Papers
Position: Build Agent Advocates, Not Platform Agents
Sayash Kapoor, Noam Kolt, Seth Lazar
Position: The Most Expensive Part of an LLM *should* be its Training Data
Nikhil Kandpal, Colin Raffel
Position: AI Safety Must Embrace an Antifragile Perspective
Ming Jin, Hyunin Lee
Position: Not All Explanations for Deep Learning Phenomena Are Equally Valuable
Alan Jeffares, Mihaela Van Der Schaar
Position: We Can’t Understand AI Using our Existing Vocabulary
John Hewitt, Robert Geirhos, Been Kim
Position: AI Safety should prioritize the Future of Work
Sanchaita Hazra, Bodhisattwa Prasad Majumder, Tuhin Chakrabarty
Position: AI Should Not Be An Imitation Game: Centaur Evaluations
Andreas Haupt, Erik Brynjolfsson
Position: We Need Responsible, Application-Driven (RAD) AI Research
Sarah Hartman, Cheng Soon Ong, Julia Powles et al.
Position: General Intelligence Requires Reward-based Pretraining
Seungwook Han, Jyothish Pari, Samuel J. Gershman et al.
Position: Spectral GNNs Rely Less on Graph Fourier Basis than Conceived
Yuhe Guo, Huayi Tang, Jiahong Ma et al.
Position: Future Research and Challenges Remain Towards AI for Software Engineering
Alex Gu, Naman Jain, Wen-Ding Li et al.
Position: It Is Time We Test Neural Computation In Vitro
Frithjof Gressmann, Ashley Chen, Lily Hexuan Xie et al.
Position: Enough of Scaling LLMs! Lets Focus on Downscaling
Yash Goel, Ayan Sengupta, Tanmoy Chakraborty
Position: Political Neutrality in AI Is Impossible — But Here Is How to Approximate It
Jillian Fisher, Ruth Elisabeth Appel, Chan Young Park et al.
Position: We Need An Algorithmic Understanding of Generative AI
Oliver Eberle, Thomas Austin Mcgee, Hamza Giaffar et al.
Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance
Moming Duan, Mingzhe Du, Rui Zhao et al.
Position: Constants are Critical in Regret Bounds for Reinforcement Learning
Simone Drago, Marco Mussi, Alberto Maria Metelli
Position: The Categorization of Race in ML is a Flawed Premise
Miriam Doh, Benedikt Höltgen, Piera Riccio et al.
Position: A Theory of Deep Learning Must Include Compositional Sparsity
David A. Danhofer, Davide D’Ascenzo, Rafael Dubach et al.
Position: Certified Robustness Does Not (Yet) Imply Model Security
Andrew Craig Cullen, Paul Montague, Sarah Monazam Erfani et al.
Position: Don’t Use the CLT in LLM Evals With Fewer Than a Few Hundred Datapoints
Sam Bowyer, Laurence Aitchison, Desi R. Ivanova
Position: Rethinking Explainable Machine Learning as Applied Statistics
Sebastian Bordt, Eric Raidl, Ulrike Von Luxburg
Position: Stop treating ‘AGI’ as the north-star goal of AI research
Borhane Blili-Hamelin, Christopher Graziul, Leif Hancox-Li et al.
Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks
Maya Bechler-Speicher, Ben Finkelshtein, Fabrizio Frasca et al.