Every paper checked — nothing hidden

All papers

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

All17,312 In brief24 More (on-topic)3,333 Not relevant13,753 Not assessed87 Older351 Could not confirm93 Blogs with no paper266 All blogs284

Paper	Topic	Relevance
A Quantum Machine Learning Approach to Content-Based Media Recommendation The paper applies quantum natural language processing circuits to content-based TV programme recommendations, comparing efficiency against classical models. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Does not address AI safety, jailbreaks, or deployment guardrails.
Sharp and Stable Label-Free Calibration Certificates from Logical Coherence The paper derives mathematical bounds to evaluate the calibration of query-based probabilistic models using logical consistency without ground-truth labels. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on theoretical calibration bounds, not empirical LLM benchmarks.
Sharp and Stable Label-Free Calibration Certificates from Logical Coherence The paper derives mathematical bounds to evaluate the calibration of query-based probabilistic models using logical consistency without ground-truth labels. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Deals with statistical calibration certificates, not model compression.
From Perception to Prediction and Intervention: Benchmarking Proactive Video Assistants in Real-Life Scenarios The paper introduces a benchmark to evaluate proactive video assistants across perception, risk prediction, and timely real-world intervention tasks. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Focuses on proactive multimodal video benchmarking, not deployment guardrails.
From Perception to Prediction and Intervention: Benchmarking Proactive Video Assistants in Real-Life Scenarios The paper introduces a benchmark to evaluate proactive video assistants across perception, risk prediction, and timely real-world intervention tasks. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on benchmarking video assistants, not model compression.
Transfer Learning in Bandits from Unknown Adaptive Policies: Problem-Dependent Lower and Upper Bounds The paper establishes regret bounds and an algorithm for transfer learning in multi-armed bandits when historical data comes from unknown adaptive policies. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Analyzes bandit transfer learning theory, not LLM evaluation benchmarks.
Transfer Learning in Bandits from Unknown Adaptive Policies: Problem-Dependent Lower and Upper Bounds The paper establishes regret bounds and an algorithm for transfer learning in multi-armed bandits when historical data comes from unknown adaptive policies. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on multi-armed bandit theory, not model compression.
Transfer Learning in Bandits from Unknown Adaptive Policies: Problem-Dependent Lower and Upper Bounds The paper establishes regret bounds and an algorithm for transfer learning in multi-armed bandits when historical data comes from unknown adaptive policies. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Covers theoretical bandit regret bounds, not deployment safety.
Market Incentives for AI Safety Investment The paper applies game theory to analyze market incentives for AI safety investments across upstream foundation model providers and downstream firms. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Analyzes market economic incentives, not model efficiency or distillation.
Market Incentives for AI Safety Investment The paper applies game theory to analyze market incentives for AI safety investments across upstream foundation model providers and downstream firms. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on economic game theory, not technical LLM benchmarks.
Local Temporal Contrasts on Human Motion Data Induce a Latent Geometry The paper uses local temporal contrastive learning on human motion data to preserve multi-scale latent geometric trajectories in representations. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Covers time-series representation learning, not AI safety or reliability.
Local Temporal Contrasts on Human Motion Data Induce a Latent Geometry The paper uses local temporal contrastive learning on human motion data to preserve multi-scale latent geometric trajectories in representations. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on representation learning for motion, not model efficiency.
Local Temporal Contrasts on Human Motion Data Induce a Latent Geometry The paper uses local temporal contrastive learning on human motion data to preserve multi-scale latent geometric trajectories in representations. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Studies motion sequence representations, not LLM evaluation or benchmarks.
ReFlow: Replay-Driven Decoupling for Efficient RL Post-Training The paper presents a replay-driven decoupled reinforcement learning framework to improve system efficiency during language model post-training. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Addresses RL system throughput, not safety guardrails or reliability.
ReFlow: Replay-Driven Decoupling for Efficient RL Post-Training The paper presents a replay-driven decoupled reinforcement learning framework to improve system efficiency during language model post-training. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on training system design, not evaluation or benchmarking.
