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
Minimax estimation of the pseudo-spectral gap of continuous Markov chains, with applications to empirical concentration bounds The paper develops a minimax-optimal estimator for the pseudo-spectral gap of continuous-state Markov chains from a single observed trajectory. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on statistical Markov chain theory, not language model evaluation.
Outperforming 1000 Euclidean Layers With Hyperbolic Goal-Conditioned RL The paper demonstrates that hyperbolic contrastive reinforcement learning achieves strong navigation performance with much shallower networks than Euclidean models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Investigates hyperbolic reinforcement learning architectures, not LLM evaluation benchmarks.
Outperforming 1000 Euclidean Layers With Hyperbolic Goal-Conditioned RL The paper demonstrates that hyperbolic contrastive reinforcement learning achieves strong navigation performance with much shallower networks than Euclidean models. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Applies hyperbolic geometry to reinforcement learning navigation, not LLM efficiency.
Outperforming 1000 Euclidean Layers With Hyperbolic Goal-Conditioned RL The paper demonstrates that hyperbolic contrastive reinforcement learning achieves strong navigation performance with much shallower networks than Euclidean models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on RL navigation dynamics, not AI safety or deployment robustness.
ChemArgus: Automated Scientific Diagnostics for Lab-Grade Multimodal Chemistry Reasoning: A Frontier Benchmark The paper presents a multimodal benchmark for competition-level chemistry that grades reasoning step-by-step with partial credit. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on scientific reasoning evaluation, not model compression or efficiency.
ChemArgus: Automated Scientific Diagnostics for Lab-Grade Multimodal Chemistry Reasoning: A Frontier Benchmark The paper presents a multimodal benchmark for competition-level chemistry that grades reasoning step-by-step with partial credit. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Proposes an academic benchmark rather than deployment safety or guardrail frameworks.
Weighted Adjoint Matching: Finetuning Flow Matching Models with Time-Dependent Control Cost The paper introduces a time-dependent control-cost weighting method to fine-tune diffusion and flow-matching models with reward guidance. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents a fine-tuning method for diffusion models, not LLM benchmarks.
Weighted Adjoint Matching: Finetuning Flow Matching Models with Time-Dependent Control Cost The paper introduces a time-dependent control-cost weighting method to fine-tune diffusion and flow-matching models with reward guidance. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on diffusion model fine-tuning control, not efficient model architectures.
Weighted Adjoint Matching: Finetuning Flow Matching Models with Time-Dependent Control Cost The paper introduces a time-dependent control-cost weighting method to fine-tune diffusion and flow-matching models with reward guidance. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies continuous generative path regularization, not deployment safety or jailbreaks.
Sequential Learning-Based Physics-Informed Neural Network Modeling for Parameter Estimation on a Power Converter The paper proposes a staged physics-informed neural network to estimate electrical parameters of a direct-current buck power converter. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Applies PINNs to electrical engineering, unrelated to AI deployment safety.
Sequential Learning-Based Physics-Informed Neural Network Modeling for Parameter Estimation on a Power Converter The paper proposes a staged physics-informed neural network to estimate electrical parameters of a direct-current buck power converter. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on power converter modeling rather than efficient LLM inference.
Sequential Learning-Based Physics-Informed Neural Network Modeling for Parameter Estimation on a Power Converter The paper proposes a staged physics-informed neural network to estimate electrical parameters of a direct-current buck power converter. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Examines physics-informed neural networks for circuits, not LLM evaluation.
GBTSVR: Twin Support Vector Regression via Granular Ball Computing The paper develops twin support vector regression methods using granular ball computing to improve robustness against noise and outliers. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies traditional tabular regression robustness, not LLM safety or guardrails.
GBTSVR: Twin Support Vector Regression via Granular Ball Computing The paper develops twin support vector regression methods using granular ball computing to improve robustness against noise and outliers. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Proposes a support vector regression variant, not small or efficient LLMs.
