Population Dynamics

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10 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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Latest papers 43

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Awakening of the Buddha: Subspace Learning During Population-Loss Plateaus
Sep 30, 2026Akash KumarAnisotropic Loss Landscapes Singular Learning Theory
Department of Computer Science & Engineering University of California-San Diego
Demographic Pluralism: Inference-Time Modeling of Pluralistic Human Preference Distributions
Sep 29, 2026Meng-Chen Wu, Qipin Chen, Ansh Jain +3Internal Pluralism Demographics
Amazon Science
MetaPersona: Task-Grounded Synthetic Populations from Empirical Social Science
Sep 29, 2026Jinyi Ye, Yuangang Li, Chenxiao Yu +7Synthetic Data Demographics
University of Southern California · University of California, Irvine · 2077AI +1
Population Physics, Population Problems: Safety and Emergence in LLM Societies
Sep 27, 2026Adrian de WynterCollective Behaviors Self-Organization
Microsoft & The University of York
KoopCell: Koopman-Based Generative Model for Learning Single-Cell Dynamics from Distribution Snapshots
Sep 27, 2026Wanfeng Lu, Yutong Zhang, Keyi Zhou +3Koopman Operator Snapshots
School of Mathematical Sciences, SCMS, SCAM, and CCSB, Fudan University, China · Research Institute of Intelligent Complex Systems, Fudan University, China · State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, China +1
Stochastic Flow Map for Count Data
Sep 20, 2026Ganchao WeiFlow Map Invertible Flows
Flatiron Institute New York, NY 10010, USA
Self-Replicating Neural Cellular Automata: Quantifying Emergent Phenotypic and Genotypic Diversity in an OpenEnded Substrate
Sep 17, 2026Sanyam Jain, Felix Simon Reimers, Stefano NicheleNeural Cellular Automata Self-Organization
Department of Computer Science and Communication, Østfold University of Applied Sciences, B R A veien 4, 1757 Halden, Norway · Department of Computer Science, Oslo Metropolitan University, Pilestredet 35, 0166 Oslo, Norway
The evolution of sex for artificial intelligence: a population-genetic framework for multigenerational model populations
Sep 16, 2026Giorgio F. GilestroArtificial Intelligence Models Population Dynamics
Department of Life Sciences, Imperial College London, London, UK
The average-farmer illusion in language-model simulations of agricultural decisions
Sep 14, 2026Zhanliang Zhu, Ziwei Li, Yuchen Liu +5User Simulation Population Dynamics
1State Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210098, China · 2Yangtze Institute for Conservation and Development, Hohai University, Nanjing 210098, China · 3Meta Platforms Inc. +2
Population Ecology of Tunes
Sep 8, 2026John M. McBride, Armand. M. LeroiCultural Evolution Population Dynamics
Department of Behavioral and Cognitive Biology, University of Vienna, Vienna, Austria · Acoustic Research Institute, Austrian Academy of Science, Vienna, Austria · Department of Life Sciences, Imperial College London, London, UK +1
Data-Driven Persona-Conditioned Agents for A/B Test Simulation
Sep 1, 2026Ziyad Benomar, Weronika Łajewska, Leonardo Perelli +1User Simulation Data-Driven
Amazon · Luxembourg, Luxembourg · Barcelona, Spain
IO Factory: Simulating AI-Enabled Influence Campaigns at Scale
Aug 11, 2026Lukasz Olejnik, Wenchao Dong, Jonas R. Kunst +4Social Influence Ai-Based Framework
Department of War Studies, King’s College London, London, United Kingdom · Independent researcher · Max Planck Institute for Security and Privacy, Bochum, Germany +4
When One Modality Is Not Enough: Multimodal Sex and Life-Stage Classification of Red Deer from Aerial RGB-Thermal Video
Aug 7, 2026Hugo Markoff, Christoph Praschl, Ivan Ludoški +3Species Bird
Aalborg University, Department of Chemistry and Bioscience, Aalborg, Denmark · University of Applied Sciences Upper Austria, Hagenberg, Austria · University of Novi Sad, Faculty of Agriculture, Novi Sad, Serbia +1
Provable Speedups From Dynamic Population Sizes in Evolutionary Algorithms for Multiobjective Optimization
Jul 26, 2026Andre OprisMulti-Objective Optimization Genetic Algorithms
Faculty of Computer Science and Mathematics University of Passau
CAPT: A Multi-task Continuous Autoregressive Transformer enabling Cross-dataset and Cross-species Transfer for Calcium Population Dynamics
Jul 25, 2026Xinhong Xu, Yimeng Zhang, Yuanlong ZhangAutoregressive Transformers Cross-Dataset Benchmark
School of Life Sciences Tsinghua University Beijing, China
Distribution-First Population Simulation: Collapse, Calibration, and Recall in Non-WEIRD LLM Persona Modeling
Jul 17, 2026Gurkan OzkanArtificial Intelligence Personas User Simulation
Istanbul Technical University
Anamnesis: An Open-Source Platform for Large-Scale Backstory-Conditioned Survey Simulation
Jul 12, 2026Song-Ze Yu, Joseph Suh, Serina Chang +1Survey Population Dynamics
University of California, Berkeley
Wasserstein Residuals: Learning Gradient Flows from Population Dynamics
Jul 6, 2026Markus Heinonen, Yair Shenfeld, Ricardo Baptista +4Wasserstein Gradient Flows Wasserstein Distance
Basis Research Institute · Aalto University · Brown University +2
A Bayesian Framework for Evaluating Scenario Compatibility in Generative Population Synthesis
Jul 3, 2026Zhenlin Qin, Leizhen Wang, Yancheng Ling +1Population Dynamics Generative Models
Department of Civil and Architectural Engineering, KTH Royal Institute of Technology, Stockholm, Sweden. · Department of Data Science and Artificial Intelligence, Monash University, Melbourne, Australia. · Digital Futures, KTH Royal Institute of Technology, Stockholm, Sweden.
Calibrating the Instrument: Controllability of an LLM-Driven Synthetic Population
Jul 1, 2026Mirko Degli EspostiPopulation Dynamics Synthetic Data
Department of Physics and Astronomy, University of Bologna
Heterogeneous synaptic motifs bridge microscale structure and macroscale nonlinear dynamics
Jun 26, 2026Meiyi Zhang, Jinjian Yu, Louis Tao +1Synaptic Plasticity Functional Connectivity
Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, China · Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China · School of Physics, Peking University, China +7
ProfileFoundry: A Synthetic Person-Object Substrate for Privacy, Memory, and Tool-Use Evaluation in LLM Agent
