Every paper checked — nothing hidden

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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
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	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.
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.
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	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	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	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.
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.
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	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.
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.
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.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on constrained k-means clustering, not LLM evaluation or benchmarks.
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.
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.
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	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	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	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.
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.
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.
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.
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.
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.
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	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	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	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	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	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	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with abstract probability compression, not efficient LLM serving or quantization.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on theoretical probability operads, not LLM benchmarks or evaluation.
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.
Conformal Sequential Decision Making With Cost and Sublinear Policy Regret Guarantees Develops an episodic sequential decision agent providing hard budget guarantees and sublinear regret bounds using conformal prediction. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on sequential decision algorithms, not LLM evaluation.
Conformal Sequential Decision Making With Cost and Sublinear Policy Regret Guarantees Develops an episodic sequential decision agent providing hard budget guarantees and sublinear regret bounds using conformal prediction. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies formal regret bounds for agents, not deployment safety guardrails.
Conformal Sequential Decision Making With Cost and Sublinear Policy Regret Guarantees Develops an episodic sequential decision agent providing hard budget guarantees and sublinear regret bounds using conformal prediction. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies online conformal regret algorithms, not model efficiency.
Algorithmic Fairness Over Time: Advances & Prospects Presents a taxonomy and research roadmap for measuring algorithmic fairness over time in systems with feedback loops. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Surveys algorithmic fairness taxonomy, not LLM benchmark evaluation.
Algorithmic Fairness Over Time: Advances & Prospects Presents a taxonomy and research roadmap for measuring algorithmic fairness over time in systems with feedback loops. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses algorithmic fairness over time, not model compression.
PATTERN Can Be Solved from Memory: Generator-Aware Baselines for Synthetic Node Classification Shows the PATTERN graph benchmark can be solved by memorization because pattern subgraphs are reused across splits. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Analyzes benchmark contamination, not safety guardrails or alignment.
PATTERN Can Be Solved from Memory: Generator-Aware Baselines for Synthetic Node Classification Shows the PATTERN graph benchmark can be solved by memorization because pattern subgraphs are reused across splits. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Critiques graph benchmarks, not model compression or efficiency.
Sparsity Saves Compute, Not Samples: Oracle Inequalities for Dense and Sparse Mixture of Experts Provides statistical error bounds for dense and sparse mixture-of-experts models, showing sparsity reduces compute rather than sample complexity. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Statistical study of MoE estimation, not LLM benchmarks.
Sparsity Saves Compute, Not Samples: Oracle Inequalities for Dense and Sparse Mixture of Experts Provides statistical error bounds for dense and sparse mixture-of-experts models, showing sparsity reduces compute rather than sample complexity. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Theoretical statistical analysis of MoE bounds, not model efficiency.
Sparsity Saves Compute, Not Samples: Oracle Inequalities for Dense and Sparse Mixture of Experts Provides statistical error bounds for dense and sparse mixture-of-experts models, showing sparsity reduces compute rather than sample complexity. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on statistical oracle inequalities, not AI safety risks.
Information-Theoretic Foundations of Online Temperature Sampling Formulates an online sampling algorithm that adjusts temperature dynamically under cross-entropy budgets to prevent repetitive language model output. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Analyzes repetitive generation decoding theory, not deployment safety guardrails.
Information-Theoretic Foundations of Online Temperature Sampling Formulates an online sampling algorithm that adjusts temperature dynamically under cross-entropy budgets to prevent repetitive language model output. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies online temperature sampling theory, not LLM evaluation.
Information-Theoretic Foundations of Online Temperature Sampling Formulates an online sampling algorithm that adjusts temperature dynamically under cross-entropy budgets to prevent repetitive language model output. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Theoretical study of entropy in sampling, not model compression.
The Optimal Decoder is Tilted Temperature Derives a verifier-tilted temperature sampling formula that optimizes pass-at-k metrics under entropy diversity and plausibility constraints. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Analyzes sampling temperature optimization, not AI safety or guardrails.
The Optimal Decoder is Tilted Temperature Derives a verifier-tilted temperature sampling formula that optimizes pass-at-k metrics under entropy diversity and plausibility constraints. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Explores sampling temperature theory, not small or compressed models.
The Optimal Decoder is Tilted Temperature Derives a verifier-tilted temperature sampling formula that optimizes pass-at-k metrics under entropy diversity and plausibility constraints. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies decoding temperature formulations, not benchmark evaluation design.
Toward a Multimodal Foundation Model for Plasma State Reconstruction Reconstructs magnetic reconnection plasma states from sparse multimodal observations using a residual 3D U-Net architecture. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on plasma state reconstruction, not model compression.
Toward a Multimodal Foundation Model for Plasma State Reconstruction Reconstructs magnetic reconnection plasma states from sparse multimodal observations using a residual 3D U-Net architecture. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Applies U-Net to plasma physics, not LLM evaluation.

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