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
Target-Constrained Pooling for Trajectory Extrapolation Proposes target-constrained pooling to improve trajectory extrapolation by combining complementary endpoint estimates from related source signals. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Proposes target-constrained pooling for time-series trajectories, not model compression.
Target-Constrained Pooling for Trajectory Extrapolation Proposes target-constrained pooling to improve trajectory extrapolation by combining complementary endpoint estimates from related source signals. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Numerical trajectory extrapolation method, not AI safety, alignment, or guardrails.
Target-Constrained Pooling for Trajectory Extrapolation Proposes target-constrained pooling to improve trajectory extrapolation by combining complementary endpoint estimates from related source signals. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Introduces a trajectory extrapolation algorithm for numerical forecasting, not LLM evaluation.
Towards Easier-to-Tune Training Algorithms: New Results from AlgoPerf Evaluates deep learning optimizers under a self-tuning fixed-hardware benchmark across nine distinct workloads. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Benchmark report on optimizer training efficiency, not small language models or efficient inference.
Towards Easier-to-Tune Training Algorithms: New Results from AlgoPerf Evaluates deep learning optimizers under a self-tuning fixed-hardware benchmark across nine distinct workloads. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Analyzes optimizer performance and tuning, unrelated to AI safety or robustness.
Lightweight Time-Frequency Vibration Sensing for Consumer Device Condition Monitoring Presents a lightweight vibration sensing framework transforming time-series into time-frequency representations for edge deep learning monitoring. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Condition monitoring for consumer rotating devices, not AI safety or LLM deployment risks.
Lightweight Time-Frequency Vibration Sensing for Consumer Device Condition Monitoring Presents a lightweight vibration sensing framework transforming time-series into time-frequency representations for edge deep learning monitoring. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Deploys tiny vision models for consumer vibration sensing, not language model efficiency.
Lightweight Time-Frequency Vibration Sensing for Consumer Device Condition Monitoring Presents a lightweight vibration sensing framework transforming time-series into time-frequency representations for edge deep learning monitoring. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Focuses on appliance vibration sensing, not LLM benchmarking or evaluation frameworks.
CB-Orchestrator: Adaptive Workflow Optimization for LLM Agents via Contextual Bandits Proposes a framework combining evolutionary candidate search and contextual bandits to optimize and select LLM agent workflows adaptively. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Addresses agent workflow selection and API cost efficiency, not safety or guardrails.
CB-Orchestrator: Adaptive Workflow Optimization for LLM Agents via Contextual Bandits Proposes a framework combining evolutionary candidate search and contextual bandits to optimize and select LLM agent workflows adaptively. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Optimizes agentic workflows via bandits, not small language models, quantization, or serving.
CB-Orchestrator: Adaptive Workflow Optimization for LLM Agents via Contextual Bandits Proposes a framework combining evolutionary candidate search and contextual bandits to optimize and select LLM agent workflows adaptively. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Proposes contextual bandit workflow optimization for LLM agents, not evaluation methodology.
Double-Take: Improving Capacity, Binding, and Selectivity In Long-Context Within Bidirectional Memory Introduces Double-Take to mitigate capacity and cross-position binding failures in long-context test-time training architectures. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Proposes test-time training memory architecture for long contexts, not LLM evaluation or benchmarks.
Double-Take: Improving Capacity, Binding, and Selectivity In Long-Context Within Bidirectional Memory Introduces Double-Take to mitigate capacity and cross-position binding failures in long-context test-time training architectures. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on memory capacity in long-context processing, not AI safety or deployment risks.
Human and LLM Facilitation in Collaborative Design Meetings: A Computational Behavioral Comparison Compares human and Llama 3.1 8B facilitation turns across design meeting transcripts using multiple computational behavioral metrics. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Analyzes behavioral differences between human and LLM meeting facilitation, not model efficiency.
Human and LLM Facilitation in Collaborative Design Meetings: A Computational Behavioral Comparison Compares human and Llama 3.1 8B facilitation turns across design meeting transcripts using multiple computational behavioral metrics. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on linguistic and behavioral comparison of meeting facilitation, not AI safety.
