Note: We make Atlas. This is a comparison written by the team that built it, not a neutral third-party review. Where Mural has the better answer for a given research job, the article says so plainly. See the table rows where Mural wins and the "When to choose Mural" 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. Mural is a collaborative whiteboard tool: infinite canvas, sticky notes, frameworks, and real-time multi-user editing, designed for enterprise teams running design thinking, workshops, and strategic sessions. 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. Mural's brand and integration with enterprise workflows and workshop methodologies are genuinely best-in-class, the framework templates, voting, and timer features are well-executed for design-thinking and workshop hosting. 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?

Mural 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

Mural 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. Mural'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

Mural 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. Mural 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 Mural

Both Atlas and Mural touch a researcher's daily work, but they live in different categories. Atlas spans paper deconstruction, project navigation, source-cited AI answers, and compounding context across a research corpus, Mural spans collaborative whiteboarding for enterprise workshops. Mural's integration with enterprise design-thinking methodologies is broader, Atlas's research depth at the citation surface 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 Mural 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 Mural. 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: Mural'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, 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, Mural's session-isolated design is the right choice, 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 Mural doesn't have at any tier.

When to choose Atlas vs Mural

• 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 enterprise-grade collaborative whiteboarding with workshop templates? Go with Mural.
• Tied: running a workshop where research findings are mapped onto a canvas**: both work fine, different jobs. The wedge only opens up once you're building a corpus you'll return to.

Recommendations by user type

• PhD researchers: Atlas. Lit-review-heavy years 1–2 benefit most from the Knowledge Map (deconstruct each paper without re-reading). Thesis-writing years 3–4 benefit from claim-source-justification (every thesis sentence anchored to a passage). Mural works for one-off tasks, the multi-year compounding graph is what makes Atlas the right tool here.
• 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 reading and citing papers is the core work, Mural when large-org workshop hosting is the daily need.
• 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. Mural 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, Mural'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 Mural to Atlas

If you've been running your literature work inside Mural and are evaluating Atlas as a mural alternative for research, the practical migration is narrower than it looks. Mural's surface is an infinite canvas with sticky notes, framework templates (design-thinking grids, sprint boards, journey maps, affinity clusters), real-time co-editing, voting, and timers. Its export options are PDF, PNG (the canvas as an image), and CSV (the sticky-note content as a flat table, one row per sticky). That CSV is the bridge.

What migrates cleanly: the text on your sticky notes. If your murals contain quotes, paraphrases, themes, and source labels typed into stickies, the CSV export gives you a flat row-per-sticky list you can re-import as notes against the underlying source PDFs. The work then continues inside Atlas with the same content, indexed against the paper it came from.

What does not migrate: spatial layout, voting state, framework templates as templates, real-time collaborator state, and any session history (who summoned whom, who voted on what, the order stickies were added). Atlas is not a whiteboard, so the canvas geometry that holds meaning in Mural ("these four stickies are clustered because they're the same theme") does not carry over as geometry, it carries over as the cluster label you typed plus the four sticky texts. If the cluster labels were implicit, you'll want to add them to the CSV before import so the grouping survives.

The recommended migration sequence: (1) export each mural as CSV, (2) export the underlying source PDFs you were synthesizing, (3) upload the PDFs to Atlas, where they're deconstructed into Knowledge Maps on ingest, (4) attach the relevant sticky-text rows as notes against the matching paper, and (5) re-run the synthesis as a chat over the new project. The chat will produce claim-source-justification triples grounded in the actual passages, replacing the manual sticky-clustering step. Most teams who migrate end up with a smaller, denser corpus than their original mural held.

A worked example: literature-review section from 8 papers

Concretely, here's the same job done two ways, so the wedge is visible. The job: write a 600-word literature-review section that synthesizes the contributions of eight related papers, with citations and a defensible argument structure.

