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
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.
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	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	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	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	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	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents an advertising operations framework rather than evaluation benchmarks.
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.
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	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on RL group-based policy gradient algorithms.
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	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Addresses RL mode collapse, not model quantization or serving efficiency.
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	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Deals with memory retrieval architectures, not safety or guardrails.
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.
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	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.
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.
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	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	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	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on insole sensor fusion, not multimodal vision-language models.
Structure-Preserving Document-Level Data Augmentation via Discourse Parsing The paper introduces a document-level data augmentation method using discourse structure parsing to preserve logical flow during text generation. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Not focused on model compression, quantization, or efficient inference.
Structure-Preserving Document-Level Data Augmentation via Discourse Parsing The paper introduces a document-level data augmentation method using discourse structure parsing to preserve logical flow during text generation. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Works purely on document-level text without multimodal modalities.
Structure-Preserving Document-Level Data Augmentation via Discourse Parsing The paper introduces a document-level data augmentation method using discourse structure parsing to preserve logical flow during text generation. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 15:06	Not relevant Focuses on data augmentation rather than retrieval-augmented generation systems.
Structure-Preserving Document-Level Data Augmentation via Discourse Parsing The paper introduces a document-level data augmentation method using discourse structure parsing to preserve logical flow during text generation. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Deals with NLP data augmentation, not multi-step agent execution.
Structure-Preserving Document-Level Data Augmentation via Discourse Parsing The paper introduces a document-level data augmentation method using discourse structure parsing to preserve logical flow during text generation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Proposes a data augmentation method, not an evaluation framework.
Structure-Preserving Document-Level Data Augmentation via Discourse Parsing The paper introduces a document-level data augmentation method using discourse structure parsing to preserve logical flow during text generation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Studies text augmentation without addressing deployment safety or guardrails.
Structure-Preserving Document-Level Data Augmentation via Discourse Parsing The paper introduces a document-level data augmentation method using discourse structure parsing to preserve logical flow during text generation. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Applies to text summarization and QA, not software code.
Structure Before Intelligence: Gating What Agentic Scientific Workflows Release The paper proposes an execution harness that validates intermediate results before allowing scientific agent workflows to release outputs. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Focuses on execution harnesses rather than general LLM benchmarking.
VLRLA: Deployment-Time Adaptation of Vision-Language-Action Models through In-Context Reinforcement Learning The paper proposes an in-context reinforcement learning method enabling vision-language-action policies to adapt during robot deployment. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 15:06	Not relevant Focuses on general robotic manipulation rather than industrial inspection or defect detection.
VLRLA: Deployment-Time Adaptation of Vision-Language-Action Models through In-Context Reinforcement Learning The paper proposes an in-context reinforcement learning method enabling vision-language-action policies to adapt during robot deployment. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Presents an adaptation method rather than benchmarking or evaluation protocols.
Revisiting Triplet Contrastive Learning for 3D Shape Understanding The paper learns 3D shape representations by mapping text hierarchies into hyperbolic space without relying on synthetic 2D renderings. Preprint	AI agents via arxiv · 15 Oct, 15:06	Not relevant Focuses on 3D shape representation learning rather than language model agents.
Revisiting Triplet Contrastive Learning for 3D Shape Understanding The paper learns 3D shape representations by mapping text hierarchies into hyperbolic space without relying on synthetic 2D renderings. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Develops representation learning losses rather than LLM evaluation benchmarks.
Revisiting Triplet Contrastive Learning for 3D Shape Understanding The paper learns 3D shape representations by mapping text hierarchies into hyperbolic space without relying on synthetic 2D renderings. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Does not address AI deployment safety, hallucination, or guardrails.
Retrieve What Helps, Only When It Helps: Agentic Reinforcement Learning for Selective Retrieval-Augmented Machine Translation The paper proposes a reinforcement learning framework that decides when and what external knowledge to retrieve for machine translation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 15:06	Not relevant Develops a retrieval framework rather than proposing LLM evaluation methodology.
Retrieve What Helps, Only When It Helps: Agentic Reinforcement Learning for Selective Retrieval-Augmented Machine Translation The paper proposes a reinforcement learning framework that decides when and what external knowledge to retrieve for machine translation. Preprint	Small and efficient models via arxiv · 15 Oct, 15:06	Not relevant Addresses selective retrieval rather than model pruning, distillation, or serving.
Retrieve What Helps, Only When It Helps: Agentic Reinforcement Learning for Selective Retrieval-Augmented Machine Translation The paper proposes a reinforcement learning framework that decides when and what external knowledge to retrieve for machine translation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 15:06	Not relevant Focuses on translation quality and retrieval policies rather than safety guardrails.
Retrieve What Helps, Only When It Helps: Agentic Reinforcement Learning for Selective Retrieval-Augmented Machine Translation The paper proposes a reinforcement learning framework that decides when and what external knowledge to retrieve for machine translation. Preprint	Multimodal models via arxiv · 15 Oct, 15:06	Not relevant Focuses on text translation without multimodal image or audio components.
Retrieve What Helps, Only When It Helps: Agentic Reinforcement Learning for Selective Retrieval-Augmented Machine Translation The paper proposes a reinforcement learning framework that decides when and what external knowledge to retrieve for machine translation. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Focuses on machine translation rather than code generation or repair.
Typed Decision Models on Tabular Data The paper compares typed probabilistic decision models with large language models on tabular data classification and format robustness. Preprint	LLMs for code via arxiv · 15 Oct, 15:06	Not relevant Evaluates structured tabular data reasoning rather than software code development.

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