Automatic Prompt Optimization

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

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Multi-Objective Coevolution of Prompts and Templates for Circuit Approximation
Jun 11, 2026Martin Tomasovic, Lukas SekaninaMulti-Objective Evolutionary Optimization Large Language Model-Guided Optimization
Brno University of Technology, Faculty of Information Technology Brno, Czech Republic
TAHOE: Text-to-SQL with Automated Hint Optimization from Experience
Jun 10, 2026Zhiyi Chen, Jie Song, Peng LiLLM Prompting Text-to-SQL
ByteDance Inc. · Georgia Institute of Technology · Work done during an internship at the ByteBrain team, ByteDance
APEX: Automated Prompt Engineering eXpert with Dynamic Data Selection
Jun 9, 2026Fei Wang, Si Si, Cho-Jui Hsieh +1Evolutionary Optimization LLM Prompting
Google · UCLA
CRAFT: Cost-aware Refinement And Front-aware Tuning of Prompts
Jun 3, 2026Shanu Kumar, Shubhanshu Khandelwal, Akhila Yesantarao Venkata +3LLM Prompting Cost-Aware Inference
MBZUAI · Microsoft
SePO: Self-Evolving Prompt Agent for System Prompt Optimization
Jun 3, 2026Wangcheng Tao, Han Wu, Weng-Fai WongLLM Agent Self-Improvement Automatic Prompt Optimization
National University of Singapore · City University of Hong Kong
Training Prompt Matters: State-Adaptive Optimization for Robust Fine-Tuning
Jun 1, 2026Wenhang Shi, Yiren Chen, Shuqing Bian +5Continual Learning for LLMs Prompt Learning
School of Information, Renmin University of China, Beijing, China · Peking University, Beijing, China · Tencent, Beijing, China
Prompt Codebooks: Discrete Compositional Optimization for Language Model Instruction Refinement
May 27, 2026Jyotirmoy Nath, Neeraj Kumar, Brejesh LallLLM Prompting Large Language Model-Guided Optimization
1IIT Delhi, India
LLMs Are Already Good Tutors: Training-Free Prompt Optimization for Pedagogical Math Tutoring
May 26, 2026Unggi Lee, Minchul Shin, Yeil Jeong +5Intelligent Tutoring Systems LLM Prompting
Korea University Sejong Campus · 1Korea University Sejong Campus · Gyeonggi Institute of Education +9
Why Prompt Optimization Works, and Why It Sometimes Doesn't: A Causal-Inspired Edit-Level Analysis
May 26, 2026Shuzhi Gong, Hechuan WenAutomatic Prompt Optimization LLM Reasoning
The University of Melbourne, Melbourne, Victoria, Australia · The University of Queensland, Brisbane, Queensland, Australia
SPEAR: Code-Augmented Agentic Prompt Optimization
May 25, 2026Mengyin Lu, Cong Feng, Huimin Han +6Tool-Augmented Language Model Agents Automatic Prompt Optimization
LinkedIn Corporation
PACE: Two-Timescale Self-Evolution for Small Language Model Agents
May 21, 2026Chen Ling, Pei Chen, Albert Guan +4Small Language Models LLM Agent Workflow Optimization
Amazon
Reflective Prompt Tuning through Language Model Function-Calling
May 20, 2026Farima Fatahi Bayat, Moin Aminnaseri, Pouya Pezeshkpour +1LLM Self-Refinement LLM Prompting
Megagon Labs
APM: Evaluating Style Personalization in LLMs with Arbitrary Preference Mappings
May 20, 2026Philipp Spohn, Leander Girrbach, Zeynep AkataLLM Evaluation LLM Personalization
Technical University of Munich, Helmholtz Munich · Munich Center for Machine Learning (MCML)
Embedding by Elicitation: Dynamic Representations for Bayesian Optimization of System Prompts
May 18, 2026Zhiyuan Jerry Lin, Benjamin Letham, Samuel Dooley +2Bayesian Optimization Large Language Model-Guided Optimization
Meta
iPOE: Interpretable Prompt Optimization via Explanations
May 18, 2026Jiahui Li, Yarik Menchaca Resendiz, Sean Papay +1LLM Interpretability LLM-Assisted Annotation
Fundamentals of Natural Language Processing, University of Bamberg, Germany · Leibniz-Institut für Psychologie (ZPID), Trier, Germany
Towards Robust Argumentative Essay Understanding via TIDE: An Interactive Framework with Trial and Debate
May 17, 2026Zheqin Yin, Yupei Ren, Yadong Zhang +2Argument Mining Automated Essay Scoring
School of Computer Science and Technology, East China Normal University · 2Shanghai Institute of Artificial Intelligence for Education, East China Normal University · Lab of Artificial Intelligence for Education, East China Normal University
MO-CAPO: Multi-Objective Cost-Aware Prompt Optimization
May 15, 2026Jan Büssing, Moritz Schlager, Timo Heiß +2Cost-Aware Inference Automatic Prompt Optimization
LMU Munich, Germany · Technical University of Munich (TUM), Munich Center for Machine Learning (MCML), Germany · LMU Munich, Munich Center for Machine Learning (MCML), Germany +2
Prompt Segmentation and Annotation Optimisation: Controlling LLM Behaviour via Optimised Segment-Level Annotations