In Mural: open a new mural, drop in the eight PDFs as link tiles or attached images. Create a sticky for each paper, with title and one-sentence summary. Read each paper (Mural does not deconstruct PDFs), and as you read, add stickies for each notable claim, evidence, and method, color-coded by paper. Cluster the stickies by theme. Add labeled connector lines between clusters to express relations. Vote on the top three themes if you're working with collaborators. Open a separate document and type the literature-review section, manually cross-referencing the stickies to remember which paper each claim came from, and manually inserting citation keys. Time investment: a full afternoon for the read-and-cluster phase, plus a second session for the write. The output is a section that depends on your memory of which sticky maps to which passage of which paper, defending a sentence three months later means re-finding the paragraph by hand.

In Atlas: create a new project, upload the eight PDFs. Atlas ingests each one and builds a Knowledge Map per paper, with claims as nodes, evidence as supporting nodes, and labeled relations between them (motivates, causes, enables, contradicts), with node text drawn faithfully from the paper. Open the Semantic Map for the project, the eight papers cluster by topic and you can re-project the canvas under your chosen literature-review angle ("methodological contributions", "open problems", "competing frameworks") without re-reading. Open a chat and ask "synthesize the contributions of these eight papers into a 600-word section organized by methodological frame." Atlas returns the draft as claim-source-justification triples: each claim cited to a passage, each citation accompanied by a one-sentence explanation of why the passage justifies the claim. You jump to each cited paragraph, confirm the reasoning holds, and edit. Time investment: roughly an hour, most of it spent verifying citations rather than re-finding them. The output is a section where every sentence has a paragraph anchor you can re-open in three months.

The structural difference: in Mural the synthesis is your job, with the canvas as scaffolding for your memory, in Atlas the synthesis is the tool's draft, with the canvas (Knowledge Map + Semantic Map) as the verification surface. Both can produce the same finished section, but the cost and the auditability differ by an order of magnitude.

When Mural is the right call

Mural is genuinely the better tool for a set of jobs that Atlas does not try to do, and it's worth naming them so the recommendation is honest. The five places Mural is the right call:

• Live workshops where a workshop lead needs voting, timers, summoning, and private mode to run a distributed session in real time. Atlas has none of those, by design.
• Design-thinking and double-diamond exercises where the framework template (empathy maps, journey grids, How-Might-We boards, 2x2 prioritization) is itself the deliverable. Mural's template library is broad and well-executed.
• Sprint planning and retrospectives for product teams that already live inside a Microsoft Teams or Atlassian stack. The integration depth is real.
• Enterprise team collaboration with frameworks, where the value is in the shared canvas the whole org returns to, not in the per-document deconstruction of a paper.
• Customer-journey mapping and service blueprinting, where spatial layout is the artifact, not a scaffolding for an underlying corpus.

If your week is built around live group sessions and the deliverable is a canvas the team will revisit together, Mural is the answer and we'll say so plainly. Atlas's surface is the dedicated research workspace for citation-grounded synthesis. The two tools cover different jobs and many teams keep both.

Common objections and edge cases

"My team already runs everything in Mural. Won't a second tool fragment the workflow?" Less than it sounds. The realistic split most teams settle on is Mural for the live sessions (workshops, retros, journey maps) and Atlas for the dedicated research corpus that feeds them. Source PDFs and sticky-note text move between the two via export-import, there's no live integration, and for most workflows that's fine because the cadence is different (Mural is real-time, Atlas is read-and-synthesize). The fragmentation cost is one extra login, the gain is source-cited answers your whiteboard tool was never built to produce.

"Can Atlas do real-time multi-user editing on a shared canvas like Mural?" No. Atlas is a single-user-at-a-time research workspace, there is no live cursor presence, no co-editing of a shared canvas, no summon-to-here feature. Where Atlas does support collaboration is at the project-handoff level (sharing projects, sources, and chats with collaborators asynchronously). If your job requires synchronous canvas collaboration, Mural is the right tool and Atlas is not trying to replace that surface.

"Does Atlas have framework templates like How-Might-We or empathy maps?" No. Atlas's surface is the Knowledge Map (per-paper argument structure) and the Semantic Map (per-project topic projection), not a library of design-thinking templates. If your work depends on the templates, Mural wins that row outright. Atlas's bet is that for paper-heavy research the argument-structure surface is more valuable than a template library, for design-thinking work the template library is more valuable. Pick by the job in front of you, not by the longer feature list.