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
A Physics-Informed Hybrid Forward-Inverse Machine Learning Framework for High-Dimensional Sparse Recipe Reconstruction The paper proposes a physics-informed forward-inverse machine learning framework for sparse recipe formulation in industrial dyeing. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on chemical dye formulations, not evaluating language models.
A Physics-Informed Hybrid Forward-Inverse Machine Learning Framework for High-Dimensional Sparse Recipe Reconstruction The paper proposes a physics-informed forward-inverse machine learning framework for sparse recipe formulation in industrial dyeing. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not address small models, pruning, or efficient inference.
A Physics-Informed Hybrid Forward-Inverse Machine Learning Framework for High-Dimensional Sparse Recipe Reconstruction The paper proposes a physics-informed forward-inverse machine learning framework for sparse recipe formulation in industrial dyeing. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Concerns industrial chemical recipes, not AI deployment safety risks.
From Prototype to Massing: Learning Graph Correspondence for Scalable Facade Modeling The paper develops a graph attention network to transfer prototype facade articulation patterns onto building massing targets. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on architectural design workflows, not operational AI safety.
From Prototype to Massing: Learning Graph Correspondence for Scalable Facade Modeling The paper develops a graph attention network to transfer prototype facade articulation patterns onto building massing targets. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on graph correspondence for architecture, not model efficiency.
From Prototype to Massing: Learning Graph Correspondence for Scalable Facade Modeling The paper develops a graph attention network to transfer prototype facade articulation patterns onto building massing targets. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Investigates architectural facade modeling rather than benchmarking language models.
Early Detection of Pension-Sponsor Distress from Public Filings: A Sponsor Vulnerability Index, Stress-Augmented VaR, and Dynamic Self-Investment Caps The paper develops an early-warning pipeline using public balance-sheet filings to predict defined-benefit pension sponsor distress. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Deals with financial risk modeling, not AI system deployment safety.
Early Detection of Pension-Sponsor Distress from Public Filings: A Sponsor Vulnerability Index, Stress-Augmented VaR, and Dynamic Self-Investment Caps The paper develops an early-warning pipeline using public balance-sheet filings to predict defined-benefit pension sponsor distress. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not study model compression, distillation, or efficient model architectures.
Early Detection of Pension-Sponsor Distress from Public Filings: A Sponsor Vulnerability Index, Stress-Augmented VaR, and Dynamic Self-Investment Caps The paper develops an early-warning pipeline using public balance-sheet filings to predict defined-benefit pension sponsor distress. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on financial distress prediction, not evaluating language models.
Hierarchical Multi-Agent Reasoning Systems for Knowledge-Grounded Material Substitution The paper introduces a hierarchical multi-agent reasoning system combining retrieval and knowledge graphs to find chemical material substitutes. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses materials design workflows, not deployment AI safety, red-teaming, or guardrails.
Hierarchical Multi-Agent Reasoning Systems for Knowledge-Grounded Material Substitution The paper introduces a hierarchical multi-agent reasoning system combining retrieval and knowledge graphs to find chemical material substitutes. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on scientific agent workflows, not model compression or efficient inference.
Hierarchical Multi-Agent Reasoning Systems for Knowledge-Grounded Material Substitution The paper introduces a hierarchical multi-agent reasoning system combining retrieval and knowledge graphs to find chemical material substitutes. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes a multi-agent domain system, not an LLM evaluation benchmark.
On physical implementability of reinforcement-learning-driven reasoning The paper presents a theoretical negative result on physically implementing reinforcement-learning-driven reasoning in certain physical neural networks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Analyzes physical neural network dynamics, not LLM benchmark evaluation.
On physical implementability of reinforcement-learning-driven reasoning The paper presents a theoretical negative result on physically implementing reinforcement-learning-driven reasoning in certain physical neural networks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Theoretical physics paper on hardware stability, not efficient model compression or serving.
On physical implementability of reinforcement-learning-driven reasoning The paper presents a theoretical negative result on physically implementing reinforcement-learning-driven reasoning in certain physical neural networks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies physical stability in neural hardware, not AI deployment safety or jailbreaks.
A Stochastic Analysis Perspective on Diffusion Models The paper provides a stochastic analysis perspective linking score matching, Langevin sampling, and diffusion model time reversal. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Mathematical survey of diffusion models, not LLM benchmark design.
A Stochastic Analysis Perspective on Diffusion Models The paper provides a stochastic analysis perspective linking score matching, Langevin sampling, and diffusion model time reversal. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Covers stochastic analysis of diffusion processes, not model distillation or compression.
A Stochastic Analysis Perspective on Diffusion Models The paper provides a stochastic analysis perspective linking score matching, Langevin sampling, and diffusion model time reversal. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Explores stochastic foundations of diffusion sampling, not deployment safety or guardrails.
ULPack: Reference-Conditioned Lossless Compression of Checkpoint Trajectories The paper introduces a lossless compression codec for model checkpoint trajectories conditioned on reference floating-point exponents. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Presents a checkpoint compression algorithm, not an LLM benchmark.
