Tag
Jev-Mem introduces an agentic memory architecture inspired by System-One/System-Two cognition, enhancing efficiency and effectiveness for long-horizon AI agents with improved scores and faster operations.
This paper introduces Fortunate Recall, an ontology-driven policy layer for managing memory lifecycle in LLMs, which classifies personal facts into behavioral categories and applies differential policies to improve coherence and reduce confabulation, outperforming existing systems on benchmarks.
The author developed a Datalog-based memory system for LLMs to maintain accurate state during investigations like vulnerability research, automatically updating conclusions when facts change.
The article introduces RENDER, a benchmark that controls reader-facing evidence in LLM memory evaluations, showing significant accuracy variations based on how memory entries are rendered to the model.
The author argues that for users who already subscribe to LLM services like ChatGPT Pro, running local embedding and reranker models for a memory system is more practical than running local LLMs, and details their GBrain-based setup.
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.