Every paper checked — nothing hidden

All papers

Every paper checked for brief #4 of 12–19 Oct 2026 — nothing is hidden. Brief papers first.

All17,312 In brief24 More (on-topic)3,333 Not relevant13,753 Not assessed87 Older351 Could not confirm93 Blogs with no paper266 All blogs284

Paper	Topic	Relevance
Latent Annealed Posterior Sampling with a Variance-Preserving Bridge for Sparsely Observed Random Fields This paper introduces a latent annealed posterior sampling framework with a variance-preserving bridge for inferring random fields from sparse observations. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Deals with spatial uncertainty quantification, not LLM deployment safety.
Latent Annealed Posterior Sampling with a Variance-Preserving Bridge for Sparsely Observed Random Fields This paper introduces a latent annealed posterior sampling framework with a variance-preserving bridge for inferring random fields from sparse observations. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Addresses posterior sampling in spatial statistics, not LLM benchmarking.
Graph Additive Models disentangling feature, topology, and interactions This paper proposes an interpretable graph neural network architecture that separates node features, topological motifs, and feature-topology interactions into additive terms. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Deals with graph architecture interpretability, not deployed LLM safety or guardrails.
Graph Additive Models disentangling feature, topology, and interactions This paper proposes an interpretable graph neural network architecture that separates node features, topological motifs, and feature-topology interactions into additive terms. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Investigates graph model interpretability, not small or efficient LLM inference.
Graph Additive Models disentangling feature, topology, and interactions This paper proposes an interpretable graph neural network architecture that separates node features, topological motifs, and feature-topology interactions into additive terms. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Proposes a graph neural additive network, not LLM benchmarking or evaluation.
On the Stability and Convergence of Flow Map Training This paper provides theoretical analysis explaining why stop gradients and adaptive weighting are necessary for stable training of generative flow maps. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Theoretical convergence study for generative flow maps, not model compression or efficiency.
On the Stability and Convergence of Flow Map Training This paper provides theoretical analysis explaining why stop gradients and adaptive weighting are necessary for stable training of generative flow maps. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Focuses on training stability of generative models, not AI safety guardrails.
On the Stability and Convergence of Flow Map Training This paper provides theoretical analysis explaining why stop gradients and adaptive weighting are necessary for stable training of generative flow maps. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Analyzes generative flow maps, not LLM benchmarking or evaluation methods.
Spectral Indistinguishability and Learning-Curve Identifiability in Quadratic Networks This paper theoretically analyzes what spectral measurements and learning curves reveal about internal representations in quadratic neural networks. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Theoretical study of quadratic networks unrelated to deployment safety risks.
Spectral Indistinguishability and Learning-Curve Identifiability in Quadratic Networks This paper theoretically analyzes what spectral measurements and learning curves reveal about internal representations in quadratic neural networks. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Theoretical learning theory paper on quadratic networks, not LLM benchmark evaluation.
Spectral Indistinguishability and Learning-Curve Identifiability in Quadratic Networks This paper theoretically analyzes what spectral measurements and learning curves reveal about internal representations in quadratic neural networks. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Not related to model efficiency, quantization, distillation, or serving.
Diminishing Returns of Data in Sparse Autoencoders: Functional Behavior does not Guarantee Feature Reproducibility This paper examines how training data size affects the functional performance and feature reproducibility of sparse autoencoders on language models. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Evaluates sparse autoencoder training properties rather than LLM benchmarks or evaluation methods.
Diminishing Returns of Data in Sparse Autoencoders: Functional Behavior does not Guarantee Feature Reproducibility This paper examines how training data size affects the functional performance and feature reproducibility of sparse autoencoders on language models. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Focuses on mechanistic interpretability tooling rather than deployed AI safety risks.
Diminishing Returns of Data in Sparse Autoencoders: Functional Behavior does not Guarantee Feature Reproducibility This paper examines how training data size affects the functional performance and feature reproducibility of sparse autoencoders on language models. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Studies SAE data requirements, not efficient serving, compression, or small models.
Causal Syntax Circuits in CodeLMs: Budget and Probe Decide What Looks Localised This paper investigates internal model circuits and prompt properties that drive code language models to default to Python code output. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Does not study model compression, efficient inference, or distillation techniques.
