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
Learning Kilometer-Scale Weather Prediction with Global-Regional Alignment This paper introduces a regional weather forecasting framework that aligns pretrained global model guidance with high-resolution regional representations. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Deals with meteorological forecasting rather than AI deployment safety or guardrails.
A Unified Bellman Operator for Safety-Critical Reinforcement Learning This paper develops a unified Bellman operator to optimize task performance and enforce constraints within safety-critical reinforcement learning. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Presents a safe RL theoretical operator rather than LLM benchmarking.
A Unified Bellman Operator for Safety-Critical Reinforcement Learning This paper develops a unified Bellman operator to optimize task performance and enforce constraints within safety-critical reinforcement learning. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on reinforcement learning algorithms rather than small models or efficient serving.
Transformer-Based Masked Diffusion Models Provably Learn Distributions with Latent Structure This paper provides statistical sample complexity guarantees for transformer-based masked diffusion models learning distributions with latent tree structures. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant A theoretical statistical analysis of masked diffusion, not practical model efficiency or compression.
Transformer-Based Masked Diffusion Models Provably Learn Distributions with Latent Structure This paper provides statistical sample complexity guarantees for transformer-based masked diffusion models learning distributions with latent tree structures. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on theoretical statistical guarantees, not safety, guardrails, or alignment.
Transformer-Based Masked Diffusion Models Provably Learn Distributions with Latent Structure This paper provides statistical sample complexity guarantees for transformer-based masked diffusion models learning distributions with latent tree structures. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Presents theoretical sample complexity bounds for masked diffusion, not LLM evaluation.
Toward Joint Optimization of Circuit Depth and Training Data Size in Adaptively Grown Quantum Classifiers This paper investigates the relationship between circuit depth, training dataset size, and generalization bounds in adaptively grown quantum classifiers. Peer-reviewed	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on quantum classifier scaling laws, not AI safety or deployment reliability.
Toward Joint Optimization of Circuit Depth and Training Data Size in Adaptively Grown Quantum Classifiers This paper investigates the relationship between circuit depth, training dataset size, and generalization bounds in adaptively grown quantum classifiers. Peer-reviewed	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Investigates quantum machine learning circuits rather than efficient classical neural networks.
Toward Joint Optimization of Circuit Depth and Training Data Size in Adaptively Grown Quantum Classifiers This paper investigates the relationship between circuit depth, training dataset size, and generalization bounds in adaptively grown quantum classifiers. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Studies quantum circuit generalization on MNIST rather than LLM evaluation.
FAITH: Feasibility-Aware Safety-Filtered RL for High-Dimensional Systems This paper presents a feasibility-aware safety filter that amortizes minimal-intervention constraints using a feedforward neural network in reinforcement learning. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:29	Not relevant Focuses on RL safety filtering for physical systems rather than industrial computer vision.
FAITH: Feasibility-Aware Safety-Filtered RL for High-Dimensional Systems This paper presents a feasibility-aware safety filter that amortizes minimal-intervention constraints using a feedforward neural network in reinforcement learning. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Does not study small language models or efficient deployment techniques.
FAITH: Feasibility-Aware Safety-Filtered RL for High-Dimensional Systems This paper presents a feasibility-aware safety filter that amortizes minimal-intervention constraints using a feedforward neural network in reinforcement learning. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Proposes a safe RL framework rather than LLM benchmarking or evaluation.
VioLA: Learning Generalist Humanoid Control Policies from Human Data This paper trains a generalist humanoid policy by predicting body and hand motion latents mapped from human demonstration data. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on learning generalist robot control policies rather than deployment safety mechanisms.
VioLA: Learning Generalist Humanoid Control Policies from Human Data This paper trains a generalist humanoid policy by predicting body and hand motion latents mapped from human demonstration data. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Develops a humanoid control policy without focusing on LLM evaluation or benchmarks.
VioLA: Learning Generalist Humanoid Control Policies from Human Data This paper trains a generalist humanoid policy by predicting body and hand motion latents mapped from human demonstration data. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:29	Not relevant Focuses on humanoid locomotion and motor control, not industrial computer vision.
