Memory as Action: Autonomous Context Curation for Long-Horizon Agentic Tasks
📰 ArXiv cs.AI
Learn how to improve long-horizon agentic tasks with Memory-as-Action, a framework that treats working memory management as learnable policy actions
Action Steps
- Formulate context management as a learnable policy action using MemAct
- Implement working memory management as a part of the agent's reasoning state
- Train the agent to curate context autonomously for long-horizon tasks
- Evaluate the performance of MemAct in mitigating attention dilution
- Apply MemAct to real-world applications such as dialogue systems or game playing
Who Needs to Know This
AI researchers and engineers working on long-horizon tasks can benefit from this framework to improve decision-making and mitigate attention dilution
Key Insight
💡 Treating working memory management as learnable policy actions can lead to better decision-making in long-horizon tasks
Share This
💡 Improve long-horizon tasks with Memory-as-Action (MemAct) framework! 🤖
Key Takeaways
Learn how to improve long-horizon agentic tasks with Memory-as-Action, a framework that treats working memory management as learnable policy actions
Full Article
Title: Memory as Action: Autonomous Context Curation for Long-Horizon Agentic Tasks
Abstract:
arXiv:2510.12635v3 Announce Type: replace Abstract: Long-context Large Language Models, despite their expanded capacity, require careful working memory management to mitigate attention dilution during long-horizon tasks. Yet existing approaches rely on external mechanisms that lack awareness of the agent's reasoning state, leading to suboptimal decisions. We propose Memory-as-Action (MemAct), a framework that treats working memory management as learnable policy actions. By formulating context ma
Abstract:
arXiv:2510.12635v3 Announce Type: replace Abstract: Long-context Large Language Models, despite their expanded capacity, require careful working memory management to mitigate attention dilution during long-horizon tasks. Yet existing approaches rely on external mechanisms that lack awareness of the agent's reasoning state, leading to suboptimal decisions. We propose Memory-as-Action (MemAct), a framework that treats working memory management as learnable policy actions. By formulating context ma
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