Causal Syntax Circuits in CodeLMs: Budget and Probe Decide What Looks Localised This paper investigates internal model circuits and prompt properties that drive code language models to default to Python code output. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on mechanistic interpretability of code models, not general benchmark evaluation.
Causal Syntax Circuits in CodeLMs: Budget and Probe Decide What Looks Localised This paper investigates internal model circuits and prompt properties that drive code language models to default to Python code output. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Does not address AI safety, jailbreaks, hallucination, or deployment guardrails.
RoLaQ: Joint Rotation and Range Calibration for 2-Bit KV Caches in Multi-Head Latent Attention This paper presents a calibration method to compress multi-head latent attention key-value caches to two bits for efficient long-context language model inference. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Focuses on KV cache quantization techniques, not AI safety or reliability guardrails.
RoLaQ: Joint Rotation and Range Calibration for 2-Bit KV Caches in Multi-Head Latent Attention This paper presents a calibration method to compress multi-head latent attention key-value caches to two bits for efficient long-context language model inference. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Evaluates 2-bit quantization schemes, not general LLM evaluation benchmarks.
On-Policy Residual Learning for Diffusion Language Models This paper introduces an on-policy residual learning framework with an entropy-contraction objective to improve generation speed and quality in diffusion language models. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Focuses on throughput acceleration for diffusion language models, not AI safety or alignment.
On-Policy Residual Learning for Diffusion Language Models This paper introduces an on-policy residual learning framework with an entropy-contraction objective to improve generation speed and quality in diffusion language models. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Studies diffusion language model training and serving, not evaluation benchmarks.
Quantile-Guided Diffusion for Probabilistic Time-Series Forecasting This paper proposes a quantile-guided residual control framework to improve uncertainty representation and accuracy in diffusion-based time-series forecasting models. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Evaluates time-series diffusion forecasting methods, not LLM evaluation or benchmarks.
Quantile-Guided Diffusion for Probabilistic Time-Series Forecasting This paper proposes a quantile-guided residual control framework to improve uncertainty representation and accuracy in diffusion-based time-series forecasting models. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Studies quantile guidance for time-series forecasting, not deployment AI safety.
Quantile-Guided Diffusion for Probabilistic Time-Series Forecasting This paper proposes a quantile-guided residual control framework to improve uncertainty representation and accuracy in diffusion-based time-series forecasting models. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on time-series diffusion forecasting architectures, not model efficiency or compression.
Vertical Sliced Wasserstein Distances for Meta-Measures This paper introduces a scalable vertical sliced Wasserstein distance to measure distances between probability distributions whose individual elements are themselves distributions. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Proposes a mathematical optimal transport distance, not LLM evaluation benchmarks.
Vertical Sliced Wasserstein Distances for Meta-Measures This paper introduces a scalable vertical sliced Wasserstein distance to measure distances between probability distributions whose individual elements are themselves distributions. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Develops theoretical metrics for meta-measures, not neural network efficiency or compression.
Vertical Sliced Wasserstein Distances for Meta-Measures This paper introduces a scalable vertical sliced Wasserstein distance to measure distances between probability distributions whose individual elements are themselves distributions. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Theoretical optimal transport paper, not operational AI safety or reliability.
Ergodic topology sampling toward the continuum with Stochastic Normalizing Flows This paper uses stochastic normalizing flows with open boundary conditions to mitigate topological freezing in lattice gauge theory simulations. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on physics simulation methods, not efficient model inference or compression.
Ergodic topology sampling toward the continuum with Stochastic Normalizing Flows This paper uses stochastic normalizing flows with open boundary conditions to mitigate topological freezing in lattice gauge theory simulations. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Studies quantum field theory sampling, not AI deployment safety or guardrails.
Ergodic topology sampling toward the continuum with Stochastic Normalizing Flows This paper uses stochastic normalizing flows with open boundary conditions to mitigate topological freezing in lattice gauge theory simulations. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Applies normalizing flows to lattice gauge theory, not LLM evaluation.
Semantic Alignment of EEG and Text: A Contrastive Learning Framework for Decoding Imagined Speech This paper aligns electroencephalography signals with text representations in a shared embedding space to improve the decoding of imagined speech intent. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Deals with brain-computer interfaces, not AI deployment safety or reliability risks.