VioLA: Learning Generalist Humanoid Control Policies from Human Data This paper trains a generalist humanoid policy by predicting body and hand motion latents mapped from human demonstration data. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Does not address model compression, pruning, or efficient inference serving.
Density Ratio Estimation with Stein Displacement Fields This paper estimates density ratios by parameterizing distribution shift as a displacement field using the Stein operator. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Investigates mathematical density ratio estimation, not language model evaluation.
Density Ratio Estimation with Stein Displacement Fields This paper estimates density ratios by parameterizing distribution shift as a displacement field using the Stein operator. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Covers distribution shift estimation in statistics, not deployment guardrails or AI safety.
Density Ratio Estimation with Stein Displacement Fields This paper estimates density ratios by parameterizing distribution shift as a displacement field using the Stein operator. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Presents theoretical density ratio algorithms rather than model compression or efficient inference.
Rounding in Preconditioner Space: Redesigning 4-bit AdamW Optimizer-State Quantization This paper redesigns 4-bit AdamW optimizer-state quantization by performing stochastic rounding directly within preconditioner space. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Deals with numerical optimization and quantization, not AI deployment safety or robustness.
Rounding in Preconditioner Space: Redesigning 4-bit AdamW Optimizer-State Quantization This paper redesigns 4-bit AdamW optimizer-state quantization by performing stochastic rounding directly within preconditioner space. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Studies optimizer-state quantization rather than language model evaluation methods.
A Balanced Data Diet: Addressing the Exploration Bottleneck in Mega-Scale RL for Robot Control This paper analyzes data sampling distributions across parallel simulations to improve exploration efficiency in reinforcement learning for robot control. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:29	Not relevant Investigates RL exploration sampling for robotic control rather than industrial vision inspection.
A Balanced Data Diet: Addressing the Exploration Bottleneck in Mega-Scale RL for Robot Control This paper analyzes data sampling distributions across parallel simulations to improve exploration efficiency in reinforcement learning for robot control. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Targets general robot control learning rather than deployment safety constraints or guardrails.
A Balanced Data Diet: Addressing the Exploration Bottleneck in Mega-Scale RL for Robot Control This paper analyzes data sampling distributions across parallel simulations to improve exploration efficiency in reinforcement learning for robot control. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Addresses simulation sample efficiency in RL, not language model evaluation.
A Balanced Data Diet: Addressing the Exploration Bottleneck in Mega-Scale RL for Robot Control This paper analyzes data sampling distributions across parallel simulations to improve exploration efficiency in reinforcement learning for robot control. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on RL training sample selection, not small or efficient model architectures.
CSF: Contextual Safety Filtering for Motion Generators This paper introduces a training-free safety filter that evaluates scene context to constrain generated humanoid motion trajectories. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Proposes a control safety filter for robot motion, not LLM benchmark evaluation.
CSF: Contextual Safety Filtering for Motion Generators This paper introduces a training-free safety filter that evaluates scene context to constrain generated humanoid motion trajectories. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:29	Not relevant Focuses on robotic motion generation safety rather than industrial computer vision inspection.
CSF: Contextual Safety Filtering for Motion Generators This paper introduces a training-free safety filter that evaluates scene context to constrain generated humanoid motion trajectories. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Does not address model compression, quantization, or efficient model serving.
Sample-Adaptive Zeroth-Order Post-Training This paper proposes a sample-adaptive zeroth-order optimization method that estimates sample importance online to accelerate large language model fine-tuning. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Develops a post-training optimization method without focusing on LLM evaluation.
Sample-Adaptive Zeroth-Order Post-Training This paper proposes a sample-adaptive zeroth-order optimization method that estimates sample importance online to accelerate large language model fine-tuning. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on optimization convergence and memory efficiency rather than AI safety or reliability.
An all-transistor physical-learning transformer can be trained to perform in-context learning This paper designs and simulates an all-transistor physical circuit using sub-threshold MOSFETs to run transformer attention and in-context learning. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Focuses on hardware-level physical learning circuits rather than LLM benchmarks or evaluation.
