arXiv subjects

Jesse Geneson

Publications and source records attributed to Jesse Geneson.

At least 19 recordsLinked to original sources

arXiv2026

Enumerating forcing and strongly forcing (0,1)-matrices

Let $Q$ be a nonzero $s\times t$ $(0,1)$-pattern, and let $m\ge s$ and $n\ge t$. An $m\times n$ matrix is strongly $Q$-forcing if every $1$-entry belongs to an $s\times t$ submatrix equal to $Q$. Let $F^{*}(m,n,Q)$ count these matrices. Put $H=m-s+1$ and $W=n-t+1$. We prove \[ F^{*}(m,n,Q)\ge 2^{HW}. \] Writing $r$ and $c$ for the numbers of nonzero rows and columns of $Q$, equality holds if and only if \[ (H=1\text{ or }r=1)\qquad\text{and}\qquad(W=1\text{ or }c=1). \] Thus the minimum over all nonzero $s\times t$ patterns is $2^{HW}$, attained exactly by singleton patterns when $H,W>1$, and every fixed nonzero pattern has square growth rate $1$. We also refine the count by weight. If $o(Q)$ is the number of $1$-entries of $Q$, then the number of strongly $Q$-forcing matrices at the minimum positive weight $o(Q)$ is $\binom{H+r-1}{r}\binom{W+c-1}{c}$; at every fixed density in $(0,1)$, the logarithmic growth rate is the binary entropy when $m$ and $n$ are comparable. For ordinary forcing, where every $s\times t$ submatrix contains the $1$-entries of $Q$ in their prescribed positions, let $F(m,n,Q)$ be the number of forcing matrices and let $\mathfrak m(m,n,Q)$ be their minimum weight. We prove \[ F(m,n,Q)=2^{mn-\mathfrak m(m,n,Q)} \quad\text{and}\quad 2^{\,mn-\mathfrak m(m,n,Q)+HW} \le F(m,n,Q)F^{*}(m,n,Q) \le 2^{mn}. \] The lower product bound has the same equality cases as the strong-forcing lower bound above, while the upper product bound is attained exactly by singleton patterns. In particular, the product is at least $2$, with equality exactly when $s=m$, $t=n$, and $Q$ is the all-ones pattern.

math.CO↗

arXiv2026

Density bounds for permutations avoiding monotone arithmetic progressions

For $X\in\{\mathbb{N},\mathbb{Z}\}$, let $\alpha_X(\ell)$ and $\beta_X(\ell)$ denote the supremal upper and lower densities of subsets of $X$ admitting $\omega$-permutations without monotone $\ell$-term arithmetic progressions. We strengthen the published lower bounds for the three-term upper-density parameters by proving \[ \alpha_{\mathbb{N}}(3)\geq\frac23,\qquad \alpha_{\mathbb{Z}}(3)\geq\frac23. \] We also prove $\beta_{\mathbb{Z}}(4)=1$ by constructing four-permutable subsets of the integers whose lower symmetric densities approach one.

math.NT↗

arXiv2026

A six-neuron counterexample to the target-free clique conjecture

The target-free clique conjecture asserts that the supports of stable fixed points of a nondegenerate combinatorial threshold-linear network (CTLN) are exactly its target-free cliques: bidirected cliques for which no outside vertex receives an edge from every clique vertex. We give an explicit six-neuron counterexample. For every sufficiently small $\varepsilon>0$, the CTLN defined by one fixed graph at $\delta=29\varepsilon/25$ is nondegenerate and has a stable fixed point with nonclique full support. Its values of $q=\delta(1-\varepsilon)/\varepsilon$ tend to $29/25$. In the complementary direction, for any CTLN on $n\geq3$ vertices, we prove that in the parameter range \[ q\geq n-2-\frac{n-3}{2}\varepsilon, \] no nonclique support can satisfy both the fixed-point positivity and linear stability conditions. Consequently, throughout this range, every nondegenerate CTLN has exactly its target-free cliques as supports of stable fixed points. In particular, this holds when $\varepsilon\leq\delta/(\delta+n-2)$.