Conditional Mean Flows: Few-Step Densities for Simulation-Based Inference The paper proposes conditional mean flows to enable few-step posterior sampling and tractable density evaluation for simulation-based inference. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Deals with posterior estimation algorithms, not AI deployment safety.
Conditional Mean Flows: Few-Step Densities for Simulation-Based Inference The paper proposes conditional mean flows to enable few-step posterior sampling and tractable density evaluation for simulation-based inference. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Covers statistical simulation-based inference, not small language models.
Conditional Mean Flows: Few-Step Densities for Simulation-Based Inference The paper proposes conditional mean flows to enable few-step posterior sampling and tractable density evaluation for simulation-based inference. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on flow matching for simulation inference, not LLM benchmarks.
Approaching Backpropagation Accuracy with Weight Perturbation on MNIST The paper analyzes the accuracy and efficiency trade-offs of derivative-free weight perturbation compared with standard backpropagation on MNIST. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Studies gradient-free weight perturbation on MNIST, not LLM benchmarks.
Approaching Backpropagation Accuracy with Weight Perturbation on MNIST The paper analyzes the accuracy and efficiency trade-offs of derivative-free weight perturbation compared with standard backpropagation on MNIST. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Analyzes gradient-free optimization theory, not model compression.
Approaching Backpropagation Accuracy with Weight Perturbation on MNIST The paper analyzes the accuracy and efficiency trade-offs of derivative-free weight perturbation compared with standard backpropagation on MNIST. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Examines theoretical optimization methods, not AI safety in deployment.
Classical methods for modern applications: synthetic tabular data generation using lightweight discrete response continuous latent variable model The paper presents a lightweight classical latent variable method for generating synthetic mixed tabular data without complex deep architectures. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on synthetic tabular data generation, not LLM benchmarks.
Classical methods for modern applications: synthetic tabular data generation using lightweight discrete response continuous latent variable model The paper presents a lightweight classical latent variable method for generating synthetic mixed tabular data without complex deep architectures. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Covers tabular latent variable modeling, not small model efficiency.
Classical methods for modern applications: synthetic tabular data generation using lightweight discrete response continuous latent variable model The paper presents a lightweight classical latent variable method for generating synthetic mixed tabular data without complex deep architectures. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Presents tabular data generation, not AI safety or guardrails.
MARS: A Momentum-Aware Adaptive Rating System for Personalized Assessment The paper proposes an interpretable, momentum-aware adaptive testing system that updates learner proficiency ratings dynamically during timed assessments. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Deals with computerized adaptive testing, not model compression.
MARS: A Momentum-Aware Adaptive Rating System for Personalized Assessment The paper proposes an interpretable, momentum-aware adaptive testing system that updates learner proficiency ratings dynamically during timed assessments. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on educational student testing, not LLM evaluation benchmarks.
MARS: A Momentum-Aware Adaptive Rating System for Personalized Assessment The paper proposes an interpretable, momentum-aware adaptive testing system that updates learner proficiency ratings dynamically during timed assessments. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Covers student assessment algorithms, not AI safety in deployment.
ShiftQAT: Quantization-Aware Training for the Numerical Contract of a Fixed-Function Edge CNN Accelerator The paper details a quantization-aware training workflow that trains neural networks to match the exact hardware constraints of edge accelerators. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Focuses on edge quantization hardware constraints, not safety guardrails.
ShiftQAT: Quantization-Aware Training for the Numerical Contract of a Fixed-Function Edge CNN Accelerator The paper details a quantization-aware training workflow that trains neural networks to match the exact hardware constraints of edge accelerators. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on edge accelerator training, not LLM evaluation benchmarks.
Robust Covariance Estimation for Entry-wise Contaminated and Partially Observed Data via Expectation Maximization Algorithm The paper introduces a stochastic approximation expectation maximization algorithm for robust covariance estimation with coordinate-wise contaminated and missing data. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on statistical covariance estimation, not LLM evaluation benchmarks.
