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
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.
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.
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	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.
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.
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.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on unsupervised domain adaptation bounds, not language model evaluation.
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.
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.
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.
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.
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.
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.
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.
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.
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	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.
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.
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.
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.
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.
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.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical analysis of flow matching penalties, not LLM evaluation.
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.
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	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	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.
Adaptive Robust Confidence Sets in High Dimensions This paper analyzes the statistical costs and computational lower bounds of constructing adaptive confidence sets under unknown Huber contamination. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on statistical query lower bounds, not model efficiency.
Adaptive Robust Confidence Sets in High Dimensions This paper analyzes the statistical costs and computational lower bounds of constructing adaptive confidence sets under unknown Huber contamination. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant High-dimensional mathematical statistics paper, not LLM evaluation.
Adaptive Robust Confidence Sets in High Dimensions This paper analyzes the statistical costs and computational lower bounds of constructing adaptive confidence sets under unknown Huber contamination. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Statistical theory on Huber contamination, not AI deployment safety.
A Dependency-Aware Multi-GPU Training Framework for Temporal Graph Neural Networks This paper presents a multi-GPU training framework for temporal graph neural networks that relaxes event dependencies to reduce synchronization. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Multi-GPU systems paper for graph networks, not LLM evaluation.
A Dependency-Aware Multi-GPU Training Framework for Temporal Graph Neural Networks This paper presents a multi-GPU training framework for temporal graph neural networks that relaxes event dependencies to reduce synchronization. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on GPU training throughput, not AI safety or reliability.
Inference on the Value of Policy Improvement in Adaptive Experiments This paper develops an asymptotically normal estimator to measure the value of policy improvement in adaptive experimental settings. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical econometrics and statistics paper, not LLM evaluation benchmarks.
Inference on the Value of Policy Improvement in Adaptive Experiments This paper develops an asymptotically normal estimator to measure the value of policy improvement in adaptive experimental settings. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Statistical theory for adaptive experiments, not AI safety or guardrails.
Inference on the Value of Policy Improvement in Adaptive Experiments This paper develops an asymptotically normal estimator to measure the value of policy improvement in adaptive experimental settings. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Statistical inference on policy improvement, not model efficiency or compression.
Grounding Deep Learning Framework Testing in Data Flow This paper introduces a data-flow graph representation and mutation testing technique to uncover bugs in deep learning frameworks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on software testing of DL frameworks, not LLM benchmarks.
Grounding Deep Learning Framework Testing in Data Flow This paper introduces a data-flow graph representation and mutation testing technique to uncover bugs in deep learning frameworks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant About bug detection in frameworks, not small or efficient models.
Grounding Deep Learning Framework Testing in Data Flow This paper introduces a data-flow graph representation and mutation testing technique to uncover bugs in deep learning frameworks. Preprint	LLMs for code via arxiv · 15 Oct, 06:18	Not relevant Tests deep learning execution frameworks, not code generation or assistants.
Benchmarking Deep Learning Models for Anomaly Detection in Real-World Cloud Telemetry This paper benchmarks deep learning architectures for anomaly detection on real-world cloud telemetry using timing-aware evaluation metrics. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Benchmarks time-series models for cloud telemetry, not LLM evaluation.
Benchmarking Deep Learning Models for Anomaly Detection in Real-World Cloud Telemetry This paper benchmarks deep learning architectures for anomaly detection on real-world cloud telemetry using timing-aware evaluation metrics. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Evaluates existing time-series models, not techniques for model efficiency.
Benchmarking Deep Learning Models for Anomaly Detection in Real-World Cloud Telemetry This paper benchmarks deep learning architectures for anomaly detection on real-world cloud telemetry using timing-aware evaluation metrics. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on IT cloud telemetry monitoring, not AI model safety.
Guarded Block Transfers for Polytope Distance: Linear Convergence with Explicit Constants and Gap-Certified Screening for SVM Training This paper presents guarded block transfers for polytope distance computation to accelerate support vector machine training. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with classical SVM optimization algorithms, not LLM efficiency.
Guarded Block Transfers for Polytope Distance: Linear Convergence with Explicit Constants and Gap-Certified Screening for SVM Training This paper presents guarded block transfers for polytope distance computation to accelerate support vector machine training. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on convex optimization for SVMs, not AI safety.
Guarded Block Transfers for Polytope Distance: Linear Convergence with Explicit Constants and Gap-Certified Screening for SVM Training This paper presents guarded block transfers for polytope distance computation to accelerate support vector machine training. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Optimization theory paper for SVMs, not LLM evaluation benchmarks.
How Shared Representations Become Specialized: Formation, Maintenance, and Latent Susceptibility This paper analyzes how shared representations specialize among multiple targets and how optimizer state influences this specialization. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Analyzes representation formation in toy neural networks, not LLM benchmarks.
How Shared Representations Become Specialized: Formation, Maintenance, and Latent Susceptibility This paper analyzes how shared representations specialize among multiple targets and how optimizer state influences this specialization. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Basic science study of representation drift, not deployment AI safety.
How Shared Representations Become Specialized: Formation, Maintenance, and Latent Susceptibility This paper analyzes how shared representations specialize among multiple targets and how optimizer state influences this specialization. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Study of representation specialization, not practical model compression or efficiency.

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