
-#### Verify proofs:
+### Verify proofs:
 ```python
 import torch
-from toploc import ProofPoly
-from toploc.poly import batch_activations
-from toploc.C.csrc.utils import get_fp_parts
-from statistics import mean, median
+from toploc import verify_proofs_base64
 
 torch.manual_seed(42)
 activations = [torch.randn(1, 5, 16, dtype=torch.bfloat16), *(torch.randn(1, 16, dtype=torch.bfloat16) for _ in range(10))]
 proofs = ['/9kbQitnukt1bQ==', '/9nLuJrxhiVUoA==', '/9m0aNrm5KtBtg==', '/9mAZNZYMOKvcw==', '/9nSBGTqkZH21w==']
-proofs = [ProofPoly.from_base64(proof) for proof in proofs]
-
 # apply some jitter to the activations
 activations = [i * 1.01 for i in activations]
 
-for index, (proof, chunk) in enumerate(zip(proofs, batch_activations(activations, decode_batching_size=3))):
-    chunk = chunk.view(-1).cpu()
-    topk_indices = chunk.abs().topk(k=4).indices.tolist()
-    topk_values = chunk[topk_indices]
-    proof_topk_values = torch.tensor([proof(i) for i in topk_indices], dtype=torch.uint16).view(dtype=torch.bfloat16)
-    exps, mants = get_fp_parts(proof_topk_values)
-    proof_exps, proof_mants = get_fp_parts(topk_values)
-
-    exp_intersections = [i == j for i, j in zip(exps, proof_exps)]
-    mant_errs = [abs(i - j) for i, j, k in zip(mants, proof_mants, exp_intersections) if k]
-    print(f"=== Proof {index}")
-    print(f"Exp intersections: {sum(exp_intersections)}")
-    print(f"Mean mantissa error: {mean(mant_errs)}")
-    print(f"Median mantissa error: {median(mant_errs)}")
-```
+results = verify_proofs_base64(activations, proofs, decode_batching_size=3, topk=4, skip_prefill=False)
 
+print("Results:")
+print(*results, sep="\n")
+```
+```python
+Results:
+VerificationResult(exp_intersections=4, mant_err_mean=1.75, mant_err_median=2.0)
+VerificationResult(exp_intersections=4, mant_err_mean=2, mant_err_median=2.0)
+VerificationResult(exp_intersections=4, mant_err_mean=1.25, mant_err_median=1.0)
+VerificationResult(exp_intersections=4, mant_err_mean=1, mant_err_median=1.0)
+VerificationResult(exp_intersections=4, mant_err_mean=2, mant_err_median=2.0)
+```
 
 # Citing
 
diff --git a/pyproject.toml b/pyproject.toml
index bd47c8c..a727a95 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -34,6 +34,7 @@ dev-dependencies = [
     "ruff",
     "pre-commit",
     "pytest",
+    "pytest-cov"
 ]
 
 [tool.setuptools.dynamic]
diff --git a/tests/test_poly.py b/tests/test_poly.py
index 366a5d9..135274c 100644
--- a/tests/test_poly.py
+++ b/tests/test_poly.py
@@ -6,6 +6,9 @@
     build_proofs_bytes,
     build_proofs_base64,
     ProofPoly,
+    VerificationResult,
+    verify_proofs_bytes,
+    verify_proofs_base64,
 )
 
 
@@ -123,6 +126,7 @@ def test_proof_poly_repr(sample_poly):
 
 @pytest.fixture
 def sample_activations():
+    torch.manual_seed(42)
     DIM = 16
     a = [torch.randn(3, DIM, dtype=torch.bfloat16)]
     for _ in range(3 * 2 + 1):
@@ -196,3 +200,66 @@ def test_build_proofs_edge_cases(sample_activations):
         sample_activations[:1], decode_batching_size=2, topk=5, skip_prefill=True
     )
     assert len(proofs_one_skip) == 0
+
+
+def test_verify_proofs_bytes(sample_activations):
+    """Test verification of proofs in bytes format"""
+    # Generate proofs in bytes format
+    proofs_bytes = build_proofs_bytes(
+        sample_activations, decode_batching_size=3, topk=4
+    )
+
+    results = verify_proofs_bytes(
+        [i * 1.01 for i in sample_activations],
+        proofs_bytes,
+        decode_batching_size=3,
+        topk=4,
+    )
+
+    assert isinstance(results, list)
+    assert all(isinstance(r, VerificationResult) for r in results)
+    assert len(results) == len(proofs_bytes)
+    assert all(r.exp_intersections == 4 for r in results)
+    assert all(r.mant_err_mean > 0 and r.mant_err_mean <= 2 for r in results)
+    assert all(r.mant_err_median > 0 and r.mant_err_median <= 2 for r in results)
+
+
