#system-design News & Analysis

5 articles tagged with #system-design. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

5 articles

AIBearisharXiv – CS AI · Apr 207/10

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When the Loop Closes: Architectural Limits of In-Context Isolation, Metacognitive Co-option, and the Two-Target Design Problem in Human-LLM Systems

Researchers document a case study where a user's custom LLM system designed for self-regulation inadvertently caused loss of agency within 48 hours due to architectural flaws in prompt isolation. The study identifies context contamination and metacognitive co-option as failure mechanisms and proposes physical rather than logical isolation as a solution, raising critical ethical questions about protective versus restrictive AI system design.

AIBullisharXiv – CS AI · Mar 117/10

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MASEval: Extending Multi-Agent Evaluation from Models to Systems

MASEval introduces a new framework-agnostic evaluation library for multi-agent AI systems that treats entire systems rather than just models as the unit of analysis. Research across 3 benchmarks, models, and frameworks reveals that framework choice impacts performance as much as model selection, challenging current model-centric evaluation approaches.

AINeutralGoogle Research Blog · Jan 287/106

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Towards a science of scaling agent systems: When and why agent systems work

The article discusses the scientific principles behind scaling agent systems in generative AI, examining the conditions and factors that determine when agent systems perform effectively. It appears to focus on understanding the theoretical foundations for building and deploying AI agent systems at scale.

AINeutralarXiv – CS AI · Apr 146/10

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ClawVM: Harness-Managed Virtual Memory for Stateful Tool-Using LLM Agents

ClawVM is a virtual memory management system designed for stateful LLM agents that addresses critical failures in current context window management. The system implements typed pages, multi-resolution representations, and validated writeback protocols to ensure deterministic state residency and durability, adding minimal computational overhead.

AINeutralarXiv – CS AI · Apr 146/10

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Cooperation in Human and Machine Agents: Promise Theory Considerations

A theoretical research paper examines Promise Theory as a framework for understanding cooperation between human and machine agents in autonomous systems. The work revisits established principles of agent cooperation to address how diverse components—humans, hardware, software, and AI—maintain alignment with intended purposes through signaling, trust, and feedback mechanisms.