An all-transistor physical-learning transformer can be trained to perform in-context learning This paper designs and simulates an all-transistor physical circuit using sub-threshold MOSFETs to run transformer attention and in-context learning. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Studies circuit design for physical learning, not deployment safety or reliability.
DiffusionART: Efficient, Stable and Scalable Reinforcement of Diffusion Models This paper proposes a reward aggregation method to stabilize and scale reinforcement learning fine-tuning for diffusion models. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Develops an RL algorithm for diffusion models without proposing LLM evaluation frameworks.
DiffusionART: Efficient, Stable and Scalable Reinforcement of Diffusion Models This paper proposes a reward aggregation method to stabilize and scale reinforcement learning fine-tuning for diffusion models. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Targets RL fine-tuning stability for diffusion generation, not model efficiency or compression.
DiffusionART: Efficient, Stable and Scalable Reinforcement of Diffusion Models This paper proposes a reward aggregation method to stabilize and scale reinforcement learning fine-tuning for diffusion models. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on general diffusion model RL optimization, not safety, guardrails, or alignment.
Realism VS Accuracy: Event Sequence Forecasting from a Generative Modeling Perspective This paper introduces a benchmark and a diffusion aggregation algorithm to evaluate and balance accuracy and realism in event sequence forecasting. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on event sequence generative modeling, not model compression or efficient serving.
Realism VS Accuracy: Event Sequence Forecasting from a Generative Modeling Perspective This paper introduces a benchmark and a diffusion aggregation algorithm to evaluate and balance accuracy and realism in event sequence forecasting. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Addresses forecast realism versus accuracy, not AI deployment safety, guardrails, or alignment.
Realism VS Accuracy: Event Sequence Forecasting from a Generative Modeling Perspective This paper introduces a benchmark and a diffusion aggregation algorithm to evaluate and balance accuracy and realism in event sequence forecasting. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Evaluates event sequence forecasting models, not large language models or LLM evaluation.
STAPLE: Static Sparse Upcycling of Pretrained Checkpoints via Embedding Modules This paper presents a method for upcycling pretrained language models into sparse architectures using token-indexed embeddings in residual pathways. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Evaluates on standard benchmarks but does not introduce or study LLM evaluation methods.
STAPLE: Static Sparse Upcycling of Pretrained Checkpoints via Embedding Modules This paper presents a method for upcycling pretrained language models into sparse architectures using token-indexed embeddings in residual pathways. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on sparse pretraining architectures, not AI safety, robustness, or reliability.
SynAct: Synergistic Active Feedback Learning for LLM Agents This paper proposes an active learning framework that selectively requests expert feedback at high-value decision steps during LLM agent workflows. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on active feedback mechanisms for agents, not model compression or efficiency.
SynAct: Synergistic Active Feedback Learning for LLM Agents This paper proposes an active learning framework that selectively requests expert feedback at high-value decision steps during LLM agent workflows. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Focuses on active learning algorithms for agent workflows, not LLM benchmark evaluation.
Data-driven Optimal Sensor Placement for Aerosol Monitoring in the UK This paper applies a convolutional Gaussian neural process and genetic optimization to select optimal aerosol monitoring sensor locations across the UK. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Covers sensor placement for atmospheric monitoring, not LLM evaluation.
Data-driven Optimal Sensor Placement for Aerosol Monitoring in the UK This paper applies a convolutional Gaussian neural process and genetic optimization to select optimal aerosol monitoring sensor locations across the UK. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on environmental sensor optimization, not small or efficient language models.
Data-driven Optimal Sensor Placement for Aerosol Monitoring in the UK This paper applies a convolutional Gaussian neural process and genetic optimization to select optimal aerosol monitoring sensor locations across the UK. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Deals with environmental sensor network optimization, not AI safety or guardrails.
Amortised Inference through One-Step Implicit Sampling This paper introduces a one-step implicit sampler using short-time MCMC dynamics to enable efficient amortized inference for Bayesian posteriors. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on high-dimensional Bayesian sampling algorithms, not small or efficient LLMs.
