Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
AI Research
arXiv CS.AI·Sep 10·5 min read
What Should an Agent Forget? Separating What Is Stored from What Is Used
TL;DR
RD-Forget is a novel framework that enables language agents to distinguish between stored knowledge and its relevance to current queries. This approach enhances answer accuracy by managing historical data while preventing outdated information from misleading responses.
✦ Why It Matters
Implement RD-Forget to enhance the accuracy of your language agents by managing historical data effectively.
Key Takeaways
- Utilize RD-Forget to improve language agent responses.
- Implement query conditioning to manage historical data influence.
- Organize facts into semantic slots for better reasoning.
- Focus on retaining relevant historical data while suppressing outdated information.
How It Works
RD-Forget operates by maintaining a source archive of observations while employing a query-conditioned memory view to filter which pieces of information are relevant for current queries. A language-model curator extracts pertinent evidence and organizes it into semantic slots, which allows the agent to perform multi-hop reasoning.
This structure enables the agent to suppress outdated facts in real-time while still having access to historical data when necessary.