30
Generalized Agent Iteration: One Formal Framework for Iterative Policy Improvement and Recursive Self-Improvement
Generalized Agent Iteration formally unifies iterative policy improvement and recursive self-improvement, defining axes that distinguish anchored, goal-drifting, and self-referential agents.
The paper proposes Generalized Agent Iteration (GAI), a formal framework that models learning as a cycle of agent evaluation and agent improvement, defining the agent as a configuration of modifiable components. Two dials—whether the improving mechanism is part of the agent and whether the evaluation standard is grounded outside it—separate generalized policy iteration (GPI) from recursive self-improvement (RSI) and classify systems as anchored, goal drift, or fully self-referential. The framework places existing systems on shared axes and makes defects of recursive self-improvement statable one condition at a time.
30
A Deep Generative Model for Synthesizing Labeled Wireless Signals
Researchers propose IIns-GAN, a GAN that synthesizes realistic labeled ultra-wideband wireless signals, cutting dataset costs for wireless sensing training.
The paper introduces Inter-Instance Generative Adversarial Networks (IIns-GAN), a deep generative method that synthesizes realistic wireless signals with position-related labels to avoid costly real-world measurement and labeling. Unlike environment-model-based synthesis, the generated signals adapt to different environment scenarios and support training tasks such as distance estimation and environment identification. Experiments on public Ultra-Wideband (UWB) datasets show the synthetic signals closely mirror real measurements and improve model training performance.
20
30