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
Vision-Language Models Integrate Conflicting Evidence Like Biased Bayesian Observers The paper proposes a Bayesian cue-combination model to explain how vision-language models integrate conflicting visual and textual evidence. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Analyzes multimodal cue conflict, not external document retrieval systems.
Vision-Language Models Integrate Conflicting Evidence Like Biased Bayesian Observers The paper proposes a Bayesian cue-combination model to explain how vision-language models integrate conflicting visual and textual evidence. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Studies vision-language perception rather than code generation or repair.
Vision-Language Models Integrate Conflicting Evidence Like Biased Bayesian Observers The paper proposes a Bayesian cue-combination model to explain how vision-language models integrate conflicting visual and textual evidence. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Theoretical study of VLM cue combination, not industrial defect inspection.
Vision-Language Models Integrate Conflicting Evidence Like Biased Bayesian Observers The paper proposes a Bayesian cue-combination model to explain how vision-language models integrate conflicting visual and textual evidence. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Examines multimodal cue combination, not multi-step tool-using agents.
Vision-Language Models Integrate Conflicting Evidence Like Biased Bayesian Observers The paper proposes a Bayesian cue-combination model to explain how vision-language models integrate conflicting visual and textual evidence. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies cognitive cue integration, not deployment guardrails or safety attacks.
Vision-Language Models Integrate Conflicting Evidence Like Biased Bayesian Observers The paper proposes a Bayesian cue-combination model to explain how vision-language models integrate conflicting visual and textual evidence. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Focuses on evidence integration rather than compression or inference efficiency.
Vision-Language Models Integrate Conflicting Evidence Like Biased Bayesian Observers The paper proposes a Bayesian cue-combination model to explain how vision-language models integrate conflicting visual and textual evidence. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes a Bayesian predictive model rather than an evaluation benchmark.
Recursive Formation and First-Class Reuse of Compositional Capabilities The paper introduces a framework for recursively composing, verifying, and persistently reusing modular agent capabilities across diverse problem domains. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents a functional framework rather than a general LLM evaluation benchmark.
Recursive Formation and First-Class Reuse of Compositional Capabilities The paper introduces a framework for recursively composing, verifying, and persistently reusing modular agent capabilities across diverse problem domains. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Addresses capability composition, not safety, guardrails, or alignment risks.
Recursive Formation and First-Class Reuse of Compositional Capabilities The paper introduces a framework for recursively composing, verifying, and persistently reusing modular agent capabilities across diverse problem domains. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on abstract formal capabilities, not software engineering or code LLMs.
From Failures to Learnable Worlds: Policy-Conditioned Environment Generation for Navigation Agents The paper presents a method that uses agent failure patterns to generate tailored simulation environments for embodied navigation learning. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on embodied robotic navigation, not industrial defect detection.
From Failures to Learnable Worlds: Policy-Conditioned Environment Generation for Navigation Agents The paper presents a method that uses agent failure patterns to generate tailored simulation environments for embodied navigation learning. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Generates targeted training environments, not a general evaluation benchmark.
From Failures to Learnable Worlds: Policy-Conditioned Environment Generation for Navigation Agents The paper presents a method that uses agent failure patterns to generate tailored simulation environments for embodied navigation learning. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on embodied navigation policies rather than vision-language models.
From Failures to Learnable Worlds: Policy-Conditioned Environment Generation for Navigation Agents The paper presents a method that uses agent failure patterns to generate tailored simulation environments for embodied navigation learning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies navigation adaptation environments rather than deployment safety guardrails.
AgentSafeEvo: Adversarial Co-Evolution for LLM Agent Safety The paper introduces an adversarial co-evolution reinforcement learning framework to strengthen language model agents against tool-use manipulation attacks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces a reinforcement learning safety framework, not an evaluation benchmark.
Beyond the Trial-and-Error Loop: Hybrid Projection and Automated Tuning for Distributed Training The paper proposes a hybrid projection and simulation method to automatically configure distributed training parallelisms across GPU clusters. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents an automated systems tuning tool, not benchmark methodology.
Beyond the Trial-and-Error Loop: Hybrid Projection and Automated Tuning for Distributed Training The paper proposes a hybrid projection and simulation method to automatically configure distributed training parallelisms across GPU clusters. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on distributed cluster configuration rather than agentic tool use.
Beyond the Trial-and-Error Loop: Hybrid Projection and Automated Tuning for Distributed Training The paper proposes a hybrid projection and simulation method to automatically configure distributed training parallelisms across GPU clusters. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on training performance tuning rather than AI deployment safety.
VideoHarness-SRSI: Structured Recursive Self-Improvement for Video Understanding Harness The paper examines failure modes in recursively self-improving execution harnesses for agentic video understanding models. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Addresses general video understanding, not industrial defect or quality inspection.
VideoHarness-SRSI: Structured Recursive Self-Improvement for Video Understanding Harness The paper examines failure modes in recursively self-improving execution harnesses for agentic video understanding models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on optimizing executable harnesses rather than benchmark methodology.
