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
Fully sequential Gaussian process regression for preference learning The paper presents a sequential Gaussian process method and sampling policy for continuous optimization based on pairwise preference comparisons. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Sequential Gaussian process regression theory, not LLM evaluation.
Fully sequential Gaussian process regression for preference learning The paper presents a sequential Gaussian process method and sampling policy for continuous optimization based on pairwise preference comparisons. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Bayesian optimization theory, not model compression or efficient LLM inference.
Fully sequential Gaussian process regression for preference learning The paper presents a sequential Gaussian process method and sampling policy for continuous optimization based on pairwise preference comparisons. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Not related to AI deployment safety or model robustness.
Selective Next-Location Prediction with MetaModelSelect: A Lightweight Post-Hoc Confidence Metamodel The paper applies a lightweight post-hoc classifier to evaluate confidence and filter out incorrect next-location predictions from recurrent models. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Specific cellular location predictor, not small language models or inference acceleration.
Selective Next-Location Prediction with MetaModelSelect: A Lightweight Post-Hoc Confidence Metamodel The paper applies a lightweight post-hoc classifier to evaluate confidence and filter out incorrect next-location predictions from recurrent models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Post-hoc confidence metamodel for cellular next-location prediction, not LLM evaluation.
ActMOF: Mapping Reticular Synthesis through Active Learning The paper introduces an experimental benchmark to evaluate active learning and Bayesian optimization strategies for synthesizing metal-organic frameworks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Not related to AI deployment safety or language model guardrails.
ActMOF: Mapping Reticular Synthesis through Active Learning The paper introduces an experimental benchmark to evaluate active learning and Bayesian optimization strategies for synthesizing metal-organic frameworks. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Active learning for chemistry experiments, not model compression or SLMs.
ActMOF: Mapping Reticular Synthesis through Active Learning The paper introduces an experimental benchmark to evaluate active learning and Bayesian optimization strategies for synthesizing metal-organic frameworks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Benchmark for chemical reticular synthesis active learning, not LLM evaluation.
W-JEPA: Wavelet Joint-Embedding Predictive Architecture for Wearable Activity Recognition The paper introduces a self-supervised wavelet predictive architecture to extract multiscale motion features from wearable activity sensors. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Sensor signal representation learning, not small language models or inference efficiency.
W-JEPA: Wavelet Joint-Embedding Predictive Architecture for Wearable Activity Recognition The paper introduces a self-supervised wavelet predictive architecture to extract multiscale motion features from wearable activity sensors. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Not related to AI safety, hallucinations, or red-teaming.
W-JEPA: Wavelet Joint-Embedding Predictive Architecture for Wearable Activity Recognition The paper introduces a self-supervised wavelet predictive architecture to extract multiscale motion features from wearable activity sensors. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Wearable sensor activity recognition, not language model evaluation.
Comprehensive Comparisons of Reinforcement Learning Algorithms in Autonomous Driving The paper benchmarks four reinforcement learning algorithms on safety, data efficiency, and stability for autonomous vehicle intersection navigation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on AV driving control algorithms rather than deployable AI safety guardrails.
Comprehensive Comparisons of Reinforcement Learning Algorithms in Autonomous Driving The paper benchmarks four reinforcement learning algorithms on safety, data efficiency, and stability for autonomous vehicle intersection navigation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Benchmarking autonomous vehicle RL algorithms, not language model evaluation.
Comprehensive Comparisons of Reinforcement Learning Algorithms in Autonomous Driving The paper benchmarks four reinforcement learning algorithms on safety, data efficiency, and stability for autonomous vehicle intersection navigation. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Autonomous driving control comparison, not small language models or compression.
Progress-Aware Distillation for Mitigating Stagnation in Small Language Model Agents The paper introduces a progress-aware distillation method to prevent small language model agents from stalling in repetitive loops during long tasks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on distillation training methods rather than new LLM evaluation benchmarks.
