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
PDE-PFN: Prior-Data Fitted Neural PDE Solver Proposes a transformer solver that uses in-context learning trained on approximate solutions to predict partial differential equation fields. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on physics-informed in-context learning solvers, not small language model compression or efficiency.
PDE-PFN: Prior-Data Fitted Neural PDE Solver Proposes a transformer solver that uses in-context learning trained on approximate solutions to predict partial differential equation fields. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Presents a neural PDE solver, not AI safety, alignment, or deployment reliability controls.
PDE-PFN: Prior-Data Fitted Neural PDE Solver Proposes a transformer solver that uses in-context learning trained on approximate solutions to predict partial differential equation fields. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant In-context PDE solver framework, not LLM evaluation or benchmark design.
AgentTune: A Unified Training Lifecycle for Tool-Using LLM Agents Presents an open-source library that unifies training, evaluation, reinforcement learning, and distillation workflows for tool-using language model agents. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on agent runtime, training, and workflows rather than small efficient model architectures.
AgentTune: A Unified Training Lifecycle for Tool-Using LLM Agents Presents an open-source library that unifies training, evaluation, reinforcement learning, and distillation workflows for tool-using language model agents. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Presents a broad framework for training and running agents, not an LLM benchmark or evaluation study.
Understanding Mamba-3 Through Oscillatory Dynamics Analyzes Mamba-3's input-dependent state rotations as oscillatory dynamics to explain their utility for tracking states and long-context language modeling. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Analyzes recurrent sequence model dynamics, not LLM evaluation benchmarks or methodologies.
Understanding Mamba-3 Through Oscillatory Dynamics Analyzes Mamba-3's input-dependent state rotations as oscillatory dynamics to explain their utility for tracking states and long-context language modeling. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Investigates recurrent oscillatory mechanics, not safety, alignment, or deployment guardrails.
Hidden Data Challenges in Clinical Machine Learning Identifies and evaluates temporal, semantic, and population preprocessing misalignments that cause data leakage and bias in clinical machine learning pipelines. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Audits clinical tabular preprocessing pipelines, not LLM evaluation or benchmark methodology.
Hidden Data Challenges in Clinical Machine Learning Identifies and evaluates temporal, semantic, and population preprocessing misalignments that cause data leakage and bias in clinical machine learning pipelines. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Studies clinical electronic health record preprocessing, not efficient models or inference techniques.
EmoGavel: Grounding-Aware Reinforcement Learning with Verifiable Rewards for Multimodal Emotion Preference Learning Proposes a reinforcement learning framework with verifiable rewards and error grounding to align multimodal emotion descriptions with human preferences. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on reinforcement learning for emotion preference alignment, not small efficient models.
EmoGavel: Grounding-Aware Reinforcement Learning with Verifiable Rewards for Multimodal Emotion Preference Learning Proposes a reinforcement learning framework with verifiable rewards and error grounding to align multimodal emotion descriptions with human preferences. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on preference learning alignment for emotion tasks, not deployment guardrails or system safety.
EmoGavel: Grounding-Aware Reinforcement Learning with Verifiable Rewards for Multimodal Emotion Preference Learning Proposes a reinforcement learning framework with verifiable rewards and error grounding to align multimodal emotion descriptions with human preferences. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Method for training emotion preference models, not an LLM evaluation framework or benchmark study.
Opening the Black Box of Classifier-Free Guidance via Information Bottleneck Analyzes classifier-free guidance in diffusion models through an information bottleneck framework to characterize when conditioning information is most effective. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Information-theoretic study of diffusion sampling schedules, not AI deployment safety or guardrails.
Opening the Black Box of Classifier-Free Guidance via Information Bottleneck Analyzes classifier-free guidance in diffusion models through an information bottleneck framework to characterize when conditioning information is most effective. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Analyzes classifier-free guidance in diffusion, not LLM evaluation or benchmark validity.
Opening the Black Box of Classifier-Free Guidance via Information Bottleneck Analyzes classifier-free guidance in diffusion models through an information bottleneck framework to characterize when conditioning information is most effective. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Studies diffusion sampling guidance schedules, not model compression, quantization, or small language models.
BLOKS: Scaling Kernel Solvers with Sketched Block Preconditioning Introduces a conjugate gradient solver for large-scale kernel ridge regression using sketched block preconditioning to improve memory efficiency and convergence. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Scalable kernel solver algorithm, not LLM evaluation or benchmark reliability.
