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
Perceptual Characterization of Scan-induced Artifacts in Dynamic 3D Meshes The paper characterizes scan-induced artifacts in dynamic 3D meshes to build a no-reference visual quality assessment method. Preprint	Multimodal models via arxiv · 15 Oct, 15:07	Not relevant Focuses on 3D mesh rendering quality, not multimodal AI models.
Perceptual Characterization of Scan-induced Artifacts in Dynamic 3D Meshes The paper characterizes scan-induced artifacts in dynamic 3D meshes to build a no-reference visual quality assessment method. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:07	Not relevant Analyzes 3D mesh artifacts, not industrial quality inspection.
Frame Rate, Not the Grid: A Confound in Single-Clip Optical ENF Video Forensics The paper shows that camera frame rate harmonics confound optical electric network frequency estimates in video forensics, failing to detect generated video. Preprint	Multimodal models via arxiv · 15 Oct, 15:07	Not relevant Focuses on forensic signal processing, not multimodal language models.
Structural Dependence, Imitation or Herding? A Multi-Agent Simulation of Global Intermedia Agenda Setting The paper uses multi-agent language model simulations to evaluate why international news outlets replicate the agenda of major Western media. Preprint	AI agents via arxiv · 15 Oct, 15:07	Not relevant Uses LLMs for computational social science simulation, not agent capabilities.
TorchSpin: Differentiable Electron Paramagnetic Resonance Simulation in PyTorch The paper presents an open-source PyTorch library providing differentiable forward simulation of electron paramagnetic resonance spectra for machine-learning workflows. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:07	Not relevant Focuses on physics simulation in PyTorch, not LLM evaluation.
TorchSpin: Differentiable Electron Paramagnetic Resonance Simulation in PyTorch The paper presents an open-source PyTorch library providing differentiable forward simulation of electron paramagnetic resonance spectra for machine-learning workflows. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:07	Not relevant About physical spectroscopy simulation, not AI deployment safety.
TorchSpin: Differentiable Electron Paramagnetic Resonance Simulation in PyTorch The paper presents an open-source PyTorch library providing differentiable forward simulation of electron paramagnetic resonance spectra for machine-learning workflows. Preprint	Small and efficient models via arxiv · 15 Oct, 15:07	Not relevant Focuses on magnetic resonance simulation, not model compression.
Delay Complexity Dimension: Learning with Delayed and Censored Labels The paper introduces a sample complexity metric to analyze binary classification learning under randomly delayed and censored labels. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Theoretical paper on delayed labels, not small model efficiency.
Delay Complexity Dimension: Learning with Delayed and Censored Labels The paper introduces a sample complexity metric to analyze binary classification learning under randomly delayed and censored labels. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Deals with delayed label learning, not safety guardrails or hallucinations.
Delay Complexity Dimension: Learning with Delayed and Censored Labels The paper introduces a sample complexity metric to analyze binary classification learning under randomly delayed and censored labels. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Studies theoretical PAC learning bounds, not LLM benchmark evaluation.
Wong--Zakai Convergence and FNO Learning for Stochastic Volterra Equations The paper proves Wong-Zakai convergence and applies Fourier neural operators to learn solution operators for stochastic Volterra integral equations. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Studies neural operators for integral equations, not model compression.
Wong--Zakai Convergence and FNO Learning for Stochastic Volterra Equations The paper proves Wong-Zakai convergence and applies Fourier neural operators to learn solution operators for stochastic Volterra integral equations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on solving stochastic differential equations, not LLM evaluation benchmarks.
Wong--Zakai Convergence and FNO Learning for Stochastic Volterra Equations The paper proves Wong-Zakai convergence and applies Fourier neural operators to learn solution operators for stochastic Volterra integral equations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on mathematical operator learning, not AI deployment safety.
