arXiv subjects

Izhar Ali

Publications and source records attributed to Izhar Ali.

3 recordsLinked to original sources

arXiv2026

SWORD: Spectral Wasserstein Online Regime Detection in Dynamic Networks

Online change point detection in dynamic graphs requires comparing graphs as they arrive, in time linear in the number of edges, without parametric assumptions. Recent spectral methods address scale via the Kernel Polynomial Method (KPM): SCPD computes Chebyshev moments of the normalized Laplacian, discretizes them into a density-of-states histogram, and scores the histogram with SVD plus cosine similarity. We introduce SWORD, which computes the same moments and instead compares their mean across two adjacent time windows by their $L_1$ distance. On three real-world benchmarks (MIT Reality, AskUbuntu, Enron), this lifts mean $F_1$ from SCPD's $0.27$ to $0.79$, with SCPD failing to detect any change on Enron. A controlled cascading ablation attributes the gap to two design choices: the two-window mean structure (dominant on MIT) and the $L_1$ metric on those mean vectors (dominant on Enron). A bin-width sweep rules out histogram discretization -- SCPD's most visible architectural choice -- as the driver. SWORD inherits SCPD's KPM core, so per-graph cost is $O(KRm)$ with no eigendecomposition, scaling to $86{,}000$-node networks. With per-dataset tuning it matches the offline TIRE autoencoder on mean $F_1$ and attains the highest precision among online methods ($0.91$, only $2$ false positives across the three benchmarks).

cs.CG↗

arXiv2026

Stochastic Sampling is Epistemically Shallow: The Dimensionality Gap Between Temperature Variation and Model Diversity in LLMs

When a language model gives different answers on repeated runs, does that variation reveal what it does not know? Self-consistency turns the variation into a per-question uncertainty estimate via majority voting. But does the same variation reveal cross-question structure -- related questions flipping together, the way a diverse ensemble does? We compare two regimes on the same questions: one model run $100$ times at $\tau=1$ versus an ensemble of $24$ LLMs run once each at $\tau=0$. A Marchenko--Pastur random-matrix test separates signal from sampling noise on both sides. Within any single model, at most one dimension rises above noise across five families and three benchmarks (MMLU, HellaSwag, GSM8K). Across the ensemble, four eigenvalues clear the noise edge, while a matched-difficulty Bernoulli null produces at most one in $500$ Monte Carlo draws. Self-consistency gives accurate per-question uncertainty but no detectable cross-question structure; only a diverse ensemble surfaces what a model does not know.

cs.AI↗

arXiv2024

Automated Extraction of Acronym-Expansion Pairs from Scientific Papers

This project addresses challenges posed by the widespread use of abbreviations and acronyms in digital texts. We propose a novel method that combines document preprocessing, regular expressions, and a large language model to identify abbreviations and map them to their corresponding expansions. The regular expressions alone are often insufficient to extract expansions, at which point our approach leverages GPT-4 to analyze the text surrounding the acronyms. By limiting the analysis to only a small portion of the surrounding text, we mitigate the risk of obtaining incorrect or multiple expansions for an acronym. There are several known challenges in processing text with acronyms, including polysemous acronyms, non-local and ambiguous acronyms. Our approach enhances the precision and efficiency of NLP techniques by addressing these issues with automated acronym identification and disambiguation. This study highlights the challenges of working with PDF files and the importance of document preprocessing. Furthermore, the results of this work show that neither regular expressions nor GPT-4 alone can perform well. Regular expressions are suitable for identifying acronyms but have limitations in finding their expansions within the paper due to a variety of formats used for expressing acronym-expansion pairs and the tendency of authors to omit expansions within the text. GPT-4, on the other hand, is an excellent tool for obtaining expansions but struggles with correctly identifying all relevant acronyms. Additionally, GPT-4 poses challenges due to its probabilistic nature, which may lead to slightly different results for the same input. Our algorithm employs preprocessing to eliminate irrelevant information from the text, regular expressions for identifying acronyms, and a large language model to help find acronym expansions to provide the most accurate and consistent results.

cs.CL↗