BLOKS: Scaling Kernel Solvers with Sketched Block Preconditioning Introduces a conjugate gradient solver for large-scale kernel ridge regression using sketched block preconditioning to improve memory efficiency and convergence. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Scales kernel ridge regression solvers, not deep model compression, quantization, or efficient LLM inference.
BLOKS: Scaling Kernel Solvers with Sketched Block Preconditioning Introduces a conjugate gradient solver for large-scale kernel ridge regression using sketched block preconditioning to improve memory efficiency and convergence. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Kernel regression optimization, not AI safety, alignment, or deployment guardrails.
PlacePulse: Learning Transferable Place Representations from Structural Geographic Data Presents a geospatial foundation model that creates structural place embeddings from sociodemographic and business data across hexagonal spatial units. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Introduces a geospatial foundation model and benchmark, not an evaluation method for language models.
PlacePulse: Learning Transferable Place Representations from Structural Geographic Data Presents a geospatial foundation model that creates structural place embeddings from sociodemographic and business data across hexagonal spatial units. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Geospatial embedding representation model, not small language model distillation, quantization, or efficient serving.
PlacePulse: Learning Transferable Place Representations from Structural Geographic Data Presents a geospatial foundation model that creates structural place embeddings from sociodemographic and business data across hexagonal spatial units. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Geospatial representation learning, not deployment safety, guardrails, or alignment risks.
Model-free Bayesian Clustering and Multimodal Posterior Summarisation Develops a model-free Bayesian clustering method using martingale posteriors and Wasserstein summarization to bypass explicit likelihoods and MCMC sampling. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Statistical clustering algorithms, not AI safety, jailbreak defense, or deployment guardrails.
Model-free Bayesian Clustering and Multimodal Posterior Summarisation Develops a model-free Bayesian clustering method using martingale posteriors and Wasserstein summarization to bypass explicit likelihoods and MCMC sampling. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Bayesian clustering methodology, not LLM evaluation frameworks or benchmark metrics.
Model-free Bayesian Clustering and Multimodal Posterior Summarisation Develops a model-free Bayesian clustering method using martingale posteriors and Wasserstein summarization to bypass explicit likelihoods and MCMC sampling. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Addresses Bayesian clustering posterior computation, not model compression or efficient LLM deployment.
Why Linear Models Can Still Work in a Complex and Changing World Analyzes theoretically and empirically why linear models often out-predict complex nonlinear models under finite samples and environment distribution shifts. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Analyzes linear versus nonlinear model generalization, not small language model compression or efficient serving.
Why Linear Models Can Still Work in a Complex and Changing World Analyzes theoretically and empirically why linear models often out-predict complex nonlinear models under finite samples and environment distribution shifts. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on transfer risk of linear models, not operational LLM safety, red-teaming, or guardrails.
Why Linear Models Can Still Work in a Complex and Changing World Analyzes theoretically and empirically why linear models often out-predict complex nonlinear models under finite samples and environment distribution shifts. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Theoretical paper explaining linear model transferability, not LLM benchmark design or evaluation protocols.
Desertification Forecasting in Semi-Arid Regions: Using Deep Learning to Probe the Observability Limits of Land Degradation Dynamics Uses a convolutional LSTM on satellite data to evaluate the predictability and observational limits of year-to-year land degradation dynamics. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:29	Not relevant Focuses on satellite time-series land degradation forecasting, not industrial computer vision or inspection.
Desertification Forecasting in Semi-Arid Regions: Using Deep Learning to Probe the Observability Limits of Land Degradation Dynamics Uses a convolutional LSTM on satellite data to evaluate the predictability and observational limits of year-to-year land degradation dynamics. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Environmental forecasting modeling, not AI safety, alignment, guardrails, or deployment reliability risks.
Desertification Forecasting in Semi-Arid Regions: Using Deep Learning to Probe the Observability Limits of Land Degradation Dynamics Uses a convolutional LSTM on satellite data to evaluate the predictability and observational limits of year-to-year land degradation dynamics. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Application of ConvLSTM to Earth observation data, not model compression or efficient serving.
Desertification Forecasting in Semi-Arid Regions: Using Deep Learning to Probe the Observability Limits of Land Degradation Dynamics Uses a convolutional LSTM on satellite data to evaluate the predictability and observational limits of year-to-year land degradation dynamics. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Satellite land degradation forecasting study, not general LLM evaluation or benchmark design.