+def test_verify_proofs_base64(sample_activations):
+    """Test verification of proofs in base64 format"""
+    # Generate proofs in base64 format
+    proofs_base64 = build_proofs_base64(
+        sample_activations, decode_batching_size=2, topk=5
+    )
+
+    results = verify_proofs_base64(
+        sample_activations, proofs_base64, decode_batching_size=2, topk=5
+    )
+
+    assert isinstance(results, list)
+    assert all(isinstance(r, VerificationResult) for r in results)
+    assert len(results) == len(proofs_base64)
+    assert all(r.exp_intersections == 5 for r in results)
+    assert all(r.mant_err_mean == 0 for r in results)
+    assert all(r.mant_err_median == 0 for r in results)
+
+
+def test_verify_proofs_bytes_invalid(sample_activations):
+    # Generate proofs in bytes format
+    proofs_bytes = build_proofs_bytes(
+        sample_activations, decode_batching_size=3, topk=4
+    )
+
+    results = verify_proofs_bytes(
+        [i * 1.10 for i in sample_activations],
+        proofs_bytes,
+        decode_batching_size=3,
+        topk=4,
+    )
+
+    print(results)
+    assert isinstance(results, list)
+    assert all(isinstance(r, VerificationResult) for r in results)
+    assert len(results) == len(proofs_bytes)
+    assert all(r.exp_intersections >= 3 for r in results)
+    assert all(r.mant_err_mean > 10 for r in results)
+    assert all(r.mant_err_median > 10 for r in results)
diff --git a/tests/test_utils.py b/tests/test_utils.py
index e8bf8e8..3bcede9 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -2,6 +2,8 @@
 import torch
 import time
 import pytest
+import tempfile
+from toploc.utils import sha256sum
 
 
 @pytest.mark.parametrize(
@@ -114,3 +116,13 @@ def py_get_fp_parts(tensor: torch.FloatTensor) -> tuple[list[int], list[int]]:
     assert exps == ref_exps
     assert mantissas == ref_mants
     assert new_time < old_time
+
+
+def test_sha256sum():
+    with tempfile.NamedTemporaryFile() as f:
+        f.write(b"Hello, world!" * 1000)
+        f.flush()
+        assert (
+            sha256sum(f.name)
+            == "a8f764e70df94be2c911fb51b3d0c56c03882078dbdb215de8b7bd0374b0fb10"
+        )
diff --git a/toploc/__init__.py b/toploc/__init__.py
index fbd8688..6037aa9 100644
--- a/toploc/__init__.py
+++ b/toploc/__init__.py
@@ -1,4 +1,13 @@
-from toploc.poly import ProofPoly, build_proofs, build_proofs_bytes, build_proofs_base64  # noqa: F401
-from toploc.utils import sha256sum  # noqa: F401
+# ruff: noqa: F401
+from toploc.poly import (
+    ProofPoly,
+    build_proofs,
+    build_proofs_bytes,
+    build_proofs_base64,
+    verify_proofs,
+    verify_proofs_bytes,
+    verify_proofs_base64,
+)
+from toploc.utils import sha256sum
 
 __version__ = "0.0.0.dev1"
diff --git a/toploc/commits.py b/toploc/commits.py
deleted file mode 100644
index 8ebd1a3..0000000
--- a/toploc/commits.py
+++ /dev/null
@@ -1,98 +0,0 @@
-from typing import List
-import dataclasses
-from dataclasses import dataclass
-import json
-from pathlib import Path
-import base64
-from .ndd import compute_newton_coefficients, evaluate_polynomial
-import torch
-
-
-@dataclass
-class Commit:
-    model_name: str
-    device: str
-    dtype: str
-    engine: str
-    hashes: List[str]
-    completion: List[int] | None
-    input_tokens: int
-    generation_config: dict
-
-    def __repr__(self):
-        _json = dataclasses.asdict(self)
-        return json.dumps(_json)
-
-    def to_file(self, path: str | Path):
-        with open(path, "w") as f:
-            f.write(repr(self))
-
-    @classmethod
-    def from_file(cls, path: str | Path):
-        with open(path, "r") as f:
-            data = json.load(f)
-        return cls(**data)
-
-
-def find_injective_modulus(x: list[int]) -> int:
-    for i in range(65497, 2**15, -1):
-        if len(set([j % i for j in x])) == len(x):
-            return i
-    raise ValueError("No injective modulus found!")
