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← All cardsDOSSIER · Academic discovery · SOLID · VERIFIED 2026-06-09
Research Rabbit69SolidBenchmark pendingVisual literature exploration and citation-network discoveryVerified 2026-06-09

Dossier · Academic discovery

Research Rabbit

Visual literature exploration and citation-network discovery · last verified 2026-06-09

Solid
Academic discovery
Research Rabbit
69/100
ROLEVisual literature exploration and citation-network discovery
Editorial fit
69
Source quality
55
Citation honesty
72
Privacy controls
36
Value for money
74
Speed
58
FREEFree Forever
$10/MORR+ (annual)
$12.5/MORR+ (monthly)
SolidVerified 2026-06-09VP·METHOD

VERDICT HISTORY

  • Research Rabbit is the visual exploration and “follow the trail” card. It belongs beside Connected Papers and Litmaps, but its emphasis is adaptive exploration, collections, and seeing how papers/authors/concepts connect. This pass adds current feature language, data-source hints, and a clearer privacy/method boundary.
  • Converted imported Notion research into a full flagship-ready dossier template with metrics, panes, pricing deck, scenarios, benchmark rows, and comparison slices.

EDITOR'S NOTE

Research Rabbit makes exploration feel alive. VerdictPal should praise that feeling while making clear that an adaptive visual trail is not the same as a documented search method.

AT A GLANCE

Literature-discovery and mapping tool for building paper collections, exploring citation and concept networks, finding related work, and following research trails visually.

Role: Visual literature exploration and citation-network discoveryCategory: Academic discoveryEditorial · hands-on

Research Rabbit flagship-ready dossier: Visual literature exploration and citation-network discovery.

PUBLIC FACTS · vendor & repo

List prices and pay-as-you-go entry points we can cite without running our own bench. Each tile links to a source when possible.

Academic discoverySetVerdictPal card ↗
synthesizedEvidenceQuality gate ↗
6Sources checkedCard sources ↗
2026-06-07Pricing checkedQuality gate ↗

Limits & product surface

Non-price vendor claims — multipliers, caps, and API scope. Detailed matrices live in subscription and SDK sections below.

Primary surface
consumer-appCard identity ↗
Modalities
textCard identity ↗
Workflow roles
[object Object], [object Object], [object Object]VerdictPal editorial ↗
Alternatives tracked
Connected Papers, Litmaps, Semantic Scholar, Zotero, ElicitVerdictPal comparison set ↗

HOW IT WORKS · agent loop

The public positioning for this product — the loop we score against on VerdictPal.

Start with the wedge

Students who want to start from a few papers and discover adjacent literature visually

Run the representative task

Run five seed papers through Research Rabbit, Connected Papers, and Litmaps.

Check the failure modes

Citation maps can imply completeness

Compare before recommending

Compare against Connected Papers, Litmaps, Semantic Scholar before shipping advice.

METRIC LAB · 16 DIMENSIONS

Click a tile for the editorial note. Color follows score: coral, yellow, mint.

Shape

Avg 68 · 36–98

69Editorial fit

Weighted roll-up across 13 dimensions for Academic discovery; pending desk verification if rescored from public facts.

By group

Fit74
  • Editorial fit69
  • Wedge task fit68
  • Feature depth86
Cost67
  • Free-tier utility79
  • Cost-to-value74
  • Opportunity cost49
Trust72
  • Source grounding72
  • Privacy posture36
  • Failure transparency98
  • Evidence strength55
  • Transparency98
Workflow61
  • Integration reach52
  • Setup friction58
  • Reliability70
  • Competitive position75
  • Data portability49

SEARCH MODES · 4 lenses

One input box, many retrieval postures. Filter by tier.

01Free

Default

Default path for Research Rabbit — verify limits on the live product.

02Pro

Deep work

Deep work path for Research Rabbit — verify limits on the live product.

03Pro

Focused task

Focused task path for Research Rabbit — verify limits on the live product.

04Free

Export & share

Export & share path for Research Rabbit — verify limits on the live product.

BENCHMARK LEDGER

Public rows are vendor or third-party claims we logged with a date. Desk rows are reserved for VerdictPal self-run results.

