Every paper checked — nothing hidden
All papers
Every paper checked for brief #4 of 12–19 Oct 2026 — nothing is hidden. Brief papers first.
All17,313In brief24More (on-topic)3,333Not relevant13,754Not assessed87Older352Could not confirm93Blogs with no paper266All blogs284
| Paper | Topic | Relevance | Feedback |
|---|---|---|---|
| Large-Scale Benchmarking of Quantum Neural Network Configurations for Financial Time Series Forecasting The paper evaluates 1,368 quantum neural network configurations on financial time series forecasting using spot exchange rate data. Preprint | AI safety and reliability in deployment via arxiv · 12 Oct, 16:29 | Not relevant Evaluates financial time series forecasting, not AI safety, hallucination, or guardrails. | |
| Large-Scale Benchmarking of Quantum Neural Network Configurations for Financial Time Series Forecasting The paper evaluates 1,368 quantum neural network configurations on financial time series forecasting using spot exchange rate data. Preprint | LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29 | Not relevant Evaluates quantum neural networks on financial forecasting, not LLMs or language benchmarks. | |
| Bayesian Optimisation under State-Preservation Constraints The paper proposes a Bayesian optimization approach that uses linear response approximations to maintain state-preservation constraints in PDE systems. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on engineering optimization, not small language models or efficient inference. | |
| Bayesian Optimisation under State-Preservation Constraints The paper proposes a Bayesian optimization approach that uses linear response approximations to maintain state-preservation constraints in PDE systems. Preprint | LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29 | Not relevant Covers PDE-constrained Bayesian optimization for engineering, not LLM evaluation. | |
| Bayesian Optimisation under State-Preservation Constraints The paper proposes a Bayesian optimization approach that uses linear response approximations to maintain state-preservation constraints in PDE systems. Preprint | AI safety and reliability in deployment via arxiv · 12 Oct, 16:29 | Not relevant Deals with physical design optimization, not AI safety or deployment reliability. | |
| Toward Optimal Regret in Adversarial MDPs with Stochastic Hard Constraints The paper introduces MA-OPS to achieve optimal regret in adversarial Markov decision processes subject to stochastic hard constraints. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Theoretical reinforcement learning regret bounds, not small language models or compression. | |
| Toward Optimal Regret in Adversarial MDPs with Stochastic Hard Constraints The paper introduces MA-OPS to achieve optimal regret in adversarial Markov decision processes subject to stochastic hard constraints. Preprint | LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29 | Not relevant Theoretical analysis of constrained Markov decision processes, not LLM evaluation. | |
| Toward Optimal Regret in Adversarial MDPs with Stochastic Hard Constraints The paper introduces MA-OPS to achieve optimal regret in adversarial Markov decision processes subject to stochastic hard constraints. Preprint | AI safety and reliability in deployment via arxiv · 12 Oct, 16:29 | Not relevant Covers abstract constrained MDP theory, not AI safety or deployment guardrails. | |
| Is Real-World Training Data Necessary for Generalist Graph Anomaly Detection? The paper investigates synthetic graph generation and introduces TS-GGAD to train generalist graph anomaly detection models without real-world training graphs. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Addresses synthetic data for graph models, not small language models or efficient serving. | |
| Is Real-World Training Data Necessary for Generalist Graph Anomaly Detection? The paper investigates synthetic graph generation and introduces TS-GGAD to train generalist graph anomaly detection models without real-world training graphs. Preprint | LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29 | Not relevant Focuses on graph neural networks and graph anomaly detection, not LLM evaluation. | |
| Is Real-World Training Data Necessary for Generalist Graph Anomaly Detection? The paper investigates synthetic graph generation and introduces TS-GGAD to train generalist graph anomaly detection models without real-world training graphs. Preprint | AI safety and reliability in deployment via arxiv · 12 Oct, 16:29 | Not relevant Covers graph anomaly detection algorithms, not LLM safety, jailbreaks, or red-teaming. | |
