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
PANDA: Prior-guided Attentional Dual-path Architecture Presents a dual-path architecture combining normalized attention and incidence priors for hypergraph message-passing neural networks. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Deals with graph representation learning, not deployment safety, guardrails, or alignment.
PANDA: Prior-guided Attentional Dual-path Architecture Presents a dual-path architecture combining normalized attention and incidence priors for hypergraph message-passing neural networks. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Presents hypergraph neural network architecture, unrelated to LLM evaluation benchmarks.
Electron-Density-Informed Molecular Property Prediction Using Surrogate Functionals Applies a surrogate functional framework incorporating ground-state electron density to improve molecular property prediction models. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on surrogate functionals for chemistry, not model compression or efficient serving.
Electron-Density-Informed Molecular Property Prediction Using Surrogate Functionals Applies a surrogate functional framework incorporating ground-state electron density to improve molecular property prediction models. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Concerned with quantum chemistry calculations, not AI deployment safety or robustness.
Electron-Density-Informed Molecular Property Prediction Using Surrogate Functionals Applies a surrogate functional framework incorporating ground-state electron density to improve molecular property prediction models. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Addresses chemical molecular property prediction, not LLM evaluation or benchmarks.
A Critical Look at Power Sampling when Test Time Sharpening LLMs Examines sampling methods for test-time sharpening in language models and shows simple best-of-N approaches yield comparable gains. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on reasoning generation sampling rather than AI safety, red-teaming, or guardrails.
A Critical Look at Power Sampling when Test Time Sharpening LLMs Examines sampling methods for test-time sharpening in language models and shows simple best-of-N approaches yield comparable gains. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Investigates power sampling versus Best-of-N, not model compression or efficient on-device serving.
A Critical Look at Power Sampling when Test Time Sharpening LLMs Examines sampling methods for test-time sharpening in language models and shows simple best-of-N approaches yield comparable gains. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Studies test-time sampling distributions for reasoning rather than developing evaluation benchmarks.
TabReliBench: Evaluating Tabular Foundation Models Beyond Predictive Accuracy Introduces a benchmark evaluating tabular foundation models across point prediction, distributional calibration, and prediction interval efficiency. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Benchmark is specifically for tabular foundation models, not LLM evaluation or language benchmarks.
TabReliBench: Evaluating Tabular Foundation Models Beyond Predictive Accuracy Introduces a benchmark evaluating tabular foundation models across point prediction, distributional calibration, and prediction interval efficiency. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Studies tabular prediction calibration, not small or efficient language model methods.
TabReliBench: Evaluating Tabular Foundation Models Beyond Predictive Accuracy Introduces a benchmark evaluating tabular foundation models across point prediction, distributional calibration, and prediction interval efficiency. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Evaluates calibration in tabular models, not LLM deployment safety, jailbreaks, or hallucinations.
Scalable Sparse Autoregressive Variational Inference for Non-Gaussian Spatial Fields Proposes a scalable variational inference method using sparse autoregressive structures and normalizing flows for non-Gaussian spatial field models. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Addresses spatial statistics inference algorithms, not small or efficient language models.
Scalable Sparse Autoregressive Variational Inference for Non-Gaussian Spatial Fields Proposes a scalable variational inference method using sparse autoregressive structures and normalizing flows for non-Gaussian spatial field models. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Focuses on variational inference for spatial statistics, not LLM benchmarks or evaluation.
Scalable Sparse Autoregressive Variational Inference for Non-Gaussian Spatial Fields Proposes a scalable variational inference method using sparse autoregressive structures and normalizing flows for non-Gaussian spatial field models. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Theoretical statistical inference paper with no focus on AI safety, guardrails, or reliability.
On the Encoding of Calendar Features in Covariate-Informed Time Series Foundation Models Compares seven calendar feature encoding techniques across four time series foundation models to evaluate impact on forecast accuracy. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Deals with time series forecasting accuracy rather than deployment safety, guardrails, or hallucination.
