The arboricity of a graph is the minimum number of forests needed to partition its edges. We present a randomized algorithm that computes the exact arboricity of a simple graph and constructs an optimal forest partition in $\min\{n^{2+o(1)},m^{4/3+o(1)}\}$ time both in expectation and with high probability, where $n$ a...
Iman Gholami, M. Hajiaghayi, Mahdi JafariRaviz et al.· 0 citations
Kearns, Roth, and Ryu show that the output agent's error approaches the global predictor's error along sufficiently deep paths with suitable feature coverage, while insufficient depth can prevent aggregation even in large networks.
M. Bateni, Z. Hadizadeh, Mohammadtaghi Hajiaghayi et al.· 0 citations
Graph Neural Networks (GNNs) are widely used for representation learning on graphs, but most methods assume static topologies, making them inefficient on evolving networks where edges change over time. Existing dynamic approaches either model graph evolution through temporal GNN architectures without focusing on effici...
Kiarash Banihashem, Mohammadtaghi Hajiaghayi, Mahdi JafariRaviz et al.· 0 citations
Building on the pioneering paper of Kearns, Roth, and Ryu (SODA'26), we study information aggregation in a networked learning model. The model captures a central pattern in agentic AI: each agent sees only part of the data and passes on only its own conclusion. Their model considers a linear regression problem with the...
M. Bateni, Z. Hadizadeh, Mohammadtaghi Hajiaghayi et al.· 1 citation
This work introduces *Theo*, an agentic autoformalization framework powered by general coding LLMs, and successfully formalizes their main theorems and proofs and validate the generated formalizations with human experts.
Arshia Soltani Moakhar, Iman Gholami, Max Springer et al.· arXiv.org· 2 citations
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