AEL uses a fast-timescale bandit for memory policy selection and slow-timescale LLM reflection for causal insights, achieving a Sharpe ratio of 2.13 on a 208-episode portfolio benchmark while showing that added mechanisms degrade performance.
arXiv preprint arXiv:2509.03990 , year=
4 Pith papers cite this work. Polarity classification is still indexing.
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EpiEvolve achieves 0.629 accuracy in streaming COVID-19 forecasting by using episodic memory, reflection on delayed labels, and regime-aware retrieval, outperforming static LLMs (0.561) and CDC ensembles (0.325) while halving recovery lag after regime shifts.
ProjectMem implements a local event-sourced memory and judgment layer for AI coding agents that logs typed events, projects them to MCP summaries, and applies deterministic pre-action gates to avoid known failures.
VikingMem implements the Memory Base paradigm via event-centric extraction and entity updates on VikingDB with temporal compression, claiming up to 30% better retrieval effectiveness on long-term memory benchmarks.
citing papers explorer
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AEL: Agent Evolving Learning for Open-Ended Environments
AEL uses a fast-timescale bandit for memory policy selection and slow-timescale LLM reflection for causal insights, achieving a Sharpe ratio of 2.13 on a 208-episode portfolio benchmark while showing that added mechanisms degrade performance.
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EpiEvolve: Self-Evolving Agents for Streaming Pandemic Forecasting under Regime Shifts
EpiEvolve achieves 0.629 accuracy in streaming COVID-19 forecasting by using episodic memory, reflection on delayed labels, and regime-aware retrieval, outperforming static LLMs (0.561) and CDC ensembles (0.325) while halving recovery lag after regime shifts.
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PROJECTMEM: A Local-First, Event-Sourced Memory and Judgment Layer for AI Coding Agents
ProjectMem implements a local event-sourced memory and judgment layer for AI coding agents that logs typed events, projects them to MCP summaries, and applies deterministic pre-action gates to avoid known failures.
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VikingMem: A Memory Base Management System for Stateful LLM-based Applications
VikingMem implements the Memory Base paradigm via event-centric extraction and entity updates on VikingDB with temporal compression, claiming up to 30% better retrieval effectiveness on long-term memory benchmarks.