GBTSVR: Twin Support Vector Regression via Granular Ball Computing The paper develops twin support vector regression methods using granular ball computing to improve robustness against noise and outliers. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on support vector regression rather than language model evaluation.
A Large-Dimensional Analysis of Least-Squares Federated Learning via Generalized Total Variation Minimization The paper uses random matrix theory to analyze the trade-off between noise reduction and bias in decentralized federated learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Covers theoretical federated learning convergence rather than deployment safety or guardrails.
A Large-Dimensional Analysis of Least-Squares Federated Learning via Generalized Total Variation Minimization The paper uses random matrix theory to analyze the trade-off between noise reduction and bias in decentralized federated learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Theoretical study of federated learning, not language model evaluation benchmarks.
A Large-Dimensional Analysis of Least-Squares Federated Learning via Generalized Total Variation Minimization The paper uses random matrix theory to analyze the trade-off between noise reduction and bias in decentralized federated learning. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not focus on model compression, small models, or efficient serving.
What Do Foundation Models for Market Microstructure Learn? A Controlled Study with Synthetic Limit Order Books The paper evaluates autoregressive foundation models trained on simulated market order flows to analyze whether they learn underlying Bayesian dynamics. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not study deployment safety, alignment, hallucinations, or adversarial attacks.
What Do Foundation Models for Market Microstructure Learn? A Controlled Study with Synthetic Limit Order Books The paper evaluates autoregressive foundation models trained on simulated market order flows to analyze whether they learn underlying Bayesian dynamics. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not study small language models or inference efficiency techniques.
What Do Foundation Models for Market Microstructure Learn? A Controlled Study with Synthetic Limit Order Books The paper evaluates autoregressive foundation models trained on simulated market order flows to analyze whether they learn underlying Bayesian dynamics. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Investigates market microstructure representation learning rather than LLM evaluation methodology.
Exhaustive Canonical Skeletons Generation: A Formal Dataset for Decomposable Symbolic Regression The paper introduces an exhaustive dataset of univariate expression skeletons generated by a formal grammar to improve symbolic regression. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Not related to safety, hallucinations, jailbreaks, or deployment guardrails.
Exhaustive Canonical Skeletons Generation: A Formal Dataset for Decomposable Symbolic Regression The paper introduces an exhaustive dataset of univariate expression skeletons generated by a formal grammar to improve symbolic regression. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on symbolic regression datasets, not language model evaluation or benchmarks.
Exhaustive Canonical Skeletons Generation: A Formal Dataset for Decomposable Symbolic Regression The paper introduces an exhaustive dataset of univariate expression skeletons generated by a formal grammar to improve symbolic regression. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model compression, quantization, distillation, or efficient inference.
KANLab: TensorFlow Playground for Kolmogorov-Arnold Networks The paper presents an interactive, browser-based tool for visualizing and learning the mechanics of Kolmogorov-Arnold Networks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Interactive educational tool for neural networks, not an evaluation benchmark.
KANLab: TensorFlow Playground for Kolmogorov-Arnold Networks The paper presents an interactive, browser-based tool for visualizing and learning the mechanics of Kolmogorov-Arnold Networks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Educational visualization software, not AI deployment safety.
KANLab: TensorFlow Playground for Kolmogorov-Arnold Networks The paper presents an interactive, browser-based tool for visualizing and learning the mechanics of Kolmogorov-Arnold Networks. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Browser-based visualization tool, not focused on model efficiency.
A Bregman Proximal Viewpoint on Neural Operators The paper introduces a Bregman proximal framework for neural operators to interpret activation functions and improve the training of deeper models. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on mathematical neural operators, not deployment safety.
A Bregman Proximal Viewpoint on Neural Operators The paper introduces a Bregman proximal framework for neural operators to interpret activation functions and improve the training of deeper models. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies continuous operator learning rather than efficient model inference.
