Tag
This position paper argues that improving machine learning peer review requires enforceable procedural safeguards and a spendable credit system, such as OpenReview Points, to incentivize good reviewing and limit submission volume.
This position paper argues that AI safety research must address AI Lock-In, the phenomenon where excessive reliance on AI leads to human deskilling and systemic vulnerabilities, and provides guidance on mitigation strategies.
This position paper argues that AI evaluation should pivot to assessing human-AI teams rather than superhuman performance to foster better societal outcomes.
This position paper argues that AI systems used in high-stakes decision-making should reason similarly to their users and faithfully communicate that reasoning, and outlines a research agenda for achieving such 'cognitively-aligned AI'.
This position paper argues that modern AI alignment techniques, though designed to prevent harmful outputs, are dual-use technologies that can be misused for censorship and manipulation, and urges the community to address this risk.
This position paper argues that AI reasoning lacks clear operational definitions, undermining evaluation validity, and proposes defining reasoning as a learnable rule-based process with a checklist for research best practices.
This position paper argues that many LLM failures stem from evaluating outputs independently and proposes a self-consistency framework that treats diverse techniques as special cases of consistency optimization.
A position paper arguing that large language models have fundamental limitations, using the metaphor 'can't jump' to highlight gaps in reasoning or generalization.
This position paper argues that long-horizon benchmark failures must be compared against baselines built from matched short stages, introducing the 'horizon residual' metric to distinguish task size from task difficulty in LLM agent evaluation.
This position paper argues that language model evaluation scores should be treated as perishable knowledge claims, not ground truth, and proposes explicit metadata such as formality tier, scope declaration, and expiration date to counter 'trust inflation' caused by averaging weak and strong signals.
This position paper argues against the claim that natural language can fully replace formal languages such as programming languages, proposing an information-theoretic specificity framework and proving a crossover theorem showing formal languages are better for high-specificity tasks.
This position paper argues for shifting from reactive AI flywheel maintenance (patching errors as they occur) to a proactive test-driven approach that maps feedback to a test space of task conditions, showing mathematically that the proactive method achieves better long-term scaling with fewer iterations.
This position paper argues for theory-level autoformalization, which formalizes entire theories including axioms, definitions, and lemmas as coherent libraries, rather than isolated statements. It discusses the significance, alternative views, open challenges, and proposes paths forward for this shift in formalization research.
This position paper argues that ground truth datasets in machine learning are not objective truths but human constructions shaped by choices, and advocates for articulating these choices to improve reliability, transparency, and accountability.
This position paper reviews the current state of LLM-driven formal mathematics, identifies key limitations in applying these systems to open-ended research mathematics, and proposes a strategic roadmap for developing AI agents capable of advancing mathematical frontiers.
This position paper argues that RL researchers need to distinguish between solving simulators and using simulators as a proxy for real deployment, highlighting issues that arise when this distinction is not made.
This position paper argues that the term 'machine unlearning' is overused in LLM research, advocating for stricter terminology tied to dataset-defined deletion and retraining-equivalence guarantees.
A position paper by Subbarao Kambhampati and researchers at Arizona State University argues that chain-of-thought reasoning in LLMs creates an illusion of reasoning, and the industry needs to move beyond costly token generation to alternative reasoning mechanisms.
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.