OpenAI's GPT-5 Systems Show Mixed Reliability Under Stress, DeepStress Framework Reveals
Orange Research, in collaboration with Aix-Marseille University, has released the DeepStress framework, a new tool designed to stress-test the reliability of search agents. Officially launched on November 13, 2026, DeepStress evaluates how these agents perform when faced with degraded evidence by replacing their retrieval modules with synthetic documents. These documents can be controlled across three dimensions: trustworthiness, relevance, and factuality. The initial findings, based on testing 12 systems with 33,600 synthetic traces, indicate significant reliability gaps. For instance, several open-weight agents, including Search-R1, SpiqaDR, AutoRefine, and ASearcher, confidently provide answers even when their sources are known to be misleading or fake. In contrast, DR-Tulu, an 8-billion-parameter model, shows some capacity to detect untrustworthy sources, with its correctness dropping and its no-answer rate increasing as trust collapses. The closed GPT-5 systems exhibit varied responses, with GPT-5-min...