Semantic Alignment of EEG and Text: A Contrastive Learning Framework for Decoding Imagined Speech This paper aligns electroencephalography signals with text representations in a shared embedding space to improve the decoding of imagined speech intent. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on EEG-text contrastive learning, not efficient model architectures or compression.
Semantic Alignment of EEG and Text: A Contrastive Learning Framework for Decoding Imagined Speech This paper aligns electroencephalography signals with text representations in a shared embedding space to improve the decoding of imagined speech intent. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Evaluates imagined speech decoding from EEG, not LLM evaluation benchmarks.
Selection-Safe Conformal Early Exit: Distribution-Free Coverage under Adaptive Depth This paper presents a pathwise conformal prediction method that guarantees valid coverage across adaptive early-exit stopping policies without multi-exit score inflation. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on conformal prediction theory for early exits, not LLM evaluation benchmarks.
Sliced Wasserstein Sketches with Error Certificates This paper develops sliced Wasserstein sketches with computable error certificates for compact representation and fast comparison of probability measures. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Studies mathematical distribution sketching, not LLM benchmarks or evaluation methods.
Sliced Wasserstein Sketches with Error Certificates This paper develops sliced Wasserstein sketches with computable error certificates for compact representation and fast comparison of probability measures. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on optimal transport distance sketches, not model compression or serving.
Sliced Wasserstein Sketches with Error Certificates This paper develops sliced Wasserstein sketches with computable error certificates for compact representation and fast comparison of probability measures. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Theoretical work on Wasserstein sketches rather than AI deployment safety.
EEG-Based Prediction of Music Enjoyment Using Deep Learning with Explainable Temporal Analysis This paper evaluates deep learning models and temporal interpretability techniques to predict continuous music enjoyment ratings from electroencephalography signals. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Focuses on EEG signal interpretation, not deployment AI safety or reliability.
EEG-Based Prediction of Music Enjoyment Using Deep Learning with Explainable Temporal Analysis This paper evaluates deep learning models and temporal interpretability techniques to predict continuous music enjoyment ratings from electroencephalography signals. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Presents an EEG music enjoyment regression benchmark, not LLM evaluation.
EEG-Based Prediction of Music Enjoyment Using Deep Learning with Explainable Temporal Analysis This paper evaluates deep learning models and temporal interpretability techniques to predict continuous music enjoyment ratings from electroencephalography signals. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Applies standard deep learning models to EEG data, not model efficiency.
Optimal Explicit and Implicit Regularization Strengths in High-Dimensional Continual Linear Regression This paper derives closed-form generalization loss expressions for high-dimensional continual linear regression, characterizing optimal step sizes and equivalent regularization strengths. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Analyzes theoretical continual linear regression, not LLM evaluation benchmarks.
Optimal Explicit and Implicit Regularization Strengths in High-Dimensional Continual Linear Regression This paper derives closed-form generalization loss expressions for high-dimensional continual linear regression, characterizing optimal step sizes and equivalent regularization strengths. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on high-dimensional regression regularization theory, not model efficiency or compression.
Optimal Explicit and Implicit Regularization Strengths in High-Dimensional Continual Linear Regression This paper derives closed-form generalization loss expressions for high-dimensional continual linear regression, characterizing optimal step sizes and equivalent regularization strengths. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Theoretical generalization study of linear regression, not operational AI safety.
O-RAN-Native Jamming Detection Under Network Heterogeneity This paper develops a statistical ensemble method to detect radio-frequency jamming in heterogeneous open radio access networks using standard telemetry. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on cellular network jamming detection, not model compression or efficiency.
O-RAN-Native Jamming Detection Under Network Heterogeneity This paper develops a statistical ensemble method to detect radio-frequency jamming in heterogeneous open radio access networks using standard telemetry. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Addresses cellular RF jamming detection rather than AI model safety, hallucinations, or jailbreaks.
O-RAN-Native Jamming Detection Under Network Heterogeneity This paper develops a statistical ensemble method to detect radio-frequency jamming in heterogeneous open radio access networks using standard telemetry. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Evaluates RF jamming detection methods, not language model evaluation benchmarks.