| DataSense-Bench: The First Step Toward an AI Scientist The paper introduces DataSense-Bench to evaluate whether AI agents can select and rank effective training data subsets for fine-tuning. Preprint | AI safety and reliability in deployment via arxiv · 12 Oct, 16:29 | Not relevant Focuses on evaluating data selection capabilities, not safety, guardrails, or alignment risks. | |
| DataSense-Bench: The First Step Toward an AI Scientist The paper introduces DataSense-Bench to evaluate whether AI agents can select and rank effective training data subsets for fine-tuning. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on agent data-selection benchmarking, not model compression or efficient architectures. | |
| Quickest Change Detection with Diffusion-Integrated Scores The paper proposes DI-SCUSUM, a training-free quickest change detection method using diffusion-smoothed empirical score differences. Preprint | LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29 | Not relevant Introduces a statistical quickest change detection method, not LLM evaluation. | |
| Quickest Change Detection with Diffusion-Integrated Scores The paper proposes DI-SCUSUM, a training-free quickest change detection method using diffusion-smoothed empirical score differences. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on classical distribution shift detection, not small language models or efficient serving. | |
| Quickest Change Detection with Diffusion-Integrated Scores The paper proposes DI-SCUSUM, a training-free quickest change detection method using diffusion-smoothed empirical score differences. Preprint | AI safety and reliability in deployment via arxiv · 12 Oct, 16:29 | Not relevant Covers statistical change-point detection, not AI safety, jailbreaks, or model reliability. | |
| ISBO: Scalable Spatio-Temporal Bayesian Optimization with Log Gaussian Cox Process Models via the INLA-SPDE Approach The paper proposes ISBO, combining log Gaussian Cox processes with the INLA-SPDE approach for scalable spatio-temporal Bayesian optimization. Preprint | LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29 | Not relevant Presents Bayesian optimization for spatio-temporal data, not LLM evaluation. | |
| ISBO: Scalable Spatio-Temporal Bayesian Optimization with Log Gaussian Cox Process Models via the INLA-SPDE Approach The paper proposes ISBO, combining log Gaussian Cox processes with the INLA-SPDE approach for scalable spatio-temporal Bayesian optimization. Preprint | AI safety and reliability in deployment via arxiv · 12 Oct, 16:29 | Not relevant Covers spatio-temporal Bayesian optimization, not deployment safety or LLM guardrails. | |
| ISBO: Scalable Spatio-Temporal Bayesian Optimization with Log Gaussian Cox Process Models via the INLA-SPDE Approach The paper proposes ISBO, combining log Gaussian Cox processes with the INLA-SPDE approach for scalable spatio-temporal Bayesian optimization. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on Gaussian processes and spatial modeling, not small language models. | |
| Training on the Future: A Delay-Aware Audit of Test-Time Adaptation for Time-Series Forecasting The paper audits test-time adaptation methods for time-series forecasting under realistic, delay-aware conditions to evaluate actual streaming performance. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on time-series forecasting filters, not small language models or model distillation. | |
| Training on the Future: A Delay-Aware Audit of Test-Time Adaptation for Time-Series Forecasting The paper audits test-time adaptation methods for time-series forecasting under realistic, delay-aware conditions to evaluate actual streaming performance. Preprint | AI safety and reliability in deployment via arxiv · 12 Oct, 16:29 | Not relevant Studies time-series label delays, not AI safety, hallucination, or alignment guardrails. | |
| Training on the Future: A Delay-Aware Audit of Test-Time Adaptation for Time-Series Forecasting The paper audits test-time adaptation methods for time-series forecasting under realistic, delay-aware conditions to evaluate actual streaming performance. Preprint | LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29 | Not relevant Audits test-time adaptation for time-series forecasting, not LLMs or language benchmarks. | |
| RIFT: Relative Isolation From Trees For Anomaly Detection The paper introduces RIFT, a deterministic, parameter-free anomaly detection algorithm based on minimum spanning trees inspired by Isolation Forests. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on tabular anomaly detection, not small language models or efficient inference. | |
| RIFT: Relative Isolation From Trees For Anomaly Detection The paper introduces RIFT, a deterministic, parameter-free anomaly detection algorithm based on minimum spanning trees inspired by Isolation Forests. Preprint | AI safety and reliability in deployment via arxiv · 12 Oct, 16:29 | Not relevant Presents an unsupervised anomaly detection baseline, not LLM safety, red-teaming, or guardrails. | |