A Bregman Proximal Viewpoint on Neural Operators The paper introduces a Bregman proximal framework for neural operators to interpret activation functions and improve the training of deeper models. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Theoretical paper on neural operators, not an evaluation framework.
Seeing at the Edge: Energy Characterization of Vision-Language Model Inference on Edge Platforms The paper measures the energy consumption, latency, and thermal behavior of running vision-language models on edge hardware accelerators. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Measures hardware energy profiles, not model quality evaluation benchmarks.
Seeing at the Edge: Energy Characterization of Vision-Language Model Inference on Edge Platforms The paper measures the energy consumption, latency, and thermal behavior of running vision-language models on edge hardware accelerators. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Examines hardware power characteristics, not deployment safety risks.
In-Context Batch Black-box Optimization via Flow Matching The paper proposes an amortized flow-matching method to generate diverse batches for parallel black-box optimization over sequential rounds. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents a black-box optimization algorithm, not language model evaluation.
In-Context Batch Black-box Optimization via Flow Matching The paper proposes an amortized flow-matching method to generate diverse batches for parallel black-box optimization over sequential rounds. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on batch Bayesian optimization, not efficient language models.
In-Context Batch Black-box Optimization via Flow Matching The paper proposes an amortized flow-matching method to generate diverse batches for parallel black-box optimization over sequential rounds. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on black-box optimization methods rather than deployment safety.
Optimizing Sparse Boolean Rule Networks using Mixed Integer Linear Programming The paper presents a mixed integer linear programming formulation to train sparse, interpretable Boolean rule networks with reduced decision variables. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on integer programming for rule networks, not safety.
Optimizing Sparse Boolean Rule Networks using Mixed Integer Linear Programming The paper presents a mixed integer linear programming formulation to train sparse, interpretable Boolean rule networks with reduced decision variables. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies small Boolean rule networks, not small language models.
Optimizing Sparse Boolean Rule Networks using Mixed Integer Linear Programming The paper presents a mixed integer linear programming formulation to train sparse, interpretable Boolean rule networks with reduced decision variables. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on Boolean rule optimization algorithms, not evaluation benchmarks.
Dimension-Optimal Dynamic Regret in Exp-Concave Online Learning The paper derives dimension-optimal dynamic regret bounds for online learning with exp-concave losses under time-varying comparator paths. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Theoretical optimization theory paper, not focused on efficient models.
Dimension-Optimal Dynamic Regret in Exp-Concave Online Learning The paper derives dimension-optimal dynamic regret bounds for online learning with exp-concave losses under time-varying comparator paths. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents theoretical online convex optimization regret bounds, not benchmarking.
Dimension-Optimal Dynamic Regret in Exp-Concave Online Learning The paper derives dimension-optimal dynamic regret bounds for online learning with exp-concave losses under time-varying comparator paths. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Theoretical online learning regret analysis, not AI deployment safety.
Investigating Behavior Cloning from Few Demonstrations for Autonomous Driving based on Bird's-Eye View in Simulated Cities The paper improves behavior cloning for autonomous driving using bird's-eye view inputs and action-density weighting for rare events like traffic lights. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies driving policy imitation rather than model compression.
Investigating Behavior Cloning from Few Demonstrations for Autonomous Driving based on Bird's-Eye View in Simulated Cities The paper improves behavior cloning for autonomous driving using bird's-eye view inputs and action-density weighting for rare events like traffic lights. Peer-reviewed	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Autonomous driving simulation is outside the industrial inspection focus.
Investigating Behavior Cloning from Few Demonstrations for Autonomous Driving based on Bird's-Eye View in Simulated Cities The paper improves behavior cloning for autonomous driving using bird's-eye view inputs and action-density weighting for rare events like traffic lights. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses autonomous vehicle behavior cloning, not general AI deployment safety.
