Small Models Scout Bottleneck Order for Large-Model Data Control

Small proxy models are commonly used to identify data mixtures for larger-scale training. We ask whether their training trajectories reveal another transferable structure: the order in which larger models should resolve skill bottlenecks. We formulate first-passage skill training, where each monitored skill has a target floor and the objective is to minimize the tokens required to reach all floors. We introduce LogFloor, a closed-loop controller that directs each round toward current bottlenecks, producing phase-ordered resolution trajectories. Across five bAbI skill slices on Qwen2.5-1.5B, LogFloor reduces token cost by 56.2% on average. In 70M-to-12B transfer, three-round replay of a 70M scout path reaches every floor in all eight target runs, saving 30.9% by pair mean, 39.4% in pooled training tokens, and 37.6% under source-cost accounting. On MMLU-control, a frozen scout path succeeds across all eight 12B runs. Collapsing a path to its static marginal mixture or reversing its phase order removes most benefits, while bottleneck labels alone remain partially useful. These results identify phase-ordered bottleneck resolution as a transferable curriculum structure for monitored skill-targeted training.

Power-Constrained and Quantized MIMO-RSMA Systems with Imperfect CSIT: Joint Precoding, Antenna Selection, and Power Control

To utilize the full potential of the available power at a base station (BS), we propose a joint precoding, antenna selection, and transmit power control algorithm for a total power budget at the BS. We formulate a sum spectral efficiency (SE) maximization problem for downlink multi-user multiple-input multiple-output (MIMO) rate-splitting multiple access (RSMA) systems with arbitrary-resolution digital-to-analog converters (DACs). We reformulate the problem by defining the ergodic sum SE using the conditional average rate approach to handle imperfect channel state information at the transmitter (CSIT), and by using approximation techniques to make the problem more tractable. Then, we decompose the problem into precoding direction and power control subproblems. We solve the precoding direction subproblem by identifying a superior Lagrangian stationary point, and the power control subproblem using gradient descent. We also propose a complexity-reduction approach that is more suitable for massive MIMO systems. Simulation results not only validate the proposed algorithm but also reveal that when utilizing the full potential of the power budget at the BS, medium-resolution DACs with 8-11 bits may actually be more power-efficient than low-resolution DACs.

A New Interpretation of the Time-Interleaved ADC Mismatch Problem: A Tracking-Based Hybrid Calibration Approach

Time-interleaved ADCs (TI-ADCs) achieve high sampling rates by interleaving multiple sub-ADCs in parallel. Mismatch errors between the sub-ADCs, however, can significantly degrade the signal quality, which is a main performance bottleneck. This paper presents a hybrid calibration approach by interpreting the mismatch problem as a tracking problem, and uses the extended Kalman filter for online estimation and compensation of the mismatch errors. After estimation, the desired signal is reconstructed using a truncated fractional delay filter and a high-pass filter. Simulations demonstrate that our algorithm substantially outperforms the existing hybrid calibration method in both mismatch estimation and compensation.