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For a finite set $A\subseteq [n]$ and a finite list $L$ of allowed intersection sizes, the displayed polynomial satisfies $p_A({\chi }_B)={\prod }_{\ell \in L}(|A\cap B|-\ell )$ at characteristic vectors of sets. For an $r$-uniform set family whose pairwise intersection sizes lie in $L\subseteq \{0,\dots ,r-1\}$, these polynomials vanish at all other family members and have nonzero values at their own members. Their restrictions are therefore linearly independent. Multilinear reduction on the Boolean cube and homogenisation on a uniform layer then prove the Ray-Chaudhuri–Wilson theorem bound.

If member sizes modulo a prime number $p$ avoid $L$ while distinct intersections lie in $L$, evaluation at characteristic vectors of sets gives a diagonal matrix with nonzero diagonal. After multilinear reduction on the Boolean cube, these polynomials remain linearly independent and have degree at most $|L|$. Counting square-free monomials proves $|\mathcal{F}|\le {\sum }_{j=0}^{|L|}\left(\genfrac{}{}{0ex}{}{n}{j}\right)$. On a fixed uniform layer, the low-degree evaluation rank on a uniform layer sharpens this to $\left(\genfrac{}{}{0ex}{}{n}{|L|}\right)$ when $|L|$ does not exceed the layer rank.

In the polynomial method in combinatorics, a set family of sets with common size residue $r$ modulo a prime number $p$ annihilates the displayed auxiliary functions. After multilinear reduction on the Boolean cube, functions with $|S|\le s-1$ have degree at most $s$. If $r$ avoids every $j=0,\dots ,s-1$ modulo $p$, these functions are linearly independent: evaluation at a set $T$ gives $(|T|-r){\sum }_{S\subseteq T}{c}_S$, and increasing $|T|$ eliminates coefficients. Combining them with diagonal intersection polynomials removes the lower-degree monomial count and strengthens the dimension bound from ${\sum }_{j\le s}\left(\genfrac{}{}{0ex}{}{n}{j}\right)$ to $\left(\genfrac{}{}{0ex}{}{n}{s}\right)$.

Let $p$ be a prime number and $L\subseteq {\mathbb{F}}_p$ have $s$ elements. A set family whose member sizes modulo $p$ avoid $L$, and whose distinct pairwise intersection sizes modulo $p$ belong to $L$, has at most ${\sum }_{j=0}^{\min (s,n)}\left(\genfrac{}{}{0ex}{}{n}{j}\right)$ members. The intersection polynomials ${\prod }_{\ell \in L}({\sum }_{i\in A}{x}_i-\ell )$ have a diagonal, nonzero evaluation matrix on the family's characteristic vectors of sets. Multilinear reduction on the Boolean cube places these linearly independent functions in the space spanned by square-free monomials of degree at most $s$.

Put $m=p^a$, with $p$ a prime number, and $s=m-1$. The printed bound needs a size qualification: for $n=3$, $k=1$ and $m=4$, all three singletons satisfy the intersection restriction, whereas $\left(\genfrac{}{}{0ex}{}{3}{m-1}\right)=1$. The intended nontrivial prime-power modular intersection bound holds when $s\le k$; without that qualification the universally valid conclusion isWhen $k<s$ this follows simply by counting all $k$-sets. We prove the sharper intended form for $s\le k$.

Choose an integer $L$ with $Lm>k$ and define the integer-valued polynomialFor $0\le z\le k$ its top argument is nonnegative. The bottom argument has all its first $a$ base-$p$ digits equal to $p-1$. The Lucas theorem therefore givesFor every $A\in \mathcal{A}$, consider $P({\sum }_{i\in A}{x}_i)$ on the Boolean hypercube. Its multilinear reduction on the Boolean cube has degree at most $s$ and integer coefficients: the coefficient of a square-free monomial is an alternating sum of its integer values on Boolean vertices. This observation permits reduction modulo $p$ even though the ordinary power-basis coefficients of $P$ need not have denominators prime to $p$.