Semi-Amortized Bayesian Inference with Reusable Local Posteriors The paper proposes a semi-amortized Bayesian inference method that trains neural estimators for parameter blocks and alternates with Markov chain updates. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies statistical Markov chain inference, not AI safety risks.
Semi-Amortized Bayesian Inference with Reusable Local Posteriors The paper proposes a semi-amortized Bayesian inference method that trains neural estimators for parameter blocks and alternates with Markov chain updates. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on Bayesian parameter inference, not LLM evaluation benchmarks.
Semi-Amortized Bayesian Inference with Reusable Local Posteriors The paper proposes a semi-amortized Bayesian inference method that trains neural estimators for parameter blocks and alternates with Markov chain updates. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Deals with amortized Bayesian computation, not small language models.
Virtual-Alloy Physics-Aware Semantic System for extrapolative prediction of perovskite solar cells The paper introduces a physics-aware machine learning framework that interpolates crystal graphs to predict performance in perovskite solar cells. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Deals with materials science machine learning, not small models.
Virtual-Alloy Physics-Aware Semantic System for extrapolative prediction of perovskite solar cells The paper introduces a physics-aware machine learning framework that interpolates crystal graphs to predict performance in perovskite solar cells. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Applies ML to solar cell design, not AI safety.
Virtual-Alloy Physics-Aware Semantic System for extrapolative prediction of perovskite solar cells The paper introduces a physics-aware machine learning framework that interpolates crystal graphs to predict performance in perovskite solar cells. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on solar cell property prediction, not LLM benchmarking.
PMO-Dock: Benchmarking Docking, Specificity, and Generalization in Molecular Optimization The paper introduces a standardized 25-task benchmark and evaluation protocol for docking-based molecular optimization in drug design. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on molecular docking benchmarks, not small efficient models.
PMO-Dock: Benchmarking Docking, Specificity, and Generalization in Molecular Optimization The paper introduces a standardized 25-task benchmark and evaluation protocol for docking-based molecular optimization in drug design. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Benchmarking molecular drug design, not language model safety risks.
Data Density Scaling Laws for Image Self-Distillation The paper proposes a revised scaling law for image self-distillation that incorporates sample repetition alongside unique sample counts. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Examines sample repetition in image pretraining, not AI safety.
Data Density Scaling Laws for Image Self-Distillation The paper proposes a revised scaling law for image self-distillation that incorporates sample repetition alongside unique sample counts. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on visual self-distillation scaling laws, not efficient model inference.
Data Density Scaling Laws for Image Self-Distillation The paper proposes a revised scaling law for image self-distillation that incorporates sample repetition alongside unique sample counts. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Studies image self-distillation scaling laws, not LLM evaluation.
IR-PRM+ with Previous-Utility Prediction: Constant Regret and Best-Iterate Convergence The paper establishes constant regret and convergence guarantees for simultaneous and alternating regret matching variants using previous-utility prediction in matrix games. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Deals with zero-sum game theory algorithms, not LLM benchmarks.
IR-PRM+ with Previous-Utility Prediction: Constant Regret and Best-Iterate Convergence The paper establishes constant regret and convergence guarantees for simultaneous and alternating regret matching variants using previous-utility prediction in matrix games. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Theoretical game solver algorithm, not deployment AI safety guardrails.
IR-PRM+ with Previous-Utility Prediction: Constant Regret and Best-Iterate Convergence The paper establishes constant regret and convergence guarantees for simultaneous and alternating regret matching variants using previous-utility prediction in matrix games. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Presents game-theoretic regret minimization, not efficient model deployment.
Discrete Diffusion Models are Minimax Optimal Under Sparsity The paper establishes minimax-optimal statistical rates for discrete diffusion models generating text and categorical data under sparsity assumptions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on statistical rates of diffusion, not deployment safety.
Discrete Diffusion Models are Minimax Optimal Under Sparsity The paper establishes minimax-optimal statistical rates for discrete diffusion models generating text and categorical data under sparsity assumptions. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Covers theoretical minimax bounds, not practical model efficiency.
