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Large Result Offloading
Demand-Driven Context Management for Tool-Augmented Language Models
About This Project
Section titled “About This Project”This is an open-access academic paper exploring Large Result Offloading (LRO). a demand-driven approach to context management in tool-augmented language models. When tool calls return datasets too large for the context window, LRO offloads results to external storage and provides structured summaries with extraction recipes, letting models retrieve exactly what they need on demand.
This paper is published as a living document. We welcome peer review, feedback, and academic collaboration.
Specification
The formal specification covering system model, protocol, data structures, and conformance requirements. View specification
Contributing
How to provide feedback, report issues, and collaborate on this research. How to contribute
Source Code
View the source and revision history on GitHub. View on GitHub
Seeking Peer Review
Section titled “Seeking Peer Review”This paper is actively seeking peer review. If you have expertise in language model architectures, context management, tool-augmented AI systems, or related fields, we welcome your feedback through GitHub Issues or GitHub Discussions.