Quality-Diversity Optimization

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Quality-diversity optimization combines evolutionary search with archives that organize solutions by behavioral traits, preserving strong candidates from different niches. Methods such as MAP-Elites and novelty search, often paired with reinforcement learning or learned behavior representations, are used to discover repertoires of robot skills, designs, heuristics, and generated outputs. These repertoires can improve adaptation and expose alternatives that a single-score optimizer would overlook, including in language-model and scientific-design settings. A central challenge is making diversity meaningful and measurable: broad archive coverage does not by itself guarantee robust performance or genuinely distinct solutions.

Latest papers

39 papers5 papers in the last four weeks, down 17% on the four weeks before. 0.0% of all new papers.

QD-Rec: Open-Ended Evolution of Recommendations via Quality-Diversity
Oct 16, 2026Takashi Ishida, Taro Makino, Issa SugiuraRecommender Systems AI-Assisted Decision Making
Sakana AI
EliteReplay: Preserving Diverse Solutions in RL Post-Training via Quality-Diversity Search
Oct 15, 2026Hanane Nour Moussa, Doeun Lee, Sachin KumarLanguage Model Post-Training RL Post-Training
The Ohio State University
Uncovering the Generative Landscape of Diffusion Models: A Spectral-Guided Quality-Diversity Diffusion Framework
Oct 11, 2026Hannah Janmohamed, Joao Correia, Penousal Machado +1Diffusion Model Sampling Quality-Diversity Optimization
University of Coimbra
Beyond Action Entropy: Quotient-Space Exploration for Genome-Scale Metabolic Model Repair
Oct 8, 2026Xuan Gong, Hanbo Huang, Wenbin Dai +5Quality-Diversity Optimization
Shanghai Jiao Tong University · Shanghai AI Laboratory
Behavioral Foundation Models for Quality Diversity
Sep 28, 2026Nazim Bendib, Nicolas Perrin-Gilbert, Olivier SigaudReinforcement Learning Quality-Diversity Optimization
Sorbonne Université, ISIR Paris, France
OPDiv: Optimal Selection of Top-K High-Scoring, Diverse Compounds
Sep 23, 2026Miroslav LžičařData Selection Virtual Screening
Deep MedChem
Graph-Based Design of Soft Grippers with Multi-Objective Quality-Diversity Optimisation
Sep 17, 2026Andre Farinha, Ge Shi, Harry Bowman +3Robotic Grasping Robotic Gripper Design
CSIRO Robotics, Australia
Forty Shades of Blue: Quality-Diversity Alignment via Mode-Conditioned Reinforcement Learning
Sep 14, 2026Jiayi Yuan, Hangoo Kang, James Jihao Liu +4RL for Language Models Multi-Objective Language Model Alignment
University of Washington · 2Stanford University
Niching Agents in The Core
Sep 11, 2026Gary B. Parker, Jim O'Connor, John AsaroGame-Playing Agents Quality-Diversity Optimization
Connecticut College, New London CT 06320-4125, USA
GoAnt: Quality-Diversity Multi-Agent Search for Alpha Factor Discovery in Market Microstructure Data
Sep 8, 2026Stella Zhao, Tommy ShaMulti-Agent System Optimization Quantitative Finance
University of Minnesota, Minneapolis, MN, USA · Stony Brook University, Stony Brook, NY, USA
Autonomously Acquiring Robot Manipulation Skills with Language-Driven Quality-Diversity
Aug 31, 2026Émiland Garrabé, Mahdi Khoramshahi, Stéphane DoncieuxRobot Policy Learning Robot Skill Learning
ISIR, Sorbonne Universit´e, France
Loreley: Repository-Scale Program Evolution with Quality-Diversity Search
Aug 20, 2026Mohan ChenCode Optimization Software Engineering
ATLAS: Scaffold-Free Algorithm Synthesis by LLMs via Embedding-Guided Quality-Diversity Search
Aug 16, 2026Danial Yazdani, Mohammad Nabi Omidvar, Yuan Sun +2Automated Algorithm Discovery LLM-Based Program Synthesis
School of Computing Technologies, RMIT University, Melbourne, VIC, Australia · School of Computing and Leeds University Business School, University of Leeds, Leeds, UK · La Trobe Business School, La Trobe University, Melbourne, VIC, Australia +1
Contextual Quality-Diversity Evolutionary Reinforcement Learning for HVAC Control in Tropical Commercial Buildings
Aug 11, 2026Tran Le VuReinforcement Learning Building Energy Management
Discovering Diverse Planning Policies for Multimodal Embodied Agents with Quality-Diversity Optimization
Aug 9, 2026Pengfei Xu, Yong Liu, Xiaoya Nan +2Quality-Diversity Optimization Multimodal Agents
School of Artificial Intelligence, Nanjing University of Information Science and Technology Nanjing, Jiangsu, China
Quality-Diversity Stress Tests for Process Reward Models:What Archive Coverage Can and Cannot Certify
Aug 8, 2026Ibne Farabi Shihab, Fariya AfrinProcess Reward Models Adversarial Attacks
Department of Computer Science, Iowa State University · Department of Computer Science, Kalinga Institute of Industrial Technology
Quality Diversity for Reliable Data Driven Time-Use Optimization
Aug 5, 2026Aneta Neumann, Ty Stanford, Dorothea Dumuid +1Uncertainty Quantification Quality-Diversity Optimization
Optimisation and Logistics School of Computer and Mathematical Sciences Adelaide University Adelaide, Australia · School of Allied Health and Human Performance Adelaide University Adelaide, Australia
