The Context Gathering Decision Process: A POMDP Framework for Agentic Search
📰 ArXiv cs.AI
Learn how to apply POMDP frameworks for agentic search to improve context gathering in large language models
Action Steps
- Apply POMDP frameworks to model agentic search in complex environments
- Use iterative exploration to find relevant information in massive codebases or databases
- Configure the POMDP framework to handle lossy representations of the search state
- Test the framework using simulated environments or real-world datasets
- Compare the performance of the POMDP framework with other context gathering methods
Who Needs to Know This
Research teams working on large language models and agentic search can benefit from this framework to improve their models' context gathering capabilities
Key Insight
💡 POMDP frameworks can be used to model agentic search and improve context gathering in complex environments
Share This
🤖 Improve context gathering in LLMs with POMDP frameworks for agentic search! 💡
Key Takeaways
Learn how to apply POMDP frameworks for agentic search to improve context gathering in large language models
Full Article
Title: The Context Gathering Decision Process: A POMDP Framework for Agentic Search
Abstract:
arXiv:2605.07042v1 Announce Type: new Abstract: Large Language Model (LLM) agents are deployed in complex environments -- such as massive codebases, enterprise databases, and conversational histories -- where the relevant state far exceeds their context windows. To navigate these spaces, an agent must iteratively explore the environment to find relevant information. However, without explicit infrastructure, an agent's working memory can degrade into lossy representations of the search state, res
Abstract:
arXiv:2605.07042v1 Announce Type: new Abstract: Large Language Model (LLM) agents are deployed in complex environments -- such as massive codebases, enterprise databases, and conversational histories -- where the relevant state far exceeds their context windows. To navigate these spaces, an agent must iteratively explore the environment to find relevant information. However, without explicit infrastructure, an agent's working memory can degrade into lossy representations of the search state, res
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