Clarify research papers for research & learning with AI
Explainpaper is an AI-driven tool that turns PDFs and arXiv papers into sentence-linked, plain-language explanations; it’s best for graduate students and researchers who need source-anchored Q&A and quick method extraction. The core capability is paragraph-level Q&A that links each explanation to the exact sentence in the paper, and the company offers a free tier with a modest paid Pro plan (price noted below) for heavier use.
Explainpaper is an online research & learning tool that explains academic papers in plain language by linking answers back to exact sentences in PDFs or arXiv entries. Its primary capability is interactive question-and-answer over a paper: upload a PDF or paste an arXiv link, ask a focused question, and get an explanation with highlighted source text. The key differentiator is source-anchored explanations that show the original sentence alongside the simplified text. Explainpaper serves students, researchers, and product/R&D teams doing literature triage. A free tier exists; paid plans unlock larger uploads and increased usage (price listed below).
Explainpaper is a web application focused on making scholarly papers readable for non-specialists and speeding literature review work for researchers. Launched by a small team to address the common problem of jargon-heavy abstracts and opaque method sections, Explainpaper positions itself as a bridge between dense technical writing and accessible summaries. Its core value proposition is to surface line-level evidence from the paper alongside AI-generated explanations, so users can verify each claim against the original text instead of trusting a standalone summary.
Feature-wise, Explainpaper supports direct PDF uploads and accepts arXiv links or DOIs to fetch public preprints, then parses the document into sections for interactive questioning. The Q&A interface allows sentence-anchored answers: when you ask a question, Explainpaper returns an explanation and highlights one or more exact sentences in the paper as the supporting source. It also offers sentence-level quoting so users can copy the original phrasing, plus simple exports: copy explanations to clipboard or download plain-text summaries. Behind the scenes the product routes API calls to large language models (commonly GPT-3.5-turbo; paid users may see access to higher-capacity models depending on plan) and shows the model source in the UI for transparency.
On pricing, Explainpaper maintains a free tier suitable for occasional users and students—this typically includes a limited number of explanations per month and caps on file size or number of stored papers. A Pro plan (approximately $6/month as a consumer-facing price) expands monthly explanation quotas, increases upload size limits, and may unlock higher-capacity model backends and private project saving. Team or enterprise options are available via custom quotation for organizations needing user management and SSO. Pricing and exact quotas can change; check the site for the most recent plan specs and any free-trial promotions.
Who uses Explainpaper? PhD students use it to convert dense methods sections into 200–400 word, source-linked summaries for weekly literature reviews, and data scientists use it to extract algorithms and reproducibility steps from 5–10 papers during model research. Product managers and R&D engineers use Explainpaper to triage papers quickly and extract actionable method or metric details. For users comparing tools, SciSpace (Typeset) is the nearest competitor; Explainpaper differentiates by prioritizing sentence-level source highlighting and a minimal, question-driven workflow rather than full-document rewrite features.
Three capabilities that set Explainpaper apart from its nearest competitors.
Current tiers and what you get at each price point. Verified against the vendor's pricing page.
| Plan | Price | What you get | Best for |
|---|---|---|---|
| Free | Free | Limited explanations per month; small upload size; public model access | Students and casual readers testing the tool |
| Pro | $6/month (approx.) | Expanded monthly quota, larger uploads, access to higher-capacity models | Active researchers or frequent literature reviewers |
| Team | Custom | Shared team seats, SSO, higher API or usage quotas by negotiation | Institutions and research labs needing multiple users |
Copy these into Explainpaper as-is. Each targets a different high-value workflow.
Role: You are an Explainpaper assistant. Task: produce a single 300-word plain-language synopsis of the uploaded PDF/arXiv paper that links each key sentence to the exact sentence(s) in the source. Constraints: (1) Keep the synopsis to 300 words ±10 words; (2) For each paragraph (3–5 paragraphs total), include one or two source anchors shown as the original sentence quoted verbatim; (3) Avoid technical jargon where possible and explain one core technical term in parentheses. Output format: 3–5 short paragraphs, each followed by the quoted source sentence(s) with page/section if available. Example: short paragraph, then: "Source: '...exact sentence...' (p.3)".
Role: You are an Explainpaper assistant. Task: identify and list the five most important contributions/claims of the paper. Constraints: (1) Produce exactly five numbered items; (2) For each item include: a one-sentence plain-language restatement (15–25 words), the exact source sentence quoted verbatim, and the page/section; (3) Mark any claim that is empirical vs. theoretical. Output format: numbered list 1–5 with three lines per item: (a) Restatement, (b) Source: '...exact sentence...' (p./sec), (c) Type: empirical/theoretical. Example item: 1. Restatement... Source: '...' (p.2) Type: empirical.
Role: Act as an Explainpaper extraction assistant focused on reproducibility. Task: extract a numbered, step-by-step experimental protocol from the Methods section that another researcher could follow. Constraints: (1) Max 12 steps; (2) For each step include: step description (10–30 words), exact quoted source sentence(s) that justify it (with page/section), all parameter values or hyperparameters mentioned, and a confidence flag (High/Medium/Low) if any value is ambiguous; (3) If a required detail is missing, add a 'Missing detail' line proposing a reasonable default. Output format: JSON array of step objects: {"step_number":n, "description":"...", "source":"...", "params":{...}, "confidence":"...", "missing_detail":"..."}.
Role: You are an Explainpaper assistant extracting evaluation results. Task: produce a structured table of all reported quantitative results and baselines. Constraints: (1) For each reported experiment row include: Experiment name/figure/table label, dataset, metric name, reported value(s) with units, baseline value(s), and the exact source sentence(s) with page/section; (2) Group rows by table or figure and preserve the original order; (3) If values are presented graphically, report approximate numeric values and mark them as 'approx.'. Output format: CSV lines with columns: Experiment, Grouping(Table/Fig), Dataset, Metric, Value, Baseline, Source. Example row: "Exp A, Table 2, CIFAR-10, accuracy, 94.2%, 93.5%, '...'(p.5)".
Role: Act as an expert peer reviewer using Explainpaper. Task: produce a structured claim-audit with suggested follow-up experiments. Steps & constraints: (1) Identify the top 6 claims in the paper; for each claim provide: (a) verbatim source sentence(s) that state the claim, (b) supporting evidence sentences (quoting exact text), (c) rate evidence strength (Strong/Moderate/Weak) with one-sentence justification, (d) one specific experiment or analysis to strengthen or falsify the claim (include outcome metrics and expected direction). (2) At the end list 3 high-impact follow-up experiments ranked by feasibility. Output format: numbered claim entries plus a 3-item follow-up list. Few-shot example: Claim: 'Model X reduces error by 10%.' Source: '...' Evidence: '...' Strength: Moderate — small sample size. Follow-up: run on larger held-out dataset, metric: error rate, expected: <previous error.'
Role: You are a senior research engineer generating a replication blueprint using Explainpaper anchors. Multi-step task: (A) Extract dataset(s), preprocessing, model architecture, training hyperparameters, loss functions, and optimization details, each with the exact quoted source sentence(s). (B) Produce concise runnable pseudocode (Python-style) for data loading, preprocessing, model definition, training loop, and evaluation matching the paper's descriptions. (C) Provide a short checklist of 10 items that must be confirmed in the code to replicate results (e.g., random seed, data splits). Constraints: keep pseudocode to ~50–120 lines and annotate each block with source anchors. Output format: sections A, B, C with quoted sources inline. Include a mini example: show one annotated pseudocode block with its source anchor.
Choose Explainpaper over SciSpace if you prioritize sentence-level source highlighting and quick Q&A-driven explanations.
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