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
Feasibility Ceilings Before Learning: Sizing and Timing Bounds for Drone-Mesh Interception This paper establishes analytical geometric bounds on coverage probability and timing uncertainty for multi-agent drone meshes intercepting approaching objects. Preprint	AI agents via arxiv · 17 Oct, 06:18	Not relevant Analyzes geometric drone interceptor bounds rather than LLM tool use or multi-agent planning.
Feasibility Ceilings Before Learning: Sizing and Timing Bounds for Drone-Mesh Interception This paper establishes analytical geometric bounds on coverage probability and timing uncertainty for multi-agent drone meshes intercepting approaching objects. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Mathematical coverage bounds for robotics, not language model evaluation.
Linear Independence of Polynomial Compositions with Bounded Input Degree This paper resolves mathematical conjectures on polynomial compositions, proving the identifiability of neural networks using general polynomial activation functions of sufficient degree. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Mathematical proof on polynomial activations, not model compression or efficient inference.
Linear Independence of Polynomial Compositions with Bounded Input Degree This paper resolves mathematical conjectures on polynomial compositions, proving the identifiability of neural networks using general polynomial activation functions of sufficient degree. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Theoretical algebraic geometry paper, not practical AI safety or reliability.
Linear Independence of Polynomial Compositions with Bounded Input Degree This paper resolves mathematical conjectures on polynomial compositions, proving the identifiability of neural networks using general polynomial activation functions of sufficient degree. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Pure algebraic geometry proof regarding neural identifiability, not LLM evaluation.
Task-Phase-Conditioned Expert Routing Is Not a Reliable Proxy for Quantization Sensitivity in MoE FFN Tensors This paper shows that routing statistics across task phases fail to provide an effective guide for mixed-precision quantization in mixture-of-experts models. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Addresses MoE weight quantization techniques, not AI safety or alignment risks.
Task-Phase-Conditioned Expert Routing Is Not a Reliable Proxy for Quantization Sensitivity in MoE FFN Tensors This paper shows that routing statistics across task phases fail to provide an effective guide for mixed-precision quantization in mixture-of-experts models. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Tests routing-based quantization sensitivity heuristics rather than broad LLM evaluation methodology.
A Phenomenology of Neural Geometry in Vision-Language-Action Models This paper investigates internal geometric representations of task-relevant physical concepts within vision-language-action policies for robotic manipulation. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Examines internal representation geometry rather than safety guardrails, jailbreaks, or hallucinations.
A Phenomenology of Neural Geometry in Vision-Language-Action Models This paper investigates internal geometric representations of task-relevant physical concepts within vision-language-action policies for robotic manipulation. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Investigates representation geometry in VLA policies, not model compression or efficiency.
A Phenomenology of Neural Geometry in Vision-Language-Action Models This paper investigates internal geometric representations of task-relevant physical concepts within vision-language-action policies for robotic manipulation. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Analyzes internal geometric representations in VLA models, not general LLM evaluation benchmarks.
A Phenomenology of Neural Geometry in Vision-Language-Action Models This paper investigates internal geometric representations of task-relevant physical concepts within vision-language-action policies for robotic manipulation. Preprint	Computer vision for industrial applications via arxiv · 17 Oct, 06:18	Not relevant Focuses on robotic manipulation geometry rather than industrial visual inspection or defect detection.
Analog RF Computing for Integrated Data and AI Flows Over Wireless Networks This paper investigates using analog radio frequency computing over wireless networks to deliver low-power neural network inference to resource-constrained edge devices. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on wireless RF computing, not LLM evaluation benchmarks.
Analog RF Computing for Integrated Data and AI Flows Over Wireless Networks This paper investigates using analog radio frequency computing over wireless networks to deliver low-power neural network inference to resource-constrained edge devices. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Deals with wireless MU-MIMO beamforming rather than AI deployment safety risks.
Denoising Score Matching in Diffusion Models: The Effect of Rank Constraints on Generalization This paper analyzes the effect of rank constraints on generalization and test error in diffusion score matching networks. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Analyzes diffusion generalization theory rather than operational AI safety and reliability.
