Graph-based Target Back-Propagation for Context Adaptation in Multi-LLM Agentic Systems
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Computer Science > Machine Learning
Title:Graph-based Target Back-Propagation for Context Adaptation in Multi-LLM Agentic Systems
Abstract:Context adaptation automates prompt engineering in LLM-based systems by iteratively revising tunable prompts from task feedback, without modifying model weights. Extending this paradigm to multi-LLM agentic systems is crucial: existing methods suffer from inaccurate credit assignment and lack convergence guarantees. We propose \textbf{G}raph-based \textbf{T}arget \textbf{B}ack-\textbf{P}ropagation (GTBP), a context adaptation framework for agentic workflows modeled as directed acyclic graphs. GTBP propagates local target outputs backward through the workflow graph and uses target--output discrepancies to guide a stage-wise prompt update mechanism. Theoretically, we show that GTBP's stage-wise prompt updates become stable over iterations, and that a sufficiently capable LLM optimizer can decrease the overall objective. Empirically, GTBP consistently outperforms strong baselines across three benchmarks while maintaining comparable computational cost.
| Subjects: | Machine Learning (cs.LG); Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.14155 [cs.LG] |
| (or arXiv:2606.14155v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.14155
arXiv-issued DOI via DataCite (pending registration)
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