Last month the community had a lot to say as a result of which we have a rare treat featuring eighteen papers. Results ranging from (almost) tight lower bounds for testing boolean monotonicity to low rank approximation of Hankel Matrices, to compressed sensing, to PCPs, to results about testing and learning convex functions, a trio of papers with Oded Goldreich as (co)-author, there are many many many more papers covered in this month’s potpourri. Featured in this spectacular collection of papers, we have a rare treat which seeks to understand in this age of LLMs, how Mathematical Explorations and Discoveries work at scale. Without further ado, let us examine our spread.

Boolean function monotonicity testing requires (almost) n^1/2 queries by Mark Chen, Xi Chen, Hao Cui, William Pires, Jonah Stockwell (arXiv) The problem of testing boolean monotonicity over the hypercube, ({0,1}^{n), is no stranger to regular PTReview readers. Finally, we have a result which puts this venerable to rest by proving an adaptive two-sided lower bound of (\Omega(N}{1/2-c})) queries for testing boolean monotonicity over the boolean hypercube. The authors introduce what they call are multilevel Talagrand Functions. Before I detail this paper a little more, let me just say that right off the bat, the paper also uses these functions to obtain a nearly linear in (n) two-sided lower bound for testing unateness. Alright, let me say a little about multilevel Talagrand Functions. Our starting point comes from one story we covered in News for November 2015 where we covered a result by Belovs-Blais where they proved a polynomial in (n) two-sided lowerbound for the same problem. As mentioned in that post, the key insight of Belovs and Blais was to work with Talagrand’s random DNF. Perturbations to this DNF are sufficiently non-monotone and were shown to require (\Omega(n^{1/4})) queries to test. Our News for February 2017 post covered the paper by Chen-Waingarten-Xie which showed that one could adaptively test the Belovs-Blais instance with a budget of (O(n^{1/4})) queries and then went on to present an improved lower bound of (\Omega(n^{1/3})) queries by defining what they called two-level Talagrand functions. The featured paper presents an intricate way to define the multilevel extensions of these Talagrand functions which are then used to squeeze out an almost (\Omega(\sqrt n)) lower bound.

Low-soundness direct-product testers and PCPs from Kaufman–Oppenheim complexes by Ryan O’Donnell and Noah Singer (arXiv) This paper shows an agreement test for direct product testing with two provers in the low soundness regime. Such results were previously known using high dimensional complexes, constructed using deep facts form number theory. This paper proves that a known simpler and strongly explicit complex (KO complex) can be used instead to design the test. Although the methods are elementary, the proof is long and somewhat involved. The featured paper shows a new expansion property of KO complex in the required regime for building the test with low soundness. Additionally, this paper also confirms that their complex can be used to build an alternate efficient PCP with only (poly(\log n)) blow up. Since the methods are elementary, it is more accessible. And, perhaps, we will have easier time to optimize the parameters to build more efficient PCPs.

A sufficient condition for characterizing the one-sided testable properties of families of graphs in the Random Neighbour Oracle Model by Christine Awofeso, Patrick Greaves, Oded Lachish, Amit Levi, Felix Reidl (arXiv) The story of this paper begins from a 2019 paper by Czumaj and Sohler which we covered in our September 2019 news. In that paper, Czumaj-Sohler gave algorithms efficient property testers for testing (H)-freeness when the input graph is known to be (K_r)-minor-free in the random neighbor query model. The algorithm presented in that result recursively explores a few random vertices of the current vertex (in particular, you can think of the algorithm as a truncated BFS with bounded recursion depth). The featured paper considers graph families parameterized by a certain parameter (in particular, it considers the class of graphs with universally bounded “(r)-admissibility”) . The key result of the paper is a theorem which asserts that restricted to the class of graphs with bounded (r)-admissibility, for every fixed finite graph (H), (H)-freeness is testable with one-sided error (assuming access to a random neighbor oracle).

Testing H-freeness on sparse graphs, the case of bounded expansion by Samuel Humeau, Mamadou Moustapha Kanté, Daniel Mock, Timothé Picavet, Alexandre Vigny (arXiv) This paper continues the thread picked up by the former result. The algorithm presented in Czumaj-Sohler result recursively explores a few random vertices of the current vertex (in particular, you can think of the algorithm as a truncated BFS with bounded recursion depth). The proof presented in Czumaj-Sohler was fairly intricate. The featured paper uses ideas rooted in sparsity literature and shows how these ideas can be used to efficiently test (H)-freeness even when the input graph is only promised to come from a polynomially bounded-expansion graph class.