Discrete Diffusion Models are Minimax Optimal Under Sparsity The paper establishes minimax-optimal statistical rates for discrete diffusion models generating text and categorical data under sparsity assumptions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents theoretical rates for diffusion models, not LLM benchmarks.
Optimization for One, Feasibility for Most: Decoupling Optimization from Feasibility in Chance-Constrained Programs The paper proposes a framework that decouples candidate generation from feasibility assessment using structured sampling for chance-constrained stochastic programs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on chance-constrained mathematical optimization, not LLM evaluation.
Optimization for One, Feasibility for Most: Decoupling Optimization from Feasibility in Chance-Constrained Programs The paper proposes a framework that decouples candidate generation from feasibility assessment using structured sampling for chance-constrained stochastic programs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies numerical operations research problems, not AI deployment safety.
Optimization for One, Feasibility for Most: Decoupling Optimization from Feasibility in Chance-Constrained Programs The paper proposes a framework that decouples candidate generation from feasibility assessment using structured sampling for chance-constrained stochastic programs. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Deals with stochastic programming, not efficient language models.
How Many Ranks Does a Token Need? A Noise-Floor Squelch for Rank-One LoRA Experts The paper introduces a router-free method that adaptively thresholds rank-one LoRA adapter responses to remove cross-talk noise per token. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on parameter-efficient fine-tuning adaptation, not deployment safety.
How Many Ranks Does a Token Need? A Noise-Floor Squelch for Rank-One LoRA Experts The paper introduces a router-free method that adaptively thresholds rank-one LoRA adapter responses to remove cross-talk noise per token. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents a dynamic LoRA pruning method, not an evaluation benchmark.
Lag: A Mean-Field Optimal Policy for Online Assignment with Reusable Resources The paper develops a closed-form index policy for online task assignment with reusable resources, establishing its asymptotic optimality in fluid limits. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on online resource assignment policies, not LLM evaluation.
Lag: A Mean-Field Optimal Policy for Online Assignment with Reusable Resources The paper develops a closed-form index policy for online task assignment with reusable resources, establishing its asymptotic optimality in fluid limits. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies online scheduling algorithms, not AI safety or reliability.
Lag: A Mean-Field Optimal Policy for Online Assignment with Reusable Resources The paper develops a closed-form index policy for online task assignment with reusable resources, establishing its asymptotic optimality in fluid limits. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Deals with multi-armed bandits, not efficient language models.
What Information Buys in Active Inference The paper compares active inference to standard exploration in reinforcement learning and proposes a square-root calibration to prevent sub-optimal actions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Theoretical cognitive reinforcement learning study, not deployment AI safety.
What Information Buys in Active Inference The paper compares active inference to standard exploration in reinforcement learning and proposes a square-root calibration to prevent sub-optimal actions. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Analyzes theoretical exploration bounds in RL, not small models.
What Information Buys in Active Inference The paper compares active inference to standard exploration in reinforcement learning and proposes a square-root calibration to prevent sub-optimal actions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on theoretical active inference regret, not LLM benchmarks.
Deep Generative Models for Phylogenetic Inference with Complex Evolutionary Processes The paper studies deep generative simulation-based inference to estimate phylogenetic tree posteriors from sequences under complex evolutionary models. Peer-reviewed	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Deals with biological tree generative models, not model efficiency.
Deep Generative Models for Phylogenetic Inference with Complex Evolutionary Processes The paper studies deep generative simulation-based inference to estimate phylogenetic tree posteriors from sequences under complex evolutionary models. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on phylogenetic tree inference, not LLM benchmarks.
Deep Generative Models for Phylogenetic Inference with Complex Evolutionary Processes The paper studies deep generative simulation-based inference to estimate phylogenetic tree posteriors from sequences under complex evolutionary models. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on computational biology, not deployment safety or robustness.
