AI for Research Papers: The Right Tool for Each Job

The best AI for research papers depends on which job you're doing. To find papers, use Semantic Scholar, Elicit, Consensus or Google Scholar Labs. To map a field, use a citation-graph tool like Connected Papers, ResearchRabbit or Litmaps. To read one paper closely, use SciSpace, Gemini Notebook (the new name for NotebookLM) or Claude with the PDF attached. To turn a 5,000-paper conference into a reading list, combine a general chatbot with the venue's own data.

Most people pick one tool and expect it to do all four. A search engine that's great at "what exists on this topic?" is weak at "what does Table 3 actually show?", and the reverse is also true. Below: the tools for each job, a worked workflow for cutting ICLR 2026's 5,355 accepted papers down to 20, and why every AI-supplied reference needs checking.

Quick comparison: AI for research papers, by task

For one pick per stage of the whole research process, including citation management, see the best AI tools for research. This article goes deeper on the paper-specific jobs.

Job 1: AI to find research papers

Semantic Scholar is the free baseline. It searches over 214 million papers across every field, adds AI-generated TLDRs for nearly 60 million papers in computer science, biology and medicine, and lets you save papers to library folders. Its Research Feeds learn from papers you rate and recommend new ones, and alerts email you when a paper gets new citations or an author publishes.

Elicit is built for questions rather than keywords. Ask "does creatine improve short-term memory in adults?" and it returns relevant papers with summaries. On paid plans you can add extraction columns (sample size, method, outcome) to build a comparison table. Elicit's pricing page lists a free Basic plan with unlimited search across more than 138 million papers and unlimited chat with papers that have full text. The heavier tools, such as research reports and a systematic review workflow that can screen 5,000 papers, sit on paid plans.

Consensus suits yes/no questions about the evidence. It searches 220M+ papers and shows a Consensus Meter that summarises how studies lean, plus Study Snapshots with key findings and methods. Treat the meter as a starting point, not a verdict: open the studies behind it and check how strong they are.

Google Scholar now has two AI features, both announced on the Google Scholar blog. Scholar Labs splits a detailed research question into its topics, searches Scholar for each, and explains how every result helps answer it. As of June 2026 it's available to all logged-in users. Quick Read, added in August 2026, gives a short, question-focused read of a paper split into Answers, Approach and Considerations, for papers you have access to.

One habit works across all four: search two ways, once in plain language and once in your field's technical terms.

Job 2: Map a field with citation graphs

Search gives you a list. A citation graph shows how it hangs together: which papers everyone builds on and which clusters exist.

Connected Papers starts from one paper you already trust. It builds a graph of similar papers using co-citation (two papers that later work often cites together) and bibliographic coupling (two papers that cite many of the same sources). Similar papers cluster together even if they never cite each other, so you find related work your keyword search would have missed.

ResearchRabbit works from a collection of seed papers rather than one. You add papers, it recommends related ones based on their references, citations and authors, and you keep following the trail as the collection grows. It's now run by Litmap Limited, the company behind Litmaps. ResearchRabbit's pricing page lists a free plan with unlimited searches across 310+ million articles and up to 50 seed articles, plus a paid tier that allows 300 seeds for large reviews.

Litmaps lays papers out on a visual map and adds monitoring: give it seed papers and it alerts you to new ones on your topic. Litmaps' pricing page shows a free plan capped at 20 inputs and a limited number of maps and articles, with paid plans removing those caps and making alerts more frequent.

Short version: Connected Papers for one paper's neighbourhood, ResearchRabbit for a review collection you grow over weeks, Litmaps for tracking a topic. All three share one blind spot: papers from the last few months haven't collected many citations yet, so pair a graph tool with a search tool for recent work.

Job 3: Read and summarise one paper

This is where general chatbots are genuinely good, as long as you give them the PDF rather than asking from memory.

SciSpace is built around reading papers. Its Chat with PDF answers questions about an uploaded paper. SciSpace also bundles literature review, an AI writer, data extraction and an agent for multi-step research tasks.

Gemini Notebook is what NotebookLM became. Google renamed it on July 16, 2026 and kept it as a standalone research tool. You add sources (PDFs, web pages, Google Docs, YouTube videos), and its answers cite the passage they came from. The free version allows up to 50 sources per notebook. It's strongest when you load 10 to 20 related papers and ask across them. Outgrown it? See NotebookLM alternatives.

Claude and ChatGPT both accept PDF uploads. In Claude's apps, Anthropic's help centre says PDFs can run up to 1,000 pages, but Claude only reads images, charts and graphics in PDFs of 100 pages or fewer. Longer files are read as text only, so for a long thesis, split out the chapter with the figures you need.

Questions that get more out of any of them:

1. "What is the main claim, and which experiment or table supports it? Quote the sentence."
2. "What are the baselines, and were they tuned as carefully as the proposed method?"
3. "List every limitation the authors state."
4. "Explain Figure 2 in plain words. What would it look like if the method didn't work?"