MOSAIC: Adversarial Co-evolution of Specialist Heuristics and Problem Instances for LLM-based Automated Heuristic Design
Jul 31, 2026Oguzhan Gungordu, Siheng Xiong, Faramarz FekriAlgorithm Selection Automated Heuristic Design
Georgia Institute of Technology
IDEAgent: Agentic Quality-Diversity Search for Research Idea Generation
Jul 24, 2026Varun Gumma, Navonil Majumder, Soumitra Sinhahajari +1Multi-Agent LLM Systems Quality-Diversity Optimization
DeCLaRe Lab, Nanyang Technological University, Singapore
Self-Evolving Agent Harnesses via Gated Semantic Quality-Diversity
Jul 15, 2026Xiaotian Luo, Fengxingyu Wang, Chuanrui Hu +2LLM Agent Self-Improvement Quality-Diversity Optimization
EverMind AI · Shanda Group
QDEvo: A Multi-Objective Quality-Diversity Framework for Automated Heuristic Design
Jul 6, 2026Nam Do Khanh, Nhat Nguyen Tran Minh, Dat Pham Vu Tuan +2Automated Algorithm Discovery Automated Heuristic Design
Hanoi University of Science and Technology Hanoi, Vietnam · George Mason University Fairfax, Virginia, USA
Heuresis: Search Strategies for Autonomous AI Research Agents Across Quality, Diversity and Novelty
Jun 23, 2026Antonis Antoniades, Deepak Nathani, Ritam Saha +6Novelty Search AI Agent Evaluation
University of California, Santa Barbara · Hexo AI
Distributed Quality-Diversity Search for Toxicity in Large Language Models
Jun 23, 2026Onkar Shelar, Travis DesellAdversarial Prompt Generation LLM Red Teaming
Rochester Institute of Technology, Rochester NY 14623, USA
On the Use of Survival Selection Methods for Evolutionary Diversity Optimisation
Jun 19, 2026Adel Nikfarjam, Jakob Bossek, Aneta Neumann +1Evolutionary Optimization Quality-Diversity Optimization
Optimisation and Logistics School of Computer Science Adelaide University · Department of Computer Science Machine Learning and Optimisation Paderborn University
Spokes: Optimizing for Diverse Pretraining Data Selection
Jun 13, 2026Clarence Lee, Yejin Choi, Luke Zettlemoyer +2Language Model Pretraining Diversity-Aware Sampling
DSO National Laboratories · Stanford University · University of Washington
Range-Aware Bayesian Optimization for Discovering Diverse Designs within Target Property Windows
Jun 10, 2026Shengli Jiang, Jason Wu, Charles M. Schroeder +1Molecular Optimization Bayesian Optimization
Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08540, USA
Generation of Diverse and Functional Robot Designs using Superquadrics Parametrisation and Quality-Diversity
Jun 9, 2026Leni Le Goff, Simon Smith, Emma HartEvolutionary Optimization Superquadric Representation
Edinburgh Napier University, Scotland UK
Quality-Diversity Search in Sound Generation: Investigating Innovation Engines for Audio Exploration
Jun 8, 2026Björn Þór Jónsson, Çağrı Erdem, Stefano Fasciani +1Evolutionary Optimization Quality-Diversity Optimization
1*Department of Informatics, University of Oslo, Norway. · Department of Musicology, University of Oslo, Norway.
LLM-Guided Evolution for Medical Decision Pipelines
Jun 5, 2026Ivan Sviridov, Artem Oskin, Ivan Panin +4Clinical Triage Large Language Model-Guided Optimization
1Sber AI Lab · 2AIRI
U-Net-Accelerated Quality-Diversity Optimization for Climate-Adaptive Urban Layouts
Jun 3, 2026Alexander Hagg, Tania Guerrero, Dirk ReithNeural Surrogate Modeling Surrogate-Assisted Optimization
Institute of Technology, Resource and Energy-efficient Engineering (TREE) Bonn-Rhein-Sieg University of Applied Sciences Sankt Augustin, 53757, Germany · Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany

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QD-Rec: Open-Ended Evolution of Recommendations via Quality-Diversity

Takashi Ishida, Taro Makino, Issa Sugiura

Sakana AI

Recommender Systems AI-Assisted Decision Making Explainable Recommendation Quality-Diversity Optimization

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Open-ended search combined with foundation models has produced advances such as faster algorithms, new results on open mathematical problems, and self-improving agents. We extend this paradigm to recommendation for decision-making, focusing on decisions where the set of possible choices is fixed but the reasons for choosing them are not. In high-stakes decisions, e.g., which company to acquire or which customer to target, comparing diverse, well-reasoned options can be more useful than a single ranked list. We formalize open-ended recommendation as building a diverse, high-quality set of recommendations, each an item together with a hypothesis, a natural-language reason why the item fits the needs of a decision maker. We propose QD-Rec, a quality--diversity method that selects items with a contextual bandit and generates hypotheses with a foundation model from earlier ones kept in an archive as stepping stones. A new recommendation enters the archive if it is novel or better than its nearest neighbor. Across M&A, customer targeting, and paper and book recommendation, QD-Rec gives the highest mean quality and builds the largest archive in most settings.