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
Random Probing for Structural Self-Interactions in Graph Neural Networks This paper proposes a message-passing layer using randomized probing of operator diagonals to capture node self-interactions in graph neural networks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Graph neural network architecture, not AI safety or reliability.
Random Probing for Structural Self-Interactions in Graph Neural Networks This paper proposes a message-passing layer using randomized probing of operator diagonals to capture node self-interactions in graph neural networks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Proposes a graph message passing layer, not model compression.
Reinforcement Learning for Large-Scale Lifelong Multi-Agent Path Finding with Shared Spatial Fields This paper introduces a reinforcement learning navigation policy using shared spatial fields for large-scale lifelong multi-agent path finding. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Grid navigation pathfinding, not deployed AI safety or guardrails.
Reinforcement Learning for Large-Scale Lifelong Multi-Agent Path Finding with Shared Spatial Fields This paper introduces a reinforcement learning navigation policy using shared spatial fields for large-scale lifelong multi-agent path finding. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Covers multi-agent path planning, not model compression or efficiency.
Reinforcement Learning for Large-Scale Lifelong Multi-Agent Path Finding with Shared Spatial Fields This paper introduces a reinforcement learning navigation policy using shared spatial fields for large-scale lifelong multi-agent path finding. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Addresses multi-agent path finding, not language model evaluation.
Link Both Views: Cross-Image Routing Attention for Dual-Endpoint V2I Beam-Pair Power Prediction This paper proposes a cross-image routing attention mechanism to predict millimeter-wave beam power from dual-endpoint vehicle-to-infrastructure camera views. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Covers wireless beam prediction, not efficient model architectures.
Link Both Views: Cross-Image Routing Attention for Dual-Endpoint V2I Beam-Pair Power Prediction This paper proposes a cross-image routing attention mechanism to predict millimeter-wave beam power from dual-endpoint vehicle-to-infrastructure camera views. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Wireless channel communication application, not AI safety or reliability.
Link Both Views: Cross-Image Routing Attention for Dual-Endpoint V2I Beam-Pair Power Prediction This paper proposes a cross-image routing attention mechanism to predict millimeter-wave beam power from dual-endpoint vehicle-to-infrastructure camera views. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Vehicle-to-infrastructure wireless power prediction, not language model evaluation.
Hierarchical Time Series Forecasting with Operator-Valued Kernels This paper presents an operator-valued kernel regression framework that enforces aggregation constraints directly in hierarchical time series forecasting. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Covers kernel methods for forecasting, not model efficiency.
Hierarchical Time Series Forecasting with Operator-Valued Kernels This paper presents an operator-valued kernel regression framework that enforces aggregation constraints directly in hierarchical time series forecasting. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Time-series forecasting theory, not AI safety and reliability.
Hierarchical Time Series Forecasting with Operator-Valued Kernels This paper presents an operator-valued kernel regression framework that enforces aggregation constraints directly in hierarchical time series forecasting. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on hierarchical time-series methods, not LLM evaluation.
Operator Surrogates This book chapter provides an overview and mathematical introduction to finite-parametric operator surrogates for approximating maps between infinite-dimensional spaces. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Mathematical survey of operator surrogates, not LLM evaluation.
Operator Surrogates This book chapter provides an overview and mathematical introduction to finite-parametric operator surrogates for approximating maps between infinite-dimensional spaces. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical operator approximation, not operational AI system safety.
Operator Surrogates This book chapter provides an overview and mathematical introduction to finite-parametric operator surrogates for approximating maps between infinite-dimensional spaces. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Covers infinite-dimensional operator approximation, not efficient language modeling.
When Does Opponent Modeling Pay Off? Spatial Inference Stabilises Reinforcement Learning under Partial Observability This paper introduces a spatial transformer opponent model that stabilizes reinforcement learning training under partial observability in competitive foraging games. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant RL training stability in games, not deployment safety or guardrails.
