Papers

Nonasymptotic convergence rates for optimization problems have been extensively studied across a wide range of settings, with carefully constructed worst-case instances establishing matching lower bounds for many algorithm classes. Recent work has shown, however, that these rates can often be improved in the asymptotic regime, while the corresponding asymptotic lower bounds remain largely unknown. We propose a general technique for converting existing nonasymptotic lower bound constructions into asymptotic lower bounds on Hilbert spaces. This allows us to show that several known asymptotic convergence upper bounds in Hilbert spaces are tight. In particular, we recover tightness of the $o(n^{-2})$ suboptimality guarantee for accelerated gradient descent on smooth convex functions \cite{attouch2016rate}, as well as the $o(n^{-1/2})$ suboptimality guarantee for gradient descent on smooth nonconvex functions \cite{gratton2025refining}. For stochastic convex optimization, we also develop a method that achieves an $o(n^{-1/2})$ suboptimality guarantee in finite dimensions, and prove a matching one-dimensional lower bound.

We give an affirmative answer to the planar local smoothing problem in Question~1.7 of D.~Ntalampekos and positive and negative answers to the basic approximation and level-set parts of his higher-dimensional Question~1.8. In every dimension $n\ge2$, each continuous Lebesgue monotone function in $W^{1,p}$ on a bounded open set admits uniform and strong $W^{1,p}$ approximation by monotone $C^{1,\alpha}_{\loc}$ functions, with unchanged Sobolev boundary values and no increase of the $p$-Dirichlet energy, for $1<p<\infty$. The energy can be made strictly smaller unless the original function is $p$-harmonic. In the plane we obtain smooth local replacement at every isolated $p$-harmonic critical point, with arbitrarily small $C^1$ and Sobolev error. A point of gradient index $-m$ can be resolved into exactly $m$ nondegenerate saddles. Together with Ntalampekos's planar theorem, this gives smooth monotone density with fixed boundary values; at a nonsmooth $p$-harmonic minimizer the energy increase is unavoidable but can tend to zero. In dimensions $n\ge3$, an explicit Lipschitz monotone function has a nonmanifold point on every level in an interval, although it admits smooth monotone approximation. A product extension of a planar homogeneous $7$-harmonic function also rules out a general discrete exceptional set under the exact boundary and energy constraints. The answer to Question~1.8 thus distinguishes the five basic approximation properties from the stronger topological and exceptional-set conclusions. Applications include constrained integral functionals, strict $BV$ convergence of superlevel sets, nonlinear flux convergence, and stability of persistence diagrams under tameness assumptions.

We construct an embedded torus and an entire graph in $\mathbb{R}^4$ whose normal bundles are initially flat but lose this property instantaneously under mean curvature flow. We also give an example showing that the parallel principal normal condition is not preserved under mean curvature flow, even though the normal bundle remains flat along the flow.

We study convex optimization over compact convex sets for twice differentiable objective functions with positive definite Hessians, assuming access to the feasible set through a linear minimization oracle. Existing second-order Frank–Wolfe methods either provide only a local linear rate over general convex sets or achieve global linear and local quadratic convergence only over polytopes. We propose damped Newton Frank–Wolfe methods that approximately solve constrained damped Newton subproblems using Frank–Wolfe or away-step Frank–Wolfe inner iterations. We develop residual backtracking, using a root Newton stepsize as a safe reference, together with a switching rule that eventually takes full steps. Under Hölder smoothness assumptions for $p$th derivatives, we establish global linear convergence and local convergence with Q-order at least $1+\nu$ ($p=2$) and local quadratic convergence ($p=3$). Experiments on matrix sensing and ridge-regularized logistic regression show that the residual-backtracking variant is consistently robust and often achieves the best performance among the tested first- and second-order baselines.

Canonical ideal quotients of extriangulated categories admit natural one-sided triangulated or pretriangulated structures. A fundamental question is whether these quotient structures still retain enough information to recover higher orthogonality properties of the subcategory being factored out. We show that, for a strongly functorially finite $n$-rigid subcategory, such information is encoded by the nilpotency of the canonical suspension and, equivalently, of the canonical loop. More precisely, this nilpotency characterizes two-sided maximal $n$-orthogonality. We further establish an objectwise recognition criterion that combines one-sided higher orthogonality with the vanishing of the $n$th suspension or loop. In the triangulated setting, our results give a converse to the known truncation construction and show that the quotient is $n$-truncated if and only if the subcategory is $(n+1)$-cluster tilting. Moreover, in the nonsplit case, the nilpotency index is exactly $n$. These results turn truncation from a consequence of higher orthogonality into a sharp criterion for recognizing it.