Sublinear Time Low-Rank Approximation of Hankel Matrices by Michael Kapralov, Cameron Musco, Kshiteej Sheth (arXiv) Our November 2022 News featured a paper which obtained low-rank approximation of Toeplitz matrices (which remarkably, were still Toeplitz!) in sublinear time. In this paper, a subset of authors carries the exploration forward and considers the following task: Suppose you are given as input a matrix obtained by noising a PSD Hankel matrix of order (n) and a parameter (\varepsilon > 0). The feature paper presents algorithms which run in time (poly(\log n, 1/\varepsilon)) and returns a bonafide Hankel matrix (\widehat{H}) with (rank(\widehat{H}) \approx poly(\log n)) which is fairly close to the unknown Hankel Matrix which we started with (which was noised to produce the original input).

Support Recovery in One-bit Compressed Sensing with Near-Optimal Measurements and Sublinear Time by Xiaxin Li, Arya Mazumdar (arXiv) Let us begin by reviewing the setup for the One-Bit Compressed Sensing Problem. You want to recover an unknown signal which is modeled as a vector (\boldsymbol{x} \in \mathbb{R}^n) which we further assume is (k)-sparse. To help you out, I give you a Sign of Dot-Product machine which on input your favorite vector (\boldsymbol{a} \in \mathbb{R}^n) tells you (sign(\boldsymbol{a}^{T\boldsymbol{x})). So, in all you can look at the quantity (\boldsymbol{y} = sign(\boldsymbol{A}\boldsymbol{x})) where (\boldsymbol{A} \in \mathbb{R}}{m \times n}). I want to keep (m) as small as possible. Another related goal seeks to recover just the support of the unknown (k)-sparse signal. The featured paper presents efficient algorithms that return a sparse vector (\widehat{\boldsymbol{x}}) whose support is a great proxy for the support of our unknown signal (in the sense they have very small symmetric difference).

Homomorphism Testing with Resilience to Online Manipulations by Esty Kelman, Uri Meir, Debanuj Nayak, Sofya Raskhodnikova (arXiv) Let us consider the task of testing properties of functions (f \colon G \to H) where ((G, +)) and ((H, \oplus)) are groups. A lot of classic results in this area (like the linearity test of BLR) assume that we have access to a reliable oracle which when given any (x \in G) returns (f(x) \in H). This assumption is no longer valid if the oracle is unreliable or worse adversarial. The featured paper considers the situation where the oracle is adversarial and can in fact manipulate the responses (like replacing (f(x)) by some value (y \in H \cup {\bot})). Moreover, the power of adversary is parameterized by a number (t). One of the results in this paper shows the following: Take (f \colon G \to H) and fix some $\varepsilon > 0$. Then one can test whether (f) is a group homomorphism where the adversary can manipulate (t) values after every query is answered using a budget of (O(1/\varepsilon + \log_2t)) queries.

** Computational Complexity in Property Testing** by Renato Ferreira Pinto Jr., Diptaksho Palit, Sofya Raskhodnikova (arXiv) This paper studies the tradeoff between the query complexity and the time complexity aspects of property testing tasks. Most of the research works in property testing have focused on obtaining testers with better and better query bounds. The present work explores the query/time interplay in property testing and develops tools to prove computational hardness of property testers. The paper proves an unconditional and a conditional hierarchy theorem. The first theorem, for instance, asserts that there are property testing problems which witness a strict separation between the query complexity of the task and the running time required to solve the task. The second theorem asserts that assuming SETH, you can separation between the query bounds and the running time bounds can be improved.

Interactive proof systems for FARNESS by Oded Goldreich Tal Herman Guy N. Rothblum (ECCC) This paper continues a line of work on interactive proofs of proximity for distribution testing problems, building on earlier work of Chiesa and Gur (see our October 2017 news). The central question here is whether interaction can reduce the sample complexity barriers that appear unavoidable in standard (non-interactive) distribution testing. The paper answers this in the affirmative for natural promise problems such as deciding whether two distributions are identical (the NO case in the interactive setup) or (\varepsilon)-far (the YES case in the interactive setup), as well as testing whether a distribution is far from uniform (the far case being the YES case in the interactive setup yet again). The authors construct explicit interactive proof systems in which the verifier uses asymptotically fewer samples than required by the best testers, while simultaneously the honest prover uses asymptotically fewer samples than would be needed to learn the distribution. The protocols rely on collision-based ideas and admit clean tradeoffs between the verifier’s and prover’s sample complexities. Conceptually, the result shows that interaction can beat both testing and learning bounds at the same time for concrete, well-studied distribution testing problems.

