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| The alien space of science: Coherent but cognitively unavailable directions (arxiv.org) | |
| 75 points by martincsweiss 21 hours ago | hide | past | pdf | 17 comments on HN | |
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In plain words: It breaks papers into small idea pieces, then finds combinations that make scientific sense but that no group of researchers would likely think of. On 16,068 AI-language-model papers, it found more varied idea sets than top AI tools while matching or beating their quality. Abstract · The Alien Space of Science: Sampling Coherent but Cognitively Unavailable Research DirectionsScientific discovery is constrained not only by what is true, but by what is cognitively available to researchers. Many directions are coherent in light of the literature yet unlikely to be proposed because no existing community combines the relevant concepts, methods, and intuitions. Modern language models inherit this bias, recombining high-density regions of the literature when prompted for novel ideas. We target the complementary region, the alien space of science: directions that are scientifically plausible but unlikely under the distribution of existing researchers. Our method decomposes papers into conceptual units and clusters them into a vocabulary of idea atoms. A coherence model scores whether atom combinations form viable research directions, while an availability model scores whether any existing author community is positioned to produce them. We rank combinations to maximize coherence while minimizing availability. On 16,068 peer-reviewed LLM papers from NeurIPS, ICLR, ICML, and major NLP venues, the sampler produces more diverse research portfolios than frontier LLM baselines, including batch-prompted variants that see their entire output, while matching or exceeding their per-idea quality under blind human, LLM, and downstream experimental evaluation. In temporal validation, all components use only pre-2025 data. Selecting less than 1% of the candidate space recovers 2025 combinations at 35.9 times chance; weighting unavailability increases recovery of strictly novel combinations to 3.24 times chance. By separating scientific plausibility from community availability, our framework points toward AI ideation that complements rather than merely accelerates human science, expanding exploration into coherent directions the community may overlook. | |
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We built a way to sample diverse ideas in machine learning - ideas that make scientific sense, but that no existing research community is positioned to propose.
In short, the way it works is like this:
1. Take 16,068 LLM papers (NeurIPS, ICLR, ICML, ACL, EMNLP, 2017 to 2025) are broken into 273 "idea atoms". Every paper, and every candidate idea, becomes a set of atoms.
2. Train a Coherence Model: a 42.8M-parameter transformer trained on papers as sequences of atoms. Basically a language model whose tokens are ideas.
3. Train a Cognitive-Availability Model: a dual encoder trained on the publication histories of 39,791 researchers, which predicts where each person could plausibly go next.
4. Use those models to score sampled tuples using reciprocal rank fusion (you want ideas that go together well, but that are not cognitively available to anyone).
We tested it in a bunch of ways. One interesting one was that we took all the papers at the end of 2024, then checked which high-scoring tuples showed up in 2025 papers. It worked well - one examples is an ICLR 2025 oral, goes from rank 1,489 with coherence alone to the top 25 of 1.14M samples.
A funny one: if you ask Claude to come up with an ML research idea, it is often about interpretability. If you then run an autoresearch pipeline with some A100s on the idea, the result tends to be interpretability 8/10 times. Our Alien Sampling method stayed at 0 interpretability ideas even after letting claude cook on them.
We also paid 50 people with graduate degrees in CS/AI to judge the research reports and found that Alien’s reports had the best average quality rank and produced 3 of the top 5 reports.
Oh and then we scaled it way the hell up to a couple million papers and 10,000,000's of concept elements and generated 25,000 new research proposals that you can search over here: https://lacuna.tiptreesystems.com/explore?view=hypotheses
Code (MIT): https://github.com/alejandrohdez00/alien-space-of-science