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
Money-Mule Detection as a Relational Learning Problem: A Study on the RBIH NFPC Dataset The paper applies heterogeneous graph neural networks directly to bank relational schemas to detect money-mule accounts prior to flag dates. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Focuses on tabular/graph fraud detection, not LLM evaluation.
Money-Mule Detection as a Relational Learning Problem: A Study on the RBIH NFPC Dataset The paper applies heterogeneous graph neural networks directly to bank relational schemas to detect money-mule accounts prior to flag dates. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Studies graph neural networks for financial fraud, not agents.
Money-Mule Detection as a Relational Learning Problem: A Study on the RBIH NFPC Dataset The paper applies heterogeneous graph neural networks directly to bank relational schemas to detect money-mule accounts prior to flag dates. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Applies classical GNNs to fraud, not LLM safety.
Loud and Clear: Making Visual Evidence Count in Multimodal Document RAG The paper examines how increasing visual token budgets and cropping key evidence improves document retrieval-augmented generation in vision-language models. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Studies generator visual fidelity, not benchmark evaluation methodology.
Loud and Clear: Making Visual Evidence Count in Multimodal Document RAG The paper examines how increasing visual token budgets and cropping key evidence improves document retrieval-augmented generation in vision-language models. Preprint	Computer vision for industrial applications via arxiv · 17 Oct, 06:17	Not relevant Analyzes document understanding, not industrial manufacturing inspection.
Loud and Clear: Making Visual Evidence Count in Multimodal Document RAG The paper examines how increasing visual token budgets and cropping key evidence improves document retrieval-augmented generation in vision-language models. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on visual document retrieval-augmented generation, not autonomous agents.
Loud and Clear: Making Visual Evidence Count in Multimodal Document RAG The paper examines how increasing visual token budgets and cropping key evidence improves document retrieval-augmented generation in vision-language models. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Studies visual token efficiency, not safety or guardrails.
Constrained Multi-Agent Reinforcement Learning in Imagination The paper evaluates a constrained multi-agent reinforcement learning method that learns policies inside world models to reduce environment interaction costs. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on reinforcement learning world models, not LLM safety.
Constrained Multi-Agent Reinforcement Learning in Imagination The paper evaluates a constrained multi-agent reinforcement learning method that learns policies inside world models to reduce environment interaction costs. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Addresses reinforcement learning for continuous-control robotics, not LLM agents.
Constrained Multi-Agent Reinforcement Learning in Imagination The paper evaluates a constrained multi-agent reinforcement learning method that learns policies inside world models to reduce environment interaction costs. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Evaluates classic MARL benchmarks, not LLM evaluation.
DocPilot: Interactive Agent Orchestration and Training for Long Visual Document Understanding The paper presents an agent orchestration framework that trains smaller language models to navigate, inspect, and reason over long multimodal documents. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Presents an agent architecture and training, not evaluation benchmarking.
DocPilot: Interactive Agent Orchestration and Training for Long Visual Document Understanding The paper presents an agent orchestration framework that trains smaller language models to navigate, inspect, and reason over long multimodal documents. Preprint	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Uses code execution as a tool, not software engineering.
DocPilot: Interactive Agent Orchestration and Training for Long Visual Document Understanding The paper presents an agent orchestration framework that trains smaller language models to navigate, inspect, and reason over long multimodal documents. Preprint	Computer vision for industrial applications via arxiv · 17 Oct, 06:17	Not relevant Targets multimodal document understanding, not industrial manufacturing inspection.
DocPilot: Interactive Agent Orchestration and Training for Long Visual Document Understanding The paper presents an agent orchestration framework that trains smaller language models to navigate, inspect, and reason over long multimodal documents. Preprint	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Focuses on agentic document reasoning rather than retrieval-augmented generation pipelines.
DocPilot: Interactive Agent Orchestration and Training for Long Visual Document Understanding The paper presents an agent orchestration framework that trains smaller language models to navigate, inspect, and reason over long multimodal documents. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on document task orchestration, not safety guardrails.
Does On-Policy Distillation Need to Complete Every Rollout? The paper introduces a method that accelerates on-policy knowledge distillation by truncating student rollouts and applying inverse-probability weighting. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Proposes an optimization algorithm, not LLM evaluation benchmarking.