math.CO↗

arXiv2026

Linear extremal bounds for a family of forbidden $0$-$1$ matrices

Fulek defined the $0$-$1$ matrix \[ L_3=\begin{pmatrix} 1&0&0&1&0\\ 0&0&0&0&1\\ 0&1&1&0&0 \end{pmatrix} \] and asked whether $\text{ex}(n,L_3) = O(n)$. We prove that every $r\times s$ $0$-$1$ matrix avoiding $L_3$ has at most $27r+2s$ $1$ entries. Fulek's general lower bound construction has $6n-8$ $1$ entries, so \[ 6n-8\leq \text{ex}(n,L_3)\leq29n \] for $n\geq5$. The same argument applies to an infinite family. If $Q_{a,b,k,\ell}$ is the light three-row matrix with column word $1^a3^k1^b2^\ell$, where $a,b,\ell\geq1$ and $k\geq2$, then \[ \text{ex}(r,s,Q_{a,b,k,\ell}) \leq\bigl(5(k-1)(4b+1)+a+b+\ell-1\bigr)r+2s. \] This verifies a conjecture of Pettie and Tardos on linear light patterns for an infinite family that includes the previously unresolved weight-five pattern $L_3$. The proof assigns matrix entries to edges of a bar $1$-visibility hypergraph, cuts gaps to control the multiplicity of these edges, and charges the cuts to a noncrossing graph on the rows.

math.CO↗

arXiv2026

The order of long rainbow arithmetic progressions

Let $T_k$ be the minimum positive integer $t$ such that, for every positive integer $n$, every equinumerous $t$-coloring of $[tn]$ contains a rainbow $k$-term arithmetic progression. Jungi\'{c}, Licht, Mahdian, Ne\v{s}et\v{r}il and Radoi\v{c}i\'{c} conjectured that $T_k=\Theta(k^2)$, while Conlon, Fox and Sudakov proved that $T_k=O(k^2\log k)$. We prove the matching lower bound $T_k=\Omega(k^2\log k)$, and hence $T_k=\Theta(k^2\log k)$.

math.CO↗

arXiv2026

CrowdMath: A Dataset of Crowdsourced Mathematical Research Discussions

Large language models have made substantial progress on mathematical reasoning, but existing benchmarks typically evaluate well-specified problems with final answers, step-by-step solutions, or complete proofs. They do not capture collaborative open-problem solving: a setting in which participants propose partial arguments, identify gaps or errors in prior steps, repair flawed reasoning, and gradually synthesize incremental contributions into a proof. We introduce CrowdMath, a dataset of 164 expert-annotated progress chains from the MIT PRIMES--Art of Problem Solving (AoPS) CrowdMath program (2016-2025), a collaborative research initiative whose discussions have led to peer-reviewed publications. Each chain traces a multi-participant forum discussion from an open-problem statement to a completed proof. Posts are labeled by their functional roles in the evolving solution process, including partial progress, proof completion, erroneous reasoning, and error identification. We define evaluation tasks and benchmark six frontier models. Models achieve 83-88% accuracy on next-post prediction, suggesting that they can follow the local flow of mathematical discussion. However, they struggle to identify the functional significance of individual contributions with the best model achieving only 0.42 macro-F1 on post-role classification. CrowdMath exposes a gap between solving well-specified mathematical problems and understanding collaborative mathematical progress as it unfolds.

cs.AI↗

arXiv2026

Online learning of smooth functions on $\mathbb{R}$

We study adversarial online learning of real-valued functions on $\mathbb{R}$. In each round the learner is queried at $x_t\in\mathbb{R}$, predicts $\hat y_t$, and then observes the true value $f(x_t)$; performance is measured by cumulative $p$-loss $\sum_{t\ge 1}|\hat y_t-f(x_t)|^p$. For the class \[ \mathcal{G}_q=\Bigl\{f:\mathbb{R}\to\mathbb{R}\ \text{absolutely continuous}:\ \int_{\mathbb{R}}|f'(x)|^q\,dx\le 1\Bigr\}, \] we show that the standard model becomes ill-posed on $\mathbb{R}$: for every $p\ge 1$ and $q>1$, an adversary can force infinite loss. Motivated by this obstruction, we analyze three modified learning scenarios that limit the influence of queries that are far from previously observed inputs. In Scenario 1 the adversary must choose each new query within distance $1$ of some past query. In Scenario 2 the adversary may query anywhere, but the learner is penalized only on rounds whose query lies within distance $1$ of a past query. In Scenario 3 the loss in round $t$ is multiplied by a weight $g(\min_{j<t}|x_t-x_j|)$. We obtain sharp characterizations for Scenarios 1-2 in several regimes. For Scenario 3 we identify a clean threshold phenomenon: if $g$ decays too slowly, then the adversary can force infinite weighted loss. In contrast, for rapidly decaying weights such as $g(z)=e^{-cz}$ we obtain finite and sharp guarantees in the quadratic case $p=q=2$. Finally, we study a natural multivariable slice generalization $\mathcal{G}_{q,d}$ of $\mathcal{G}_q$ on $\mathbb{R}^d$ and show a sharp dichotomy: while the one-dimensional case admits finite opt-values in certain regimes, for every $d\ge 2$ the slice class $\mathcal{G}_{q,d}$ is too permissive, and even under Scenarios 1-3 an adversary can force infinite loss.

