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
Counting with Attention and LayerNorm: Norm Scaling and Representation Geometry The paper studies sequence-length parameter bounds and representation geometry required for attention layers to perform counting tasks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Theoretical representation analysis of single-layer attention, not efficient model architectures.
Guidance Without Labels: Flow Matching from Coupled Gaussian Mixtures The paper applies optimal transport with Gaussian mixtures to improve flow matching sample efficiency without requiring training labels. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Deals with flow matching image generation, not LLM evaluation.
Guidance Without Labels: Flow Matching from Coupled Gaussian Mixtures The paper applies optimal transport with Gaussian mixtures to improve flow matching sample efficiency without requiring training labels. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Generative flow matching method, not model quantization, distillation, or serving.
Guidance Without Labels: Flow Matching from Coupled Gaussian Mixtures The paper applies optimal transport with Gaussian mixtures to improve flow matching sample efficiency without requiring training labels. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Image generation technique, not AI safety or deployment monitoring.
RFWeave: Learning Wireless Propagation Across Scenes and Transceiver Configurations The paper introduces a model that predicts wireless propagation across varying physical scenes, transceiver positions, and antenna configurations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Wireless signal modeling, not AI safety or deployment reliability.
RFWeave: Learning Wireless Propagation Across Scenes and Transceiver Configurations The paper introduces a model that predicts wireless propagation across varying physical scenes, transceiver positions, and antenna configurations. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Applies deep learning to wireless propagation, not small efficient models.
RFWeave: Learning Wireless Propagation Across Scenes and Transceiver Configurations The paper introduces a model that predicts wireless propagation across varying physical scenes, transceiver positions, and antenna configurations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Wireless propagation modeling paper, not language model evaluation.
Compression Through Time: An Interplay of Separability, Stationarity and Capacity The paper investigates information-theoretic and geometric compression mechanisms over time within recurrent neural networks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies generalization dynamics in LSTMs, not LLM evaluation benchmarks.
Compression Through Time: An Interplay of Separability, Stationarity and Capacity The paper investigates information-theoretic and geometric compression mechanisms over time within recurrent neural networks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Investigates theoretical compression in recurrent networks, not safety guardrails.
Compression Through Time: An Interplay of Separability, Stationarity and Capacity The paper investigates information-theoretic and geometric compression mechanisms over time within recurrent neural networks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Information-theoretic analysis of LSTM dynamics, not model compression or deployment.
Making LoRA Identifiable: Orthogonal Alignment for Continual Learning The paper introduces an orthogonal parameter alignment approach for low-rank adaptation to mitigate catastrophic forgetting in continual learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on continual parameter-efficient fine-tuning, not LLM benchmark evaluation.
Making LoRA Identifiable: Orthogonal Alignment for Continual Learning The paper introduces an orthogonal parameter alignment approach for low-rank adaptation to mitigate catastrophic forgetting in continual learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses catastrophic forgetting in continual learning, not deployment safety risks.
Amortized Variational Inference of Latent Intensities from Replicated Point Patterns The paper develops an amortized variational inference method to estimate latent point process intensities from replicated event patterns. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies latent point processes, not language model evaluation or benchmarks.
Amortized Variational Inference of Latent Intensities from Replicated Point Patterns The paper develops an amortized variational inference method to estimate latent point process intensities from replicated event patterns. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on Bayesian point process inference, not LLM efficiency.
Amortized Variational Inference of Latent Intensities from Replicated Point Patterns The paper develops an amortized variational inference method to estimate latent point process intensities from replicated event patterns. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Concerns spatial-temporal point processes, not deployment AI safety.
Spectral Costs of Multinomial and Residual Resampling in High-Dimensional Particle Ensembles: A Free-Probability Analysis The paper analyzes the spectral properties and limits of multinomial and residual resampling in high-dimensional particle filtering. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies particle filter resampling theory, not language model evaluation.
Spectral Costs of Multinomial and Residual Resampling in High-Dimensional Particle Ensembles: A Free-Probability Analysis The paper analyzes the spectral properties and limits of multinomial and residual resampling in high-dimensional particle filtering. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Mathematical theory of particle filters, not AI safety deployment.
Spectral Costs of Multinomial and Residual Resampling in High-Dimensional Particle Ensembles: A Free-Probability Analysis The paper analyzes the spectral properties and limits of multinomial and residual resampling in high-dimensional particle filtering. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Mathematical analysis of particle ensembles, not efficient model inference.
Universal Distributed Feature Extraction Complementary to Side Information via H-Score The paper introduces a nested H-score method for distributed feature extraction that avoids redundancy with receiver-side auxiliary information. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on distributed estimation theory, not AI safety guardrails.
Universal Distributed Feature Extraction Complementary to Side Information via H-Score The paper introduces a nested H-score method for distributed feature extraction that avoids redundancy with receiver-side auxiliary information. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Information-theoretic distributed feature extraction, not small or efficient models.
