The paper introduces “ℝD_{CF5}”, a probe‑based estimator that predicts the region‑wise gain of a dynamic ensemble over the best static blend in regression tasks under distribution shift. Across 12 benchmark dataset‑shift pairs, the estimator achieves a Spearman correlation of +0.98 with actual test gains, outperforming alternative diagnostics. The authors also present a Probe‑Validated Ensemble Selector that chooses between a static affine stacker and dynamic realizers, demonstrating risk reductions of up to 16% in prospective deployments.
By Tianxin Zhou, Ruixi Lin
A stable compression score can still select the worse model. In our dense study, a split-half reliable path-quadratic score predicted a 16.
ESPO (Error-Structured Prompt Optimization) addresses prompt bloat in evolutionary prompt optimizers by splitting the optimization process into Diagnose, Propose, and Select phases. It clusters training errors into structural patterns, generates diverse candidate prompts through four complementary strategies, and applies bootstrap stability selection. Across seven NLP benchmarks, ESPO improves average accuracy by +3.76 pp over GEPA, produces prompts 47 % shorter, and achieves higher accuracy on four additional student models, with the largest gain on Qwen3 GSM8K.
By Lihao Liu, Peng Tang, Kunwar Yashraj Singh, Shabnam Ghadar
arXiv:2607. 24562v1 Announce Type: new Abstract: Large language models serve heterogeneous populations structured by domain, topic difficulty, and linguistic style.
By Murilo Salem, Lu\'isa B\"ohm, Daniel Pontes, Anderson Ferrugem
arXiv:2606. 17649v1 Announce Type: cross Abstract: The high cost of fine-tuning LLMs poses a significant economic barrier; pre-hoc performance prediction offers a critical solution to substantially reduce this expense.
By Yuxiang Luo, Chen Wang, Nan Tang
arXiv:2608.23601v1 Announce Type: cross
Abstract: EDA flow parameter tuning is critical for quality-of-results~(QoR), yet the parameter space is large, tightly coupled, and full evaluations are prohi...
By Kunlong Li, Shangshang Yao, Su Zheng, Lingli Wang