Does On-Policy Distillation Need to Complete Every Rollout? The paper introduces a method that accelerates on-policy knowledge distillation by truncating student rollouts and applying inverse-probability weighting. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on distillation training speed, not deployment safety.
Does On-Policy Distillation Need to Complete Every Rollout? The paper introduces a method that accelerates on-policy knowledge distillation by truncating student rollouts and applying inverse-probability weighting. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Presents an on-policy distillation method, not agent planning.
Quantum Versus Classical Machine Learning for Neuropsychiatric Disorders: A Feature-Matched Multi-Cohort Evaluation The paper compares quantum and classical machine learning models on multi-cohort fMRI datasets to test their utility for neuropsychiatric classification. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Focuses on medical ML classification comparisons, not LLM benchmarks.
Quantum Versus Classical Machine Learning for Neuropsychiatric Disorders: A Feature-Matched Multi-Cohort Evaluation The paper compares quantum and classical machine learning models on multi-cohort fMRI datasets to test their utility for neuropsychiatric classification. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Addresses clinical neuroimaging classification, not AI deployment safety.
Quantum Versus Classical Machine Learning for Neuropsychiatric Disorders: A Feature-Matched Multi-Cohort Evaluation The paper compares quantum and classical machine learning models on multi-cohort fMRI datasets to test their utility for neuropsychiatric classification. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Evaluates quantum machine learning for neuropsychiatric classification, not agents.
ARGUS: An Agenda-Driven Framework for Reliable User Simulation The paper introduces an agenda-driven neuro-symbolic framework to improve the reliability of user simulation when testing interactive conversational agents. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on user simulation fidelity, not safety red-teaming.
FedSkillGit: Federated Skill Evolution via Version Control The paper proposes a federated learning framework that uses version control operations instead of full document aggregation to evolve agent skills. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Focuses on federated skill learning, not benchmark evaluation.
FedSkillGit: Federated Skill Evolution via Version Control The paper proposes a federated learning framework that uses version control operations instead of full document aggregation to evolve agent skills. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on skill evolution communication, not AI safety guardrails.
Hierarchical Graph Convolutional Network Approach for Detecting Low-Quality Documents The paper introduces a hierarchical graph convolutional network and dataset to detect internal inconsistencies and headline mismatches in news documents. Peer-reviewed	AI agents via arxiv · 17 Oct, 06:17	Not relevant Addresses document quality classification using GCNs, not LLM agents.
Hierarchical Graph Convolutional Network Approach for Detecting Low-Quality Documents The paper introduces a hierarchical graph convolutional network and dataset to detect internal inconsistencies and headline mismatches in news documents. Peer-reviewed	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Introduces a text classification dataset, not LLM evaluation methods.
Hierarchical Graph Convolutional Network Approach for Detecting Low-Quality Documents The paper introduces a hierarchical graph convolutional network and dataset to detect internal inconsistencies and headline mismatches in news documents. Peer-reviewed	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on news text consistency, not AI deployment safety.
LTM: Latent Transition Based In-Context Memory for Vision-Language-Action Models The paper introduces an in-context memory method storing latent transitions between visual observations to assist robot manipulation without extra annotation. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on robotic memory, not AI safety or guardrails.
LTM: Latent Transition Based In-Context Memory for Vision-Language-Action Models The paper introduces an in-context memory method storing latent transitions between visual observations to assist robot manipulation without extra annotation. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Proposes a memory architecture, not benchmark evaluation methodology.
LTM: Latent Transition Based In-Context Memory for Vision-Language-Action Models The paper introduces an in-context memory method storing latent transitions between visual observations to assist robot manipulation without extra annotation. Preprint	Computer vision for industrial applications via arxiv · 17 Oct, 06:17	Not relevant Targets general robot manipulation, not industrial inspection or defect detection.
LTM: Latent Transition Based In-Context Memory for Vision-Language-Action Models The paper introduces an in-context memory method storing latent transitions between visual observations to assist robot manipulation without extra annotation. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on robotic manipulation and visual-action models, not LLM agents.
