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arXiv · 2606.14155

Graph-based Target Back-Propagation for Context Adaptation in Multi-LLM Agentic Systems

Tan Zhu·Tong Yao·Kananart Kuwaranancharoen·Amit Singh·Yushang Lai·Deepa Mohan·Shankara Bhargava
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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.

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Tan Zhu, Tong Yao, Kananart Kuwaranancharoen, Amit Singh, Yushang Lai, Deepa Mohan, Shankara Bhargava. 2026-06-12. Graph-based Target Back-Propagation for Context Adaptation in Multi-LLM Agentic Systems. https://arxiv.org/abs/2606.14155

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