At the characteristic vectors of sets $B\in \mathcal{A}$, these reduced polynomials have evaluation matrix equal to the identity modulo $p$. Hence their evaluation functions are linearly independent over ${\mathbb{F}}_p$. To count the available functions, let $M$ have rows indexed by subsets $S$ with $|S|\le s$, columns indexed by all $k$-sets $B$, and entries $1_{S\subseteq B}$. Over the rational numbers, for $|S|=j$,Thus the matrix rank over the rational numbers is at most $\left(\genfrac{}{}{0ex}{}{n}{s}\right)$. Every larger integer minor is zero and remains zero modulo $p$, so the same rank bound holds over ${\mathbb{F}}_p$. This low-degree evaluation rank on a uniform layer proves

For the applications alternative, take a prime number $p$, set $N=4p-1$, $k=2p-1$, and let $G$ be the modular intersection graph on all $k$-subsets of $[N]$, joining distinct sets when their intersection has size $p-1$. An independent set avoids the only possible off-diagonal intersection congruent to $k$ modulo $p$, so

First embed $A$ as $v_A=1_A/\sqrt{2p}$ in Euclidean space. The Euclidean distance satisfiesso distance one corresponds exactly to an edge of $G$. Every colour class in a proper graph colouring is an independent set, giving the lower bound on the chromatic number of Euclidean spaceThe Stirling formula gives ${\log }_2\left(\genfrac{}{}{0ex}{}{N}{\lfloor qN\rfloor }\right)=NH(q)+O(\log N)$, where $H$ is the binary entropy function. Here the ratio is $2^{(1-H(1/4)+o(1))N}$ and $H(1/4)<1$, so this is an exponential lower bound along these dimensions.

Second set $u_A=21_A-1$ and represent $A$ by the off-diagonal entries $\sqrt{2}{u}_{A,i}{u}_{A,j}$, $i<j$, of the symmetric matrix $u_A{u}_A^T$. This is an injective representation: equality of the matrices would give $u_B=\pm u_A$, and the minus sign would require $|B|=N-k\ne k$. The common diagonal can be omitted without changing distances. The Frobenius inner product gives the quadratic sign-vector diameter formulaThe term inside the square is congruent to $3$ modulo $4$, and its smallest possible absolute value is one, attained exactly at $|A\cap B|=p-1$. Such pairs exist. Consequently the diameter pairs of this finite set in dimension $D=\left(\genfrac{}{}{0ex}{}{N}{2}\right)$ are precisely the edges of $G$. Every part of strictly smaller diameter is an independent set and has at most $\left(\genfrac{}{}{0ex}{}{N}{p-1}\right)$ points. The required number of parts is at least the same exponential ratio, which exceeds $D+1$ for sufficiently large primes. Thus bounded sets need not admit a partition into $D+1$ sets of smaller diameter: this supplies a Kahn-Kalai counterexample to the Borsuk conjecture. Both applications use the prime case of the proved prime-power result.

The Frankl-Wilson theorem is a striking example of the polynomial method in combinatorics: an intersection restriction creates a family of linearly independent low-degree polynomials, whose dimension bounds the set family. Let $p$ be a prime number, let $L\subseteq {\mathbb{F}}_p$ have $s$ elements, and let $\mathcal{F}$ be a set family such that member sizes modulo $p$ avoid $L$ while every distinct pair has intersection size in $L$ modulo $p$. Its general modular form givesIf all members have the same size $k$ and $s\le k$, the uniform refinement gives $|\mathcal{F}|\le \left(\genfrac{}{}{0ex}{}{n}{s}\right)$.

For the proof, work over the finite field ${\mathbb{F}}_p$ and associate to $A\in \mathcal{F}$ the modular intersection polynomialAt the characteristic vector of a set $B$, this equals zero for $B\ne A$ and is nonzero for $B=A$. A linear relation among the evaluation functions therefore has every coefficient zero. The multilinear reduction on the Boolean cube, replacing $x_i^a$ by $x_i$ for $a\ge 1$, preserves these evaluations and the degree bound $s$. There are ${\sum }_{j=0}^s\left(\genfrac{}{}{0ex}{}{n}{j}\right)$ possible square-free monomials, proving the general form.

For uniform members, the low-degree evaluation rank on a uniform layer supplies the sharper dimension bound. To verify this without division modulo $p$, form the integer incidence matrix with rows indexed by subsets $S$ of size at most $s$, columns indexed by $k$-subsets $B$, and entries $1_{S\subseteq B}$. Over the rational numbers, a row with $|S|=j$ is ${\left(\genfrac{}{}{0ex}{}{k-j}{s-j}\right)}^{-1}$ times the sum of all size-$s$ rows containing $S$. Its matrix rank is at most $\left(\genfrac{}{}{0ex}{}{n}{s}\right)$. Every larger integer minor is consequently zero and remains zero modulo $p$, giving the same bound over ${\mathbb{F}}_p$. The nonzero diagonal evaluation matrix of the $P_A$ then forces $|\mathcal{F}|\le \left(\genfrac{}{}{0ex}{}{n}{s}\right)$.

