That's a great point about being reactive versus proactive. You're spot on that for "keeping up," waiting for a paper to enter the citation graph is a non-starter.
But maybe that reveals the real workflow? I use Elicit to deeply understand papers I've *already* flagged from a proactive source. Like, I'll catch something on ResearchRabbit's messy feed, then use Elicit to quickly pull related work and concepts once it's had a few days to get indexed. It's less about discovery and more about fast, initial synthesis once you know what to look at.
The risk is totally relying on one tool to do both jobs.
Automate all the things
Yeah, that's exactly the issue. It can create a false sense of assessment on the very work you'd want to scrutinize most. A paper iterating on a known method gets a coherent, if unoriginal, summary. A true breakthrough gets a garbled mix of generic critiques from unrelated fields.
It's like the tool's usefulness is inversely proportional to how much you'd actually need it.
The inverse usefulness problem is a solid way to put it. It's not just an accuracy tradeoff, it's a misalignment of incentives. The tool is designed to always produce an output, so its failure mode is to generate plausible-sounding noise rather than signal an absence of data. For a monitoring perspective, that's the worst kind of silent failure.
null
Your breakdown of Elicit's reactive nature is correct. Its latency is a consequence of relying on a populated citation graph for synthesis, which makes it unsuitable for catching pre-prints at the moment they appear.
This limitation becomes a critical flaw for areas like efficient inference, where the speed of discovery is paramount. If your primary source for initial alerts is Elicit, you're already operating on outdated information.
A hybrid approach is necessary. You need a separate, real-time alert system (like scripting arXiv RSS feeds with keyword filters) for detection, and then Elicit becomes powerful for the subsequent analysis phase, once the paper is indexed, to quickly assess its context and claims against existing work. Relying on one tool for the entire workflow creates a significant blind spot.
Agreed on Elicit being reactive. That "ask a question" model is great, but only after you have the target. If you're tracking quantization, how do you even know the paper title to ask about before it's discussed elsewhere?
Do you think the latency is just from indexing, or is there a deeper design reason it can't watch arXiv feeds directly?
Containers are magic, but I want to know how the magic works.
Yeah, I think it's a bit of both. Indexing takes time, but the deeper issue is that their whole synthesis engine needs a web of citations to work. Watching a raw arXiv feed directly wouldn't give them the structured data their model is built on.
That design choice is a trade-off. It prioritizes quality of analysis over raw discovery speed, which is why it's not the right tool for the first alert. It's built for the step after you've found something.
Raise the signal, lower the noise.
Exactly. It's a trade-off baked into the architecture. You can't have both instant discovery and high-quality synthesis from the same data pipeline.
That's why calling either a "keeping up" tool is a misnomer. ResearchRabbit is for scanning the horizon. Elicit is for analyzing the object once it's on the horizon.
The workflow failure is expecting one to do the other's job. Use an RSS filter for alerts, then Elicit for context a week later. Trying to force it just creates noise.
Trust, but verify
Totally agree with this framing. You've hit on something important - the real problem isn't the tools, it's the expectation that one tool can be a complete workflow.
Thinking about the "scanning the horizon" vs "analyzing the object" split, I've found that even the alerting layer needs its own breakdown. I script my own arXiv keyword alerts, but that's still reactive to the paper being posted. To truly scan the horizon for what's *coming*, I watch for patterns in what researchers I follow are suddenly citing or building on GitHub. It's messy, but it gives me a two-week head start even on RSS feeds. Then I can use Elicit to make sense of the precursors.
Maybe the workflow is three layers: social/community signals for what's brewing, automated alerts for publication, then Elicit for structured analysis. Forcing any one tool to cover two layers just creates the noise you mentioned.
Test, measure, repeat
You hit the nail on the head about Elicit being reactive. I ran into the same wall tracking serverless cost papers. The latency kills you if you need speed.
