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Every lab has a rhythm. Raw material comes in. It gets cleaned, sorted, shaped, then assembled into something useful. Research should feel the same. Inputs are papers, datasets, transcripts, and notes. The assembly line is a set of small, reliable agents that summarize, cite, and draft with traceable logic. The goal is not to worship automation. The goal is to build a Research Factory that saves time without sacrificing truth. That phrase is also our SEO key phrase: The Research Factory: Agents That Summarize, Cite, And Draft.
The promise is real when you ground language models in evidence and you keep humans in the loop. Retrieval augmented generation raises accuracy by pulling context from a vetted corpus. Hallucinations drop when claims must match sources. A steady editorial process turns fragments into clear prose. The rest of this guide shows the blueprint, the prompts, and the guardrails that make it safe and useful, with references to the best public surveys and standards available today. (arXiv)
Why build a Research Factory now
Large language models are astonishing storytellers, but they are not omniscient. They will sound confident even when they are wrong. Surveys on hallucination document the problem, classify failure types, and catalog mitigation tactics. These surveys point to factors like missing context, retrieval failure, weak prompts, and training data drift. Any serious research workflow must assume that ungrounded generations can mislead. That is the risk case for The Research Factory: Agents That Summarize, Cite, And Draft. (arXiv)
Policy bodies have noticed. NIST released a Generative AI profile for the AI Risk Management Framework that calls for traceability, evaluation, and documentation across the lifecycle. The U.S. AI Safety Institute has also pushed draft guidance and tooling for developers who deploy generative systems. These are not academic niceties. They are rails that help teams ship research outputs they can defend. (NIST)
The good news is practical. Retrieval augmented generation, usually called RAG, gives researchers a disciplined way to tether generation to sources. Several comprehensive surveys explain how RAG architectures fetch relevant passages from a corpus before the model writes. The model then drafts with citations and can be forced to quote. The approach works well for knowledge intensive tasks like literature reviews, evidence tables, and claim checking. This is the technical backbone of the factory you are about to build. (arXiv)
The assembly line at a glance
The Research Factory runs on five stations and three quality gates. Everything else is optional.
Station A. Ingest
Collect PDFs, web pages, data summaries, transcripts, and prior memos. Normalize them into text with metadata.
Station B. Retrieve
Search across the corpus using embeddings or hybrid lexical plus vector search. Pull top passages with citations.
Station C. Summarize
Ask an agent to compress each source into a structured card with facts, claims, and uncertainties. Include direct quotes with line numbers or page numbers.
Station D. Synthesize
Combine cards into an outline. Ask the agent to group by question, by method, or by finding. Keep links to the underlying passages.
Station E. Draft
Write sections in plain English. Insert inline citations and a reference list generated from canonical identifiers.
Quality Gate 1. Attribution
Reject any sentence that lacks a source when a source is required.
Quality Gate 2. Consistency
Compare the draft to the original passages. Check that numbers and qualifiers match.
Quality Gate 3. Risk
Run a short checklist from the NIST profile: document data sources, note model versions, log known limitations, and capture approval. (NIST)
This pattern is model agnostic. It is a mindset. If you prefer concrete tools, Elicit and scite are sensible companions. Elicit focuses on literature discovery, tabular evidence extraction, and sentence level citations for claims. Scite provides Smart Citations that label whether later papers support or contrast a claim. Together they encourage an evidence first habit. (Elicit)
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Agents that do the work without doing harm
You do not need a swarm. You need three specialists that play nicely with your corpus and your editor.
1) The Summarizer
Mission: Turn one source into a structured note card that a future you will understand at a glance.
Inputs: A single paper, report, or transcript.
Outputs:
- 5 to 9 bullet summary in neutral language
- Key numbers with units and study scope
- Methods in one short paragraph
- Limitations in one short paragraph
- 3 direct quotes with page or line markers
- 3 linked citations to related sources from your corpus
Prompt: You are the Summarizer in The Research Factory: Agents That Summarize, Cite, And Draft. Read the source and produce a structured card with: abstract level summary, method, key numbers, three verbatim quotes with page or section markers, and explicit limitations. Use sentence level citations after any claim that is not obvious. If a claim lacks a source, write “Source needed.”
Why this works: it forces a separation between what the paper says and what you wish it said. It also builds a reusable unit that can be checked by a colleague without rereading the full paper. Surveys on hallucination recommend such grounding and traceable claims. (arXiv)
2) The Citer
Mission: Attach trustworthy citations to each nontrivial claim and assemble a reference list.
Inputs: Draft paragraphs with citation placeholders.
Outputs:
- A mapping from each claim to candidate passages
- Inline citations that resolve to stable identifiers
- A reference section in the required style
- A shortlist of claims that need human verification
Prompt: You are the Citer for The Research Factory: Agents That Summarize, Cite, And Draft. For each sentence, search the corpus for supporting or contrasting passages. Insert citations in author-year style and include direct quotes for sensitive facts. If evidence is weak or contradictory, flag the sentence and propose a safer rewrite. Prefer sources with Smart Citations that indicate support or contrast.