Asking for quotes is the key move. It forces the answer back to the text and makes a wrong answer easy to spot.

Job 4: Build and triage an AI paper reading list

The hardest job is deciding which 20 papers out of thousands deserve your time. No single product owns this yet, so most people combine venue data, an interest profile and an LLM doing the first pass.

ML conferences make this unusually doable, because many run their reviews on OpenReview. ICLR lists its accepted papers there with titles, abstracts and keywords, and its reviews are public, so you can see what reviewers praised or doubted. OpenReview also has a documented API with a Python client if you want the whole list in a spreadsheet.

People also package this kind of screening as reusable tools. Research dashboards and paper-screening workflows show up in the Research section of Taku's marketplace categories, where creators publish workflows they've already got working.

Workflow: from 5,355 accepted papers to a 20-paper reading list

ICLR 2026 received 19,525 valid submissions and accepted 5,355, according to the ICLR program chairs' retrospective. Here's how to get to 20 in an afternoon. Treat the numbers at each step as a guide.

Step 1. Get the list. Pull titles, abstracts, keywords and links for every accepted paper, either through the OpenReview API or by exporting from the conference site. Save it as a spreadsheet with one row per paper.

Step 2. Write your interest profile. It decides the quality of everything after it. Write:

• three to five sentences on what you work on and what you want to learn
• three to five papers you loved recently, as examples of "yes"
• topics to exclude, as examples of "no"

Step 3. First pass with an LLM (5,355 to about 200). Feed abstracts in batches of 50 to 100 with your profile. Ask for a 0 to 3 relevance score, a one-line reason, and the abstract sentence that justifies the score. Keep everything scored 2 or 3. The quoted sentence lets you check a reason in seconds.

Step 4. Your pass (about 200 to 50). Skim the survivors' titles and the model's reasons yourself. You'll disagree with maybe one in five scores, and those disagreements tell you how to tighten your profile for next time.

Step 5. Second look (catch what the screen missed). Give the LLM your 10 favourite survivors and every paper it scored 1. Ask which of the 1s share a problem or method with the favourites. A handful usually come back, often good papers with vague abstracts. Brand-new papers have too few citations for graph tools to help much here.

Step 6. Read the reviews (about 50 to 20). For the shortlist, open the OpenReview page and skim the reviews and author responses. Reviews often surface the weakness the abstract hides. Keep the 20 that still look worth an hour each.

Step 7. Read deeply, then save the setup. Load the 20 into Gemini Notebook or Claude and work through the questions from Job 3. Then save your profile and prompts. Next conference, you reuse them instead of starting from scratch.

Accuracy caveats: verify every reference

Chatbots asked for sources from memory can produce citations that look real and don't exist. This isn't hypothetical. ICLR's own retrospective says many 2026 submissions included references to documents that didn't exist or had badly wrong bibliographic details. The program chairs checked flagged references by hand, and every paper with confirmed hallucinated references was desk rejected.

Fake citations are the obvious failure. Summaries that misread a table, and searches that silently miss a paper, are harder to spot:

The rule is simple: AI can suggest a reference. Only you can confirm it. Never paste a citation into a draft without opening the paper itself.

Key points

• Pick the tool by job. Search tools find papers, graph tools show how they connect, PDF chat helps you read one. None does all four well.
• For conference triage, a written interest profile, an LLM first pass and your own review cut thousands of papers to 20.
• For reading, upload the PDF and ask for quotes.
• Verify every reference. ICLR desk-rejected papers over hallucinated citations in 2026.

A paper-screening setup like the one above is the kind of workflow that gets rebuilt from scratch every conference season. Taku is an AI-native desktop workspace where you can mirror a research workflow someone already got working, run it on your own reading list, and save it to use again next time. Taku is in Beta, and the Mac app is available now.

FAQ

What is the best AI for research papers?

There isn't one best tool, because research papers involve several jobs. Semantic Scholar and Google Scholar Labs are the best free starting points for finding papers. Connected Papers and ResearchRabbit are best for mapping a field. For reading one paper, Gemini Notebook, SciSpace or Claude with the PDF attached work well.

Can ChatGPT or Claude find research papers?

They can suggest papers, but a reference given from memory may not exist. Use a research paper AI that searches a real index, such as Semantic Scholar, Elicit or Consensus, to find papers. Then use ChatGPT or Claude to read and question the PDFs you've found.

Is there a free AI to find research papers?

Yes. Semantic Scholar is free to use, and Google Scholar Labs is free with a Google account. Elicit and ResearchRabbit both let you search without paying, and Consensus is free to use, with paid plans for heavier use.

How do I build an AI paper reading list from a conference?

Export the accepted papers (for ICLR and similar venues, OpenReview has them). Write a short interest profile with example papers, have an LLM score abstracts against it, then review the shortlist and the reviews yourself.

Can AI summarise a research paper accurately?

Mostly, when it has the actual PDF. Accuracy drops on figures, tables and limitations, so ask for the quote behind each claim.