cs.LG↗

arXiv2026

Randomized Zero Forcing

We introduce randomized zero forcing (RZF), a stochastic color-change process on directed graphs in which a white vertex turns blue with probability equal to the fraction of its incoming neighbors that are blue. Unlike probabilistic zero forcing, RZF is governed by in-neighborhood structure and can fail to propagate globally due to directionality. The model extends naturally to weighted directed graphs by replacing neighbor counts with incoming weight proportions. We study the expected propagation time of RZF, establishing monotonicity properties with respect to enlarging the initial blue set and increasing weights on edges out of initially blue vertices, as well as invariances that relate weighted and unweighted dynamics. Exact values and sharp asymptotics are obtained for several families of directed graphs, including arborescences, stars, paths, cycles, and spiders, and we derive tight extremal bounds for unweighted directed graphs in terms of basic parameters such as order, degree, and radius. We conclude with an application to an empirical input-output network, illustrating how expected propagation time under RZF yields a dynamic, process-based notion of centrality in directed weighted systems.

math.CO↗

arXiv2026

Asymptotic Tightness of the Pigeonhole Bound for Large-Order Davenport-Schinzel Sequences

We prove that the pigeonhole upper bound $\lambda(s,m) \leq \binom{m}{2}(s+1)$ is asymptotically tight whenever $s/\!\sqrt{m} \to \infty$. In particular, $\lambda(s,m) \sim \binom{m}{2}\,s$ in this regime. As corollaries: $\lambda(n,n)/n^3 \to \frac{1}{2}$, resolving the leading constant from the previously known interval $[\frac{1}{3}, \frac{1}{2}]$; and more generally $\lambda(an,bn) \sim \frac{ab^2}{2}\,n^3$ for any constants $a,b > 0$.

math.CO↗

arXiv2025

Stable non-minimal fixed points of threshold-linear networks

In threshold-linear networks (TLNs), a fixed point is called minimal if no proper subset of its support is also a fixed point. Curto et al (Advances in Applied Mathematics, 2024) conjectured that every stable fixed point of any TLN must be a minimal fixed point. We provide a counterexample to this conjecture: an explicit competitive TLN on 3 neurons that exhibits a stable fixed point whose support is not minimal (it contains the support of another stable fixed point). We prove that there is no competitive TLN on 2 neurons which contains a stable non-minimal fixed point, so our 3-neuron construction is the smallest such example. By expanding our base example, we show for any positive integers $i, j$ with $i < j-1$ that there exists a competitive TLN with stable fixed point supports $\tau \subsetneq \sigma$ for which $|\tau| = i$ and $|\sigma| = j$. Using a different expansion of our base example, we also show that chains of nested stable fixed points in competitive TLNs can be made arbitrarily long.

q-bio.NC↗

arXiv2025

Throttling for metric dimension and its variants

Metric dimension is a graph parameter that has been applied to robot navigation and finding low-dimensional vector embeddings. Throttling entails minimizing the sum of two available resources when solving certain graph problems. In this paper, we introduce throttling for metric dimension, edge metric dimension, and mixed metric dimension. In the context of vector embeddings, metric dimension throttling finds a low-dimensional, low-magnitude embedding with integer coordinates. We show that computing the throttling number is NP-hard for all three variants. We give formulas for the throttling numbers of special families of graphs, and characterize graphs with extremal throttling numbers. We also prove that the minimum possible throttling number of a graph of order $n$ is $\Theta\left(\frac{\log{n}}{\log{\log{n}}}\right)$, while the minimum possible throttling number of a tree of order $n$ is $\Theta(n^{1/3})$ or $\Theta(n^{1/2})$ depending on the variant of metric dimension.