Tools like scite make this agent more reliable because they distinguish supportive from contrasting citations. That saves you from accidentally citing a paper that refutes your claim. (Scite)
3) The Drafter
Mission: Compose readable sections that explain what is known, what is debated, and what the next step should be.
Inputs: Batches of cards from the Summarizer and links from the Citer.
Outputs:
- An outline with headings framed as questions
- A first draft that uses active voice and plain terms
- Tables for conflicting results or caveats
- A short box titled “What this does not show”
Prompt: You are the Drafter in The Research Factory: Agents That Summarize, Cite, And Draft. Using the cards and citations, write a 600-word section that explains the state of evidence for the research question. Use short paragraphs. Avoid claims that lack a source. Include a box of limitations at the end and a one-sentence next-step recommendation.
This agent benefits most from RAG. The better your retriever, the stronger your draft. Current surveys explain that RAG helps with knowledge update, long tail facts, and robustness. That is exactly what researchers need when literature shifts quickly. (arXiv)
Guardrails that keep your footing
A factory without safety rails is a hazard. Three rules make the system sturdy.
Rule 1. Ground before you generate.
Every section draft should begin with a retrieval step. Do not let the model free write on memory alone. RAG surveys show accuracy gains when generation is conditioned on retrieved passages. (arXiv)
Rule 2. Separate synthesis from opinion.
Mark speculation clearly. Use phrases like “A plausible interpretation is” or “Open question.” The hallucination literature shows that readers struggle to tell fact from fluent inference. Your markup is a courtesy and a control. (arXiv)
Rule 3. Log the work.
Record the model version, retrieval settings, and corpus snapshot. The NIST profile advocates documentation and traceability. You cannot reproduce results if you do not keep notes. (NIST)
A final caution is timely. Media coverage in mid 2025 noted that some frontier models improved reasoning while also surfacing different hallucination profiles on certain benchmarks. Treat that as a reminder to test your stack on your own tasks rather than relying on headline metrics. (Live Science)
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Evidence tables that do not crumble
Narrative reviews are useful. Tables are faster to scan. Your factory should produce both. Here is a compact pattern.
Columns: claim, population or scope, effect or number, method note, limitations, citation.
Rows: one per source, one extra row for your synthesis.
Prompt: Create a six-column evidence table from these cards. Use one short line per paper. Quote numbers as they appear, with units. If two papers conflict, bold the row labels and add a footnote that explains the difference in method or sample. Include author-year citations in each row.
This layout discourages wishful synthesis. If results disagree, the conflict becomes visible. RAG surveys recommend explicit handling of conflicts rather than averaging them away. (arXiv)
Citing with integrity
Citation is not decoration. It is navigation. Your Citer agent should favor stable IDs and context windows that include the quoted sentence plus the sentences before and after it. When possible, use tools that expose whether a citation supports or contrasts your claim. Scite’s Smart Citations do exactly this, which is why it belongs in a research stack. If you rely on an AI research tool for discovery or synthesis, follow its recommended citation practices as well. Elicit publishes guidance for how to cite its outputs and underlying papers. Use that transparency to your advantage. (Scite)
Prompt: For each citation in this draft, append a parenthetical tag of [supports], [mentions], or [contradicts] based on the surrounding context. If uncertain, mark [unclear] and add a comment for human review. Prefer citations with Smart Citations when available.
The end-to-end walkthrough
Let us run a small example to see how The Research Factory: Agents That Summarize, Cite, And Draft behaves on a real question: “Do retrieval augmented systems reduce hallucination in knowledge heavy tasks.”
- Ingest: you load four surveys on RAG and two surveys on hallucination. The corpus now includes overviews that define terms, list methods, and point to benchmarks. (arXiv)
- Retrieve: the retriever pulls sections on accuracy, robustness, and knowledge update, along with the taxonomy of hallucinations. (arXiv)
- Summarize: the Summarizer creates cards for each paper with methods and key claims.
- Synthesize: the agent groups claims into three themes. First, RAG helps with freshness and long tail facts. Second, failures still occur if the retriever misses. Third, evaluation remains hard because benchmarks vary. (arXiv)
- Draft: the Drafter writes 600 words that explain the tradeoffs. It quotes a definition of RAG from a survey and places the quote in quotation marks with a page or section marker. It lists known failure modes like retrieval miss, chunking errors, and spurious matches. (arXiv)
- Cite: the Citer adds inline references, pulls stable IDs, and adds a reference list.
- Quality gates: you run the attribution pass. Every claim about performance gains ties back to a survey or benchmark. You run the consistency pass. Numbers match the originals. You run the risk pass from the NIST profile. The draft logs its sources and flags open evaluation questions. (NIST)
The output is not only readable. It is defensible. That is the point.
