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Autoresearch on an old research idea
- hahaddmmm12x 6mo ago[flagged]
- love2read 6mo agoSo... It did work. It found bugs (that he didn't know about) and it did optimization (that he hadn't done).
- trcf23 6mo agoFrom what i understood, not so much. Most of the gains came from fixing a bug + hyperparameters with optuna which is supposed to be already quite automatic (you set the list of all the var with values you want to try and voilà). I guess a simple claude code session would fix that in a few minutes instead of a full day. To me, I guess the main value of Autoresearch would be to test different kind of architectures. It's sometimes hard to know what to choose and it would probably give a nice overview. Anyone used it for exploratory modeling?
- nadavdebi 6mo ago[flagged]
- datsci_est_2015 6mo agoI often use LLMs to explore prior art and maybe find some alternative ways of thinking of problems. About 90% of what it tells me is useless or inapplicable to my domain due to a technicality it could not have known, but the other 10% is nice and has helped me learn some great new things. I can’t imagine letting an agent try everything that the LLM chatbot had recommended ($$$). Often coming up in recommendations are very poorly maintained / niche libraries that have quite a lot of content written about them but what I can only imagine is very limited use in real production environments. On the other hand, we have domain expert “consultants” in our leadership’s ears making equally absurd recommendations that we constantly have to disprove. Maybe an agent can occupy those consultants and let us do our work in peace.
- MattGaiser 6mo ago> agent try everything that the LLM chatbot had recommended ($$$) A lot depends on whether it is expensive to you. I use Claude Code for the smallest of whims and rarely run out of tokens on my Max plan.
- datsci_est_2015 6mo agoOur experiments aren’t free. We use cloud infrastructure. An experiment costs on the order of tens of dollars, so massively parallelizing “spaghetti at wall” simulators is costly before we even talk about LLMs.
- victorbjorklund 6mo agoIf it is an experiment. Can’t you just make a POC for the experiment that doesn’t need to use half of AWS to just run? And if the experiment is actually positive you can then bring it to the real application and test it there (and spending the 10-100 usd it costs to test it live)?
- deleted 6mo ago[deleted]
- datsci_est_2015 6mo agoI wouldn’t want the LLM-based agent to hyperspecialize its solution to a subset of the data. That’s a basic tenet of machine learning. Steelmanning your question though, I guess you could come up with some sort of tiered experimentation scheme where you slowly expose it to more data and more compute based on prior success or failures.
- Eufrat 6mo agoI find LLMs useful in regurgitating one-liners that I can’t be bothered to remember or things where even being flat out wrong is okay and you just do it yourself. For all the folks spending a lot of time and energy in setting up MCP servers, AGENTS.md, etc. I think this represents more that the LLM cannot do what it is being sold as by AI boosters and needs extreme amounts of guidance to reach a desired goal, if it even can. This is not an argument that the tech has no value. It clearly can be useful in certain situations, but this is not what OpenAI/Anthropic/Perplexity are selling and I don’t think the actual use cases have a sustainable business model. People who spend the energy to tailor the LLMs to their specific workflows and get it to be successful, amazing. Does this scale? What’s going to happen if you don’t have massive amounts of money subsidizing the training and infrastructure? What’s the actual value proposition without all this money propping it up?
- the_arun 6mo agoTry this if the main link is not responsive - https://archive.is/6xLiU https://archive.is/6xLiU
- lamroger 6mo agoAwesome breakdown! It really feels like a hyper-hyper parameter search + bug fixer. I started looking at Kaggle again and autoresearch seems to converge to many of the solution vibes there. Wild ensembles, squeezing a bit of loss out. More engineering than research IMO
- sdenton4 6mo agoFor raw hyperparameter search, though, I would expect a proper Bayesian framework to be much better. Eg, vizier.
- ainch 6mo agoI think it depends whether you can leverage some knowledge. It's possible for a person/LLM to look at a loss curve and say "oh that's undertraining, let's bump the lr" - whereas a Bayesian method doesn't necessarily have deeper understanding, so it'll waste a lot of time exploring the search space on poor options. If you're resource unconstrained then BO should ofc do very well though.
