Every paper checked — nothing hidden

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Every paper checked for brief #4 of 12–19 Oct 2026 — nothing is hidden. Brief papers first.

All17,312 In brief24 More (on-topic)3,333 Not relevant13,753 Not assessed87 Older351 Could not confirm93 Blogs with no paper266 All blogs284

Paper	Topic	Relevance
Scene Reconstruction Using Ubiquitous Wireless Signals via Diffusion Posterior Inference Over physics-Grounded Representations This paper presents a diffusion-based posterior inference framework to reconstruct physical scenes from ubiquitous wireless channel measurements. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Presents wireless signal diffusion models, not small models or efficient inference.
Beyond Fixed Acquisition Functions: Temporal Difference Active Learning for Perturbation Discovery This paper introduces a temporal difference active learning framework to adaptively learn experiment selection policies for budgeted perturbation discovery. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on biological perturbation active learning, not LLM evaluation benchmarks.
Beyond Fixed Acquisition Functions: Temporal Difference Active Learning for Perturbation Discovery This paper introduces a temporal difference active learning framework to adaptively learn experiment selection policies for budgeted perturbation discovery. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Deals with experimental biological screening, not AI deployment safety or guardrails.
Beyond Fixed Acquisition Functions: Temporal Difference Active Learning for Perturbation Discovery This paper introduces a temporal difference active learning framework to adaptively learn experiment selection policies for budgeted perturbation discovery. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Addresses drug screen active learning, not model compression or efficient serving.
The importance of tensor handling and implementation of physics-informed DeepONets Reorganizes tensor operations in physics-informed DeepONet architectures to eliminate redundancy and reduce training costs for higher-dimensional problems. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Focuses on solving differential equations, not safety, guardrails, or alignment.
The importance of tensor handling and implementation of physics-informed DeepONets Reorganizes tensor operations in physics-informed DeepONet architectures to eliminate redundancy and reduce training costs for higher-dimensional problems. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Targets scientific operator networks, not small language models or LLM efficiency.
The importance of tensor handling and implementation of physics-informed DeepONets Reorganizes tensor operations in physics-informed DeepONet architectures to eliminate redundancy and reduce training costs for higher-dimensional problems. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on physics-informed neural network architectures, not LLM benchmarks.
LCT: Losslessly Compressed Tensors with GPU friendly execution for Training Provides a GPU-accelerated Huffman compression framework for tensors to lower memory overhead during model training. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on GPU tensor compression systems, not LLM benchmark evaluation.
LCT: Losslessly Compressed Tensors with GPU friendly execution for Training Provides a GPU-accelerated Huffman compression framework for tensors to lower memory overhead during model training. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Deals with training memory compression, not AI deployment safety or guardrails.
Attention over noisy examples: generalization and memorization in the exponential Hopfield network Analyzes generalization and memorization dynamics in exponential Hopfield networks trained with softmax attention over noisy examples. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Analyzes theoretical Hopfield models, not practical model compression or efficiency.
Attention over noisy examples: generalization and memorization in the exponential Hopfield network Analyzes generalization and memorization dynamics in exponential Hopfield networks trained with softmax attention over noisy examples. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Statistical physics analysis of Hopfield networks, not LLM evaluation benchmarks.
Attention over noisy examples: generalization and memorization in the exponential Hopfield network Analyzes generalization and memorization dynamics in exponential Hopfield networks trained with softmax attention over noisy examples. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Studies associative memory theory rather than AI deployment safety or guardrails.
Generalization Guarantees of Randomized Block Coordinate Descent for Nonconvex Learning Derives theoretical high-probability generalization guarantees and early stopping bounds for randomized block coordinate descent on nonconvex problems. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Theoretical nonconvex optimization analysis, not LLM evaluation or benchmarking.
Generalization Guarantees of Randomized Block Coordinate Descent for Nonconvex Learning Derives theoretical high-probability generalization guarantees and early stopping bounds for randomized block coordinate descent on nonconvex problems. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Presents theoretical generalization bounds, not small models or efficient inference.