VideoHarness-SRSI: Structured Recursive Self-Improvement for Video Understanding Harness The paper examines failure modes in recursively self-improving execution harnesses for agentic video understanding models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on harness search optimization, not safety or alignment.
Evaluating Agentic Capabilities Before Post-Training: An End-to-End Approach The paper presents an evaluation framework to test the interactive agentic capabilities of base language models before post-training. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on measuring agentic pretraining capability rather than deployment safety.
How Do LLMs Understand Legal Philosophy? Diagnosing Legal-Philosophical Reasoning in LLMs The paper introduces a benchmark to evaluate how well language models understand legal philosophy and apply it to legal reasoning. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Examines legal philosophy reasoning rather than software code generation.
How Do LLMs Understand Legal Philosophy? Diagnosing Legal-Philosophical Reasoning in LLMs The paper introduces a benchmark to evaluate how well language models understand legal philosophy and apply it to legal reasoning. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Does not cover model compression, distillation, or serving efficiency.
How Do LLMs Understand Legal Philosophy? Diagnosing Legal-Philosophical Reasoning in LLMs The paper introduces a benchmark to evaluate how well language models understand legal philosophy and apply it to legal reasoning. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies legal philosophical reasoning rather than AI deployment safety.
How Do LLMs Understand Legal Philosophy? Diagnosing Legal-Philosophical Reasoning in LLMs The paper introduces a benchmark to evaluate how well language models understand legal philosophy and apply it to legal reasoning. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Investigates legal-philosophical text analysis, not autonomous agent planning.
How Do LLMs Understand Legal Philosophy? Diagnosing Legal-Philosophical Reasoning in LLMs The paper introduces a benchmark to evaluate how well language models understand legal philosophy and apply it to legal reasoning. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Assesses text-based legal opinions without multimodal capabilities.
How Do LLMs Understand Legal Philosophy? Diagnosing Legal-Philosophical Reasoning in LLMs The paper introduces a benchmark to evaluate how well language models understand legal philosophy and apply it to legal reasoning. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Does not explore retrieval-augmented generation architectures or grounding.
Recognition Is Not Grounding: Benchmarking Multilingual Financial Evidence Extraction The paper introduces a multilingual benchmark to evaluate how language models identify financial concepts and extract supporting numerical evidence. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Benchmarks extraction accuracy, not safety, guardrails, or jailbreaks.
Recognition Is Not Grounding: Benchmarking Multilingual Financial Evidence Extraction The paper introduces a multilingual benchmark to evaluate how language models identify financial concepts and extract supporting numerical evidence. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Tests factual extraction from financial reports, not agent workflows.
EvoProbe: Evaluation-Driven Benchmark Evolution for LLM Agents The paper introduces an evaluation framework that diagnoses bottlenecks and dynamically regenerates benchmark tasks for language model agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on dynamic benchmarking methods rather than safety guardrails.
Integrating Simulation and Chain-of-thought Reasoning in Multimodal-Language Models For Physical Reasoning The paper compares physics simulations and chain-of-thought language reasoning for physical problem solving in multimodal language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents a hybrid reasoning method, not an evaluation benchmark.
Integrating Simulation and Chain-of-thought Reasoning in Multimodal-Language Models For Physical Reasoning The paper compares physics simulations and chain-of-thought language reasoning for physical problem solving in multimodal language models. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Explores reasoning paradigms for VLMs rather than tool-using agents.
Integrating Simulation and Chain-of-thought Reasoning in Multimodal-Language Models For Physical Reasoning The paper compares physics simulations and chain-of-thought language reasoning for physical problem solving in multimodal language models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies physical reasoning capabilities, not safety or deployment risks.
xLightGCN: Explainable Graph Neural Network Based Recommendation Systems with Most Influential Peers The paper proposes a graph-based method to generate explainable recommendations by identifying influential user peers in graph neural networks. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on recommender explainability rather than LLM deployment safety.
xLightGCN: Explainable Graph Neural Network Based Recommendation Systems with Most Influential Peers The paper proposes a graph-based method to generate explainable recommendations by identifying influential user peers in graph neural networks. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Introduces an explainable recommender method, not an LLM benchmark.
xLightGCN: Explainable Graph Neural Network Based Recommendation Systems with Most Influential Peers The paper proposes a graph-based method to generate explainable recommendations by identifying influential user peers in graph neural networks. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on recommendation system graph algorithms, not LLM agents.
Are You Reading the Right Thing? Rethinking Internal Signals for Prompt-Injection Detection The paper analyzes five internal representation reading methods to examine how they detect prompt injections in language models. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on internal model states for prompt injection detection.
Are You Reading the Right Thing? Rethinking Internal Signals for Prompt-Injection Detection The paper analyzes five internal representation reading methods to examine how they detect prompt injections in language models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Studies prompt injection detection signals rather than general benchmark validity.
AdCoEvo: A Co-Evolving Multi-Agent Advertising Delivery System The paper presents a multi-agent system designed to automate advertising operations and coordinate tasks like bidding and budget allocation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Covers automated advertising decisions rather than deployment safety guardrails.