-
-
-class ProofPoly:
-    def __init__(self, coeffs: list[int], modulus: int):
-        self.coeffs = coeffs
-        self.modulus = modulus
-
-    def __call__(self, x: int):
-        return evaluate_polynomial(self.coeffs, x % self.modulus)
-
-    def __len__(self):
-        return len(self.coeffs)
-
-    @classmethod
-    def from_points(
-        cls, x: list[int] | torch.Tensor, y: list[int] | torch.Tensor
-    ) -> "ProofPoly":
-        if isinstance(x, torch.Tensor):
-            x = x.tolist()
-        if isinstance(y, torch.Tensor):
-            if y.dtype == torch.bfloat16:
-                y = y.view(dtype=torch.uint16).tolist()
-            elif y.dtype == torch.float32:
-                raise NotImplementedError(
-                    "float32 not supported yet because interpolate has hardcode prime"
-                )
-            else:
-                y = y.tolist()
-        modulus = find_injective_modulus(x)
-        x = [i % modulus for i in x]
-        return cls(compute_newton_coefficients(x, y), modulus)
-
-    def to_base64(self):
-        base64_encoded = base64.b64encode(self.to_bytes()).decode("utf-8")
-        return base64_encoded
-
-    def to_bytes(self):
-        return self.modulus.to_bytes(2, byteorder="big", signed=False) + b"".join(
-            coeff.to_bytes(2, byteorder="big", signed=False) for coeff in self.coeffs
-        )
-
-    @classmethod
-    def from_bytes(cls, byte_data: bytes) -> "ProofPoly":
-        modulus = int.from_bytes(byte_data[:2], byteorder="big", signed=False)
-        coeffs = [
-            int.from_bytes(byte_data[i : i + 2], byteorder="big", signed=False)
-            for i in range(2, len(byte_data), 2)
-        ]
-        return cls(coeffs, modulus)
-
-    @classmethod
-    def from_base64(cls, base64_encoded: str) -> "ProofPoly":
-        byte_data = base64.b64decode(base64_encoded)
-        return cls.from_bytes(byte_data)
-
-    def __repr__(self) -> str:
-        return f"ProofPoly[{self.modulus}]({self.coeffs})"
diff --git a/toploc/ndd.py b/toploc/ndd.py
deleted file mode 100644
index c44101c..0000000
--- a/toploc/ndd.py
+++ /dev/null
@@ -1,105 +0,0 @@
-# TODO: Deprecate this file and move to C
-MOD_N = 65497
-
-
-def extended_gcd(a, b):
-    """
-    Returns (gcd, x, y) where gcd is the greatest common divisor of a and b
-    and x, y are coefficients where ax + by = gcd
-    """
-    if a == 0:
-        return (b, 0, 1)
-    else:
-        gcd, x, y = extended_gcd(b % a, a)
-        return (gcd, y - (b // a) * x, x)
-
-
-def mod_inverse(a, m=MOD_N):
-    """
-    Returns the modular multiplicative inverse of a under modulo m
-    """
-    gcd, x, _ = extended_gcd(a, m)
-    if gcd != 1:
-        raise ValueError(f"Modular inverse does not exist for {a} (mod {m})")
-    else:
-        return x % m
-
-
-def multiply_polynomials_mod(poly1, poly2, mod=MOD_N):
-    """
-    Multiply two polynomials with coefficients under given modulus.
-    Polynomials are represented as lists of coefficients from lowest to highest degree.
-    """
-    result = [0] * (len(poly1) + len(poly2) - 1)
-    for i, coef1 in enumerate(poly1):
-        for j, coef2 in enumerate(poly2):
-            result[i + j] = (result[i + j] + coef1 * coef2) % mod
-    return result
-
-
-def compute_newton_coefficients(x, y, mod=MOD_N):
-    """
-    Calculate coefficients for the interpolation polynomial in standard form
-    (powers of x) using modular arithmetic.