Denoising Score Matching in Diffusion Models: The Effect of Rank Constraints on Generalization This paper analyzes the effect of rank constraints on generalization and test error in diffusion score matching networks. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Theoretical generalization bounds for diffusion score matching, not LLM evaluation.
Denoising Score Matching in Diffusion Models: The Effect of Rank Constraints on Generalization This paper analyzes the effect of rank constraints on generalization and test error in diffusion score matching networks. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Investigates theoretical score matching rank constraints, not efficient model compression or serving.
Complete or Sparse: A Tale of Two Identifiabilities This paper proves conditions under which supervised neural networks can uniquely identify and recover true underlying data latents in generative and discriminative settings. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Studies representation identifiability theory rather than model compression or efficiency.
Complete or Sparse: A Tale of Two Identifiabilities This paper proves conditions under which supervised neural networks can uniquely identify and recover true underlying data latents in generative and discriminative settings. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Theoretical mechanistic interpretability study, not LLM benchmark or evaluation methodology.
Complete or Sparse: A Tale of Two Identifiabilities This paper proves conditions under which supervised neural networks can uniquely identify and recover true underlying data latents in generative and discriminative settings. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Theoretical study of latent variable identifiability rather than AI safety or guardrails.
Time-partitioning Gaussian Process Mixture Model This paper introduces a Bayesian nonparametric Gaussian process mixture model using an adaptive Pitman-Yor prior to detect persistent regimes in time series. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Concerns Gaussian process regime modeling, not efficient neural architectures or inference.
Time-partitioning Gaussian Process Mixture Model This paper introduces a Bayesian nonparametric Gaussian process mixture model using an adaptive Pitman-Yor prior to detect persistent regimes in time series. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Studies nonstationary financial time series, not deployed AI safety or guardrails.
Time-partitioning Gaussian Process Mixture Model This paper introduces a Bayesian nonparametric Gaussian process mixture model using an adaptive Pitman-Yor prior to detect persistent regimes in time series. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Proposes a Bayesian time-series model rather than LLM evaluation.
SNOAR: Self-training with Noise filtering, Observation adding, and Remixing for Speech Denoising Without Clean References This paper proposes a self-training speech denoising framework that corrects teacher predictions via noise filtering and observation mixing without clean references. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Evaluates speech denoising algorithms rather than LLM benchmarks or evaluation methods.
SNOAR: Self-training with Noise filtering, Observation adding, and Remixing for Speech Denoising Without Clean References This paper proposes a self-training speech denoising framework that corrects teacher predictions via noise filtering and observation mixing without clean references. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Studies audio denoising rather than AI safety or reliability in deployment.
SNOAR: Self-training with Noise filtering, Observation adding, and Remixing for Speech Denoising Without Clean References This paper proposes a self-training speech denoising framework that corrects teacher predictions via noise filtering and observation mixing without clean references. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on unsupervised speech denoising rather than model efficiency or compression.
Long-Context Drafting Does Not Require Long-Context Drafters This paper demonstrates that frozen short-context drafting models can effectively serve ultra-long context speculative decoding without fine-tuning on long sequences. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on speculative decoding speedups, not benchmark or evaluation methodology.
Long-Context Drafting Does Not Require Long-Context Drafters This paper demonstrates that frozen short-context drafting models can effectively serve ultra-long context speculative decoding without fine-tuning on long sequences. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Focuses on speculative decoding acceleration, not safety, jailbreaks, or guardrails.
Clinically Interpretable Biological Age Estimation Using CatBoost and SHAP with Continuous and Categorical Features from NHANES This paper uses CatBoost and an automated SHAP analysis pipeline on clinical data to estimate biological age with interpretable biomarker insights. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Focuses on healthcare biomarker interpretation, not AI safety or guardrails.
Clinically Interpretable Biological Age Estimation Using CatBoost and SHAP with Continuous and Categorical Features from NHANES This paper uses CatBoost and an automated SHAP analysis pipeline on clinical data to estimate biological age with interpretable biomarker insights. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Clinical tabular regression interpretability, not LLM evaluation or benchmarks.