Generalization Guarantees of Randomized Block Coordinate Descent for Nonconvex Learning Derives theoretical high-probability generalization guarantees and early stopping bounds for randomized block coordinate descent on nonconvex problems. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Theoretical optimization theory, not deployment safety, guardrails, or hallucination.
Architecting for Hardware Diversity: Meta's End-to-End Co-Design for High-Performance AI Training on AMD GPUs Shares hardware-model co-design techniques and analytical kernel selection for training large recommendation models on AMD GPUs. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Addresses hardware optimization and porting, not AI safety, hallucination, or guardrails.
Architecting for Hardware Diversity: Meta's End-to-End Co-Design for High-Performance AI Training on AMD GPUs Shares hardware-model co-design techniques and analytical kernel selection for training large recommendation models on AMD GPUs. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on hardware-model co-design, not LLM evaluation or benchmark methodology.
Query-Efficient Rejection Sampling for Parallel Masked Diffusion Proposes a single-increment rejection sampling algorithm to correct token correlation bias efficiently in parallel masked diffusion. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on diffusion sampling algorithms, not language model evaluation benchmarks.
Query-Efficient Rejection Sampling for Parallel Masked Diffusion Proposes a single-increment rejection sampling algorithm to correct token correlation bias efficiently in parallel masked diffusion. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Presents diffusion sampling theory, not AI guardrails, jailbreaks, or safety.
SurvICL: a Foundation Model for in-context Survival Analysis Introduces a tabular foundation model pretrained on synthetic priors to predict survival curves in-context. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Studies clinical survival predictions rather than AI safety or reliability guardrails.
SurvICL: a Foundation Model for in-context Survival Analysis Introduces a tabular foundation model pretrained on synthetic priors to predict survival curves in-context. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Introduces a tabular survival analysis benchmark, not LLM or language model evaluation.
SurvICL: a Foundation Model for in-context Survival Analysis Introduces a tabular foundation model pretrained on synthetic priors to predict survival curves in-context. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on in-context survival models, not model compression or efficient serving.
Explore Once, Evaluate Many: Multiple Policy Evaluation for Stochastic Linear Bandits Introduces an algorithm to evaluate multiple target policies simultaneously in contextual linear bandits with shared sampling. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Focuses on bandit sample complexity bounds, not deployment safety or jailbreaks.
Explore Once, Evaluate Many: Multiple Policy Evaluation for Stochastic Linear Bandits Introduces an algorithm to evaluate multiple target policies simultaneously in contextual linear bandits with shared sampling. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Theoretical bandit policy evaluation, not benchmarking or evaluating LLMs.
Explore Once, Evaluate Many: Multiple Policy Evaluation for Stochastic Linear Bandits Introduces an algorithm to evaluate multiple target policies simultaneously in contextual linear bandits with shared sampling. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Studies stochastic bandit sample complexity, not efficient or small model inference.
BitSparse: Compact Activation Storage for ReLU Feed-Forward Networks Presents a GPU format that losslessly compresses sparse activations to reduce memory usage during neural network training. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on memory compression engineering, not evaluating or benchmarking LLMs.
BitSparse: Compact Activation Storage for ReLU Feed-Forward Networks Presents a GPU format that losslessly compresses sparse activations to reduce memory usage during neural network training. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Focuses on memory reduction techniques, not safety, guardrails, or alignment.
Topology-Inspired Fine-Tuning for Wind-Field Super-Resolution Applies topology-inspired loss functions to fine-tune super-resolution neural networks on scientific wind-field datasets. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Focuses on weather super-resolution fine-tuning, not AI safety and reliability.
Topology-Inspired Fine-Tuning for Wind-Field Super-Resolution Applies topology-inspired loss functions to fine-tune super-resolution neural networks on scientific wind-field datasets. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Evaluates wind-field super-resolution topology, not language model benchmarking.
Topology-Inspired Fine-Tuning for Wind-Field Super-Resolution Applies topology-inspired loss functions to fine-tune super-resolution neural networks on scientific wind-field datasets. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Presents scientific image super-resolution, not efficient or small language models.