Universal Distributed Feature Extraction Complementary to Side Information via H-Score The paper introduces a nested H-score method for distributed feature extraction that avoids redundancy with receiver-side auxiliary information. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Deals with information theory in distributed estimation, not LLM evaluation.
Amortised adaptive importance sampling The paper proposes an amortized adaptive importance sampling method using a lightweight neural network to iteratively update proposal distributions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Statistical sampling paper rather than AI safety or reliability.
Amortised adaptive importance sampling The paper proposes an amortized adaptive importance sampling method using a lightweight neural network to iteratively update proposal distributions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents Monte Carlo sampling algorithms, not LLM evaluation benchmarks.
Amortised adaptive importance sampling The paper proposes an amortized adaptive importance sampling method using a lightweight neural network to iteratively update proposal distributions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Covers statistical adaptive importance sampling, not model compression or serving.
CopyQuad: Quadrupedal Skill Transfer from a Single Side-View Video Without a Depth Model The paper transfers quadrupedal locomotion skills from single side-view videos to robots by pinning unobserved lateral motion to stance width. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on quadrupedal locomotion transfer, not deployment safety systems.
CopyQuad: Quadrupedal Skill Transfer from a Single Side-View Video Without a Depth Model The paper transfers quadrupedal locomotion skills from single side-view videos to robots by pinning unobserved lateral motion to stance width. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant About robot kinematics without depth models, not LLM efficiency.
CopyQuad: Quadrupedal Skill Transfer from a Single Side-View Video Without a Depth Model The paper transfers quadrupedal locomotion skills from single side-view videos to robots by pinning unobserved lateral motion to stance width. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on quadrupedal robotics locomotion, not industrial visual inspection.
CopyQuad: Quadrupedal Skill Transfer from a Single Side-View Video Without a Depth Model The paper transfers quadrupedal locomotion skills from single side-view videos to robots by pinning unobserved lateral motion to stance width. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Applies to robot imitation learning rather than language model benchmarks.
NFMap: Topographic 2D Manifold Learning with Invertible Normalizing Flows The paper presents an invertible normalizing flow method for two-dimensional topographic manifold learning and data visualization. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Deals with 2D manifold dimensionality reduction, not LLM evaluation.
NFMap: Topographic 2D Manifold Learning with Invertible Normalizing Flows The paper presents an invertible normalizing flow method for two-dimensional topographic manifold learning and data visualization. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies 2D manifold learning rather than AI safety guardrails.
NFMap: Topographic 2D Manifold Learning with Invertible Normalizing Flows The paper presents an invertible normalizing flow method for two-dimensional topographic manifold learning and data visualization. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on 2D data visualization methods, not efficient model deployment.
Efficient Preconditioned Optimization Methods with Frequent Directions The paper integrates matrix sketching via frequent directions into adaptive optimization methods to reduce preconditioning computation overhead. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant General mathematical optimization paper rather than small models, distillation, or serving.
Efficient Preconditioned Optimization Methods with Frequent Directions The paper integrates matrix sketching via frequent directions into adaptive optimization methods to reduce preconditioning computation overhead. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes general optimization algorithms, not LLM evaluation frameworks.
Efficient Preconditioned Optimization Methods with Frequent Directions The paper integrates matrix sketching via frequent directions into adaptive optimization methods to reduce preconditioning computation overhead. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Concerns optimization theory, not deployment safety or AI reliability.
One-Layer Transformers Provably Learn In-Context K-Nearest Neighbor Prediction with Chain-of-Thought The paper theoretically analyzes how one-layer transformers learn in-context nearest-neighbor prediction when using chain-of-thought reasoning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on mathematical theory of in-context learning, not LLM evaluation benchmarks.
One-Layer Transformers Provably Learn In-Context K-Nearest Neighbor Prediction with Chain-of-Thought The paper theoretically analyzes how one-layer transformers learn in-context nearest-neighbor prediction when using chain-of-thought reasoning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Theoretical reasoning analysis, not deployment safety, guardrails, or hallucination.
One-Layer Transformers Provably Learn In-Context K-Nearest Neighbor Prediction with Chain-of-Thought The paper theoretically analyzes how one-layer transformers learn in-context nearest-neighbor prediction when using chain-of-thought reasoning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Theoretical analysis of one-layer transformers rather than practical model efficiency or compression.
When Repeated Measurements Hurt Tabular Foundation Models The paper studies how repeated input measurements can degrade out-of-sample prediction quality in tabular foundation models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies tabular foundation model behavior, not model compression.
Predicting Audio Popularity: A Comparative Study of Metadata and Acoustic Features Across Music and Sound Effects The paper evaluates machine learning models on metadata and acoustic features to predict audio popularity on an online sharing platform. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Predicts online audio popularity, not LLM evaluation.
Predicting Audio Popularity: A Comparative Study of Metadata and Acoustic Features Across Music and Sound Effects The paper evaluates machine learning models on metadata and acoustic features to predict audio popularity on an online sharing platform. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on audio popularity prediction, not AI deployment safety.