Clinically Interpretable Biological Age Estimation Using CatBoost and SHAP with Continuous and Categorical Features from NHANES This paper uses CatBoost and an automated SHAP analysis pipeline on clinical data to estimate biological age with interpretable biomarker insights. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Clinical tabular modeling study, not small or efficient neural models.
Machine-learning-based neutrino-event generation constrained by experimental measurements This paper introduces a generative surrogate model for neutrino-nucleus scattering calibrated directly to measured experimental cross sections without event-level data. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant High-energy physics simulation paper, not LLM evaluation or benchmarks.
Machine-learning-based neutrino-event generation constrained by experimental measurements This paper introduces a generative surrogate model for neutrino-nucleus scattering calibrated directly to measured experimental cross sections without event-level data. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Physics generative modeling application, not AI safety or alignment.
Machine-learning-based neutrino-event generation constrained by experimental measurements This paper introduces a generative surrogate model for neutrino-nucleus scattering calibrated directly to measured experimental cross sections without event-level data. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on neutrino event simulation, not efficient model architectures.
Do Emergent Misalignment Models Share a Circuit? This paper shows that fine-tuned language models exhibiting emergent misalignment across different harmful domains share specific attention heads responsible for the behavior. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on circuit analysis of misalignment, not model compression.
Do Emergent Misalignment Models Share a Circuit? This paper shows that fine-tuned language models exhibiting emergent misalignment across different harmful domains share specific attention heads responsible for the behavior. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Mechanistic interpretability study, not LLM benchmark development or evaluation.
Learning Warm Starts for the Simplex Method from Measured Solver Cost This paper analyzes why machine-learning-predicted warm starts for the simplex method reduce iterations much more than actual solve time. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on simplex algorithm optimization, not neural network efficiency.
Learning Warm Starts for the Simplex Method from Measured Solver Cost This paper analyzes why machine-learning-predicted warm starts for the simplex method reduce iterations much more than actual solve time. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Studies linear programming solver speedups, not LLM benchmark evaluation.
Learning Warm Starts for the Simplex Method from Measured Solver Cost This paper analyzes why machine-learning-predicted warm starts for the simplex method reduce iterations much more than actual solve time. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Focuses on simplex algorithm solver acceleration, not AI safety.
Flexible Doubly Robust Proximal Causal Inference for Censored Outcomes This paper presents a flexible doubly robust proximal learning framework to estimate causal effects on right-censored time-to-event outcomes under unmeasured confounding. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Studies biostatistical methods, not AI safety or deployment robustness.
Flexible Doubly Robust Proximal Causal Inference for Censored Outcomes This paper presents a flexible doubly robust proximal learning framework to estimate causal effects on right-censored time-to-event outcomes under unmeasured confounding. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Biostatistical causal inference framework, not LLM evaluation or benchmarks.
Flexible Doubly Robust Proximal Causal Inference for Censored Outcomes This paper presents a flexible doubly robust proximal learning framework to estimate causal effects on right-censored time-to-event outcomes under unmeasured confounding. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on survival causal inference, not model compression or efficiency.
Stochastic Gradient Descent: A Nonlinear Time Series Perspective The paper analyzes the convergence and asymptotic properties of stochastic gradient descent with constant step size using nonlinear time series theory. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Mathematical analysis of SGD, not small models or inference efficiency.
Stochastic Gradient Descent: A Nonlinear Time Series Perspective The paper analyzes the convergence and asymptotic properties of stochastic gradient descent with constant step size using nonlinear time series theory. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Theoretical analysis of stochastic optimization, not LLM evaluation benchmarks.
Stochastic Gradient Descent: A Nonlinear Time Series Perspective The paper analyzes the convergence and asymptotic properties of stochastic gradient descent with constant step size using nonlinear time series theory. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Focuses on mathematical optimization theory, not deployment safety or robustness.
Sign Betting for Misspecified Regression: Variance Adaptivity and Optimal Approximation The paper analyzes the theoretical statistical properties and oracle inequalities of sign-betting regression under model misspecification. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Studies statistical regression theory, not small or efficient models.
Sign Betting for Misspecified Regression: Variance Adaptivity and Optimal Approximation The paper analyzes the theoretical statistical properties and oracle inequalities of sign-betting regression under model misspecification. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Theoretical regression analysis, not LLM evaluation or benchmark design.
