High-dimensional curved diffusions show abrupt convergence at a critical time.
problem Understanding abrupt convergence in high-dimensional curved diffusions.
method Functional inequalities and spectral rigidity.
result Abrupt convergence (cutoff) occurs in high dimensions, linked to spectral rigidity.
New cutoff phenomenon found for geodesic paths on hyperbolic manifolds.
problem Understanding the cutoff phenomenon for geodesic paths on hyperbolic manifolds.
method Spectral strategy and detailed spectral analysis of the spherical mean operator.
result Geodesic paths on compact hyperbolic manifolds exhibit cutoff for spatially localized initial conditions.
New transport method simplifies cutoff phenomenon for Markov processes.
problem Understanding the cutoff phenomenon for Markov processes.
method A new W-TV transport inequality combined with a parabolic regularization estimate.
result Recovery and extension of previous results on cutoff phenomena.
Study shows long-term existence of IMCF on non-compact graphs in hyperbolic space.
problem Long-term existence of IMCF on non-compact graphs in hyperbolic space.
method Investigation of IMCF on bounded graphs over horospheres, use of cutoff functions, and development of a non-compact ODE maximum principle.
result Long time existence of IMCF on non-compact graphs in hyperbolic space.
LLMs can memorize economic data and recall exact values before their training cutoff.
problem Evaluating the trustworthiness of LLMs' economic forecasts during their training period.
method Demonstrated through counterfactual forecasting and analysis of LLMs' recall ability.
result LLMs have memorized economic and financial data, leading to recall-level accuracy before their knowledge cutoff.
The paper examines distances and random walks on hyperbolic surfaces.
problem Analyzing distances and random walks on hyperbolic surfaces.
method Utilizing density theorems of exceptional eigenvalues and algebraic group representations.
result The distances on hyperbolic surfaces are highly concentrated around the minimal value, and the discrete random walk exhibits cutoff.
Study finds the cutoff for exact recovery in Gaussian mixture models.
problem Determining the separation of cluster centers for exact recovery in Gaussian mixture models.
method Used information theory and SDP relaxation of K-means clustering. result Sharp threshold for exact recovery of cluster labels without assuming cluster center symmetry.
New method bounds graph curvature with exceptions.
problem Global curvature bounds with no exceptions.
method Perpetual cutoff method.
result Sharp upper bounds on distance to exception sets.
New method detects and adjusts for temporal leakage in LLM backtests.
problem Standard backtest leakage detection is ineffective for modern models.
method Developed new methods to measure and adjust for temporal leakage.
result Demonstrated that models legitimately know more about times near their cutoffs, leading to structural leakage.
Non-negative curvature affects Markov chains' mixing and expansion properties.
problem Understanding the behavior of Markov chains with non-negative curvature.
method Analyzing conductance, displacement, and cutoff phenomenon in sparse Markov chains.
result Non-negatively curved Markov chains exhibit specific, non-standard behavior in terms of mixing and expansion.
New algorithm boosts deep learning training speed.
problem Efficiently train deep neural networks on large clusters.
method Synchronous distributed SGD with amortized inference model.
result Dynamic cutoff improves convergence and training time.
We show that Chern-Simons gauge theory with appropriate cutoffs is equivalent, term by term in perturbation theory, to a Fermionic theory with a nonlocal interaction term. When an additional cutoff is placed on the Fermi fields, this Fermionic theory gives rise to a convergent perturbation expansion. This leads us to c…
The CGMY model's ATM call-price asymptotics are derived using characteristic function.
problem Deriving short-time asymptotics for the CGMY model's ATM call prices.
method Using the characteristic function, derived short-time asymptotics for the CGMY model's ATM call prices. Extracted higher-order coefficients by dynamic cutoff partitioning.
result Higher-order coefficients are derived for the CGMY model's ATM call prices.
Paper generalizes paracomposition and change of variables for paradifferential operators.
problem Generalizing paracomposition and change of variables for paradifferential operators in low regularity settings.
method Drops diffeomorphism hypothesis, estimates in Sobolev and Zygmund spaces, discusses pull-back of pseudodifferential and paradifferential operators.
result Sharp estimates for composition in Sobolev and Zygmund spaces, change of variables in paradifferential operators.
We show that the Yang-Mills quantum field theory with momentum and spacetime cutoffs in four Euclidean dimensions is equivalent, term by term in an appropriately resummed perturbation theory, to a Fermionic theory with nonlocal interaction terms. When a further momentum cutoff is imposed, this Fermionic theory has a co…
DatedGPT prevents lookahead bias in financial forecasting models.
problem Lookahead bias in large language models trained on internet-scale data.
method Time-aware pretraining with annual data cutoffs and instruction fine-tuning.
result Models' knowledge is effectively bounded by their data cutoff year, improving forecasting validity.
