Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 17·5 min read
Wrong but Useful: Trajectory Value Beyond Answer Correctness in Multi-Agent Messages
TL;DR
Multi-agent reasoning systems can benefit from incorporating messages that may be incorrect but still provide valuable insights. The study reveals that over 40% of wrong answers can enhance final outcomes in reasoning tasks.
✦ Why It Matters
Incorporate wrong but insightful messages in your multi-agent systems to enhance reasoning outcomes.
Key Takeaways
- Evaluate messages based on trajectory value, not just correctness.
- Incorporate reasoning from incorrect messages to improve outcomes.
- Utilize DHD to assess message impact in multi-agent systems.
- Focus on retaining reasoning over correctness for better results.
How It Works
Diverse Hypothesis Deliberation (DHD) measures the impact of messages by caching multiple independently generated responses and analyzing their effects on a downstream solver. By replaying scenarios with different messages, the system can determine whether the inclusion of a message aids or hinders reasoning, thus quantifying its trajectory value.