Robust Covariance Estimation for Entry-wise Contaminated and Partially Observed Data via Expectation Maximization Algorithm The paper introduces a stochastic approximation expectation maximization algorithm for robust covariance estimation with coordinate-wise contaminated and missing data. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Addresses robust statistics methods, not model compression or serving.
Robust Covariance Estimation for Entry-wise Contaminated and Partially Observed Data via Expectation Maximization Algorithm The paper introduces a stochastic approximation expectation maximization algorithm for robust covariance estimation with coordinate-wise contaminated and missing data. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Deals with classical statistical estimation, not AI system safety.
Sparse Federated Echo State Networks for Time-Series Classification via Smooth-L1 Regularization The paper presents a sparse federated Echo State Network using smooth L1 regularization for efficient edge time-series classification. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Covers reservoir computing sparsity, not AI safety or guardrails.
Sparse Federated Echo State Networks for Time-Series Classification via Smooth-L1 Regularization The paper presents a sparse federated Echo State Network using smooth L1 regularization for efficient edge time-series classification. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on time-series classification, not LLM evaluation benchmarks.
Pairwise AUC Optimization Needs Corrective Power: A Unified View The paper analyzes direct pairwise area-under-curve training to explain why mini-batch stochastic gradient descent can fail on severe ranking errors. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Analyzes training dynamics of AUC loss, not model efficiency.
Pairwise AUC Optimization Needs Corrective Power: A Unified View The paper analyzes direct pairwise area-under-curve training to explain why mini-batch stochastic gradient descent can fail on severe ranking errors. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Covers loss function theory, not deployment safety or robustness.
Pairwise AUC Optimization Needs Corrective Power: A Unified View The paper analyzes direct pairwise area-under-curve training to explain why mini-batch stochastic gradient descent can fail on severe ranking errors. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on optimization surrogates for AUC, not LLM benchmarks.
Q($γ_H$): Entropy-Guided Horizon Mixing in Multi-Discount Q-Learning The paper introduces a multi-horizon reinforcement learning framework that dynamically mixes multiple discount factors using policy confidence signals. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Presents a reinforcement learning algorithm, not LLM evaluation or benchmarks.
Q($γ_H$): Entropy-Guided Horizon Mixing in Multi-Discount Q-Learning The paper introduces a multi-horizon reinforcement learning framework that dynamically mixes multiple discount factors using policy confidence signals. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on Q-learning horizons, not model compression or serving.
Q($γ_H$): Entropy-Guided Horizon Mixing in Multi-Discount Q-Learning The paper introduces a multi-horizon reinforcement learning framework that dynamically mixes multiple discount factors using policy confidence signals. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Investigates standard reinforcement learning optimization, not safety guardrails.
Same Spectrum, Harder First-Order Training: Phase-Locking in Single- versus Multi-Qubit Re-Uploading Circuits The paper investigates optimization dynamics in variational quantum circuits, showing single-qubit re-uploading circuits encounter phase-locking during first-order training. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on quantum circuits, not LLM evaluation benchmarks.
Same Spectrum, Harder First-Order Training: Phase-Locking in Single- versus Multi-Qubit Re-Uploading Circuits The paper investigates optimization dynamics in variational quantum circuits, showing single-qubit re-uploading circuits encounter phase-locking during first-order training. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Covers quantum computing theory, not AI safety or reliability.
Same Spectrum, Harder First-Order Training: Phase-Locking in Single- versus Multi-Qubit Re-Uploading Circuits The paper investigates optimization dynamics in variational quantum circuits, showing single-qubit re-uploading circuits encounter phase-locking during first-order training. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Examines quantum circuit optimization rather than small model efficiency.
Measuring the local shape of learning-rate transfer curves The paper provides analytical formulas to characterize and explain the gradient of learning-rate transfer curves across different model scales. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on optimization dynamics and learning rates, not LLM benchmarks.