Large-Step Last-Iterate Convergence of Random-Reshuffling Stochastic Gradient Descent The paper establishes theoretical last-iterate convergence rates for random-reshuffling stochastic gradient descent using larger stepsizes. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Not related to AI deployment safety or reliability guardrails.
Large-Step Last-Iterate Convergence of Random-Reshuffling Stochastic Gradient Descent The paper establishes theoretical last-iterate convergence rates for random-reshuffling stochastic gradient descent using larger stepsizes. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant General convex optimization convergence analysis, not model compression or efficient serving.
Large-Step Last-Iterate Convergence of Random-Reshuffling Stochastic Gradient Descent The paper establishes theoretical last-iterate convergence rates for random-reshuffling stochastic gradient descent using larger stepsizes. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Theoretical optimization theory for SGD, not LLM evaluation.
State-Matched Sequence Conformal Prediction The paper proposes a sequential conformal prediction algorithm that groups calibration scores by state similarity rather than pure temporal recency. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on conformal prediction theory, not LLM benchmarking.
State-Matched Sequence Conformal Prediction The paper proposes a sequential conformal prediction algorithm that groups calibration scores by state similarity rather than pure temporal recency. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not study small language models or inference optimization.
Digital Self-Interference Cancellation for In-Band Full-Duplex Systems Based on Transformer Neural Operators The paper applies transformer neural operators to model interference channels and cancel digital self-interference in full-duplex radio systems. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Neural operator for full-duplex radio, not general model compression.
Digital Self-Interference Cancellation for In-Band Full-Duplex Systems Based on Transformer Neural Operators The paper applies transformer neural operators to model interference channels and cancel digital self-interference in full-duplex radio systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Radio frequency signal cancellation, not language model evaluation.
Digital Self-Interference Cancellation for In-Band Full-Duplex Systems Based on Transformer Neural Operators The paper applies transformer neural operators to model interference channels and cancel digital self-interference in full-duplex radio systems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Not related to AI safety, hallucinations, or model reliability.
SKG: A Law-Induced Engine from Inference to Induction The paper presents a mathematical framework unifying symplectic geometry, kernel methods, and physical conservation laws for physics-constrained learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not address deployment safety, jailbreaks, or AI guardrails.
SKG: A Law-Induced Engine from Inference to Induction The paper presents a mathematical framework unifying symplectic geometry, kernel methods, and physical conservation laws for physics-constrained learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Theoretical physics-informed gradient reasoning engine, not LLM benchmarks.
SKG: A Law-Induced Engine from Inference to Induction The paper presents a mathematical framework unifying symplectic geometry, kernel methods, and physical conservation laws for physics-constrained learning. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Geometric mechanical inference framework, not small language models or distillation.
CktHGVAE: A Symmetric Design/State Heterogeneous Graph Variational Autoencoder for Transistor-level Analog/Mixed-signal Circuit Design and Analysis The paper introduces a heterogeneous graph variational autoencoder to model design and state variables jointly for analog circuit analysis. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Circuit design optimization, not small or efficient language models.
CktHGVAE: A Symmetric Design/State Heterogeneous Graph Variational Autoencoder for Transistor-level Analog/Mixed-signal Circuit Design and Analysis The paper introduces a heterogeneous graph variational autoencoder to model design and state variables jointly for analog circuit analysis. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Heterogeneous graph VAEs for analog circuits, not LLM evaluation.
CktHGVAE: A Symmetric Design/State Heterogeneous Graph Variational Autoencoder for Transistor-level Analog/Mixed-signal Circuit Design and Analysis The paper introduces a heterogeneous graph variational autoencoder to model design and state variables jointly for analog circuit analysis. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Not concerned with AI safety, guardrails, or model hallucinations.
High-Probability Regret Bounds for Feel-Good Thompson Sampling The paper establishes theoretical high-probability regret bounds for feel-good Thompson sampling algorithms in contextual bandit problems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Not related to AI safety, red-teaming, or model deployment risks.