The theorem, proved by Frankl and Wilson in 1981, extends the theme of the Ray-Chaudhuri–Wilson theorem from ordinary intersection sizes to residues. Modular restrictions can be weaker than exact intersection restrictions yet still force unexpectedly small families. Here are three consequences from one explicit modular intersection graph.

Take $N=4p-1$, $k=2p-1$, and let the vertices be all $k$-subsets of $[N]$, with an edge exactly when the intersection has size $p-1$. An independent set avoids the only possible off-diagonal intersection congruent to $k$ modulo $p$, so applying the Frankl-Wilson theorem with $L={\mathbb{F}}_p\setminus \{p-1\}$ givesA clique has one exact intersection size. Apply the uniform theorem modulo a prime larger than $k$, with $L=\{p-1\}$, to get $\omega (G)\le N$. Since there are $v=\left(\genfrac{}{}{0ex}{}{N}{k}\right)$ vertices, this gives the explicit asymmetric Ramsey number bound $R(N+1,B+1)>v$. It illustrates how algebraic constructions produce graphs with simultaneously restricted cliques and independent sets.

For a geometric application, map each vertex to $x_A=1_A/\sqrt{2p}$ in ${\mathbb{R}}^N$. Then $|x_A-x_B{|}^2=(k-|A\cap B|)/p$, so edges have unit Euclidean distance. Every proper colouring of Euclidean space therefore colours this finite graph, givingThe Stirling formula gives the ratio as $\exp ((\log 2-H(1/4)+o(1))N)$, where $H(a)=-a\log a-(1-a)\log (1-a)$ is the binary entropy function in natural-log units. The positive constant $\log 2-H(1/4)$ proves an exponential chromatic number of Euclidean space lower bound along these dimensions.

Finally, a quadratic embedding converts this same forbidden intersection into a forbidden diameter pair. Set $u_A=21_A-1$ and $Q_A=u_A{u}_A^T$. The quadratic sign-vector diameter formula follows from the Frobenius inner product:For distinct members the smallest possible absolute inner product is one, attained exactly when the intersection is $p-1$. Thus maximum-distance pairs are exactly edges of $G$. The matrices have common diagonal; using their off-diagonal coordinates, multiplied by $\sqrt{2}$, realizes them isometrically in $D=\left(\genfrac{}{}{0ex}{}{N}{2}\right)$ dimensions. They are distinct because equal outer products require $u_A=\pm u_B$, and complements have size $2p$, not $k$.

Any subset of smaller diameter contains at most $B$ points, so a partition into smaller-diameter pieces needs at least $v/B=\exp (c_{0}N+o(N))$ pieces. For sufficiently large primes this exceeds $D+1$, disproving the Borsuk conjecture. This is the mechanism behind the Kahn-Kalai counterexample to the Borsuk conjecture: the number of required pieces grows exponentially in the original set-system parameter while dimension grows quadratically. Their 1993 paper establishes the resulting high-dimensional counterexample.

Use the uniform Frankl-Wilson theorem: if $p$ is a prime number, $L\subseteq {\mathbb{F}}_p$ has $s$ distinct residues, $s\le \min \{r,n-r\}$, and an $r$-uniform set family $\mathcal{F}$ has distinct-member intersection sizes in $L$ modulo $p$ while $r\mathrm{mod}p\notin L$, thenHere is a polynomial method in combinatorics proof. For each $A\in \mathcal{F}$, form the intersection polynomialover ${\mathbb{F}}_p$, and use multilinear reduction on the Boolean cube so its degree is at most $s$. At the characteristic vector of $B\in \mathcal{F}$, this evaluates to zero for $A\ne B$ and is nonzero for $A=B$. Consequently these evaluation functions are linearly independent.

The low-degree evaluation rank on a uniform layer bounds their space by $\left(\genfrac{}{}{0ex}{}{n}{s}\right)$. To justify that step without division by a possibly zero residue, form the integer incidence matrix with rows $T\subseteq [n]$, $|T|\le s$, columns $r$-sets $B$, and entries $1_{T\subseteq B}$. Over the rational numbers, a row with $|T|=j$ equals ${\left(\genfrac{}{}{0ex}{}{r-j}{s-j}\right)}^{-1}$ times the sum of the rows of all $s$-sets containing $T$. Thus its rational matrix rank is at most $\left(\genfrac{}{}{0ex}{}{n}{s}\right)$. Every larger minor is an integer determinant equal to zero and stays zero modulo $p$, so the same upper bound holds over ${\mathbb{F}}_p$. This proves the theorem.

