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
Enabling Token-wise Importance Sampling in Off-Policy RLVR with Proximal KL Regularization The paper develops an off-policy reinforcement learning method with token-wise importance sampling and proximal KL regularization. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes an RL training algorithm, not an evaluation methodology or benchmark.
Emergent Loop Geometry in Gauge-Equivariant Graph Neural Networks The paper analyzes how network depth influences the internal loop geometry and spatial reach of gauge-equivariant graph neural networks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on theoretical physics GNN representations, not model efficiency.
Emergent Loop Geometry in Gauge-Equivariant Graph Neural Networks The paper analyzes how network depth influences the internal loop geometry and spatial reach of gauge-equivariant graph neural networks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on lattice gauge theory, not AI safety or reliability.
Emergent Loop Geometry in Gauge-Equivariant Graph Neural Networks The paper analyzes how network depth influences the internal loop geometry and spatial reach of gauge-equivariant graph neural networks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on mathematical properties of graph neural networks in physics.
Depth Memory The paper formalizes layer-to-layer representation sharing as a memory storage and retrieval framework for deep transformer networks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on internal layer representation mixing, not safety, jailbreaks, or hallucinations.
Depth Memory The paper formalizes layer-to-layer representation sharing as a memory storage and retrieval framework for deep transformer networks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Proposes a model architecture framework, not an evaluation benchmark or method.
Statistical Vignettes on the Grokking Phenomenon The paper analytically characterizes the mechanisms and conditions behind the grokking phenomenon in a simplified learning setup. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Theoretical analysis of grokking dynamics, not LLM evaluation or benchmarks.
Statistical Vignettes on the Grokking Phenomenon The paper analytically characterizes the mechanisms and conditions behind the grokking phenomenon in a simplified learning setup. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Studies theoretical generalization dynamics, not deployment safety or guardrails.
Statistical Vignettes on the Grokking Phenomenon The paper analytically characterizes the mechanisms and conditions behind the grokking phenomenon in a simplified learning setup. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Theoretical learning study, not small models, pruning, or efficient inference.
Joint Neural Posterior Estimation and Model Selection for Chemical Reaction Networks The paper presents a neural network architecture for simultaneous model selection and parameter posterior estimation in chemical reaction networks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on chemical reaction Bayesian inference, not LLM benchmarking.
Joint Neural Posterior Estimation and Model Selection for Chemical Reaction Networks The paper presents a neural network architecture for simultaneous model selection and parameter posterior estimation in chemical reaction networks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on chemical parameter estimation, not AI safety or deployment reliability.
Joint Neural Posterior Estimation and Model Selection for Chemical Reaction Networks The paper presents a neural network architecture for simultaneous model selection and parameter posterior estimation in chemical reaction networks. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Addresses Bayesian inference for chemistry, not small or efficient language models.
Exact Topological Compliance: Generating Persistence-Equivalent Graphs The paper introduces methods to generate graphs whose topological persistence diagrams match target descriptors exactly rather than approximately. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on topological graph generation, not model compression or efficient serving.
Exact Topological Compliance: Generating Persistence-Equivalent Graphs The paper introduces methods to generate graphs whose topological persistence diagrams match target descriptors exactly rather than approximately. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on topological graph theory, not deployment safety or reliability guardrails.
Exact Topological Compliance: Generating Persistence-Equivalent Graphs The paper introduces methods to generate graphs whose topological persistence diagrams match target descriptors exactly rather than approximately. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Addresses theoretical graph generation, not LLM benchmarking or evaluation methods.
Data Free Training of Diffusion Models for Lattice Field Theories The paper presents a method for training diffusion models in lattice field theory directly from their own generated samples. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Studies lattice field theory sampling rather than efficient LLM inference or distillation.
Data Free Training of Diffusion Models for Lattice Field Theories The paper presents a method for training diffusion models in lattice field theory directly from their own generated samples. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on physics simulation methods, not AI safety, jailbreaks, or alignment.
Data Free Training of Diffusion Models for Lattice Field Theories The paper presents a method for training diffusion models in lattice field theory directly from their own generated samples. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Deals with physics sampling in lattice field theory, not LLM evaluation.
