AgentDropoutV2: Optimizing Information Flow in Multi-Agent Systems via Test-Time Rectify-or-Reject Pruning

Researchers propose AgentDropoutV2, a test-time framework that optimizes multi-agent systems by dynamically correcting or removing erroneous outputs without requiring retraining. The system acts as an active firewall with retrieval-augmented rectification, achieving 6.3 percentage point accuracy gains on math benchmarks while preventing error propagation between AI agents.