Can Failed Trajectories Reveal Where Agents Go Wrong? Agentic Failure Attribution Without Step-Level Labels Proposes a spectral representation framework to attribute agent failures to specific decisive steps using only trajectory-level outcome labels. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on trajectory failure attribution for agents, not model efficiency or distillation.
Scaling RNA Foundation Models through Iterative Refinement Presents a compact RNA foundation model utilizing learned iterative refinement to allow variable inference depth at fixed parameter scale. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Domain-specific biological foundation model for RNA, unrelated to AI safety or reliability.
Scaling RNA Foundation Models through Iterative Refinement Presents a compact RNA foundation model utilizing learned iterative refinement to allow variable inference depth at fixed parameter scale. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on biological RNA modeling with iterative refinement, not general efficient LLM deployment.
Scaling RNA Foundation Models through Iterative Refinement Presents a compact RNA foundation model utilizing learned iterative refinement to allow variable inference depth at fixed parameter scale. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Proposes an RNA biology foundation model, not general LLM benchmarks or evaluation methods.
Why Heavy-Tailed Weight Spectra Predict Model Quality Provides a probabilistic marginal likelihood derivation explaining why heavy-tailed weight spectral metrics correlate with deep network generalization quality. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Studies statistical justification for model quality metrics, not AI safety or deployment guardrails.
Why Heavy-Tailed Weight Spectra Predict Model Quality Provides a probabilistic marginal likelihood derivation explaining why heavy-tailed weight spectral metrics correlate with deep network generalization quality. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Investigates theoretical quality metrics from weight spectra, not model compression or efficiency.
Fast Gradient-Based Approximation of Continuous Dynamic Time Warping Introduces fast gradient-based approximation schemes for continuous dynamic time warping to enable scalable, differentiable time series alignment. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Proposes a fast time-series alignment solver, not small or efficient language model methods.
Fast Gradient-Based Approximation of Continuous Dynamic Time Warping Introduces fast gradient-based approximation schemes for continuous dynamic time warping to enable scalable, differentiable time series alignment. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Presents an approximation algorithm for Dynamic Time Warping, not LLM evaluation benchmarks.
Fast Gradient-Based Approximation of Continuous Dynamic Time Warping Introduces fast gradient-based approximation schemes for continuous dynamic time warping to enable scalable, differentiable time series alignment. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on time-series dynamic time warping optimization, unrelated to AI safety or reliability.
WaSA: Wavelet Scattering Autoencoders for Unsupervised Offline Detection of Malicious Clients in Federated Learning Uses wavelet scattering autoencoders to detect malicious client data updates offline in federated learning without requiring attack labels. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Focuses on federated learning poisoning defense, not evaluating or benchmarking language models.
WaSA: Wavelet Scattering Autoencoders for Unsupervised Offline Detection of Malicious Clients in Federated Learning Uses wavelet scattering autoencoders to detect malicious client data updates offline in federated learning without requiring attack labels. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Addresses federated learning data poisoning in IoT, not LLM safety, guardrails, or alignment.
WaSA: Wavelet Scattering Autoencoders for Unsupervised Offline Detection of Malicious Clients in Federated Learning Uses wavelet scattering autoencoders to detect malicious client data updates offline in federated learning without requiring attack labels. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on malicious client detection in federated learning, not model compression or efficient serving.
Prediction-Informed Landing Window Planning for UAV Landing on USV in High Sea States Develops a motion-prediction hierarchical control framework to plan safe touchdown windows for autonomous drone landings on surface vessels. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:29	Not relevant Focuses on UAV flight trajectory planning and control, not computer vision defect inspection or perception.
Prediction-Informed Landing Window Planning for UAV Landing on USV in High Sea States Develops a motion-prediction hierarchical control framework to plan safe touchdown windows for autonomous drone landings on surface vessels. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Develops robotic landing control algorithms, completely unrelated to LLM evaluation benchmarks.
Prediction-Informed Landing Window Planning for UAV Landing on USV in High Sea States Develops a motion-prediction hierarchical control framework to plan safe touchdown windows for autonomous drone landings on surface vessels. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on autonomous vehicle control dynamics rather than AI safety, red-teaming, or LLM reliability.
Prediction-Informed Landing Window Planning for UAV Landing on USV in High Sea States Develops a motion-prediction hierarchical control framework to plan safe touchdown windows for autonomous drone landings on surface vessels. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Addresses UAV/USV maritime control systems, not small language models or efficient AI serving.
