Note: We make Atlas. This is a comparison written by the team that built it, not a neutral third-party review. Where Elicit has the better answer for a given research job, the article says so plainly. See the table rows where Elicit wins and the "When to choose Elicit" section below. The goal is to give you the data you need to choose the right tool for the kind of work in front of you, not to convince you Atlas is the answer to every research job.

Atlas is a visual research workspace for people whose work depends on understanding a body of papers: a thesis, a treatment decision, a major-purchase teardown, a literature review. Elicit is an AI-assisted research assistant focused on systematic literature search: enter a research question, Elicit retrieves relevant papers from Semantic Scholar, extracts structured data into a table, and synthesises summaries with citations. Both tools touch a researcher's daily work. The wedge is what happens after the first answer. Atlas deconstructs each paper into a Knowledge Map (a visual map of the argument), projects a whole corpus into a Semantic Map, runs every answer through claim-source-justification (the citation-grounded surface that explains why a passage supports a claim), and compounds prior work into a persistent knowledge graph so projects get smarter the longer you use Atlas. Elicit's brand and integration with Semantic Scholar are genuinely strong for systematic-review-style discovery, when you don't yet know which papers exist on a topic, Elicit's search-and-extract pipeline is one of the better starting points. If you need to trust the answers (for a thesis, a treatment plan, a brief, a hire), the visual maps, claim-source-justification, and compounding graph are where Atlas earns the comparison.

How is Atlas different?

Elicit and Atlas overlap at the surface: both touch the work of reading and reasoning over sources. But they diverge on three capabilities that decide whether the output is shareable, defensible work. This section walks through the three differences, in order.

1. Visual maps of every paper and project

Atlas builds two kinds of visual map automatically as you read. A Knowledge Map deconstructs each paper into its argument structure: claims, evidence, definitions, and labeled relations between them (motivates, causes, enables, contradicts), laid out as a multi-level zoom. You see the paper's spine at the top level and drop into the supporting passages with a click. A Semantic Map projects your whole project (sources, notes, chats, citations) into a spatial canvas where related items cluster by topic, and you can re-project the same canvas under a new topic angle without re-reading anything. The Semantic Map is how 200 papers stop being a folder and start being a corpus.

"It's like an ultimate GPT. I can finally see what I've read." Kyle Lao, NUS researcher

Elicit does not have a per-paper claim-evidence deconstruction or a topic-angle re-projection across an entire project. If you've ever spent an afternoon trying to recover the structure of a paper you read three weeks ago, the Knowledge Map is the surface that pays for itself first. Visual maps make a body of papers legible at a glance, and the multi-level zoom of the Knowledge Map is the surface Atlas is built around.

2. Every claim traces to a source, and Atlas explains why the source supports it

The hallucination problem in AI research tools isn't "the model made something up." It's "the model put a citation next to a claim that the cited passage doesn't justify." Atlas renders every answer as a claim-source-justification triple: the claim, the passage, and a one-sentence explanation of why the passage supports the claim. You can click into the source paragraph and read the highlighted sentences in context.

The benchmark Atlas runs internally is the H/V ratio: the proportion of generated sentences whose citation does not survive a passage-level re-check, divided by the proportion that does. Atlas targets H/V < 0.1 on the citation-grounding benchmark, and we publish how the benchmark is constructed in Verifiable AI Research (2026): What It Actually Means. Elicit's answers may include citations or links to sources, but they're grounded at the sentence-citation level (or not at all), not at the claim-justification level. For most casual question-answering the gap doesn't matter. For a thesis sentence, a legal brief paragraph, or a treatment-decision summary, it does. The wedge in one sentence: every claim traces to its source, and Atlas explains why the source justifies it.

3. Your projects compound: the second month is 10× the first

Elicit treats each session (or project, or workspace) as a separable container: work goes in, an answer comes out, and the next session starts fresh. Atlas builds a persistent per-user knowledge graph across projects: every citation you jump to, every annotation you make, every Knowledge Map and Semantic Map you generate accumulates into a four-layer graph (citations + mentions + KMs + SMs) that the next chat can draw from. Open a new project on a related topic and Atlas can pull in the relevant sources, prior annotations, and chat history without re-ingesting.

This is the capability we hear about most from long-term users: the second month is 10× the first because the graph has something to work with. John Tan, a postdoc using Atlas for a multi-year literature review, describes it as "the only tool where the work I did last semester is still doing work for me this semester." Put plainly: projects get smarter the longer you use Atlas. Elicit does not have an equivalent persistent compounding graph across projects, which is the wedge for sustained, multi-month research.