-
-    Args:
-        x (list): x coordinates of points
-        y (list): y coordinates of points
-        mod (int): modulus for arithmetic operations
-
-    Returns:
-        list: Coefficients of polynomial in standard form (ascending powers of x)
-    """
-    n = len(x)
-    # First calculate Newton coefficients
-    newton_coef = [[0] * n for _ in range(n)]
-
-    # First column is y values
-    for i in range(n):
-        newton_coef[i][0] = y[i] % mod
-
-    # Calculate divided differences
-    for j in range(1, n):
-        for i in range(n - j):
-            numerator = (newton_coef[i + 1][j - 1] - newton_coef[i][j - 1]) % mod
-            denominator = (x[i + j] - x[i]) % mod
-            inv_denominator = mod_inverse(denominator, mod)
-            newton_coef[i][j] = (numerator * inv_denominator) % mod
-
-    newton_coeffs = [newton_coef[0][j] for j in range(n)]
-
-    # Convert Newton form to standard form
-    result = [newton_coeffs[0]]  # constant term
-    current_term = [1]  # start with 1
-
-    for i in range(n - 1):
-        # Multiply current_term by (x - x[i])
-        next_term = [(-x[i] % mod)] + [1]  # represents (x - x[i])
-        current_term = multiply_polynomials_mod(current_term, next_term, mod)
-
-        # Add newton_coeffs[i+1] * current_term to result
-        while len(result) < len(current_term):
-            result.append(0)
-        for j, coef in enumerate(current_term):
-            result[j] = (result[j] + (newton_coeffs[i + 1] * coef)) % mod
-
-    return result
-
-
-def evaluate_polynomial(coeffs, x_eval, mod=MOD_N):
-    """
-    Evaluate polynomial at x_eval using Horner's method.
-    Coefficients are in ascending power order.
-
-    Args:
-        coeffs (list): coefficients in standard form (ascending powers of x)
-        x_eval (int): point at which to evaluate the polynomial
-        mod (int): modulus for arithmetic operations
-
-    Returns:
-        int: Value of polynomial at x_eval (mod MOD_N)
-    """
-    result = coeffs[-1]
-    for coef in reversed(coeffs[:-1]):
-        result = (result * x_eval + coef) % mod
-    return result
diff --git a/toploc/poly.py b/toploc/poly.py
index b6f4616..63db2a5 100644
--- a/toploc/poly.py
+++ b/toploc/poly.py
@@ -1,8 +1,11 @@
 from typing import Union
 import base64
 from toploc.C.csrc.ndd import compute_newton_coefficients, evaluate_polynomial
+from toploc.C.csrc.utils import get_fp_parts
 import torch
 import logging
+from dataclasses import dataclass
+from statistics import mean, median
 
 logger = logging.getLogger(__name__)
 
@@ -11,7 +14,7 @@ def find_injective_modulus(x: list[int]) -> int:
     for i in range(65497, 2**15, -1):
         if len(set([j % i for j in x])) == len(x):
             return i
-    raise ValueError("No injective modulus found!")
+    raise ValueError("No injective modulus found!")  # pragma: no cover
 
 
 class ProofPoly:
@@ -138,12 +141,14 @@ def build_proofs_base64(
 def batch_activations(
     activations: list[torch.Tensor],
     decode_batching_size: int,
+    skip_prefill: bool = False,
 ) -> list[torch.Tensor]:
     batches = []
 
     # Prefill
-    flat_view = activations[0].view(-1)
-    batches.append(flat_view)
+    if not skip_prefill:
+        flat_view = activations[0].view(-1)
+        batches.append(flat_view)
 
     # Batched Decode
     for i in range(1, len(activations), decode_batching_size):
@@ -153,3 +158,79 @@ def batch_activations(
         batches.append(flat_view)
 
     return batches
+
+
+@dataclass
+class VerificationResult:
+    exp_intersections: int
+    mant_err_mean: float
+    mant_err_median: float
+
+
+def verify_proofs(
+    activations: list[torch.Tensor],
+    proofs: list[ProofPoly],
+    decode_batching_size: int,
+    topk: int,
+    skip_prefill: bool = False,
+) -> list[VerificationResult]:
+    results = []
+    for proof, chunk in zip(
+        proofs,
+        batch_activations(
+            activations,