math.CO↗

arXiv2025

Mistake-bounded online learning with operation caps

We investigate the mistake-bound model of online learning with caps on the number of arithmetic operations per round. We prove general bounds on the minimum number of arithmetic operations per round that are necessary to learn an arbitrary family of functions with finitely many mistakes. We solve a problem on agnostic mistake-bounded online learning with bandit feedback from (Filmus et al, 2024) and (Geneson \& Tang, 2024). We also extend this result to the setting of operation caps.

cs.LG↗

arXiv2025

Saturation of 0-1 Matrices

A 0-1 matrix $M$ contains a 0-1 matrix $P$ if $M$ has a submatrix $P'$ which can be turned into $P$ by changing some of the ones to zeroes. Matrix $M$ is $P$-saturated if $M$ does not contain $P$, but any matrix $M'$ derived from $M$ by changing a zero to a one must contain $P$. The saturation function $sat(n,P)$ is defined as the minimum number of ones of an $n \times n$ $P$-saturated 0-1 matrix. Fulek and Keszegh showed that each pattern $P$ has $sat(n,P) = O(1)$ or $sat(n,P) = \Theta(n)$. This leads to the natural problem of classifying forbidden 0-1 matrices according to whether they have linear or bounded saturation functions. Some progress has been made on this problem: multiple infinite families of matrices with bounded saturation function and other families with linear saturation function have been identified. We answer this question for all patterns with at most four ones, as well as several specific patterns with more ones, including multiple new infinite families. We also consider the effects of certain matrix operations, including the Kronecker product and insertion of empty rows and columns. Additionally, we consider the simpler case of fixing one dimension, extending results of (Fulek and Keszegh, 2021) and (Berendsohn, 2021). We also generalize some results to $d$-dimensional saturation.

math.CO↗

arXiv2025

Fault tolerance for metric dimension and its variants

Hernando et al. (2008) introduced the fault-tolerant metric dimension $\text{ftdim}(G)$, which is the size of the smallest resolving set $S$ of a graph $G$ such that $S-\left\{s\right\}$ is also a resolving set of $G$ for every $s \in S$. They found an upper bound $\text{ftdim}(G) \le \dim(G) (1+2 \cdot 5^{\dim(G)-1})$, where $\dim(G)$ denotes the standard metric dimension of $G$. It was unknown whether there exists a family of graphs where $\text{ftdim}(G)$ grows exponentially in terms of $\dim(G)$, until recently when Knor et al. (2024) found a family with $\text{ftdim}(G) = \dim(G)+2^{\dim(G)-1}$ for any possible value of $\dim(G)$. We improve the upper bound on fault-tolerant metric dimension by showing that $\text{ftdim}(G) \le \dim(G)(1+3^{\dim(G)-1})$ for every connected graph $G$. Moreover, we find an infinite family of connected graphs $J_k$ such that $\dim(J_k) = k$ and $\text{ftdim}(J_k) \ge 3^{k-1}-k-1$ for each positive integer $k$. Together, our results show that \[\lim_{k \rightarrow \infty} \left( \max_{G: \text{ } \dim(G) = k} \frac{\log_3(\text{ftdim}(G))}{k} \right) = 1.\] In addition, we consider the fault-tolerant edge metric dimension $\text{ftedim}(G)$ and bound it with respect to the edge metric dimension $\text{edim}(G)$, showing that \[\lim_{k \rightarrow \infty} \left( \max_{G: \text{ } \text{edim}(G) = k} \frac{\log_2(\text{ftedim}(G))}{k} \right) = 1.\] We also obtain sharp extremal bounds on fault-tolerance for adjacency dimension and $k$-truncated metric dimension. Furthermore, we obtain sharp bounds for some other extremal problems about metric dimension and its variants. In particular, we prove an equivalence between an extremal problem about edge metric dimension and an open problem of Erd\H{o}s and Kleitman (1974) in extremal set theory.