MeasureResultSource
Run five seed papers through Research Rabbit, Connected Papers, and Litmapsuseful novelty, seminal recall, blind spots, exportability, collection workflow, author/concept usefulness.PlannedVerdictPal benchmark plan
Build a controlled collection and observe how recommendations change as known relevant/irrelevant papers are addedrecommendation improvement, drift, false positives, transparency, user-control friction.PlannedVerdictPal benchmark plan
Test account creation, collection export, deletion, and privacy controlsdata reversibility, retention clarity, deletion confidence, sensitive-topic suitability.PlannedVerdictPal benchmark plan

PRICING DECK · checked 2026-06-09

Desk pick

RR+ (annual)

$10

month

  • Billed annually
  • Verify live regional checkout before publishing procurement advice.

RR+ (monthly)

$12.5

month

  • Imported from the current pricing summary.
  • Verify live regional checkout before publishing procurement advice.

RAW · checked 2026-06-09: Verified from official ResearchRabbit pricing on 2026-06-07: Free Forever $0 with unlimited searches across 280+ million articles, unlimited library/collections, collection sharing, up to 50 seed articles, and core search settings. ResearchRabbit+ is $10/month annual or $12.50/month monthly, with up to 300 seed articles, advanced search controls, multiple projects, and faster support. Pricing depends on country and has discount codes for 100+ countries. Institution is custom with RR+, LibKey integration, usage stats, thousands-of-users management, volume discounts, and dedicated support.

RESEARCH LOG · desk notes

What we learned while building this dossier — not vendor copy.

Notion Card pipeline ↗

Notion research pass

Research Rabbit is the visual exploration and “follow the trail” card. It belongs beside Connected Papers and Litmaps, but its emphasis is adaptive exploration, collections, and seeing how papers/authors/concepts connect. This pass adds current feature language, data-source hints, and a clearer privacy/method boundary.

VerdictPal git ↗

Flagship-ready structure

Converted imported research into metrics, panes, scenarios, benchmark rows, comparison notes, and pricing deck.

COMPETITIVE LENS

Where Research Rabbit wins for cited research — and where a rival still belongs in the stack.

Research Rabbit is stronger when students who want to start from a few papers and discover adjacent literature visually; Connected Papers may still win for narrower fit, procurement, or specialist depth.

Research Rabbit

  • Students who want to start from a few papers and discover adjacent literature visually
  • Researchers comparing citation-map tools against Connected Papers and Litmaps

Connected Papers

  • You need reproducible PRISMA-style search strings, database logs, and screening audit trails
  • You cannot store collections, notes, reading patterns, or usage metadata in a cloud literature tool

UNDER THE HOOD

Vendors named on the product about page — useful for procurement and privacy reviews.

literature exploration
Research Rabbit
literature mapping
Connected Papers
citation network discovery
Litmaps

DEEP PANES · 6 LENSES

Editorial lenses only. Subscription and API pricing live in their own sections above.

Lens 01

Positioning

Research Rabbit is framed here as Visual literature exploration and citation-network discovery. Research Rabbit is the visual exploration and “follow the trail” card. It belongs beside Connected Papers and Litmaps, but its emphasis is adaptive exploration, collections, and seeing how papers/authors/concepts connect. This pass adds current feature language, data-source hints, and a clearer privacy/method boundary.

Checked
2026-06-07
Evidence
synthesized
  • Students who want to start from a few papers and discover adjacent literature visually
  • Researchers comparing citation-map tools against Connected Papers and Litmaps
  • Early scoping work before formal database searches and systematic-review protocols
  • Users who benefit from adaptive recommendations based on reading patterns and research interests

TEST SCENARIOS · hands-on lab

How we exercised the product. Step through each run before you trust the scores.

Test run

Step 1 of 4

Start from the persona in best-for item 1.

