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This ICML 2026 spotlight position paper identifies a failure mode in image-generation alignment where aesthetic preference optimization overrides explicit user intent, terming it 'reversed alignment' and testing on anti-aesthetic prompts.
This position paper argues that current AI paradigms are insufficient for addressing global systemic risks and proposes Planet-Centered AI (PCAI) as a new design philosophy that treats Earth's interconnected systems as first-class concerns.
This position paper argues that integrating explicit memory, analogous to human hippocampal memory, is essential for advancing LLMs toward AGI. It draws on neuroscience to propose that higher-order cognitive functions require explicit memory beyond implicit statistical learning.
This position paper argues that current methods for evaluating AI resource usage are insufficient and advocates for the adoption of life cycle assessment (LCA) to properly account for energy and environmental costs across the entire ML pipeline, from hardware manufacturing to training and inference.
This position paper argues that large language models should learn from personalized rather than aggregated human preferences, highlighting theoretical limitations from social choice theory and practical issues from demographic diversity. It proposes bounded personalization frameworks that respect individual autonomy while maintaining universal safety constraints.
This position paper argues that a scientific understanding of AI must go beyond post-hoc analysis and instead study the training dynamics that shape model behavior, with implications for predicting, intervening, and designing training procedures for desired properties like capabilities and safety.
This position paper argues that deployed RL agents should never stop learning, as the train-then-fix paradigm inherently fails to address non-stationarity and distribution shift in real-world environments. The authors identify four sources of post-deployment non-stationarity and advocate for continual RL as the standard approach for deployed systems.
This position paper argues that sampling-based inference in Bayesian neural networks has achieved computational parity with optimization-based methods and is poised to supersede them, offering superior uncertainty quantification and prediction performance.
This position paper advocates for developing 'data probes'—synthetic sequences from random processes—to systematically study how data characteristics affect LLM performance, aiming to move beyond empirical heuristics.
This position paper argues that current uncertainty quantification methods for large language models are essentially unsupervised clustering, measuring internal consistency rather than external correctness, and therefore fail to detect confident hallucinations. The authors advocate for a paradigm shift to ground uncertainty in objective truth.
This position paper argues that machine learning research should prioritize ideas over benchmarks and theoretical guarantees, proposing an 'Ideas First' framework that values behavioral signatures and tailored experiments to promote equity and scientific understanding.
This position paper argues that incorporating metacognition as a design principle can lead to more accurate, secure, and efficient AI systems, and demonstrates the concept through a Federated Learning case study and a software framework for experimentation.
The author proposes two architectures, Internal KV-Sphere Architecture (IKSA) and Background Micro Fine-Tuning (BMFT), for enabling LLMs to learn continually from personal interactions without GPU requirements and without catastrophic forgetting.
This position paper argues that agentic AI systems—incorporating memory, reasoning, tool use, self-improvement, and alignment—are a more foreseeable route to AGI than simply scaling monolithic models, and it formalizes these components as separable axes with distinct bottlenecks.
This position paper argues that interpretability research should be evaluated based on actionability—the extent to which insights enable concrete decisions and interventions. The authors propose a framework with evaluation criteria aligned with practical outcomes to address the lack of real-world impact in current interpretability work.
Bert Hubert deelt zijn position paper voor het rondetafeloverleg in de Tweede Kamer over de overname van Solvinity en de gevolgen voor DigiD, waarin hij waarschuwt voor operationele afhankelijkheden van de overheid van private IT-bedrijven.