Superstatistics with cut-off tails models financial data with fat tails and cutoffs.
problem Capturing the fat-tailed and cutoff shapes in financial time series.
method Incorporates cut-off effects into superstatistics to model financial data.
result The model accurately describes real financial time series properties.
Develops a tool to identify abnormal blood smear results based on CBC tests.
problem Manual review of blood smears by technologists is time-consuming and inconsistent.
method Cost-sensitive Lasso-penalized additive logistic regression combined with stability selection.
result The tool correctly identifies true cutoff values for abnormal smear results.
New models avoid lookahead bias by training on past data only.
problem Lookahead bias in language models.
method Chronologically consistent training on data before a knowledge-cutoff date.
result Elimination of lookahead bias in predictions.
Proposes a method to detect anomalies in financial time series using PCA and neural networks.
problem Anomalies in financial time series lead to miscalibrated risk models.
method Extract features using PCA, define anomaly score with neural network, calibrate cutoff value.
result The proposed PCA NN approach outperforms other anomaly detection methods.
Paper derives gradient estimates for porous medium equations on Riemannian manifolds.
problem Gradient estimates for porous medium equations on Riemannian manifolds.
method Employing cutoff functions and the maximum principle.
result Derives Hamilton-Souplet-Zhang type gradient estimates for porous medium type equations.
A new calibration metric bridges testability and actionability.
problem Combining testability and actionable insights for forecast probabilities.
method Cutoff Calibration Error (CCE) that assesses calibration over intervals of forecasted probabilities.
result Cutoff Calibration Error is both testable and actionable.
Bayesian model estimates treatment effects near cutoffs in regression discontinuity designs.
problem Estimating conditional average treatment effects in regression discontinuity designs.
method Develops a Bayesian additive regression tree (BART) model with linear leaf-level regressions.
result Adapts to different slopes on the running variable near the cutoff, providing interpretable inference.
Study challenges the necessity of data augmentation for improving predictions on imbalanced text datasets.
problem Improving predictions on imbalanced text datasets.
method Comparing classifier cutoff adjustments to data augmentation techniques.
result Classifier cutoff adjustments can produce similar results to data augmentation without the need for additional data.
Blockchain markets with paid-priority trading can lead to biased prices and reduced liquidity.
problem Discrete clearing and paid-priority in blockchain markets lead to biased prices and reduced liquidity.
method Developed a model to evaluate the viability of blockchain markets under discrete clearing and paid-priority.
result Paid-priority ordering induces endogenous selection, leading to biased prices and reduced liquidity.
Smooth KANs improve model reliability in computational biomedicine.
problem Limited convergence of KANs in representing generic smooth functions.
method Introducing smooth, structurally informed KANs that can approximate MLPs in specific function classes.
result Smooth KANs can achieve equivalence to MLPs in specific function classes, enhancing model reliability and performance.
The so-called Pareto-Levy or power-law distribution has been successfully used as a model to describe probabilities associated to extreme variations of worldwide stock markets indexes data and it has the form Pr(X>x) x∗∗(−alpha)forgamma<x<infinity.Theselectionofthethresholdparametergamma from empirical d…
The dynamics of generalized Lotka-Volterra systems is studied by theoretical techniques and computer simulations. These systems describe the time evolution of the wealth distribution of individuals in a society, as well as of the market values of firms in the stock market. The individual wealths or market values are gi…
In this paper we tackle the problem of estimating the power-law tail exponent of income distributions by using the Hill's estimator. A subsample semi-parametric bootstrap procedure minimising the mean squared error is used to choose the power-law cutoff value optimally. This technique is applied to personal income data…
We detect lookahead bias in LLM forecasts using a novel statistical method.
problem Detecting lookahead bias in LLM-generated economic forecasts.
method Developed a statistical procedure using date-only recall queries and estimated Lookahead Propensity (LAP).
result LLM forecasts are contaminated with lookahead bias, as indicated by a positive interaction between LAP and the forecast in accuracy regressions.
AI bias arises from human-defined goals, not algorithmic flaws.
problem AI bias due to human-defined goals in LLMs.
method Purpose-conditioned cognition and revealing downstream use of LLM outputs.
result AI bias can be reduced by purpose-aware prompting but not fully by regularization.
The paper proves a hierarchy of Liouville theorems for polyharmonic functions on manifolds with nonnegative Ricci curvature.
problem Establishing Liouville theorems for polyharmonic functions on manifolds with nonnegative Ricci curvature.
method A new L2 estimate for the Laplacian of a polyharmonic function, obtained by induction through a cutoff construction combined with a hole-filling argument. result All polyharmonic functions of sublinear growth on manifolds of nonnegative Ricci curvature are constant.