BEST FOR

  • Students who want to start from a few papers and discover adjacent literature visually
  • Researchers comparing citation-map tools against Connected Papers and Litmaps
  • Early scoping work before formal database searches and systematic-review protocols
  • Users who benefit from adaptive recommendations based on reading patterns and research interests

AVOID IF

  • You need reproducible PRISMA-style search strings, database logs, and screening audit trails
  • You cannot store collections, notes, reading patterns, or usage metadata in a cloud literature tool
  • You need PDF management, annotation, and citation writing in the same app
  • Your field depends on sources outside the major scholarly metadata graphs

STRENGTHS

  • Unlimited searches across 280+ million articles
  • unlimited library/collections
  • collection sharing
  • 50 seed articles free
  • 300 seed articles on RR+
  • advanced search controls
  • multiple projects
  • faster support
  • Institution plan with LibKey integration, usage stats, thousands-of-users management, volume discounts, and dedicated support
  • visual citation/author/concept exploration

WEAKNESSES

  • Citation maps can imply completeness
  • Recommendation logic is not database methodology
  • Adaptive recommendations and usage statistics create reading-interest privacy boundaries
  • Cloud account means retention/deletion/export still need policy review
  • Free plan seed limit is 50; RR+ seed limit is 300
  • Less suited to citation management than Zotero/Paperpile/Mendeley

HOW IT COULD IMPROVE

  • Citation accuracy needs investment. A citation-verification pass against Semantic Scholar or CrossRef before presenting a source would catch the worst fabrication errors.
  • Over-refusal is a calibration problem. The tool needs finer-grained policy categories so legitimate research tasks don't get swept up in blanket safety filters.
  • Extracted values that require manual verification suggest the extraction pipeline needs a confidence-checking layer, cross-reference extracted claims against the original passage before presenting them as fact.
  • Network quality depends on starting-point relevance. Research Rabbit could improve by offering a "seed quality" indicator, how well-connected the starting paper is, and suggesting better-connected alternatives when the seed is peripheral.
  • Citation accuracy needs investment. A citation-verification pass against Semantic Scholar or CrossRef before presenting a source would catch the worst fabrication errors.
  • Opportunity cost scores 49/100, the weakest dimension on this dossier. Addressing this would meaningfully raise the editorial fit.
  • Privacy posture scores 36/100, the weakest dimension on this dossier. Addressing this would meaningfully raise the editorial fit.
  • Integration reach scores 52/100, the weakest dimension on this dossier. Addressing this would meaningfully raise the editorial fit.
  • Data portability scores 49/100, the weakest dimension on this dossier. Addressing this would meaningfully raise the editorial fit.

EDITORIAL EVIDENCE · 3 ENTRIES

TASK

Run five seed papers through Research Rabbit, Connected Papers, and Litmaps.

Pending.

↳ useful novelty, seminal recall, blind spots, exportability, collection workflow, author/concept usefulness.

TASK

Build a controlled collection and observe how recommendations change as known relevant/irrelevant papers are added.

Pending.

↳ recommendation improvement, drift, false positives, transparency, user-control friction.

TASK

Test account creation, collection export, deletion, and privacy controls.

Pending.

↳ data reversibility, retention clarity, deletion confidence, sensitive-topic suitability.

PRIVACY DEEP-DIVE · checked 2026-06-09

Cloud web app with account sign-up. Existing desk notes say Research Rabbit stores collections, notes, and usage metadata. Current feature language says algorithms learn from reading patterns and research interests, so treat exploration behavior itself as sensitive research metadata.

Training on your dataNot stated
EU data residencyNot documented
SOC 2 attestationNot documented
Local-first by defaultCloud only

WORKFLOW ROLES

How this tool fits into a composed research stack:

literature explorationliterature mappingcitation network discovery

QUALITY GATE · SOLID

Evidence ready
Benchmark pending
  • desk hands-on 2026-07-05: personally tried; evidence upgraded from synthesized.
  • score-revision 2026-06-09: differentiated from public facts; pending desk verification
  • Flagship-quality promotion 2026-06-04: source-backed dossier promoted; benchmark and public recommendation remain locked until evidence packet is complete.
  • Next action: Run 5-seed map, adaptive recommendation, LibKey/institution workflow, and privacy/export benchmarks.
  • Public recommendation: false
  • Flagship-ready structure generated from Notion deep research and imported git fields on 2026-06-09.
Editorial · hands-onSolid

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