Predicting Audio Popularity: A Comparative Study of Metadata and Acoustic Features Across Music and Sound Effects The paper evaluates machine learning models on metadata and acoustic features to predict audio popularity on an online sharing platform. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Applies standard ML to audio data, not model compression.
DASS: A Solver-Adaptable Dynamic Auxiliary Search Strategy for Symbolic Regression The paper proposes a dynamic auxiliary search strategy to guide symbolic regression solvers by constructing a high-potential functional subspace. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses symbolic regression search, not model efficiency.
DASS: A Solver-Adaptable Dynamic Auxiliary Search Strategy for Symbolic Regression The paper proposes a dynamic auxiliary search strategy to guide symbolic regression solvers by constructing a high-potential functional subspace. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on symbolic regression algorithms, not LLM benchmarking.
DASS: A Solver-Adaptable Dynamic Auxiliary Search Strategy for Symbolic Regression The paper proposes a dynamic auxiliary search strategy to guide symbolic regression solvers by constructing a high-potential functional subspace. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Deals with symbolic search spaces, not AI deployment safety.
On the Calibration of Crowdsourcing Aggregators The paper provides theoretical and empirical analyses of calibration in crowdsourced label aggregation across human and language model datasets. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on label aggregation calibration, not model compression.
Statistical Downscaling of Daily Precipitation from ERA5 Atmospheric Fields to IMERG over West Africa The paper evaluates neural network architectures to downscale ERA5 atmospheric data to high-resolution satellite precipitation estimates across West Africa. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on climate modeling, not AI deployment safety.
Statistical Downscaling of Daily Precipitation from ERA5 Atmospheric Fields to IMERG over West Africa The paper evaluates neural network architectures to downscale ERA5 atmospheric data to high-resolution satellite precipitation estimates across West Africa. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Applies U-Nets to meteorology, not efficient LLM serving.
Statistical Downscaling of Daily Precipitation from ERA5 Atmospheric Fields to IMERG over West Africa The paper evaluates neural network architectures to downscale ERA5 atmospheric data to high-resolution satellite precipitation estimates across West Africa. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Covers weather precipitation downscaling, not LLM benchmarking.
Calibrated $α$-Barycenters for Multiobjective $f$-Divergence Optimization The paper develops a calibrated barycenter framework to accelerate multiobjective optimization over divergence matching problems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Mathematical multiobjective optimization, not AI deployment safety.
Calibrated $α$-Barycenters for Multiobjective $f$-Divergence Optimization The paper develops a calibrated barycenter framework to accelerate multiobjective optimization over divergence matching problems. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with abstract divergence optimization, not model compression.
Calibrated $α$-Barycenters for Multiobjective $f$-Divergence Optimization The paper develops a calibrated barycenter framework to accelerate multiobjective optimization over divergence matching problems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on mathematical optimization of f-divergences, not LLM evaluation.
Sample-Size Extrapolation in Simulation-Based Inference via Statistical Scaling The paper introduces a center-estimated neural posterior estimator to improve sample-size extrapolation in simulation-based inference. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies statistical posterior estimation, not model efficiency.
Sample-Size Extrapolation in Simulation-Based Inference via Statistical Scaling The paper introduces a center-estimated neural posterior estimator to improve sample-size extrapolation in simulation-based inference. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on simulation-based inference rather than LLM benchmarking.
Sample-Size Extrapolation in Simulation-Based Inference via Statistical Scaling The paper introduces a center-estimated neural posterior estimator to improve sample-size extrapolation in simulation-based inference. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Investigates neural posterior estimators, not AI deployment safety.
Mean Accuracy Is the Wrong Estimand for a Release Decision The paper shows mean benchmark accuracy misses tail risks in fine-tuned models, proposing recipe certification cards for deployment decisions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses evaluation methodology rather than model compression or inference.
Feature Normalization under Finite Training Budgets The paper analyzes when feature standardization improves risk bounds for rare subgroups under finite stochastic gradient training budgets. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies SGD feature standardization theory, not LLM evaluation.
Feature Normalization under Finite Training Budgets The paper analyzes when feature standardization improves risk bounds for rare subgroups under finite stochastic gradient training budgets. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Theoretical statistics on feature normalization, not efficient model serving.
Feature Normalization under Finite Training Budgets The paper analyzes when feature standardization improves risk bounds for rare subgroups under finite stochastic gradient training budgets. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Investigates statistical optimization theory rather than AI deployment risks.
Radial Tail Repair: Post-Training Correction for Diffusion and Flow Models The paper proposes a post-training tail correction method for diffusion and flow models using radial-angular extreme value decomposition. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies diffusion tail statistics rather than AI deployment safety.
Radial Tail Repair: Post-Training Correction for Diffusion and Flow Models The paper proposes a post-training tail correction method for diffusion and flow models using radial-angular extreme value decomposition. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Addresses distribution correction in diffusion models, not LLM evaluation.

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