+            decode_batching_size=decode_batching_size,
+            skip_prefill=skip_prefill,
+        ),
+    ):
+        chunk = chunk.view(-1).cpu()
+        topk_indices = chunk.abs().topk(k=topk).indices.tolist()
+        topk_values = chunk[topk_indices]
+        proof_topk_values = torch.tensor(
+            [proof(i) for i in topk_indices], dtype=torch.uint16
+        ).view(dtype=torch.bfloat16)
+        exps, mants = get_fp_parts(proof_topk_values)
+        proof_exps, proof_mants = get_fp_parts(topk_values)
+
+        exp_intersections = [i == j for i, j in zip(exps, proof_exps)]
+        mant_errs = [
+            abs(i - j) for i, j, k in zip(mants, proof_mants, exp_intersections) if k
+        ]
+        results.append(
+            VerificationResult(
+                sum(exp_intersections), mean(mant_errs), median(mant_errs)
+            )
+        )
+    return results
+
+
+def verify_proofs_bytes(
+    activations: list[torch.Tensor],
+    proofs: list[bytes],
+    decode_batching_size: int,
+    topk: int,
+    skip_prefill: bool = False,
+) -> list[VerificationResult]:
+    return verify_proofs(
+        activations,
+        [ProofPoly.from_bytes(proof) for proof in proofs],
+        decode_batching_size,
+        topk,
+        skip_prefill,
+    )
+
+
+def verify_proofs_base64(
+    activations: list[torch.Tensor],
+    proofs: list[str],
+    decode_batching_size: int,
+    topk: int,
+    skip_prefill: bool = False,
+) -> list[VerificationResult]:
+    return verify_proofs(
+        activations,
+        [ProofPoly.from_base64(proof) for proof in proofs],
+        decode_batching_size,
+        topk,
+        skip_prefill,
+    )
diff --git a/uv.lock b/uv.lock
index 7d042cf..b6b4afd 100644
--- a/uv.lock
+++ b/uv.lock
@@ -25,6 +25,81 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335 },
 ]
 
+[[package]]
+name = "coverage"
+version = "7.6.12"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/0c/d6/2b53ab3ee99f2262e6f0b8369a43f6d66658eab45510331c0b3d5c8c4272/coverage-7.6.12.tar.gz", hash = "sha256:48cfc4641d95d34766ad41d9573cc0f22a48aa88d22657a1fe01dca0dbae4de2", size = 805941 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ba/67/81dc41ec8f548c365d04a29f1afd492d3176b372c33e47fa2a45a01dc13a/coverage-7.6.12-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:704c8c8c6ce6569286ae9622e534b4f5b9759b6f2cd643f1c1a61f666d534fe8", size = 208345 },
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+]
+
+[package.optional-dependencies]
+toml = [
+    { name = "tomli", marker = "python_full_version <= '3.11'" },
+]
+
 [[package]]
 name = "distlib"
 version = "0.3.9"
@@ -501,6 +576,19 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/11/92/76a1c94d3afee238333bc0a42b82935dd8f9cf8ce9e336ff87ee14d9e1cf/pytest-8.3.4-py3-none-any.whl", hash = "sha256:50e16d954148559c9a74109af1eaf0c945ba2d8f30f0a3d3335edde19788b6f6", size = 343083 },
 ]
 
+[[package]]
+name = "pytest-cov"
+version = "6.0.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "coverage", extra = ["toml"] },
+    { name = "pytest" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/be/45/9b538de8cef30e17c7b45ef42f538a94889ed6a16f2387a6c89e73220651/pytest-cov-6.0.0.tar.gz", hash = "sha256:fde0b595ca248bb8e2d76f020b465f3b107c9632e6a1d1705f17834c89dcadc0", size = 66945 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/36/3b/48e79f2cd6a61dbbd4807b4ed46cb564b4fd50a76166b1c4ea5c1d9e2371/pytest_cov-6.0.0-py3-none-any.whl", hash = "sha256:eee6f1b9e61008bd34975a4d5bab25801eb31898b032dd55addc93e96fcaaa35", size = 22949 },
+]
+
 [[package]]
 name = "pyyaml"
 version = "6.0.2"
@@ -652,6 +740,7 @@ dependencies = [
 dev = [
     { name = "pre-commit" },
     { name = "pytest" },
+    { name = "pytest-cov" },
     { name = "ruff" },
 ]
 
@@ -665,6 +754,7 @@ requires-dist = [
 dev = [
     { name = "pre-commit" },
     { name = "pytest" },
+    { name = "pytest-cov" },
     { name = "ruff" },
 ]