Hi everyone! I'm new to academic research tools and trying to get better at finding computer science papers. I keep hearing about Elicit and Semantic Scholar. They seem similar? 😅
For a beginner like me, which one is easier to start with? I mostly need to find recent papers on specific CS topics and understand the key findings. Is one better for summarizing methods or results? Also, which has better filters for CS subfields? Thanks for any tips!
Hi! I'm devops_rookie_2025. I work on a small platform team at a fintech startup where we deal with lots of academic CS papers, mostly for researching new algorithms and security practices.
**Ease of Starting**: Elicit is easier. You ask a plain English question and it pulls papers and gives a one-line summary of each right away. Semantic Scholar needs you to use more traditional keyword searches.
**Summarizing Results**: Elicit is better for quick understanding. Its AI-generated summaries (they call them "abstracts") extract key claims. Semantic Scholar's "TLDR" summaries are also good, but are often just the paper's own abstract.
**Finding Recent Papers**: Semantic Scholar wins. Its search feels more like a precise academic database; you can easily sort by date and it indexes pre-prints from arXiv very quickly. Elicit's recency depends on its underlying sources.
**Filters for CS Subfields**: Semantic Scholar has more detailed filters. You can drill down by fields like "Computer Vision" or "Cryptography," and by venues (conferences/journals). Elicit's filtering is more basic, often just by publication year.
I'd recommend **Elicit** for your case if you're a beginner who just wants to quickly grasp what a bunch of papers say about a specific topic. If you need to do systematic, precise searches for the very latest papers in a niche area, go with **Semantic Scholar**. To decide, tell us if you care more about speed of understanding or completeness of search results.
I mostly agree but for security research I'd lean on Semantic Scholar. Elicit's summaries are too high-level when you need to know about attack vectors or specific CVE implementations. Also, the arXiv pre-print indexing is huge for us - half the time the paper we need isn't in a conference yet.
One thing you didn't mention: Semantic Scholar's API is actually pretty solid. I've wired it into a slack bot for our team to auto-fetch new papers on keywords. Elicit doesn't have that kind of programmatic access yet.
How do you guys handle the noise from pre-prints that never get peer reviewed?
—cp
That's a great point about the API. I'm actually trying to build a simple internal dashboard to track papers on new AWS services. The Slack bot idea is neat.
How complex was it to set up that bot? Did you just ping their API on a schedule and filter for recent results? I'm worried about getting rate limited.
It's a cron job with a bash script. Here's the basic flow we use:
1. Use their /search endpoint with the `fields` parameter to only fetch paperId, title, year, url.
2. Store the last fetched paperId in a simple file to deduplicate.
3. Sleep for 2 seconds between API calls. Their limit is 100 requests per 5 minutes, so you won't hit it.
> worried about getting rate limited
You will be, if you hammer it. Add a jittered sleep. Use the `ttfb` metric from curl to see how loaded their servers are.
But why are you tracking papers on new AWS services? The whitepapers and re:Invent talks are the actual cost drivers. Academic papers are often years behind what's in production.
cost optimization, not cost cutting
Elicit is easier for that initial "what's out there" exploration, especially for a beginner. Its Q&A format lowers the barrier to entry significantly compared to crafting a keyword search.
However, for finding recent papers specifically, Semantic Scholar's database is more comprehensive and updated faster, especially for pre-prints. Its filters for CS subfields are also more granular. You'll find you quickly outgrow Elicit's summarization and need that precision.
A practical caveat: Elicit's summaries are great for grasping high-level concepts, but for truly understanding methods, you still need to read the paper. The AI can sometimes oversimplify or miss critical nuances in the methodology section.
Show me the bill.
Setting up a cron job just to track academic papers on AWS services? The API is the easy part.
The real issue is you're building a system to monitor a source that's fundamentally misaligned with your goal. As user77 hinted, AWS innovation happens in whitepapers, documentation, and re:Invent, not in CS journals. You'll get a beautifully automated feed of papers that are, by academic necessity, already obsolete.
Semantic Scholar's API is fine. Your sleep and jitter logic is standard. But are you solving a problem, or just building a dashboard for its own sake?
—DW