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
NV-Tesseract-AD: Curriculum-Enhanced Conditional Diffusion for Multivariate Time-Series Anomaly Detection Introduces a dual-curriculum conditional diffusion framework for multivariate time-series anomaly detection across varying masking and noise levels. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Addresses multivariate time-series anomaly detection rather than AI deployment safety or guardrails.
Provenance-grouped protocol for evaluating machine learning in perovskite photovoltaics Proposes an institutional provenance-grouped evaluation protocol to eliminate data leakage when benchmarking models for perovskite photovoltaics. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on materials science machine learning, not small or efficient language models.
Provenance-grouped protocol for evaluating machine learning in perovskite photovoltaics Proposes an institutional provenance-grouped evaluation protocol to eliminate data leakage when benchmarking models for perovskite photovoltaics. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on scientific dataset contamination in photovoltaics, not LLM deployment safety.
Provenance-grouped protocol for evaluating machine learning in perovskite photovoltaics Proposes an institutional provenance-grouped evaluation protocol to eliminate data leakage when benchmarking models for perovskite photovoltaics. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Investigates evaluation splits in perovskite materials science, not LLM evaluation or language benchmarks.
Credit Attribution in Timestamped Soccer Networks Using Multivariate Hawkes Processes Applies multivariate Hawkes processes to sequence data of soccer passes and shots to distribute attacking credit across multiple players. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on sports credit attribution, not AI safety or deployment reliability.
Credit Attribution in Timestamped Soccer Networks Using Multivariate Hawkes Processes Applies multivariate Hawkes processes to sequence data of soccer passes and shots to distribute attacking credit across multiple players. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Develops sports analytics metrics using point processes, not LLM evaluation benchmarks.
Credit Attribution in Timestamped Soccer Networks Using Multivariate Hawkes Processes Applies multivariate Hawkes processes to sequence data of soccer passes and shots to distribute attacking credit across multiple players. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Addresses soccer event sequences rather than efficient language models or compression.
Coordinate-Free Automatic Differentiation Presents a coordinate-free automatic differentiation formulation that expresses geometric differential operations within standard machine learning primitives. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on mathematical automatic differentiation operators, not model compression or small language models.
Coordinate-Free Automatic Differentiation Presents a coordinate-free automatic differentiation formulation that expresses geometric differential operations within standard machine learning primitives. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Deals with coordinate-free automatic differentiation, not LLM evaluation or benchmarking.
Coordinate-Free Automatic Differentiation Presents a coordinate-free automatic differentiation formulation that expresses geometric differential operations within standard machine learning primitives. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Addresses mathematical AD operators rather than deployment safety, robustness, or reliability.
Polyconvex neural-network surrogates with learned paired directional features for anisotropic hyperelastic homogenization Develops polyconvex neural network surrogate models using learned paired directional features for anisotropic hyperelastic material homogenization. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Covers mechanics constraints rather than AI deployment safety, alignment, or guardrails.
Polyconvex neural-network surrogates with learned paired directional features for anisotropic hyperelastic homogenization Develops polyconvex neural network surrogate models using learned paired directional features for anisotropic hyperelastic material homogenization. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Concerns neural surrogates for hyperelastic homogenization, not small language models or efficient LLM serving.
Polyconvex neural-network surrogates with learned paired directional features for anisotropic hyperelastic homogenization Develops polyconvex neural network surrogate models using learned paired directional features for anisotropic hyperelastic material homogenization. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Focuses on mechanical surrogate models rather than LLM evaluation or benchmarking methods.
Trainable Quantum-Native Language Models: Dissipative Memory, Capacity Limits, and Hardware Validation Investigates quantum-native byte-level language models, analyzing memory limits under learned dissipation and testing them on quantum hardware. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on quantum register architectures, not classical model compression, distillation, or efficient inference.
Trainable Quantum-Native Language Models: Dissipative Memory, Capacity Limits, and Hardware Validation Investigates quantum-native byte-level language models, analyzing memory limits under learned dissipation and testing them on quantum hardware. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Studies quantum language models, not general LLM evaluation frameworks or benchmarks.
