Every paper checked — nothing hidden

All papers

Every paper checked for brief #4 of 12–19 Oct 2026 — nothing is hidden. Brief papers first.

All17,313 In brief24 More (on-topic)3,333 Not relevant13,754 Not assessed87 Older352 Could not confirm93 Blogs with no paper266 All blogs284

Paper	Topic	Relevance
Personalized Physiological-Model-Based Neural Network for Glucose Forecasting in Type 1 Diabetes The paper develops a neural network constrained by a personalized four-parameter physiological model to forecast blood glucose levels in diabetes. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Covers physiological glucose forecasting rather than AI safety or reliability.
Personalized Physiological-Model-Based Neural Network for Glucose Forecasting in Type 1 Diabetes The paper develops a neural network constrained by a personalized four-parameter physiological model to forecast blood glucose levels in diabetes. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on glucose prediction networks rather than small language model efficiency.
Personalized Physiological-Model-Based Neural Network for Glucose Forecasting in Type 1 Diabetes The paper develops a neural network constrained by a personalized four-parameter physiological model to forecast blood glucose levels in diabetes. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Pertains to clinical glucose forecasting, not language model evaluation.
Split Conformal Interval Length under Huber Contamination The paper establishes theoretical bounds on interval lengths for split conformal prediction under Huber data contamination. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on statistical interval analysis rather than small model efficiency.
Split Conformal Interval Length under Huber Contamination The paper establishes theoretical bounds on interval lengths for split conformal prediction under Huber data contamination. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies statistical conformal prediction interval bounds, not language model benchmarks.
RF Fingerprinting of DSP Implementations using Machine Learning: An Over-the-Air Case Study The paper demonstrates that convolutional neural networks can classify transmitter digital signal processing implementations from over-the-air radio signals. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on RF signal fingerprinting, not language model evaluation.
RF Fingerprinting of DSP Implementations using Machine Learning: An Over-the-Air Case Study The paper demonstrates that convolutional neural networks can classify transmitter digital signal processing implementations from over-the-air radio signals. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on radio transmitter identification, not small language model efficiency.
RF Fingerprinting of DSP Implementations using Machine Learning: An Over-the-Air Case Study The paper demonstrates that convolutional neural networks can classify transmitter digital signal processing implementations from over-the-air radio signals. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Deals with radio transmitter identification rather than AI safety risks.
Revisited Support Vector Machines: A Unified Joint Optimization Framework with Nyström Approximation for Scalable Adaptive Learning The paper presents a continuous joint optimization framework for support vector machines that updates multipliers and kernel hyperparameters together. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on Support Vector Machine optimization, not language model evaluation.
Revisited Support Vector Machines: A Unified Joint Optimization Framework with Nyström Approximation for Scalable Adaptive Learning The paper presents a continuous joint optimization framework for support vector machines that updates multipliers and kernel hyperparameters together. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses kernel machine optimization rather than AI safety or reliability.
Revisited Support Vector Machines: A Unified Joint Optimization Framework with Nyström Approximation for Scalable Adaptive Learning The paper presents a continuous joint optimization framework for support vector machines that updates multipliers and kernel hyperparameters together. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses classical SVM optimization rather than small language model efficiency.
Sensitivity Fidelity Reveals Physical Dependencies Beyond Forecast Accuracy in Deep-Learning Earth-System Models The paper proposes sensitivity fidelity as an evaluation criterion to verify physical consistency in deep-learning Earth-system forecasting models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Addresses Earth-system physical models rather than language model evaluation benchmarks.
Sensitivity Fidelity Reveals Physical Dependencies Beyond Forecast Accuracy in Deep-Learning Earth-System Models The paper proposes sensitivity fidelity as an evaluation criterion to verify physical consistency in deep-learning Earth-system forecasting models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on physical consistency in climate models, not efficient language models.
Sensitivity Fidelity Reveals Physical Dependencies Beyond Forecast Accuracy in Deep-Learning Earth-System Models The paper proposes sensitivity fidelity as an evaluation criterion to verify physical consistency in deep-learning Earth-system forecasting models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on Earth-system model physics rather than AI safety or reliability.
Beyond Aggregate Drift: Optimal Client-Specific Windows in Federated Learning The paper presents an accelerated federated learning method that assigns client-specific memory windows based on individual noise and drift rates. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Deals with federated learning convergence bounds rather than deployment safety.
Beyond Aggregate Drift: Optimal Client-Specific Windows in Federated Learning The paper presents an accelerated federated learning method that assigns client-specific memory windows based on individual noise and drift rates. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on federated data drift optimization rather than small model efficiency.
Beyond Aggregate Drift: Optimal Client-Specific Windows in Federated Learning The paper presents an accelerated federated learning method that assigns client-specific memory windows based on individual noise and drift rates. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on federated optimization algorithms, not language model evaluation.
Vision-Predictor-Action: Towards Efficient Embodied AI via Neuro-Symbolic JEPA Predictors The paper proposes a robot control framework using predictive architectures and flow matching to generate action chunks without autoregressive decoding. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on general robot manipulation policies, not industrial defect inspection.
Vision-Predictor-Action: Towards Efficient Embodied AI via Neuro-Symbolic JEPA Predictors The paper proposes a robot control framework using predictive architectures and flow matching to generate action chunks without autoregressive decoding. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents an embodied AI architecture, not an evaluation framework for language models.