Sign Betting for Misspecified Regression: Variance Adaptivity and Optimal Approximation The paper analyzes the theoretical statistical properties and oracle inequalities of sign-betting regression under model misspecification. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Theoretical statistics paper, not AI safety, guardrails, or alignment.
Recursive Offline Distillation via Entropy Gain Sampling and Answer Mixing The paper presents an offline distillation method combining entropy-difference sampling and answer mixing to transfer reasoning abilities into smaller language models. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Focuses on reasoning distillation, not AI safety or jailbreak prevention.
Recursive Offline Distillation via Entropy Gain Sampling and Answer Mixing The paper presents an offline distillation method combining entropy-difference sampling and answer mixing to transfer reasoning abilities into smaller language models. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on distillation training methods, not LLM evaluation frameworks.
Planning to Learn: Real-Time Robust Adaptive Control on the GPU with Guaranteed Future Learning The paper presents a GPU-accelerated robust adaptive control framework that accounts for guaranteed future parameter learning during real-time robotic trajectory planning. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Robotic flight control stability, not AI safety guardrails or alignment.
Planning to Learn: Real-Time Robust Adaptive Control on the GPU with Guaranteed Future Learning The paper presents a GPU-accelerated robust adaptive control framework that accounts for guaranteed future parameter learning during real-time robotic trajectory planning. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on GPU robotic control solvers, not model compression.
Planning to Learn: Real-Time Robust Adaptive Control on the GPU with Guaranteed Future Learning The paper presents a GPU-accelerated robust adaptive control framework that accounts for guaranteed future parameter learning during real-time robotic trajectory planning. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Robotics control algorithm, not LLM evaluation or benchmark methodology.
Planning to Learn: Real-Time Robust Adaptive Control on the GPU with Guaranteed Future Learning The paper presents a GPU-accelerated robust adaptive control framework that accounts for guaranteed future parameter learning during real-time robotic trajectory planning. Preprint	Computer vision for industrial applications via arxiv · 17 Oct, 06:18	Not relevant Addresses robotic adaptive control, not industrial vision defect inspection.
Ranked Right, Sized Wrong: Ensemble Spread Fails Silently at Streamflow Extremes The paper demonstrates that ensemble spread in streamflow forecasting models becomes significantly under-dispersed and underestimates error during extreme flood events. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Studies streamflow forecasting, not efficient model inference or compression.
Ranked Right, Sized Wrong: Ensemble Spread Fails Silently at Streamflow Extremes The paper demonstrates that ensemble spread in streamflow forecasting models becomes significantly under-dispersed and underestimates error during extreme flood events. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on hydrological forecasting uncertainty, not LLM benchmark evaluation.
Ranked Right, Sized Wrong: Ensemble Spread Fails Silently at Streamflow Extremes The paper demonstrates that ensemble spread in streamflow forecasting models becomes significantly under-dispersed and underestimates error during extreme flood events. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Focuses on hydrology modeling, not AI deployment safety or LLM guardrails.
Cross-subject decoding of imagined speech from scalp EEG: a leakage-controlled evaluation on three public corpora The paper evaluates cross-subject transfer and accuracy limits of decoding imagined speech from scalp electroencephalography signals across three public datasets. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Neuroscience EEG decoding study, not small or efficient neural models.
Cross-subject decoding of imagined speech from scalp EEG: a leakage-controlled evaluation on three public corpora The paper evaluates cross-subject transfer and accuracy limits of decoding imagined speech from scalp electroencephalography signals across three public datasets. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Neurotechnology decoding paper, not AI safety, alignment, or guardrails.
Cross-subject decoding of imagined speech from scalp EEG: a leakage-controlled evaluation on three public corpora The paper evaluates cross-subject transfer and accuracy limits of decoding imagined speech from scalp electroencephalography signals across three public datasets. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Studies EEG brain decoding transfer, not language model evaluation.
MIDI-REPA: Improving Text-to-Music Generation with Symbolic Representation Alignment The paper aligns intermediate representations of text-to-music models with symbolic music transcriptions to improve generation quality efficiently. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on text-to-music representation learning, not LLM evaluation.

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