Trainable Quantum-Native Language Models: Dissipative Memory, Capacity Limits, and Hardware Validation Investigates quantum-native byte-level language models, analyzing memory limits under learned dissipation and testing them on quantum hardware. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Does not evaluate safety, robustness, hallucinations, or deployment guardrails.
The Expressive Power of Random-Feature MPNNs Across Graph Sizes Analyzes the expressive power and size-uniform approximation limits of message-passing neural networks using continuous random node features. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Theoretical analysis of GNN expressivity unrelated to AI deployment safety or robustness.
The Expressive Power of Random-Feature MPNNs Across Graph Sizes Analyzes the expressive power and size-uniform approximation limits of message-passing neural networks using continuous random node features. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Theoretical study of message-passing graph neural network expressivity, not LLM evaluation.
The Expressive Power of Random-Feature MPNNs Across Graph Sizes Analyzes the expressive power and size-uniform approximation limits of message-passing neural networks using continuous random node features. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Theoretical graph expressivity paper, not about small models, quantization, or efficient serving.
Deploying RDMA for Large-Scale Inference and Training Workloads Presents deployment experiences and design strategies for ByteExpress, an RDMA communication layer for distributed training and inference workloads. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Network systems paper on RDMA transport, not LLM evaluation or benchmarking.
Deploying RDMA for Large-Scale Inference and Training Workloads Presents deployment experiences and design strategies for ByteExpress, an RDMA communication layer for distributed training and inference workloads. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Covers networking and infrastructure optimization rather than AI safety or reliability guardrails.
Inverse Synthesis of Shock Time Series from Shock Response Spectra Using Ensembles of Conditional Variational Autoencoders Uses ensembles of conditional variational autoencoders with wavelet decoders to synthesize acceleration time series from shock response spectra. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Mechanical shock synthesis paper, unrelated to deployed AI safety or reliability.
Inverse Synthesis of Shock Time Series from Shock Response Spectra Using Ensembles of Conditional Variational Autoencoders Uses ensembles of conditional variational autoencoders with wavelet decoders to synthesize acceleration time series from shock response spectra. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Engineering signal processing using CVAEs, unrelated to efficient language models.
Inverse Synthesis of Shock Time Series from Shock Response Spectra Using Ensembles of Conditional Variational Autoencoders Uses ensembles of conditional variational autoencoders with wavelet decoders to synthesize acceleration time series from shock response spectra. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Studies shock response spectrum time-series synthesis, not LLM evaluation.
TreeHCA: Hindsight-Guided Exploration and Credit Assignment for Multi-Turn LLM Agents Develops a tree-based exploration and hindsight credit assignment method to improve multi-turn reinforcement learning for language model agents. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Introduces an RL exploration and credit assignment method, not a benchmark or evaluation framework.
TreeHCA: Hindsight-Guided Exploration and Credit Assignment for Multi-Turn LLM Agents Develops a tree-based exploration and hindsight credit assignment method to improve multi-turn reinforcement learning for language model agents. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on agent reinforcement learning algorithms rather than model compression or efficient serving.
TreeHCA: Hindsight-Guided Exploration and Credit Assignment for Multi-Turn LLM Agents Develops a tree-based exploration and hindsight credit assignment method to improve multi-turn reinforcement learning for language model agents. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on question-answering accuracy in agents, not AI safety, red-teaming, or guardrails.
Scalable Learning of Determinantal Point Processes with Kernel-Structured Interactions Introduces a scalable training method for determinantal point processes using nonlinear kernels, latent features, and stochastic optimization. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on kernel estimation for point processes, not small or efficient neural models.
Scalable Learning of Determinantal Point Processes with Kernel-Structured Interactions Introduces a scalable training method for determinantal point processes using nonlinear kernels, latent features, and stochastic optimization. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Not related to AI safety, hallucination, or deployment risks.
Scalable Learning of Determinantal Point Processes with Kernel-Structured Interactions Introduces a scalable training method for determinantal point processes using nonlinear kernels, latent features, and stochastic optimization. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Mathematical study on determinantal point processes, not LLM benchmarking.