Feasibility Ceilings Before Learning: Sizing and Timing Bounds for Drone-Mesh Interception This paper establishes analytical geometric bounds on coverage probability and timing uncertainty for multi-agent drone meshes intercepting approaching objects. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Concerns geometric coverage bounds for drones, not efficient model inference.
Feasibility Ceilings Before Learning: Sizing and Timing Bounds for Drone-Mesh Interception This paper establishes analytical geometric bounds on coverage probability and timing uncertainty for multi-agent drone meshes intercepting approaching objects. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Focuses on physical drone interception geometry rather than AI system safety and reliability.
Feasibility Ceilings Before Learning: Sizing and Timing Bounds for Drone-Mesh Interception This paper establishes analytical geometric bounds on coverage probability and timing uncertainty for multi-agent drone meshes intercepting approaching objects. Preprint	AI agents via arxiv · 17 Oct, 06:18	Not relevant Analyzes geometric drone interceptor bounds rather than LLM tool use or multi-agent planning.
Feasibility Ceilings Before Learning: Sizing and Timing Bounds for Drone-Mesh Interception This paper establishes analytical geometric bounds on coverage probability and timing uncertainty for multi-agent drone meshes intercepting approaching objects. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Mathematical coverage bounds for robotics, not language model evaluation.
Feasibility Ceilings Before Learning: Sizing and Timing Bounds for Drone-Mesh Interception This paper establishes analytical geometric bounds on coverage probability and timing uncertainty for multi-agent drone meshes intercepting approaching objects. Preprint	Computer vision for industrial applications via arxiv · 17 Oct, 06:18	Not relevant Deals with drone mesh interception rather than industrial computer vision.
Linear Independence of Polynomial Compositions with Bounded Input Degree This paper resolves mathematical conjectures on polynomial compositions, proving the identifiability of neural networks using general polynomial activation functions of sufficient degree. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Pure algebraic geometry proof regarding neural identifiability, not LLM evaluation.
Linear Independence of Polynomial Compositions with Bounded Input Degree This paper resolves mathematical conjectures on polynomial compositions, proving the identifiability of neural networks using general polynomial activation functions of sufficient degree. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Mathematical proof on polynomial activations, not model compression or efficient inference.
Linear Independence of Polynomial Compositions with Bounded Input Degree This paper resolves mathematical conjectures on polynomial compositions, proving the identifiability of neural networks using general polynomial activation functions of sufficient degree. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Theoretical algebraic geometry paper, not practical AI safety or reliability.
Task-Phase-Conditioned Expert Routing Is Not a Reliable Proxy for Quantization Sensitivity in MoE FFN Tensors This paper shows that routing statistics across task phases fail to provide an effective guide for mixed-precision quantization in mixture-of-experts models. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Tests routing-based quantization sensitivity heuristics rather than broad LLM evaluation methodology.
Task-Phase-Conditioned Expert Routing Is Not a Reliable Proxy for Quantization Sensitivity in MoE FFN Tensors This paper shows that routing statistics across task phases fail to provide an effective guide for mixed-precision quantization in mixture-of-experts models. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Addresses MoE weight quantization techniques, not AI safety or alignment risks.
A Phenomenology of Neural Geometry in Vision-Language-Action Models This paper investigates internal geometric representations of task-relevant physical concepts within vision-language-action policies for robotic manipulation. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Analyzes internal geometric representations in VLA models, not general LLM evaluation benchmarks.
A Phenomenology of Neural Geometry in Vision-Language-Action Models This paper investigates internal geometric representations of task-relevant physical concepts within vision-language-action policies for robotic manipulation. Preprint	Computer vision for industrial applications via arxiv · 17 Oct, 06:18	Not relevant Focuses on robotic manipulation geometry rather than industrial visual inspection or defect detection.
A Phenomenology of Neural Geometry in Vision-Language-Action Models This paper investigates internal geometric representations of task-relevant physical concepts within vision-language-action policies for robotic manipulation. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Investigates representation geometry in VLA policies, not model compression or efficiency.
A Phenomenology of Neural Geometry in Vision-Language-Action Models This paper investigates internal geometric representations of task-relevant physical concepts within vision-language-action policies for robotic manipulation. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Examines internal representation geometry rather than safety guardrails, jailbreaks, or hallucinations.

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