Conformalized Physics-Informed Weak Regression for Parameter Estimation in Epidemic Models The paper presents a weak regression approach combined with conformal prediction to estimate parameters and uncertainty in epidemic models without numerical derivatives. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Covers scientific differential equation regression, not AI safety risks.
Conformalized Physics-Informed Weak Regression for Parameter Estimation in Epidemic Models The paper presents a weak regression approach combined with conformal prediction to estimate parameters and uncertainty in epidemic models without numerical derivatives. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on physics-informed epidemic regression, not model compression.
Conformalized Physics-Informed Weak Regression for Parameter Estimation in Epidemic Models The paper presents a weak regression approach combined with conformal prediction to estimate parameters and uncertainty in epidemic models without numerical derivatives. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Studies parameter estimation for epidemic models, not LLM benchmarking.
Theoretical and Practical Advances in Hierarchical Bayesian Deep Learning Methodology The paper introduces a multilevel particle marginal Metropolis-Hastings method for joint Bayesian inference of neural network parameters and prior hyperparameters. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Presents hierarchical Bayesian MCMC theory, not efficient language models.
Theoretical and Practical Advances in Hierarchical Bayesian Deep Learning Methodology The paper introduces a multilevel particle marginal Metropolis-Hastings method for joint Bayesian inference of neural network parameters and prior hyperparameters. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on MCMC methodology, not LLM evaluation benchmarks.
Theoretical and Practical Advances in Hierarchical Bayesian Deep Learning Methodology The paper introduces a multilevel particle marginal Metropolis-Hastings method for joint Bayesian inference of neural network parameters and prior hyperparameters. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses theoretical uncertainty estimation, not AI deployment safety risks.
Backpropagating Verifier Rewards Through Continuous Flow Language Models The paper introduces a post-training framework using intermediate token critics to backpropagate verifier rewards through continuous flow language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies model post-training methods, not safety guardrails or reliability.
Backpropagating Verifier Rewards Through Continuous Flow Language Models The paper introduces a post-training framework using intermediate token critics to backpropagate verifier rewards through continuous flow language models. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Deals with RL training for continuous flow models, not efficiency.
Backpropagating Verifier Rewards Through Continuous Flow Language Models The paper introduces a post-training framework using intermediate token critics to backpropagate verifier rewards through continuous flow language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces an RL framework for continuous flow models, not benchmarks.
Contextual Credit Policy Optimization The paper presents a method that weights trajectory peers by context similarity to improve step-level credit assignment for language model agents. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on RL credit assignment algorithms, not model compression.
Contextual Credit Policy Optimization The paper presents a method that weights trajectory peers by context similarity to improve step-level credit assignment for language model agents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes an RL credit assignment method, not a benchmark.
Contextual Credit Policy Optimization The paper presents a method that weights trajectory peers by context similarity to improve step-level credit assignment for language model agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies reinforcement learning optimization, not deployment safety or guardrails.
Neural Supervised Clustering for Learning Individualized Treatment Rules with Many Treatments The paper introduces a neural Q-learning framework that clusters treatments by response similarity to improve individualized treatment rules across many options. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on statistical treatment clustering, not deployment AI safety.
Neural Supervised Clustering for Learning Individualized Treatment Rules with Many Treatments The paper introduces a neural Q-learning framework that clusters treatments by response similarity to improve individualized treatment rules across many options. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on biostatistical treatment rule estimation, not LLM evaluation.
Neural Supervised Clustering for Learning Individualized Treatment Rules with Many Treatments The paper introduces a neural Q-learning framework that clusters treatments by response similarity to improve individualized treatment rules across many options. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Deals with individualized treatment rules, not efficient language models.
TRACER: Trace-Regularized Training Against Indirect Prompt Injection The paper introduces an adversarial training framework that hardens models against indirect prompt injection across retrieval and tool interaction channels. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on prompt injection defense training, not general benchmarking.

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