Cross-Behavior Policy Optimization: Unified Learning from Offline Demonstrations and On- and Off-Policy Rollouts via a Shared Regression Target Proposes a cross-behavior policy optimization framework unifying offline demonstrations and online rollouts via a shared regression objective. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on policy optimization for reasoning, not model compression or efficient serving.
Cross-Behavior Policy Optimization: Unified Learning from Offline Demonstrations and On- and Off-Policy Rollouts via a Shared Regression Target Proposes a cross-behavior policy optimization framework unifying offline demonstrations and online rollouts via a shared regression objective. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Addresses RL/SFT reasoning performance rather than safety, guardrails, or alignment risks.
Cross-Behavior Policy Optimization: Unified Learning from Offline Demonstrations and On- and Off-Policy Rollouts via a Shared Regression Target Proposes a cross-behavior policy optimization framework unifying offline demonstrations and online rollouts via a shared regression objective. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Proposes a post-training optimization algorithm, not a benchmark or evaluation method.
Reinforcement Learning via Fixed-point Flow Matching Presents fixed-point flow matching, a data-free procedure that trains flow policies in reinforcement learning without requiring demonstration data. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Proposes a reinforcement learning optimization method, not LLM benchmarks or evaluation.
Reinforcement Learning via Fixed-point Flow Matching Presents fixed-point flow matching, a data-free procedure that trains flow policies in reinforcement learning without requiring demonstration data. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Studies flow-matching RL policies, not small or efficient model serving.
Reinforcement Learning via Fixed-point Flow Matching Presents fixed-point flow matching, a data-free procedure that trains flow policies in reinforcement learning without requiring demonstration data. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:29	Not relevant A general reinforcement learning algorithm, not computer vision for industrial use.
Reinforcement Learning via Fixed-point Flow Matching Presents fixed-point flow matching, a data-free procedure that trains flow policies in reinforcement learning without requiring demonstration data. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on policy optimization in RL, not AI safety or deployment guardrails.
Two-way Guidance: Simultaneous Learning of Summary Statistics for Generative Bayes Introduces a two-way guidance framework that jointly learns sufficient summary statistics and posteriors for scalable simulation-based inference. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Statistical inference paper unrelated to efficient language models or model compression.
Two-way Guidance: Simultaneous Learning of Summary Statistics for Generative Bayes Introduces a two-way guidance framework that jointly learns sufficient summary statistics and posteriors for scalable simulation-based inference. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Focuses on simulation-based Bayesian inference, not LLM evaluation.
Two-way Guidance: Simultaneous Learning of Summary Statistics for Generative Bayes Introduces a two-way guidance framework that jointly learns sufficient summary statistics and posteriors for scalable simulation-based inference. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Does not address AI safety, jailbreaks, or deployment reliability.
Universal Bounds for Self-Normalized Kullback-Leibler Estimation Establishes finite-sample bounds for self-normalized Kullback-Leibler divergence estimators and applies them to test-time alignment sampling techniques. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on theoretical information bounds rather than AI safety or reliability.
Universal Bounds for Self-Normalized Kullback-Leibler Estimation Establishes finite-sample bounds for self-normalized Kullback-Leibler divergence estimators and applies them to test-time alignment sampling techniques. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Theoretical bounds on importance sampling, not efficient model compression or serving.
Universal Bounds for Self-Normalized Kullback-Leibler Estimation Establishes finite-sample bounds for self-normalized Kullback-Leibler divergence estimators and applies them to test-time alignment sampling techniques. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Provides mathematical bounds for KL estimation rather than LLM benchmarking.
Robust Langevin Sampling under State-Dependent Heavy-Tailed Noise with Applications to LLM Fine-Tuning Analyzes Langevin sampling algorithms under state-dependent heavy-tailed gradient noise with applications to Bayesian fine-tuning of language models. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on theoretical sampling convergence rather than efficient inference or small models.
Robust Langevin Sampling under State-Dependent Heavy-Tailed Noise with Applications to LLM Fine-Tuning Analyzes Langevin sampling algorithms under state-dependent heavy-tailed gradient noise with applications to Bayesian fine-tuning of language models. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Theoretical Langevin Monte Carlo optimization paper, not LLM benchmark evaluation.