Nonlinear preconditioning makes it possible to adapt gradient updates to the growth of the objective's curvature, but combining its design with suitable stepsize selection is challenging. We propose adaptive dual preconditioned gradient descent (adaptive DPGD), which combines curvature-based preconditioner design with a linesearch-free stepsize rule based on local information. For convex objectives, we establish linear convergence under local strong convexity of the objective. By adapting the stepsize rule, we extend the method to weakly convex objectives and establish asymptotic stationarity without global Lipschitz smoothness. Moreover, our local assumptions give greater freedom in preconditioner design. We exploit this freedom to develop a semiautomatic recipe guided by the objective's curvature, yielding a family of preconditioners that includes those underlying normalized and hyperbolic gradient descent. Experiments on convex and weakly convex problems with real data demonstrate the effectiveness of the preconditioner design and adaptive stepsize selection.

For every $n\geq 4$ and every $H$ in a neighborhood of $n-1$ (depending on $n$), we prove the existence of a properly embedded strongly stable CMC hypersurface in $\mathbb H^n$ with mean curvature $H$ and infinitely many ends, invariant under the action of a Schottky group. In particular, stable Bernstein-type rigidity fails in this range.

This paper examines idempotents in algebras and categories that arise from factorizations of the identity morphism. In the diagrammatic and combinatorial contexts considered here, these factorizations correspond to generalizations of meanders, where a meander is understood to be a curve in the plane that wanders transversely back and forth across a given straight line. By formulating the algebra of the Temperley-Lieb Monoid in terms of planar curve combinatorics, one can understand idempotents in the Temperley-Lieb Monoid in terms of meanders. Corresponding results are shown for the Brauer Monoid and for the Tangle Monoid and Tangle Category.

We establish strong and explicit forms of a one-sided Nullstellensatz for polynomial rings D[x1, . . . , xn] over arbitrary division rings D with central indeterminates. To this end, we employ the theory of integral dependence over left ideals and the notion of G-left ideals, which plays a role analogous to that of G-ideals in the classical theory. We prove that the Jacobson radical of any left ideal of D[x1, . . . , xn] coincides with the intersection of all G-left ideals containing it. As a consequence, we obtain an explicit description of Jacobson radicals of left ideals of polynomial rings over division rings in terms of integral dependence.

In a predictor-corrector arc-search method, a point on the predictor arc is first selected and a corrector is then computed at that point. Since the Karush-Kuhn-Tucker (KKT) matrix changes with the selected point, this correction may require a second factorization in each iteration. We propose a one-factorization arc-search method (OFAS) for semidefinite programming (SDP) that avoids this second factorization, retains the corrector after the arc step, and uses only one KKT factorization per iteration. The key is a three-term decomposition of the complementarity mismatch at the selected point on the arc, which allows the correction to be assembled from linear-system solutions using the factorization at the current iterate. We also propose a curvature-amplified variant (CA-OFAS) that amplifies the second-order arc term. The proposed methods are formulated with a homogeneous self-dual embedding. For a wide long-step neighborhood, we prove that both methods reduce the homogeneous complementarity and residuals below $\varepsilon$ in $O(n \log(1/\varepsilon))$ iterations for an $n$-by-$n$ SDP matrix variable. Numerical experiments show that the one-factorization structure and the correction after the arc step are associated with reductions in computation time. Compared with the Mehrotra-type method, CA-OFAS reduces the geometric-mean computation time by 17% on SDPLIB and 13% on neural network verification SDP problems.

BF16 is now standard in large-scale pretraining, including in fused attention kernels such as FlashAttention, and these kernels are widely trusted. When we used FlashAttention-3 to pretrain a 450M-parameter transformer on 50B tokens, however, we ran into a problem: training was healthy for 25B tokens, then the gradient norm grew a thousandfold and the loss ended 0.2 nats above FP32 attention, without a single NaN. Recomputing the attention backward of just two layers in FP32 removes almost all of the excess gradient. Part of the cause is known: a fused multiply-add in the forward softmax, so far treated as an extreme-input NaN case and never fixed in FlashAttention-3. Repairing it stops the blow-up, but the query gradient is still wrong by more than its own size, and training still drives attention logits to thousands of times their size under accurate gradients. The remaining error comes from a broken conservation law. The softmax score gradient sums to zero along every row, which makes the query gradient blind to where the keys sit as a group; rounding it to BF16 leaves a small nonzero sum that leaks the mean key into the gradient, and the leak grows exactly as late training makes keys large and attention sharp. We introduce GProj (gauge projection), which restores the zero sum after the cast with two rank-one corrections per row. It cuts the remaining median query/key gradient errors from 219%/13% to 0.34%/0.37%, on par with FP32 attention, for 4.7% more time per training step. In matched from-scratch runs it trains to the same loss as FP32 attention, while FlashAttention-3 and key smoothing both destabilize.

