Behnam Ojaghi | Search arXiv

arXiv2026

From Intents to Algorithms: Verified Algorithm Discovery for Transport Networks

Intent-based networking decouples desired outcomes from device-level configuration, but most systems still map intents to parameters of an algorithm selected in advance. Large language models (LLMs) create an opportunity to automate algorithm design, yet unrestricted generated code is unsuitable for transport-network control because feasibility, reproducibility, and robustness must be enforced independently of the model. We present VERA-TN, a verification-guided framework that compiles a network intent into a bounded algorithm-design specification. The target architecture uses an LLM as a semantic variation operator over typed request-ordering and path-ranking programs; generated logic remains separated from a trusted allocator that enforces path validity, latency, capacity, and single-path constraints. We prove feasibility preservation under explicit assumptions and establish a sufficient bound for the lexicographic latency tie-break in the exact reference model. The released proof-of-concept instantiates the same interface with a bounded ten-parameter numerical candidate and deterministic replay, rather than a completed live-LLM/AST study. Across 150 certified held-out cases on a 28-node TEFNET24-derived hierarchy, evolutionary search reaches a mean priority-utility ratio of 0.958, compared with 0.952 for equal-budget random search and 0.940 for priority-greedy routing. The gain over random search is small but statistically detectable (Holm- adjusted p = 0.0083). The candidate does not improve congestion relative to MILP-C, and the effect of failure-aware training is inconclusive at the 0.05 level (p = 0.051). Eight discovery runs on the official national topology and replay on 12 unseen metro-regional topologies show no stable intent-specific specialization. These results support the trust-boundary and numerical-evolution claims but do not establish a benefit from LLM generation.

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arXiv2023

A supervised active learning method for identifying critical nodes in Wireless Sensor Network

Energy Efficiency of a wireless sensor network (WSN) relies on its main characteristics, including hop-number, user's location, allocated power, and relay. Identifying nodes, which have more impact on these characteristics, is, however, subject to a substantial computational overhead and energy consumption. In this paper, we proposed an active learning approach to address the computational overhead of identifying critical nodes in a WSN. The proposed approach can overcome biasing in identifying non-critical nodes and needs much less effort in fine-tuning to adapt to the dynamic nature of WSN. This method benefits from the cooperation of clustering and classification modules to iteratively decrease the required number of data in a typical supervised learning scenario and to increase the accuracy in the presence of uninformative examples, i.e., non-critical nodes. Experiments show that the proposed method has more flexibility, compared to the state-of-the-art, to be employed in large scale WSN environments, the fifth-generation mobile networks (5G), and massively distributed IoT (i.e., sensor networks), where it can prolong the network lifetime.

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