Rethinking Relative Policy Differences Transfer of On-Policy Distillation Formulates relative policy difference transfer as an implicit reward in KL-regularized optimization for flexible on-policy knowledge distillation. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Investigates distillation mechanics and dynamics, not LLM evaluation benchmarks or methodologies.
Rethinking Relative Policy Differences Transfer of On-Policy Distillation Formulates relative policy difference transfer as an implicit reward in KL-regularized optimization for flexible on-policy knowledge distillation. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Studies distillation dynamics, not AI safety, jailbreaking, hallucination, or deployment guardrails.
How Much Computation Does Every Forecast Need? Structured Block Skipping in Two Time-Series Foundation Models Evaluates static Transformer block skipping in time-series foundation models to reduce inference latency with minimal forecasting degradation. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on dynamic computation skipping, not AI safety, alignment, or guardrails.
How Much Computation Does Every Forecast Need? Structured Block Skipping in Two Time-Series Foundation Models Evaluates static Transformer block skipping in time-series foundation models to reduce inference latency with minimal forecasting degradation. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Evaluates layer skipping in time-series models, not LLM evaluation or benchmarking methodology.
Verifiable Sampling for Delegated Generative Quantum Machine Learning Proposes a blind and verifiable sampling protocol for securely delegating quantum generative machine learning inference to remote servers. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Studies verifiable protocols for quantum computing, not LLM evaluation benchmarks.
Verifiable Sampling for Delegated Generative Quantum Machine Learning Proposes a blind and verifiable sampling protocol for securely delegating quantum generative machine learning inference to remote servers. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Examines cryptographic verification for quantum computing protocols, not standard AI safety or guardrails.
Verifiable Sampling for Delegated Generative Quantum Machine Learning Proposes a blind and verifiable sampling protocol for securely delegating quantum generative machine learning inference to remote servers. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on quantum generative model protocols, not small language models or efficient LLM serving.
Monarch-MoE: Structured Experts for Multimodal Continual Instruction Tuning Replaces dense LoRA experts with block-diagonal factorizations to isolate gradients and prevent catastrophic forgetting during continual multimodal tuning. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Presents parameter-efficient continual tuning architectures, not evaluation benchmarks or metrics.
Monarch-MoE: Structured Experts for Multimodal Continual Instruction Tuning Replaces dense LoRA experts with block-diagonal factorizations to isolate gradients and prevent catastrophic forgetting during continual multimodal tuning. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Addresses catastrophic forgetting in parameter-efficient tuning, not AI safety, red-teaming, or guardrails.
Tensor-based Fusion of Multi-modal Data for Predicting Cancer Risk Levels Applies tensor decomposition and clustering to fuse heterogeneous clinical and genomic datasets for unsupervised cancer risk prediction. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on clinical data clustering rather than small language models or LLM efficiency.
Tensor-based Fusion of Multi-modal Data for Predicting Cancer Risk Levels Applies tensor decomposition and clustering to fuse heterogeneous clinical and genomic datasets for unsupervised cancer risk prediction. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Applies tensor decomposition to cancer risk prediction data, not LLM evaluation.
Tensor-based Fusion of Multi-modal Data for Predicting Cancer Risk Levels Applies tensor decomposition and clustering to fuse heterogeneous clinical and genomic datasets for unsupervised cancer risk prediction. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Addresses clinical risk modeling, unrelated to AI safety, hallucination, or deployment security.
Learning Schwarzschild Geodesic Motion with Lagrangian Neural Networks Introduces coordinate normalization and energy-constrained sampling to train Lagrangian neural networks on complex Schwarzschild geodesic dynamics. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Addresses computational physics equations, not AI deployment safety or reliability guardrails.
Learning Schwarzschild Geodesic Motion with Lagrangian Neural Networks Introduces coordinate normalization and energy-constrained sampling to train Lagrangian neural networks on complex Schwarzschild geodesic dynamics. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on relativistic geodesic physics modeling, not small language models or model compression.
Learning Schwarzschild Geodesic Motion with Lagrangian Neural Networks Introduces coordinate normalization and energy-constrained sampling to train Lagrangian neural networks on complex Schwarzschild geodesic dynamics. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Investigates neural networks for relativistic physics, not language model evaluation.