| RIFT: Relative Isolation From Trees For Anomaly Detection The paper introduces RIFT, a deterministic, parameter-free anomaly detection algorithm based on minimum spanning trees inspired by Isolation Forests. Preprint | LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29 | Not relevant Introduces a tree-based anomaly detection algorithm, not LLM benchmarks or evaluation. | |
| AdaptLSTM: Efficient Adaptive Online Learning for Cloud Workload Forecasting under Distribution Drift The paper presents AdaptLSTM, an adaptive online learning method that selectively updates cloud workload forecasts upon detecting distribution drift. Preprint | AI safety and reliability in deployment via arxiv · 12 Oct, 16:29 | Not relevant Covers cloud workload drift forecasting, not AI safety, jailbreaks, or guardrails. | |
| AdaptLSTM: Efficient Adaptive Online Learning for Cloud Workload Forecasting under Distribution Drift The paper presents AdaptLSTM, an adaptive online learning method that selectively updates cloud workload forecasts upon detecting distribution drift. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Applies to time-series drift adaptation, not small language models or model compression. | |
| AdaptLSTM: Efficient Adaptive Online Learning for Cloud Workload Forecasting under Distribution Drift The paper presents AdaptLSTM, an adaptive online learning method that selectively updates cloud workload forecasts upon detecting distribution drift. Preprint | LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29 | Not relevant Focuses on time-series online learning for cloud workloads, not LLM evaluation. | |
| Spatial Pattern Formation from Multi-Agent Learning in Public Goods Dilemmas The paper examines how multi-agent Q-learning movement policies generate spatial clustering and traveling bands in spatial public goods games. Preprint | AI safety and reliability in deployment via arxiv · 12 Oct, 16:29 | Not relevant Covers spatial pattern formation in RL, not deployment safety or guardrails. | |
| Spatial Pattern Formation from Multi-Agent Learning in Public Goods Dilemmas The paper examines how multi-agent Q-learning movement policies generate spatial clustering and traveling bands in spatial public goods games. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on spatial RL dynamics, not small or efficient language models. | |
| Spatial Pattern Formation from Multi-Agent Learning in Public Goods Dilemmas The paper examines how multi-agent Q-learning movement policies generate spatial clustering and traveling bands in spatial public goods games. Preprint | AI agents via arxiv · 12 Oct, 16:29 | Not relevant Studies game-theoretic tabular Q-learning in public goods games, not LLM-based agents. | |
| Spatial Pattern Formation from Multi-Agent Learning in Public Goods Dilemmas The paper examines how multi-agent Q-learning movement policies generate spatial clustering and traveling bands in spatial public goods games. Preprint | LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29 | Not relevant Studies multi-agent reinforcement learning dynamics, not LLM evaluation. | |
| Composite Online-to-Nonconvex Conversion with Optimal Oracle Complexity The paper develops an online-to-nonconvex conversion framework for stochastic nonsmooth nonconvex composite optimization achieving optimal oracle complexity bounds. Preprint | AI safety and reliability in deployment via arxiv · 12 Oct, 16:29 | Not relevant Studies abstract optimization convergence bounds, not deployment safety or robustness. | |
| Composite Online-to-Nonconvex Conversion with Optimal Oracle Complexity The paper develops an online-to-nonconvex conversion framework for stochastic nonsmooth nonconvex composite optimization achieving optimal oracle complexity bounds. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on optimization theory, not small language models, distillation, or serving. | |
| Composite Online-to-Nonconvex Conversion with Optimal Oracle Complexity The paper develops an online-to-nonconvex conversion framework for stochastic nonsmooth nonconvex composite optimization achieving optimal oracle complexity bounds. Preprint | LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29 | Not relevant A theoretical paper on nonsmooth nonconvex optimization, not LLM benchmarks. | |
| asdex: Automatic Sparse Differentiation in JAX The paper presents asdex, an automatic sparse differentiation library in JAX that uses graph coloring to compute sparse Jacobians efficiently. Preprint | AI safety and reliability in deployment via arxiv · 12 Oct, 16:29 | Not relevant Covers scientific computing automatic differentiation, not safety, guardrails, or reliability. | |