On the Encoding of Calendar Features in Covariate-Informed Time Series Foundation Models Compares seven calendar feature encoding techniques across four time series foundation models to evaluate impact on forecast accuracy. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on feature encoding techniques, not small or efficient model compression or serving.
On the Encoding of Calendar Features in Covariate-Informed Time Series Foundation Models Compares seven calendar feature encoding techniques across four time series foundation models to evaluate impact on forecast accuracy. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Evaluates calendar feature encodings in time series models, not language model evaluation benchmarks.
VQIL: Visible Quantum Interference Learning for Perturbation Response Ranking on Graphs Introduces a parameterized quantum-walk model optimized via backpropagation to rank network node responses to seed perturbations. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Proposes a quantum walk method for graph ranking, not an LLM benchmark or evaluation.
VQIL: Visible Quantum Interference Learning for Perturbation Response Ranking on Graphs Introduces a parameterized quantum-walk model optimized via backpropagation to rank network node responses to seed perturbations. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Addresses graph perturbation response ranking rather than AI safety or deployment robustness.
VQIL: Visible Quantum Interference Learning for Perturbation Response Ranking on Graphs Introduces a parameterized quantum-walk model optimized via backpropagation to rank network node responses to seed perturbations. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on graph perturbation ranking via quantum walk algorithms, not efficient model architectures.
Transformer-based In-Context Score Operator Learning for Diffusion Models Proposes an in-context score operator framework allowing diffusion models to adapt to unseen target distributions without retraining. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Theoretical diffusion operator learning framework unrelated to AI safety or guardrails.
Transformer-based In-Context Score Operator Learning for Diffusion Models Proposes an in-context score operator framework allowing diffusion models to adapt to unseen target distributions without retraining. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Presents an in-context operator framework for diffusion models, not LLM benchmarking.
Transformer-based In-Context Score Operator Learning for Diffusion Models Proposes an in-context score operator framework allowing diffusion models to adapt to unseen target distributions without retraining. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on score operator learning for diffusion adaptation, not compression or efficient serving.
Towards Understanding the Training of GFlowNets: A Theoretical Perspective Develops a theoretical framework linking training objective losses to sampling accuracy and finite-time convergence in Generative Flow Networks. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Addresses theoretical sampling bounds of GFlowNets, not small models or efficient inference.
Towards Understanding the Training of GFlowNets: A Theoretical Perspective Develops a theoretical framework linking training objective losses to sampling accuracy and finite-time convergence in Generative Flow Networks. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Mathematical convergence theory for GFlowNets unrelated to deployment safety or alignment.
Towards Understanding the Training of GFlowNets: A Theoretical Perspective Develops a theoretical framework linking training objective losses to sampling accuracy and finite-time convergence in Generative Flow Networks. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Theoretical analysis of GFlowNets convergence, not language model evaluation.
Machine Unlearning for Federated Socially Fair k-Means Clustering Presents an exact machine unlearning method for federated socially fair k-means clustering using separate group-level local summaries. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on unlearning speed in federated clustering rather than lightweight LLM inference.
Machine Unlearning for Federated Socially Fair k-Means Clustering Presents an exact machine unlearning method for federated socially fair k-means clustering using separate group-level local summaries. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Addresses machine unlearning in federated clustering, not language model evaluation.
SeoulMMOD: A Large-Scale Multimodal Origin-Destination Flow Benchmark Introduces a multi-mode urban mobility benchmark across Seoul to evaluate origin-destination travel demand forecasting models. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Benchmark is for urban origin-destination traffic flow forecasting, not language model evaluation.
SeoulMMOD: A Large-Scale Multimodal Origin-Destination Flow Benchmark Introduces a multi-mode urban mobility benchmark across Seoul to evaluate origin-destination travel demand forecasting models. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on urban mobility spatio-temporal modeling, not efficient inference or model compression.
SeoulMMOD: A Large-Scale Multimodal Origin-Destination Flow Benchmark Introduces a multi-mode urban mobility benchmark across Seoul to evaluate origin-destination travel demand forecasting models. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Urban traffic forecasting benchmark unrelated to AI safety, guardrails, or alignment.
