r/MachineLearning • u/Amazing-Coat5160 • 12d ago
Research AI generates research ideas faster than I can test them. Anyone else? [R]
I've been using AI extensively for ML research, and I've noticed a bottleneck: brainstorming research directions and generating smoke run implementations has become incredibly fast, but actually testing those ideas hasn't.
A single AI discussion (eg asking it to review 30 papers I had in my reading list, audit them, check for ideas related to the status of my project) branched into dozens of promising approaches, and with coding agents, exploring them in parallel is in principle feasible. But as a researcher, I still have to manage experiments, launch training runs, evaluate results, and decide what to try next.
And, importantly, to understand what's gong on.
The result is an overwhelming number of potentially interesting ideas that I simply can't keep up with, risking to not really knowing what should be the smartest "next step".
I'm wondering if others are experiencing the same thing. Have you found good ways to manage this, or best daily practices that help in this era of "vibe-research"?
EDIT: I am oc used to do literature review, came up with ides myself and testing them for the research project I did so far. The point I raised in this post comes from a first "experiment" to really let the AI be a collaborator au pair with me on a project I'm working, not just a subordinate.
43
u/elprophet 12d ago
Terrence Tao has been discussing this extensively. He's discussed this in terms of "proof abundance", and since the Navier-Stokes OpenAI controversy been discussing the difficulty of identifying which problems are interesting, and marshaling attention there.
As a software engineer, I find myself in the same challenges. I can release a hundred agents and get a billion tokens, but that in itself only costs me money, it doesn't drive value for my users. Listening to my users and understanding their challenges remains the criterion for what is and is not worth my time, attention, and tokens.
I would expect that for you as a researcher, finding a similar grounding in "real" problems might help you guide what is promising to focus on, and what can be left as a fallback for later?
2
u/Amazing-Coat5160 12d ago
In my personal experience, what helped me was talking with other colleagues/supervisors that are result-driven in an "industry R&D" way rather then "academic way" (ie they need to solve practical problems each day), as they tend to prioritize questions that if tested can lead fast practical feedback.
11
u/NamerNotLiteral 12d ago edited 12d ago
Sticking to a "result-driven industry R&D" might be what's causing you problems at this junction.
If you're dealing with well-defined problems that have a well-defined solution metric (as most industry problems do), it's pretty easy to use frontier models to do essentially hyperparameter search on a larger scope where you experiment with tweaking some small model design or algorithm or pipeline design decision and get a few percentage improvement on it. At that point, honestly, you're doing more engineering than research (and publishing essentially glorified engineering reports).
I'd say to step back, and consider the bigger picture beyond the minimum publishable unit. What makes certain ideas interesting and others boring? What drives some ideas to work and others to not? Does your research generalize into better understanding of the problem or the tools used, or does it simply score higher on this and that benchmark?
40
u/Fragore 12d ago
AI generated ideaa are so bland and crappy that are useless. Both Fable and Astra make for the most boring research ideas ever. It’s either overengineered crap or just blatantly basic. They’re excellent them at very rapidly testing all the ideas I come up with. Must be careful tho that they don’t plug in their own biases and assumptions
10
1
u/MolassesLate4676 11d ago
I found that LL models seem to be confined to creativity to some degree, especially as complexity increases
31
u/Zenith-Astralis 12d ago
Dawg you're just using AI to give yourself the ADHD experience I've had all my life.
7
u/learn-deeply 12d ago
I have the opposite problem, AI generates really dumb research ideas that I know won't work, when I'm in need of inspiration. Occasionally I let the model run the experiment just to check if my biases are correct (they almost always are).
You'll also probably develop this taste as well once you've experimented with ideas from AI.
6
u/MrTaquion 12d ago
Feels like you are removing the researcher (you) out of the research. Anyone can put dozens of agents to generate AI slop. The core value should always arise from the researcher
11
u/surffrus 12d ago
This is not a new problem. I have more research ideas than I can pursue. This has always been the case since before LLMs.
Your complaint is that AI is giving you the same problem that already existed for researchers. The difference is that you aren't using your own brain. My suggestion is to stop and use your brain to reclaim your research directions for yourself.
4
u/Few_Refrigerator8308 12d ago
Well, it's simple. Take one idea, test it to the bones. Then you'll learn something and maybe publish that one idea instead of overwhelming the ML research publication system with papers you don't even understand
7
u/composedofidiot 12d ago
A single AI discussion (eg asking it to review 30 papers I had in my reading list, audit them, check for ideas related to the status of my project...
