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
CLIVO: Differentiable Cluster-Based Instruction Mixing with Learned Validation Objectives The paper proposes CLIVO, a target-aware method combining semantic clustering and learned validation objectives for differentiable multimodal instruction data mixing. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a data selection method rather than an evaluation benchmark.
CLIVO: Differentiable Cluster-Based Instruction Mixing with Learned Validation Objectives The paper proposes CLIVO, a target-aware method combining semantic clustering and learned validation objectives for differentiable multimodal instruction data mixing. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on instruction selection efficiency rather than safety, red-teaming, or guardrails.
CLIVO: Differentiable Cluster-Based Instruction Mixing with Learned Validation Objectives The paper proposes CLIVO, a target-aware method combining semantic clustering and learned validation objectives for differentiable multimodal instruction data mixing. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on instruction dataset selection rather than multi-step agent systems.
Acceptable Misalignment The paper argues that full robotic value alignment is impossible and examines how to manage acceptable misalignment in autonomous systems. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Philosophical paper on robot ethics rather than industrial visual inspection.
Acceptable Misalignment The paper argues that full robotic value alignment is impossible and examines how to manage acceptable misalignment in autonomous systems. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Theoretical discussion of value alignment rather than autonomous LLM agent execution.
Acceptable Misalignment The paper argues that full robotic value alignment is impossible and examines how to manage acceptable misalignment in autonomous systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Theoretical paper on misalignment risks rather than an empirical benchmark.
Transparency as Answerability: Role-Dependent Information Flow Modeling The paper formalizes transparency as role-dependent answerability using information flow networks to evaluate AI-supported decision systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Formulates an analytical framework for transparency rather than a model benchmark.
Transparency as Answerability: Role-Dependent Information Flow Modeling The paper formalizes transparency as role-dependent answerability using information flow networks to evaluate AI-supported decision systems. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Proposes a conceptual transparency framework rather than autonomous LLM agents.
Observe, Share, or Predict? Disentangling Partner Information in Embodied Multi-Agent Collaboration The paper investigates how observing partner states, sharing knowledge, and predicting partner actions affect coordination in embodied multi-agent teams. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Studies embodied multi-agent coordination rather than industrial inspection.
Observe, Share, or Predict? Disentangling Partner Information in Embodied Multi-Agent Collaboration The paper investigates how observing partner states, sharing knowledge, and predicting partner actions affect coordination in embodied multi-agent teams. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Analyzes agent coordination strategies rather than proposing an LLM benchmark.
Observe, Share, or Predict? Disentangling Partner Information in Embodied Multi-Agent Collaboration The paper investigates how observing partner states, sharing knowledge, and predicting partner actions affect coordination in embodied multi-agent teams. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on collaboration efficiency rather than AI safety or guardrails.
Neural Bayesian Networks for Modular and Scalable Decision-Making Under Uncertainty The paper proposes Neural Bayesian Networks, replacing classical probability tables with differentiable tensor operations for scalable probabilistic reasoning on GPUs. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Discusses probabilistic inference rather than deployment safety, red-teaming, or guardrails.
Neural Bayesian Networks for Modular and Scalable Decision-Making Under Uncertainty The paper proposes Neural Bayesian Networks, replacing classical probability tables with differentiable tensor operations for scalable probabilistic reasoning on GPUs. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Introduces a probabilistic graphical model architecture rather than LLM autonomous agents.
Neural Bayesian Networks for Modular and Scalable Decision-Making Under Uncertainty The paper proposes Neural Bayesian Networks, replacing classical probability tables with differentiable tensor operations for scalable probabilistic reasoning on GPUs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a neural probabilistic framework rather than an LLM benchmark.
Neural Bayesian Networks for Modular and Scalable Decision-Making Under Uncertainty The paper proposes Neural Bayesian Networks, replacing classical probability tables with differentiable tensor operations for scalable probabilistic reasoning on GPUs. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on tensorized Bayesian networks rather than language model compression.
Transparent Information Flow Modeling for Role-Aware AI Act Assessment The paper applies an information-flow modeling methodology to assess public-sector AI recruitment tools under European Union AI Act requirements. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on AI governance modeling rather than autonomous tool-using agents.
Transparent Information Flow Modeling for Role-Aware AI Act Assessment The paper applies an information-flow modeling methodology to assess public-sector AI recruitment tools under European Union AI Act requirements. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents a compliance assessment methodology rather than an LLM evaluation benchmark.
Comparing GenAI to Machine Learning and Manual Review for eDiscovery The paper analyzes legal eDiscovery review data to compare generative AI tools against continuous active machine learning and manual human review. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Examines legal document review tools rather than autonomous agent systems.
Comparing GenAI to Machine Learning and Manual Review for eDiscovery The paper analyzes legal eDiscovery review data to compare generative AI tools against continuous active machine learning and manual human review. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Compares task effectiveness rather than model compression or efficient inference.
Comparing GenAI to Machine Learning and Manual Review for eDiscovery The paper analyzes legal eDiscovery review data to compare generative AI tools against continuous active machine learning and manual human review. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Examines eDiscovery evaluation metrics rather than safety, alignment, or guardrails.
