Good Papers

Showing Automated scientific discovery Show all papers

70%Highly rated
?Highly ratedVote to see the score
arXivAutomated scientific discovery

The AI Theorist reveals excitonic structure in $α$-RuCl$_3$

AI Theorist autonomously develops a first-principles model identifying distinct excitonic states with contrasting selection rules in α-RuCl3 optical spectra.

Hongjian Zhou, Xianfan Nie, Sean Wu, Tarun Patel and 4 more

Published Oct 1, 2026 · 0 citations · ▲ 3 on Hugging Face

– ReadersNo votes yet
4/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 4 of 20 reviewers recommend it
lenient 3/5
medium 1/10
strict 0/5
88%Must read
?Must readVote to see the score
arXivAutomated scientific discovery

Autoregressive Drillhole Modelling Under Distribution Shift

DrillBench benchmarks autoregressive drillhole modeling, finding lithology-sequence models transfer more robustly than spatial methods, and combining pretraining with retrieval improves cross-province generalization.

Yihao Ding, Daniel Yitian Su, Yiran Zhang, Christopher M. Gonzalez and 1 more

Published Oct 1, 2026 · 0 citations

– ReadersNo votes yet
15/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 15 of 20 reviewers recommend it
lenient 5/5
medium 8/10
strict 2/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026HHMI Janelia Research CampusHHMI JaneliaAutomated scientific discovery

Discovering dynamical parameters of synthetic multicellular systems from image sequences

Corey Herr, Cedric Allier, Stephan Saalfeld, Allyson E. Sgro

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026U Science and Technology of ChinHunanAutomated scientific discovery

DETS: An Interval-Censored Evidential Sampling Framework for Cross-Domain Scientific Discovery

Jiahang Shen, Hongxin Xiang, Wenjie Du

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
?Worth a lookVote to see the score
NeurIPS 2026Virginia Polytechnic Institute aSandia National LaboratoriesVirginia Tech / AmazonAutomated scientific discovery

LLM-AutoSciLab: Closed-Loop Scientific Law Discovery via Active Experimentation with LLMs

Sanchit Kabra, Nikhil Abhyankar, Saaketh Desai, Prasad Iyer and 1 more

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet
1/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
45%Niche pick
?Niche pickVote to see the score
NeurIPS 2026Computer Science, CornellCornellDepartment of Computer Science, Automated scientific discovery

Contrastive Discovery: Open-Ended Scientific Discovery over Competing Explanations

Ziang Liu, James J Kim, Yijia Dai, Jennifer Sun

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

– ReadersNo votes yet
0/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
86%Must read
?Must readVote to see the score
NeurIPS 2026Department of Computer Science aU California, BerkeleyDartmouth CollegeUC BerkeleyAutomated scientific discovery

Learning to Discover Iterative Spectral Algorithms

AutoSpec is a neural framework that discovers iterative spectral algorithms via self-supervised prediction of recurrence coefficients, yielding order-of-magnitude speedups over classical baselines.

Zihang Liu, Oleg Balabanov, Yaoqing Yang, Michael Mahoney

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet
14/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 14 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 2/5
83%Must read
?Must readVote to see the score
NeurIPS 2026TsinghuaAutomated scientific discovery

FunctionEvolve: Structure-Guided Symbolic Regression with LLMs

FunctionEvolve guides LLM symbolic regression via expression-tree structures, local edits, and structure-aware fitting to recover 82.9% of benchmark forms, 4.5x over baselines.

Zeyu Xia, Jun Zhu, Dong Yan

Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

– ReadersNo votes yet
13/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 13 of 20 reviewers recommend it
lenient 4/5
medium 8/10
strict 1/5
80%Must read
?Must readVote to see the score
NeurIPS 2026SpotlightMITGoogleThe Institute for Bird PopulatioAutomated scientific discovery

Targeted Review for AI-Assisted Biodiversity Surveys: Active Continuous-Score Occupancy Modeling

ACORN strategically selects ML predictions for expert review to optimize occupancy model inference, recovering ecological conclusions near fully human-labeled levels with far fewer reviews.

Timm Haucke, Lauren Harrell, Justin Kay, Mary K Clapp and 1 more

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

– ReadersNo votes yet
12/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 12 of 20 reviewers recommend it
lenient 5/5
medium 7/10
strict 0/5
74%Highly rated
?Highly ratedVote to see the score
NeurIPS 2026U Science and Technology of ChinU Science and Technology of ChinAutomated scientific discovery

Interpretable Relational Inference with LLM-Guided Symbolic Dynamics Modeling

COSINE jointly infers interaction graphs and sparse symbolic dynamics via LLM-guided adaptive libraries, recovering interpretable structures and compact equations in synthetic and epidemic data.

Xiaoxiao Liang, Juyuan Zhang, Liming Pan, Linyuan Lü

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

– ReadersNo votes yet
9/20 AI panelreviewers recommend it

Readers and the AI panel: vote on this paper to see what they said.

Only vote on papers you've read. Sign in with GitHub to vote.

AI panel: 9 of 20 reviewers recommend it
lenient 5/5
medium 4/10
strict 0/5