- sdenton4 6mo agoYah, I'm a bit skeptical - ime humans tend to under explore due to incorrect assumptions. Often this is due to forming a narrative to explain some result, and then over attaching to it. Also, agents aren't actually good at reasoning yet. Good Bayesian exploration is much, much better than grid search, and does indeed learn to avoid low value regions of the parameter space. If we're talking about five minute experiments (as in the blog post), Bayesian optimization should chew through the task no problem.
- BrokenCogs 6mo agoDoes autoresearch work for projects that are not llm based? Eg in karpathy's example he is optimizing the nanogpt. What if I wanted to improve a Unet for image segmentation?
- sdenton4 6mo agoThe gist of these things is you point them at an eval metric and say 'make it go better.' so, you can point it at anything you can measure. The example in the blog post here is bonding boxes on wood cut images.
- simonw 6mo agoTobi from Shopify used a variant of autoresearch to optimize the Liquid template engine, and found a 53% speedup after ~120 experiments: https://github.com/Shopify/liquid/pull/2056 https://github.com/Shopify/liquid/pull/2056 I wrote up some more notes on that here: https://simonwillison.net/2026/Mar/13/liquid/ https://simonwillison.net/2026/Mar/13/liquid/
- Denzel 6mo agoHow much did this cost? Has there ever been an engineering focus on performance for liquid? It’s certainly cool, but the optimizations are so basic that I’d expect a performance engineer to find these within a day or two with some flame graphs and profiling.
- simonw 6mo agoHe used Pi as the harness but didn't say which underlying model. My stab-in-the-air guess would be no more than a few hundred dollars in token spend (for 120 experiments run over a few days assuming Claude Opus 4.6 used without the benefits of the Claude Max plan.) So cheaper than a performance engineer for a day or two... but the Shopify CEO's own time is likely a whole lot more expensive than a regular engineer!
- deleted 6mo ago[deleted]
- carlsborg 6mo ago> “ The agent acted like a hyperparameter optimization algorithm with some basic reasoning baked in.” Good lens. The crux of the auto research repo is basically one file - program.md which is a system prompt that can be summarized as “do this in a loop: improve train.py, run the training, run evals, record result. Favor simplicity”. The other files are an arbitrary ML model that is being trained.
- MITSardine 6mo agoThis is something I could almost never be bothered to do before, but I can now very lazily set up large parameter sweeps and visualization scripts to really probe things. There's a danger of "analysis paralysis" but I've still found it quite useful. Although I'm not sure it saves me time as much as sanity.
- dvt 6mo agoOk, so looking at the commit log[1], I was mostly interested in seeing what the "moonshot ideas" implementations looked like, but basically everything is just hyperparameter tuning. Which is nice, but likely not worth the $$$ spent on the tokens. Am I missing something here? [1] https://github.com/ykumards/eCLIP/commits/main/autoresearch https://github.com/ykumards/eCLIP/commits/main/autoresearch
- DoctorOetker 6mo agoIt would seem wise to modify the autoresearch instructions to first estimate the computational costs rigorously and then sort and compare the proposals for human review, and for each actually executed attempt to feed back the computational costs with LoRa adapter? i.e. perhaps minimal changes to autoresearch can take control for cost-effective research to occur.
- stingraycharles 6mo agoYes but at that point you may as well use a proper hyperparameter tuning framework like optuna if all the LLM agent is supposed to do is do hyperparameter tuning.
- DoctorOetker 6mo agoDoes optuna think abstractly (i.e. use LLM to interpret the code and come up with insights), or just perform hyperparameter tuning experiments on user-indicated parameters?