High-Probability Regret Bounds for Feel-Good Thompson Sampling The paper establishes theoretical high-probability regret bounds for feel-good Thompson sampling algorithms in contextual bandit problems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Contextual bandit regret theory, not language model evaluation or benchmarks.
High-Probability Regret Bounds for Feel-Good Thompson Sampling The paper establishes theoretical high-probability regret bounds for feel-good Thompson sampling algorithms in contextual bandit problems. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Theoretical bandit algorithms, not model compression or efficient serving.
HankelKANet: Lightweight Path Loss Estimation via Physics-Inspired Hankel Convolution The paper proposes a neural network using physics-inspired Hankel convolution kernels for lightweight path loss estimation in wireless networks. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Specific wireless physics-informed network, not general small language models or compression.
HankelKANet: Lightweight Path Loss Estimation via Physics-Inspired Hankel Convolution The paper proposes a neural network using physics-inspired Hankel convolution kernels for lightweight path loss estimation in wireless networks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not address AI safety, hallucination, or deployment risks.
HankelKANet: Lightweight Path Loss Estimation via Physics-Inspired Hankel Convolution The paper proposes a neural network using physics-inspired Hankel convolution kernels for lightweight path loss estimation in wireless networks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on wireless path loss estimation, not LLM evaluation.
Two-stage Residual Learning for Modelling Strength-Informed Optimisation of Thermal Systems The paper introduces a two-stage residual learning framework to detect local inaccuracies and guide the optimization of thermal systems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Not focused on AI safety, guardrails, or language model reliability.
Two-stage Residual Learning for Modelling Strength-Informed Optimisation of Thermal Systems The paper introduces a two-stage residual learning framework to detect local inaccuracies and guide the optimization of thermal systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Surrogate modeling for thermal systems, not LLM evaluation or benchmarks.
Two-stage Residual Learning for Modelling Strength-Informed Optimisation of Thermal Systems The paper introduces a two-stage residual learning framework to detect local inaccuracies and guide the optimization of thermal systems. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on thermal systems optimization, not efficient model architectures.
Noisy-Label Calibration with Multiple Unreliable Labels The paper develops methods to evaluate and recalibrate binary predictors using noisy, multi-annotator labels without requiring clean ground truth. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address model compression, distillation, or efficient inference.
Noisy-Label Calibration with Multiple Unreliable Labels The paper develops methods to evaluate and recalibrate binary predictors using noisy, multi-annotator labels without requiring clean ground truth. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Addresses general binary classification calibration under noisy labels, not LLM benchmarking.
Test-Time Forecasting: Recurrent Depth Enables In-Context Dynamics Prediction The paper uses recurrent gradient descent iterations during test time to enable foundation models to forecast unseen dynamical systems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Not related to AI safety, hallucinations, or deployment guardrails.
Test-Time Forecasting: Recurrent Depth Enables In-Context Dynamics Prediction The paper uses recurrent gradient descent iterations during test time to enable foundation models to forecast unseen dynamical systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on dynamical systems forecasting architectures, not LLM evaluation.
Test-Time Forecasting: Recurrent Depth Enables In-Context Dynamics Prediction The paper uses recurrent gradient descent iterations during test time to enable foundation models to forecast unseen dynamical systems. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Addresses dynamical systems forecasting via recurrent depth, not general LLM compression.
Geometric Kernel Diversity: An Axiomatic Diversity Measure in Euclidean Space The paper introduces a continuous, monotonic geometric kernel measure and algorithms to efficiently compute diversity over Euclidean points. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Theoretical diversity measure algorithms, not model efficiency or compression.
Geometric Kernel Diversity: An Axiomatic Diversity Measure in Euclidean Space The paper introduces a continuous, monotonic geometric kernel measure and algorithms to efficiently compute diversity over Euclidean points. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not address AI safety, jailbreaks, or deployment guardrails.
