Step 1: Identify the Claim and Its Source Paper

Start with a scientific claim you want to evaluate — perhaps from a news article, a colleague’s presentation, or your own research hypothesis. Identify the specific paper that originally proposed this claim. Search for the paper in Semantic Scholar by title, author, or DOI. If you don’t know the source paper, search for the claim as a semantic query — Semantic Scholar will surface papers that address it directly.

Step 2: Read the TLDR and Abstract for Core Finding

On the paper’s Semantic Scholar page, read the TLDR summary first — this single sentence distills the paper’s core finding. Then read the full abstract for additional context on methodology and scope. The TLDR gives you the essential claim in seconds; the abstract provides the evidential framework. If the TLDR doesn’t clearly state a specific finding, the paper may be primarily methodological or theoretical rather than empirical — note this for your evaluation.

Step 3: Examine Citation Velocity and Volume

Check the paper’s citation statistics. Citation velocity (how quickly citations accumulate) indicates whether the finding is actively influencing current research. High velocity with moderate total citations suggests a rising influential paper. High total citations with low velocity suggests an established but possibly dated finding. Low citations overall suggests either a niche finding, a recently published paper, or a finding that hasn’t gained traction. This quantitative context informs how much weight to give the claim in your evaluation.

Step 4: Analyze Citation Context for Support vs. Contrast

Click “Citations” and review the citation context section. Semantic Scholar categorizes citations as Supporting, Contrasting, or Mentioning. Supporting citations cite the paper to endorse its findings. Contrasting citations cite it to challenge or present contradictory evidence. Mentioning citations reference it without taking a position. A well-supported claim will have predominantly supporting citations. A contested claim will have significant contrasting citations. A claim with few citations of any type hasn’t been adequately tested by the community. Pay special attention to contrasting citations — these are the papers that might invalidate or refine the original claim.

Step 5: Check for More Recent Influential Papers

In the citation context, look for highly-cited subsequent papers that cite your source paper. These represent newer research that has built upon, modified, or superseded the original finding. Open these influential citing papers, read their TLDRs, and assess whether they strengthen, weaken, or replace the original claim. A claim from 2020 that has been refined by a highly-cited 2024 paper should be evaluated through the 2024 paper’s lens, not solely through the original.

Step 6: Form Your Evidence-Based Assessment

Synthesize your evaluation into a structured assessment: Claim (what the original paper asserts), Support Level (predominantly supported, contested, or insufficiently tested by citation context), Refinements (how subsequent papers have modified the claim), Current Status (the most recent influential finding on this topic). This structured assessment is far more rigorous than simply checking citation count — it’s a genuine evidence-based evaluation that accounts for the living, evolving nature of scientific knowledge.

Step 1: Formulate Your Research Question as a Semantic Query

Open Semantic Scholar and enter your research question in natural language rather than keywords. For example, instead of searching “attention mechanism transformer NLP” (keyword approach), search “What evidence supports the effectiveness of attention mechanisms in transformer models for natural language processing tasks?” The semantic engine understands your question’s meaning and returns papers that substantively address it. Review the initial results and refine your query based on what appears — if results are too broad, add specificity; if too narrow, simplify.

Step 2: Rapid Screening with TLDR Summaries

Your search will return dozens to hundreds of papers. Instead of reading abstracts one by one, scan the TLDR summaries displayed under each result. Each TLDR is a single-sentence AI-generated summary of the paper’s core finding. In 30 minutes, you can screen 100 papers by TLDR — a task that would take 5+ hours with traditional abstract reading. Mark papers as “relevant,” “possibly relevant,” or “not relevant” based on TLDR assessment. This initial screening reduces your working set from hundreds to 20-30 genuinely relevant papers.

Step 3: Deep Evaluation with Paper Reader AI Highlights

For your 20-30 relevant papers, open each in Semantic Scholar’s Paper Reader. The AI highlights key sections: methodology (how the study was conducted), results (what was found), limitations (what constraints apply), and novel contributions (what’s new). These highlights let you evaluate each paper’s rigor and relevance in 5-10 minutes instead of 30-45 minutes of full reading. Take notes directly in the reader interface, saving annotations to your personal library for later synthesis.

Step 4: Trace Citation Influence with Context

For your most relevant papers, explore their citation context. Click “Citations” to see not just how many times the paper is cited, but how each citing paper references it — whether citations support, contrast, or extend the original findings. This context is invaluable for literature reviews because it reveals: which findings are widely supported, which are contested, and which have been extended or refined by subsequent research. Build your review narrative around citation patterns rather than isolated paper summaries.

Step 5: Build and Organize Your Review Library

Create a Semantic Scholar library for your review project. Save all relevant papers to this library, organized by subtopic or thematic category. Add your notes and annotations from Step 3. Share the library with collaborators if you’re conducting a team review. The library becomes your structured working dataset — all papers, notes, and organization in one searchable location rather than scattered across downloads, browser tabs, and note-taking apps.

Step 6: Set Up Alerts for Ongoing Monitoring

Literature reviews aren’t one-time events — they require ongoing updates as new papers publish. Create a research feed based on your core query and set weekly email alerts. Each alert lists newly published papers relevant to your topic with TLDR summaries, letting you evaluate and incorporate new findings into your review without repetitive manual searching. This continuous monitoring ensures your review stays current throughout your research project’s lifecycle.