Infra papers

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16 papers found

ReviewPaperPublishedTopics

7.5Semantic Quorum Assurance: Collective Certification for Non-Deterministic AI InfrastructureAs AI systems become more integrated into critical infrastructure, ensuring their operational safety is paramount, making SQA a relevant contribution to the field.Jun 9, 2026Infra Security Safety

6.8Agent Memory: Characterization and System Implications of Stateful Long-Horizon WorkloadsUnderstanding and optimizing agent memory systems is crucial for improving the efficiency and scalability of long-horizon AI tasks.Jun 6, 2026LLM Agent Infra

6.8AI Assurance: A Comprehensive Testing Strategy for Enterprise AI SystemsAI engineers and researchers should care because it addresses the unique challenges of assuring enterprise AI systems, which differ from traditional software, and provides structured guidance for managing these systems effectively.May 25, 2026Evaluation Safety Infra

6.8TorchUMM: A Unified Multimodal Model Codebase for Evaluation, Analysis, and Post-trainingA unified codebase like TorchUMM can streamline the evaluation process for multimodal models, making it easier for researchers to compare different architectures and improve model development.May 22, 2026Multimodal Evaluation Benchmark

7.2AgentWall: A Runtime Safety Layer for Local AI AgentsAs AI agents gain more capabilities, ensuring their safe operation in local environments is crucial to prevent unintended or malicious actions, making AgentWall relevant for developers and researchers working with autonomous systems.May 19, 2026Agent Safety Security

6.5Safactory: A Scalable Agentic Infrastructure for Training Trustworthy Autonomous IntelligenceAs AI systems evolve towards autonomy, a cohesive infrastructure like Safactory could streamline the development and evaluation of these systems, enhancing safety and reliability.May 11, 2026Agent Infra Safety

6.8Collaborative Agent Reasoning Engineering (CARE): A Three-Party Design Methodology for Systematically Engineering AI Agents with Subject Matter Experts, Developers, and Helper AgentsThis methodology could streamline the development of AI agents in scientific domains, potentially leading to more efficient and reliable systems.May 1, 2026LLM Agent Reasoning

7.2Sovereign Agentic Loops: Decoupling AI Reasoning from Execution in Real-World SystemsThis work is crucial for AI engineers and researchers focused on deploying AI systems safely, as it addresses the risk of unsafe executions by ensuring that AI intents are validated before affecting real-world systems.Apr 27, 2026LLM Agent Safety

6.8Focus Session: Hardware and Software Techniques for Accelerating Multimodal Foundation ModelsThis work is relevant for AI engineers and researchers looking to improve the efficiency and performance of multimodal foundation models, which are increasingly used in diverse applications.Apr 27, 2026Multimodal Infra Architecture

7.5Mesh Memory Protocol: Semantic Infrastructure for Multi-Agent LLM SystemsAs LLM agents increasingly work together on complex tasks, MMP provides a structured approach to enhance their collaborative capabilities, which is crucial for improving efficiency and effectiveness in multi-agent systems.Apr 22, 2026LLM Agent Infra

6.5Agentic AI Optimisation (AAIO): what it is, how it works, why it matters, and how to deal with itUnderstanding AAIO is crucial for AI engineers and researchers as it addresses the integration challenges posed by autonomous AI systems in digital environments.Apr 21, 2026Agent Infra Other

7.5The Cognitive Circuit Breaker: A Systems Engineering Framework for Intrinsic AI ReliabilityAs LLMs are increasingly integrated into critical systems, ensuring their reliability without significant performance trade-offs is essential for maintaining trust and functionality.Apr 17, 2026LLM Safety Evaluation

7.5SciFi: A Safe, Lightweight, User-Friendly, and Fully Autonomous Agentic AI Workflow for Scientific ApplicationsThis framework could significantly reduce the manual workload for researchers, allowing them to focus on more complex and creative aspects of their work.Apr 17, 2026Agent Safety Infra

8.5Parallax: Why AI Agents That Think Must Never ActAs AI agents increasingly gain execution capabilities, ensuring their safe operation becomes critical to prevent potential security breaches and unintended actions, making Parallax's approach highly relevant.Apr 16, 2026Agent Evaluation Infra

8.5KumoRFM-2: Scaling Foundation Models for Relational LearningThis paper introduces a foundation model that significantly improves the handling of relational data, a critical component in many real-world applications, by outperforming existing methods and scaling to large datasets.Apr 16, 2026Benchmark Data Infra

7.5From Agent Loops to Structured Graphs:A Scheduler-Theoretic Framework for LLM Agent ExecutionThis paper matters because it addresses critical weaknesses in the current LLM agent execution paradigm, potentially leading to more reliable and debuggable AI systems.Apr 14, 2026Agent Infra LLM