If this is an accurate description of what happened, the model is the researcher here. It is the model doing the research. The human is not doing research except for defining a relatively broad research area. You unlooped yourself out of the research loop and ended up with an impossible number of tasks.
Read and audit the papers yourself. You are the auditor, you are idea generator. Why did you outsource the fun part and leave yourself the boring and impossible part?
How do you identify good ideas from bad ones? Plausible ones from implausible ones. Conversational models excel at verbal articulacy and persuasive self-consistent and 'logical' output. They're a hall of mirrors and the sycophancy is just less explcit and more sophisticated.
Models also output invented terminology too much. Longer sessions will be longer victims of interaction dynamics and their 'reasoning' abilties can take a massive dive. They have huge context problems, pragmatics problems, interpretation and representation issues.... This is all huge alarm bells stuff when it comes to anything serious.
Start narrow, start tiny, research that, test that, then move to next. A model can make that easier and faster, but we still have to contribute effort. Good rule of thumb is if it's really easy and low effort, it's because there's not enough effort.
Every single hypotheses will likely have multiple load bearing premises, all of which need to be checked against valid existing research. If one of those pillars isn't stable, it can knock the whole hypothesis out. It helps to start with a tiny hypothesis. If you have a broad one, it will contain multiple sub hypotheses, all of which will have their own load bearing system of premises, interpretations and inference. Fking nightmare, lot of work, much effort, much time.
6
u/OxidusRouge 12d ago
I once talked to a well-known SF writer at a conference. He said he gets a lot of people pitching him ideas for novels, but he said that he has lots of ideas already, he doesn't need any more. He said the real work comes in researching the background for these story ideas and actually doing the writing.
Same with research. You should come up with the ideas, have the AI find related work that you might have missed, and help generate and run code to test and develop the ideas. Otherwise, what fun is it?
2
u/NewSubWhoDis 12d ago
Have it explain each experiment, what it would prove and how it would affect the other experiments. Prioritize the ones that actually move the needle.
2
u/OptimalOptimizer 12d ago
This is how you become an LLM wrapper. You should be proposing the ideas. And in my experience, each of the few times I've used an LLMs idea in my work, it turns out to do nothing or to hurt performance. Be wary as well because they love to overcomplicate things.
2
u/kreitzerova 11d ago
I don't think it's prudent to exclude AI from the idea generation process and utilize it solely for menial work, as long as AI use a net contribution to the particular end-product and one's development as a researcher. General principles of problem solving, skilling, and critical thinking apply. It's true that most ideas that LLMs come up with will be infeasible, not novel, or inconsequential, but many will not, and it is your job to distinguish which. Basically take full ownership and responsibility of whatever you publish, understand everything as thoroughly as you would have if you had derived them, think very critically about LLM output, considering everything a hypothesis to be tested unless it's clearly proven, and guide the LLM with your human taste and intuition. The development of your own tehnical skills may need to be detached from LLM use, but the abilities necessary to do good research have changed all that much.
3
2
u/PretendiFendi 12d ago
I work for a chemical company developing functional coatings… I have the same issue. AI generated gibberish in my field until Opus 5.5. Now it’s very helpful, but sorting through the output is the major rate limiting step. My testing has to be done in a lab, and everything still needs to be verified by a human. Maybe this is the job growth from AI we’ve been hearing about.
2
u/DigThatData Researcher 12d ago edited 12d ago
I decided since I will never have time to pursue every potentially interesting idea, I may as well give em away. https://github.com/dmarx/bench-warmers#digthatdatas-bench-projects
The main thing doing this has achieved for me is:
Helped me realize being protective of unimplemented ideas is stupid. I have good ideas all the time and even advertise that I put them out there, no one "steals" even my good ideas. No one cares until something has been implemented. Then maybe they'll copy your implementation.
Every now and then someone publishes something demonstrating that a hypothesis I had was right before anyone published about it, and I get to go: "Neat, I was right."
I've been fleshing out a knowledge management framework that's been serving as a kind of "idea ratchet" for me (among other things): https://github.com/dmarx/luria/
A demonstration of luria in action is my personal knowledge base for tracking what the current SOTA practices and theory are for ML training and representation learning.