We study the weighted Karcher mean on the Lie group $\operatorname{UT}_n$ of upper triangular matrices with positive diagonal entries. Using a divided-difference description of analytic functions on upper triangular matrices and the Parlett recurrence, we prove the existence, uniqueness, and analyticity of the weighted Karcher mean and derive recursive formulas for its computation. The theory yields explicit expressions in low dimensions and formulas for weighted geometric means on $\operatorname{UT}_n$. More generally, the results reveal a close relationship between divided differences, analytic matrix functions, and nonlinear matrix means on triangular Lie groups.

For a finite simple graph $G = (V, E)$, we consider the quotient algebra $\mathcal M(G)$ of the polynomial ring in the variables $u_e$ (for $e \in E$) by the ideal generated by all products $u_e u_f$ for non-disjoint edges $e$ and $f$ (including all squares $u_e^2$). This quotient is called the matching algebra of $G$, since it has a basis indexed by the matchings of $G$. In this quotient, we define a subalgebra $\mathcal D(G)$ generated by the signed incidence sums $\theta_v=\sum_{e=(v,w)}u_e-\sum_{e=(w,v)}u_e$ for all $v \in V$ (where all edges of $G$ are oriented arbitrarily); we call this the Dunkl matching algebra. We show that, as a graded vector space, $\mathcal D(G)$ is dual to the span of all polynomials $p_M = \prod_{(i,j) \in M} (x_i - x_j)$, where $M$ ranges over all matchings of $G$. For the complete graph $K_n$, the latter span is a direct sum of two-row Specht modules (one in each degree); thus its Hilbert series is that of the Catalan triangle, and, in characteristic zero, the Dunkl matching algebra can be presented by linear and quadratic relations. For arbitrary graphs, we formulate the saturation problem of deciding when the selected matching Specht generators $p_M$ in a given degree $k$ span the full two-row Specht module $S^{(n-k,k)}$. We show that saturation in degree $k$ forces $k$-connectivity, that saturation in degree $2$ is equivalent to $2$-connectivity, and that $k$-linked graphs are saturated in degree $k$. We also prove saturation in every possible degree whenever the complement of $G$ is a matching. We show that the Dunkl matching algebra equals the full matching algebra exactly for forests, and give an explicit Hilbert series formula for unicyclic graphs.

We establish a parameterized Heintze–Karcher-type inequality for positive solutions of nonlinear Dirichlet problems involving the $p$-Laplacian, with $p\geq2$, on bounded Riemannian domains. Under a Ricci curvature lower bound, positive boundary mean curvature, and suitable structural and approximation assumptions, the estimate follows from a regularized Reilly-type identity and a weighted Hessian inequality. We compare the resulting bound with previous estimates and investigate the geometry associated with equality. For $p=2$, an exact deficit identity allows the pointwise condition $f'\leq nk$ to be replaced by a weighted integral condition. As applications, we obtain Heintze–Karcher and Soap Bubble-type rigidity results, with equality forcing the domain to be a metric ball and the solution to be radial.

We establish an exact correspondence between renormalization-group (RG) evolution and real-time quantum dynamics in the anisotropic current-current perturbed $SU(2)_1$ Wess-Zumino-Witten (WZW) model. Using its integrable fermionic representation, we construct the exact time-dependent many-body wavefunction by means of the generalized Bethe ansatz and show that periodic boundary conditions lead to quantum Knizhnik-Zamolodchikov equations associated with the XXZ trigonometric $R$-matrix, which corresponds to the quantum affine algebra $\mathcal{U}_q(\widehat{\mathfrak{sl}_2})$ evaluated in its spin-$1/2$ evaluation representation. Consistency of these equations constrains the temporal evolution of the longitudinal and transverse interaction strengths. In the universal regime, these integrability conditions are exactly equivalent to the Berezinskii-Kosterlitz-Thouless RG flow equations upon identifying physical time with the logarithmic RG scale. Consequently, the RG limit cycles of the anisotropic current-current perturbed $SU(2)_1$ WZW model are realized as periodic real-time evolution of its interaction strengths. Our results provide an exact dynamical realization of RG limit cycles and establish a direct connection between cyclic RG flows, quantum integrability, and periodically driven interacting quantum field theories.