State-OPD: State-Grounded On-Policy Distillation for Multi-Turn Agents Investigates on-policy distillation for multi-turn agents by decoupling objective interaction states from internal student reasoning trajectories. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on distillation performance rather than AI safety, guardrails, or alignment risks.
State-OPD: State-Grounded On-Policy Distillation for Multi-Turn Agents Investigates on-policy distillation for multi-turn agents by decoupling objective interaction states from internal student reasoning trajectories. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Focuses on knowledge distillation techniques for agent policies, not evaluating or benchmarking LLMs.
Neural Surrogate Modeling of Ultrasonic Wave Propagation in Heterogeneous Media Combines Fourier neural operators with local-integral resampling in a U-Net to model high-frequency ultrasonic waves in heterogeneous media. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Presents surrogate models for ultrasonic wave simulation, not LLM evaluation.
Neural Surrogate Modeling of Ultrasonic Wave Propagation in Heterogeneous Media Combines Fourier neural operators with local-integral resampling in a U-Net to model high-frequency ultrasonic waves in heterogeneous media. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Deals with ultrasonic physics simulation, having no relation to AI safety or reliability.
Neural Surrogate Modeling of Ultrasonic Wave Propagation in Heterogeneous Media Combines Fourier neural operators with local-integral resampling in a U-Net to model high-frequency ultrasonic waves in heterogeneous media. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Addresses PDE wave equation surrogates, not small language models or efficient LLM inference.
Data-Adaptive Mahalanobis Metric Learning for Cross-Head Attention in Transformers Generalizes multi-head attention by learning data-adaptive, full cross-head Mahalanobis metric matrices to capture inter-head feature interactions. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on attention architecture formulation rather than AI safety, red-teaming, or guardrails.
Data-Adaptive Mahalanobis Metric Learning for Cross-Head Attention in Transformers Generalizes multi-head attention by learning data-adaptive, full cross-head Mahalanobis metric matrices to capture inter-head feature interactions. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Proposes an attention variant for Transformers, unrelated to LLM evaluation or benchmarks.
Data-Adaptive Mahalanobis Metric Learning for Cross-Head Attention in Transformers Generalizes multi-head attention by learning data-adaptive, full cross-head Mahalanobis metric matrices to capture inter-head feature interactions. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Introduces a general attention mechanism for model expressiveness, not model compression or efficiency serving.
CopRoute: Dependence-Aware Conformal Routing for Language Models Proposes a conformal routing method that accounts for model failure dependencies to optimize multi-model query coverage under risk budgets. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Studies routing algorithms to minimize LLM inference calls, not evaluation benchmarks or metrics.
MHR-Router: Multimodal Heterogeneous Retrieval-Enhanced Adaptive Routing for VLM Inference Develops a retrieval-enhanced routing framework that dispatches multimodal queries to specialized vision-language models based on local failure patterns. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Proposes an adaptive query router for VLM inference, not an evaluation framework or benchmark.
MHR-Router: Multimodal Heterogeneous Retrieval-Enhanced Adaptive Routing for VLM Inference Develops a retrieval-enhanced routing framework that dispatches multimodal queries to specialized vision-language models based on local failure patterns. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Optimizes expert routing for performance rather than addressing AI safety, hallucination, or red-teaming.
Recovering Irreducible Three-Body Interactions with Lagrangian Neural Networks Applies Lagrangian neural networks with targeted sampling to recover non-pairwise three-body dispersion interactions directly from trajectory data. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Studies physics-informed neural networks for molecular physics, not LLM evaluation or benchmarks.
Recovering Irreducible Three-Body Interactions with Lagrangian Neural Networks Applies Lagrangian neural networks with targeted sampling to recover non-pairwise three-body dispersion interactions directly from trajectory data. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Deals with physical dynamic modeling, unrelated to deployment safety, alignment, or LLM guardrails.
Recovering Irreducible Three-Body Interactions with Lagrangian Neural Networks Applies Lagrangian neural networks with targeted sampling to recover non-pairwise three-body dispersion interactions directly from trajectory data. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Investigates Lagrangian neural networks for three-body physics, not model compression or small language models.

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