Vision-Predictor-Action: Towards Efficient Embodied AI via Neuro-Symbolic JEPA Predictors The paper proposes a robot control framework using predictive architectures and flow matching to generate action chunks without autoregressive decoding. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on inference latency and control rather than safety or reliability guardrails.
UNISON: A Unified Framework for Learning from Scored Bags The paper presents a unified three-stage framework for multiple instance learning and recommendation from scored bag collections. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on recommender systems and multiple instance learning, not language model evaluation.
UNISON: A Unified Framework for Learning from Scored Bags The paper presents a unified three-stage framework for multiple instance learning and recommendation from scored bag collections. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on recommender system architectures rather than AI safety guardrails.
UNISON: A Unified Framework for Learning from Scored Bags The paper presents a unified three-stage framework for multiple instance learning and recommendation from scored bag collections. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with recommendation frameworks rather than small language model inference.
Latent-Space Importance Weighting under Covariate Shift The paper proposes a latent-space importance weighting algorithm to improve prediction accuracy under covariate shift. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on latent representation learning theory, not model efficiency.
Latent-Space Importance Weighting under Covariate Shift The paper proposes a latent-space importance weighting algorithm to improve prediction accuracy under covariate shift. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on statistical covariate shift bounds, not language model benchmarking.
Latent-Space Importance Weighting under Covariate Shift The paper proposes a latent-space importance weighting algorithm to improve prediction accuracy under covariate shift. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Covers covariate shift bounds rather than AI safety or reliability.
An Attention-Guided Graph Neural Network Surrogate for Boundary Integral Computation in Physical Field Reconstruction The paper introduces an attention-guided graph neural network surrogate to reconstruct missing boundary information in physical field simulations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on boundary integral reconstruction, not language model benchmarks.
An Attention-Guided Graph Neural Network Surrogate for Boundary Integral Computation in Physical Field Reconstruction The paper introduces an attention-guided graph neural network surrogate to reconstruct missing boundary information in physical field simulations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Pertains to physical field engineering computation, not AI safety risks.
An Attention-Guided Graph Neural Network Surrogate for Boundary Integral Computation in Physical Field Reconstruction The paper introduces an attention-guided graph neural network surrogate to reconstruct missing boundary information in physical field simulations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses physical field computation rather than small language model efficiency.
Joint Contrastive-Adversarial Representation Learning for Unsupervised Domain Adaptation The paper establishes theoretical risk bounds for a two-stage unsupervised domain adaptation method combining contrastive learning and adversarial alignment. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on unsupervised domain adaptation bounds, not language model evaluation.
Joint Contrastive-Adversarial Representation Learning for Unsupervised Domain Adaptation The paper establishes theoretical risk bounds for a two-stage unsupervised domain adaptation method combining contrastive learning and adversarial alignment. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Analyzes statistical bounds for domain adaptation, not safety or reliability.
Joint Contrastive-Adversarial Representation Learning for Unsupervised Domain Adaptation The paper establishes theoretical risk bounds for a two-stage unsupervised domain adaptation method combining contrastive learning and adversarial alignment. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on domain adaptation theory rather than small model efficiency.
ProML: Progressive Multi-label Learning The paper introduces a progressive multi-label learning framework that decouples direct label fitting from predictor optimization via an auxiliary variable. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Pertains to multi-label learning optimization rather than efficient language models.
ProML: Progressive Multi-label Learning The paper introduces a progressive multi-label learning framework that decouples direct label fitting from predictor optimization via an auxiliary variable. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on mathematical optimization for multi-label tasks, not AI safety.
ProML: Progressive Multi-label Learning The paper introduces a progressive multi-label learning framework that decouples direct label fitting from predictor optimization via an auxiliary variable. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Covers multi-label machine learning optimization, not language model evaluation.
Screening Collapse under Recursive Synthetic Data The paper analyzes screening collapse, showing how recursive training on synthetic data degrades feature selection prior to model training. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Analyzes linear variable screening theory rather than language model benchmark suites.
Screening Collapse under Recursive Synthetic Data The paper analyzes screening collapse, showing how recursive training on synthetic data degrades feature selection prior to model training. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on synthetic data degradation in statistical models, not model compression.
Adaptive Role Assignment and Controlled Feature Reconstruction for Federated Image Semantic Communication The paper introduces an adaptive role-assignment and feature reconstruction framework to improve hybrid federated image communication under wireless constraints. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Deals with wireless network transmission rather than deployment safety or jailbreaks.
Adaptive Role Assignment and Controlled Feature Reconstruction for Federated Image Semantic Communication The paper introduces an adaptive role-assignment and feature reconstruction framework to improve hybrid federated image communication under wireless constraints. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on radio semantic communication rather than small language model efficiency.