Optimal cutoff interval for risk scores improves binary classification accuracy.
problem Improving binary classification accuracy with abstention.
method Determines optimal cutoff interval for risk scores, refraining from decisions outside this interval.
result Minimizes classification margin and maximizes accuracy within the interval.
Graph neural networks fail to distinguish certain 3D atom configurations.
problem Graph neural networks (GNN) fail to distinguish certain 3D atom configurations.
method Construction of degenerate 3D atom configurations that are indistinguishable by first-order GNNs.
result First-order GNNs are incomplete for 3D atom configurations.
Study of loops in sums of Laplace eigenfunctions on surfaces.
problem Uniform bound for the number of nested loops in sums of Laplace eigenfunctions.
method Real-analytic category analysis and biharmonic function construction.
result Uniform bound for the number of rooted double nests in terms of surface, root, and spectral cutoff.
New method calibrates uncertainty estimates for image classifiers without labeled data.
problem Uncertainty estimates for modern classifiers are unreliable without labeled calibration data.
method Calibrates uncertainty estimates using unlabeled examples for distribution shifts.
result Proposes a method that provides excellent uncertainty estimates under natural distribution shifts.
Equivalence of norms on manifolds with curvature bounds established.
problem Establishing equivalence of norms on manifolds with bounded sectional curvature.
method Using spectral projector and thickness condition for subsets.
result Constant in equivalence depends only on manifold dimension, curvature bounds, and frequency threshold.
Survival trees exhibit end-cut preference, leading to biased splits.
problem End-cut preference in survival trees causes biased splits and poor interpretability.
method Proposed a smooth sigmoid surrogate (SSS) approach to replace hard-threshold indicator function.
result Smooth sigmoid surrogate (SSS) effectively mitigates end-cut preference in survival trees.
ChatGPT snapshots predict future stock returns.
problem Predicting future stock returns using pre-cutoff text.
method Extracted LLM outlook scores from OpenAI snapshots.
result Outlook scores positively correlate with future stock returns.
FinanceHarness automates financial deep research with specialized tools and benchmarks.
problem Lack of specialized financial deep research for financial analysis.
method FinanceHarness uses a layered harness and FinanceGym for specialized research questions and validation.
result FinanceHarness improves rubric scores by 7.1% and achieves 82% pass rate with expert validation.
It is shown phenomenologically that the fractional derivative ξ=Dαu of order α of a multifractal function has a power-law tail ∝∣ξ∣−p⋆ in its cumulative probability, for a suitable range of α's. The exponent is determined by the condition ζp⋆=αp⋆, where ζp is the exponent of…
We prove precompactness in an orbifold Cheeger-Gromov sense of complete gradient Ricci shrinkers with a lower bound on their entropy and a local integral Riemann bound. We do not need any pointwise curvature assumptions, volume or diameter bounds. In dimension four, under a technical assumption, we can replace the loca…
The paper provides gradient estimates for solutions to nonlinear parabolic equations under Ricci flow.
problem Gradient estimates for positive solutions to nonlinear parabolic equations under Ricci flow.
method Maximum principle and cutoff function approach.
result Gradient estimates and Harnack inequalities for positive solutions to the heat equation under Ricci flow.
Bayesian approach for estimating heterogeneous treatment effects in RDD designs.
problem Heterogeneity in treatment effects in RDD designs can lead to misleading conclusions.
method Direct Bayesian Additive Regression Trees (BART) for modeling heterogeneous treatment effects.
result Flexibly captures complicated structures of heterogeneous treatment effects as a function of covariates.
This paper proposes BAT to balance accuracy and robustness in adversarial training.
problem Balancing accuracy and robustness in adversarial training models.
method Blind adversarial training (BAT) uses a cutoff-scale strategy to adaptively estimate a nonuniform budget for AEs.
result BAT improves the overall robustness of adversarial training models.
Some general features of kinetic multi-agent models are reviewed, with particular attention to the relation between the agent saving propensities and the form of the equilibrium wealth distribution. The effect of a finite cutoff of the saving propensity distribution on the corresponding wealth distribution is studied. …
The paper decomposes unsupervised learning's generalization error into model, data, and variance components.
problem Understanding the components of unsupervised learning's generalization error.
method Information-geometric decomposition of the Kullback-Leibler generalization error.
result The optimal rank in ε-PCA is the noise floor, balancing model-error gain and data-bias cost. A new method embeds data using Gaussian processes based on the heat kernel.
problem Embedding high-dimensional data in a low-dimensional space.
method Computing embeddings based on the Karhunen-Loève expansion of the heat kernel.
result The embedding approximates diffusion distances and is robust to outliers.