Model Discovery

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7 papers in the last four weeks, level with the four weeks before. 0.1% of all new papers.

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SciPaths: Forecasting Pathways to Scientific Discovery
May 14, 2026Eric Chamoun, Yizhou Chi, Yulong Chen +4Scientific Discovery Model Discovery
University of Cambridge, Cambridge, United Kingdom · Singapore University of Technology and Design, Singapore · The Alan Turing Institute, London, United Kingdom
Systematic Discovery of Semantic Attacks in Online Map Construction through Conditional Diffusion
May 14, 2026Chenyi Wang, Ruoyu Song, Raymond Muller +5Lane-Level Map Generation Autonomous Driving Perception
University of Arizona · Purdue University · Lawrence Livermore National Laboratory +2
Epistemic Uncertainty for Test-Time Discovery
May 11, 2026Kainat Riaz, Muhammad Ahmed Mohsin, Ahsan Bilal +5Model Discovery Stochastic Exploration
Stanford University · University of Oklahoma · National University of Sciences and Technology
Discovery of Nonlinear Dynamics with Automated Basis Function Generation
May 10, 2026Mohammad Amin Basiri, Charles NicholsonSymbolic Regression System Identification
Data Science and Analytics Institute, University of Oklahoma, Norman, OK, USA · School of Industrial and Systems Engineering, University of Oklahoma, Norman, OK, USA
From Mechanistic to Compositional Interpretability
May 9, 2026Ward Gauderis, Thomas Dooms, Steven T. Homer +2Mechanistic Interpretability Interpretability
Vrije Universiteit Brussel, Brussels, Belgium · Independent · UK AI Security Institute, London, United Kingdom.
Black-box model classification under the discriminative factorization
May 8, 2026Hayden Helm, Merrick Ohata, Carey PriebeBlack Box Exploratory Factor Analysis
Helivan · JHU
Discovering Ordinary Differential Equations with LLM-Based Qualitative and Quantitative Evaluation
May 8, 2026Sum Kyun Song, Bong Gyun Shin, Jae Yong LeeOrdinary Differential Equations Symbolic Regression
Department of Artificial Intelligence, Chung-Ang University, Seoul, Republic of Korea · Department of AI Big Data, Daejin University, Pocheon-si, Republic of Korea.
Arrow: A Foundation Model for Causal Discovery
May 8, 2026Ryan Thompson, He Zhao, Daniel M. Steinberg +1Causal Discovery Methods Model Discovery
University of Technology Sydney · CSIRO’s Data61
Think-Aloud Reshapes Automated Cognitive Model Discovery Beyond Behavior
May 6, 2026Hanbo Xie, Akshay K. Jagadish, Lan Pan +1Cognitive Science Large Reasoning Models
School of Psychology, Georgia Institute of Technology · Princeton AI Lab, Princeton University · Center of Excellence for Computational Cognition, Georgia Institute of Techonology
PHBench: A Benchmark for Predicting Startup Series A Funding from Product Hunt Launch Signals
May 3, 2026Yagiz Ihlamur, Ben Griffin, Rick ChenModel Discovery Large Language Model Forecasting
Amazon, Seattle, WA, USA · University of Oxford, Oxford, UK
Automated Interpretability and Feature Discovery in Language Models with Agents
May 2, 2026Arnau Marin-Llobet, Javier FerrandoMechanistic Interpretability Interpretability
Harvard University
Machine Collective Intelligence for Explainable Scientific Discovery
Apr 30, 2026Gyoung S. Na, Chanyoung ParkArtificial Intelligence Scientists Scientific Discovery
Korea Research Institute of Chemical Technology (KRICT) · Korea Advanced Institute of Science and Technology (KAIST)
Large language model-enabled automated data extraction for concrete materials informatics
Apr 24, 2026Zhanzhao Li, Kengran Yang, Qiyao He +1Data-Driven Materials Discovery Large Language Model Pipelines
Department of Civil and Environmental Engineering, Rice University, Houston, TX 77005, USA. · Rice Advanced Materials Institute, Rice University, Houston, TX 77005, USA. · Ken Kennedy Institute, Rice University, Houston, TX 77005, USA. +1
CoFEE: Reasoning Control for LLM-Based Feature Discovery
Apr 23, 2026Maximilian Westermann, Ben Griffin, Aaron Ontoyin Yin +6LLM Reasoning Strategies Model Discovery
University of Oxford · Vela Research · Amazon
Evaluation-driven Scaling for Scientific Discovery
Apr 21, 2026Haotian Ye, Haowei Lin, Jingyi Tang +22Model Discovery Scientific Discovery
Wizard Intelligence Learning Lab, Stanford University · Peking University, Tsinghua University
How Much Data is Enough? The Zeta Law of Discoverability in Biomedical Data, featuring the enigmatic Riemann zeta function
Apr 19, 2026Paul M. ThompsonScaling Laws Biomedical
Stevens Institute for Neuroimaging & Informatics · University of Southern California · Los Angeles, CA, USA
DiscoGen: Procedural Generation of Algorithm Discovery Tasks in Machine Learning
Mar 18, 2026Alexander D. Goldie, Zilin Wang, Adrian Hayler +17Model Discovery Mle-Bench Lite
University of Oxford · University of California, Santa Barbara · University College London +1
Decoding ML Decision: An Agentic Reasoning Framework for Large-Scale Ranking System
Feb 20, 2026Longfei Yun, Yihan Wu, Haoran Liu +11Ranking Agentic Reasoning
Meta
A Probabilistic Framework for LLM-Based Model Discovery
Feb 20, 2026Stefan Wahl, Raphaela Schenk, Ali Farnoud +2Model Discovery Probabilistic Model
Machine Learning in Science, University of Tübingen, Tübingen, Germany · Tübingen AI Center, Tübingen, Germany · Boehringer Ingelheim, Biberach, Germany +1
Paid Voices vs. Public Feeds: Interpretable Cross-Platform Theme-Based Analysis of Climate Discourse
Jan 19, 2026Samantha Sudhoff, Pranav Perumal, Zhaoqing Wu +1Discourse Model Discovery
Department of Computer Science, Purdue University, West Lafayette, IN 47907
When is a System Discoverable from Data? Discovery Requires Chaos
Nov 12, 2025Zakhar Shumaylov, Peter Zaika, Philipp Scholl +3Chaos Model Discovery
University of Cambridge · LMU Munich and Munich Center for Machine Learning (MCML) · IBM Research
Making Interpretable Discoveries from Unstructured Data: A High-Dimensional Multiple Hypothesis Testing Approach
Nov 3, 2025Jacob CarlsonModel Discovery Unstructured Data
Harvard University∗
Position: Use Sparse Autoencoders to Discover Unknowns
Jun 30, 2025Kenny Peng, Rajiv Movva, Jon Kleinberg +2Improving Sparse Autoencoders Model Discovery
1Cornell University · 2UC Berkeley
Automated Discovery of Operable Dynamics from Videos
Oct 14, 2024Kuang Huang, Dong Heon Cho, Boyuan ChenDynamical Systems Model Discovery
Department of Applied Physics and Applied Mathematics, Columbia University, 500 W. 120th Street, New York, 10027, NY, USA. · Department of Computer Science, Duke University, 308 Research Dr., Durham, 27705, NC, USA. · Department of Mechanical Engineering and Material Science, Duke University, 121 Hudson Hall, Durham, 27708, NC, USA. +1

