arXiv subjects

Vaibhav Krishan

Publications and source records attributed to Vaibhav Krishan.

4 recordsLinked to original sources

arXiv2026

Degree Lower Bounds for Torus Polynomials and $MAJORITY$ vs $ACC^0$

The class $ACC^0$ consists of Boolean functions that can be computed by constant-depth circuits of polynomial size with $AND, NOT$ and $MOD_m$ gates, where $m$ is a natural number. At the frontier of our understanding lies a widely believed conjecture asserting that $MAJORITY$ does not belong to $ACC^0$. A few years ago, Bhrushundi, Hosseini, Lovett and Rao (ITCS 2019) introduced torus polynomial approximations as an approach towards this conjecture. Torus polynomials approximate Boolean functions when the fractional part of their value on Boolean points is close to half the value of the function. They reduced the conjecture that $MAJORITY \notin ACC^0$ to a conjecture concerning the non-existence of low degree torus polynomials that approximate $MAJORITY$. We reduce the non-existence problem further, to a statement about finding feasible solutions for an infinite family of linear programs. The main advantage of this statement is that it allows for incremental progress, which means finding feasible solutions for successively larger collections of these programs. As an immediate first step, we find feasible solutions for a large class of these linear programs, leaving only a finite set for further consideration. Our method is inspired by the method of dual polynomials, which is used to study the approximate degree of Boolean functions. Using our method, we also propose a way to progress further. We prove several additional key results with the same method, including lower bounds for approximating the $AND$ function, lower bounds when the approximating polynomial is symmetric, showcasing the power of our machinery.

cs.CC↗

arXiv2026

On $CC^0$ Lower Bounds for AND via Torus Polynomials

We explore a torus polynomial approximation based approach towards a long-standing question: whether $AND$ can be computed by $CC^0$ circuits - the class of constant-depth polynomial size circuits containing $MOD_m$ gates for some $m$. Bhrushundi et al. (ITCS 2019) introduced torus polynomial approximations as an approach for proving lower bounds against $ACC^0$ - a class containing $CC^0$ with circuits comprising $AND$, $OR$ and $NOT$ gates. We show how lower bounds for torus polynomials approximating $AND$ can be used to make progress on this question. Using lower bounds on the degree of symmetric torus polynomials approximating $AND$ from Krishan and Vishwanathan (ITCS 2026), we prove size lower bounds for symmetric $CC^0$-circuits computing $AND$. More precisely, we prove that any depth $h$ symmetric $CC^0$ circuit requires $2^{\widetilde{\Omega}(n^{1/O(h)})}$ size to compute $AND$. A key ingredient in our proof is an argument that we can construct symmetric torus polynomials to approximate symmetric $CC^0$ circuits. Our construction exhibits an explicit correspondence between the symmetry of the circuit and that of the polynomial. Using this, we also establish lower bounds for weaker notions of circuit symmetry. Lower bounds for symmetric $CC^0$ circuits were also independently established by Pago (ICALP 2026) using different techniques. In the asymmetric regime, we establish degree upper bounds for depth three circuits of the form $MOD_p \circ MOD_m \circ AND_{O(1)}$ where $m=pq$ is a semiprime. This circuit class is a special case of the constant degree hypothesis, introduced by Barrington, Straubing and Therien (Inf. and Comp., 1990), where $m$ could be an arbitrary composite number. We argue that improved lower bounds for asymmetric torus polynomials approximating $AND$ imply size lower bounds for semiprime $m$ and hence progress on the constant-degree hypothesis.

cs.CC↗

arXiv2026

On Proof Systems for #QBF

For a quantified Boolean formula (QBF), the problem of computing the number of winning strategies is known as the #QBF problem. This problem is considered harder than the analogous #SAT problem. Recently, important proof systems for QBFs and #SAT have been studied. By extending the ideas from both fields, we show that it is possible to design proof systems for #QBF. Such proof systems are important not only for advancing the theory of #QBF but also for certifying and designing better #QBF solvers, an area that is still in its early stages. In this paper, we explore #QBF proof systems to count the number of Skolem functions. Apart from a naive system, we study #QBF systems based on the expansion rule of universal variables in QBFs. We observe that these systems have inherent structural weaknesses that lead to lower bounds. As an alternative, we propose a #QBF proof system that we call Q-MICE, which consists of sound inference rules for computing and certifying the #QBF solution, similar to the line-based #SAT proof system MICE. To demonstrate the strength of Q-MICE, we present various upper bounds, such as the quantified version of the propositional XOR-PAIRS formula, which are known to be hard for MICE. Consequently, we also separate Q-MICE from the expansion-based #QBF proof systems.

cs.LO↗

arXiv2018

A #SAT Algorithm for Small Constant-Depth Circuits with PTF gates

We show that there is a randomized algorithm that, when given a small constant-depth Boolean circuit $C$ made up of gates that compute constant-degree Polynomial Threshold functions or PTFs (i.e., Boolean functions that compute signs of constant-degree polynomials), counts the number of satisfying assignments to $C$ in significantly better than brute-force time. Formally, for any constants $d,k$, there is an $ε> 0$ such that the algorithm counts the number of satisfying assignments to a given depth-$d$ circuit $C$ made up of $k$-PTF gates such that $C$ has size at most $n^{1+ε}$. The algorithm runs in time $2^{n-n^{Ω(ε)}}$. Before our result, no algorithm for beating brute-force search was known even for a single degree-$2$ PTF (which is a depth-$1$ circuit of linear size). The main new tool is the use of a learning algorithm for learning degree-$1$ PTFs (or Linear Threshold Functions) using comparison queries due to Kane, Lovett, Moran and Zhang (FOCS 2017). We show that their ideas fit nicely into a memoization approach that yields the #SAT algorithms.

cs.CC↗