Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·Jun 9·5 min read
REFLECT: Intervention-Supported Error Attribution for Silent Failures in LLM Agent Traces
TL;DR
Locating errors in long plan-and-execution traces of large language model (LLM) agents is challenging, particularly during silent failures where no explicit error is reported. REFLECT is a method that improves error attribution by incorporating intervention outcomes to refine the diagnosis of suspect steps.
✦ Why It Matters
Engineers can leverage REFLECT to enhance error diagnosis in LLM agents, improving reliability in complex task execution.
Key Takeaways
- Use REFLECT to improve error localization in LLMs.
- Test potential error steps with controlled replays.
- Refine error attribution based on intervention outcomes.
- Achieve higher accuracy in structured tool-use scenarios.
How It Works
REFLECT operates by first identifying candidate error steps in LLM agent traces. It then tests these steps through controlled replays, applying diagnosis-specific patches.
The outcomes of these interventions provide contrastive evidence that helps refine the final error attribution, leading to more accurate localization of errors.