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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
Explore This! Beat and Downbeat Tracking From a Learned Tatum Grid This paper proposes a neural architecture that predicts a tatum grid to track musical beats and downbeats without dynamic Bayesian networks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Investigates audio music tracking, not LLM evaluation frameworks.
Sequence-Structured Decision Learning for Branch-and-Bound in Mixed-Integer Linear Programming This paper introduces a sequence-structured framework that models the global search trajectory to learn branching and node selection in branch-and-bound. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on algorithmic search efficiency, not language model compression.
Sequence-Structured Decision Learning for Branch-and-Bound in Mixed-Integer Linear Programming This paper introduces a sequence-structured framework that models the global search trajectory to learn branching and node selection in branch-and-bound. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses combinatorial optimization decisions, not deployed AI safety risks.
Sequence-Structured Decision Learning for Branch-and-Bound in Mixed-Integer Linear Programming This paper introduces a sequence-structured framework that models the global search trajectory to learn branching and node selection in branch-and-bound. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Concerns mixed-integer linear programming solvers, not LLM benchmark evaluation.
Physics-informed random neural architectures on compact boundaryless multiply connected manifolds for random-operator models in machine learning and uncertainty quantification This paper develops stochastic physics-informed neural networks on manifolds to solve stochastic boundary-value problems with random operators. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with scientific computing architectures, not model compression or efficiency.
Physics-informed random neural architectures on compact boundaryless multiply connected manifolds for random-operator models in machine learning and uncertainty quantification This paper develops stochastic physics-informed neural networks on manifolds to solve stochastic boundary-value problems with random operators. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies stochastic boundary value problems, not deployment safety or guardrails.
Physics-informed random neural architectures on compact boundaryless multiply connected manifolds for random-operator models in machine learning and uncertainty quantification This paper develops stochastic physics-informed neural networks on manifolds to solve stochastic boundary-value problems with random operators. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on solving physics differential equations, not LLM benchmarking.
Disentangling Marginal Capacity from Geometry in Optimal-Transport Label Transfer This paper analyzes error patterns from class frequency imbalances in optimal-transport label transfer and proposes matching target composition marginals. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on distribution alignment in label transfer, not efficient model architectures or inference.
Disentangling Marginal Capacity from Geometry in Optimal-Transport Label Transfer This paper analyzes error patterns from class frequency imbalances in optimal-transport label transfer and proposes matching target composition marginals. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies optimal transport label transfer under class imbalance, not LLM evaluation.
Disentangling Marginal Capacity from Geometry in Optimal-Transport Label Transfer This paper analyzes error patterns from class frequency imbalances in optimal-transport label transfer and proposes matching target composition marginals. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Analyzes label shift in optimal transport, not AI deployment safety or guardrails.
Terrarium: Growing Personal Assistants via Scalable Synthesis of App Worlds This paper presents a framework to synthesize executable application environments and training tasks from web information for personal language agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on synthetic training environments for personal assistants, not AI safety or guardrails.
Terrarium: Growing Personal Assistants via Scalable Synthesis of App Worlds This paper presents a framework to synthesize executable application environments and training tasks from web information for personal language agents. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on synthesizing environment suites for assistants, not model compression or efficient inference.
World2TS: Real-World Event Context for Time-Series Forecasting This paper introduces a benchmark and dataset that pairs numerical time-series data with real-world event context for forecasting. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Introduces a context dataset for time-series forecasting, not LLM evaluation or benchmarks.
World2TS: Real-World Event Context for Time-Series Forecasting This paper introduces a benchmark and dataset that pairs numerical time-series data with real-world event context for forecasting. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses context alignment in time-series forecasting, not AI deployment safety or guardrails.
World2TS: Real-World Event Context for Time-Series Forecasting This paper introduces a benchmark and dataset that pairs numerical time-series data with real-world event context for forecasting. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on time-series forecasting datasets, not small or efficient model architectures.
Blending experimental electron density information to protein-ligand interaction This paper introduces a pre-trained vision transformer that combines experimental electron-density maps with atomic coordinates to model protein-ligand interactions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on protein-ligand interaction models, not language model evaluation or benchmarks.
Blending experimental electron density information to protein-ligand interaction This paper introduces a pre-trained vision transformer that combines experimental electron-density maps with atomic coordinates to model protein-ligand interactions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies multimodal molecular structural representations, not efficient LLM inference or compression.
Blending experimental electron density information to protein-ligand interaction This paper introduces a pre-trained vision transformer that combines experimental electron-density maps with atomic coordinates to model protein-ligand interactions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses biomolecular binding affinity models, not AI deployment safety or guardrails.
Contrastive Linearized Laplace: Loss Geometry, Valid Repairs, and Reliable Cores This paper proposes a post-hoc framework based on Linearized Laplace approximation to quantify uncertainty across downstream tasks in contrastive learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents uncertainty quantification for contrastive embeddings, not LLM evaluation benchmarks.
Contrastive Linearized Laplace: Loss Geometry, Valid Repairs, and Reliable Cores This paper proposes a post-hoc framework based on Linearized Laplace approximation to quantify uncertainty across downstream tasks in contrastive learning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with post-hoc Laplace approximations, not model distillation, pruning, or efficiency.
