deep-research

Thesis-grade research with verified citations is produced by running a 4-role peer-reviewed pipeline that separates known, contested, and unknown claims while flagging hallucination risks. It uses parallel subagent research, depth, breadth, and counter-argument passes to synthesize high-confidence results.

Checking AI research for hallucinations requires a post-hoc verification step that grades evidence quality and flags anything that cannot be independently verified. This process consolidates citations into a graded list, explicitly separating high-confidence findings from contested or unknown buckets.

Researching high-stakes policy claims involves applying a counter-argument pass to identify contested evidence and unknowns. The pipeline synthesizes results into structured buckets of high-confidence, contested, and unknown information, ensuring load-bearing questions receive evidence-graded validation.

Deep dive research results can be saved directly to an Obsidian-style knowledge vault or a local research directory. This optional vault persistence feature writes the synthesized, citation-forward deliverables to a configured path for ongoing reference and evidence synthesis.

Evidence synthesis works for technical claims needing validation by applying a peer-review pass over parallel subagent research. It grades source quality, checks for hallucinations, and outputs a consolidated citations list to ensure high-stakes technical assertions are properly supported.