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Introduces DMF, a deterministic memory framework for conversational AI agents that replaces LLM-based compression with classical NLP and mathematical scoring, achieving comparable accuracy to Mem0 while using zero tokens for memory preparation and up to 242× fewer tokens overall.
Introduces a framework for agent memory with three components: Remember (hot session and cross-session storage), Cite (authority ordering via AGENTS.md), and Forget (timestamped facts with Mem0-style soft decay). Argues that missing any of these leads to stale facts or unauthorized sources.
This article introduces the open-source EverOS project, which provides long-term memory capabilities for AI coding assistants like Claude Code. It automatically saves conversation history and retrieves memories in new conversations. Additionally, it includes multiple application examples.
MemForest proposes a memory framework for long-context LLM agents that improves scalability and reduces latency through parallel chunk extraction and hierarchical temporal indexing, achieving 6x higher throughput on benchmarks.
Echo-Forcing introduces a scene memory framework for interactive long video generation, using hierarchical temporal memory, scene recall frames, and difference-aware memory decay to handle prompt switching and long-term recall. The method is training-free and achieves strong performance on VBench-Long.
Introduces SimpleMem, an efficient memory framework for LLM agents that uses semantic lossless compression to improve accuracy and reduce token consumption, achieving 26.4% F1 improvement and up to 30x reduction in inference-time token usage.