When Does Opponent Modeling Pay Off? Spatial Inference Stabilises Reinforcement Learning under Partial Observability This paper introduces a spatial transformer opponent model that stabilizes reinforcement learning training under partial observability in competitive foraging games. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on RL opponent models, not model compression.
When Does Opponent Modeling Pay Off? Spatial Inference Stabilises Reinforcement Learning under Partial Observability This paper introduces a spatial transformer opponent model that stabilizes reinforcement learning training under partial observability in competitive foraging games. Preprint	AI agents via arxiv · 15 Oct, 06:18	Not relevant Studies game-theoretic reinforcement learning, not LLM-based autonomous planning agents.
When Does Opponent Modeling Pay Off? Spatial Inference Stabilises Reinforcement Learning under Partial Observability This paper introduces a spatial transformer opponent model that stabilizes reinforcement learning training under partial observability in competitive foraging games. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies spatial RL opponent modeling, not LLM evaluation benchmarks.
Teach Me After You Learn It: Lookahead Self-Distillation for RLVR This paper proposes Lookahead Self-Distillation, providing cross-instance token-level supervision to improve reinforcement learning with verifiable rewards for language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes a training method, not a benchmark or evaluation methodology.
Teach Me After You Learn It: Lookahead Self-Distillation for RLVR This paper proposes Lookahead Self-Distillation, providing cross-instance token-level supervision to improve reinforcement learning with verifiable rewards for language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on reasoning distillation, not deployment safety, jailbreaks, or guardrails.
Optimal Online Regret for Context-Lumpable Bandits This paper develops an online algorithm achieving optimal regret bounds for stochastic contextual bandits with unknown context-lumpable cluster structures. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Bandit optimization theory, not operational AI safety or alignment.
Optimal Online Regret for Context-Lumpable Bandits This paper develops an online algorithm achieving optimal regret bounds for stochastic contextual bandits with unknown context-lumpable cluster structures. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses theoretical regret bounds, not small or efficient models.
Optimal Online Regret for Context-Lumpable Bandits This paper develops an online algorithm achieving optimal regret bounds for stochastic contextual bandits with unknown context-lumpable cluster structures. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical contextual bandit paper, not LLM evaluation.
PING: physics informed graph neural network for advection-diffusion processes with applications to solar power forecasting This paper introduces a physics-informed graph neural network for modeling advection-diffusion processes to forecast solar power production. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Addresses solar power forecasting, not language model evaluation.
PING: physics informed graph neural network for advection-diffusion processes with applications to solar power forecasting This paper introduces a physics-informed graph neural network for modeling advection-diffusion processes to forecast solar power production. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on weather and solar modeling, not AI safety.
PING: physics informed graph neural network for advection-diffusion processes with applications to solar power forecasting This paper introduces a physics-informed graph neural network for modeling advection-diffusion processes to forecast solar power production. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Covers physics-informed graph neural networks, not model compression.
Reliable Risk Estimation with Kernel Mean Matching under Dependent Sampling This paper derives finite-sample risk bounds for Kernel Mean Matching under dependent sampling using effective sample sizes. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Concerns theoretical risk estimation bounds, not deployment AI safety.
Reliable Risk Estimation with Kernel Mean Matching under Dependent Sampling This paper derives finite-sample risk bounds for Kernel Mean Matching under dependent sampling using effective sample sizes. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on statistical covariate shift, not efficient language modeling.
Reliable Risk Estimation with Kernel Mean Matching under Dependent Sampling This paper derives finite-sample risk bounds for Kernel Mean Matching under dependent sampling using effective sample sizes. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Statistical kernel mean matching theory, not LLM evaluation.
Bayesian Optimization of CVaR with Unknown Hyperparameters This paper presents HE-CV-UCB, an algorithm for Bayesian optimization of Conditional Value-at-Risk under unknown Gaussian process hyperparameters. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Presents Bayesian optimization regret bounds, not model efficiency.