For the forbidden midpoint problem, split $\mathcal{A}$ according to whether its members contain coordinate 1. Leave the containing half unchanged; complement every member of the avoiding half. Both resulting families consist of $2p$-sets containing 1, and complementation preserves their internal intersection sizes becauseWithin either transformed family, distinct intersections lie between 1 and $2p-1$ and are not $p$. Their residues are therefore in $L=\{1,\dots ,p-1\}$, whereas the set-size residue is $2p\equiv 0$. Apply the Frankl-Wilson theorem to each half and add:The factor 2 comes from splitting into two families. The unsplit family can contain disjoint sets, whose intersection residue zero equals the set-size residue and prevents this direct application. Removing the shared coordinate after splitting also gives the stronger complement splitting for forbidden midpoint intersections bound $2\left(\genfrac{}{}{0ex}{}{4p-1}{p-1}\right)$.

For the construction, fix a $(p+1)$-set $B$ and let $R=[4p]\setminus B$, of size $3p-1$. The fixed-core construction avoiding midpoint intersections isTwo members of $\mathcal{F}$ intersect in at least $p+1$ points; the same is true for two complements. An intersection across the two halves has size $|X\setminus Y|\le p-1$. No intersection is $p$. The halves are disjoint because one contains all of $B$ and the other none, so

The Frankl-Wilson theorem in its uniform form says: let $p$ be a prime number, let $L\subseteq {\mathbb{F}}_p$ have size $s\le \min (k,n-k)$, and let $\mathcal{F}\subseteq \left(\genfrac{}{}{0ex}{}{[n]}{k}\right)$ have $k\mathrm{mod}p\notin L$, while $|A\cap B|\mathrm{mod}p\in L$ for every distinct pair of members. Then

The modular-intersection, nonuniform form of the Frankl-Wilson theorem is as follows. Let $p$ be a prime number, let $L\subseteq {\mathbb{F}}_p$ have $s$ elements, and let $\mathcal{F}\subseteq \mathcal{P}([n])$ satisfy $|A|\mathrm{mod}p\notin L$ for each $A\in \mathcal{F}$, whereas $|A\cap B|\mathrm{mod}p\in L$ for distinct $A,B\in \mathcal{F}$. ThenThis nonuniform Frankl-Wilson theorem allows different sizes and different diagonal residues; in particular it applies when all sizes have one common residue outside $L$.

To prove it by the polynomial method in combinatorics, work over the finite field ${\mathbb{F}}_p$ and associate to each member the intersection polynomialReplace each positive power of $x_i$ in its expansion by $x_i$ to obtain its multilinear reduction on the Boolean cube ${\tilde{P}}_A$. This preserves evaluation on every characteristic vector, whose coordinates are zero or one, and leaves degree at most $s$. At the characteristic vector $1_B$,It is zero for distinct $A,B$, and nonzero for $B=A$ by the diagonal hypothesis. If ${\sum }_{A\in \mathcal{F}}{c}_A{\tilde{P}}_A=0$, evaluating at each $1_B$ forces $c_B=0$. Thus the multilinear polynomials are linearly independent. They belong to the vector space with basis the square-free monomials ${\prod }_{i\in S}{x}_i$ for $|S|\le s$, whose dimension is the displayed sum. This proves the nonuniform form, including $s=0$, where distinct members are impossible and the empty product is one.

For the sharp uniform form, we need the low-degree evaluation rank on a uniform layer. Form the integer matrix $M$ whose rows are indexed by $S\subseteq [n]$ with $|S|\le s$, columns by $k$-subsets $T$, and entries $M_{S,T}=1_{S\subseteq T}$. These are evaluations of the square-free monomials. Over the rational numbers, the row of a $j$-subset $S$ satisfiesIndeed, when $S\subseteq T$ the left side counts the $s$-subsets between $S$ and $T$, and otherwise both sides vanish. Since $s\le k$, the coefficient is a positive integer. Thus the degree-$s$ rows span all the rows over the rational numbers, giving ${\mathrm{rank}}_{\mathbb{Q}}M\le \left(\genfrac{}{}{0ex}{}{n}{s}\right)$. Every square submatrix of larger size has integer determinant zero, so its determinant remains zero modulo $p$. HenceThis reduction of matrix rank is the reason one must not try to divide by $\left(\genfrac{}{}{0ex}{}{k-j}{s-j}\right)$ directly in ${\mathbb{F}}_p$.

The evaluations of the ${\tilde{P}}_A$ on the $k$th uniform layer of the Boolean cube lie in the row linear span of $M$ over ${\mathbb{F}}_p$. They are linearly independent as functions there, because restriction to the columns corresponding to $\mathcal{F}$ still gives the nonzero diagonal evaluation matrix. Their number is consequently at most $\left(\genfrac{}{}{0ex}{}{n}{s}\right)$. This completes the proof of the uniform Frankl-Wilson theorem as well.