Jun 24, 2026Sriram Selvam, Anneswa GhoshProfile Synthetic Data
Cellular Predictions on the Move: What about Data?
Jun 24, 2026Natalia Vesselinova, Pauliina IlmonenAccurate Short-Term Electricity Load Forecasting Accurate Traffic Forecasting
Neural Parameter Calibration for Finite-State Mean Field Games
Jun 22, 2026Anna C. M. Thöni, Grégoire Lambrecht, Gökçe Dayanıklı +3Mean Field Games Mean-Field Limit
Radboud University, Nijmegen, the Netherlands · New York University, New York, NY · University of Illinois Urbana-Champaign, Champaign, IL +2
LLM-Powered Virtual Population for Demand Simulation and Pricing
Jun 15, 2026Chengpiao Huang, Kaizheng WangUser Simulation Consumption
Department of Industrial Engineering and Operations Research, Columbia University. · Department of Industrial Engineering and Operations Research and Data Science Institute, Columbia University.
Tuning Agent-Based Predator-Prey Models Toward Lotka-Volterra Dynamics
Jun 11, 2026Corinna Mandl, Siddharth Chaturvedi, Marcel van GervenPredator-Prey Models Agent-Based Model
Department of Machine Learning and Neural Computing Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, the Netherlands
An Infectious Disease Spread Simulation Based on Large Language Model Decision Making
Jun 4, 2026Yonchanok Khaokaew, Ruochen Kong, Andreas Zufle +6Epidemiological Models Large Language Model Decisions
Computer Science and Engineering Faculty of Engineering The University of New South Wales Sydney, NSW, Australia · King Mongkut’s University of Technology North Bangkok (KMUTNB) · Department of Computer Science Emory University Atlanta, Georgia, USA +5
Mean-Field Diffuser: Scaling Offline MARL to Thousands of Agents
May 28, 2026Wenhao Li, Xiangfeng Wang, Bo JinMulti-Agent Reinforcement Learning Model-Based Reinforcement Learning
Tongji University · East China Normal University
Two-Parameter Flows for Learning Population Dynamics of Physical Systems
May 25, 2026Paul Schwerdtner, Tobias Blickhan, Benjamin PeherstorferInvertible Flows Population Dynamics
Courant Institute of Mathematical Sciences, New York University, 251 Mercer Street, New York, NY 10012, USA.
Leveraging Gauge Freedom for Learning Non-Gradient Population Dynamics of Stochastic Systems
May 24, 2026Jules Berman, Tobias Blickhan, Benjamin PeherstorferWasserstein Gradient Flows Vector Fields
Courant Institute of Mathematical Sciences, New York University, New York, NY 10012, USA.
Asymmetric Scaling Laws from Sparse Features
May 22, 2026John Sous, Michael WinerOverparameterization Scaling Laws
Department of Applied Physics, Yale University, New Haven, Connecticut 06511, USA · Energy Sciences Institute, Yale University, West Haven, Connecticut 06516, USA · Institute for Advanced Study, Princeton, NJ 08540, USA +1
The Illusion of Intervention: Your LLM-Simulated Experiment is an Observational Study
May 20, 2026Victoria Lin, Taedong Yun, Maja Matarić +3User Simulation Illusions
Google DeepMind · Carnegie Mellon University
APS: Bias-Controlled Adaptive Prototype Simulation for Population-Scale LLM Agents
May 19, 2026Quan Zheng, Yan Gao, Shaobin He +6Scaling Improve Social Simulation Large Language Model Agents
1Beijing Normal University · 2Zhongguancun Academy, Beijing, China · 3Zhongguancun Institute of Artificial Intelligence
Towards Model-Free Learning in Dynamic Population Games: An Application to Karma Economies
May 11, 2026Matteo Cederle, Saverio Bolognani, Gian Antonio SustoDeep Q-Networks Model-Free
Department of Information Engineering University of Padova · Automatic Control Laboratory ETH Zurich
A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots
May 8, 2026Vincent Guan, Lazar Atanackovic, Kirill NeklyudovLagrangian Methods Population Dynamics
University of British Columbia · Mila - Quebec AI Institute · University of Alberta +4
Neural-Actuarial Longevity Forecasting: Anchoring LSTMs for Explainable Risk Management
May 7, 2026Davide RindoriActuarial Ratemaking Risk
SAV Actuarial Candidate, ETH Zürich · PhD in Physics, University of Florence (2021)
Direct From Darwin: Deriving Advanced Optimizers From Evolutionary First Principles
May 6, 2026Daniel GrimmerEvolutionary Optimization Evolutionary Optimization Methods
Philosophy, Yale University, New Haven, 06511, USA
Geospatial foundation-model embeddings improve population estimation unevenly across space and scale
May 3, 2026Wenbin Zhang, Eimear Cleary, Francisco Rowe +4Geospatial Foundation Models Populations
WorldPop, School of Geography and Environmental Sciences, University of Southampton, United Kingdom · Geographic Data Science Lab, Department of Geography and Planning, School of Environmental Sciences, University of Liverpool, United Kingdom
Beyond Continuity: Simulation-free Reconstruction of Discrete Branching Dynamics from Single-cell Snapshots
May 1, 2026Junda Ying, Yuxuan Wang, Bowen Yang +2Schrödinger Bridges Population Dynamics
Beijing International Center for Mathematical Research, Peking University · Center for Data Science, Peking University · School of Mathematical Sciences, Peking University +4
RCMAES: A Robust CMA-ES Variant for CEC2026 Competition
Apr 29, 2026Khoirul Faiq Muzakka, Sören Möller, Martin FinsterbuschCovariance Population Dynamics
Institute of Energy Materials and Devices (IMD-2), Forschungszentrum Jülich GmbH, Germany
Population Dynamics in ARIEL Robotics Systems Featuring Embodied Evolution via Spatial Mating Mechanisms
Apr 29, 2026Victoria Peterson, Akshat Srivastava, Raghav PrabhakarEvolutionary Optimization Methods Population Dynamics
Vrije Universiteit Amsterdam, Netherlands
Using Zero-Shot LLM-Generated Survey Data for Geographically Explicit Population Synthesis
Apr 23, 2026Taylor Anderson, Sara Von Hoene, Orhan Yagizer Cinar +4Synthetic Data Populations
Dept. of Geography and Geoinformation Science, George Mason University, Fairfax, VA, USA · Dept. of Computer Science, George Mason University, Fairfax, VA, USA · College of Public Health, George Mason University, Fairfax, VA, USA +2
Simulation-free Structure Learning for Stochastic Population Dynamics
Oct 18, 2025Noah El Rimawi-Fine, Adam Stecklov, Lucas Nelson +4Population Dynamics Stochastic Dynamics
McGill University · Mila – Quebec AI Institute · AITHYRA +5

