AI Agents of the Week: Papers You Should Know About
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Executive Summary
This week in AI agents:
Researchers introduced a graph-based memory system that lets agents learn high-level strategies from experience, boosting their cross-task generalization. To tackle multi-agent coordination, new frameworks enabled agents to share learned representations and retrieve past trajectories as context, accelerating team adaptation to novel tasks.
Communication efficiency emerged as a theme: one study defined novel metrics to learn leaner communication protocols, cutting redundant messages while improving cooperative success.
Another work proposed a “Subject-DAG” multi-agent planner that decomposes complex problems by domain, assigning specialized LLM agents to each subject area and routing information among them - significantly outperforming one-size-fits-all approaches.
Collectively, we’re moving towards agent architectures that are more memory-equipped, specialized, and efficient, laying groundwork for agents that can reason, plan, and learn in increasingly sophisticated ways. Below, we’ll take a closer look at each paper’s core innovations, why they matter, and what they hint at for the future of autonomous AI agents.
