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
YOUDrive: Driving Style as a Composable Direction for End-to-End Autonomous Driving This paper introduces a method to enable composable driving-style control in end-to-end autonomous driving planners using task vectors. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focus is on motion planning and style control in driving, not general VLM reasoning.
YOUDrive: Driving Style as a Composable Direction for End-to-End Autonomous Driving This paper introduces a method to enable composable driving-style control in end-to-end autonomous driving planners using task vectors. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Evaluates driving kinematic trajectories, not language model capabilities.
YOUDrive: Driving Style as a Composable Direction for End-to-End Autonomous Driving This paper introduces a method to enable composable driving-style control in end-to-end autonomous driving planners using task vectors. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on autonomous driving motion planning, not language model agents.
ASIWorld: Benchmarking Autonomous Agents in a Persistent Survival Sandbox This paper introduces a persistent survival sandbox benchmark to evaluate sustained goal execution and program synthesis in autonomous agents. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on agent planning performance rather than safety guardrails or jailbreaks.
Depth-progressive 3D seismic velocity model building via 2D generative diffusion models This paper proposes a depth-progressive framework using 2D generative diffusion models to build 3D seismic velocity models efficiently. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Not related to AI safety or enterprise deployment risks.
Depth-progressive 3D seismic velocity model building via 2D generative diffusion models This paper proposes a depth-progressive framework using 2D generative diffusion models to build 3D seismic velocity models efficiently. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Addresses geophysics diffusion modeling, not LLM benchmarking.
Depth-progressive 3D seismic velocity model building via 2D generative diffusion models This paper proposes a depth-progressive framework using 2D generative diffusion models to build 3D seismic velocity models efficiently. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on 2D/3D geophysical seismic modeling, not autonomous agents.
Seamless Deployment of Machine Learning Inference at the Far Edge: An Empirical AWS IoT Greengrass Architecture for Industrial Predictive Maintenance This paper evaluates an edge-cloud hybrid architecture using AWS IoT Greengrass for real-time machine learning inference in predictive maintenance. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Edge machine learning deployment for telemetry, not an LLM agent.
Seamless Deployment of Machine Learning Inference at the Far Edge: An Empirical AWS IoT Greengrass Architecture for Industrial Predictive Maintenance This paper evaluates an edge-cloud hybrid architecture using AWS IoT Greengrass for real-time machine learning inference in predictive maintenance. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on edge IoT architecture rather than language model evaluation.
Seamless Deployment of Machine Learning Inference at the Far Edge: An Empirical AWS IoT Greengrass Architecture for Industrial Predictive Maintenance This paper evaluates an edge-cloud hybrid architecture using AWS IoT Greengrass for real-time machine learning inference in predictive maintenance. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses industrial predictive maintenance latency rather than AI safety or reliability risks.
A Reference Architecture for Integrating Agentic AI into Enterprise Software: Responsibilities, Interactions, and Variability This paper presents a technology-neutral reference architecture for integrating agentic AI capabilities into enterprise software systems. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant System architecture paper, not a benchmark or evaluation methodology.
AI-Native Product Management: A Vision This position paper outlines a conceptual operating model and set of principles for integrating persistent AI agents into product management. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Conceptual framework and vision paper, not an empirical LLM benchmark.
AI-Native Product Management: A Vision This position paper outlines a conceptual operating model and set of principles for integrating persistent AI agents into product management. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant High-level vision paper without technical focus on safety, guardrails, or robustness.
AI-Native Product Management: A Vision This position paper outlines a conceptual operating model and set of principles for integrating persistent AI agents into product management. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses on product management and organization rather than code generation or software repair.
Cross-Lingual TTS for Indian Languages: A Benchmark and Inference-Time Duration Estimation Strategies This paper introduces an Indian language text-to-speech benchmark and an inference-time duration estimation method for alignment-free synthesis. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Pertains to text-to-speech audio synthesis, not autonomous multi-step agents.
Cross-Lingual TTS for Indian Languages: A Benchmark and Inference-Time Duration Estimation Strategies This paper introduces an Indian language text-to-speech benchmark and an inference-time duration estimation method for alignment-free synthesis. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not investigate AI safety, guardrails, or reliability risks.