Adaptive Role Assignment and Controlled Feature Reconstruction for Federated Image Semantic Communication The paper introduces an adaptive role-assignment and feature reconstruction framework to improve hybrid federated image communication under wireless constraints. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on federated communication rather than language model evaluation benchmarks.
The Price of Precision: A Calibrated Cost Model for Mixed-Precision LLM Serving The paper presents a hardware-calibrated cost model to optimize mixed-precision serving configurations across varying batch sizes and context lengths. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on serving latency optimization rather than language model evaluation methods.
The Price of Precision: A Calibrated Cost Model for Mixed-Precision LLM Serving The paper presents a hardware-calibrated cost model to optimize mixed-precision serving configurations across varying batch sizes and context lengths. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses serving resource constraints rather than AI safety or reliability guardrails.
MERIT: Scaling KG Foundation Models with Relation Transformers The paper presents a transformer-based relation encoder for knowledge graph foundation models to reduce scaling and inference costs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Evaluates a specific model rather than proposing language model evaluation methodology.
MERIT: Scaling KG Foundation Models with Relation Transformers The paper presents a transformer-based relation encoder for knowledge graph foundation models to reduce scaling and inference costs. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on knowledge graph foundation models rather than small language model efficiency.
MERIT: Scaling KG Foundation Models with Relation Transformers The paper presents a transformer-based relation encoder for knowledge graph foundation models to reduce scaling and inference costs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on link prediction efficiency rather than AI safety or reliability.
Context-Aware Precision Scheduling for Small Language Models The paper proposes a context-aware mixed-precision scheduling method to balance execution latency and accuracy for small language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on latency and precision tradeoffs rather than safety guardrails or robustness.
Context-Aware Precision Scheduling for Small Language Models The paper proposes a context-aware mixed-precision scheduling method to balance execution latency and accuracy for small language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on runtime scheduling, not language model benchmark methodologies.
Testing Central Structure for Complex Networks The paper proposes a hypothesis testing framework using spectral radius to determine if a complex network contains true central structure. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on complex network graph theory rather than efficient language models.
Testing Central Structure for Complex Networks The paper proposes a hypothesis testing framework using spectral radius to determine if a complex network contains true central structure. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on network centrality tests, not language model evaluation or benchmarks.
Testing Central Structure for Complex Networks The paper proposes a hypothesis testing framework using spectral radius to determine if a complex network contains true central structure. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Deals with graph centrality testing rather than AI safety or reliability.
Drift, Then Spread: Learning One-Step Generative Policies for Online Reinforcement Learning The paper proposes a one-step generative reinforcement learning policy that balances reward improvement and entropy without iterative sampling. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on RL sample and inference speed, not AI safety.
Drift, Then Spread: Learning One-Step Generative Policies for Online Reinforcement Learning The paper proposes a one-step generative reinforcement learning policy that balances reward improvement and entropy without iterative sampling. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on online RL policy design, not LLM evaluation benchmarks.
When do explicit penalties improve flow matching? The paper derives geometric and statistical criteria showing when explicit penalties improve sample accuracy in flow matching generative models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Analyzes statistical trade-offs in generative modeling, not deployment safety.
When do explicit penalties improve flow matching? The paper derives geometric and statistical criteria showing when explicit penalties improve sample accuracy in flow matching generative models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Theoretical analysis of error trade-offs, not efficient model serving.
When do explicit penalties improve flow matching? The paper derives geometric and statistical criteria showing when explicit penalties improve sample accuracy in flow matching generative models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical analysis of flow matching penalties, not LLM evaluation.
Notes on Uniform Policy Score Bounds and Lipschitz Continuity for Truncated Gaussian, Product Cauchy, Boltzmann, and Softmax Policies The paper establishes uniform policy score bounds and Lipschitz continuity conditions across four common reinforcement learning policy parameterizations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Brief mathematical note on policy bounds, not LLM evaluation.
Notes on Uniform Policy Score Bounds and Lipschitz Continuity for Truncated Gaussian, Product Cauchy, Boltzmann, and Softmax Policies The paper establishes uniform policy score bounds and Lipschitz continuity conditions across four common reinforcement learning policy parameterizations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Pure theory on Lipschitz continuity in RL policies, not efficiency.
Notes on Uniform Policy Score Bounds and Lipschitz Continuity for Truncated Gaussian, Product Cauchy, Boltzmann, and Softmax Policies The paper establishes uniform policy score bounds and Lipschitz continuity conditions across four common reinforcement learning policy parameterizations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Mathematical statistics note on RL bounds, not AI safety.
DRanker: A Transformer-Based Multi-Task Ranking Model for the Disney+ Homepage The paper presents an encoder-only transformer ranking model deployed at Disney+ that uses lightweight debiasing to improve recommendations. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Covers recommendation ranking systems, not AI safety or guardrails.
DRanker: A Transformer-Based Multi-Task Ranking Model for the Disney+ Homepage The paper presents an encoder-only transformer ranking model deployed at Disney+ that uses lightweight debiasing to improve recommendations. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Describes a production recommender model, not LLM evaluation benchmarks.

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