SmolWAM: A Latent World-Action Model for Robotic Manipulation on Edge Devices Presents a compressed latent world-action model enabling faster robotic manipulation inference on resource-constrained edge hardware. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:29	Not relevant Targets general robotic manipulation on edge devices, not industrial inspection or defect detection.
SmolWAM: A Latent World-Action Model for Robotic Manipulation on Edge Devices Presents a compressed latent world-action model enabling faster robotic manipulation inference on resource-constrained edge hardware. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on robotic edge-device latency and compute reduction, not AI safety or guardrails.
SmolWAM: A Latent World-Action Model for Robotic Manipulation on Edge Devices Presents a compressed latent world-action model enabling faster robotic manipulation inference on resource-constrained edge hardware. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Proposes a lightweight robotic policy model rather than an LLM evaluation benchmark.
Studying the Effects of Task Diversity Using Small Mathematical Worlds Introduces two synthetic mathematical task datasets to study how task diversity affects model learning during pretraining. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Investigates task diversity's effect on internal representations, not efficient models or compression.
Studying the Effects of Task Diversity Using Small Mathematical Worlds Introduces two synthetic mathematical task datasets to study how task diversity affects model learning during pretraining. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Introduces synthetic math environments to analyze training dynamics, not LLM benchmark evaluation.
Studying the Effects of Task Diversity Using Small Mathematical Worlds Introduces two synthetic mathematical task datasets to study how task diversity affects model learning during pretraining. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Studies internal representations in synthetic settings, not deployment safety or robustness.
How Well Do Prior-Data Fitted Networks Amortize Bayesian Inference? Empirically evaluates the Bayesian posterior approximation quality and amortization errors of prior-data fitted networks across synthetic prior tasks. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Analyzes Bayesian approximation error in Prior-Data Fitted Networks, not LLM benchmarks.
How Well Do Prior-Data Fitted Networks Amortize Bayesian Inference? Empirically evaluates the Bayesian posterior approximation quality and amortization errors of prior-data fitted networks across synthetic prior tasks. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Deals with Bayesian posterior predictive distribution error, not AI safety or deployment guardrails.
How Well Do Prior-Data Fitted Networks Amortize Bayesian Inference? Empirically evaluates the Bayesian posterior approximation quality and amortization errors of prior-data fitted networks across synthetic prior tasks. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Examines Bayesian inference error decomposition rather than model compression or efficient serving.
From Pixels to Privacy-Sensitive Features: An Information-Theoretic Framework for Reconstruction Bounds Introduces an information-theoretic framework using rate-distortion theory to establish computable lower bounds on feature reconstruction under DP-SGD. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Investigates privacy distortion bounds under DP-SGD, not model compression or efficient serving.
From Pixels to Privacy-Sensitive Features: An Information-Theoretic Framework for Reconstruction Bounds Introduces an information-theoretic framework using rate-distortion theory to establish computable lower bounds on feature reconstruction under DP-SGD. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Focuses on differential privacy reconstruction bounds rather than LLM evaluation or benchmarks.
Geometry-Aware Multi-Objective Optimization: Norm-Constrained Oracles with Entropic Weight Updates Analyzes convergence rates of a multi-objective optimization method pairing norm-constrained linear minimization oracles with entropic task-weight updates. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Theoretical multi-objective descent paper unrelated to AI safety, hallucination, or guardrails.
Geometry-Aware Multi-Objective Optimization: Norm-Constrained Oracles with Entropic Weight Updates Analyzes convergence rates of a multi-objective optimization method pairing norm-constrained linear minimization oracles with entropic task-weight updates. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Examines multi-objective optimization algorithms, not language model evaluation or benchmarks.
Geometry-Aware Multi-Objective Optimization: Norm-Constrained Oracles with Entropic Weight Updates Analyzes convergence rates of a multi-objective optimization method pairing norm-constrained linear minimization oracles with entropic task-weight updates. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Concerns optimization theory rather than small language models, quantization, or inference serving.
Optimal Algorithms for Nonsmooth Nonconvex Stochastic Optimization with Constraints Establishes stochastic first-order and zeroth-order algorithms achieving optimal rates for constrained nonsmooth nonconvex optimization. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on pure mathematical optimization algorithms, not model compression or efficient serving.
