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
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.
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.
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.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Applies to robot imitation learning rather than language model benchmarks.
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	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on quadrupedal robotics locomotion, not industrial visual inspection.
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	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on 2D data visualization methods, not efficient model deployment.
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.
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.
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.
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	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.
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.
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	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.
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.
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	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Deals with symbolic search spaces, not AI deployment safety.
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.
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	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.
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.
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	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	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	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	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on distribution tail modeling, not model compression.
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.
GpFlash: Maximizing Speed and Minimizing Memory for Gaussian Process Training and Prediction The paper introduces GpFlash, an open library designed to speed up Gaussian process training and prediction while reducing memory footprint. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Covers Gaussian process libraries, not LLM benchmark evaluation.
GpFlash: Maximizing Speed and Minimizing Memory for Gaussian Process Training and Prediction The paper introduces GpFlash, an open library designed to speed up Gaussian process training and prediction while reducing memory footprint. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on Gaussian processes rather than small language models.
GpFlash: Maximizing Speed and Minimizing Memory for Gaussian Process Training and Prediction The paper introduces GpFlash, an open library designed to speed up Gaussian process training and prediction while reducing memory footprint. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Deals with Gaussian process computing, not AI deployment safety.
Better together? A performance theory of human-machine skill policies under generalization difficulty The paper develops a formal performance theory analyzing skill allocation and workflow design in joint human-machine systems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on theoretical workflow economics rather than deployment safety.
Better together? A performance theory of human-machine skill policies under generalization difficulty The paper develops a formal performance theory analyzing skill allocation and workflow design in joint human-machine systems. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Covers task allocation theory, not model compression or efficiency.
Better together? A performance theory of human-machine skill policies under generalization difficulty The paper develops a formal performance theory analyzing skill allocation and workflow design in joint human-machine systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents theoretical framework for human-machine pairing, not LLM benchmarks.
Efficient Resource-Shiftable RL Post-training with Generative Rewards The paper presents PivotRL, a system that dynamically shifts GPU resources among rollout, reward modeling, and training during post-training. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Addresses training cluster resource scheduling rather than benchmark evaluation.
Efficient Resource-Shiftable RL Post-training with Generative Rewards The paper presents PivotRL, a system that dynamically shifts GPU resources among rollout, reward modeling, and training during post-training. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on cluster systems engineering rather than AI safety.
From Parallel to Sequential: Small-Batch Noise Restores Simplicity Bias of Muon The paper demonstrates that small-batch stochastic gradient noise restores sequential feature learning in the Muon optimizer. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Analyzes optimizer dynamics rather than language model evaluation methods.
From Parallel to Sequential: Small-Batch Noise Restores Simplicity Bias of Muon The paper demonstrates that small-batch stochastic gradient noise restores sequential feature learning in the Muon optimizer. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on optimizer theory rather than inference efficiency.
From Parallel to Sequential: Small-Batch Noise Restores Simplicity Bias of Muon The paper demonstrates that small-batch stochastic gradient noise restores sequential feature learning in the Muon optimizer. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies optimizer simplicity bias rather than deployment safety.
Sequence Composition in EHR Foundation Model Pretraining The paper analyzes sequence composition strategies during electronic health record foundation model pretraining to improve downstream clinical prediction. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies pretraining data composition rather than model compression methods.
Sequence Composition in EHR Foundation Model Pretraining The paper analyzes sequence composition strategies during electronic health record foundation model pretraining to improve downstream clinical prediction. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses EHR pretraining sequences rather than AI safety guardrails.
Sequence Composition in EHR Foundation Model Pretraining The paper analyzes sequence composition strategies during electronic health record foundation model pretraining to improve downstream clinical prediction. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Explores EHR pretraining sequences rather than general LLM benchmarking.
Learning Operators for Symmetry Discovery: From Lie Groups to Local Symmetry Geometry The paper formulates continuous symmetry discovery across function families as an inverse symmetry operator problem using differential characteristics. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with Lie groups and operators, not model efficiency.
Learning Operators for Symmetry Discovery: From Lie Groups to Local Symmetry Geometry The paper formulates continuous symmetry discovery across function families as an inverse symmetry operator problem using differential characteristics. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies mathematical continuous symmetry discovery, not LLM evaluation.
Learning Operators for Symmetry Discovery: From Lie Groups to Local Symmetry Geometry The paper formulates continuous symmetry discovery across function families as an inverse symmetry operator problem using differential characteristics. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on operator theory rather than deployment safety.
FORGE: Procedural Weight Generation for Sparse MoE Inference The paper introduces FORGE, an on-chip datapath that regenerates sparse mixture-of-experts weights from compact seeds during inference. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on hardware inference acceleration rather than safety risks.
FORGE: Procedural Weight Generation for Sparse MoE Inference The paper introduces FORGE, an on-chip datapath that regenerates sparse mixture-of-experts weights from compact seeds during inference. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes hardware weight generation rather than LLM benchmarking methods.

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