arXiv:2609.31688v2 Announce Type: replace
Abstract: In verifiable domains such as math and coding, finding one correct solution among many attempts can matter more than the pass rate of each attempt....
By Eric Fithian, Kirill Skobelev, X. Y. Han
arXiv:2609.25575v1 Announce Type: cross
Abstract: Fine-tuned Large Language Models (LLMs) significantly advance Automated Theorem Proving (ATP), but are often deployed as guiding policies within tree...
By Adam Ousherovitch, Ambuj Tewari
The paper investigates how increasing inference-time computation—via wider beam search or sample‑plus‑vote—affects performance on grammar‑constrained text‑to‑SQL tasks for small language models. Using the Qwen2.5‑Instruct family (0.5B–7B parameters) on the Spider benchmark, the authors find that larger models consistently outperform higher inference compute on the same model size, and that beam search yields better accuracy than sample‑plus‑vote under matched budgets. These results suggest that, unlike unconstrained settings, scaling inference compute does not compensate for smaller model size when strict grammar constraints are applied.
By Ty Chermsirivatana, John MacCormick
arXiv:2609.38672v1 Announce Type: cross
Abstract: Beam-search-based test-time methods provide an effective way to improve large language model (LLM) performance on long-horizon generation by pruning...
By Qijia He, Yu Huang, Yuan Cheng, Yuxin Chen, Yingbin Liang
ESPO (Error-Structured Prompt Optimization) addresses prompt bloat in evolutionary prompt optimizers by separating optimization into Diagnose, Propose, and Select phases. It clusters training errors, generates diverse candidates, and applies bootstrap stability selection, achieving a 3.76‑point accuracy gain over GEPA on seven NLP benchmarks while producing 47% shorter prompts. Cross‑model tests on four additional student models confirm ESPO’s superior average accuracy, notably improving Qwen3 GSM8K from 15.00% to 91.40%.
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