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This paper presents the design and implementation of a safety-constrained LLM system for maternal and child health resource navigation, using a multi-layered architecture with domain-restricted RAG and boundary enforcement to ensure safety and reliability.
This paper investigates the optimal placement of expensive 'oracle' correctors within a swarm of unreliable agents to achieve correct consensus, revealing a submodular property and a budget-correctness frontier that depends on the cost–strength curvature.
An analysis of multi-agent LLM pipeline failures, citing the Berkeley MAST paper which attributes most failures to coordination issues (specification, inter-agent misalignment) rather than model capability, and suggests dedicated verifier agents as a fix.
This paper introduces MemClaw, a governed shared memory architecture for multi-agent LLM systems, formalizing failure modes like unauthorized leakage and stale propagation, and evaluating the system via the ArgusFleet harness.
A carefully curated collection of papers related to large model systems, covering training, inference, multimodality, and more. It is continuously updated and includes technical reports, frameworks, and courses, making it a valuable reference for researchers and developers.
LLMSys-PaperList is a curated reading list on GitHub that organizes LLM systems research papers and resources into practical categories such as training systems, serving systems, and multi-modal coverage, helping AI/ML engineers and researchers stay updated.
The article highlights a critical failure mode in production RAG systems where confident but incorrect answers arise from versioning issues and lack of uncertainty mechanisms. It proposes architectural improvements like routing layers, retrieval scoring, and hallucination checks to mitigate these errors.