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Long-Running LLM Workflows
This repository contains exploratory notes and conceptual work related to building AI systems that support long-running, multi-step workflows using large language models.
Current research interests include persistent memory and state management across sessions, strategies for reducing context loss and conversational drift, and approaches to improving reliability and consistency in applied LLM-based systems.
The focus is on system-level design considerations rather than specific model implementations, with emphasis on real-world use cases that require sustained interaction, task continuity, and incremental progress over time.
Content in this repository is intentionally high-level and may evolve as experimentation, evaluation, and applied research continue.
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