On doubly-sublinear interactive proofs for distributions by Oded Goldreich Guy N. Rothblum (ECCC) As I understand it, this paper takes a step back from specific testing problems and asks how general the phenomenon exhibited above really is. So let’s try to place the context for this paper with the preceding paper in mind. Motivated with the interactive protocols presented for FARNESS, the authors introduce the notion of doubly-sublinear interactive proofs of proximity** **(ds-IPPs), where the verifier’s sample complexity is asymptotically smaller than testing complexity and the honest prover’s sample complexity is asymptotically smaller than learning complexity. The main contribution is conceptual rather than algorithmic: the paper shows that ds-IPPs do exist, by constructing distribution properties (algebraically defined and somewhat artificial) that admit such protocols. Together with the above paper, this work shows that interaction changes the game in the sense that the situation with sample complexity looks different with interaction than without.

Proving the PCP Theorem with 1.5 proof compositions (or yet another PCP construction) by Oded Goldreich (ECCC) This paper revisits the classic PCP Theorem and offers a new PCP construction of constant query complexity and sublinear randomness complexity (n^{\alpha}) for any constant (\alpha > 0). The traditional Arora–Lund–Motwani–Sudan–Szegedy proof uses two generic proof compositions (Reed–Muller with itself, then with a Hadamard-based PCP). Inspired from a recent work of Amireddy et al. which constructs a PCP (without composition) with constant query complexity and randomness complexity (n^{\alpha}), this work approaches the same challenge. It combines a Reed-Muller based PCP with Hadamard encoding in a novel way. By working with a constant number of dimensions and a large alphabet, then encoding field elements via Hadamard codes and emulating key tests (e.g., low-degree and sum-check), the paper achieves a PCP system that can serve as an inner verifier without full generic composition. In all, this paper presents another route to the PCP Theorem with constant queries and (n^{\alpha}) randomness.

Interactive Proofs For Distribution Testing With Conditional Oracles by Ari Biswas, Mark Bun, (Our Own) Clement Canonne, Satchit Sivakumar (arXiv) Another paper whose roots lie in the seminal work of Chiesa and Gur. This paper poses the following twist in distribution testing (on top of the Chiesa-Gur twist):

Can label-invariant properties be verified in a (query, sample and communication)-efficient way when the tester has access to a PCOND oracle.

The paper proves that there exist label invariant properties of distributions sample complexity of testing which is not allayed with the PCOND oracle (that is, it remains the same as the sample complexity of the task without one). However, when interaction is allowed a different picture emerges. In particular, the paper presents a round protocol which can be used to interactively test a label-invariant distribution property (over a universe of size (N)) in the PCOND model with a query complexity that grows as (poly(\log N)).

Verification of Statistical Properties: Redefining the Possible by (Again, our own) Clement Canonne, Sam Polgar, Aditya Singh, Aravind Thyagarajan, Qiping Yang (ECCC) Let us again start our story from the Chiesa-Gur paper. So, we have an unreliable prover and a verifier where the verifier is interested in testing some label-invariant properties of a distribution and is allowed to chit-chat with the prover. One problematic aspect which prevents this construct from being applicable to some practical scenarios is that there are cursed properties (eg, things as basic as UNIFORMITY testing), testing which requires the verifier to hold (\Omega(\sqrt n)) items where (n) denotes the support size of the distribution whose properties we want to test. Undeterred by this news, the featured paper contemplates how to evade this curse. The eye-opening realization is to consider these tasks with the roles of completeness and soundness flipped — eg akin to the “Interactive proofs of farness” paper above, you think your goal is like testing FARNESS from UNIFORMITY. The paper shows this task indeed admits a protocol with sample complexity being (O(1)). The paper also considers the multiplayer and the zero-knowledge variations of these tasks and looks like a nice conceptual read for the PTReview readers.

Learning and Testing Convex Functions by Renato Ferreira Pinto Jr., Cassandra Marcussen, Elchanan Mossel, Shivam Nadimpalli (arXiv) The paper studies functions (f \colon \mathbb{R}^{d \to \mathbb{R}) under Gaussian measure and formalizes convexity testing as distinguishing convex functions from those that are (\varepsilon)-far in (L^2) equipped with the Gaussian Measure. A central theorem shows that if (f) is Lipschitz (or satisfies comparable regularity), then convexity can be tested with sample complexity (n}{poly(1/\varepsilon^2)}). Conversely, the paper supplements these results with a sample complexity lower bound in a natural query model.