AdCoEvo: A Co-Evolving Multi-Agent Advertising Delivery System The paper presents a multi-agent system designed to automate advertising operations and coordinate tasks like bidding and budget allocation. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Coordinates advertising tasks without retrieval-augmented generation techniques.
AdCoEvo: A Co-Evolving Multi-Agent Advertising Delivery System The paper presents a multi-agent system designed to automate advertising operations and coordinate tasks like bidding and budget allocation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents an advertising operations framework rather than evaluation benchmarks.
MIPS: Mitigating Mode Collapse in Reinforcement Learning for Large Outcome Spaces The paper proposes marginal inverse probability scaling to reduce mode collapse in reinforcement learning with large outcome spaces. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Develops an RL policy gradient variant, not benchmark methodology.
MIPS: Mitigating Mode Collapse in Reinforcement Learning for Large Outcome Spaces The paper proposes marginal inverse probability scaling to reduce mode collapse in reinforcement learning with large outcome spaces. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Addresses RL mode collapse, not model quantization or serving efficiency.
MIPS: Mitigating Mode Collapse in Reinforcement Learning for Large Outcome Spaces The paper proposes marginal inverse probability scaling to reduce mode collapse in reinforcement learning with large outcome spaces. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Deals with general reinforcement learning, not safety or guardrails.
MIPS: Mitigating Mode Collapse in Reinforcement Learning for Large Outcome Spaces The paper proposes marginal inverse probability scaling to reduce mode collapse in reinforcement learning with large outcome spaces. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on RL group-based policy gradient algorithms.
PFedMA: Who Should Join Next as Federated Multi-Agent Orchestration Evolves? The paper introduces a federated multi-agent framework that guides agent selection and query refinement under dynamic privacy constraints. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Designs a federated framework rather than an evaluation benchmark.
MemCode: Discrete Semantic Representations for Long-Term Agent Memory The paper introduces a discrete codebook representation that pairs associative indexing with vector search for long-term agent memory. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes an agent memory method rather than testing benchmarks.
MemCode: Discrete Semantic Representations for Long-Term Agent Memory The paper introduces a discrete codebook representation that pairs associative indexing with vector search for long-term agent memory. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Deals with memory retrieval architectures, not safety or guardrails.
Beyond Retrieval: Evidence Access and Event Aggregation in Agent Memory The paper introduces an event ledger and executable aggregation framework to help agents accurately query and summarize trajectory history. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on memory architectures rather than deployment safety guardrails.
Beyond Retrieval: Evidence Access and Event Aggregation in Agent Memory The paper introduces an event ledger and executable aggregation framework to help agents accurately query and summarize trajectory history. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes a memory system rather than an evaluation benchmark.
IMU-Bench: Benchmarking Wrist IMU Representations Across Kinematic, Semantic and Clinical Tasks The paper introduces a multi-task benchmark and a masked autoencoder to evaluate transfer learning for wrist-worn inertial measurement units. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on IMU wearable representations, not language-model planning agents.
IMU-Bench: Benchmarking Wrist IMU Representations Across Kinematic, Semantic and Clinical Tasks The paper introduces a multi-task benchmark and a masked autoencoder to evaluate transfer learning for wrist-worn inertial measurement units. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Evaluates wearable IMU sensor representation, not LLM benchmark methodologies.
IMU-Bench: Benchmarking Wrist IMU Representations Across Kinematic, Semantic and Clinical Tasks The paper introduces a multi-task benchmark and a masked autoencoder to evaluate transfer learning for wrist-worn inertial measurement units. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Covers wearable motion tracking, not industrial manufacturing defect inspection.
IMU-Bench: Benchmarking Wrist IMU Representations Across Kinematic, Semantic and Clinical Tasks The paper introduces a multi-task benchmark and a masked autoencoder to evaluate transfer learning for wrist-worn inertial measurement units. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies wearable motion transfer rather than AI deployment safety risks.
Robust Real-Time Monocular Human Pose Estimation with Insole-Based Sensor Fusion The paper combines camera video and insole sensor signals to improve real-time monocular human pose estimation under visual occlusion. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Evaluates sensor fusion architectures, not LLM benchmark methodologies.
Robust Real-Time Monocular Human Pose Estimation with Insole-Based Sensor Fusion The paper combines camera video and insole sensor signals to improve real-time monocular human pose estimation under visual occlusion. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not address AI safety, jailbreaks, or deployment guardrails.
Robust Real-Time Monocular Human Pose Estimation with Insole-Based Sensor Fusion The paper combines camera video and insole sensor signals to improve real-time monocular human pose estimation under visual occlusion. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on human pose tracking, not industrial inspection or defect detection.
Robust Real-Time Monocular Human Pose Estimation with Insole-Based Sensor Fusion The paper combines camera video and insole sensor signals to improve real-time monocular human pose estimation under visual occlusion. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on human pose estimation, not autonomous language agents.
Robust Real-Time Monocular Human Pose Estimation with Insole-Based Sensor Fusion The paper combines camera video and insole sensor signals to improve real-time monocular human pose estimation under visual occlusion. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on insole sensor fusion, not multimodal vision-language models.

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