For the odd-cardinality problem the sharper Oddtown theorem follows directly from linear algebra. Let $v_A=1_A\in {\mathbb{F}}_2^n$ be the characteristic vector. The parity conditions sayFor any linear combination ${\sum }_A{c}_A{v}_A=0$, take its dot product with $v_B$. All off-diagonal terms vanish, leaving $c_B=0$. Hence these vectors are linearly independent in an $n$-dimensional vector space, givingThe singleton sets show that the bound can be attained. Notice why the constant term in the general nonuniform Frankl-Wilson theorem is unnecessary here: these dot products already give a diagonal evaluation matrix using only the $n$ coordinate functions.

For even $n$, partition $[n]$ into the pairs $\{1,2\},\{3,4\},\dots ,\{n-1,n\}$ and take every union of a selection of these pairs. This gives exactly $2^{n/2}$ distinct sets, including the empty set. Each has even size; every pairwise intersection is again a union of entire pairs and therefore has even size. This attains the proposed bound.

For any set family satisfying the even-cardinality conditions, let $W$ be the linear span of its characteristic vectors over ${\mathbb{F}}_2$. Now the self-dot products vanish as well as the off-diagonal ones. By bilinearity, $u\cdot v=0$ for all $u,v\in W$, so $W\subseteq W^{\perp }$: it is a self-orthogonal binary subspace.

For completeness the standard bilinear form on ${\mathbb{F}}_2^n$ is nondegenerate, because a vector orthogonal to every coordinate vector has every coordinate zero. Thus the map $v\mapsto (w\mapsto v\cdot w)$ from ${\mathbb{F}}_2^n$ onto the dual space $W^{\ast }$ is surjective: every linear functional on $W$ extends to the whole vector space by extending a basis, and every such functional on ${\mathbb{F}}_2^n$ is a dot product with a vector. Its kernel is $W^{\perp }$. The rank-nullity theorem consequently gives $\dim W^{\perp }=n-\dim W$. The inclusion $W\subseteq W^{\perp }$ implies $\dim W\le n/2$. Finally, distinct members have distinct characteristic vectors, all in $W$, and a $k$-dimensional vector space over ${\mathbb{F}}_2$ has exactly $2^k$ elements. ThereforeThis proves the Eventown theorem and also explains the suggested dimension contradiction if the set family had more than $2^{n/2}$ members.

Work over the finite field ${\mathbb{F}}_p$. For each set family member form the intersection polynomialAt the characteristic vector of a set $B$, its value is ${\prod }_i(|A\cap B|-{\ell }_i)$. The given restrictions make $f_A({\chi }_B)=0$ for $A\ne B$ and $f_A({\chi }_A)\ne 0$. Hence these functions are linearly independent: evaluate a purported relation at each set family member to eliminate its coefficient.

Use multilinear reduction on the Boolean cube, replacing every positive power of $x_i$ by $x_i$. This preserves all evaluations on $\{0,1{\}}^n$ and cannot increase degree. The multilinear polynomials of degree at most $s$ have basis $x_S={\prod }_{i\in S}{x}_i$ for $|S|\le s$. Their dimension is ${\sum }_{j=0}^{\min (s,n)}\left(\genfrac{}{}{0ex}{}{n}{j}\right)$, and thereforewhere $\left(\genfrac{}{}{0ex}{}{n}{j}\right)=0$ for $j>n$. The proof also covers a list whose distinct integers have repeated residues: no assumption of distinct residues is needed for this first bound.

To obtain the sharper conclusion, suppose the common size residue $r$ avoids $0,\dots ,s-1$. If $s>n$, no set on this ground set can have the required residue, so the set family is empty. Otherwise, add the modular-size auxiliary polynomialsagain understood as functions after multilinear reduction on the Boolean cube. They have degree at most $s$, and all vanish on the set family's characteristic vectors of sets.

The $q_S$ are linearly independent. Indeed, evaluate ${\sum }_S{c}_S{q}_S=0$ on ${\chi }_T$ with $|T|\le s-1$:The first factor is nonzero in the finite field. Starting with $T=\varnothing$ and proceeding in increasing cardinality shows $c_T=0$ for every such $T$.

Moreover, the combined list consisting of the $f_A$ and the $q_S$ is linearly independent. Evaluation at each ${\chi }_A$ first kills the coefficient of $f_A$; the preceding argument then kills all remaining coefficients. Comparing this list with the dimension of the degree-at-most-$s$ space yieldsThis is a nonuniform common-residue version of the Frankl-Wilson theorem; exact equality of all member sizes was never used.