We study boundary regularity for the fully nonlinear singular elliptic equation of the form $|Du|^\gamma F(D^2u)=f$, where $-1<\gamma<0$. First, we prove pointwise boundary $C^{1,\alpha}$ estimates with pointwise $C^{1,\alpha}$ Dirichlet data, which weakens the $C^2$ domain smoothness requirement from Birindelli and Demengel~\cite{BD10}. The proof uses compactness and perturbation, without flattening the boundary. Second, we extend the $W^{2,\delta}$ estimates of Li and Li~\cite{LiLi17} from balls to $C^{1,\alpha}$ domains with $C^{1,\alpha}$ Dirichlet data{, for $0<\delta\le\delta_0<1$. Here $\delta_0$ is an exponent in the interior $W^{2,\delta_0}$ estimate}. The proof combines pointwise boundary $C^{1,\alpha}$ estimates, interior $W^{2,\delta_0}$ estimates, and a Whitney decomposition.

In this paper, we investigate three fundamental convexity parameters of graphs under cycle convexity, namely the Helly number, Radon number, and rank. We first study the computational complexity of these parameters. For each of these parameters, we consider the associated threshold decision problem of determining whether the parameter of a given graph is at least a prescribed integer. We establish that all three problems are $\NP$-hard and $\W[1]$-hard when parameterized by the threshold. Moreover, we strengthen these results by showing that the $\NP$-hardness persists even when the input is restricted to planar graphs of maximum degree at most $6$. We also focus on the structural properties of connected graphs corresponding to extremal values of these parameters. In particular, we characterize the graph classes for which the three parameters attain the values $n-1$ and $n-2$, where $n$ is the order of $G$.

We give an alternative variational proof that non-signaling correlations shared between the sender and receiver cannot increase the Rényi capacity of a classical channel. This monotonicity follows from the data-processing inequality for channel divergences established by Oufkir, Yao and Berta. Our approach works directly with the information-radius representation of the capacity. The constraints defining non-signaling boxes are linear in the box, while the Rényi capacity depends on the channel nonlinearly. We use a Legendre transform to express the capacity as an extremum over a family of affine functionals in which the channel appears linearly. Combining this method with separate arguments for the Shannon capacity and endpoint orders, we recover monotonicity at every order from zero to infinity. We then derive consequences for standard coding exponent functions: non-signaling simulation cannot increase the random-coding or sphere-packing exponent below capacity, or decrease the strong-converse exponent above capacity.

Transcatheter edge-to-edge repair (TEER) treats mitral regurgitation with a clip that grasps and coapts the mitral valve (MV) leaflets. Predicting its outcome requires resolving the interaction between the blood flow and the complete MV apparatus, in which highly deformable leaflets and chordae tendineae undergo large deformation and complex contact. In this work, we introduce an immersogeometric fluid-structure interaction (FSI) formulation in which the coupling among the valvular components, the surrounding flow, and the repair is enforced weakly, without requiring conforming discretizations. A fully parameterized reconstruction pipeline is developed to convert segmented images into analysis-ready isogeometric meshes. The leaflets and chordae are modeled as Kirchhoff-Love shells and cables, and the shell formulation is modified to include an in-vivo prestrain tensor that accounts for the loading already present in the imaged geometry. We propose a subcell approach that improves the quadrature for the interfacial constraint and eliminates the over-refinement otherwise needed to prevent unphysical leakage through the leaflets. The MV repair is modeled by a penalty formulation that enforces leaflet coaptation over a prescribed region, allowing clip number and placement to be varied without remeshing or modeling the device. A lumped-parameter model at the outflow tract of the left heart imposes a flow-dependent pressure rather than a prescribed waveform, so that the split between forward and regurgitant flow matches physiological behavior. We validate the reconstruction against the segmented imaging data, demonstrate mesh convergence of the proposed formulations, and apply the framework to a prolapsed MV and its single- and double-clip repairs to quantify pre- and post-operative changes in valve dynamics and left-heart hemodynamics.

