Leading AI models like GPT and Gemini routinely cite text passages in document analyses that don't actually support their answers. Even when the answer is right, the cited evidence is often wrong. Researchers at Peking University call this "attribution hallucination," a risk for regulated fields like law and medicine. Their new CiteVQA benchmark is the first to test for it systematically.<br /> The article AI models often give the right answers but point to the wrong sources appeared first on The Decoder. [...]
Across 101 enterprises, the context feeding AI agents is failing often and repeatedly. Sixty-eight percent have traced a confident but wrong agent answer to missing or inconsistent business context in [...]
An enterprise AI agent answers with total confidence, but the number is wrong. Nobody catches it until someone traces it back to a stale metric definition or a document the retrieval system never pull [...]
Across 101 enterprises, the infrastructure that feeds AI agents their business context is being built faster than it can be trusted. Retrieval-augmented generation is already the default context sourc [...]
You spend weeks tuning an AI chatbot. Answers are accurate. Stakeholders sign off, and you ship it. Three months later, the system is confidently wrong about a third of what users ask. Nobody changed [...]
Google on Monday unveiled the most significant upgrade to its autonomous research agent capabilities since the product's debut, launching two new agents — Deep Research and Deep Research Max †[...]