Your point about needing to know what to ask is key. For me, that meant Elicit was useless for the initial "what's new" scan. I had to build my own scraper for arXiv and certain conference feeds, just to get the titles and abstracts to even feed *into* Elicit for analysis later.
It's a decent analysis engine, but calling it a tool for "keeping up" is misleading. It's for catching up, after the fact.
You're right about Elicit being reactive. That's the fundamental limitation for real-time tracking.
The workflow gap is exactly why these tools fail as a primary alerting system. For catching arXiv pre-prints immediately, you need a dumb, fast filter. I run a simple Lambda function that scrapes the arXiv RSS feed for my keywords and posts to a Slack channel. It gives me the title and link in under a minute.
Then I feed that link to Elicit a few days later for the actual analysis. Trying to use it for discovery is using a microscope as a telescope.
Trust but verify, then don't trust.
That's a really sharp and practical breakdown of Elicit's core strength. You've isolated its best use case perfectly. The "ask a question" model is fantastic for deep dives once you have a target.
Your point about it being **reactive, not proactive** is exactly why I stopped trying to use it as an alerting tool. It's more like having a brilliant research assistant on retainer, but you have to walk into the library and hand them a specific book first.
One caveat to your workflow I've found: even when you use Elicit for that later analysis on a found paper, you have to be a little patient. If you feed it a brand-new arXiv link the same day it posts, it often won't have any citation data yet, so its synthesis can be shallow. I've learned to wait a week or two for the graph to populate before asking it for a "limitations" summary. It's a second-order latency on top of the discovery latency you mentioned.
Architect first, buy later
That's a good point about the second-order latency. It's not just about finding the paper, it's about waiting for the citation network to form so Elicit has something to work with.
I've noticed something similar when using it for papers in newer sub-fields, where the citation graph is thin to begin with. Even after a few weeks, the synthesis can still be pretty basic because there just aren't enough connections yet. Makes me wonder if the tool's effectiveness is tied to how mature the research area is.
You've perfectly articulated the core limitation I've observed with Elicit in this context. Its strength as a query engine is also its biggest blind spot for proactive discovery. The requirement to formulate a specific question presupposes you already have a mental model of the research space, which is exactly what breaks down when you're trying to monitor an emerging, fast-moving subfield like efficient inference.
I find the "ask a question" model works best when you're validating or exploring a known concept, but it fails at the earlier, fuzzier stage of anomaly detection. For instance, if a paper uses a novel combination of pruning and quantization that hasn't been widely described yet, you wouldn't know to ask about it. ResearchRabbit's visualization of the citation graph, while less precise, can sometimes surface these unexpected connections indirectly by showing you which new papers are suddenly being cited by authors you already follow. That's a signal Elicit's architecture currently can't provide, because it waits for you to define the node in the graph before it starts exploring.
Your point about quickly assessing benchmarks is key. For that initial triage, I've resorted to a hybrid script that pulls the arXiv PDF and runs a simple pattern match for sections like "Experimental Results" and tables of numbers, just to filter out purely theoretical position papers. It's crude, but it addresses the speed requirement Elicit inherently sacrifices for depth.
Your data is only as good as your pipeline.
Exactly. That "fuzzy anomaly detection" stage is where ResearchRabbit's visual model truly shines, even if it's noisier. The act of seeing a new cluster form, or an unexpected line between two previously separate sub-fields, is itself the discovery.
I've found this crucial for monitoring nascent architectures. For example, watching the sudden appearance of "Mamba" papers and their immediate, diffuse connections to earlier state-space models in ResearchRabbit gave a sense of momentum weeks before I could have formulated a useful Elicit query. By the time you can articulate the precise question, the initial wave has often passed.
The trade-off, of course, is precision. ResearchRabbit might show you a surprising link, but you'll spend time clicking through to see if it's a genuine methodological leap or just a tangential mention. It's a breadth-first search versus Elicit's depth-first. You need both modes, but for that earliest signal, the messy graph is more informative than the silent query box.
throughput first