ClusterFewshot is a new strategy for selecting few‑shot demonstrations in large language model workflows. It combines semantic structuring with utility‑aware scoring to build representative demonstration sets, improving accuracy over prior bootstrap‑based methods. In DSPy‑based pipelines, it substantially reduces optimization cost across multiple benchmarks while consistently outperforming earlier approaches in both standalone prompt tuning and hybrid prompt‑weight optimization.
arXiv:2506.17251v3 Announce Type: replace-cross Abstract: Although large language models (LLMs) have achieved remarkable performance, the inherent stochasticity of their reasoning processes and varyi...
arXiv:2606. 30704v1 Announce Type: cross Abstract: Large language models (LLMs) excel across a wide range of tasks, yet their instance-specific solutions often lack the structural consistency needed for reliable deployment.
Frontier large language models (LLMs) are examined as batch optimizers in both continuous and discrete settings. The study finds that while LLMs perform competitively in zero‑shot optimization of numerical test functions, their performance is less robust than classical non‑LLM methods. However, LLMs excel in semantically rich, discrete spaces that resemble their pretraining data, demonstrating strong batch optimization behavior in such contexts.
InternBootcamp is an open‑source framework that offers over 1,000 domain‑diverse task environments for large language model (LLM) reasoning research. It introduces Bootcamp‑Eval, an automatically generated benchmark for comprehensive performance assessment. Experiments show that training on InternBootcamp significantly improves reasoning performance, with a 32B model achieving state‑of‑the‑art results on Bootcamp‑Eval and other established benchmarks, demonstrating that scaling the number of training tasks yields consistent gains.
arXiv:2506. 00400v4 Announce Type: replace-cross Abstract: LLM-based prompt optimization, which uses LLM-provided ``textual gradients'' (feedback) to refine prompts, has emerged as an effective method for automatic prompt engineering.
The paper introduces SWE-Flux, a repository‑level benchmark designed to test large language models’ ability to reason about runtime behavior. It contains 480 execution‑grounded instances from 12 real Python repositories, with gold answers automatically harvested from instrumented test executions. Evaluation of five LLMs shows the task remains difficult, with the best model achieving only 37% accuracy, and the benchmark can generate challenging variants through input perturbation.
arXiv:2601. 03808v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have achieved notable performance in code synthesis; however, data-aware augmentation remains a limiting factor, handled via heuristic design or brute-force approaches.
arXiv:2606. 11209v1 Announce Type: cross Abstract: Visual question answering increasingly requires multi-step reasoning.
arXiv:2608.30426v1 Announce Type: new Abstract: Current dialogue systems struggle with dynamic information retrieval, often leading to hallucinations and lower response accuracy. We address this by a...
arXiv:2609.39927v1 Announce Type: new Abstract: Prompt optimization improves the performance of language-model systems on downstream tasks by refining their prompts. Classical methods evaluate prompt...
OptSkills is an archetype‑centric agent that learns and reasons about optimization problems using large language models. It clusters problems by underlying archetypes, explores diverse modeling and solver configurations within each cluster, and distills successful trajectories into reusable workflow‑level skills. The system achieves state‑of‑the‑art accuracy on multiple datasets, outperforming prior methods on challenging benchmarks such as MIPLIB‑NL and OOD NLCO.
arXiv:2512. 14332v2 Announce Type: replace-cross Abstract: The field of Language Reasoning Models (LRMs) has been very active over the past few years with advances in training and inference techniques enabling LRMs to reason longer, and more accurately.