Accelerating Linear Bandits with Persistent Candidate Sets Introduces an efficient linear bandit algorithm that maintains a small candidate set of actions to reduce per-round evaluation costs. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on linear multi-armed bandit optimization, not small language models or deep model efficiency.
Accelerating Linear Bandits with Persistent Candidate Sets Introduces an efficient linear bandit algorithm that maintains a small candidate set of actions to reduce per-round evaluation costs. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Bandit regret minimization analysis, not AI safety, hallucination mitigation, or deployment guardrails.
Accelerating Linear Bandits with Persistent Candidate Sets Introduces an efficient linear bandit algorithm that maintains a small candidate set of actions to reduce per-round evaluation costs. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Theoretical bandit algorithm acceleration, not LLM evaluation or benchmarking frameworks.
One Thing Leads to Another: The Essentials of Time Series Causal Discovery Provides a comprehensive tutorial on time series causal discovery methods, covering underlying principles, evaluation techniques, and practical applications. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Tutorial on time series causal discovery methods, not LLM evaluation benchmarks or testing protocols.
One Thing Leads to Another: The Essentials of Time Series Causal Discovery Provides a comprehensive tutorial on time series causal discovery methods, covering underlying principles, evaluation techniques, and practical applications. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Overview of time-series causality principles, not model compression or efficient LLM inference.
One Thing Leads to Another: The Essentials of Time Series Causal Discovery Provides a comprehensive tutorial on time series causal discovery methods, covering underlying principles, evaluation techniques, and practical applications. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Tutorial on time-series causal inference methods, not deployment safety, guardrails, or red-teaming.
PPG-Bench: A Unified Benchmark for PPG Foundation Models Introduces a unified benchmark evaluating five public photoplethysmography foundation models across nine datasets and 26 clinical and wearable tasks. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Benchmarking representation quality on biosignals, not deployment safety, guardrails, or alignment.
PPG-Bench: A Unified Benchmark for PPG Foundation Models Introduces a unified benchmark evaluating five public photoplethysmography foundation models across nine datasets and 26 clinical and wearable tasks. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on benchmarking photoplethysmography foundation models, not small efficient model techniques.
Learning by Compression: Risk Estimates Attain the Law-of-Large-Numbers Rate Proves that risk estimates derived from preferent compression schemes achieve standard law-of-large-numbers rates without excess logarithmic factors. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Statistical learning theory on risk bounds, not empirical LLM evaluation or benchmark frameworks.
Learning by Compression: Risk Estimates Attain the Law-of-Large-Numbers Rate Proves that risk estimates derived from preferent compression schemes achieve standard law-of-large-numbers rates without excess logarithmic factors. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Theoretical sample-compression risk bounds, not practical model compression, quantization, or efficient inference.
Learning by Compression: Risk Estimates Attain the Law-of-Large-Numbers Rate Proves that risk estimates derived from preferent compression schemes achieve standard law-of-large-numbers rates without excess logarithmic factors. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Pure statistical learning theory, not deployed AI safety, guardrails, or hallucination mitigation.
Harmonic analysis and circuit approximation of Sobolev functions on quantum input spaces Derives theoretical approximation rates for Sobolev functions on quantum state and gate spaces using harmonic analysis and finite-depth quantum circuits. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Theoretical quantum circuit approximation rates, not classical small language models or efficient inference.
Harmonic analysis and circuit approximation of Sobolev functions on quantum input spaces Derives theoretical approximation rates for Sobolev functions on quantum state and gate spaces using harmonic analysis and finite-depth quantum circuits. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Addresses quantum circuit mathematical approximation theory, not real-world AI deployment safety or robustness.
Harmonic analysis and circuit approximation of Sobolev functions on quantum input spaces Derives theoretical approximation rates for Sobolev functions on quantum state and gate spaces using harmonic analysis and finite-depth quantum circuits. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Focuses on quantum Sobolev function approximation, not LLM benchmarks or evaluation trustworthiness.
A Scalable Learned Iterative Solver for Nonlinear MPC via Temporal Decomposition Introduces a learned primal-dual solver for nonlinear model predictive control that uses temporal decomposition to scale independently of the prediction horizon. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Focuses on numerical control solvers, not AI deployment safety, alignment, or guardrail systems.
A Scalable Learned Iterative Solver for Nonlinear MPC via Temporal Decomposition Introduces a learned primal-dual solver for nonlinear model predictive control that uses temporal decomposition to scale independently of the prediction horizon. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Concerns optimal control solver architectures, not efficient small language models or LLM inference serving.