Bayesian Optimization of CVaR with Unknown Hyperparameters This paper presents HE-CV-UCB, an algorithm for Bayesian optimization of Conditional Value-at-Risk under unknown Gaussian process hyperparameters. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Addresses Bayesian optimization algorithms, not language model evaluation.
Bayesian Optimization of CVaR with Unknown Hyperparameters This paper presents HE-CV-UCB, an algorithm for Bayesian optimization of Conditional Value-at-Risk under unknown Gaussian process hyperparameters. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Mathematical risk optimization, not LLM safety, guardrails, or hallucination.
Over-parameterization Is Neither Robust nor Fragile in Matrix Sensing: A Geometric Theory of Solution Robustness This paper analyzes the geometric robustness of over-parameterized low-rank matrix sensing under the Burer-Monteiro factorization. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Examines mathematical optimization stability, not operational AI system safety.
Over-parameterization Is Neither Robust nor Fragile in Matrix Sensing: A Geometric Theory of Solution Robustness This paper analyzes the geometric robustness of over-parameterized low-rank matrix sensing under the Burer-Monteiro factorization. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Covers theoretical over-parameterization in matrix sensing, not model compression.
Over-parameterization Is Neither Robust nor Fragile in Matrix Sensing: A Geometric Theory of Solution Robustness This paper analyzes the geometric robustness of over-parameterized low-rank matrix sensing under the Burer-Monteiro factorization. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies matrix sensing optimization geometry, not LLM evaluation.
On the Optimality of Random Features under General Conditions: Misspecified Cases and Robust Learning This paper provides optimal learning rate bounds for random feature kernel ridge regression under misspecified models and heavy-tailed noise. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Analyzes theoretical kernel ridge regression bounds, not LLM evaluation.
On the Optimality of Random Features under General Conditions: Misspecified Cases and Robust Learning This paper provides optimal learning rate bounds for random feature kernel ridge regression under misspecified models and heavy-tailed noise. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with kernel random feature theory, not language model efficiency.
On the Optimality of Random Features under General Conditions: Misspecified Cases and Robust Learning This paper provides optimal learning rate bounds for random feature kernel ridge regression under misspecified models and heavy-tailed noise. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Statistical noise robustness theory, not deployed AI safety or guardrails.
Beyond Task Success: A Theory of Representation Reuse This paper develops a theoretical framework characterizing when representations learned on a source task remain recoverable and reusable for future tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical representation study unrelated to AI safety and deployment guardrails.
Beyond Task Success: A Theory of Representation Reuse This paper develops a theoretical framework characterizing when representations learned on a source task remain recoverable and reusable for future tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents general representation learning theory, not LLM evaluation benchmarks.
Beyond Task Success: A Theory of Representation Reuse This paper develops a theoretical framework characterizing when representations learned on a source task remain recoverable and reusable for future tasks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Discusses downstream representation reuse, not model compression or efficiency.
Beyond RMSE and Granger Gain: Multi-Horizon Forecast-Error Entropy for Nonlinear Predictive Coupling This paper proposes a forecast-error distribution entropy framework to detect nonlinear predictive coupling across multiple time horizons. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on time-series forecasting metrics, not model efficiency.
Beyond RMSE and Granger Gain: Multi-Horizon Forecast-Error Entropy for Nonlinear Predictive Coupling This paper proposes a forecast-error distribution entropy framework to detect nonlinear predictive coupling across multiple time horizons. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies time-series predictive causality metrics, not language model evaluation.
Beyond RMSE and Granger Gain: Multi-Horizon Forecast-Error Entropy for Nonlinear Predictive Coupling This paper proposes a forecast-error distribution entropy framework to detect nonlinear predictive coupling across multiple time horizons. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Covers time-series entropy bounds, not deployment safety or reliability.
LunaTP: Enabling Efficient and Scalable Tensor Product for Equivariant MLIPs This paper introduces LunaTP, a parallel execution framework that accelerates tensor product convolutions in equivariant machine learning interatomic potentials. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on equivariant interatomic simulations, not small/efficient LLMs.
