GraphBit：一种基于图的代理框架用于非线性代理编排
GraphBit: A Graph-based Agentic Framework for Non-Linear Agent Orchestration
AI总结：
GraphBit通过显式定义DAG流程提高代理编排的可重复性和可审计性，在多个基准任务中表现优于现有框架，实现高准确率、低延迟和高吞吐量。
摘要：
GraphBit通过显式定义DAG流程提高代理编排的可重复性和可审计性，在多个基准任务中表现优于现有框架，实现高准确率、低延迟和高吞吐量。
英文摘要：
Agentic LLM frameworks that rely on prompted orchestration, where the model itself determines workflow transitions, often suffer from hallucinated routing, infinite loops, and non-reproducible execution. We introduce GraphBit, an engine-orchestrated framework that defines workflows explicitly and deterministically as a directed acyclic graph (DAG). Unlike prompted orchestration, agents in GraphBit operate as typed functions, while a Rust-based engine governs routing, state transitions, and tool invocation, ensuring reproducibility and auditability. The engine supports parallel branch execution, conditional control flow over structured state predicates, and configurable error recovery. A three-tier memory architecture consisting of ephemeral scratch space, structured state, and external connectors isolates context across stages, preventing cascading context bloat that degrades reasoning in long-running pipelines. Across GAIA benchmark tasks spanning zero-tool, document-augmented, and web-enabled workflows, GraphBit outperforms six existing frameworks, achieving the highest accuracy (67.6 percent), zero framework-induced hallucinations, the lowest latency (11.9 ms overhead), and the highest throughput. Ablation studies demonstrate that each memory tier contributes measurably to performance, with deterministic execution providing the greatest gains on tool-intensive tasks representative of real-world deployments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
