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Mnemo is an open-source, local-first memory layer for any LLM that extracts entities and relationships into a persistent knowledge graph using SQLite and petgraph, providing automatic context injection for enhanced conversations.
SAGE proposes a novelty gate for memory evolution in agentic LLMs, using a von Mises-Fisher-based density estimator to decide whether to add, merge, or ignore new facts, reducing LLM calls while maintaining memory quality.
Introduces SeqMem-Eval, a diagnostic evaluation framework for sequentially evolving LLM memory that measures multiple dimensions beyond aggregate metrics, revealing trade-offs between adaptability and stability.
GroupMemBench is a new benchmark for evaluating LLM agent memory in multi-party conversations, exposing failures in current memory systems with the best achieving only 46% average accuracy.
This research demonstrates that continuously updating LLM agent memories through distillation and consolidation loops causes performance regression, even when trained on ground-truth solutions. The study finds that episodic-only retention outperforms text-based consolidation, highlighting significant flaws in current self-improvement paradigms.
An independent developer quietly shipped a working “LLM Wiki” system—persistent memory for language models—weeks before Andrej Karpathy publicized the same concept.
Someone implemented a working "LLM Wiki" system a month before Andrej Karpathy publicized the concept, addressing the problem that LLMs restart from zero without memory or learning.
Researchers introduce BEHEMOTH benchmark and CluE cluster-based prompt optimization to enable LLMs to extract and retain heterogeneous memory across diverse tasks, achieving 9% gains over prior self-evolving frameworks.
Mem0 introduces a scalable memory-centric architecture using graph-based representations to improve long-term conversational coherence in LLMs, significantly reducing latency and token costs while outperforming existing memory systems.