Cross-Lingual TTS for Indian Languages: A Benchmark and Inference-Time Duration Estimation Strategies This paper introduces an Indian language text-to-speech benchmark and an inference-time duration estimation method for alignment-free synthesis. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Benchmark is for cross-lingual speech synthesis, not language model evaluation.
EvoCowork: A Benchmark for Real-World Office Tasks This paper introduces a 240-task benchmark to evaluate recursive self-improvement in agents across diverse real-world office workflows. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on benchmark performance rather than safety guardrails or risk mitigation.
Pre-Registered Genesis Accounting for Recursive Self-Improvement: First Audited Operator-Genesis Events in a Physics-Discovery Runtime This paper introduces an accounting framework to measure recursive self-improvement and audits machine-authored operator generation in a physics runtime. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Not concerned with enterprise deployment safety, guardrails, or alignment.
Pre-Registered Genesis Accounting for Recursive Self-Improvement: First Audited Operator-Genesis Events in a Physics-Discovery Runtime This paper introduces an accounting framework to measure recursive self-improvement and audits machine-authored operator generation in a physics runtime. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an accounting methodology for symbolic physics discovery, not LLM evaluation.
Pre-Registered Genesis Accounting for Recursive Self-Improvement: First Audited Operator-Genesis Events in a Physics-Discovery Runtime This paper introduces an accounting framework to measure recursive self-improvement and audits machine-authored operator generation in a physics runtime. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on automated scientific discovery and symbolic regression, not LLM agents.
Getting Space Right: Agentic Rewards for Image Generation This paper proposes a curriculum-guided reinforcement learning framework and an agentic reward model for spatial text-to-image generation. Preprint	Computer vision for industrial applications via arxiv · 15 Oct, 06:17	Not relevant Applies to text-to-image generation, not industrial inspection or defect detection.
Getting Space Right: Agentic Rewards for Image Generation This paper proposes a curriculum-guided reinforcement learning framework and an agentic reward model for spatial text-to-image generation. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Uses agentic critic solely as a tool to provide rewards for image generation.
Getting Space Right: Agentic Rewards for Image Generation This paper proposes a curriculum-guided reinforcement learning framework and an agentic reward model for spatial text-to-image generation. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on reward modeling and reinforcement learning for generative models, not LLM evaluation.
Getting Space Right: Agentic Rewards for Image Generation This paper proposes a curriculum-guided reinforcement learning framework and an agentic reward model for spatial text-to-image generation. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not address AI safety, jailbreaks, or deployment guardrails.
PEAM: On-device Communication Agent for Personalized, Efficient and Autonomous Mobile Network Configuration This paper presents an on-device communication agent that uses small language models to generate low-latency mobile network configuration rules. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on network configuration optimization rather than safety or jailbreak risks.
PEAM: On-device Communication Agent for Personalized, Efficient and Autonomous Mobile Network Configuration This paper presents an on-device communication agent that uses small language models to generate low-latency mobile network configuration rules. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents an application and architecture rather than an evaluation benchmark.
WeatherBridge: Forecast-Aware Multi-Source Input Adaptation for Pretrained Weather Forecasting Models This paper introduces an adapter that aligns operational numerical weather prediction inputs with ERA5 reference states for pretrained forecasting models. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on adapter architectures for weather forecasting, not LLM benchmarks.
WeatherBridge: Forecast-Aware Multi-Source Input Adaptation for Pretrained Weather Forecasting Models This paper introduces an adapter that aligns operational numerical weather prediction inputs with ERA5 reference states for pretrained forecasting models. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Not related to AI safety, hallucination, or deployment guardrails.
WeatherBridge: Forecast-Aware Multi-Source Input Adaptation for Pretrained Weather Forecasting Models This paper introduces an adapter that aligns operational numerical weather prediction inputs with ERA5 reference states for pretrained forecasting models. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Domain-specific numerical weather prediction adaptation, not an LLM agent.