Sep 30, 2026, cs.LGJ/K move · Enter open · S save

Awakening of the Buddha: Subspace Learning During Population-Loss Plateaus

Akash Kumar

Department of Computer Science & Engineering University of California-San Diego

Anisotropic Loss Landscapes Singular Learning Theory Subspace Teacher Population Dynamics Bodhi

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Population loss can remain nearly constant while a neural network learns a substantially more predictive representation. We establish this separation for two-layer ReLU and leaky-ReLU networks trained on Gaussian inputs by simultaneous fixed-step population gradient descent on all parameters. For structured additive teachers whose links are positive mixtures of Gaussian-damped cubics in

H^1(γ)

, we give explicit conditions under which small IID Gaussian initialization yields a high-probability guarantee: at a checkpoint during a high-loss plateau, minimum alignment between the rank-

r

teacher subspace and the leading

r

-dimensional eigenspace of the predictor's average gradient outer product (AGOP) increases by at least

1/2

, and the minimum refit MSE under unchanged coefficient budgets decreases by more than

0.399

, both relative to initialization. The same trajectory subsequently attains a trained loss below every value in the plateau window. A complementary result treats unequal-weight cubic teachers and small additive Sobolev perturbations using projected-feature refits. For SwiGLU networks with an exactly fitted intercept, we prove leading-AGOP alignment during a loss plateau at fixed width and dimension as Gaussian initialization vanishes, for square-integrable teachers with nonzero Hermite content of degree one, two, or three. A rank-one cubic specialization also gives simultaneous unrestricted-refit gains at a prescribed width. An approximation lower bound further shows that certain interaction targets retain nonzero error when ridge neurons are restricted to shared orthogonal axes within the teacher subspace. Population-moment experiments with ReLU students across 21 teachers and 50 initializations per teacher complement the analysis.

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Latest papers 43