AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Multimodal large language models (MLLMs) often struggle to identify and use geometric relations in diagrams. Recent methods address this challenge by converting geometric entities, relations, and constraints into explicit textual representations for the model to reason over. However, effective formalization is highly n...
This work introduces AndroidReality, a perturbation-based framework for evaluating and improving the robustness of mobile agents, and builds a perturbed mobile benchmark on top of AndroidWorld with realistic and controllable perturbation injections, enabling systematic robustness evaluation of mobile agents.
Sim2Signal is presented, a benchmark that decomposes the Sim-to-Real gap into observation, action, transition, and reward gaps, corresponding to mismatches in the four components of the underlying MDP, and induces each gap in isolation under a shared protocol.
MASkills presents a new agent-optimization pipeline that integrates skill-conditioned credit assignment, hierarchical credit aggregation, and momentum-smoothed optimization, enabling agent skill libraries to evolve through refinement, induction, consolidation, and pruning.
An agenda for a shared MDP vocabulary, uncertainty-aware oversight, and scaling human-AI collaboration across volume, complexity, and expertise is opened.
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