Comparing GenAI to Machine Learning and Manual Review for eDiscovery The paper analyzes legal eDiscovery review data to compare generative AI tools against continuous active machine learning and manual human review. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Studies text classification in eDiscovery rather than retrieval-augmented generation.
WeFair: Fidelity-Aware Alignment from Human Feedback for Personalized Query Rewriting in Short-Video Search The paper proposes an alignment framework using human feedback signals to personalize search query rewriting without causing intent drift. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on search query fidelity rather than AI safety risks or red-teaming.
WeFair: Fidelity-Aware Alignment from Human Feedback for Personalized Query Rewriting in Short-Video Search The paper proposes an alignment framework using human feedback signals to personalize search query rewriting without causing intent drift. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an alignment framework rather than an evaluation benchmark.
WeFair: Fidelity-Aware Alignment from Human Feedback for Personalized Query Rewriting in Short-Video Search The paper proposes an alignment framework using human feedback signals to personalize search query rewriting without causing intent drift. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Presents a query rewriting model rather than multi-step autonomous agents.
KNN Implementation Details Can Dramatically Change Performance: An Example from Cover Trees The paper analyzes how Cover Tree nearest-neighbor implementation details and query modes materially change runtime performance across datasets. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on general vector search implementation rather than retrieval-augmented generation.
KNN Implementation Details Can Dramatically Change Performance: An Example from Cover Trees The paper analyzes how Cover Tree nearest-neighbor implementation details and query modes materially change runtime performance across datasets. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies nearest neighbor data structures rather than LLM agent workflows.
KNN Implementation Details Can Dramatically Change Performance: An Example from Cover Trees The paper analyzes how Cover Tree nearest-neighbor implementation details and query modes materially change runtime performance across datasets. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Analyzes algorithmic implementation details rather than LLM benchmark design.
KNN Implementation Details Can Dramatically Change Performance: An Example from Cover Trees The paper analyzes how Cover Tree nearest-neighbor implementation details and query modes materially change runtime performance across datasets. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses vector search runtime rather than AI safety or reliability.
KNN Implementation Details Can Dramatically Change Performance: An Example from Cover Trees The paper analyzes how Cover Tree nearest-neighbor implementation details and query modes materially change runtime performance across datasets. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Focuses on classical k-NN search rather than model compression or LLM inference.
UA-Bench: Benchmarking Large Audio-Language Models on Underwater Acoustic Recognition The paper introduces a large-scale benchmark of synthetic underwater audio records to evaluate audio-language models on acoustic recognition tasks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on acoustic recognition benchmarking rather than AI safety risks.
UA-Bench: Benchmarking Large Audio-Language Models on Underwater Acoustic Recognition The paper introduces a large-scale benchmark of synthetic underwater audio records to evaluate audio-language models on acoustic recognition tasks. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Presents an audio evaluation benchmark rather than autonomous agent systems.
Social Priors: Descriptions of AI Users Shape How Language Models Trust and Cooperate The paper investigates how fine-tuning language models on descriptions of user motivations influences their downstream trust and cooperative behavior. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Investigates behavioral alignment rather than model compression or efficient serving.
Social Priors: Descriptions of AI Users Shape How Language Models Trust and Cooperate The paper investigates how fine-tuning language models on descriptions of user motivations influences their downstream trust and cooperative behavior. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Examines social behavior shifts in models rather than agent planning or tool use.
Social Priors: Descriptions of AI Users Shape How Language Models Trust and Cooperate The paper investigates how fine-tuning language models on descriptions of user motivations influences their downstream trust and cooperative behavior. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies behavioral shifts from finetuning rather than introducing evaluation benchmarks.
SarcasTraps: Effect of Sarcasm on Turkish Hate Speech Detection The paper evaluates the impact of sarcasm on Turkish hate speech detection models using a targeted external test dataset. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Studies natural language hate speech, not software code or programming.
SarcasTraps: Effect of Sarcasm on Turkish Hate Speech Detection The paper evaluates the impact of sarcasm on Turkish hate speech detection models using a targeted external test dataset. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Evaluates existing models rather than introducing model compression techniques.
SarcasTraps: Effect of Sarcasm on Turkish Hate Speech Detection The paper evaluates the impact of sarcasm on Turkish hate speech detection models using a targeted external test dataset. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Exclusively investigates text classification without any multimodal component.
SarcasTraps: Effect of Sarcasm on Turkish Hate Speech Detection The paper evaluates the impact of sarcasm on Turkish hate speech detection models using a targeted external test dataset. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focuses on text classification rather than retrieval-augmented generation.
SarcasTraps: Effect of Sarcasm on Turkish Hate Speech Detection The paper evaluates the impact of sarcasm on Turkish hate speech detection models using a targeted external test dataset. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies hate speech classification rather than multi-step tool-using agents.
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	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on proactive robotic timing rather than safety guardrails or alignment.
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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Introduces a multi-agent application system, not an evaluation benchmark.
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.
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	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Evaluates general vision-language benchmarks, not industrial defect detection.
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	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on visual in-context learning rather than multi-step agent planning.
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	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 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	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	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an implementation guide for scientists, not an LLM benchmark.
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	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses scientific model reproducibility rather than small efficient model design.
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	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Discusses RL optimization rather than safety, red-teaming, or guardrails.
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.
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.

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