Prompt Optimization

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Prompt optimization studies how to adapt instructions and other inputs so language and multimodal models perform better without necessarily changing their weights. Common approaches use textual feedback or gradients, evolutionary search, Bayesian optimization, bandits, and reinforcement learning, with validation examples guiding candidate selection. Work increasingly optimizes prompts alongside tool schemas, workflows, agent routing, and communication, across settings such as reasoning, retrieval, tool use, and image generation. Gains can depend heavily on the evaluation data and objective: prompts may overfit, break requirements they were not scored on, or trade accuracy against cost, readability, and robustness, making held-out testing and regression checks central challenges.

Latest papers

141 papers21 papers in the last four weeks, up 75% on the four weeks before. 0.1% of all new papers.

GEPA-Curator: Curated Learning for Self Improving Agents
Oct 27, 2026Zack Yu, Nicholas Lee, Minjae Lee +8LLM Agent Self-Improvement Prompt Optimization
UC Berkeley · Furiosa AI · ICSI +2
Batching the Reflective Optimization Loop: Parallel Proposals Make GEPA Faster and Better
Oct 27, 2026Jialin Zhang, Lakshya A Agrawal, Shangyin Tan +6Prompt Optimization Large Language Model-Guided Optimization
University of California, Berkeley
Intent-Preserving Adversarial User Simulation for Multi-Turn Agent Evaluation
Oct 26, 2026Atri Vivek Sharma, Brian Formento, Alessio LomuscioLLM Agent Evaluation User Simulation
Imperial College London · Safe Intelligence
CEPO: A General Framework for Improving Prompt Optimization in Multi-Agent Systems
Oct 26, 2026Jongha Kim, Seohyun Lee, Jonathan Minho Chung +2Prompt Optimization Multi-Agent System Optimization
Korea University · KAIST · Google Cloud AI Research
When Does GEPA Merge Help? A Mechanism-Level Diagnostic
Oct 25, 2026Chong Zhao, Ruyi Yang, Changming Liu +1Automatic Prompt Optimization Prompt Optimization
Harvard University, Cambridge, MA, USA.
Reproducible Evaluation as the Substrate for Safe Prompt Compression in Production Agents
Oct 22, 2026Prabal Vashisht, Zheng Hu, Ying YangPrompt Compression LLM Agent Evaluation
Mx ML, DoorDash Inc.
Training-Free Continual Learning for Multimodal Forecasting via Prompt Evolution
Oct 20, 2026Laurent Mombaerts, Jonathan Taws, Jacopo Gargano +4LLM-Based Time Series Forecasting Prompt Optimization
Amazon Web Services · University of California
Explore More, Validate Less: Efficient Reflective Prompt Optimization
Oct 19, 2026Xiaofan Zhou, Lu ChengPrompt Optimization Automatic Prompt Optimization
Pennsylvania State University, USA
FlowCred: Hierarchical Credit Assignment and Functional Coordination for Joint Text-to-Video Post-Training
Oct 13, 2026Mingxuan Zhu, Yanwei Huang, Shiyuan Liu +4T2V Generation Prompt Optimization for T2V Generation
Shanghai Jiao Tong University · KlingAI · The Hong Kong University of Science and Technology +2
From Agents to Edges: Paired Optimization for Multi-Agent Collaboration
Oct 13, 2026Feng Lin, Xiaotian Yu, Yifan Yang +1Multi-Agent Communication Multi-Agent Collaboration
Intellifusion Inc.
Learning to Design SR Solvers: Harness Optimization for Symbolic Regression
Oct 12, 2026Qian Li, Yuxiao Hu, Qihao Duan +2Symbolic Regression Agent Harness Optimization
Shanghai Jiao Tong University, Shanghai, China · Eastern Institute of Technology, Ningbo, China · The Hong Kong Polytechnic University, Hong Kong, China +1
PID-Gated Continual Prompt Adaptation Under Noisy Feedback
Oct 12, 2026Zhentao XuPID Control Prompt Optimization
Independent Researcher, Sunnyvale, CA, USA
MoPrO-SD: Modular Prompt Optimization for Stance Detection
Oct 9, 2026Meysam Sabbaghan, Nikesh Gyawali, Arman Zareian Jahromi +3Stance Detection Prompt Optimization
Kansas State University · University of Illinois Chicago
Do Prompt Optimizers Break Requirements They Are Not Scored On? A Preregistered Study
Oct 9, 2026Sangdo Han, Moonsoo KimPrompt Optimization Instruction Following
CEO Business School
From a Prompt to Repertoires: Evolving Functional REpertoires Enable LLM Continual Learning
Oct 8, 2026Fengyuan Liu, Yue Wang, Hangxi Guo +4Continual Learning for LLMs Prompt Learning
The Chinese University of Hong Kong, Shenzhen · Shanghai AI Laboratory · University of Science and Technology of China +2
MIRA: A Musical Intent Refinement Agent for Aligning Text-to-Music Generation with User Intent
Oct 7, 2026Zekai Liu, Zhilin Wang, Xuzheng He +2Text-to-Music Generation Music Generation Evaluation
Shandong University · University of Science and Technology of China · Central Conservatory of Music +2
A Study on Improving Multi-class Audio Source Separation Via Decoupled CLAP Query Optimization and an Automated Data Engine
Oct 7, 2026Amirhossein Hajavi, Hanhee Lee, Pushya Jain +4Audio Source Separation Prompt Optimization
Noah’s Ark Lab, Huawei Technologies Canada co.
How Should a Prompt Optimizer Spend a Tight Budget? BudgetAPO with Noise-Adaptive Evaluation
Oct 5, 2026Haoyue Liu, Zhichao Wang, Huanyu Yan +1Automatic Prompt Optimization Prompt Optimization
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen · Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen) · Guangdong Provincial Key Laboratory of Future Networks of Intelligence, CUHK(SZ)
Building LLM Agent Systems the Deep Learning Way: From Modular Design to Architecture Search
Oct 4, 2026Tao Feng, Pengrui Han, Zhongjie Dai +1LLM Agents AI Agent Architecture
University of Illinois Urbana-Champaign
Increasing Resilience of Smart Home Agents
Oct 4, 2026Christopher Terrazas, Eduardo Cotilla-SanchezLLM Agent Reliability LLM Agent Training
School of Electrical Engineering and Computer Science Oregon State University Corvallis, Oregon, USA
VideoEvolve: Evolving Agent Harnesses for Video Temporal Grounding
Oct 1, 2026Bingjun Luo, Yuhuan Fan, Jialin Guo +1Temporal Video Grounding Evolutionary Optimization
Tsinghua University · Columbia University · Harbin Engineering University
Ranking-Aware Prompt Optimization for Multimodal Clinical Diagnosis
Sep 30, 2026Tian Xia, Minghao Liu, Yiqing Liang +2Medical Diagnosis Multimodal Large Language Models
Harvard University · University of California, Santa Cruz · Brown University +2
AdaGEPA: Adaptive Feedback Allocation for Reflective Prompt Optimization
Sep 30, 2026Junyang Chen, Zecheng Wang, Jingbang ChenAutomatic Prompt Optimization Prompt Optimization
The Chinese University of Hong Kong, Shenzhen · Shenzhen Loop Area Institute
LLM unbranding: Erasing Commercial Identity while Preserving Generic Utility
Sep 29, 2026Kajetan Ożóg, Alicja Wojciechowska, Dawid Malarz +3Prompt Optimization LLM Unlearning
Jagiellonian University · IDEAS Research Institute · Heinrich Heine Universität Düsseldorf
How Much Prompt Is Enough? A Blackbox Minimization of Few-Shots in LLMs
Sep 28, 2026Ali Alfageeh, Rahul Gopinath, Amin AlipourLLM Prompting Few-Shot Prompting
University of Houston · The University of Sydney
COEVO: Co-Evolving Context and Parameters for Recursive Self-Improvement
Sep 27, 2026Siwei Chen, Xinping Bao, Xinyu Cai +3RL for Language Models Prompt Optimization
Peking University · Emotional Machine
What Should the Reflector See? An Empirical Study of Evidence in Reflective Prompt Optimization
Sep 26, 2026Xiaofan Zhou, Lu ChengPrompt Optimization Automatic Prompt Optimization
Pennsylvania State University
Prompt Minimization: Reducing Input Redundancy Without Sacrificing Output Fidelity
Sep 25, 2026Marius F. R. Juston, Kevin A. Karim, Jonathan Gao +2LLM Prompting Prompt Optimization
UIUC University of Illinois Urbana-Champaign, Illinois, USA · KTH Royal Institute of Technology, Stockholm, Sweden
Optimizing the Score, Losing Sight of the Task: Reward Hacking Across Weights, Selection, and Prompts
Sep 22, 2026Vansh WahiReward Hacking LLM Evaluation
EvolveTrade: Experience-Driven Policy Refinement for Self-Evolving LLM Trading Agents
Sep 15, 2026Sehee Kim, Yumin Choi, Minki Kang +1LLM Agent Self-Improvement Prompt Optimization
KAIST