Halfspaces Are Hard to Test with Relative Error by Xi Chen, Anindya De, Yizhi Huang, Shivam Nadimpalli, Rocco A. Servedio, Tianqi Yang (arXiv) This paper considers property testing of Boolean halfspaces in the relative-error model (which we covered earlier, eg see here), where distance between a pair of functions is defined as the fractional symmetric difference between the supports of the two functions. We assume that algorithms have access to a uniformly random element in the support of the input function. Alright, with these details behind us, let us state the main result of this paper. It asserts that for some constant (\varepsilon_0 > 0), any relative-error ​(\varepsilon_0)-tester for halfspaces over ({0,1}^n) must take (\Omega(\log n)) samples, showing a super-constant lower bound in this model. This result sharply contrasts with classic absolute-error testing results, where halfspaces admit constant-query testers, and thus demonstrates that changing the relative-error model differs significantly from the classical model.

Testing Noisy Low-Degree Polynomials for Sparsity by Yiqiao Bao, Anindya De, Shivam Nadimpalli, Rocco A. Servedio, Nathan White (arXiv) Let us start from the setup considered in the Chen-De-Servedio paper we covered in our November 2019 post. In their setup, you are given samples from a noisy linear model (\boldsymbol{y} = \mathbf{w \cdot x} + \texttt{Noise}). On the other hand, this paper considers a generalized situation where you are given (\boldsymbol{y} = \mathbf{p(x)}+\texttt{Noise}). You are interested in knowing whether (\mathbf{p}) is (s)-sparse or far-from (s)-sparse. The main result gives explicit conditions under which sparsity can be tested with a constant number of samples, depending on the degree, noise rate, and sparsity parameter (s). Outside this regime, the authors prove logarithmic lower bounds, showing that once noise overwhelms certain structural signals, sparsity testing becomes nearly as hard as learning the polynomial itself.

Efficient Testing Implies Structured Symmetry by Cynthia Dwork, Pranay Tankala (arXiv) This paper addresses a structural question in property testing: which Boolean properties admit testers with small sample complexity and efficient computation? The paper shows that if a property (P \subseteq {0,1}^n) is testable using (i) few samples and (ii) a circuit of small size, then (P) must be close to a property (\mathcal{Q}) which enjoy some kind of stuctured symmetry. In other words, efficient testability forces the property to exhibit a form of low-complexity symmetry. Conversely, the paper shows that properties with such structured symmetry admit efficient testers.

Mathematical exploration and discovery at scale by Bogdan Georgiev, Javier Gómez-Serrano, Terence Tao, Adam Zsolt Wagner (arXiv) This paper asks, fairly openly (see Section 1.2), what it might even mean for modern AI systems to participate in mathematical discovery. The authors place their work in the long lineage of computer-assisted mathematics, from early symbolic systems to contemporary automated reasoning, but are careful to frame large language models not as theorem-provers or replacements for mathematicians, but as exploratory engines: components inside a closed-loop pipeline where proposal, evaluation, and refinement happen repeatedly and at scale. Concretely, the paper studies a setup in which LLM-based code generation is coupled with automated evaluators, allowing the system to search large spaces of mathematical constructions and algorithms with minimal human intervention. Rather than optimizing for a single headline result, the authors emphasize breadth, reporting on 44 separate “adventures” spanning combinatorics, number theory, analysis, and even Olympiad-style geometry. In many cases, the system rediscovers known constructions or matches best-known bounds; in others, it produces plausible variants or refinements that look less like finished theorems and more like the kind of halfway-formed ideas one might jot down while exploring a problem for the first time. Quoting from the paper:

“One illustrative example comes from experiments related to the Erdős discrepancy problem. First, when the system was run with no human guidance, it found a sequence of length 200 before progress started to slow down. Next, when the prompt was augmented with the advice to try a function which is multiplicative, or approximately multiplicative, the system performed significantly better and found constructions of length 380 in the same amount of time. These constructions were still far from the optimal value of 1160, and the authors explicitly note that other hints (for example, suggesting the use of SAT solvers) might have improved the score further, but were not explored due to time limitations.”

The broader point of the paper is less about getting optimal bounds and more about scale and exploration. The results are empirical and depend on carefully chosen experimental settings, but the diversity of examples makes this an engaging and unusually readable account of what happens when machines are allowed to roam around mathematical landscapes with a long leash.