For $1<p<2$ we prove weak and strong unique continuation properties for the solutions to $\Delta_pu+f(u)=0$ in a planar domain, with $f$ continuous and such that $|f(s)|\leq C|s|^{p-1}$: a solution vanishing on an open set vanishes identically, and so does a solution vanishing to infinite order at a single point. We can therefore make progress towards a proof of some long-standing nonexistence results available only for $p=2$, such as a celebrated one of Esteban and Lions, establishing here that for the critical $p$-Laplace equation with zero Dirichlet boundary condition on a half-plane, there are no nontrivial finite energy solutions. In the plane, unique continuation thus provides the missing ingredient for a generalisation to the $p$-Laplacian operator of a classical result of Struwe on the bubble-profile decomposition of possibly sign-changing Palais-Smale sequences associated to the Brezis-Nirenberg problem, which remains open in dimension $N\geq3$ for $p\neq2$. We obtain a characterisation of their loss of compactness in terms of the finite energy solutions of $\Delta_pu+|u|^{p^*-2}u=0$ in $\mathbb R^2$.

We introduce the notion of a quasi-cellular category, which is a variant of cellular algebras and cellular categories. Many diagram categories, such as the Brauer category and the partition category, are quasi-cellular. We study the Brauer category $B$ over a commutative ring $\mathbb k$ with parameter $\delta$ through its quasi-cellular structure. We construct a linear functor $L:B\to mB$ to the matrix Brauer category $mB$, which has the same objects and hom-spaces as $B$ but a matrix-like composition. For a non-singular parameter $\delta$, we show that $L$ is an isomorphism of linear categories, and that $B$ is Morita equivalent to its subcategory $\mathbb S$ spanned by permutations, whose endomorphism algebras are the group algebras of symmetric groups. These results generalize those of Brown and König and Xi for the Brauer algebras.

We prove nonlinear stability of positive nonconstant steady states for a chemotaxis–consumption system with nutrient-dependent motility and a fixed boundary nutrient concentration $b$. The motility satisfies two monotonicity conditions met by power laws and saturating functions. For each prescribed population mass, sufficiently large nutrient diffusivity $D$ yields a unique positive steady state. Small compatible perturbations generate unique global solutions converging exponentially to the steady state with their conserved mass. The result holds on general bounded domains in two and three dimensions, with a lower-regularity version on an interval. At order $D^{-1}$, the nutrient deficit is the torsion function multiplied by the mean population density and boundary nutrient value; the population contrast also depends on $\kappa_b=b\phi'(b)/\phi(b)$. Computations illustrate relaxation from compatible data and convergence to different steady states when the initial mass changes.

Nakayama found the way to limit the contexts in the Döring-Isham topos quantum theory and unified such limitation and the definition of probability as a single choice of topology. We explain how this unification supports the Bayesian perspective where the probability changes as data is obtained. We describe this change as a local shift of the applicability of a predictive theory and apply it to quantum computing.

The concept of tensor eigenpairs has attracted increasing research attention in the past decades. Recent works have focused on a special class termed regular simplex tensors, which are constructed from an equiangular tight frame of n vectors in (n-1) dimensional space for n >= 3 and order m >= 3. Existing works focus on analyzing the robustness of eigenpairs obtained by the tensor power method. At the end of that work, a conjecture was made that if they exist, the only robust eigenvectors of a regular simplex tensor, up to sign equivalence, are the vectors in the regular simplex frame. A subsequent study theoretically proved that this holds for the simplest triangle case where n = 3. However, for cases with higher n, the process becomes complicated in both checking all eigenpairs and determining the explicit formula for the robustness criterion. In this paper, to deal with this issue, a connection between robust and locally optimal eigenpairs is built, recognizing the latter as another pivotal concept in the field of optimization. Then, we turn to checking the local optimality of all eigenpairs, for which we have developed an efficient model with a favorable structure that facilitates the enumeration of all eigenpairs and delineates the optimization landscape for the model. Then, integrating the two advances enables us to narrow the scope of the robust eigenpairs to those locally maximized ones, which are exactly the vectors in the regular simplex frame. Finally, the proof of the conjecture reduces to only examining the vectors in the frame, whose robustness can be easily checked for any higher n and m. This work shows that, up to sign equivalence, excluding the exceptional cases (m, n) = (3, 3)/(3, 4)/(4, 3) where no robust eigenpairs exist, the only robust eigenvectors of a regular simplex tensor are the vectors in the regular simplex frame.

Keller's strong form of the homological conjectures asserts: any finite dimensional algebra cogenerates its unbounded derived category of modules. Here we record an example of a (coconnective) dg algebra with finite dimensional cohomology, which does not cogenerate its module category, along with some related phenomena: a smooth dg category whose dualizing bimodule fails to be nondegenerate, and a nontrivial fully faithful left Calabi-Yau morphism. All examples and most proofs were produced by ChatGPT. In an appendix we explain the reason we were looking for such examples: their existence would follow from the existence of Weinstein symplectic manifolds which failed to satisfy Arnol'd's chord conjecture.