Baselines and Score Centering in Policy Gradient Estimation Analyzes score centering and covariance estimands to maintain reward shift invariance under regularized importance sampling. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Theoretical analysis of policy gradient estimation, not LLM evaluation benchmarks.
Baselines and Score Centering in Policy Gradient Estimation Analyzes score centering and covariance estimands to maintain reward shift invariance under regularized importance sampling. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Discusses RL post-training optimization theory rather than deployment safety or guardrails.
Baselines and Score Centering in Policy Gradient Estimation Analyzes score centering and covariance estimands to maintain reward shift invariance under regularized importance sampling. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Covers policy gradient math for post-training, not model compression or efficiency.
Learning Rational Strategies for Temporal Objectives in Multi-Agent Systems Analyzes rational strategy synthesis and equilibrium guarantees for stochastic multi-agent systems with probabilistic temporal logic objectives. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Presents theoretical RL verification rather than testing or benchmarking LLMs.
Learning Rational Strategies for Temporal Objectives in Multi-Agent Systems Analyzes rational strategy synthesis and equilibrium guarantees for stochastic multi-agent systems with probabilistic temporal logic objectives. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Focuses on game-theoretic synthesis rather than AI safety guardrails or hallucinations.
Learning Rational Strategies for Temporal Objectives in Multi-Agent Systems Analyzes rational strategy synthesis and equilibrium guarantees for stochastic multi-agent systems with probabilistic temporal logic objectives. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Does not study efficient serving, quantization, or small language models.
Learning Rational Strategies for Temporal Objectives in Multi-Agent Systems Analyzes rational strategy synthesis and equilibrium guarantees for stochastic multi-agent systems with probabilistic temporal logic objectives. Preprint	AI agents via arxiv · 17 Oct, 06:18	Not relevant Theoretical multi-agent RL synthesis, not language model agents or planning.
Backward Compatibility in Zero-Bit Neural Audio Watermarking Establishes a training approach to ensure backward compatibility and prevent false positives across versions of neural audio watermarking. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Does not involve model pruning, distillation, quantization, or efficient serving.
Backward Compatibility in Zero-Bit Neural Audio Watermarking Establishes a training approach to ensure backward compatibility and prevent false positives across versions of neural audio watermarking. Preprint	Multimodal models via arxiv · 17 Oct, 06:18	Not relevant Focuses on zero-bit audio watermarking, not multimodal reasoning or vision-language models.
Backward Compatibility in Zero-Bit Neural Audio Watermarking Establishes a training approach to ensure backward compatibility and prevent false positives across versions of neural audio watermarking. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Focuses on audio watermarking compatibility instead of AI guardrails or jailbreaks.
Backward Compatibility in Zero-Bit Neural Audio Watermarking Establishes a training approach to ensure backward compatibility and prevent false positives across versions of neural audio watermarking. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Covers audio watermarking backward compatibility rather than LLM evaluation benchmarks.
Robust Reinforcement Learning for Dynamic Treatment Regimes under Model and Measurement Uncertainty Develops a robust reinforcement learning framework for dynamic treatment policies under patient measurement and model uncertainty. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Does not address model compression, quantization, or efficient inference techniques.
Robust Reinforcement Learning for Dynamic Treatment Regimes under Model and Measurement Uncertainty Develops a robust reinforcement learning framework for dynamic treatment policies under patient measurement and model uncertainty. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on medical RL treatment regimes, not evaluating language models.
Robust Reinforcement Learning for Dynamic Treatment Regimes under Model and Measurement Uncertainty Develops a robust reinforcement learning framework for dynamic treatment policies under patient measurement and model uncertainty. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Concerns clinical RL robustness rather than AI deployment safety or guardrails.
Lean Graph Search for Formal Theorem Proving Introduces a graph-based retrieval method that traces multi-hop library dependencies to find lemmas for formal theorem proving. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Addresses premise retrieval algorithms, not model quantization or efficient inference.
