Academic Research Assistant
Turn a research question into an evidence-grounded literature review.
Search academic literature extract evidence validate citations generate a structured research paper.
Recent empirical analyses (Ji et al., 2023) categorize model unfaithfulness into intrinsic contradictions and extrinsic fabrications. Retrieval-augmented architectures (Lewis et al., 2020) significantly reduce factual errors by grounding generation on indexed document corpora. Furthermore, post-hoc verification agents (Gao et al., 2023) enforce bidirectional citation tracing, reducing unsupported claims across multi-turn reasoning workflows...
- Ji, Z. et al. (2023). Survey of Hallucination in Natural Language Generation.
- Lewis, P. et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.
| Study & Year | Mitigation Focus | Methodology | Key Empirical Finding | Evidence Level |
|---|---|---|---|---|
| Ji et al. (2023) | Taxonomy & Evaluation | Comprehensive Survey | Establishes intrinsic vs. extrinsic hallucination categorization. | High (Foundational) |
| Lewis et al. (2020) | Retrieval Grounding | RAG Architecture | Dense retrieval conditioning cuts factual hallucinations by ~68%. | High (Pivotal) |
| Gao et al. (2023) | Citation Verification | RARR Automated Editing | External search verification resolves factual mistakes post-generation. | High (Empirical) |
| Dhuliawala et al. (2023) | Self-Contradiction Probing | Chain-of-Verification | Decomposing verification into sub-queries improves reliability. | Moderate-High |
Formulates research objectives, questions, and Boolean search keywords.
You approve scope and search queries before retrieving papers.
Queries OpenAlex, Semantic Scholar, and arXiv simultaneously.
Filters out duplicates and evaluates abstracts on 0–10 relevance score.
You inspect literature synthesis and research gaps.
Drafts paper sections with strict evidence-to-claim citation checks.
Research shouldn't be a black box.
Traditional AI chatbots give you a one-shot answer from unseen training data. DeepResearch executes a structured, verifiable research process with human review at every stage.
One-Shot Generation
- One-shot prompt: Tries to write everything in a single ungrounded pass.
- Opaque sources: Hallucinates non-existent DOIs and author citations.
- No review gates: Runs entirely uncontrolled until output completes.
- Chat responses: Produces unstructured conversational text.
Multi-Stage Evidence Pipeline
- Structured pipeline: Scope → Literature → Evidence → Synthesis → Paper.
- Traceable evidence: Queries 320M+ indexed papers (OpenAlex, S2, Crossref, Europe PMC, PubMed, DOAJ, DataCite & arXiv).
- Human review gates: Pauses at 3 checkpoints so you can guide the direction.
- Publication artifacts: PRISMA-style flow, evidence matrix, PDF and Word export.
Everything you need to write literature reviews.
Built for researchers, academics, and analysts who need reliable, citation-backed literature synthesis.
Find the literature
Search multiple academic indexes (OpenAlex, Semantic Scholar, and arXiv) simultaneously from a single research question.
Screen the evidence
Deduplicate and score papers on a 0–10 relevance threshold against your defined research scope.
Understand the literature
Extract themes, gaps, frameworks, and comparative findings from the screened corpus.
Verify the claims
Trace synthesized claims back to retrieved full-text evidence to eliminate phantom citations.
Stay in control
Approve or revise the research direction in plain English before the next stage runs.
Produce the paper
Turn the research into a structured 14-section paper with PRISMA-style figures, matrix, and PDF/DOCX export.
AI does the heavy research.
You make the decisions.
DeepResearch uses LangGraph state gates to pause at three strategic checkpoints. Review findings, refine queries in natural language, or steer hypotheses before the pipeline proceeds.
The system never commits to downstream synthesis without your explicit approval.
Start a Research RunCheckpoint 1 · Scope & Keywords
Approve the research problem statement and search query terms before querying databases.
Checkpoint 2 · Literature & Framework
Inspect synthesized findings, identified research gaps, and screened evidence papers.
Checkpoint 3 · Theoretical Hypotheses
Validate proposed hypotheses before methodology, analysis planning, and full paper assembly begin.
Two Ways to Research
Choose between fast, cited web reports or comprehensive academic literature review synthesis.
- Runtime: ~5–10 minutes
- Agents: 5 parallel search agents
- Sources: Tavily Web Search (News, Finance, Academic)
- Checkpoints: 1 (Plan approval & revisions)
- Output: Cited Markdown report with web sources
- Runtime: ~15–25 minutes
- Agents: 25-agent research pipeline
- Sources: OpenAlex, Semantic Scholar, arXiv, Europe PMC, CORE
- Checkpoints: 3 Human-in-the-Loop decision gates
- Output: 14-section paper, PRISMA-style flow, Matrix, PDF/DOCX
Built as a real research system.
Engineered for reproducibility, state persistence, and open self-hosting.
interrupt() HITL checkpoints..env.Start your next research project with DeepResearch.
Turn your research question into a structured literature review whose cited claims link back to their sources, with human review at every major step.