Understanding and Mitigating Training Instability in Large-Scale Recommender Systems: A Cascade Perspective The paper analyzes training instability cascades in large recommender systems and proposes targeted gradient, weight, and loss interventions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on recommender training divergence rather than model compression or efficient inference.
Understanding and Mitigating Training Instability in Large-Scale Recommender Systems: A Cascade Perspective The paper analyzes training instability cascades in large recommender systems and proposes targeted gradient, weight, and loss interventions. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Addresses recommendation system training instability, not retrieval-augmented generation systems.
Understanding and Mitigating Training Instability in Large-Scale Recommender Systems: A Cascade Perspective The paper analyzes training instability cascades in large recommender systems and proposes targeted gradient, weight, and loss interventions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Analyzes training convergence instability rather than LLM benchmarking or evaluation frameworks.
EVISTA: Unified Multimodal Video Search with Evidence-Aware Temporal Path Optimization The paper introduces a multimodal video retrieval system that optimizes temporal paths to locate ordered events across video, text, and audio. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Focuses on multimodal video keyframe search, not RAG or knowledge-intensive LLM retrieval.
A Stateful Research Information Infrastructure for Scientific Discovery The paper introduces an infrastructure that organizes scientific works as persistent, stateful research objects to maintain continuity across tools. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Presents data infrastructure for scientific objects, not retrieval-augmented generation or grounding mechanisms.
MoSAIC: Learning DAG Scheduling Heuristics from Optimal Schedules through Motif-Aware Recovery and LLM-Guided Reasoning The paper introduces a framework that uses language models to extract task-scheduling priority functions from optimal solver solutions on directed acyclic graphs. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Focuses on DAG scheduling heuristics rather than text retrieval or knowledge grounding.
MoSAIC: Learning DAG Scheduling Heuristics from Optimal Schedules through Motif-Aware Recovery and LLM-Guided Reasoning The paper introduces a framework that uses language models to extract task-scheduling priority functions from optimal solver solutions on directed acyclic graphs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Focuses on DAG scheduling optimization, not LLM evaluation or benchmark methodology.
MoSAIC: Learning DAG Scheduling Heuristics from Optimal Schedules through Motif-Aware Recovery and LLM-Guided Reasoning The paper introduces a framework that uses language models to extract task-scheduling priority functions from optimal solver solutions on directed acyclic graphs. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Deals with DAG task scheduling, not model compression or efficient serving.
MoSAIC: Learning DAG Scheduling Heuristics from Optimal Schedules through Motif-Aware Recovery and LLM-Guided Reasoning The paper introduces a framework that uses language models to extract task-scheduling priority functions from optimal solver solutions on directed acyclic graphs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on task scheduling, not deployment safety, jailbreaks, or hallucinations.
Toward Explainability in Multimodal Recommendation The paper reviews explainability methods and evaluation metrics in multimodal recommender systems, categorizing approaches into modality and post-hoc explanations. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Surveys explainability in multimodal recommender systems, not retrieval-augmented generation.
Federated Semantic Residuals for Multimodal Recommendation The paper proposes a federated recommendation adapter that adds bounded semantic residual scores to on-device collaborative filtering models. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Studies federated multimodal recommender systems rather than retrieval-augmented generation.
Pathwise Alignment for Collaborative Filtering: Bilateral Conditional Flow Regularization The paper uses conditional flow matching during training to regularize matrix factorization embeddings across sparse user interactions in collaborative filtering. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Deals with collaborative filtering algorithms, not retrieval-augmented generation systems.
Rethinking Evidence Modeling: Context-Coupled Graph Learning for Multimodal Recommendation The paper presents a context-coupled graph learning method that jointly models collaborative and multimodal item relations for recommendation systems. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Focuses on graph-based multimodal recommendation systems rather than retrieval-augmented generation.
UniSGR-V2: Stepwise Target-Side Feature Anticipation and Crossing for Generative Recommendation The paper proposes a generative recommendation framework that reconstructs continuous target-side features during autoregressive decoding to improve ranking accuracy. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Focuses on generative recommendation systems, not retrieval-augmented generation for language models.