A Scalable Learned Iterative Solver for Nonlinear MPC via Temporal Decomposition Introduces a learned primal-dual solver for nonlinear model predictive control that uses temporal decomposition to scale independently of the prediction horizon. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Addresses learned optimization for nonlinear predictive control, not LLM benchmarks or evaluation methods.
FedPCD: Priority-Constrained Federated Aggregation Proposes a federated optimization framework that preserves global aggregate objectives while applying minimal corrections to meet individual client performance constraints. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Proposes a federated aggregation constraint algorithm, not LLM safety, guardrails, or deployment vulnerability mitigation.
FedPCD: Priority-Constrained Federated Aggregation Proposes a federated optimization framework that preserves global aggregate objectives while applying minimal corrections to meet individual client performance constraints. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Covers federated learning optimization across clients, not model compression, quantization, or efficient serving.
FedPCD: Priority-Constrained Federated Aggregation Proposes a federated optimization framework that preserves global aggregate objectives while applying minimal corrections to meet individual client performance constraints. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Focuses on federated optimization algorithms, not LLM evaluation methods or benchmark datasets.
Asymptotic Analysis of Discrete-Time Self-Attention Dynamics in Transformers Analyzes the discrete-time dynamical system of transformer self-attention, classifying its equilibria and showing consensus alignment with the value matrix's principal eigenvector. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Theoretical analysis of self-attention dynamical systems, not LLM evaluation benchmarks or methodologies.
Asymptotic Analysis of Discrete-Time Self-Attention Dynamics in Transformers Analyzes the discrete-time dynamical system of transformer self-attention, classifying its equilibria and showing consensus alignment with the value matrix's principal eigenvector. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Studies mathematical dynamical stability of attention layers, not deployment safety, alignment, or guardrails.
Asymptotic Analysis of Discrete-Time Self-Attention Dynamics in Transformers Analyzes the discrete-time dynamical system of transformer self-attention, classifying its equilibria and showing consensus alignment with the value matrix's principal eigenvector. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Investigates theoretical equilibria of attention dynamics, not efficient inference or compact model architectures.
Simulating Training Spikes of Deep Neural Networks by Layer-wise Random Feature Models Simulates transformer training loss spikes using quadratic models with heavy-tailed inputs and derives an adaptive optimizer hyperparameter schedule to prevent them. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Examines training loss spikes, not deployment safety, jailbreaks, prompt injection, or operational reliability.
Simulating Training Spikes of Deep Neural Networks by Layer-wise Random Feature Models Simulates transformer training loss spikes using quadratic models with heavy-tailed inputs and derives an adaptive optimizer hyperparameter schedule to prevent them. Preprint	Small and efficient models via arxiv · 12 Oct, 16:29	Not relevant Focuses on training stability dynamics, not model compression, distillation, quantization, or efficient inference.
Simulating Training Spikes of Deep Neural Networks by Layer-wise Random Feature Models Simulates transformer training loss spikes using quadratic models with heavy-tailed inputs and derives an adaptive optimizer hyperparameter schedule to prevent them. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Studies pre-training loss spike dynamics theoretically, not LLM benchmark design or evaluation trustworthiness.
Probabilistic Ship Trajectory Forecasting using Transformer Models on AIS Data Evaluates Transformer models trained with quantile loss on historical vessel position data to produce probabilistic ship trajectory forecasts. Preprint	AI safety and reliability in deployment via arxiv · 12 Oct, 16:29	Not relevant Studies vessel collision avoidance forecasting, not LLM deployment safety, guardrails, or alignment.
Probabilistic Ship Trajectory Forecasting using Transformer Models on AIS Data Evaluates Transformer models trained with quantile loss on historical vessel position data to produce probabilistic ship trajectory forecasts. Preprint	LLM evaluation and benchmarks via arxiv · 12 Oct, 16:29	Not relevant Evaluates maritime vessel trajectory forecasting, not LLM evaluation or benchmarks.
Probabilistic Ship Trajectory Forecasting using Transformer Models on AIS Data Evaluates Transformer models trained with quantile loss on historical vessel position data to produce probabilistic ship trajectory forecasts. Preprint	Computer vision for industrial applications via arxiv · 12 Oct, 16:29	Not relevant Uses AIS coordinates and positional heat maps, not industrial computer vision or defect inspection.

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