- stingraycharles 6mo agoThe latter, but it uses fairly optimized approaches to ensure it selects the best candidates. If you look at the commits, you can see that all it does is just set different values for different parameters of continuous values: the type of thing that I trust statistics a lot more than reasoning. Optuna can make very informed decisions when making lots of different changes at once, slowly converging towards optimal parameters, where the LLM seems to be throwing stuff at a wall and see what sticks. What would work best if the LLM would try to approach things on a higher level, ie use Optuna, but reason about better approaches for algorithms and/or data or whatever. But what it ends up doing is tuning parameters manually, only one / a few at a time, extremely inefficient and unlikely to be optimal.
- jpcompartir 6mo agoThere are better techniques for hyper-parameter optimisation, right? I fear I have missed something important, why has Autoresearch blown up so much? The bottleneck in AI/ML/DL is always data (volume & quality) or compute. Does/can Autoresearch help improve large-scale datasets? Is it more compute efficien than humans?
- hun3 6mo ago> There are better techniques for hyper-parameter optimisation, right? There always are. You need to think about what those would be, though. Autoresearch outsources the thinking to LLMs.
- nextos 6mo agoAFAIK, it's a bit more than hyper-parameter tuning as it can also make non-parametric (structural) changes. Non-parametric optimization is not a new idea. I guess the hype is partly because people hope it will be less brute force now.
- coppsilgold 6mo agoPerhaps LLM-guided Superoptimization: <https://en.wikipedia.org/wiki/Superoptimization https://en.wikipedia.org/wiki/Superoptimization> I recall reading about a stochastic one years ago: <https://github.com/StanfordPL/stoke https://github.com/StanfordPL/stoke>
- gwerbin 6mo agoIt's an LLM-powered evolutionary algorithm.
- ainch 6mo agoI'd like see a system like this take more inspiration from the ES literature, similar to AlphaEvolve. Let's see an archive of solutions, novelty scoring and some crossover rather than purely mutating the same file in a linear fashion.
- _pdp_ 6mo agoTake some working code. Ask an LLM to fix bugs. Measure performance and test coverage. Feed the results back into the LLM. Repeat. This has been the standard approach for more complex LLM deployments for a while now in our shop. Using different models across iterations is also something I've found useful in my own experiments. It's like getting a fresh pair of eyes.
- cyanydeez 6mo agoCan we modify this approach to get LLMs that are good at specific programming languages or frameworks? That seems to be where local LLMs could really shine.
- barrenko 6mo agoIt's just RL-everything.
- nico 6mo agoWould love to have a small local model that only knows about rails and mvc web development Alternatively, a modular model with multiple “experts” that I could mix and match for my specific stack I don’t need the model to know all of the Internet plus 20 different human languages. I just want it to be really good with the stack of the project
- mememememememo 6mo agoLLMs shine through emergent behaviour. Finding an LLM that does Rails doesn't know poetry is like finding a Rails human developer who doesn't have a hobby e.g. basketball. So what if they play basketball? They can code too!
- nico 6mo agoThen it might need a new type of architecture to work. I’m not attached to LLMs. If a new model comes out that can do only the things I want it do it, then great
- lucasay 6mo agoThis feels less like automated research and more like structured trial and error with a decent feedback loop. Still useful, but I think the real bottleneck is how good your eval metric is. If that’s weak, the whole loop just optimizes for the wrong thing faster.
- kridsdale1 6mo agoI mean, isn’t that “the scientific method”?
- Almondsetat 6mo agoDesigning a good fitness function, a tale as old as time...
- edwardsrobbie 6mo ago[flagged]
- 1970-01-01 6mo ago> The original paper used several medical X-ray datasets which I don’t have access to anymore, so I needed a new dataset with spatial annotations to test the expert attention mechanism. I picked the Ukiyo-eVG dataset: ~11K Japanese woodblock prints That's such a weird switch. There's lots of free medical imaging online. Example: https://www.cancerimagingarchive.net/ https://www.cancerimagingarchive.net/
- ykumards 6mo agoThat’s true! It felt a bit flippant to give medical data to an agent. Also, I wanted to see if the model would work in other domains!
