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
Proactive Physical AI through Continuous Observation and Timely Intervention The paper proposes a system for physical AI that continuously monitors environments to identify intervention needs and execute timely actions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on proactive robotics architecture rather than LLM benchmarking methods.
Proactive Physical AI through Continuous Observation and Timely Intervention The paper proposes a system for physical AI that continuously monitors environments to identify intervention needs and execute timely actions. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Deals with embodied physical robotics rather than industrial defect inspection.
MADDIE: Multi-Agent Drug Discovery Engine The paper introduces a multi-agent framework comprising an orchestrator and four specialized agents to automate iterative drug discovery workflows. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent coordination for drug design, not safety guardrails or red-teaming.
MADDIE: Multi-Agent Drug Discovery Engine The paper introduces a multi-agent framework comprising an orchestrator and four specialized agents to automate iterative drug discovery workflows. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a multi-agent application system, not an evaluation benchmark.
The Value of Being Wrong: Self-Mined Visual In-Context Learning from Errors The paper introduces an in-context learning method that selects demonstration examples by mining a vision-language model's own past prediction mistakes. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an in-context learning method rather than an evaluation benchmark.
The Value of Being Wrong: Self-Mined Visual In-Context Learning from Errors The paper introduces an in-context learning method that selects demonstration examples by mining a vision-language model's own past prediction mistakes. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on visual in-context learning rather than multi-step agent planning.
The Value of Being Wrong: Self-Mined Visual In-Context Learning from Errors The paper introduces an in-context learning method that selects demonstration examples by mining a vision-language model's own past prediction mistakes. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on visual prompting selection rather than AI safety and guardrails.
The Value of Being Wrong: Self-Mined Visual In-Context Learning from Errors The paper introduces an in-context learning method that selects demonstration examples by mining a vision-language model's own past prediction mistakes. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Evaluates general vision-language benchmarks, not industrial defect detection.
WeGR: Unified Generative Retrieval for Industrial Search Systems in WeChat The paper introduces an industrial generative retrieval framework featuring collaborative tokenization and preference optimization for WeChat search systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a generative retrieval method rather than an evaluation benchmark.
WeGR: Unified Generative Retrieval for Industrial Search Systems in WeChat The paper introduces an industrial generative retrieval framework featuring collaborative tokenization and preference optimization for WeChat search systems. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on search retrieval algorithms, not autonomous LLM agents.
WeGR: Unified Generative Retrieval for Industrial Search Systems in WeChat The paper introduces an industrial generative retrieval framework featuring collaborative tokenization and preference optimization for WeChat search systems. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses retrieval optimization rather than AI safety, jailbreaking, or guardrails.
PRAISE: A Reproducible Workflow for Non-Programmer Scientists to Implement Published AI Models The paper presents a structured step-by-step workflow designed to help non-programmer scientists implement and validate published machine learning models. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses scientific model reproducibility rather than small efficient model design.
PRAISE: A Reproducible Workflow for Non-Programmer Scientists to Implement Published AI Models The paper presents a structured step-by-step workflow designed to help non-programmer scientists implement and validate published machine learning models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on human workflow and education rather than LLM autonomous agents.
PRAISE: A Reproducible Workflow for Non-Programmer Scientists to Implement Published AI Models The paper presents a structured step-by-step workflow designed to help non-programmer scientists implement and validate published machine learning models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Deals with workflow reproducibility rather than AI safety or hallucination prevention.
PRAISE: A Reproducible Workflow for Non-Programmer Scientists to Implement Published AI Models The paper presents a structured step-by-step workflow designed to help non-programmer scientists implement and validate published machine learning models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an implementation guide for scientists, not an LLM benchmark.
From Suboptimal to Optimal: One-Step Latent Flow Policies for Offline Reinforcement Learning The paper proposes an offline reinforcement learning method that optimizes generative decision chunks in latent space using conditional velocity fields. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on mathematical offline RL policies rather than LLM-based autonomous agents.
From Suboptimal to Optimal: One-Step Latent Flow Policies for Offline Reinforcement Learning The paper proposes an offline reinforcement learning method that optimizes generative decision chunks in latent space using conditional velocity fields. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on RL policy optimization efficiency, not model compression or serving.
From Suboptimal to Optimal: One-Step Latent Flow Policies for Offline Reinforcement Learning The paper proposes an offline reinforcement learning method that optimizes generative decision chunks in latent space using conditional velocity fields. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a reinforcement learning algorithm rather than an evaluation benchmark.
From Suboptimal to Optimal: One-Step Latent Flow Policies for Offline Reinforcement Learning The paper proposes an offline reinforcement learning method that optimizes generative decision chunks in latent space using conditional velocity fields. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Discusses RL optimization rather than safety, red-teaming, or guardrails.
AUGUstus: Autonomous Driving VLA Model with Unified Generation and Understanding The paper introduces an autonomous driving model unifying diffusion-based trajectory generation with autoregressive chain-of-thought reasoning to reduce latency. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Primary focus is model architecture for driving, not deployment safety or guardrails.