Tackling the Modality Inhibitance Effect with Wavelet-based Modality Integration in Multi-modal Recommendation The paper introduces a wavelet-based integration method to resolve performance degradation caused by feature redundancy in trimodal recommender systems. Peer-reviewed	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Deals with recommender feature fusion, not general multimodal models like vision-language foundation models.
Tackling the Modality Inhibitance Effect with Wavelet-based Modality Integration in Multi-modal Recommendation The paper introduces a wavelet-based integration method to resolve performance degradation caused by feature redundancy in trimodal recommender systems. Peer-reviewed	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Focuses on multimodal recommendation algorithms rather than retrieval-augmented generation for language models.
From a Large-Scale Teacher to Efficient Multimodal Students for Search Relevance Prediction The paper distills an LLM-based search relevance judge into smaller multimodal student models for efficient e-commerce search evaluation. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Studies e-commerce search relevance prediction rather than retrieval-augmented generation.
From a Large-Scale Teacher to Efficient Multimodal Students for Search Relevance Prediction The paper distills an LLM-based search relevance judge into smaller multimodal student models for efficient e-commerce search evaluation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Focuses on model distillation for relevance prediction, not AI safety, jailbreaks, or guardrails.
Tracing Money Flows in the Multi-Token Cryptocurrency Network The paper develops a transaction-aware graph method to trace multi-token fund flows and asset transformations across cryptocurrency networks. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Analyzes cryptocurrency transaction network graphs, not retrieval-augmented generation or document search.
The Scored Population, the Operating Point, and the Pipeline: Three Objects That Decide What an Open-Web Event System Looks Like The paper studies how pipeline selection and scored populations introduce evaluation blind spots in open-web event admission systems. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Analyzes web event extraction and admission pipelines rather than grounding language models with external knowledge.
Mitigating Popularity Bias in Sequential Recommendations with Sparse Prior-Regularized Objective The paper proposes a sparse prior-regularized objective that makes popularity bias correction context-dependent during sequential recommender model training. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:18	Not relevant Focuses on recommender systems popularity bias rather than retrieval-augmented generation for language models.
Human Pose Estimation by Probabilistic Mixtures of Gaussian and Uniform Distributions The paper proposes a continuous probabilistic Gaussian mixture framework to improve human pose estimation under dense occlusions without retraining models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:18	Not relevant Focuses on probabilistic pose estimation modeling rather than model compression or efficient serving.
Human Pose Estimation by Probabilistic Mixtures of Gaussian and Uniform Distributions The paper proposes a continuous probabilistic Gaussian mixture framework to improve human pose estimation under dense occlusions without retraining models. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on general human pose estimation rather than industrial inspection or defect detection.
Human Pose Estimation by Probabilistic Mixtures of Gaussian and Uniform Distributions The paper proposes a continuous probabilistic Gaussian mixture framework to improve human pose estimation under dense occlusions without retraining models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:18	Not relevant Covers pose estimation robustness rather than LLM deployment safety, jailbreaks, or hallucinations.
Human Pose Estimation by Probabilistic Mixtures of Gaussian and Uniform Distributions The paper proposes a continuous probabilistic Gaussian mixture framework to improve human pose estimation under dense occlusions without retraining models. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Deals with computer vision pose heatmaps, not multimodal text-image foundation models.
Human Pose Estimation by Probabilistic Mixtures of Gaussian and Uniform Distributions The paper proposes a continuous probabilistic Gaussian mixture framework to improve human pose estimation under dense occlusions without retraining models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:18	Not relevant Introduces a pose estimation framework, not an LLM benchmark or evaluation methodology.
Scenes as an Attack Surface: Uncovering Scene-Grounded Safety Failures in MLLMs The paper introduces a benchmark to show that visual scene semantics alone can cause multimodal models to bypass safety guardrails without adversarial prompts. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Studies multimodal LLM jailbreaks, not industrial inspection or defect detection.
RePrior: Measurement-Reconciled Prior Correction for Sparse-View CBCT Reconstruction The paper introduces a multi-stage framework that corrects learned priors using X-ray measurements for sparse-view computed tomography reconstruction. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Addresses 3D CT volumetric reconstruction, not multimodal vision-language models or cross-modal reasoning.