ULPack: Reference-Conditioned Lossless Compression of Checkpoint Trajectories The paper introduces a lossless compression codec for model checkpoint trajectories conditioned on reference floating-point exponents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses binary checkpoint file compression, not safety, guardrails, or model alignment.
Refined Grid Sampling for Efficient Implicit Neural Representations of Large-Scale Scientific Data The paper proposes a dynamic stratified grid sampling approach to train implicit neural representations on large scientific data faster. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Deals with grid coordinate sampling speed, not deployment safety or guardrails.
Refined Grid Sampling for Efficient Implicit Neural Representations of Large-Scale Scientific Data The paper proposes a dynamic stratified grid sampling approach to train implicit neural representations on large scientific data faster. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on scientific implicit neural representations, not LLM benchmark evaluation.
Embedding Graph Kernel Entropy (EGKE): Fine-grained Hallucination Detection for LLMs from Embeddings The paper proposes an embedding-based graph kernel entropy method for computationally efficient hallucination detection in language models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Uses sentence embeddings for entropy calculation, not small model design or compression.
Embedding Graph Kernel Entropy (EGKE): Fine-grained Hallucination Detection for LLMs from Embeddings The paper proposes an embedding-based graph kernel entropy method for computationally efficient hallucination detection in language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes a hallucination detection method rather than evaluating benchmark trustworthiness.
Learning Non-Gaussian SEMs with Conditional Risk, and Why the Direction of Recursion Matters The paper extends recursive linear structural equation model identification to non-Gaussian noise using conditional prediction risk. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Analyzes causal model recovery, not AI safety or deployment monitoring.
Learning Non-Gaussian SEMs with Conditional Risk, and Why the Direction of Recursion Matters The paper extends recursive linear structural equation model identification to non-Gaussian noise using conditional prediction risk. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Presents statistical causality algorithms, not small or efficient neural models.
Learning Non-Gaussian SEMs with Conditional Risk, and Why the Direction of Recursion Matters The paper extends recursive linear structural equation model identification to non-Gaussian noise using conditional prediction risk. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Deals with causal discovery in structural equation models, not LLM evaluation.
Adaptive-Depth Is a Finishing Stage The paper proposes training looped transformers at variable depths only during a short finishing stage to reduce compute costs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on transformer training recipes, not LLM evaluation frameworks.
Adaptive-Depth Is a Finishing Stage The paper proposes training looped transformers at variable depths only during a short finishing stage to reduce compute costs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Investigates compute efficiency in transformers, not safety, guardrails, or hallucinations.
Do Artificial Neural Networks Need Dendritic Nanotubes? Two Reasons for Shrinking Importance Estimates in Continual Learning The paper examines importance estimate shrinkage to mitigate catastrophic forgetting in regularization-based continual learning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses continual learning parameter importance, not model compression or efficient inference.
Do Artificial Neural Networks Need Dendritic Nanotubes? Two Reasons for Shrinking Importance Estimates in Continual Learning The paper examines importance estimate shrinkage to mitigate catastrophic forgetting in regularization-based continual learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies continual learning parameter shrinkage, not deployment safety or guardrails.
Do Artificial Neural Networks Need Dendritic Nanotubes? Two Reasons for Shrinking Importance Estimates in Continual Learning The paper examines importance estimate shrinkage to mitigate catastrophic forgetting in regularization-based continual learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Investigates continual learning regularization techniques, not LLM evaluation benchmarks.
Variance Fingerprints: Component-Wise Identification of Content Factors by Augmentation Design The paper establishes theoretical conditions for identifying individual content factors in self-supervised learning via designed augmentation strengths. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical self-supervised learning identifiability paper, not an LLM benchmark.
Variance Fingerprints: Component-Wise Identification of Content Factors by Augmentation Design The paper establishes theoretical conditions for identifying individual content factors in self-supervised learning via designed augmentation strengths. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on data augmentation theory, not safety guardrails or reliability.
Variance Fingerprints: Component-Wise Identification of Content Factors by Augmentation Design The paper establishes theoretical conditions for identifying individual content factors in self-supervised learning via designed augmentation strengths. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses factor identification in self-supervised models, not model efficiency or compression.
Learning to Sync at the Edge: Client-Side Adaptive Synchronization for Federated Learning The paper introduces a client-side reinforcement learning scheduler to optimize synchronization decisions in edge federated learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses federated synchronization efficiency, not AI safety or hallucination prevention.
Learning to Sync at the Edge: Client-Side Adaptive Synchronization for Federated Learning The paper introduces a client-side reinforcement learning scheduler to optimize synchronization decisions in edge federated learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on federated learning communication protocols, not LLM benchmarking.
Layer-wise Input Recoverability in Deep Neural Networks The paper probes convolutional neural networks to measure how much layer input information can be reconstructed from layer outputs. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Investigates input recoverability in CNNs, not model compression or efficient serving.
Layer-wise Input Recoverability in Deep Neural Networks The paper probes convolutional neural networks to measure how much layer input information can be reconstructed from layer outputs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Analyzes feature recoverability in vision backbones, not AI safety or deployment robustness.