AUGUstus: Autonomous Driving VLA Model with Unified Generation and Understanding The paper introduces an autonomous driving model unifying diffusion-based trajectory generation with autoregressive chain-of-thought reasoning to reduce latency. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on autonomous driving control policy rather than tool use or agent planning.
AUGUstus: Autonomous Driving VLA Model with Unified Generation and Understanding The paper introduces an autonomous driving model unifying diffusion-based trajectory generation with autoregressive chain-of-thought reasoning to reduce latency. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Deals with autonomous driving, which is explicitly excluded from this topic.
AUGUstus: Autonomous Driving VLA Model with Unified Generation and Understanding The paper introduces an autonomous driving model unifying diffusion-based trajectory generation with autoregressive chain-of-thought reasoning to reduce latency. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a model architecture and training method, not an evaluation framework.
TabGift: A Context-Efficient Foundation Model for Tabular Data Generation The paper proposes a context-efficient foundation model for generating realistic tabular data from limited context observations. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on tabular diffusion generation rather than autonomous planning agents.
TabGift: A Context-Efficient Foundation Model for Tabular Data Generation The paper proposes a context-efficient foundation model for generating realistic tabular data from limited context observations. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Covers tabular data synthesis rather than safety, red-teaming, or guardrails.
TabGift: A Context-Efficient Foundation Model for Tabular Data Generation The paper proposes a context-efficient foundation model for generating realistic tabular data from limited context observations. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a tabular foundation model rather than an evaluation benchmark.
Glasseek: A Transparent Recipe for Training and Trustworthy Evaluation of DeepSearch Agents The paper introduces an open-source framework and recipe for training and evaluating deep-search language agents on complex web exploration tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on search agent recipes rather than safety guardrails or jailbreaks.
Governing Copyright Responsibly in the Era of Generative AI The paper analyzes forty legal instruments to examine how copyright governance protects scholarly authors whose works train generative AI models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Legal and policy analysis of copyright, not agentic architectures.
Governing Copyright Responsibly in the Era of Generative AI The paper analyzes forty legal instruments to examine how copyright governance protects scholarly authors whose works train generative AI models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Examines copyright governance frameworks rather than model performance benchmarks.
MAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation The paper proposes a manifold-aware graph diffusion network to generate valid functional brain connectomes while capturing fine-grained geometric structure. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Covers brain graph generation rather than multi-step planning agents.
MAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation The paper proposes a manifold-aware graph diffusion network to generate valid functional brain connectomes while capturing fine-grained geometric structure. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Applies graph diffusion to neuroimaging connectomes, not industrial visual inspection.
MAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation The paper proposes a manifold-aware graph diffusion network to generate valid functional brain connectomes while capturing fine-grained geometric structure. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on medical connectome synthesis rather than deployment safety or jailbreaks.
MAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation The paper proposes a manifold-aware graph diffusion network to generate valid functional brain connectomes while capturing fine-grained geometric structure. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on medical graph diffusion rather than model quantization or distillation.
MAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation The paper proposes a manifold-aware graph diffusion network to generate valid functional brain connectomes while capturing fine-grained geometric structure. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a generative architecture rather than general model evaluation methods.
MAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation The paper proposes a manifold-aware graph diffusion network to generate valid functional brain connectomes while capturing fine-grained geometric structure. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on neuroimaging connectomes rather than multimodal vision-language models.
ProbabilityBench: Model Evaluation and Calibration with Exact Ground-Truth Uncertainty The paper introduces ProbabilityBench to evaluate model uncertainty calibration against analytically computed, ground-truth instance-level probabilities across three text environments. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on probability calibration rather than autonomous tool-using agents.
ProbabilityBench: Model Evaluation and Calibration with Exact Ground-Truth Uncertainty The paper introduces ProbabilityBench to evaluate model uncertainty calibration against analytically computed, ground-truth instance-level probabilities across three text environments. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses model calibration benchmarks rather than model compression or serving efficiency.
Understanding Personalization Gains in Whisper The paper studies low-rank adaptation in Whisper to evaluate whether transcription gains require group-specific adapters rather than pooled fine-tuning. Peer-reviewed	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant About speech recognition adaptation rather than software engineering or code generation.
Understanding Personalization Gains in Whisper The paper studies low-rank adaptation in Whisper to evaluate whether transcription gains require group-specific adapters rather than pooled fine-tuning. Peer-reviewed	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Evaluates personalization error rates rather than safety guardrails or alignment.
Understanding Personalization Gains in Whisper The paper studies low-rank adaptation in Whisper to evaluate whether transcription gains require group-specific adapters rather than pooled fine-tuning. Peer-reviewed	AI agents via arxiv · 15 Oct, 06:17	Not relevant Investigates speech model fine-tuning rather than autonomous tool-using agents.