Optimal Algorithms for Nonsmooth Nonconvex Stochastic Optimization with Constraints Establishes stochastic first-order and zeroth-order algorithms achieving optimal rates for constrained nonsmooth nonconvex optimization. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Presents theoretical rates for non-convex stochastic optimization, not LLM evaluation.
Optimal Algorithms for Nonsmooth Nonconvex Stochastic Optimization with Constraints Establishes stochastic first-order and zeroth-order algorithms achieving optimal rates for constrained nonsmooth nonconvex optimization. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Mathematical optimization theory with no focus on AI safety, robustness, or reliability.
Randomized Momentum Transport with Cheap Predictors Introduces randomized momentum transport to track stochastic gradients using probabilistically sampled transport corrections alongside cheap predictors. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Examines optimization theory and gradient oracle costs, not small models or inference acceleration.
Randomized Momentum Transport with Cheap Predictors Introduces randomized momentum transport to track stochastic gradients using probabilistically sampled transport corrections alongside cheap predictors. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Covers theoretical momentum optimization algorithms rather than LLM benchmarking or evaluation.
Randomized Momentum Transport with Cheap Predictors Introduces randomized momentum transport to track stochastic gradients using probabilistically sampled transport corrections alongside cheap predictors. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Theoretical optimization study without direct application to AI safety, guardrails, or alignment.
Adaptive Variance Reduction without Knowing Oracle Smoothness Proposes a two-phase variance reduction method that adapts to average, standard, and noiseless smoothness settings without predefined hyperparameter constants. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Concerns stochastic non-convex optimization convergence, not model compression or efficient serving.
Adaptive Variance Reduction without Knowing Oracle Smoothness Proposes a two-phase variance reduction method that adapts to average, standard, and noiseless smoothness settings without predefined hyperparameter constants. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Theoretical stochastic optimization study unrelated to AI safety or reliability guardrails.
Adaptive Variance Reduction without Knowing Oracle Smoothness Proposes a two-phase variance reduction method that adapts to average, standard, and noiseless smoothness settings without predefined hyperparameter constants. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant A theoretical optimization paper on variance reduction, not language model evaluation.
Constrained Probabilistic Numerics: Gaussian Process Solvers for Linear PDEs under Linear Inequality Constraints Develops a sequential conditioning framework enabling Gaussian process linear partial differential equation solvers to enforce linear inequality constraints. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Theoretical PDE solver paper with no connection to deployment AI safety or model alignment.
Constrained Probabilistic Numerics: Gaussian Process Solvers for Linear PDEs under Linear Inequality Constraints Develops a sequential conditioning framework enabling Gaussian process linear partial differential equation solvers to enforce linear inequality constraints. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Presents a Gaussian process solver for PDEs, not an LLM benchmark or evaluation study.
Constrained Probabilistic Numerics: Gaussian Process Solvers for Linear PDEs under Linear Inequality Constraints Develops a sequential conditioning framework enabling Gaussian process linear partial differential equation solvers to enforce linear inequality constraints. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on probabilistic numerics for PDEs rather than model compression or efficient LLM inference.
Diversity-Aware Ensemble of Quantum-Classical Models via Combinatorial Fusion Evaluates combinatorial fusion analysis to build diverse ensembles combining classical models and variational quantum circuits for classification tasks. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Proposes a quantum-classical model ensemble technique rather than language model evaluation.
Diversity-Aware Ensemble of Quantum-Classical Models via Combinatorial Fusion Evaluates combinatorial fusion analysis to build diverse ensembles combining classical models and variational quantum circuits for classification tasks. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Addresses combinatorial fusion of quantum circuits, not safety, robustness, or reliability guardrails.
Diversity-Aware Ensemble of Quantum-Classical Models via Combinatorial Fusion Evaluates combinatorial fusion analysis to build diverse ensembles combining classical models and variational quantum circuits for classification tasks. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on quantum-classical ensembling rather than small language models or inference efficiency.

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