Layer-wise Input Recoverability in Deep Neural Networks The paper probes convolutional neural networks to measure how much layer input information can be reconstructed from layer outputs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Studies CNN layer representations, not LLM benchmarks or evaluation methods.
Diagnosing Generalization Boundaries in AI-Based Vulnerability Detection via Calibrated Embedding Distances The paper evaluates embedding distance metrics to detect generalization boundaries and distribution shifts in AI-based code vulnerability models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on model representation geometry and degradation, not model compression or efficiency.
Learning to See through Action: Predictive Curiosity for Robots The paper proposes an active robot visual learning framework driven by self-supervised curiosity and world model prediction errors. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies active robotic exploration policies, not safety guardrails or hallucination mitigation.
Learning to See through Action: Predictive Curiosity for Robots The paper proposes an active robot visual learning framework driven by self-supervised curiosity and world model prediction errors. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses curiosity-driven robotic learning, not efficient model inference or compression.
Learning to See through Action: Predictive Curiosity for Robots The paper proposes an active robot visual learning framework driven by self-supervised curiosity and world model prediction errors. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on robotic curiosity frameworks, not LLM benchmarks or evaluations.
Learning to See through Action: Predictive Curiosity for Robots The paper proposes an active robot visual learning framework driven by self-supervised curiosity and world model prediction errors. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant General robotic visual exploration, not manufacturing quality control or defect detection.
Decentralized Bandit Learning of Markov Perfect Equilibria The paper proves convergence guarantees for decentralized bandit learning in infinite-horizon, general-sum Markov games. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical multi-agent game theory study, not LLM evaluation.
Decentralized Bandit Learning of Markov Perfect Equilibria The paper proves convergence guarantees for decentralized bandit learning in infinite-horizon, general-sum Markov games. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses continuous Markov game theory, not model efficiency or distillation.
Decentralized Bandit Learning of Markov Perfect Equilibria The paper proves convergence guarantees for decentralized bandit learning in infinite-horizon, general-sum Markov games. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies bandit equilibria convergence, not AI safety guardrails or reliability.
STREGO: A Scalable Implementation of a Trust-Region Framework for Noisy Bayesian Optimization The paper presents a trust-region Bayesian optimization framework designed for noisy, high-dimensional blackbox problems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies noisy Bayesian optimization, not deployment safety or hallucination prevention.
STREGO: A Scalable Implementation of a Trust-Region Framework for Noisy Bayesian Optimization The paper presents a trust-region Bayesian optimization framework designed for noisy, high-dimensional blackbox problems. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on Bayesian optimization algorithms, not efficient model serving or compression.
STREGO: A Scalable Implementation of a Trust-Region Framework for Noisy Bayesian Optimization The paper presents a trust-region Bayesian optimization framework designed for noisy, high-dimensional blackbox problems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Optimization framework paper, not an LLM evaluation benchmark.
Continuous-Time Cloth Dynamics with Physics-Informed Neural ODEs The paper presents a continuous-time neural ODE solver for simulating cloth dynamics at arbitrary timestamps. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on neural ODE simulation, not LLM benchmarks or evaluation.
Continuous-Time Cloth Dynamics with Physics-Informed Neural ODEs The paper presents a continuous-time neural ODE solver for simulating cloth dynamics at arbitrary timestamps. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Concerns physical cloth simulation solvers, not small language models or model compression.
Continuous-Time Cloth Dynamics with Physics-Informed Neural ODEs The paper presents a continuous-time neural ODE solver for simulating cloth dynamics at arbitrary timestamps. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Addresses deformable cloth modeling, not AI safety or deployment robustness.
Continuous-Time Cloth Dynamics with Physics-Informed Neural ODEs The paper presents a continuous-time neural ODE solver for simulating cloth dynamics at arbitrary timestamps. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Physics simulation of cloth, not industrial defect detection or visual inspection.
On the Depth Transfer of Neural Optimizers via L-Smoothness The paper analyzes depth transfer of hyperparameters in neural networks using an L-smoothness theoretical framework. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Analyzes hyperparameter depth transfer dynamics, not LLM evaluation benchmarks.
On the Depth Transfer of Neural Optimizers via L-Smoothness The paper analyzes depth transfer of hyperparameters in neural networks using an L-smoothness theoretical framework. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on optimization dynamics rather than AI safety or reliability monitoring.
Class-Conditional Conformal Prediction for Signal Temporal Logic Inference with Many Classes The paper presents a conformal prediction method to reliably infer Signal Temporal Logic formulas for multiclass time series classification. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Does not address model compression, quantization, or efficient inference.
Class-Conditional Conformal Prediction for Signal Temporal Logic Inference with Many Classes The paper presents a conformal prediction method to reliably infer Signal Temporal Logic formulas for multiclass time series classification. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Deals with signal temporal logic inference, not AI deployment safety or jailbreaks.
Class-Conditional Conformal Prediction for Signal Temporal Logic Inference with Many Classes The paper presents a conformal prediction method to reliably infer Signal Temporal Logic formulas for multiclass time series classification. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Covers conformal prediction for time-series logic, not LLM evaluation benchmarks.

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