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TRACE is an open-source hierarchical memory system for LLM agents that organizes conversation history into a topic tree, achieving 82.5% F1 on MemoryAgentBench's EventQA using an open-weight model, outperforming Mem0 and MemGPT.
HASTE introduces a hierarchical multi-agent system for ML engineering that organizes cross-competition knowledge into three tiers, achieving 77.3% medal rate on MLE-Bench Lite while reducing compute by 52% and demonstrating that structured knowledge transfer outperforms flat memory approaches.
This paper introduces ContextForge, a hierarchical memory architecture that treats the LLM context window as a recyclable workspace, achieving significant token and speed improvements on long-horizon tasks while maintaining accuracy on a 276-million-row enterprise benchmark.
This paper introduces MemSlides, a hierarchical memory framework for personalized slide generation that separates long-term user profiles, working memory for session constraints, and tool memory for localized edits, enabling multi-turn revision without full regeneration.
This paper presents HORMA, a hierarchical organize-and-retrieve memory agent that organizes agent experiences into a file-system-like structure for efficient retrieval, improving performance on long-horizon tasks while reducing token usage.
PhotoCraft proposes a training-free hierarchical memory system for photo-search agents, integrating working, episodic, and semantic memory to maintain long-horizon context and transfer knowledge across tasks, achieving up to 18.5% improvement on DISBench.
CoMIC is a cloud-edge framework for LLM agents that uses collaborative memory and insight circulation to improve long-horizon task performance without requiring parameter updates, achieving gains in progress rate and action grounding across multiple tasks.
SHARP introduces a bio-inspired framework that separates memory accumulation from pattern recognition, using accelerated replay during offline sleep phases to learn long-range non-stationary temporal patterns in streaming settings. It improves context retention on text8 and PG-19 while maintaining computational efficiency.
TMAS introduces a multi-agent framework that enhances large language model reasoning by scaling test-time compute through structured collaboration and hierarchical memory systems. The approach uses specialized agents, cross-trajectory information flow, and hybrid reward reinforcement learning to improve iterative scaling and stability on challenging reasoning benchmarks.
SafeHarbor is a novel framework for LLM agent safety that uses hierarchical memory and self-evolution to balance safety and utility, achieving state-of-the-art performance on benign and malicious tasks.