math.CO↗

arXiv2024

Sequence saturation

In this paper, we introduce saturation and semisaturation functions of sequences, and we prove a number of fundamental results about these functions. Given a forbidden sequence $u$ with $r$ distinct letters, we say that a sequence $s$ on a given alphabet is $u$-saturated if $s$ is $r$-sparse, $u$-free, and adding any letter from the alphabet to an arbitrary position in $s$ violates $r$-sparsity or induces a copy of $u$. We say that $s$ is $u$-semisaturated if $s$ is $r$-sparse and adding any letter from the alphabet to $s$ violates $r$-sparsity or induces a new copy of $u$. Let the saturation function $\operatorname{Sat}(u, n)$ denote the minimum possible length of a $u$-saturated sequence on an alphabet of size $n$, and let the semisaturation function $\operatorname{Ssat}(u, n)$ denote the minimum possible length of a $u$-semisaturated sequence on an alphabet of size $n$. For alternating sequences, we determine both the saturation function and the semisaturation function up to a constant multiplicative factor. We show for every sequence that the semisaturation function is always either $O(1)$ or $\Theta(n)$. For the saturation function, we show that every sequence $u$ has either $\operatorname{Sat}(u, n) \ge n$ or $\operatorname{Sat}(u, n) = O(1)$. For every sequence with $2$ distinct letters, we show that the saturation function is always either $O(1)$ or $\Theta(n)$.

math.CO↗

arXiv2024

Bounds on the price of feedback for mistake-bounded online learning

We improve several worst-case bounds for various online learning scenarios from (Auer and Long, Machine Learning, 1999). In particular, we sharpen an upper bound for delayed ambiguous reinforcement learning by a factor of 2 and an upper bound for learning compositions of families of functions by a factor of 2.41. We also improve a lower bound from the same paper for learning compositions of $k$ families of functions by a factor of $\Theta(\ln{k})$, matching the upper bound up to a constant factor. In addition, we solve a problem from (Long, Theoretical Computer Science, 2020) on the price of bandit feedback with respect to standard feedback for multiclass learning, and we improve an upper bound from (Feng et al., Theoretical Computer Science, 2023) on the price of $r$-input delayed ambiguous reinforcement learning by a factor of $r$, matching a lower bound from the same paper up to the leading term.

cs.LG↗

arXiv2023

Efficient quantum loading of probability distributions through Feynman propagators

We present quantum algorithms for the loading of probability distributions using Hamiltonian simulation for one dimensional Hamiltonians of the form ${\hat H}= \Delta + V(x) \mathbb{I}$. We consider the potentials $V(x)$ for which the Feynman propagator is known to have an analytically closed form and utilize these Hamiltonians to load probability distributions including the normal, Laplace and Maxwell-Boltzmann into quantum states. We also propose a variational method for probability distribution loading based on constructing a coarse approximation to the distribution in the form of a `ladder state' and then projecting onto the ground state of a Hamiltonian chosen to have the desired probability distribution as ground state. These methods extend the suite of techniques available for the loading of probability distributions, and are more efficient than general purpose data loading methods used in quantum machine learning.

quant-ph↗

arXiv2023

Estimating systemic importance with missing data in input-output graphs

In the context of the Cobb-Douglas productivity model we consider the $N \times N$ input-output linkage matrix $W$ for a network of $N$ firms $f_1, f_2, \cdots, f_N$. The associated influence vector $v_w$ of $W$ is defined in terms of the Leontief inverse $L_W$ of $W$ as $v_W = \frac{\alpha}{N} L_W \vec{\mathbf{1}}$ where $L_W = (I - (1-\alpha) W')^{-1}$, $W'$ denotes the transpose of $W$ and $I$ is the identity matrix. Here $\vec{\mathbf{1}}$ is the $N \times 1$ vector whose entries are all one. The influence vector is a metric of the importance for the firms in the production network. Under the realistic assumption that the data to compute the influence vector is incomplete, we prove bounds on the worst-case error for the influence vector that are sharp up to a constant factor. We also consider the situation where the missing data is binomially distributed and contextualize the bound on the influence vector accordingly. We also investigate how far off the influence vector can be when we only have data on nodes and connections that are within distance $k$ of some source node. A comparison of our results is juxtaposed against PageRank analogues. We close with a discussion on a possible extension beyond Cobb-Douglas to the Constant Elasticity of Substitution model, as well as the possibility of considering other probability distributions for missing data.

cs.DM↗