Amortised Inference through One-Step Implicit Sampling This paper introduces a one-step implicit sampler using short-time MCMC dynamics to enable efficient amortized inference for Bayesian posteriors. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Addresses Bayesian posterior sampling techniques, not LLM evaluation.
Amortised Inference through One-Step Implicit Sampling This paper introduces a one-step implicit sampler using short-time MCMC dynamics to enable efficient amortized inference for Bayesian posteriors. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on implicit posterior sampling, not AI deployment safety or reliability.
Expert-level detection of low-voltage intervals in EEG as a robust biomarker of neonatal encephalopathy This paper uses a ConvNeXt architecture to detect low-voltage EEG intervals as biomarkers for neonatal hypoxic-ischaemic encephalopathy. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on clinical EEG classification, not small or efficient language models.
Expert-level detection of low-voltage intervals in EEG as a robust biomarker of neonatal encephalopathy This paper uses a ConvNeXt architecture to detect low-voltage EEG intervals as biomarkers for neonatal hypoxic-ischaemic encephalopathy. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Deals with medical signal detection, not AI safety or deployment guardrails.
Expert-level detection of low-voltage intervals in EEG as a robust biomarker of neonatal encephalopathy This paper uses a ConvNeXt architecture to detect low-voltage EEG intervals as biomarkers for neonatal hypoxic-ischaemic encephalopathy. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Applies ConvNeXt to neonatal EEG data, not LLM evaluation or benchmarks.
Critical Mass: Structure-Disjoint Splits Drift With the Libraries They Are Built On This paper optimizes the Maximum Common Edge Subgraph distance calculation to evaluate structure-disjoint data splits on large molecular libraries. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Addresses molecular graph partitioning, not small language models or model efficiency.
Critical Mass: Structure-Disjoint Splits Drift With the Libraries They Are Built On This paper optimizes the Maximum Common Edge Subgraph distance calculation to evaluate structure-disjoint data splits on large molecular libraries. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Studies dataset split leakage in cheminformatics, not LLM evaluation or benchmarks.
Critical Mass: Structure-Disjoint Splits Drift With the Libraries They Are Built On This paper optimizes the Maximum Common Edge Subgraph distance calculation to evaluate structure-disjoint data splits on large molecular libraries. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on cheminformatics splitting algorithms, not AI deployment safety.
Toward Maximizing Algebraic Connectivity via Density-Dependent Backbone Construction and Reinforcement Learning Completion Combines structural graph backbone construction with reinforcement learning completion to design network topologies that maximize algebraic connectivity. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Applies RL to graph connectivity problems, not LLM evaluation.
Toward Maximizing Algebraic Connectivity via Density-Dependent Backbone Construction and Reinforcement Learning Completion Combines structural graph backbone construction with reinforcement learning completion to design network topologies that maximize algebraic connectivity. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on graph topology optimization, not small or efficient language models.
Toward Maximizing Algebraic Connectivity via Density-Dependent Backbone Construction and Reinforcement Learning Completion Combines structural graph backbone construction with reinforcement learning completion to design network topologies that maximize algebraic connectivity. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on graph theory and network topology design, not AI safety or reliability.
Primal-Dual Flow Matching for Sample-Wise Constrained Generation Introduces primal-dual flow matching to enforce sample-wise constraints in generative modeling using binary membership feedback. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Focuses on flow-matching generative models, not LLM evaluation or benchmarks.
Primal-Dual Flow Matching for Sample-Wise Constrained Generation Introduces primal-dual flow matching to enforce sample-wise constraints in generative modeling using binary membership feedback. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Deals with constrained continuous generative flow models, not small or efficient LLMs.
Primal-Dual Flow Matching for Sample-Wise Constrained Generation Introduces primal-dual flow matching to enforce sample-wise constraints in generative modeling using binary membership feedback. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Addresses mathematical constraint satisfaction in flow matching, not LLM deployment safety.

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