Pro-t-VAE: Closed-Form Student's t Mixtures for Robust Prototype Discovery This paper introduces a variational autoencoder using a closed-form Student's t mixture prior for robust unsupervised image prototype discovery. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on unsupervised image clustering via VAEs, not LLM evaluation or benchmarks.
Pro-t-VAE: Closed-Form Student's t Mixtures for Robust Prototype Discovery This paper introduces a variational autoencoder using a closed-form Student's t mixture prior for robust unsupervised image prototype discovery. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies Student's t mixture VAE formulations, not efficient LLM serving or compression.
Pro-t-VAE: Closed-Form Student's t Mixtures for Robust Prototype Discovery This paper introduces a variational autoencoder using a closed-form Student's t mixture prior for robust unsupervised image prototype discovery. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses prototype discovery in computer vision, not AI deployment safety or guardrails.
FDTD Model of Noise Pollution from an AI Data Center This paper models low-frequency sound propagation over surrounding topography to evaluate noise pollution caused by AI data centers. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Models physical acoustic noise around data centers, not small or efficient AI architectures.
FDTD Model of Noise Pollution from an AI Data Center This paper models low-frequency sound propagation over surrounding topography to evaluate noise pollution caused by AI data centers. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies acoustic noise propagation of data centers, not LLM benchmarks or evaluation.
FDTD Model of Noise Pollution from an AI Data Center This paper models low-frequency sound propagation over surrounding topography to evaluate noise pollution caused by AI data centers. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on environmental acoustic noise, not AI model safety, guardrails, or alignment.
Prediction with Expert Advice under Heavy-Tailed Losses This paper establishes regret bounds and develops adaptive algorithms for prediction with expert advice under heavy-tailed losses. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies multi-armed bandit/expert advice theory, not model compression or efficient inference.
Prediction with Expert Advice under Heavy-Tailed Losses This paper establishes regret bounds and develops adaptive algorithms for prediction with expert advice under heavy-tailed losses. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents theoretical bounds for online learning, not language model evaluation or benchmarks.
Prediction with Expert Advice under Heavy-Tailed Losses This paper establishes regret bounds and develops adaptive algorithms for prediction with expert advice under heavy-tailed losses. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on theoretical regret bounds under heavy-tailed loss, not AI deployment safety.
Minimum Volume Optimal Transport for Multivariate Conformal Prediction This paper presents a framework to minimize prediction-set volume in multivariate conformal prediction by learning a Mahalanobis ground cost. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies multivariate conformal prediction theory, not LLM benchmarks or evaluation methods.
Minimum Volume Optimal Transport for Multivariate Conformal Prediction This paper presents a framework to minimize prediction-set volume in multivariate conformal prediction by learning a Mahalanobis ground cost. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on optimal transport conformal sets, not efficient model compression or serving.
Minimum Volume Optimal Transport for Multivariate Conformal Prediction This paper presents a framework to minimize prediction-set volume in multivariate conformal prediction by learning a Mahalanobis ground cost. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies mathematical conformal prediction sets, not AI deployment safety or guardrails.
Representation Drift in Continual Learning: LoRA versus N-gram Memory Measures representation drift across continual learning methods, finding n-gram memory updates cause less internal drift than LoRA. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies representation drift across training mechanisms, not benchmark methodology or saturation.
TLA-Verus: Generating and Solving Temporal Proof Obligations with LLM Agents Uses language model agents and deterministic translation to convert TLA+ formal specifications into machine-checkable Verus proofs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies software specification translation, not AI model deployment safety or guardrails.
TLA-Verus: Generating and Solving Temporal Proof Obligations with LLM Agents Uses language model agents and deterministic translation to convert TLA+ formal specifications into machine-checkable Verus proofs. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on formal software verification with LLMs, not model compression or efficient serving.
An Algebraic Approach to Constrained K-means Clustering Uses Gröbner bases to simplify pairwise constraints and improve assignment efficiency in constrained k-means clustering. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on constrained k-means clustering, not LLM evaluation or benchmarks.
An Algebraic Approach to Constrained K-means Clustering Uses Gröbner bases to simplify pairwise constraints and improve assignment efficiency in constrained k-means clustering. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses algebraic clustering algorithms, not AI safety or deployment reliability.
An Algebraic Approach to Constrained K-means Clustering Uses Gröbner bases to simplify pairwise constraints and improve assignment efficiency in constrained k-means clustering. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies k-means clustering constraints, not small or efficient language models.
BSIPipe: Bidirectional Sequence-Level Interleaved Pipeline Parallelism for Long-Sequence Large Language Model Training Introduces bidirectional interleaved pipeline parallelism to improve throughput and activation memory scaling in long-sequence model training. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on training throughput and parallelism, not AI safety, guardrails, or hallucination.
BSIPipe: Bidirectional Sequence-Level Interleaved Pipeline Parallelism for Long-Sequence Large Language Model Training Introduces bidirectional interleaved pipeline parallelism to improve throughput and activation memory scaling in long-sequence model training. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents a pipeline parallelism training system, not an LLM benchmark or evaluation.