Geometric Kernel Diversity: An Axiomatic Diversity Measure in Euclidean Space The paper introduces a continuous, monotonic geometric kernel measure and algorithms to efficiently compute diversity over Euclidean points. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Computational geometry for axiomatic diversity measures, not language model evaluation.
On Inherent Privacy of Posterior Sampling: A Unified Rényi-Divergence Framework The paper develops a theoretical Rényi-divergence framework to measure inherent differential privacy guarantees obtained directly from Bayesian posterior sampling. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Addresses theoretical differential privacy bounds, not LLM evaluation frameworks.
On Inherent Privacy of Posterior Sampling: A Unified Rényi-Divergence Framework The paper develops a theoretical Rényi-divergence framework to measure inherent differential privacy guarantees obtained directly from Bayesian posterior sampling. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on Bayesian differential privacy theory, not efficient inference or SLMs.
Projected Wasserstein-Fisher-Rao Flows for Hawkes Background-Intensity Estimation The paper proposes a variational method using Wasserstein-Fisher-Rao geometry to estimate the background intensity of Hawkes processes. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Not related to AI safety, hallucination, or deployment robustness.
Projected Wasserstein-Fisher-Rao Flows for Hawkes Background-Intensity Estimation The paper proposes a variational method using Wasserstein-Fisher-Rao geometry to estimate the background intensity of Hawkes processes. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on statistical manifold flows, not model compression or efficient serving.
Projected Wasserstein-Fisher-Rao Flows for Hawkes Background-Intensity Estimation The paper proposes a variational method using Wasserstein-Fisher-Rao geometry to estimate the background intensity of Hawkes processes. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Theoretical statistics for Hawkes processes, not LLM benchmarking or evaluation.
InstructPro2: Natural Language and Biological Grounding Guided De Novo Protein Design The paper introduces a model for de novo protein sequence generation guided by natural language instructions and biological context encoders. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on computational biology and protein sequence design, not LLM evaluation benchmarks.
InstructPro2: Natural Language and Biological Grounding Guided De Novo Protein Design The paper introduces a model for de novo protein sequence generation guided by natural language instructions and biological context encoders. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not address small language models, distillation, pruning, or efficient inference.
InstructPro2: Natural Language and Biological Grounding Guided De Novo Protein Design The paper introduces a model for de novo protein sequence generation guided by natural language instructions and biological context encoders. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not study AI deployment safety, hallucination, guardrails, or red-teaming.
Risk-Controlled DFT Selection for Machine-Learned Interatomic Potentials The paper applies conformal risk control to decide when to trust machine-learned interatomic potentials versus running expensive physics calculations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Materials science simulation selection, not AI deployment safety.
Risk-Controlled DFT Selection for Machine-Learned Interatomic Potentials The paper applies conformal risk control to decide when to trust machine-learned interatomic potentials versus running expensive physics calculations. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Materials discovery potential selection, not small model optimization.
Risk-Controlled DFT Selection for Machine-Learned Interatomic Potentials The paper applies conformal risk control to decide when to trust machine-learned interatomic potentials versus running expensive physics calculations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Covers conformal risk control for interatomic potentials, not LLMs.
Synthetic reaction data from transformation rules for graph-to-sequence pretraining in biodegradation pathway prediction The paper develops a graph-to-sequence pretraining framework using rule-generated synthetic reactions to predict chemical biodegradation pathways. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Chemical reaction modeling, not AI deployment safety.
Synthetic reaction data from transformation rules for graph-to-sequence pretraining in biodegradation pathway prediction The paper develops a graph-to-sequence pretraining framework using rule-generated synthetic reactions to predict chemical biodegradation pathways. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Biochemical prediction framework, not model compression or efficiency.
Synthetic reaction data from transformation rules for graph-to-sequence pretraining in biodegradation pathway prediction The paper develops a graph-to-sequence pretraining framework using rule-generated synthetic reactions to predict chemical biodegradation pathways. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on biodegradation chemical pathways, not LLM benchmarks.

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