RePrior: Measurement-Reconciled Prior Correction for Sparse-View CBCT Reconstruction The paper introduces a multi-stage framework that corrects learned priors using X-ray measurements for sparse-view computed tomography reconstruction. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on medical CT reconstruction rather than industrial inspection or defect detection.
Temporal Diffusion Bridge Model for Vessel Extraction Using Physics-informed Synthetic Data The paper presents a temporal diffusion bridge model trained on physics-based synthetic data to extract coronary arteries from angiograms without manual labels. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on medical X-ray image translation, not multimodal models.
Temporal Diffusion Bridge Model for Vessel Extraction Using Physics-informed Synthetic Data The paper presents a temporal diffusion bridge model trained on physics-based synthetic data to extract coronary arteries from angiograms without manual labels. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on coronary angiography vessel extraction, not industrial inspection.
Neural Block-wise Auto-exposure for Single-Shot High Dynamic Range Imaging The paper proposes a neural block-wise auto-exposure method for single-shot high dynamic range imaging to reduce visual artifacts. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on camera exposure for HDR imaging, not industrial inspection.
Neural Block-wise Auto-exposure for Single-Shot High Dynamic Range Imaging The paper proposes a neural block-wise auto-exposure method for single-shot high dynamic range imaging to reduce visual artifacts. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on camera hardware auto-exposure algorithms, not multimodal models.
Exploring the Latent Structure of AMD Severity Using Retinal Foundation Models The paper examines how retinal foundation model embeddings represent macular degeneration severity and how that latent structure affects downstream prediction. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Investigates retinal disease diagnosis, not industrial defect detection.
Exploring the Latent Structure of AMD Severity Using Retinal Foundation Models The paper examines how retinal foundation model embeddings represent macular degeneration severity and how that latent structure affects downstream prediction. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Analyzes retinal foundation model embeddings, not general multimodal models.
Frozen Site-Probe Accuracy Is Not a Standalone Harmonization Score: An Audit of Evaluation Practice for Multi-Site MRI The paper audits frozen site-probe evaluation for multi-site brain MRI harmonization and finds probe accuracy is an unreliable standalone metric. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on medical brain MRI site harmonization, not multimodal models.
Frozen Site-Probe Accuracy Is Not a Standalone Harmonization Score: An Audit of Evaluation Practice for Multi-Site MRI The paper audits frozen site-probe evaluation for multi-site brain MRI harmonization and finds probe accuracy is an unreliable standalone metric. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on multi-site MRI harmonization evaluation, not industrial inspection.
C$^2$LUE: Clinically Aligned Patient-Level Ultrasound Diagnosis via Evidence-Aware Multi-Instance Learning This paper proposes a multi-instance learning model with risk-aligned regularization for patient-level ultrasound diagnostic classification. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on medical multi-instance learning for ultrasound, not multimodal models.
C$^2$LUE: Clinically Aligned Patient-Level Ultrasound Diagnosis via Evidence-Aware Multi-Instance Learning This paper proposes a multi-instance learning model with risk-aligned regularization for patient-level ultrasound diagnostic classification. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Deals with medical ultrasound diagnosis, not industrial inspection.
TrajCFG: Token-Wise Classifier-Free Guidance via Denoising Trajectory Feedback This paper introduces a training-free method adjusting token-wise guidance scales based on velocity trajectory directional changes in generative models. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Studies classifier-free guidance in generative diffusion, not industrial inspection.
TrajCFG: Token-Wise Classifier-Free Guidance via Denoising Trajectory Feedback This paper introduces a training-free method adjusting token-wise guidance scales based on velocity trajectory directional changes in generative models. Preprint	Multimodal models via arxiv · 15 Oct, 06:18	Not relevant Focuses on sampling algorithms for generative diffusion across modalities.
Axiom-0: Hierarchical Self-Refinement for Vision-Language-Action Policies This paper introduces a vision-language-action policy using hierarchical self-refinement across internal representations and action interfaces. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:18	Not relevant Focuses on general robotic manipulation, not industrial defect detection.

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