Investigating Behavior Cloning from Few Demonstrations for Autonomous Driving based on Bird's-Eye View in Simulated Cities The paper improves behavior cloning for autonomous driving using bird's-eye view inputs and action-density weighting for rare events like traffic lights. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on autonomous driving behavior cloning, not LLM benchmarking.
HGRP: Heterogeneous Graph Neural Network for Routability Prediction The paper proposes a heterogeneous graph neural network that combines circuit topology, physical layout, and routing resources to predict chip routability. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on circuit design automation rather than deployment safety.
HGRP: Heterogeneous Graph Neural Network for Routability Prediction The paper proposes a heterogeneous graph neural network that combines circuit topology, physical layout, and routing resources to predict chip routability. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on electronic circuit routability prediction, not evaluation frameworks.
HGRP: Heterogeneous Graph Neural Network for Routability Prediction The paper proposes a heterogeneous graph neural network that combines circuit topology, physical layout, and routing resources to predict chip routability. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies chip placement prediction, not efficient model architectures.
Mixed Poisson diffusion models and application to posterior sampling The paper introduces diffusion models driven by Poisson counting processes to solve discrete Poisson inverse problems via posterior sampling. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Mathematical diffusion framework, not deployment safety or reliability.
Mixed Poisson diffusion models and application to posterior sampling The paper introduces diffusion models driven by Poisson counting processes to solve discrete Poisson inverse problems via posterior sampling. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies Poisson generative modeling rather than efficient inference.
Mixed Poisson diffusion models and application to posterior sampling The paper introduces diffusion models driven by Poisson counting processes to solve discrete Poisson inverse problems via posterior sampling. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on mathematical diffusion models for inverse problems, not benchmarking.
The minimal width of neural networks and immersions of real projective space The paper uses differential topology to establish theoretical lower bounds on the minimal width of universal approximator neural networks. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Presents pure approximation theory bounds, not practical model compression.
The minimal width of neural networks and immersions of real projective space The paper uses differential topology to establish theoretical lower bounds on the minimal width of universal approximator neural networks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Theoretical paper on differential topology and neural network width.
The minimal width of neural networks and immersions of real projective space The paper uses differential topology to establish theoretical lower bounds on the minimal width of universal approximator neural networks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Theoretical mathematical study on function approximation, not deployment safety.
MAEM: Mitigating Recency Bias via a Replay-Free, Capacity-Controlled Dynamic Mixture of Experts for Class-Incremental Graph Learning The paper presents a replay-free dynamic mixture-of-experts method to mitigate recency bias in class-incremental graph learning without needing task identifiers. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Develops a graph learning framework, not an evaluation benchmark.
MAEM: Mitigating Recency Bias via a Replay-Free, Capacity-Controlled Dynamic Mixture of Experts for Class-Incremental Graph Learning The paper presents a replay-free dynamic mixture-of-experts method to mitigate recency bias in class-incremental graph learning without needing task identifiers. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses continual graph learning rather than AI deployment guardrails.
MAEM: Mitigating Recency Bias via a Replay-Free, Capacity-Controlled Dynamic Mixture of Experts for Class-Incremental Graph Learning The paper presents a replay-free dynamic mixture-of-experts method to mitigate recency bias in class-incremental graph learning without needing task identifiers. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on class-incremental graph learning, not model compression.
Adversarial Reinforcement Learning for Robust Planetary Landing The paper uses adversarial reinforcement learning with physically constrained perturbations to improve the robustness of autonomous planetary landing controllers. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies aerospace flight trajectory control, not production AI deployment safety.
Adversarial Reinforcement Learning for Robust Planetary Landing The paper uses adversarial reinforcement learning with physically constrained perturbations to improve the robustness of autonomous planetary landing controllers. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies landing guidance control rather than efficient machine learning.
Adversarial Reinforcement Learning for Robust Planetary Landing The paper uses adversarial reinforcement learning with physically constrained perturbations to improve the robustness of autonomous planetary landing controllers. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on aerospace trajectory control, not language model benchmarks.

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