Beyond Outcome Feedback: Trajectory-Quality-Guided Experience-based Learning This paper presents an experience-based learning framework that uses trajectory quality signals rather than final task outcomes to guide agent improvement. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Addresses learning from past executions rather than safety, guardrails, or alignment risks.
Beyond Outcome Feedback: Trajectory-Quality-Guided Experience-based Learning This paper presents an experience-based learning framework that uses trajectory quality signals rather than final task outcomes to guide agent improvement. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes an experience-learning mechanism rather than a benchmark or evaluation standard.
MARL-BattleGrounds: A Novel JAX-native Benchmark for Heterogeneous and Competitive Multi-agent RL This paper introduces a JAX-native benchmark for evaluating heterogeneous and competitive multi-agent reinforcement learning algorithms. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Benchmark compute efficiency in JAX, not small LLM compression or serving.
MARL-BattleGrounds: A Novel JAX-native Benchmark for Heterogeneous and Competitive Multi-agent RL This paper introduces a JAX-native benchmark for evaluating heterogeneous and competitive multi-agent reinforcement learning algorithms. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Benchmark is for MARL game policies, not language model evaluation.
MARL-BattleGrounds: A Novel JAX-native Benchmark for Heterogeneous and Competitive Multi-agent RL This paper introduces a JAX-native benchmark for evaluating heterogeneous and competitive multi-agent reinforcement learning algorithms. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Studies competitive game mechanics rather than enterprise AI safety or deployment robustness.
MARL-BattleGrounds: A Novel JAX-native Benchmark for Heterogeneous and Competitive Multi-agent RL This paper introduces a JAX-native benchmark for evaluating heterogeneous and competitive multi-agent reinforcement learning algorithms. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focuses on traditional multi-agent reinforcement learning games, not LLM-based agents.
Probable but Not Valid: A Psychometric Audit of Decision Models as Synthetic Survey Respondents This paper audits whether decision models and generative language models can serve as valid synthetic respondents for psychometric surveys. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Evaluates LLMs as survey respondents, not multi-step autonomous agents.
Bridging Risk Approximation Gaps in Model Predictive Task Sampling via In-Context Modeling This paper addresses failure modes in model predictive task sampling for reinforcement learning by using in-context modeling. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Theoretical reinforcement learning optimization paper, not about tool-using language model agents.
Bridging Risk Approximation Gaps in Model Predictive Task Sampling via In-Context Modeling This paper addresses failure modes in model predictive task sampling for reinforcement learning by using in-context modeling. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Focuses on policy training sampling efficiency, not LLM evaluation frameworks.
Bridging Risk Approximation Gaps in Model Predictive Task Sampling via In-Context Modeling This paper addresses failure modes in model predictive task sampling for reinforcement learning by using in-context modeling. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on general RL task sampling rather than deployment safety or guardrails.
Bridging Risk Approximation Gaps in Model Predictive Task Sampling via In-Context Modeling This paper addresses failure modes in model predictive task sampling for reinforcement learning by using in-context modeling. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Addresses RL task sampling efficiency, not model compression or lightweight serving.
WorkBorne: Discovering Agentic Tasks from Real-World Artifact Transitions This paper presents a framework for discovering and synthesizing terminal-based agent tasks grounded in real-world artifact transitions. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text and terminal environments, not multimodal models.
WorkBorne: Discovering Agentic Tasks from Real-World Artifact Transitions This paper presents a framework for discovering and synthesizing terminal-based agent tasks grounded in real-world artifact transitions. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Focuses on task synthesis rather than safety, guardrails, or hallucination mitigation.
WorkBorne: Discovering Agentic Tasks from Real-World Artifact Transitions This paper presents a framework for discovering and synthesizing terminal-based agent tasks grounded in real-world artifact transitions. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, quantization, or inference efficiency.
WorkBorne: Discovering Agentic Tasks from Real-World Artifact Transitions This paper presents a framework for discovering and synthesizing terminal-based agent tasks grounded in real-world artifact transitions. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Synthesizes agent task environments, not retrieval-augmented generation architectures.
WorkBorne: Discovering Agentic Tasks from Real-World Artifact Transitions This paper presents a framework for discovering and synthesizing terminal-based agent tasks grounded in real-world artifact transitions. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Focuses broadly on terminal-based agent tasks rather than code generation or repair.