Measuring the local shape of learning-rate transfer curves The paper provides analytical formulas to characterize and explain the gradient of learning-rate transfer curves across different model scales. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Studies learning-rate transfer curves, not model compression or serving.
Measuring the local shape of learning-rate transfer curves The paper provides analytical formulas to characterize and explain the gradient of learning-rate transfer curves across different model scales. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Examines theoretical training dynamics rather than deployment safety.
A Closed Form Depth-Width Law for Neural Collapse in Deep Linear Networks The paper derives a closed-form geometric law describing how depth and width affect representation compression in deep linear networks. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Theoretical analysis of linear networks, not model compression or efficiency.
A Closed Form Depth-Width Law for Neural Collapse in Deep Linear Networks The paper derives a closed-form geometric law describing how depth and width affect representation compression in deep linear networks. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Deals with representation geometry theory, not safety or deployment reliability.
A Closed Form Depth-Width Law for Neural Collapse in Deep Linear Networks The paper derives a closed-form geometric law describing how depth and width affect representation compression in deep linear networks. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Studies theoretical neural collapse laws, not LLM evaluation or benchmarks.
Compiling Multi-Threshold Operators for Quantized Non-Monotonic and Fine-Granular Activations in Custom-Tailored DNN Accelerators The paper proposes an ONNX compiler method to map quantized non-monotonic and fine-granular activations into multi-threshold operators for hardware accelerators. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Focuses on FPGA hardware quantization, not safety or guardrails.
Compiling Multi-Threshold Operators for Quantized Non-Monotonic and Fine-Granular Activations in Custom-Tailored DNN Accelerators The paper proposes an ONNX compiler method to map quantized non-monotonic and fine-granular activations into multi-threshold operators for hardware accelerators. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on hardware operator compilation, not LLM evaluation or benchmarks.
Local Epochs as Optimal Stopping: Theory and Evidence in Federated Learning The paper frames client local training in federated learning as an optimal stopping problem to balance local updates against drift. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Focuses on federated optimization, not AI safety or deployment guardrails.
Local Epochs as Optimal Stopping: Theory and Evidence in Federated Learning The paper frames client local training in federated learning as an optimal stopping problem to balance local updates against drift. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Deals with federated learning local epochs rather than model efficiency methods.
Local Epochs as Optimal Stopping: Theory and Evidence in Federated Learning The paper frames client local training in federated learning as an optimal stopping problem to balance local updates against drift. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on federated learning stopping rules, not LLM evaluation benchmarks.
Physics-Aware Distillation of a Grid Foundation Model for Real-Time Edge Inference The paper distils a power-grid foundation model into a compact student network for real-time edge inference on power systems. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on power-flow models, not language model evaluation.
Physics-Aware Distillation of a Grid Foundation Model for Real-Time Edge Inference The paper distils a power-grid foundation model into a compact student network for real-time edge inference on power systems. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Focuses on model distillation, not deployment safety or guardrails.
Lethe: Link Inference Attacks For Evaluation of Edge Unlearning Methods This paper presents a benchmark and link inference attack framework to evaluate edge unlearning methods in graph neural networks. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Benchmarking is for graph edge unlearning, not language models.
Lethe: Link Inference Attacks For Evaluation of Edge Unlearning Methods This paper presents a benchmark and link inference attack framework to evaluate edge unlearning methods in graph neural networks. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Does not study model compression, distillation, or efficient inference.
Learning Lean Feedback for Efficient Proof-Candidate Evaluation on GPUs This paper trains GPU-based neural evaluators to predict Lean 4 compiler verdicts and feedback, accelerating proof-candidate filtering. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Focuses on training throughput for theorem provers, not safety.
Learning Lean Feedback for Efficient Proof-Candidate Evaluation on GPUs This paper trains GPU-based neural evaluators to predict Lean 4 compiler verdicts and feedback, accelerating proof-candidate filtering. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Develops neural evaluators for Lean proofs, not general LLM benchmarks.

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