Title: DeepTRACE: Auditing Deep Research AI Systems for Tracking Reliability Across Citations and Evidence
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: deep research, generative search engines, NLP, audit framework, sociotechnical evaluation, large language models

Abstract: Generative search engines and deep research LLM agents promise trustworthy, source-grounded synthesis, yet users regularly encounter overconfidence, weak sourcing, and confusing citation practices. We introduce DeepTRACE, a novel sociotechnically grounded audit framework that turns prior community-identified failure cases into eight measurable dimensions spanning answer text, sources, and citations. DeepTRACE uses statement-level analysis (decomposition, confidence scoring) and builds citation and factual-support matrices to audit how systems reason with and attribute evidence end-to-end. Using automated extraction pipelines for popular public models (e.g., GPT-4.5/5, You.com, Perplexity, Copilot/Bing, Gemini) and an LLM-judge with validated agreement to human raters, we evaluate both web-search engines and deep-research configurations. Our findings show that generative search engines and deep research agents frequently produce one-sided, highly confident responses on debate queries and include large fractions of statements unsupported by their own listed sources. Deep-research configurations reduce overconfidence and can attain high citation thoroughness, but they remain highly one-sided on debate queries and still exhibit large fractions of unsupported statements, with citation accuracy ranging from 40–80\% across systems.

TL;DR: DeepTRACE is an audit framework that scores generative search and deep-research LLM systems on eight dimensions by decomposing answers and checking each statement against sources.