Papers
Position: An Empirically Grounded Identifiability Theory Will Accelerate Self Supervised Learning Research
Patrik Reizinger, Randall Balestriero, David Klindt et al.
Position: A Theory of Deep Learning Must Include Compositional Sparsity
David A. Danhofer, Davide D’Ascenzo, Rafael Dubach et al.
Position: Beyond Assistance – Reimagining LLMs as Ethical and Adaptive Co-Creators in Mental Health Care
Abeer Badawi, Md Tahmid Rahman Laskar, Jimmy Huang et al.
Position: Build Agent Advocates, Not Platform Agents
Sayash Kapoor, Noam Kolt, Seth Lazar
Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoption
Audrey Poinsot, Panayiotis Panayiotou, Alessandro Leite et al.
Position: Certified Robustness Does Not (Yet) Imply Model Security
Andrew Craig Cullen, Paul Montague, Sarah Monazam Erfani et al.
Position: Challenges and Future Directions of Data-Centric AI Alignment
Min-Hsuan Yeh, Jeffrey Wang, Xuefeng Du et al.
Position: Constants are Critical in Regret Bounds for Reinforcement Learning
Simone Drago, Marco Mussi, Alberto Maria Metelli
Position: Contextual Integrity is Inadequately Applied to Language Models
Yan Shvartzshnaider, Vasisht Duddu
Position: Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance
Moming Duan, Mingzhe Du, Rui Zhao et al.
Position: Deep Learning is Not So Mysterious or Different
Andrew Gordon Wilson
Position: Democratic AI is Possible. The Democracy Levels Framework Shows How It Might Work.
Aviv Ovadya, Kyle Redman, Luke Thorburn 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: Editing Large Language Models Poses Serious Safety Risks
Paul Youssef, Zhixue Zhao, Daniel Braun et al.
Position: Enough of Scaling LLMs! Lets Focus on Downscaling
Yash Goel, Ayan Sengupta, Tanmoy Chakraborty
Position: Evaluating Generative AI Systems Is a Social Science Measurement Challenge
Hanna Wallach, Meera Desai, A. Feder Cooper et al.
Position: Explainable AI Cannot Advance Without Better User Studies
Matej Pičulin, Bernarda Petek, Irena Ograjenšek et al.
Position: Formal Mathematical Reasoning—A New Frontier in AI
Kaiyu Yang, Gabriel Poesia, Jingxuan He et al.
Position: Future Research and Challenges Remain Towards AI for Software Engineering
Alex Gu, Naman Jain, Wen-Ding Li et al.
Position: General Intelligence Requires Reward-based Pretraining
Seungwook Han, Jyothish Pari, Samuel J. Gershman et al.
Position: Generative AI Regulation Can Learn from Social Media Regulation
Ruth Elisabeth Appel
Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks
Maya Bechler-Speicher, Ben Finkelshtein, Fabrizio Frasca et al.
Position: Graph Matching Systems Deserve Better Benchmarks
Indradyumna Roy, Saswat Meher, Eeshaan Jain et al.
Position: Human Baselines in Model Evaluations Need Rigor and Transparency (With Recommendations & Reporting Checklist)
Kevin Wei, Patricia Paskov, Sunishchal Dev et al.
Position: Humanity Faces Existential Risk from Gradual Disempowerment
Jan Kulveit, Raymond Douglas, Nora Ammann et al.