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SciPaths: Forecasting Pathways to Scientific Discovery

Eric Chamoun, Yizhou Chi, Yulong Chen, Rui Cao+3

University of Cambridge, Cambridge, United Kingdom · Singapore University of Technology and Design, Singapore · The Alan Turing Institute, London, United Kingdom

Scientific Discovery Model Discovery Pathways

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Scientific progress depends on sequences of enabling contributions, yet existing AI4Science benchmarks largely focus on citation prediction, literature retrieval, or idea generation rather than the dependencies that make progress possible. In this paper, we introduce discovery pathway forecasting: given a target scientific contribution and the prior literature available at a specified time, the task is to (1) identify the enabling contributions required to realize it and (2) ground each in prior work when such prior work exists. We present SciPaths, a benchmark of 262 expert-annotated gold pathways and 2,444 silver pathways constructed from machine learning and natural language processing papers, where each pathway records enabling contributions, roles, rationales, and prior-work groundings or unmapped decisions. Evaluating frontier and open-weight language models, we find that the best model reaches only 0.189 F1 under strict semantic matching, with core methodological dependencies hardest to recover. Prior-work grounding improves substantially when gold enabling contributions are provided, showing that decomposition quality is a major bottleneck for end-to-end pathway recovery. SciPaths therefore shifts evaluation toward a missing capability in scientific forecasting: reasoning backward from a target contribution to the enabling scientific building blocks and prior-work dependencies that make it feasible.

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