You want a research analyst that reads everything and tells you what matters. Here is how to actually get one.
Research AIs are only as good as the question. "Tell me about longevity" gets you a Wikipedia paraphrase. "What are the three most promising small-molecule senolytics in human trials in 2026, with the lead clinical evidence for each, and the main safety concerns" gets you something useful. Be the analyst who hires the analyst.
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Every credible research AI returns citations. Open them. Spot-check 3-5 claims per report against the actual sources. AIs in 2026 hallucinate citations less often than in 2023, but it still happens — especially with paper titles, dates, and quoted statistics. Verification is part of the workflow, not optional.
A single prompt rarely produces the best research. Treat the AI like a junior analyst: read the first pass, push back on weak claims, ask follow-ups, request the contrarian view, ask what was missed. The output of round three is usually 4x the quality of round one.
A research AI that does not remember your previous questions is starting from zero every time. The compound value of research is in the accumulated context. Use a system with persistent project memory so you can return to the topic next week without re-introducing it.
Luna's agent swarm runs PubMed (daily-fed), web search across multiple sources, Wikipedia/arXiv/Stack Overflow/ConceptNet (OmegaKnowledgeCore), document processing for PDFs you give her, and Tree-of-Thoughts synthesis to combine sources.
You can ask her by voice on a walk and have a research brief waiting when you get home. Or speak the brief out loud and have her draft it as you talk.
Sovereign — your research questions are not flowing through a third-party LLM provider.
Run a research task with Luna →
Not novel research. AI in 2026 can synthesise existing literature at high quality, surface non-obvious connections, draft literature reviews, and run experiments you design — but it is not generating new scientific knowledge unsupervised. Use it as a hyper-fast research assistant, not as the lead investigator.
On factual recall from well-trodden topics: high. On obscure topics, recent events, or anything requiring precise numerical claims: variable. Always verify quantitative claims and direct quotes against the cited source. The hallucination rate has dropped sharply but is not zero.
For specific questions with an answer, yes. For exploratory browsing where you do not yet know what you are looking for, no. Both are useful. Many people use an AI for synthesised research and Google for primary sources.
Perplexity's free tier or Luna (also free) gets you 80% of paid value. Add OpenAI Deep Research or Google Deep Research when you need long-form synthesis. Free tools are now genuinely competitive on the research workload.