CFG-Policy: Unified Diffusion Policy Learning Framework via Classifier-free Guidance Applies classifier-free guidance to diffusion policies to separate reinforcement learning exploration from core task capabilities. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Investigates policy exploration in RL, not AI safety, alignment, or guardrails.
CFG-Policy: Unified Diffusion Policy Learning Framework via Classifier-free Guidance Applies classifier-free guidance to diffusion policies to separate reinforcement learning exploration from core task capabilities. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies diffusion policy exploration in robotics, not efficient LLM compression or inference.
CFG-Policy: Unified Diffusion Policy Learning Framework via Classifier-free Guidance Applies classifier-free guidance to diffusion policies to separate reinforcement learning exploration from core task capabilities. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Deals with reinforcement learning exploration methods, not LLM benchmark evaluation.
CFG-Policy: Unified Diffusion Policy Learning Framework via Classifier-free Guidance Applies classifier-free guidance to diffusion policies to separate reinforcement learning exploration from core task capabilities. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on robotic manipulation and RL exploration, not industrial defect inspection.
Compositional Average Velocity Modeling for Efficient Diffusion Offline Reinforcement Learning Proposes a single-step generative policy for offline reinforcement learning by modeling average velocity instead of iterative denoising. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on offline reinforcement learning policies, not LLM evaluation or benchmarks.
Compositional Average Velocity Modeling for Efficient Diffusion Offline Reinforcement Learning Proposes a single-step generative policy for offline reinforcement learning by modeling average velocity instead of iterative denoising. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Examines reinforcement learning diffusion sampling, not efficient LLM inference or distillation.
Compositional Average Velocity Modeling for Efficient Diffusion Offline Reinforcement Learning Proposes a single-step generative policy for offline reinforcement learning by modeling average velocity instead of iterative denoising. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Investigates diffusion policy training in RL, not AI safety or deployment guardrails.
Exact Phase-Type Point Processes for Certified Measurement and Prediction of Event Streams Formulates a phase-type Hawkes process model with certified convex optimization to measure and predict financial event streams. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on financial market point processes, not LLM benchmarking or evaluation.
Exact Phase-Type Point Processes for Certified Measurement and Prediction of Event Streams Formulates a phase-type Hawkes process model with certified convex optimization to measure and predict financial event streams. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses temporal point processes in finance, not efficient model architectures or compression.
Exact Phase-Type Point Processes for Certified Measurement and Prediction of Event Streams Formulates a phase-type Hawkes process model with certified convex optimization to measure and predict financial event streams. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Analyzes macroeconomic event streams, not AI safety, hallucination, or guardrails.
Cross-Material Magnetic Core Loss Prediction via Operating-Condition Matching and Partial Optimal Transport Predicts magnetic core losses across materials using partial optimal transport to align operating conditions with limited data. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on magnetic core loss prediction, not LLM benchmarks or evaluation.
Cross-Material Magnetic Core Loss Prediction via Operating-Condition Matching and Partial Optimal Transport Predicts magnetic core losses across materials using partial optimal transport to align operating conditions with limited data. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Applies optimal transport to magnetic loss, not efficient model compression or serving.
Cross-Material Magnetic Core Loss Prediction via Operating-Condition Matching and Partial Optimal Transport Predicts magnetic core losses across materials using partial optimal transport to align operating conditions with limited data. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies magnetic core loss prediction, not AI deployment safety or guardrails.
Characterizing the Separability Gap in Prior-Data Fitted Networks Identifies how non-separable priors in prior-data fitted networks affect predictive accuracy under model misspecification. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies Bayesian in-context learning theory, not model compression or efficient inference.
Characterizing the Separability Gap in Prior-Data Fitted Networks Identifies how non-separable priors in prior-data fitted networks affect predictive accuracy under model misspecification. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Analyzes Bayesian prior-data fitted networks, not language model evaluation benchmarks.
Characterizing the Separability Gap in Prior-Data Fitted Networks Identifies how non-separable priors in prior-data fitted networks affect predictive accuracy under model misspecification. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Investigates prior non-separability in tabular PFNs, not AI safety or deployment robustness.
DiffGA: Steering Frozen Diffusion Models for Materials Design via Evolutionary Search Steers frozen diffusion models toward black-box materials design goals using evolutionary search along sampling trajectories. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies candidate generation for materials, not AI deployment safety or guardrails.
DiffGA: Steering Frozen Diffusion Models for Materials Design via Evolutionary Search Steers frozen diffusion models toward black-box materials design goals using evolutionary search along sampling trajectories. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on inverse materials design rather than evaluating language models.
DiffGA: Steering Frozen Diffusion Models for Materials Design via Evolutionary Search Steers frozen diffusion models toward black-box materials design goals using evolutionary search along sampling trajectories. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Pertains to evolutionary search in materials diffusion, not small or efficient models.
DisCo Algebra: Why Disintegration and Combination Are Not Inverses Introduces an algebraic framework proving information loss asymmetry when decomposing and reconstructing joint probability distributions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Investigates theoretical copulas and operads, not AI deployment safety or guardrails.

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