ESG-Kor: A Korean Dataset for ESG-related Information Extraction and Practical Use Cases The paper presents a Korean dataset extracted from corporate sustainability reports to support information extraction on environmental, social, and governance topics. Peer-reviewed	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not investigate model compression or efficient serving.
ESG-Kor: A Korean Dataset for ESG-related Information Extraction and Practical Use Cases The paper presents a Korean dataset extracted from corporate sustainability reports to support information extraction on environmental, social, and governance topics. Peer-reviewed	Retrieval-augmented generation (RAG) via arxiv · 17 Oct, 06:17	Not relevant Introduces a classification dataset, not retrieval-augmented generation.
ESG-Kor: A Korean Dataset for ESG-related Information Extraction and Practical Use Cases The paper presents a Korean dataset extracted from corporate sustainability reports to support information extraction on environmental, social, and governance topics. Peer-reviewed	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on ESG information extraction, not AI agents.
ESG-Kor: A Korean Dataset for ESG-related Information Extraction and Practical Use Cases The paper presents a Korean dataset extracted from corporate sustainability reports to support information extraction on environmental, social, and governance topics. Peer-reviewed	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on ESG extraction, not deployment safety or robustness.
ESG-Kor: A Korean Dataset for ESG-related Information Extraction and Practical Use Cases The paper presents a Korean dataset extracted from corporate sustainability reports to support information extraction on environmental, social, and governance topics. Peer-reviewed	LLMs for code via arxiv · 17 Oct, 06:17	Not relevant Targets corporate ESG documents, not code generation or repair.
ESG-Kor: A Korean Dataset for ESG-related Information Extraction and Practical Use Cases The paper presents a Korean dataset extracted from corporate sustainability reports to support information extraction on environmental, social, and governance topics. Peer-reviewed	Multimodal models via arxiv · 17 Oct, 06:17	Not relevant Handles purely text-based corporate sustainability reports.
Memorilla: Latent Semantic Memory for LLMs The paper introduces a system that retrieves document information into a fixed set of prompt embeddings for frozen language models. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Evaluates on benchmarks, but does not contribute novel benchmark methodology.
Memorilla: Latent Semantic Memory for LLMs The paper introduces a system that retrieves document information into a fixed set of prompt embeddings for frozen language models. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Proposes a memory representation system, not agent planning or tool use.
Memorilla: Latent Semantic Memory for LLMs The paper introduces a system that retrieves document information into a fixed set of prompt embeddings for frozen language models. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Focuses on document memory representation, not deployment safety.
PriorityFEC: Deadline-Aware Streaming FEC for KV-Cache Handoff in Edge Multi-Agent Systems The paper proposes an error-correction method to prioritize and transmit key-value cache tokens across edge-deployed multi-agent systems under latency deadlines. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Focuses on edge networking efficiency, not evaluation benchmarks.
PriorityFEC: Deadline-Aware Streaming FEC for KV-Cache Handoff in Edge Multi-Agent Systems The paper proposes an error-correction method to prioritize and transmit key-value cache tokens across edge-deployed multi-agent systems under latency deadlines. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Addresses edge transport deadlines, not AI safety or alignment.
Learning to Reason with Selective Internal Feedback The paper introduces a reinforcement learning method that uses internal certainty profiles and selective filtering to train models without external verifiers. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Targets reasoning accuracy via RL, not safety or reliability deployment.
Learning to Reason with Selective Internal Feedback The paper introduces a reinforcement learning method that uses internal certainty profiles and selective filtering to train models without external verifiers. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Introduces a training algorithm, not evaluation benchmarks.
Learning to Reason with Selective Internal Feedback The paper introduces a reinforcement learning method that uses internal certainty profiles and selective filtering to train models without external verifiers. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Presents an RL training technique, not an agent system.
Self-Guided LLM Reasoning for Error Diagnosis in Vehicle Routing Problems The paper presents a self-guided reasoning framework where language models generate code to detect and diagnose errors in vehicle routing problem instances. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Investigates reasoning for VRP error detection, not benchmark methodology.