Pitfalls and how to dodge them
Over-fitting to a narrow corpus.
If your document store is small, RAG may surface the same paper repeatedly. Counter this by refreshing the index with diverse sources and by using hybrid retrieval that mixes keyword and vector search. RAG surveys warn about narrow retrieval scopes that bias synthesis. (arXiv)
Citation drift.
Long drafting sessions can push citations out of alignment with claims. Run a final “evidence check” pass that re-retrieves for each claim to confirm that texts still support the line. Tools that classify supportive versus contrasting citations help here. (Scite)
False certainty.
Even with retrieval, models sometimes overstate. Require hedging where appropriate. The hallucination literature advises calibrated language and uncertainty markers when evidence is mixed. (arXiv)
Policy blind spots.
If your research touches sensitive domains, adopt the NIST profile’s emphasis on documentation, evaluation plans, and red team tests for misuse. Build those checks into the factory’s risk gate. (NIST)
Prompts you can paste today
Use these as starters. Tweak the nouns to match your field.
Prompt: Build a literature map for The Research Factory: Agents That Summarize, Cite, And Draft. Cluster papers into definitions, methods, evaluations, and applications. Output a bullet list with 3 to 5 items in each cluster, each with a one sentence note and a citation.
Prompt: From these four papers, extract every quantitative result about retrieval accuracy or hallucination rate. Present a table with metric, value, dataset, and citation. Include direct quotes for each number with page markers.
Prompt: Create a 400-word related work section that contrasts naive generation, classic search plus copy, and retrieval augmented generation. Use simple language, insert two short quotes, and include citations after each claim.
Prompt: Run a contradiction sweep on this draft. For each paragraph, search the corpus for any source that disagrees. Insert a comment with the citation and a one sentence description of the conflict.
Prompt: Produce a one page executive summary with three findings, three caveats, and three recommended next steps. Every finding must have a citation. Every caveat must reference a limit or bias in the sources.
Reporting and reproducibility
A factory is only as useful as its records. Keep a simple log at the top of every major draft.
- Model family and version
- Retrieval settings and index date
- Corpus size and high level composition
- Key prompts used for summarization, citation, and drafting
- Names of human reviewers and approval dates
This mirrors the spirit of the AI Risk Management Framework. It also saves future you from archaeology. (NIST)
Prompt: Append a provenance block to this document that lists model version, retrieval settings, and a timestamped list of sources. Render as a fenced code block that can be pasted into version control notes.
When to trust, when to verify
Trust the system for scaffolding. Verify the edges. Three quick tests help.
The Drop Test: remove your strongest source and rerun the synthesis. If conclusions shift wildly, your evidence base is fragile.
The Opponent Test: ask the Citer to find the best available counterclaim and place it side by side with your preferred reading. If you still stand by your summary after reading both, you likely did honest synthesis.
The Freshness Test: rerun retrieval with a time filter for the last six to twelve months. If new sources change the story, update gracefully.
These habits align with both the technical literature on retrieval and the policy literature on evaluation and documentation. (arXiv)
A short case scenario
A health policy team must brief leadership on whether community screening guidelines should shift this year. The team loads ten systematic reviews, three position statements, and two economic models. The Summarizer turns each into cards with methods and key numbers. The Citer attaches Smart Citations and flags two claims that have notable contrast in the literature. The Drafter produces a clear section with a conflict table and suggests a cautious recommendation, tied to data quality. The NIST style risk gate records model version and retrieval settings. The final brief gets delivered with quotes and links. The director signs off because the logic is visible and the gaps are named. That is The Research Factory: Agents That Summarize, Cite, And Draft doing its job.
What to read and why
If you are new to retrieval, start with any of the major RAG surveys. They explain architectures, evaluators, and common failure modes. If you want a single document to shape your governance, read the NIST Generative AI profile and skim the US AI Safety Institute announcements for evaluation tools and templates. If you want practical discovery and citation aids, try Elicit for literature mapping and scite for Smart Citations. These references will make your factory both faster and safer. (arXiv)
Closing thoughts
Research is a craft. Tools should make the craft steadier, not sloppier. A small roster of agents that summarize, cite, and draft can raise your throughput without lowering your standards. Retrieval keeps the model honest. Citation keeps the writer honest. Documentation keeps the team honest. If you take one idea from this guide, let it be this: build a system where every strong sentence can point to a passage.
The result is not sterile. It is liberating. You spend less time shuffling PDFs and more time thinking. You publish faster and you correct faster. You can show your reasoning to a skeptic and win them with clarity. That is the heart of The Research Factory: Agents That Summarize, Cite, And Draft. Start with one agent and one corpus this week. Add a second agent next week. When the rhythm feels natural, add the third. Keep your curiosity sharp. Keep your citations sharper.