Large Reasoning Models (LRMs) have shown remarkable reasoning capabilities, yet they still suffer from overthinking, generating redundant reasoning steps which incur substantial token consumption. Existing methods, such as suppressing reflective keywords or forcing shorter reasoning lengths, attempt to mitigate this issue but inevitably truncate necessary steps and induce underthinking, thereby compromising performance. To address this dilemma, we investigate the latent representations and observe that efficient reasoning steps naturally cluster into a concentrated region in latent space, while those deviating from this region tend to produce verbose sequences. To leverage this, we keep reasoning focused within this region via a quadratic program which projects deviating hidden states back into the region. Then we propose a novel training-free framework to achieve efficient reasoning that reduces token generation costs without sacrificing performance. Extensive experiments conducted on four models ranging from 1.5B to 14B, and across six benchmarks in math reasoning, coding, and scientific QA, validate the effectiveness of our method, up to a 12.1% improvement in accuracy while reducing generated tokens by 11.8% to 52.8%. Codes are available at \href{https://github.com/hzn18/Opt4Reasoning}{https://github.com/hzn18/Opt4Reasoning}.

We study quantitative mixing and deformation of sets and curves for a class of two-dimensional autonomous Hamiltonian flows with finitely many critical points satisfying local conditions that allow finite-order degeneracy. Variation of the period across neighboring trajectories generates transverse shear, providing a common mechanism for scalar mixing, set deformation, and curve stretching. First, for $H^1$ initial data supported away from equilibria and infinite-period trajectories, in regions where the period gradient is bounded away from zero, we establish sharp $(1+t)^{-1}$ decay in $H^{-1}$ towards the time average of the initial data along each periodic trajectory. Second, under the same geometric conditions, we prove matching upper and lower bounds of order $(1+t)^{-1}$ for an orbit-relative geometric mixing scale of transported Lipschitz subdomains whose closures are not invariant under the flow. This scale measures how closely the transported subdomain covers the union of trajectories meeting its initial position. Third, for Lipschitz curves separated from infinite-period trajectories, we derive an explicit first-order large-time expansion of their length with a remainder bounded uniformly in time. In particular, their length grows at most linearly. Counterexamples illustrate how the stated conclusions can fail when selected nondegeneracy or separation assumptions are removed. The analysis combines coordinates adapted to the periodic trajectories with quantitative estimates and asymptotic expansions for the flow Jacobian. Numerical simulations for cellular and radial flows illustrate the functional and geometric mixing rates and the evolution of curve length.

Let $\pi$ be an irreducible generic representation of $\operatorname{GL}_n(\mathbb{C})$. We classify the exceptional poles of the associated Asai $L$-function $L(s,\pi,\mathrm{As})$ and give a representation-theoretic characterization of them. When $\pi$ is in general position, we further express $L(s,\pi,\mathrm{As})$ in terms of the exceptional $L$-factors attached to the irreducible constituents occurring in the derivatives of $\pi$.

We study properties of Extremally Ricci-Pinched (ERP) $G_2$-Structures on compact $7$-manifolds. We provide characterizations of the ERP condition in terms of the traceless $\ast$-Ricci tensor, the Hodge Laplacian of the torsion, the Ricci eigenvalues, the Laplacian flow, and the $27$-dimensional Weyl curvature component. We also prove that the automorphism group of a compact ERP $G_2$-Structure is finite. We then identify perturbations of ERP $G_2$-Structures, both in general and for specific examples, that preserve the ERP condition.

Assume that smooth metrics $g_k$ converge in measure to a smooth metric $g$. Gromov asked the following question: if all the metrics $g_k$ have non-negative scalar curvature does it follow that $g$ also has non-negative scalar curvature? We answer Gromov's question in dimension three. We show that, without further assumptions, the metric $g$ need not have non-negative scalar curvature and in fact $g$ may be completely arbitrary. However, if one assumes in addition that the identity maps $(M,g_k)\to (M,g)$ are uniformly bi-Lipschitz, then indeed $g$ must have non-negative scalar curvature. Our method of proof can also be used to show that, under an almost Euclidean entropy condition, scalar curvature lower bounds will persist under convergence of the metrics together with their inverses in $L^p$ for suitably large $p$.