Oct 27, 2026J/K move · Enter open · S save

GEPA-Curator: Curated Learning for Self Improving Agents

Zack Yu, Nicholas Lee, Minjae Lee, Lakshya Agarwal+7

UC Berkeley · Furiosa AI · ICSI+2

LLM Agent Self-Improvement Prompt Optimization

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Large Language Models (LLMs) are increasingly used in multi-turn agentic workloads that require sustained interaction with complex environments. Existing reflective prompt optimizers such as GEPA learn from failed rollouts, but agentic failures can also come from prompt weaknesses, harness limitations, stochastic execution, or noisy judges. We argue that such failures may not provide useful learning signal, and may even be harmful; and thus it is essential to curate which tasks are provided to the prompt optimizer for better performance. We introduce GEPA-Curator, a framework that augments the prompt optimizer to select which agent experiences should shape prompt optimization. In particular, GEPA-Curator consists of two components: task curation, which filters out tasks that are likely to provide noisy or unreliable learning signals, and prior success retrieval, which selectively retrieves past successful trajectories for a task and provides them as additional context to the prompt optimizer. We evaluate GEPA-Curator on GoBrowse, ALFWorld, AppWorld, and

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-bench, using both Gemini 3.1 Pro and Claude Opus 5 as optimizer models. GEPA-Curator matches or exceeds the mean performance of both baselines, with gains of up to 20.7% over GEPA and 21.2% over ACE.