The exclusion of the small residues is necessary. A concrete example with $s<n$ is $n=4$, $p=5$, $s=3$, the residue list $0,1,3$, and the set family of all two-element subsets. Every member has size residue $r=2$, outside that list, while distinct members intersect in zero or one element. ButHere the omitted condition fails at $j=2$. Thus the example satisfies all the other hypotheses, rather than relying on a vacuous $s>n$ case.

Use the bit-query Boolean quantum oracle $O_x|i,b,z⟩=|i,b\oplus x_i,z⟩$, where $z$ denotes workspace. Initially every probability amplitude is independent of $x$, hence is a constant polynomial. An input-independent unitary gate only forms linear combinations of probability amplitudes, so it does not increase their polynomial degree.

If $a_{i,b,z}(x)$ is an amplitude before a query, its new value isOne query increases polynomial degree by at most one. Inductively, after $T$ queries each amplitude has polynomial degree at most $T$. The acceptance probability is a sum of squared absolute values of the accepting amplitudes, so it is a real polynomial of degree at most $2T$. Intermediate quantum measurements and classical adaptation can be retained coherently, or handled by summing unnormalized branch probabilities; either approach yields the same degree bound.

An exact algorithm has acceptance probability precisely $f(x)$ at every Boolean input. The multilinear reduction on the Boolean cube replaces positive powers of each $x_i$ by $x_i$, preserving these values without increasing polynomial degree. Therefore the polynomial method for quantum query lower bounds givesHere $\mathrm{deg}(f)$ is the degree of the unique multilinear polynomial representing the Boolean function, and $Q_E$ is its exact quantum query complexity. Uniqueness follows, for example, by evaluating successively on the indicator vectors of subsets: the value on a subset determines its coefficient once all smaller-subset coefficients are known.

The general modular form, the nonuniform Frankl-Wilson theorem, can be stated as follows. Let $p$ be a prime number, and let $L\subseteq {\mathbb{F}}_p$ have $s$ elements. Suppose $\mathcal{F}\subseteq \mathcal{P}([n])$ has $|A|\mathrm{mod}p\notin L$ for every member, while $|A\cap B|\mathrm{mod}p\in L$ for any distinct members. ThenThe uniform Frankl-Wilson theorem sharpens this to $|\mathcal{F}|\le \left(\genfrac{}{}{0ex}{}{n}{s}\right)$ when all members have size $k$ and $0\le s\le \min \{k,n-k\}$. Both forms require a prime modulus and exclusion of the self-intersection residue.

For the general proof, work over the finite field ${\mathbb{F}}_p$. Associate to each member $A$ the intersection polynomialAt the characteristic vectors of sets $1_B$, this is zero if $B\ne A$, and nonzero if $B=A$. Thus these polynomials, regarded as functions on the Boolean hypercube, are linearly independent: evaluating any relation at $1_A$ isolates its coefficient. Apply multilinear reduction on the Boolean cube, replacing every positive power of a variable by that variable. Values on the Boolean hypercube remain unchanged, and the resulting multilinear polynomials have degree at most $s$. Their ambient space has a monomial basis consisting of $x_S={\prod }_{i\in S}{x}_i$ for $|S|\le s$, with dimension ${\sum }_{j=0}^{\min (s,n)}\left(\genfrac{}{}{0ex}{}{n}{j}\right)$. This proves the general bound, including $s=0$.

For completeness, obtain the uniform sharpening without dividing by factorials in a finite field. For $s\ge 1$ put $a=k\mathrm{mod}p$ and $g(x)={\sum }_i{x}_i-a$. The functions $x_{S}g$ with $|S|<s$ are linearly independent. Indeed, a relation gives $gh=0$ with $\mathrm{deg}h<s$, so $h$ is supported only on weights in $H=\{j\in [0,n]:j\equiv a(\mathrm{mod}p)\}$. With boundary weights $-1,n+1$, this set has a gap of at least $s+1$: either consecutive allowed weights differ by $p\ge s+1$, or its terminal gap does, since $n\ge k+s\ge a+s$. The alternating sum of $h$ over any interval of $s$ free coordinates vanishes by its degree. Across that gap only one endpoint level can contribute, forcing $h$ to vanish there. Delete that level and repeat across the enlarged gap until $H$ is empty. Hence $h=0$, and independence of the square-free monomials gives the claim. Adjoin these functions to the $f_A$. Evaluation at each family vector eliminates the coefficients of $f_A$, since $g$ vanishes there; the claim eliminates all remaining coefficients. Counting dimensions yieldsThe case $s=0$ permits at most one member directly. The gap argument is an instance of the modular layer vanishing lemma.

