BOHM extracts multi-resolution attribution trees from existing routing weights in hierarchical AI systems, providing zero-cost explanations that correlate with SHAP when routing is near-optimal.
Agentic AI: a comprehensive survey of architectures, applications, and future directions.Artificial Intelligence Review, 59 (1):11
10 Pith papers cite this work, alongside 35 external citations. Polarity classification is still indexing.
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Switchcraft routes agentic tool-calling queries to the lowest-cost model that preserves correctness, reaching 82.9% accuracy and 84% cost reduction on five benchmarks.
In Agentic GraphRAG, cited evidence is necessary but not sufficient for accurate answers, as uncited traversal context and graph structure also affect results, requiring evaluation of the full retrieval trajectory.
Memanto delivers 89.8% and 87.1% accuracy on LongMemEval and LoCoMo benchmarks using typed semantic memory and information-theoretic retrieval, outperforming hybrid graph and vector systems with a single query and zero ingestion cost.
Context Kubernetes formalizes six abstractions for knowledge orchestration in agentic AI, with experiments showing a three-tier permission model blocks all five tested attack scenarios where simpler baselines fail.
QUASAR is a new autonomous LLM-based system that orchestrates multi-scale atomistic simulations and benchmarks as a general reasoning tool rather than a narrow automation script.
Agentic AI was used to rapidly reimplement Music Mouse, translate the Continuator system, and add a 3D UI to a tracker sequencer, with reflections on effective practices for audio software development.
The paper develops a four-layer AI agent architecture and the Agentic Financial Market Model linking agent parameters such as autonomy and coupling to market efficiency, liquidity, and systemic risk, with an exploratory event-study application.
Reviews AI applications in ship finance and presents ShipFinance.ai, a modular LLM-based agentic architecture for automating loan application workflows.
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BOHM: Zero-Cost Hierarchical Attribution for Compound AI Systems
BOHM extracts multi-resolution attribution trees from existing routing weights in hierarchical AI systems, providing zero-cost explanations that correlate with SHAP when routing is near-optimal.