Try Atlas: Sign up for an evaluation sample (10 sources · 5 lifetime AI chats) and run a Knowledge Map on one of your own papers. Used by researchers at NUS, NTU, SMU, and eight other universities.

Comparing Atlas and Elicit

Both Atlas and Elicit touch a researcher's daily work, but they live in different categories. Atlas spans paper deconstruction, project navigation, source-cited answers, and compounding context across a corpus you own. Elicit spans literature discovery, structured data extraction across papers, and summary synthesis. Elicit's integration with the Semantic Scholar index is broader, while Atlas's research depth on an owned library is deeper. The rest of this article walks through the five capability surfaces where the two tools differ: per-paper deconstruction, project-level navigation, source-cited answering, literature-grounded annotations, and compounding context across projects. Each section is a two-column table where every row is a real capability, and at least one row in each table is one where Elicit wins or ties.

Paper deconstruction (Knowledge Map)

The Knowledge Map is Atlas's per-paper surface. It deconstructs a single paper into a multi-level argument structure with labeled relations between claims, faithful-to-source nodes (the node text comes from the paper, not from a generated summary), and hierarchical breadcrumbs that let you read down from the high-level thesis to a specific paragraph.

Good to know: The bottom row belongs to Elicit. Atlas does not ship that surface. The Knowledge Map's payoff is recovering a paper's argument three weeks after you first read it, when topic chips alone are no longer enough.

Project / corpus view (Semantic Map)

The Semantic Map is Atlas's per-project surface. It projects all the sources, notes, chats, and citations in a project into a spatial embedding where related items cluster by topic. Re-project the same canvas under a different topic angle without re-ingesting anything.

Good to know: Elicit's strength on that row is genuine. If your work depends on it, that's the boundary. The Semantic Map's payoff is when 200 papers stop being a folder and start being a corpus you can re-project under different topic angles without re-reading.

Citation-grounded answers

Atlas produces claim-source-justification triples: the claim, the passage, and a one-sentence explanation of why the passage supports the claim. You can jump to the source paragraph, read the highlighted sentences, and check whether the reasoning holds.

Good to know: Both tools have a citation surface. The wedge is whether the surface explains why a passage justifies a claim, not just which passage was cited. For everyday Q&A the gap is invisible, but for a thesis sentence or a brief paragraph it's the whole game.

Literature-grounded annotations

Atlas auto-annotates each paper on ingest. Citations inside the paper become first-class objects: Atlas resolves the cited source (when open-access), pulls the relevant passage, and lets you see how a citation in the paper builds up its argument across multiple sources without leaving the document.

Good to know: Literature-Grounded Annotations resolve citations inside the paper you're reading. When a paper cites a source that's open-access, Atlas pulls in the cited passage. It is not a web-grounding feature. It is a way to see how a single paper builds its argument across the sources it cites.

Compounding context across projects

Atlas builds a four-layer persistent graph (citations + mentions + KMs + SMs) across all your projects, so chats, annotations, and maps from one project become context for the next.

Good to know: Compounding is the slowest capability to demonstrate in a demo and the biggest payoff in week eight. If your work is many small, unrelated projects, Elicit's session-isolated design is the right choice, because isolation is a feature, not a gap. Compounding pays off for sustained, multi-month research.

Price comparison

Atlas is a paid product. There is no perpetual no-cost plan. You get a short evaluation sample (10 sources · 5 lifetime AI chats), and after that you pay $20/mo or $204/yr for Atlas Pro. At the paid tier, Atlas is the only tool with Knowledge Map, Semantic Map, claim-source-justification, and compounding graph. You aren't paying for chat tokens. You're paying for capabilities that Elicit doesn't have at any tier.

When to choose Atlas vs Elicit

• Want paper structure deconstructed multi-level? Go with Atlas. (Knowledge Map)
• Want answers that explain how each citation justifies the claim? Go with Atlas. (claim-source-justification)
• Want your projects to compound over months? Go with Atlas. (4-layer graph)
• Want systematic literature discovery from a research question across Semantic Scholar? Go with Elicit.
• Tied: running a structured-extract across 20 papers on a topic**: both work fine for different jobs. The wedge only opens up once you're building a corpus you'll return to.

