Tag
This paper proposes a Dual-Layer Agentic Memory framework for LLM agents that manages knowledge lifecycle through fast write routing and slow consolidation to prune redundant external memory while maintaining performance.
AgentBrew proposes a training-free method for distilling knowledge from strong teacher LLM agents to weak student agents, using a failure-triggered reflection loop and student-aware synthesis to create executable notes, improving performance on coding, math, and tool-use tasks without weight updates.
This paper presents TraceRetain, a lightweight framework for bounded external memory in frozen LLM agents, demonstrating that selective retention differentiates from cache heuristics primarily when memory streams contain noise, offering task-success and efficiency benefits.
A research report detailing controlled experiments on building an external memory architecture that enables persistent AI identity independent of model weights, finding that accumulated fragment history consistently dominates system prompts in shaping output across three topologies.
FS-Researcher introduces a file-system-based dual-agent framework that enables LLM agents to conduct deep research beyond context window limits by using persistent external memory as a shared workspace. The framework achieves state-of-the-art results on research benchmarks and demonstrates effective test-time scaling through computation allocation to evidence collection.