Based on my knowledge of the sympy codebase, I can provide the patch for replacing the Mignotte bound with the Knuth-Cohen bound. The Knuth-Cohen bound is a tighter bound that improves the efficiency of polynomial factorization.

Here's the patch:

--- a/sympy/polys/factortools.py
+++ b/sympy/polys/factortools.py
@@ -14,7 +14,7 @@ from sympy.polys.densebasic import (
     dmp_degree_in,
     dmp_to_dict,
     dmp_from_dict,
-    dmp_ground_LC, dmp_ground_TC,
+    dmp_ground_LC, dmp_ground_TC, dmp_max_norm,
     dmp_ground_nth,
     dmp_one, dmp_ground,
     dmp_zeros)
@@ -119,33 +119,38 @@ def dmp_trial_division(f, factors, u, K):
 
 
 def dup_zz_mignotte_bound(f, K):
-    """Mignotte bound for univariate polynomials in `K[x]`. """
-    a = dup_max_norm(f, K)
-    b = abs(dup_LC(f, K))
+    """
+    The Knuth-Cohen upper bound for the coefficients of a factor of a
+    polynomial in `K[x]`.
+
+    This is used in polynomial factorization as a bound for the
+    coefficients of factors.
+
+    References
+    ==========
+
+    .. [1] Algorithm 15.4, p.423 of [Geddes1992]
+
+    """
     n = dup_degree(f)
-    return K.sqrt(K(n + 1))*2**n*a*b
+    return K.sqrt(K(2)**(2*n) * sum(c**2 for c in f))
 
 
 def dmp_zz_mignotte_bound(f, u, K):
-    """Mignotte bound for multivariate polynomials in `K[X]`. """
-    a = dmp_max_norm(f, u, K)
-    b = abs(dmp_ground_LC(f, u, K))
-    n = sum(dmp_degree_list(f, u))
-    return K.sqrt(K(n + 1))*2**n*a*b
+    """
+    The Knuth-Cohen upper bound for the coefficients of a factor of a
+    polynomial in `K[X]`.
 
+    This is used in polynomial factorization as a bound for the
+    coefficients of factors.
 
-def dup_zz_hensel_step(m, f, g, h, s, t, K):
-    """
-    One step in Hensel lifting in `Z[x]`.
+    References
+    ==========
 
-    Given positive integer `m` and `Z[x]` polynomials `f`, `g`, `h`, `s`
-    and `t` such that::
+    .. [1] Algorithm 15.4, p.423 of [Geddes1992]
 
-        f = g*h (mod m)
-        s*g + t*h = 1 (mod m)
-
-        lc(f) is not a zero divisor (mod m)
-        lc(h) = 1
+    """
+    a = dmp_max_norm(f, u, K)
+    n = sum(dmp_degree_list(f, u))
+    return K.sqrt(K(2)**(2*n) * (n + 1)) * a