Self-Guided LLM Reasoning for Error Diagnosis in Vehicle Routing Problems The paper presents a self-guided reasoning framework where language models generate code to detect and diagnose errors in vehicle routing problem instances. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on routing optimization via self-guided reasoning, not multi-step agents.
Self-Guided LLM Reasoning for Error Diagnosis in Vehicle Routing Problems The paper presents a self-guided reasoning framework where language models generate code to detect and diagnose errors in vehicle routing problem instances. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Does not explore model efficiency, compression, or quantization.
Self-Guided LLM Reasoning for Error Diagnosis in Vehicle Routing Problems The paper presents a self-guided reasoning framework where language models generate code to detect and diagnose errors in vehicle routing problem instances. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Addresses data error correction, not safety or guardrails.
Matoi: Selective Revalidation of Consequence Evidence for Agent Authorization The paper introduces an authorization system that reuses validated consequence evidence across agent execution branches while maintaining separate live authority. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Focuses on authorization architecture, not LLM benchmark evaluation.
When do quantum views help? Complementary classical-quantum representations in multi-view fusion The paper studies combining classical and quantum representations to determine when multi-view fusion improves machine learning predictions over single representations. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Investigates quantum ML representation learning, not AI safety, jailbreaks, or guardrails.
When do quantum views help? Complementary classical-quantum representations in multi-view fusion The paper studies combining classical and quantum representations to determine when multi-view fusion improves machine learning predictions over single representations. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Studies quantum feature fusion algorithms, not LLM benchmarks or evaluation methods.
When do quantum views help? Complementary classical-quantum representations in multi-view fusion The paper studies combining classical and quantum representations to determine when multi-view fusion improves machine learning predictions over single representations. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Focuses on classical-quantum machine learning fusion, not LLM agents or tool use.
Watts If? A Benchmark for Counterfactual Reasoning About the AI Data Center Buildout The paper introduces a benchmark to evaluate how language models perform counterfactual reasoning regarding AI data center infrastructure and siting commitments. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Introduces an infrastructure decision benchmark, not autonomous agent planning or tool use.
Watts If? A Benchmark for Counterfactual Reasoning About the AI Data Center Buildout The paper introduces a benchmark to evaluate how language models perform counterfactual reasoning regarding AI data center infrastructure and siting commitments. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Evaluates domain-specific counterfactual reasoning rather than AI deployment safety or guardrails.
Multi-Reward Hacking: From Aggregation-Induced Drift to Trust-Anchored Defense The paper analyzes multi-reward reinforcement learning failures where auxiliary objectives degrade primary performance and introduces a trust-anchored gating method. Preprint	AI agents via arxiv · 17 Oct, 06:17	Not relevant Studies reinforcement learning post-training dynamics, not LLM multi-step agent tool use.
Multi-Reward Hacking: From Aggregation-Induced Drift to Trust-Anchored Defense The paper analyzes multi-reward reinforcement learning failures where auxiliary objectives degrade primary performance and introduces a trust-anchored gating method. Preprint	Small and efficient models via arxiv · 17 Oct, 06:17	Not relevant Addresses RL post-training reward alignment rather than model compression or inference efficiency.
Multi-Reward Hacking: From Aggregation-Induced Drift to Trust-Anchored Defense The paper analyzes multi-reward reinforcement learning failures where auxiliary objectives degrade primary performance and introduces a trust-anchored gating method. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Focuses on training algorithms rather than empirical benchmark development or LLM-as-a-judge.
Jev-LiteMAS: Separating Decisions from Generation in Multi-Agent Workflows The paper proposes a framework that replaces generative model calls with lightweight decision models for routing and reviewing in multi-agent workflows. Preprint	AI safety and reliability in deployment via arxiv · 17 Oct, 06:17	Not relevant Targets coordination efficiency in agent workflows rather than AI safety or hallucination defenses.
Jev-LiteMAS: Separating Decisions from Generation in Multi-Agent Workflows The paper proposes a framework that replaces generative model calls with lightweight decision models for routing and reviewing in multi-agent workflows. Preprint	LLM evaluation and benchmarks via arxiv · 17 Oct, 06:17	Not relevant Focuses on multi-agent workflow architecture rather than evaluating general LLM benchmarks.

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