Robust Langevin Sampling under State-Dependent Heavy-Tailed Noise with Applications to LLM Fine-Tuning Analyzes Langevin sampling algorithms under state-dependent heavy-tailed gradient noise with applications to Bayesian fine-tuning of language models. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Theoretical sampling analysis without direct focus on deployment safety or guardrails.
When Solvers Get Moody: Quantifying and Predicting Performance Variability in Mixed Integer Programming Proposes a weighted metric to quantify and predict performance variability across mixed-integer programming solver runs. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Addresses solver performance variance in combinatorial optimization, not AI safety.
When Solvers Get Moody: Quantifying and Predicting Performance Variability in Mixed Integer Programming Proposes a weighted metric to quantify and predict performance variability across mixed-integer programming solver runs. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Studies classical mixed-integer programming solver variability, not LLM evaluation.
When Solvers Get Moody: Quantifying and Predicting Performance Variability in Mixed Integer Programming Proposes a weighted metric to quantify and predict performance variability across mixed-integer programming solver runs. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on operations research MIP solvers, not small or efficient neural models.
HART: Hybrid Attention-RWKV Time series Foundation Model Introduces a hybrid attention-RWKV architecture to mitigate horizon degradation in non-autoregressive time series forecasting. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Does not investigate AI safety, guardrails, or deployment security.
HART: Hybrid Attention-RWKV Time series Foundation Model Introduces a hybrid attention-RWKV architecture to mitigate horizon degradation in non-autoregressive time series forecasting. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on time-series foundation model architecture design rather than model compression/efficiency.
HART: Hybrid Attention-RWKV Time series Foundation Model Introduces a hybrid attention-RWKV architecture to mitigate horizon degradation in non-autoregressive time series forecasting. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Evaluates on time series benchmarks but focuses on a new architecture, not LLM evaluation.
CoGameReg: Cooperative-Game-Based Collaborative Semi-Supervised Regression for Seismic Reservoir Property Prediction Presents a cooperative game theoretic semi-supervised regression framework for predicting reservoir properties from seismic data. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Geophysical prediction method unrelated to AI safety, hallucination, or deployment guardrails.
CoGameReg: Cooperative-Game-Based Collaborative Semi-Supervised Regression for Seismic Reservoir Property Prediction Presents a cooperative game theoretic semi-supervised regression framework for predicting reservoir properties from seismic data. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Presents a domain-specific regression framework, not small or efficient language models.
CoGameReg: Cooperative-Game-Based Collaborative Semi-Supervised Regression for Seismic Reservoir Property Prediction Presents a cooperative game theoretic semi-supervised regression framework for predicting reservoir properties from seismic data. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Applies semi-supervised regression to seismic property prediction, not LLM benchmarking.
Personalized Federated Direct Preference Optimization via LoRA Adapters Develops a personalized federated preference optimization framework maintaining local LoRA adapters atop a globally synchronized model. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Proposes a federated preference optimization algorithm, not an evaluation framework or benchmark.
Personalized Federated Direct Preference Optimization via LoRA Adapters Develops a personalized federated preference optimization framework maintaining local LoRA adapters atop a globally synchronized model. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Uses LoRA for federated personalization, not model distillation, quantization, or efficient serving.
Generalized Determinantal Point Processes for Attraction and Repulsion with Distinct Interaction Kernels Introduces determinant-ratio point processes to jointly model attraction and repulsion with distinct symmetric positive-definite kernels. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Theoretical statistics paper on determinantal point processes, not LLM evaluation.
Generalized Determinantal Point Processes for Attraction and Repulsion with Distinct Interaction Kernels Introduces determinant-ratio point processes to jointly model attraction and repulsion with distinct symmetric positive-definite kernels. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Does not address AI safety, jailbreaks, hallucination, or deployment reliability.
Generalized Determinantal Point Processes for Attraction and Repulsion with Distinct Interaction Kernels Introduces determinant-ratio point processes to jointly model attraction and repulsion with distinct symmetric positive-definite kernels. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on point process sampling and formulation, not model compression or efficient serving.

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