Understanding Personalization Gains in Whisper The paper studies low-rank adaptation in Whisper to evaluate whether transcription gains require group-specific adapters rather than pooled fine-tuning. Peer-reviewed	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on speech recognition adaptation, not retrieval-augmented generation.
Understanding Personalization Gains in Whisper The paper studies low-rank adaptation in Whisper to evaluate whether transcription gains require group-specific adapters rather than pooled fine-tuning. Peer-reviewed	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses purely on audio/ASR adaptation rather than multimodal vision-language reasoning.
Understanding Personalization Gains in Whisper The paper studies low-rank adaptation in Whisper to evaluate whether transcription gains require group-specific adapters rather than pooled fine-tuning. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies LoRA personalization in speech rather than general LLM evaluation benchmarks.
Benchmarking Quantum Kernel SVMs for Credit Card Fraud Detection: Performance Realities Under NISQ Constraints The paper benchmarks three quantum kernel SVM encodings against classical models for imbalanced credit card fraud detection under NISQ constraints. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Discusses quantum kernel SVMs rather than autonomous agent systems.
Benchmarking Quantum Kernel SVMs for Credit Card Fraud Detection: Performance Realities Under NISQ Constraints The paper benchmarks three quantum kernel SVM encodings against classical models for imbalanced credit card fraud detection under NISQ constraints. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses fraud detection ML rather than AI safety, red-teaming, or guardrails.
Benchmarking Quantum Kernel SVMs for Credit Card Fraud Detection: Performance Realities Under NISQ Constraints The paper benchmarks three quantum kernel SVM encodings against classical models for imbalanced credit card fraud detection under NISQ constraints. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Benchmarking classical vs quantum ML rather than LLM evaluation or benchmarks.
SPLICE: Sparse Physics Library Identification and Compositional Expansion Introduces a framework that discovers and separates physical neural operators from unlabeled trajectory data containing multiple mechanisms. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces an operator discovery algorithm rather than model evaluation benchmarks.
SPLICE: Sparse Physics Library Identification and Compositional Expansion Introduces a framework that discovers and separates physical neural operators from unlabeled trajectory data containing multiple mechanisms. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Investigates physics simulations rather than AI deployment safety or robustness.
SPLICE: Sparse Physics Library Identification and Compositional Expansion Introduces a framework that discovers and separates physical neural operators from unlabeled trajectory data containing multiple mechanisms. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on physics simulation rather than model quantization or inference efficiency.
SPLICE: Sparse Physics Library Identification and Compositional Expansion Introduces a framework that discovers and separates physical neural operators from unlabeled trajectory data containing multiple mechanisms. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Addresses scientific neural operators, not language model planning agents.
Neos: Learning to Interleave Search and Visual Reasoning Develops a multimodal search framework that learns to turn intermediate visual discoveries into guidance for subsequent search actions. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an agent search framework rather than a model evaluation benchmark.
Neos: Learning to Interleave Search and Visual Reasoning Develops a multimodal search framework that learns to turn intermediate visual discoveries into guidance for subsequent search actions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on visual search agents rather than AI safety or reliability.
VL-DocIR: A Benchmark for Vision-Based Long Document Information Retrieval Introduces a visual document retrieval benchmark focused on multi-page and cross-document evidence configurations in long documents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on information retrieval benchmarks rather than safety or reliability guardrails.
VL-DocIR: A Benchmark for Vision-Based Long Document Information Retrieval Introduces a visual document retrieval benchmark focused on multi-page and cross-document evidence configurations in long documents. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Applies vision to document retrieval rather than industrial defect inspection.
VL-DocIR: A Benchmark for Vision-Based Long Document Information Retrieval Introduces a visual document retrieval benchmark focused on multi-page and cross-document evidence configurations in long documents. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on document retrieval benchmarks rather than multi-step planning agents.
PatientWM: Towards Generalizable Clinical World Models via Unified Patient States Proposes a clinical world-model architecture using unified patient states and ordered action histories to anticipate treatment outcomes. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Proposes a clinical predictive world model rather than autonomous planning agents.
PatientWM: Towards Generalizable Clinical World Models via Unified Patient States Proposes a clinical world-model architecture using unified patient states and ordered action histories to anticipate treatment outcomes. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on clinical forecasting rather than AI safety guardrails or reliability.
PatientWM: Towards Generalizable Clinical World Models via Unified Patient States Proposes a clinical world-model architecture using unified patient states and ordered action histories to anticipate treatment outcomes. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a domain modeling architecture rather than a model evaluation benchmark.
Gradient Routing Localizes and Removes Unintended Behaviors in RL Routes gradients from imperfect monitor flags into sacrificial parameters to mitigate reward hacking during reinforcement learning. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Investigates RL reward hacking rather than model compression or inference efficiency.
Gradient Routing Localizes and Removes Unintended Behaviors in RL Routes gradients from imperfect monitor flags into sacrificial parameters to mitigate reward hacking during reinforcement learning. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on RL fine-tuning methods rather than agentic tool-use workflows.

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