Lean Graph Search for Formal Theorem Proving Introduces a graph-based retrieval method that traces multi-hop library dependencies to find lemmas for formal theorem proving. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Focuses on mathematical lemma retrieval, not AI safety or deployment robustness.
Lean Graph Search for Formal Theorem Proving Introduces a graph-based retrieval method that traces multi-hop library dependencies to find lemmas for formal theorem proving. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Focuses on lemma retrieval for theorem provers, not LLM evaluation benchmarks.
PathFM: Fast Generative Forecasting of Multivariate Trajectories Uses flow matching and distillation to generate full multivariate time-series trajectory forecasts rapidly. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Evaluates time series forecasting models rather than large language models.
PathFM: Fast Generative Forecasting of Multivariate Trajectories Uses flow matching and distillation to generate full multivariate time-series trajectory forecasts rapidly. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Deals with time series forecasting calibration rather than LLM safety or deployment guardrails.
PathFM: Fast Generative Forecasting of Multivariate Trajectories Uses flow matching and distillation to generate full multivariate time-series trajectory forecasts rapidly. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Distills time series trajectory generators rather than small language models.
TransX Technical Report: Scaling Sequence Transduction for Recommendation Describes a scalable sequence-transduction architecture and serving infrastructure for large-scale industrial recommendation systems. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:18	Not relevant Focuses on e-commerce recommendation systems rather than document retrieval or RAG knowledge grounding.
Learning Topology-Aware Representations for Mechanics-Relevant TPMS Metamaterials Applies persistent homology to encode global structural connectivity into learned representations of mechanical metamaterials. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:18	Not relevant Deals with mechanical metamaterials, not LLM evaluation or benchmarks.
Learning Topology-Aware Representations for Mechanics-Relevant TPMS Metamaterials Applies persistent homology to encode global structural connectivity into learned representations of mechanical metamaterials. Preprint	Multimodal models via arxiv · 17 Oct, 06:18	Not relevant Focuses on 3D metamaterial geometry representations rather than multimodal language-vision models.
Learning Topology-Aware Representations for Mechanics-Relevant TPMS Metamaterials Applies persistent homology to encode global structural connectivity into learned representations of mechanical metamaterials. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Deals with metamaterial structural connectivity, not AI safety or deployment robustness.
Learning Topology-Aware Representations for Mechanics-Relevant TPMS Metamaterials Applies persistent homology to encode global structural connectivity into learned representations of mechanical metamaterials. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Focuses on topology-aware representation learning rather than model compression or efficient inference.
Learning Topology-Aware Representations for Mechanics-Relevant TPMS Metamaterials Applies persistent homology to encode global structural connectivity into learned representations of mechanical metamaterials. Preprint	Computer vision for industrial applications via arxiv · 17 Oct, 06:18	Not relevant Focuses on mechanical metamaterial design representations, not factory vision inspection.
Orbia: Do Generated Videos Form a Consistent 3D World? Presents a benchmark to evaluate 3D consistency, input preservation, and content persistence in interactive video world models. Preprint	Computer vision for industrial applications via arxiv · 17 Oct, 06:18	Not relevant Evaluates 3D consistency in video generators, not manufacturing defect detection.
Purify Before You Align: Improving Representation Alignment for Diffusion Models Introduces local appearance purification to filter alignment targets and improve representation learning during diffusion model training. Preprint	Small and efficient models via arxiv · 17 Oct, 06:18	Not relevant Studies visual representation alignment rather than model compression or efficient inference.
Purify Before You Align: Improving Representation Alignment for Diffusion Models Introduces local appearance purification to filter alignment targets and improve representation learning during diffusion model training. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:18	Not relevant Studies diffusion representation alignment, not AI safety or deployment guardrails.
Purify Before You Align: Improving Representation Alignment for Diffusion Models Introduces local appearance purification to filter alignment targets and improve representation learning during diffusion model training. Preprint	Multimodal models via arxiv · 17 Oct, 06:18	Not relevant Focuses on image diffusion training targets rather than multimodal integration.

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