Model Monotonicity
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Related Articles from SNS
Model Monotonicity in Autobidding Auctions: When Do Better Predictions Lead to Better Outcomes?
arXiv:2605.31036v1 Announce Type: new Abstract: Online advertising platforms rely on machine learning models to predict click-through rates (pCTR) and conversion rates (pCVR) for auction mechanisms. We introduce a novel framework to study the interaction between recommender system model quality, auction format, and autobidder behavior.
Variational Learning for Insertion-based Generation
arXiv:2606.02133v1 Announce Type: new Abstract: Non-monotonic sequence generation methods, such as masked diffusion models, provide a flexible alternative to left-to-right autoregressive modeling by allowing tokens to be generated in non-fixed and prescribed orders. Despite their practical advantages, most existing non-monotonic models are order-agnostic and rely on a fixed-length grid, limiting their ability to support variable-length generation and adaptive insertion order. In this work,...
Margin-Adaptive Confidence Ranking for Reliable LLM Judgement
arXiv:2605.15416v2 Announce Type: replace Abstract: Jung et al. (2025) introduce a hypothesis testing framework for guaranteeing agreement between large language models (LLMs) and human judgments, relying on the assumption that the model's estimated confidence is monotonic with respect to human-disagreement risk. In practice, however, this assumption may be violated, and the generalization behavior of the confidence estimator is not explicitly analyzed.
Hedging on the Frontier: Learning New Tasks with Few Samples
arXiv:2605.30997v1 Announce Type: cross Abstract: When a learner faces a new task with few samples, it must leverage any available side information. In practice, this often comes in the form of model evaluations on related tasks in public benchmarks. A key question then is how to model task relatedness such that it is both realistic and the benchmark evaluations lead to provable gains.
Cognitive Fatigue in Autoregressive Transformers: Formalization and Measurement
Announce Type: new Abstract: Autoregressive language models frequently degrade during long-horizon generation, producing repetitive text, losing instruction adherence, and exhibiting unstable entropy. Despite the prevalence of these failures, practitioners lack online diagnostics to detect them in real-time as they occur. We formalize this degradation as cognitive fatigue, a measurable generation-time state characterized by decay in attention to the original prompt, representational drift,...
ZX-Calculus:Trace-Indexed Dependent Types and Epistemic Semantics
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PE-MHL: Physics-Encoded Modular Hybrid Layers for Scalable Learning of Complex Systems
arXiv:2606.04290v1 Announce Type: new Abstract: Hybrid models that combine physics-based and data-driven components have shown strong potential for achieving accuracy and interpretability in control applications. While recent methods have made progress in incorporating physical consistency, challenges remain in scalability, robustness to noise, and control of model complexity. This paper proposes a Physics-Encoded Modular Hybrid Layer (PE-MHL) framework, in which a baseline physics-based...
RadOT-Eval: Auditable Structured-Evidence Transport for Radiology Report Evaluation
arXiv:2606.08769v1 Announce Type: new Abstract: Automatic evaluation is critical for high-stakes text generation, where errors often involve omitted findings, hallucinated content, polarity reversals, location changes, uncertainty mismatches, and temporal-comparison errors rather than low surface similarity alone. Radiology report generation provides a challenging test case because generated reports must preserve structured clinical evidence across sources. We present RadOT-Eval, an...
Harnessing Evanescent Wave Interaction for Enhanced Optical NO2 Detection with Carbon Nanotube-Coated Side-Polished Fiber
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Stresses and fluid flow in lamina cribrosa through anisotropic poroelasticty
arXiv:2511.04726v2 Announce Type: replace Abstract: To investigate the mechanical correlations between intraocular pressure (IOP) variations and glaucoma, this study presents a linear transversely isotropic poroelastic model of the lamina cribrosa (LC) based on Reissner-Mindlin plate theory. A key feature of the proposed framework is its analytical tractability, which allows the governing poroelastic equations to be solved in closed form under appropriate mechanical and hydraulic boundary...