May 14, 2026Devika Prasad, Luke Gerschwitz, Tong Li +5LLM Prompting Automatic Prompt Optimization
Commonwealth Bank of Australia
Efficient Multi-objective Prompt Optimization via Pure-exploration Bandits
May 14, 2026Donghao Li, Chengshuai Shi, Weijuan Ou +2Multi-Armed Bandits LLM Prompting
University of Virginia, Charlottesville, VA 22904, USA · Princeton University, Princeton, NJ 08544, USA · Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
Prompt Optimization for User Simulation in Conversational Recommender Systems: A Multi-Objective Framework
May 8, 2026Nipun B Nair, Tongtong Wu, Weiqing WangAutomatic Prompt Optimization Recommender Systems
Monash University, Australia
A Reproducible Optimisation Protocol for Calibrating Prompt-Based Large Language Model Workflows in Evidence Synthesis
May 7, 2026Teo SusnjakLarge Language Model-Guided Optimization Language Model Calibration
School of Mathematical and Computational Sciences Massey University, Auckland, New Zealand
Adaptive Prompt Embedding Optimization for LLM Jailbreaking
Apr 27, 2026Miles Q. Li, Benjamin C. M. Fung, Boyang Li +2Adversarial Prompt Generation Adversarial Attacks on LLMs
School of Information Studies, McGill University, Montreal, QC, Canada · Department of Computer Science, Kean University, Union, NJ, United States · Mila - Quebec AI Institute, Montreal, QC, Canada +1
PrismaDV: Automated Task-Aware Data Unit Test Generation
Apr 23, 2026Hao Chen, Arnab Phani, Sebastian SchelterAutomatic Prompt Optimization Automated Test Generation
BIFOLD & TU Berlin
Exploiting LLM-as-a-Judge Disposition on Free Text Legal QA via Prompt Optimization
Apr 22, 2026Mohamed Hesham Elganayni, Runsheng Chen, Sebastian Nagl +1LLM Evaluation LLM-as-a-Judge
Technical University of Munich Germany
From Fuzzy to Formal: Scaling Hospital Quality Improvement with AI
Apr 21, 2026Patrick Vossler, Jean Feng, Venkat Sivaraman +9Human-in-the-Loop AI Automatic Prompt Optimization
University of California, San Francisco · 2Zuckerberg San Francisco General Hospital
ContraPrompt: Contrastive Prompt Optimization via Dyadic Reasoning Trace Analysis
Apr 20, 2026Rishav Rishav, Pushpak Pujari, Pushpendre RastogiPairwise Comparison CoT Reasoning
Vizops AI
Prompt Optimization Is a Coin Flip: Diagnosing When It Helps in Compound AI Systems
Apr 16, 2026Xing Zhang, Guanghui Wang, Yanwei Cui +4Prompt Sensitivity Large Language Model-Guided Optimization
AWS Generative AI Innovation Center · HSBC Holdings Plc., HSBC Technology Center, China.
KernelFoundry: Hardware-aware evolutionary GPU kernel optimization
Mar 12, 2026Nina Wiedemann, Quentin Leboutet, Michael Paulitsch +2GPU Kernel Optimization GPU Acceleration
Intel Corporation
Synthetic Interaction Data for Scalable Personalization in Large Language Models
Feb 12, 2026Yuchen Ma, Yue Huang, Wenjie Wang +3RL for Language Models LLM Personalization
Munich Center for Machine Learning & LMU Munich · University of Notre Dame · Sichuan University
MA-SAPO: Multi-Agent Reasoning for Score-Aware Prompt Optimization
Oct 18, 2025Juhyeon Lee, Wonduk Seo, Junseo Koh +6Automatic Prompt Optimization Multi-Agent LLMs
Peking University · Enhans · Fudan University +1

Jun 11, 2026, cs.NEJ/K move · Enter open · S save

Multi-Objective Coevolution of Prompts and Templates for Circuit Approximation

Martin Tomasovic, Lukas Sekanina

Brno University of Technology, Faculty of Information Technology Brno, Czech Republic

Multi-Objective Evolutionary Optimization Large Language Model-Guided Optimization Automatic Prompt Optimization Approximation Algorithms Approximate Computing

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Approximate multipliers deliberately relax computational accuracy to achieve gains in power efficiency, latency, and silicon area, which makes them well-suited for error-resilient applications such as neural networks. In this work, we introduce a co-evolutionary algorithm that leverages an off-the-shelf large language model (LLM) without requiring domain-specific training to automate the design of optimized 8-bit approximate multipliers. The approach simultaneously evolves a population of candidate circuits and a population of prompt templates that steer LLM-driven modifications. Experimental results for several target design objectives demonstrate that the proposed method discovers approximate multipliers with improved error-area trade-offs compared to highly optimized circuits from the EvoApproxLib library.

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