| asdex: Automatic Sparse Differentiation in JAX The paper presents asdex, an automatic sparse differentiation library in JAX that uses graph coloring to compute sparse Jacobians efficiently. Preprint | LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29 | Not relevant Presents a sparse automatic differentiation toolkit in JAX, not LLM evaluation. | |
| asdex: Automatic Sparse Differentiation in JAX The paper presents asdex, an automatic sparse differentiation library in JAX that uses graph coloring to compute sparse Jacobians efficiently. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on scientific differentiation in JAX, not small language models or inference efficiency. | |
| Ambient Discrete Diffusion: Using the Wrong Data at the Right Time for Data Efficient Learning The paper introduces RefineMix to train discrete diffusion models under data scarcity using out-of-distribution data at chosen diffusion timesteps. Preprint | LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29 | Not relevant Proposes a diffusion training framework for scientific data, not LLM evaluation benchmarks. | |
| Ambient Discrete Diffusion: Using the Wrong Data at the Right Time for Data Efficient Learning The paper introduces RefineMix to train discrete diffusion models under data scarcity using out-of-distribution data at chosen diffusion timesteps. Preprint | AI safety and reliability in deployment via arxiv · 12 Oct, 16:29 | Not relevant Addresses data scarcity in diffusion, not AI safety, alignment, or deployment reliability. | |
| Ambient Discrete Diffusion: Using the Wrong Data at the Right Time for Data Efficient Learning The paper introduces RefineMix to train discrete diffusion models under data scarcity using out-of-distribution data at chosen diffusion timesteps. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on discrete diffusion data efficiency in science, not model compression or efficient serving. | |
| SplitJEPA: Learning Invariant and Variant Latent Worlds without Reconstruction The paper presents SplitJEPA, learning invariant and variant latent world state representations directly without using reconstruction objectives. Preprint | AI safety and reliability in deployment via arxiv · 12 Oct, 16:29 | Not relevant Deals with latent world models for robotics, not deployment safety, guardrails, or hallucination. | |
| SplitJEPA: Learning Invariant and Variant Latent Worlds without Reconstruction The paper presents SplitJEPA, learning invariant and variant latent world state representations directly without using reconstruction objectives. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Does not address small language models, compression, quantization, or efficient serving. | |
| SplitJEPA: Learning Invariant and Variant Latent Worlds without Reconstruction The paper presents SplitJEPA, learning invariant and variant latent world state representations directly without using reconstruction objectives. Preprint | LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29 | Not relevant Focuses on representation learning via JEPA, not evaluating or benchmarking language models. | |
| Subspace Uncertainty and Sharp Sampling Thresholds on the Boolean Cube This paper derives sharp sample complexity thresholds for Gaussian regression over bounded-degree polynomial subspaces on the Boolean cube. Preprint | AI safety and reliability in deployment via arxiv · 12 Oct, 16:29 | Not relevant Theoretical math paper on sampling thresholds, not AI safety or deployment guardrails. | |
| Subspace Uncertainty and Sharp Sampling Thresholds on the Boolean Cube This paper derives sharp sample complexity thresholds for Gaussian regression over bounded-degree polynomial subspaces on the Boolean cube. Preprint | LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29 | Not relevant Analyzes theoretical Gaussian regression on the Boolean cube, not LLM evaluation. | |
| Subspace Uncertainty and Sharp Sampling Thresholds on the Boolean Cube This paper derives sharp sample complexity thresholds for Gaussian regression over bounded-degree polynomial subspaces on the Boolean cube. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on mathematical statistical learning bounds, not model compression or efficiency. | |
| Closing the Horizon Gap in Policy Optimization for Adversarial MDPs This paper establishes tighter regret bounds for adversarial Markov decision processes by incorporating regularized Q-functions into policy optimization. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Addresses theoretical reinforcement learning regret, not model compression or efficiency. | |
