# EpiEvolve: Self-Evolving Agents for Streaming Pandemic Forecasting under Regime Shifts

arXiv: https://arxiv.org/abs/2606.05513 · HTML: https://mlsanity.com/paper/2606.05513/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Model collapse from synthetic data, one of 13 directions in Synthetic data and self-improvement for LLMs.
- Builds on 24 of its 43 references that are in the map.
- First posted on arXiv 3 Jun 2026.

## Placed in

- Synthetic data and self-improvement for LLMs / [Model collapse from synthetic data](https://mlsanity.com/synth/model-collapse-from-synthetic-data/)

## Builds on (most cited first, 24 of 24)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [A-MEM: Agentic Memory for LLM Agents](https://mlsanity.com/paper/2502.12110/) (2025)
- [Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory](https://mlsanity.com/paper/2511.20857/) (2025)
- [Mobile-Agent-E: Self-Evolving Mobile Assistant for Complex Tasks](https://mlsanity.com/paper/2501.11733/) (2025)
- [MemGen: Weaving Generative Latent Memory for Self-Evolving Agents](https://mlsanity.com/paper/2509.24704/) (2025)
- [MemEvolve: Meta-Evolution of Agent Memory Systems](https://mlsanity.com/paper/2512.18746/) (2025)
- [SE-Agent: Self-Evolution Trajectory Optimization in Multi-Step Reasoning with LLM-Based Agents](https://mlsanity.com/paper/2508.02085/) (2025)
- [StreamBench: Towards Benchmarking Continuous Improvement of Language Agents](https://mlsanity.com/paper/2406.08747/) (2024)
- [Group-Evolving Agents: Open-Ended Self-Improvement via Experience Sharing](https://mlsanity.com/paper/2602.04837/) (2026)
- [Self-Updatable Large Language Models by Integrating Context into Model Parameters](https://mlsanity.com/paper/2410.00487/) (2024)
- [Test-Time Adaptation for LLM Agents via Environment Interaction](https://mlsanity.com/paper/2511.04847/) (2025)
- [Unsupervised Concept Drift Detection from Deep Learning Representations in Real-time](https://mlsanity.com/paper/2406.17813/) (2024)
- [Towards Adaptive Memory-Based Optimization for Enhanced Retrieval-Augmented Generation](https://mlsanity.com/paper/2504.05312/) (2025)
- [Meta-Policy Reflexion: Reusable Reflective Memory and Rule Admissibility for Resource-Efficient LLM Agent](https://mlsanity.com/paper/2509.03990/) (2025)
- [STReasoner: Empowering LLMs for Spatio-Temporal Reasoning in Time Series via Spatial-Aware Reinforcement Learning](https://mlsanity.com/paper/2601.03248/) (2026)
- [EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting](https://mlsanity.com/paper/2505.12738/) (2025)
- [Research on the Online Update Method for Retrieval-Augmented Generation (RAG) Model with Incremental Learning](https://mlsanity.com/paper/2501.07063/) (2025)
- [EpidemIQs: Prompt-to-Paper LLM Agents for Epidemic Modeling and Analysis](https://mlsanity.com/paper/2510.00024/) (2025)
- [AI Agents as Policymakers in Simulated Epidemics](https://mlsanity.com/paper/2601.04245/) (2026)
- [Pre-training Epidemic Time Series Forecasters with Compartmental Prototypes](https://mlsanity.com/paper/2502.03393/) (2025)
- [Coordinated Pandemic Control with Large Language Model Agents as Policymaking Assistants](https://mlsanity.com/paper/2601.09264/) (2026)
- [Detecting Concept Drift in Neural Networks Using Chi-squared Goodness of Fit Testing](https://mlsanity.com/paper/2505.04318/) (2025)

## Built on by (most cited first, 1 of 1)

- [Self-Evolving Time-Series Forecasting Agents with Episodic Memory and Online Policy Learning](https://mlsanity.com/paper/2609.32689/) (2026)

## Abstract (arXiv)

Epidemic LLM forecasters are usually trained and evaluated as static supervised models, whereas operational pandemic forecasting is a streaming process in which labels arrive after predictions and disease regimes shift over time. We study this mismatch in weekly COVID-19 hospitalization trend forecasting across five variant regimes. We introduce EpiEvolve, a self-evolving agent that wraps an LLM forecaster trained on the warm-start period and keeps its weights fixed during streaming. EpiEvolve adapts by storing forecast outcomes in a hierarchical episodic memory, reflecting on delayed labels, retrieving cases relevant to the current regime, and distilling recurring errors into strategic rules. The resulting context lets the forecaster reuse its own past predictions and outcomes in later weeks while following a chronological protocol that prevents future leakage. On the streaming dataset, EpiEvolve reaches 0.629 average accuracy, compared with 0.561 for the static backbone and 0.325 for the external CDC ensemble, and reduces recovery lag after regime shifts from 5 to 2 weeks. Ablations show that reflection, strategic memory, and regime-aware retrieval each contribute to the gains.