arXiv AI July 24, 2026 By Yueying Cui, Renhao Xue, Yi Zhang, Mukul Prasad

From Errors to Rules: Iterative Prompt Optimization for Text Classification

arXiv:2607. 20497v1 Announce Type: new Abstract: Prompt optimization for text classification spans diverse approaches, from demonstration selection to exploration-based search to error-driven diagnosis, each with known but incompletely characterized strengths and limitations.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Sep 15

Failure-Guided Co-Evolution of Prompts and Training Data

arXiv:2609.15209v1 Announce Type: cross Abstract: Automatic prompt optimization (APO) improves language-model programs by revising prompts from task feedback, yet it typically holds its training data...

By Tianyu Yuan, Zhuzhong Qian

benchmarks

arXiv AI

Aug 21

Task-CoEvolve: Efficient Harness Optimization via Adaptive Validation Task Selection

arXiv:2608. 20169v1 Announce Type: cross Abstract: We present a novel approach to efficient LLM agent harness optimization through adaptive validation task selection.

By Atsuyuki Miyai, Kiyoharu Aizawa, Toshihiko Yamasaki

llms agents nlp

arXiv AI

Jul 28

DocHRL: A Hierarchical Reinforcement Learning Framework for Cost-Optimised Document Classification

arXiv:2607. 22644v1 Announce Type: new Abstract: Real-world document classification pipelines typically apply the same sequence of models to every incoming document, regardless of its complexity or type.

By Mohammed Yousif, Prabhjot Singh, Arjun Pankajakshan, Madhu Reddiboina

llms computer-vision reinforcement-learning benchmarks

arXiv AI

Aug 10

SkillProx: Self-Evolving Agent Skills via Proximal Textual Gradient Descent

arXiv:2608. 07449v1 Announce Type: new Abstract: LLM agents increasingly adapt to recurring tasks by accumulating procedural knowledge in skills.

By Mingxuan Zheng, Yujin Zhou, Chuxue Cao, Boqin Yin, Yuyao Zhang, Jiapeng Sun, Shuaishuai Gong, Sirui Han, Yike Guo

llms agents benchmarks

arXiv AI

Jun 9

Knowing How to Edit: Reliable Evaluation Signals for Diagnosing and Optimizing Prompts at Query Level

arXiv:2511. 19829v3 Announce Type: replace Abstract: Prompt optimization has become a central mechanism for eliciting strong performance from LLMs, and recent work has made substantial progress by proposing diverse prompt evaluation metrics and optimization strategies.

By Ke Chen, Yifeng Wang, Hassan Almosapeeh, Haohan Wang

llms benchmarks

arXiv AI

Aug 12

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models

arXiv:2608. 10471v1 Announce Type: new Abstract: Prompt optimizers automate the search for prompts that improve language-model performance, but existing methods rely on a predefined optimization procedure: the algorithm determines which candidates to explore and how the search progresses, while the language model generates or refines prompt proposals.

By Subhash Bangalore Satheesha, Nirvik Pande, Deepthi Duddempudi, Bharath Dandala

llms agents nlp benchmarks

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Related stories

Failure-Guided Co-Evolution of Prompts and Training Data

Task-CoEvolve: Efficient Harness Optimization via Adaptive Validation Task Selection

DocHRL: A Hierarchical Reinforcement Learning Framework for Cost-Optimised Document Classification

SkillProx: Self-Evolving Agent Skills via Proximal Textual Gradient Descent

Knowing How to Edit: Reliable Evaluation Signals for Diagnosing and Optimizing Prompts at Query Level

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models