Turn a research question into evidence-grounded literature reviews.
Search academic literature, extract evidence, check citations, and generate a structured research paper.
Structured literature synthesis, from query to paper.
Inspect real research runs with PRISMA study flow, evidence extraction, and drafted review sections.
"What techniques reduce hallucinations in large language models?"
Mitigating Hallucinations in Large Language Models: A Systematic Literature Review
Recent empirical analyses categorize model unfaithfulness into intrinsic contradictions and extrinsic fabrications. Retrieval-augmented architectures significantly reduce factual errors by grounding generation on indexed document corpora...
- 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 | 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) |
Works inside Claude Science
CiteProof also ships as a local MCP server. Your AI agent does the thinking; CiteProof keeps the record straight.
- Stores every review decision in a per-review local database
- Counts PRISMA numbers and checks that they add up
- Checks quotes against the source text you supply
- Records screening decisions and computes reviewer agreement (Cohen's kappa)
- Finds duplicate records and clusters them
- Checks DOIs and flags retractions through Crossref
- Exports an audit bundle you can keep with the review
The server contains no language model. The agent makes every judgement; CiteProof stores it and checks it where it can.
Works with Claude Science and other MCP clients. Claude is a trademark of Anthropic; CiteProof is not affiliated with Anthropic.
How it works
A structured four-stage research workflow built for reproducible, citation-grounded literature synthesis.
Search
Query multiple academic indexes—including OpenAlex, Semantic Scholar, and arXiv—simultaneously from a single research question.
Screen with PRISMA counts
Deduplicate papers by DOI and normalized title, then score abstracts against research criteria on a 0–10 relevance scale while tracking identification, deduplication, screening, and inclusion numbers.
Extract evidence
Extract key findings, methodologies, domain frameworks, and research gaps from the screened corpus into a structured evidence table.
Write and check citations
Draft structured review sections with claims evaluated against source text by a model, and unresolved or retracted citations flagged before export.
Evidence and citation checks
Honest validation at every step. Model judgements are backed by extracted source passages.
DOI and retraction check
Resolves identifiers against Crossref and DataCite to confirm paper records exist, identify duplicates, and flag retracted literature before synthesis.
Claim-support check by a model
Every generated claim is checked by a model against retrieved full-text evidence. Unsupported statements are flagged or rewritten before assembly.
Evidence table
Assembles a structured matrix tabulating study authors, methodology, empirical findings, and evidence levels for transparent auditability.
Prose lint
Scans draft sections for ungrounded generalizations, vague quantifiers and process narration before assembling the final document.
Two ways to research
Choose between fast cited web synthesis and comprehensive academic literature reviews.
DeepSearch
Fast web research & cited 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
Research Mode
Full academic paper & literature synthesis
- Runtime ~15–25 minutes
- Agents 24-agent research pipeline
- Sources OpenAlex, Semantic Scholar, arXiv, Europe PMC, CORE
- Checkpoints 3 Human-in-the-Loop decision gates
- Output 14-section paper, PRISMA flow, Matrix, PDF/DOCX
AI does the heavy research. You make the decisions.
CiteProof pauses at three strategic checkpoints so you can guide the direction. The system never commits to downstream synthesis without your explicit approval.
Checkpoint 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.
Start your next research project with CiteProof.
Turn your research question into a structured literature review whose cited claims link back to their sources, with human review at every major step.