Recommendations by user type

• PhD researchers: Atlas + Elicit. Use Elicit for the discovery phase (which papers exist on the topic?) and Atlas for the deep-reading phase (what does each paper argue?) once the papers are in your library.
• Students doing literature reviews and thesis research: Atlas, scoped to research workflows (dissertation, thesis, literature review). The Knowledge Map is the largest time-saver in the lit-review phase, and the compounding graph keeps prior work accessible across semesters.
• Knowledge workers (consultants, analysts, PMs, journalists): Atlas when you read reports and the occasional paper for client work, and Elicit for adjacent jobs it handles well. The claim-source-justification wedge is the difference between a slide you can defend in a meeting and a slide you can't.
• Personal researchers with stakes (medical, legal, major-purchase, deep autodidact): Atlas. Burst-usage research where the stakes are high (medical, legal, major-purchase, deep autodidact) is exactly where citation-grounded reasoning earns its keep. Elicit is a fine starting tool. Atlas is the tool you graduate to once you realize you'll need to defend the answer.

The honest one-liner across all four segments: if the research compounds, Atlas is the bet. If each session is self-contained and the next one starts fresh, Elicit's form is genuinely the better fit, and we'll say so plainly. The expensive mistake is using a session-isolated tool for compounding work (every project pays the re-ingestion tax) or using a corpus tool for one-off questions where simpler tools are faster. A useful diagnostic: ask whether you expect to come back to the same corpus in three months. If yes, the project-graph approach carries its weight. If no, lighter tools win on friction. Most research workflows we hear from at universities (Cambridge, Harvard, MIT, Stanford) sit firmly on the "yes" side: the corpus is the same corpus across semesters, advisors, and grant cycles, which is the cohort Atlas is built for. The corollary is that picking the right tool is mostly a question about your work pattern, not a question about which feature list is longer. Both tools do their job well within the form they're built for.

Migrating from Elicit to Atlas

Most Elicit-to-Atlas migrations are partial by design: people keep Elicit for search-and-extract jobs and move the deep-reading work into Atlas. The migration question is therefore less "how do I leave Elicit" and more "how do I move the artifacts I already built in Elicit into a workspace where I can deconstruct, annotate, and compound them." Practically, the things you have in Elicit are projects (saved questions plus the papers Elicit retrieved) and extracted-data tables (columns like intervention, sample, outcome, effect size, study type, year). Elicit lets you export the extracted-data table as CSV and the citation list as BibTeX, and individual PDFs can be downloaded one at a time from the linked-source view. That gets you a portable artifact: a CSV of paper references plus the columns you extracted, and a folder of PDFs corresponding to the rows you want to read more deeply.

What migrates cleanly into Atlas: the PDFs themselves and the paper references. Drop the PDFs into a new Atlas project and each one is auto-deconstructed into a Knowledge Map on ingest, with cited sources resolved (where open-access) via Literature-Grounded Annotations. The BibTeX-style citation list becomes the spine of your project library, and once the PDFs are in, every chat you run on that project is grounded in claim-source-justification against those papers. The CSV of extracted data can itself be uploaded as a source: Atlas will treat the table as ingested context, so when you ask a question that touches one of the extracted columns, Atlas can reason over the table alongside the underlying PDFs. That preserves the work you already did in Elicit's column extraction without forcing you to re-extract anything.

What does not migrate as a native object: Elicit's column-extraction templates (the prompts that define what "intervention" or "sample" means for your particular review), saved search queries against Semantic Scholar, and Elicit's saved-question-level UI for adding new columns to an existing table. Those are Elicit-native surfaces, and Atlas does not try to reproduce them. The wedge is different. If column extraction across a large paper set is the central job, keeping Elicit in the stack is the right call, and Atlas's own Export & Import is markdown-first (notes export as .md, bulk import accepts .md files) so anything you build in Atlas remains portable in standard formats. The migration is a one-afternoon job for a typical 30-paper corpus: export, upload, let the Knowledge Maps build overnight, and start the first deep-reading chat the next morning.

A worked example: literature-review section from 8 papers

Consider a literature-review subsection on, say, the effect of intermittent fasting on insulin sensitivity, where you have eight RCTs and review papers you want to synthesise into a defensible 600-word section with citations. In Elicit, the natural workflow is column extraction: you set up columns for intervention protocol, sample size, duration, primary outcome, effect size, and study limitations, run the extraction across the eight papers, and end up with a table you can read row-by-row. Elicit's summary view will then generate a short synthesis with inline citations to the rows. That is a genuinely strong workflow for extraction-table outputs, especially when the section you are writing is itself table-formed or when you want to verify a specific quantitative comparison across the corpus. Elicit is built for this and it shows.