SkillForgeBench: Evaluating Agent Reliability Grounded in Agent Skill Specifications This paper introduces a benchmark that uses agent skill specifications to evaluate the reliability and procedural correctness of AI agents. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Focus is on agent procedural execution rather than retrieval-augmented generation.
SkillForgeBench: Evaluating Agent Reliability Grounded in Agent Skill Specifications This paper introduces a benchmark that uses agent skill specifications to evaluate the reliability and procedural correctness of AI agents. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant General agent workflow evaluation, not specialized software engineering or code generation.
SkillForgeBench: Evaluating Agent Reliability Grounded in Agent Skill Specifications This paper introduces a benchmark that uses agent skill specifications to evaluate the reliability and procedural correctness of AI agents. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Does not investigate multimodal vision, audio, or document understanding architectures.
SkillForgeBench: Evaluating Agent Reliability Grounded in Agent Skill Specifications This paper introduces a benchmark that uses agent skill specifications to evaluate the reliability and procedural correctness of AI agents. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not focus on model efficiency, distillation, or quantization.
Predicting how model propensities generalize out of distribution This paper studies how language model behavioral propensities generalize out-of-distribution across different elicitation methods. Preprint	Multimodal models via arxiv · 15 Oct, 06:17	Not relevant Focuses on text language models and alignment traits, not multimodality.
Predicting how model propensities generalize out of distribution This paper studies how language model behavioral propensities generalize out-of-distribution across different elicitation methods. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Studies trait generalization across elicitation methods, not autonomous tool-using agents.
Predicting how model propensities generalize out of distribution This paper studies how language model behavioral propensities generalize out-of-distribution across different elicitation methods. Preprint	LLMs for code via arxiv · 15 Oct, 06:17	Not relevant Does not address code generation, program repair, or software engineering.
Predicting how model propensities generalize out of distribution This paper studies how language model behavioral propensities generalize out-of-distribution across different elicitation methods. Preprint	Retrieval-augmented generation (RAG) via arxiv · 15 Oct, 06:17	Not relevant Not related to retrieval-augmented generation or external document grounding.
Predicting how model propensities generalize out of distribution This paper studies how language model behavioral propensities generalize out-of-distribution across different elicitation methods. Preprint	Small and efficient models via arxiv · 15 Oct, 06:17	Not relevant Does not address model compression, quantization, distillation, or serving efficiency.
Predicting how model propensities generalize out of distribution This paper studies how language model behavioral propensities generalize out-of-distribution across different elicitation methods. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Studies behavioral propensity spillover rather than benchmarking or evaluation methodology.
VC-OPD: Visual Counterfactual On-Policy Distillation for Grounded Vision-Language Reasoning This paper proposes visual counterfactual on-policy distillation to prioritize visually grounded tokens during vision-language reasoning training. Preprint	AI safety and reliability in deployment via arxiv · 15 Oct, 06:17	Not relevant Does not address deployment safety, guardrails, or red-teaming.
VC-OPD: Visual Counterfactual On-Policy Distillation for Grounded Vision-Language Reasoning This paper proposes visual counterfactual on-policy distillation to prioritize visually grounded tokens during vision-language reasoning training. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Proposes a training method rather than an evaluation benchmark or methodology.
VC-OPD: Visual Counterfactual On-Policy Distillation for Grounded Vision-Language Reasoning This paper proposes visual counterfactual on-policy distillation to prioritize visually grounded tokens during vision-language reasoning training. Preprint	AI agents via arxiv · 15 Oct, 06:17	Not relevant Focus is on vision-language model distillation, not multi-step tool-using agents.
Probabilistic Multi-Source Data Selection via Learnable Categorical Priors: Variance Reduction and Guaranteed Derandomization This paper introduces a probabilistic relaxation method with learnable categorical priors for bilevel multi-source data selection. Preprint	LLM evaluation and benchmarks via arxiv · 15 Oct, 06:17	Not relevant Presents optimization mathematics for data selection, not LLM benchmarking methods.

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