- make3 6mo agobut doesn't it break the assumption that it should ideally be able to reproduce your original results
- ykumards 6mo agoIMO it would be hard to reproduce the results using autoresearch setup. To get CLIP to work properly we typically need large batch sizes. So the experiments in the original paper were quite heavy, and ran parallel across 8 GPUs.
- Achiyacohen 6mo ago[dead]
- motbus3 6mo agoI've done something with a small project I have and I had very similar results overall.
- wasting_time 6mo agoCare to elaborate?
- deleted 6mo ago[deleted]
- n_bhavikatti 6mo agoThe temperature clamp fix and "Optuna++" actions by the agents (the cause of basically all improvement to eCLIP) indicate they are good at finding bugs and hyper-parameter tuning. But when it comes to anything beyond that, such as novel architectural shifts, agents aren't good enough. With no clear path forward they tend to randomly change things, which is a poor approach. Agents: Optimization >> innovation
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- pikachu0625 6mo agoIt's better to outsource optimization phases. Our idea should be for constraint, assumptions etc. for breakthrough. Boyd often argues that once you can express a problem in a standard mathematical form, the implementation becomes a commodity that software can handle automatically.
- mlmonkey 6mo ago> Then I lock down Claude Code’s permissions to only edit these two files and run run.sh. No direct Python execution, no pip installs, no network access, no git push, etc. How does one run Claude Code without network access?
- shepherdjerred 6mo agoYou can do this via a Docker container or seatbelt on MacOS. in both cases you'd limit it so CC can only talk to the required Anthropic APIs. So not zero access, but as close to it as you can get.
- franktankbank 6mo agoPretty good question, also how do you update python version without network access?
- ykumards 6mo agoSorry I could have worded this part better. The docker container didn’t have network access. Claude didn’t have permission to execute anything other than the run.sh bash script, which would orchestrate the docker run
- saidnooneever 6mo agopretty cool experiment, i thought about someone maybe doing this and am happy you did it in this way. nice writeup too. this made me giggle a bit: "At one point it got tired of waiting for training to finish and just ended the conversation. I wouldn’t give it full autonomy just yet :)" thanks for sharing your results and the road to them!
- ykumards 6mo agoThank you, glad you liked it!
- leontloveless 6mo ago[dead]
- deleted 6mo ago[deleted]
- maxbeech 6mo ago[dead]
- SebastianSosa 6mo agoautoresearch is a trivial research idea "ablate through experiments with knowledge over previous experiments"
- ricksunny 6mo agoWith all the posts lately about Karpathy's autoresearch, it remains unclear to me whether this name is intended to convey that this LLM-codebase should be useful for research across all domains - like molecular biology, aircraft control, sociological, ww2 history, etc. or is it intended only to discover new LLM capabilities.
- Xx_crazy420_xX 6mo agoAutoresearch is nothing new, big players are already in the game with more sophisticated solutions: - https://arxiv.org/abs/2602.02660 (MARS) - https://arxiv.org/abs/2601.14525 (Execution-grounded automated AI research) - https://arxiv.org/abs/2601.10402 (ML-Master 2.0) The mostly used benchmark for automated AI engineering/ research is: https://github.com/openai/mle-bench https://github.com/openai/mle-bench
- bluequbit 6mo agoThe thing is, autoresearch feels more accessible that the listed solutions. I can use it trivially on virtually any problem that has verifiable rewards and a feedback loop.
- ide0666 6mo agoThe scratchpad.md for agent working memory is a nice touch. Having a persistent record of what was tried and why matters more than most people realize when debugging automated experiment loops.
- endymion-light 6mo agoThis is really cool - i'm going to try it on my old disseration.
- pu_pe 6mo ago> Like with any LLM project, the first 90% of the work was super smooth and barely needed my intervention. The last 10% was a slog. The author doesn't really describe which part was a slog, I thought autoresearch was supposed to be pretty much set and forget.
- WecoAI 6mo ago[dead]