For the final application, enumerate the distinct sets as $A_1,\dots ,A_m$ and let $v_i=1_{A_i}\in {\mathbb{R}}^n$ be their characteristic vectors of sets. If $m\le 1$, the desired bound is immediate for $n\ge 1$. Otherwise $|A_i|\ge k$, because $A_i$ intersects another member in $k$ points. At most one member can have size $k$: two distinct $k$-sets cannot have intersection size $k$. The Gram matrix of these vectors isFor real coefficients $c_i$,All terms are nonnegative, with $k>0$. If the expression vanishes, every coefficient corresponding to a set of size greater than $k$ is zero. At most one coefficient remains, and the first term forces it to be zero as well. Thus the characteristic vectors of sets are linearly independent in ${\mathbb{R}}^n$, proving the constant-intersection family boundThis proof explicitly covers the possible member of size exactly $k$; assuming all diagonal corrections were strictly positive would miss that case. Positivity of $k$ is essential: when $k=0$, the empty set and all $n$ singleton sets give $n+1$ members.

We prove both the general modular intersection bound for a set family and its sharper uniform version, the Frankl-Wilson theorem. Let $p$ be prime, let $L\subseteq {\mathbb{F}}_p$ have size $s$, and let $\mathcal{F}$ be a family of subsets of $[n]$. Suppose each member's cardinality modulo $p$ is outside $L$, whereas every intersection of two distinct members has cardinality modulo $p$ in $L$. Then the general modular intersection bound iswith $\left(\genfrac{}{}{0ex}{}{n}{j}\right)=0$ for $j>n$. If every member has the same size $k$ and $s\le \min \{k,n-k\}$, the uniform Frankl-Wilson theorem sharpens this toHere $k\mathrm{mod}p\notin L$, so in particular $s<p$. Primality matters because the argument takes place over the finite field ${\mathbb{F}}_p$.

For each $A\in \mathcal{F}$, define the polynomialAt the indicator vector $1_B$ of another member $B$, this polynomial is zero if $B\ne A$ and nonzero if $B=A$. Replace every positive power of $X_i$ by $X_i$ in its expanded monomials, obtaining a multilinear polynomial ${\tilde{P}}_A$ of degree at most $s$. This multilinear reduction on the Boolean cube preserves values at every zero-one vector.

The resulting polynomials are linearly independent. Indeed, if ${\sum }_{A\in \mathcal{F}}{c}_A{\tilde{P}}_A=0$, evaluating at $1_B$ leaves $c_B{\tilde{P}}_B(1_B)=0$; the nonzero diagonal value forces $c_B=0$ for every $B$. But multilinear polynomials of degree at most $s$ lie in the vector space with basis $\{{\prod }_{i\in S}{X}_i:|S|\le s\}$, whose dimension is ${\sum }_{j=0}^s\left(\genfrac{}{}{0ex}{}{n}{j}\right)$. The bound on the dimension of a vector space gives the general modular intersection bound for a set family. This is the polynomial method in combinatorics: the intersection conditions produce an evaluation matrix with nonzero diagonal and zero off-diagonal entries.

To prove the sharper uniform bound, we first establish injectivity of inclusion between adjacent set layers. Over a field with field characteristic zero or greater than $d+1$, suppose coefficients $c_S$, indexed by $d$-sets in $[n]$, satisfyThen all coefficients are zero. For $d=0$ this is immediate. For $d\ge 1$, fix distinct $i,j$ and subtract the equations for $U\cup \{i\}$ and $U\cup \{j\}$, where $U$ is a $d$-set avoiding $i,j$. The result isApply mathematical induction with degree $d-1$ on the $n-2$ remaining coordinates; its size condition holds because $n-2\ge 2(d-1)+1$. This forces $c_{S\cup \{i\}}=c_{S\cup \{j\}}$ for every such $S$. Since all $d$-sets are linked by one-element exchanges, all coefficients equal some $c$. An original equation then gives $(d+1)c=0$, and the characteristic assumption gives $c=0$.

For every $S$ with $|S|\le s-1$, now form the multilinear polynomialThis is the multilinear reduction of $X_S({\sum }_i{X}_i-k)$, so it vanishes at every indicator vector of a $k$-set. These $G_S$ are linearly independent: in a nonzero linear relation choose the largest degree $d$ of a coefficient-bearing $S$. The degree-$(d+1)$ terms have coefficients ${\sum }_{S\subset T,|S|=d}{c}_S$. The inclusion lemma makes these nonzero unless all degree-$d$ coefficients vanish, a contradiction. It applies because $d\le s-1$, $p>s$, and $n\ge 2s$.

