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Merge pull request #127 from shuds13/main
Add abstract to Souza talk
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_talks/2025_12_10.html

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The convergence of HPC, AI, and edge instrumentation is enabling "Autonomous
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Science," a paradigm shift capable of compressing discovery cycles from years to
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months. However, shifting to agent-driven workflows introduces risks regarding
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non-determinism and "dataflow contamination," which threaten scientific
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reproducibility. In this presentation, we argue that robust provenance data
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management is the critical enabler for trustworthy autonomous systems.
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<br /><br />
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We introduce a dual framework: "Provenance of Agents," which enforces accountability
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by systematically capturing agent decisions for root cause analysis, and
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"Provenance with Agents," which leverages Large Language Models (LLMs) as
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interfaces to democratize access to complex runtime data. Showcasing the Flowcept
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architecture and real-world applications in computational chemistry and adaptive
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additive manufacturing at Oak Ridge National Laboratory, we demonstrate how
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provenance safeguards the scientific method within autonomous loops. By ensuring
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transparency and enabling real-time human steering across the Edge-Cloud-HPC
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continuum , this approach removes manual bottlenecks, significantly reducing
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time-to-solution and accelerating the pace of trusted scientific discovery.

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