In Atlas, the same eight papers go into a project and each is deconstructed into a Knowledge Map on ingest: claims at the top level (for example, "16:8 protocol improves HOMA-IR over 8 weeks"), supporting evidence one level down (the trial arm and statistical result), and labeled relations (causes, motivates, contradicts) connecting them. You then ask the chat a thesis-style question, "Across these eight studies, what is the consensus on insulin sensitivity changes under intermittent fasting protocols of 8+ weeks, and where do the studies disagree?" The answer comes back as claim-source-justification triples: each generated sentence is anchored to a passage in one of the eight papers, and a one-sentence reasoning trace explains why that passage supports the claim. You click into the passage, see it highlighted in context, and decide whether to keep the sentence, refine it, or chase the disagreement into a follow-up question. The Semantic Map across the eight papers shows you which trials cluster together by protocol and which one is an outlier, which is the kind of view that often surfaces a confound you would otherwise have missed.

The honest read on the comparison: if the deliverable is a results table, Elicit's column extraction gets there with less ceremony. If the deliverable is a defensible 600-word paragraph where each sentence needs a passage you can point to in a viva, claim-source-justification is what carries the work, and the Knowledge Maps make sure you do not lose the structure of any single paper as you write across them. The two workflows are not in tension. Many researchers run Elicit's extraction first to get the table, then move the same eight PDFs into Atlas for the prose write-up. The week-eight payoff comes from the next subsection: when you start on, say, time-restricted eating and cardiovascular markers, three of the eight insulin-sensitivity papers reappear in the new corpus, and Atlas's persistent graph surfaces the prior annotations and chat history without re-ingesting anything.

When Elicit is the right call

There are jobs where Elicit is straightforwardly the better recommendation, and we will say so plainly. The first is column extraction across many papers: if your output is a structured table where every row is a paper and every column is a consistently-defined field (intervention, sample, outcome, effect size, study quality score), Elicit's extraction pipeline is purpose-built for it and the time-to-table is shorter than anything Atlas offers, because Atlas does not ship a column-extraction surface. The second is paper discovery via search-then-extract: you do not yet have the papers, you have a research question, and you want a tool to find candidate papers from Semantic Scholar and immediately summarise what each one contributes. Atlas is grounded in your uploaded library plus the cited-source resolution layer. It does not issue ad-hoc Semantic Scholar searches from a research question.

The third is systematic-review-style data tables, where the output artifact itself is a table (PRISMA flow, evidence summary, intervention comparison grid) and the table is the deliverable, not a write-up that draws on the table. Elicit's project view is the right shape for that work. The fourth is the very-first-pass discovery question, "what literature even exists on X," where you want a broad sweep across the index before you commit to reading anything deeply. In each of these cases, the right move is to use Elicit for what it does well and revisit Atlas once the corpus has been selected and the deep-reading or compounding phase begins. Many of the research workflows we see in practice run both tools side by side for exactly this reason.

Common objections and edge cases

"My field cares about systematic-review rigor and PRISMA flow diagrams. Does Atlas help?" Atlas is not a systematic-review tool in the PRISMA-flow sense. It does not ship a screening interface, an inclusion/exclusion log, or an automated extraction template that locks the same columns across every reviewer. For the screening and extraction phases of a formal systematic review, Elicit or a purpose-built tool is the right call. Once the included papers are selected and the review enters the synthesis-and-write-up phase, Atlas's Knowledge Maps and claim-source-justification carry their weight on the prose. The two tools live in different phases of the same project.

"I want one answer that searches the open web, not just my library. Which tool?" Neither tool searches the open web in the general sense. Elicit searches Semantic Scholar's index of academic papers from a research question. Atlas is grounded in your uploaded library plus Literature-Grounded Annotations that resolve open-access sources cited inside your papers. If web-grounded answering is the core need (news, blogs, non-academic sources), a general-purpose answering tool is the better fit than either. The choice between Atlas and Elicit is really a choice between depth on an owned library and discovery across an index.

"I am a solo researcher with no collaborators. Does the compounding graph still earn its keep?" Yes, and arguably more so. The compounding graph's value comes from prior work becoming context for future work. The unit it compounds across is "you and your projects over time," not "you and your team in one project." A solo researcher who returns to overlapping topics across semesters is the sharpest case for the four-layer graph: the citations, mentions, Knowledge Maps, and Semantic Maps you built six months ago resurface automatically in the new chat, which is the difference between starting from zero and starting from where you left off.