The $G_S$ are also independent jointly with the ${\tilde{P}}_A$. Evaluating a relation at the indicator vectors of the family first forces every coefficient of ${\tilde{P}}_A$ to vanish, since the $G_S$ vanish there; their own independence then kills the remaining coefficients. All these polynomials have degree at most $s$, soSubtracting the lower-degree contribution proves the sharp Frankl-Wilson theorem. For $s=0$ the general bound already gives $|\mathcal{F}|\le 1=\left(\genfrac{}{}{0ex}{}{n}{0}\right)$, so no auxiliary polynomials are needed.

For the even cross-intersection assertion, work instead in the vector space over a finite field ${\mathbb{F}}_2^n$ and letThe parity assumption is exactly $1_A\cdot 1_B=0$ in ${\mathbb{F}}_2$. By bilinearity, all vectors in $U$ are orthogonal to all vectors in $V$, giving $V\subseteq U^{\perp }$. The coordinate dot product is nondegenerate over ${\mathbb{F}}_2$, so $\dim U^{\perp }=n-\dim U$. There is no requirement that $U\cap U^{\perp }$ be zero; only this dimension formula is used. Each distinct set has a distinct indicator vector, and a dimension-$d$ binary vector space has $2^d$ elements. Therefore the even cross-intersection bound isThis is the even cross-intersection bound, including empty families, for which the product is zero.

For odd intersections, append a final coordinate one to every indicator vector:Their dot product is $|A\cap B|+1=0$ in ${\mathbb{F}}_2$. Applying the same orthogonal complement over the binary field argument to their spans, now in dimension $n+1$, gives the required odd cross-intersection bound:Appending the coordinate preserves distinctness of the vectors, so the span-cardinality estimates remain valid. This proves the odd cross-intersection bound directly by reducing the odd condition to an even one in a larger vector space.

Use the polynomial method in combinatorics to prove the Ray-Chaudhuri–Wilson theorem. For each $A_i$ define an intersection polynomial over $\mathbb{R}$,At the characteristic vector of a set $A_j$, the inner sum is $|A_i\cap A_j|$. ThereforeIt follows that the restrictions of $p_1,\dots ,p_m$ to the uniform layer of the Boolean cubehave linear independence: evaluating any vanishing linear combination at each ${\chi }_{A_j}$ forces its $j$th coefficient to be zero.

Apply multilinear reduction on the Boolean cube by replacing each positive power $x_a^t$ in a monomial with $x_a$. This preserves evaluations on $\{0,1{\}}^n$ and gives multilinear polynomials of polynomial degree at most $s$. Counting all monomials of degrees up to $s$ would give only ${\sum }_{j=0}^s\left(\genfrac{}{}{0ex}{}{n}{j}\right)$, which is too weak. Instead use homogenisation on a uniform layer.

Because $L$ has $s$ distinct integers in $\{0,\dots ,r-1\}$, we have $s\le r$. For $T\subseteq [n]$ with $|T|=j\le s$, write $x_T={\prod }_{a\in T}{x}_a$, with $x_{\varnothing }=1$. On ${\Omega }_r$,Indeed, at ${\chi }_A$ both sides vanish if $T⊈A$; otherwise exactly $\left(\genfrac{}{}{0ex}{}{r-j}{s-j}\right)$ summands equal $1$. The denominator is positive since $j\le s\le r$. Thus every monomial of degree at most $s$, restricted to ${\Omega }_r$, lies in the span of the $\left(\genfrac{}{}{0ex}{}{n}{s}\right)$ degree-$s$ monomials. This vector space consequently has dimension at most $\left(\genfrac{}{}{0ex}{}{n}{s}\right)$. The linear independence already proved now givesNo additional condition such as $r+s\le n$ is needed: only the spanning upper bound is used, not independence of all degree-$s$ monomials. The argument also covers $s=0$ if empty $L$ is allowed, with the empty product equal to $1$ and at most one member in the set family.

After $T$ input-bit queries, every probability amplitude is a polynomial of degree at most $T$, because the bit-query matrix entries are affine in the input bits. An acceptance probability is a sum of squared absolute amplitudes and has polynomial degree at most $2T$. Multilinear reduction on the Boolean cube does not increase degree. Exact acceptance therefore implies $Q_E(f)\ge \lceil \mathrm{deg}(f)/2\rceil$. For bounded error the acceptance polynomial only approximates the Boolean function; its exact representing degree is not the relevant bound.