LunaTP: Enabling Efficient and Scalable Tensor Product for Equivariant MLIPs This paper introduces LunaTP, a parallel execution framework that accelerates tensor product convolutions in equivariant machine learning interatomic potentials. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Scientific computing acceleration, not AI deployment safety.
LunaTP: Enabling Efficient and Scalable Tensor Product for Equivariant MLIPs This paper introduces LunaTP, a parallel execution framework that accelerates tensor product convolutions in equivariant machine learning interatomic potentials. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Hardware acceleration of tensor products, not LLM evaluation.
Overcoming Catastrophic Forgetting: Continual Learning for Radar Waveform Classification This paper benchmarks eighteen continual learning methods on a sequential radar waveform recognition task across task- and class-incremental settings. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Continual learning on radar datasets, not LLM benchmarks.
Overcoming Catastrophic Forgetting: Continual Learning for Radar Waveform Classification This paper benchmarks eighteen continual learning methods on a sequential radar waveform recognition task across task- and class-incremental settings. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Domain-specific radar signal classification, not AI deployment safety.
Overcoming Catastrophic Forgetting: Continual Learning for Radar Waveform Classification This paper benchmarks eighteen continual learning methods on a sequential radar waveform recognition task across task- and class-incremental settings. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Continual learning for radar waveforms, not efficient LLM serving.
Convergence-Rate-Guided Algorithm Design of Expected Improvement for Continuous Simulation Optimization This paper develops finite-sample bounds to guide algorithm design for Expected Improvement in continuous simulation optimization with heteroscedastic noise. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Stochastic kriging optimization algorithms, not model efficiency.
Convergence-Rate-Guided Algorithm Design of Expected Improvement for Continuous Simulation Optimization This paper develops finite-sample bounds to guide algorithm design for Expected Improvement in continuous simulation optimization with heteroscedastic noise. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Simulation optimization bounds, not AI deployment safety.
Convergence-Rate-Guided Algorithm Design of Expected Improvement for Continuous Simulation Optimization This paper develops finite-sample bounds to guide algorithm design for Expected Improvement in continuous simulation optimization with heteroscedastic noise. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Continuous simulation optimization theory, not LLM evaluation.
FastEEP: Fast Intra-Instance Elastic Scaling for Expert-Parallel MoE Serving This paper presents FastEEP, a system that speeds up intra-instance elastic scaling for Mixture-of-Experts serving by reusing runtime state. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Serving infrastructure system design, not benchmark or model evaluation.
FastEEP: Fast Intra-Instance Elastic Scaling for Expert-Parallel MoE Serving This paper presents FastEEP, a system that speeds up intra-instance elastic scaling for Mixture-of-Experts serving by reusing runtime state. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Covers inference serving systems, not safety, guardrails, or alignment.
Graph Metanetworks for Transformer Based Language Models This paper develops graph metanetworks that respect the weight-space symmetries of transformer language models, including attention matrices and token embeddings. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Weight-space symmetry modeling, not model compression or efficiency.
Graph Metanetworks for Transformer Based Language Models This paper develops graph metanetworks that respect the weight-space symmetries of transformer language models, including attention matrices and token embeddings. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Metanetworks on model weights, not AI deployment safety.
Graph Metanetworks for Transformer Based Language Models This paper develops graph metanetworks that respect the weight-space symmetries of transformer language models, including attention matrices and token embeddings. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on weight-space metanetworks, not LLM evaluation benchmarks.
ProTok: Unified Protein Tokenization via Modular Multi-Codebook Quantization This paper introduces ProTok, a unified discrete tokenizer for protein sequence, structure, and functional annotations using modular multi-codebook quantization. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Evaluates protein tokenizers, not LLM evaluation methodologies.
ProTok: Unified Protein Tokenization via Modular Multi-Codebook Quantization This paper introduces ProTok, a unified discrete tokenizer for protein sequence, structure, and functional annotations using modular multi-codebook quantization. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Protein representation tokenization, not small or efficient models.

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