| Closing the Horizon Gap in Policy Optimization for Adversarial MDPs This paper establishes tighter regret bounds for adversarial Markov decision processes by incorporating regularized Q-functions into policy optimization. Preprint | AI safety and reliability in deployment via arxiv · 12 Oct, 16:29 | Not relevant Presents theoretical online MDP bounds rather than AI safety or reliability guardrails. | |
| Closing the Horizon Gap in Policy Optimization for Adversarial MDPs This paper establishes tighter regret bounds for adversarial Markov decision processes by incorporating regularized Q-functions into policy optimization. Preprint | LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29 | Not relevant Studies theoretical regret bounds in MDPs rather than LLM evaluation. | |
| Bilevel optimization for data-driven learning of Koopman embeddings using kernel-based autoencoders This paper uses bilevel optimization and kernel autoencoders to learn finite-dimensional Koopman operator embeddings from nonlinear dynamical data. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on nonlinear dynamical system modeling, not model compression or efficiency. | |
| Bilevel optimization for data-driven learning of Koopman embeddings using kernel-based autoencoders This paper uses bilevel optimization and kernel autoencoders to learn finite-dimensional Koopman operator embeddings from nonlinear dynamical data. Preprint | LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29 | Not relevant Studies dynamical systems and Koopman embeddings, not language model evaluation. | |
| Bilevel optimization for data-driven learning of Koopman embeddings using kernel-based autoencoders This paper uses bilevel optimization and kernel autoencoders to learn finite-dimensional Koopman operator embeddings from nonlinear dynamical data. Preprint | AI safety and reliability in deployment via arxiv · 12 Oct, 16:29 | Not relevant Deals with dynamical systems theory rather than AI deployment safety or reliability. | |
| Marformer: A Transformer for Predicting Missing Data Distributions This paper presents a transformer architecture that predicts conditional marginal distributions for missing data in a single forward pass. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on predicting missing marginal distributions, not model compression or efficiency. | |
| Marformer: A Transformer for Predicting Missing Data Distributions This paper presents a transformer architecture that predicts conditional marginal distributions for missing data in a single forward pass. Preprint | LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29 | Not relevant Introduces a tabular data model for missing values rather than LLM evaluation. | |
| Marformer: A Transformer for Predicting Missing Data Distributions This paper presents a transformer architecture that predicts conditional marginal distributions for missing data in a single forward pass. Preprint | AI safety and reliability in deployment via arxiv · 12 Oct, 16:29 | Not relevant Focuses on missing data estimation rather than safety guardrails or alignment. | |
| Prospective Prediction of OOD Degradation from Source-Side Training Dynamics This paper analyzes source-side training dynamics to prospectively detect out-of-distribution performance degradation before evaluation. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Does not address small models, pruning, quantization, or efficient inference. | |
| Prospective Prediction of OOD Degradation from Source-Side Training Dynamics This paper analyzes source-side training dynamics to prospectively detect out-of-distribution performance degradation before evaluation. Preprint | LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29 | Not relevant Focuses on predicting OOD failure from training dynamics rather than LLM benchmark evaluation. | |
| Learning Kilometer-Scale Weather Prediction with Global-Regional Alignment This paper introduces a regional weather forecasting framework that aligns pretrained global model guidance with high-resolution regional representations. Preprint | LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29 | Not relevant Applies models to weather prediction rather than LLM evaluation or benchmarks. | |
| Learning Kilometer-Scale Weather Prediction with Global-Regional Alignment This paper introduces a regional weather forecasting framework that aligns pretrained global model guidance with high-resolution regional representations. Preprint | Small and efficient models via arxiv · 12 Oct, 16:29 | Not relevant Focuses on regional weather forecasting architecture, not model compression or efficient serving. |
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