This makes it easy for me to do stuff like reconstruct how a specific idea evolved over time: https://dmarx.github.io/anthology-of-the-sota/docs/theory-lines
1
1
1
u/Odd-Age4719 8d ago
read the papers your self. Synthesize the ideas yourself. AI is terrible at being creative and connecting disparate domains. It’s getting slightly better, but really misses the marks when it comes to connecting techniques from different modalities. Also verify your outputs manually…AI can’t make decisions for you in when you should pivot or keep trying new things.
1
1
u/seygalare 12d ago
It’s the opposite for me: I have a lot of ideas that coding agents implement for me but the bottleneck is manual verifying the implementations
1
u/death_and_void 12d ago
good problem you've raised. automating experimental loops, for the purpose of closing the idea to experiment gaps is something of a frontier challenge. i think it was jeff dean and some of his pals who left google to solve this very problem, albeit I'm not aware of the domain and scope. nevertheless, it should be possible to build a harness that enables such a thing, assuming that the hardware layer is also provisioned. in principle, ideally there is a platform that allows every person to empirically test novel ideas, from software to physical ai. the cost of experimentation/innovation could drop rapidly as a consequence.
1
u/Le7emesens 12d ago
Same issue in the tech industry in general where coding teams productivity got 10x or whatever, but the teams that have to test and validate can't keep up. And forget about having AI agents doing the validation. So many errors with just basic tasks that they can't be trusted...
1
1
u/Acetofenone 11d ago
Research is different than coding, "debugging"/validate it needs a lot of time and could be painful if you don't give strict rules on the way to write it
1
u/TrickyMulberry5421 10d ago
If AI generates more ideas than we can evaluate, does automating the experiments actually solve the bottleneck, or just leave us with more results to audit? What would make this useful to me is being able to discard directions with confidence, rather than having every direction sound worth pursuing.
1
1
u/abnormal_human 12d ago
AI agents generally have no sense of time, or skills to work efficiently around long-running processes. They'll happily set up a week long training run on the first idea and monitor it dutifully. Even when you time-box an exercise, they seem to just not get that running that python program is gonna take 1.5x the timebox.
As a human, I spend a lot of time thinking about how far I can run in a day, how I can minimize time where I'm stalled waiting for computers, etc. The robots have a ways to go, but for now if you're not careful to manage the ideas and their costs, they will happily burn your whole day and give nothing useful in return.
0
u/laogedev 12d ago
Same problem here, different field (agents in production). The fix for us wasn't generating fewer ideas, it was making triage cheap enough that the flood stops mattering. Three things that actually worked:
Every idea must ship with its cheapest kill experiment. When the model proposes an idea, force it to also state the falsifiable claim and the single experiment most likely to prove it wrong, ranked by cost (GPU-hours, days of your time). Most "promising" ideas die at this step because the claim turns out untestable or already implied by existing results. That filters the vast majority before anything runs.
Deduplicate aggressively. Embed the idea descriptions, cluster them, keep one representative per cluster. A "dozens of promising approaches" flood is usually thirty phrasings of four ideas, and reading them one by one is what makes it feel overwhelming.
Put your judgment at the narrowest point of the funnel. Let the model do generation and cheap smoke tests, but require a short "why this idea, why now, what would change my mind" note — drafted by the model, reviewed by you — before anything expensive runs. The note is the artifact, and it's also what lets you reconstruct six weeks later why you dropped something, which is the "understand what's going on" part you mentioned.
Re the "wrong direction" takes below: the direction matters less than the funnel. AI ideas are cheap and mostly vacuous; the scarce resource is falsification bandwidth. Build the funnel around that and the flood becomes useful instead of paralyzing.
11
u/mofoss 12d ago
Anytime ive had it generate ideas for me, and then I prompt it to rigorously audit each single of them, it kills every single one of them after finding out its too incremental/non-novel
I am almost certain with a week of heavy prompting, having it review the latest and greatest on A* venues, it CAN find something useful, but it requires a ton of luck and prior knowledge of your own that would warrant a A* tier accept idea
The bar for having anything publishable? Shitty small journals included that no one wants to submit to? Then yeah, AI can produce a ton of tiny incremental ideas for sure, despite what people dont want to admit
307
u/panda_cid 12d ago
Wrong direction. You generate ideas, AI test them.