@inproceedings{jin-etal-2026-bid,
title = "{BID}-{SPO}: Rethinking Multi-Agent Prompt Optimization as Information Contracting via Bidding-based Shapley Values",
author = "Jin, Weiqiang and
Cheng, Yuxin and
Sun, Tingting and
Tang, Shixiang and
Gao, Jing and
Qi, Jinhu and
Zhang, Wentao and
Wang, Junli and
Zhao, Biao and
Zhao, Guoshuai and
Yang, Guang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.239/",
pages = "5029--5060",
ISBN = "979-8-89176-462-0",
abstract = "Multi-agent LLM systems decompose complex tasks across specialist agents, yet joint prompt optimization remains difficult because agents are coupled through structured information handoffs. We identify interface mismatch as a structural failure mode that existing optimizers cannot diagnose: their scalar attribution signals conflate reasoning quality with format incompatibility, and the inter-agent contract is left entirely implicit. We propose BID-SPO (Bidding-based Shapley Prompt Optimization), which makes the inter-agent contract an explicit optimization variable. Each agent declares an Output Bid and an Input Demand, and a contract-aware cooperative-game characteristic function yields an attribution signal whose Shapley decomposition exposes three interpretable dimensions (global task contribution, downstream interface alignment, and upstream adaptation cost), turning attribution into a targeted diagnosis for prompt repair. A bid-conformance constraint keeps declared contracts feasible with respect to actual agent behavior throughout optimization. Across seven benchmarks and different model configurations, BID-SPO outperforms MASPO and AgentDropout, with accuracy gains of $+2.93${\%} under the Qwen3-8B + Gemini-2.5-pro configuration and $+4.38${\%} under the DeepSeek-v4 flash / pro configuration. Our findings emphasize that reliable multi-agent LLM optimization requires optimizing inter-agent interfaces as explicit objects, not just improving agents in isolation."
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%0 Conference Proceedings
%T BID-SPO: Rethinking Multi-Agent Prompt Optimization as Information Contracting via Bidding-based Shapley Values
%A Jin, Weiqiang
%A Cheng, Yuxin
%A Sun, Tingting
%A Tang, Shixiang
%A Gao, Jing
%A Qi, Jinhu
%A Zhang, Wentao
%A Wang, Junli
%A Zhao, Biao
%A Zhao, Guoshuai
%A Yang, Guang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F jin-etal-2026-bid
%X Multi-agent LLM systems decompose complex tasks across specialist agents, yet joint prompt optimization remains difficult because agents are coupled through structured information handoffs. We identify interface mismatch as a structural failure mode that existing optimizers cannot diagnose: their scalar attribution signals conflate reasoning quality with format incompatibility, and the inter-agent contract is left entirely implicit. We propose BID-SPO (Bidding-based Shapley Prompt Optimization), which makes the inter-agent contract an explicit optimization variable. Each agent declares an Output Bid and an Input Demand, and a contract-aware cooperative-game characteristic function yields an attribution signal whose Shapley decomposition exposes three interpretable dimensions (global task contribution, downstream interface alignment, and upstream adaptation cost), turning attribution into a targeted diagnosis for prompt repair. A bid-conformance constraint keeps declared contracts feasible with respect to actual agent behavior throughout optimization. Across seven benchmarks and different model configurations, BID-SPO outperforms MASPO and AgentDropout, with accuracy gains of +2.93% under the Qwen3-8B + Gemini-2.5-pro configuration and +4.38% under the DeepSeek-v4 flash / pro configuration. Our findings emphasize that reliable multi-agent LLM optimization requires optimizing inter-agent interfaces as explicit objects, not just improving agents in isolation.
%U https://aclanthology.org/2026.findings-emnlp.239/
%P 5029-5060
Markdown (Informal)
[BID-SPO: Rethinking Multi-Agent Prompt Optimization as Information Contracting via Bidding-based Shapley Values](https://aclanthology.org/2026.findings-emnlp.239/) (Jin et al., Findings 2026)
ACL
- Weiqiang Jin, Yuxin Cheng, Tingting Sun, Shixiang Tang, Jing Gao, Jinhu Qi, Wentao Zhang, Junli Wang, Biao Zhao, Guoshuai Zhao, and Guang Yang. 2026. BID-SPO: Rethinking Multi-Agent Prompt Optimization as Information Contracting via Bidding-based Shapley Values. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 5029–5060, Budapest, Hungary. Association for Computational Linguistics.