The paper introduces MetaModelSelect, a lightweight post‑hoc confidence metamodel that improves selective next‑location prediction for mobile networks. It uses only the outputs of a frozen recurrent neural network—specifically a learned embedding of the predicted location, the top four softmax probabilities, and Softmax Concentration—to determine whether the top‑1 prediction is correct, without retraining the backbone. Experiments on Changchun cellular mobility trajectories show that MetaModelSelect reduces risk–coverage metrics (AURC, E‑AURC, AUGRC) by up to 4.2% compared to maximum softmax probability and achieves the lowest mean risk across all partial coverages.
arXiv:2606. 05130v1 Announce Type: cross Abstract: Individual-level mobility prediction is central to urban simulation, transportation planning, and policy analysis.
MetaSE is an active ensemble framework that selects a small set of reliable models for mobile sensing tasks, reducing computational cost while maintaining accuracy. It leverages short-term persistence in model reliability, uses post-execution evidence to prune unreliable members, and only triggers lightweight routing when replacements are needed. Experiments on four human activity recognition datasets and four model architectures show MetaSE outperforms a fixed three-model ensemble and matches the accuracy of more expensive adaptive and full-ensemble approaches, achieving 2.7× speedup and 69% memory savings on a Raspberry Pi 4B.
arXiv:2607. 15552v1 Announce Type: cross Abstract: Generating personalized trip itineraries is a complex planning task and involves a tension between hard combinatorial feasibility and soft latent desirability.
arXiv:2610.17584v1 Announce Type: new Abstract: Operational mobile networks are rich in observations but poor in interventions: they continuously generate large volumes of passive telemetry, while co...
arXiv:2410. 05662v4 Announce Type: replace Abstract: Most federated learning (FL) approaches assume a fixed device set.
BER-PEF is a Bayes‑error‑rate‑based framework that transforms BER estimation into human mobility predictability estimation, enabling comparison of different estimators even when ground truth predictability is not observable. It maps various data types—symbolic sequences, numeric trajectories, contextual features, and learned representations—into a shared feature–label space and evaluates estimator outputs along controlled perturbation curves against a common predictability reference interval. Experiments on datasets such as Foursquare NYC/TKY, GeoLife, and T‑Drive show that several BER‑based estimators outperform existing methods on symbolic sequences and numeric trajectories, and that aggregating evidence across multiple perturbation levels yields a more reliable basis for selecting estimators.
MetaLearnNCA introduces a decentralized few‑shot meta‑learning framework that uses interacting Neural Cellular Automata (NCAs) to adapt to new tasks without backpropagation. The method splits adaptation into an Active‑NCA that performs task inference on a 2D spatial memory grid, and a learned Meta‑NCA that diffuses error residuals locally to update this grid. Experiments show competitive in‑distribution performance (96.12% on Omniglot) and superior out‑of‑distribution transfer on MNIST, KMNIST, and Fashion‑MNIST compared to canonical meta‑learners.
arXiv:2608. 12892v1 Announce Type: new Abstract: Activation steering turns localized representations into control directions, but localization alone does not reveal whether a direction has a selective operating regime.
arXiv:2606. 26424v1 Announce Type: new Abstract: Trajectory forecasting for autonomous driving has advanced rapidly, yet representative models often produce uninformative posteriors over forecast modes, causing problems for mode pruning.
arXiv:2609.00284v1 Announce Type: cross Abstract: Emerging 6G wireless networks are expected to operate across diverse deployment scenarios, where variations in network topology, user mobility, traff...
Activation steering turns localized representations into control directions, but localization alone does not reveal whether a direction has a selective operating regime. We introduce Predictive Memory Localization (PML), which treats the measured-grid intervention path as the predictive object of memory localization.
arXiv:2607. 09798v1 Announce Type: cross Abstract: Sixth-generation (6G) networks are moving toward AI-native operation, where learning modules are embedded across the radio access network (RAN), edge, and core.