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    <abstract>Foundation-model agents in multi-step, open-ended environments frequently suffer from compounding errors, where early mistakes contaminate long-horizon trajectories. While Multi-Agent Debate (MAD) succeeds in deterministic domains with external validators (CITATION), agents in subjective, non-deterministic tasks such as persuasion experience problem drift and sycophantic conformity. Focusing on subjective argumentation, we identify semantic leakage in standard Retrieval-Augmented Generation (RAG) as a reproducible trigger for these failures: standard RAG prioritizes vocabulary overlap over logical structure.\par To mitigate this leakage, we introduce Taxonomic Strategy RAG (TS-RAG), a systems intervention that routes strategy retrieval through a discrete categorical bottleneck to decouple argumentative structure from topical content. Zero-shot, cross-domain evaluations on r/ChangeMyView show that TS-RAG improves the transfer of abstract argumentative logic where standard semantic retrieval degrades (+14.5 win-rate points over an unaugmented persuader for Gemini Flash 3.0, p&lt;0.05). In asymmetric deployments that mix lightweight and frontier models, TS-RAG raises the win rate of a lightweight persuader against a stronger opponent (70.5% \rightarrow 78.5%, p&lt;0.05) and reduces rounds to consensus when the persuader is already dominant. Finally, we introduce trace-level diagnostics via a turn-by-turn Debate State Representation (DSR), and show that strict stance-holding constraints on the opponent are necessary to prevent evaluation collapse caused by default agentic sycophancy.</abstract>
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%0 Conference Proceedings
%T Retrieve the Logic, Not the Topic: Taxonomic Strategy RAG for Cross-Domain Persuasion Agents
%A Ayromlou, Sana
%A Sehgal, Purvi
%A Narayana, Pradyumna
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-439-2
%F ayromlou-etal-2026-retrieve
%X Foundation-model agents in multi-step, open-ended environments frequently suffer from compounding errors, where early mistakes contaminate long-horizon trajectories. While Multi-Agent Debate (MAD) succeeds in deterministic domains with external validators (CITATION), agents in subjective, non-deterministic tasks such as persuasion experience problem drift and sycophantic conformity. Focusing on subjective argumentation, we identify semantic leakage in standard Retrieval-Augmented Generation (RAG) as a reproducible trigger for these failures: standard RAG prioritizes vocabulary overlap over logical structure.\par To mitigate this leakage, we introduce Taxonomic Strategy RAG (TS-RAG), a systems intervention that routes strategy retrieval through a discrete categorical bottleneck to decouple argumentative structure from topical content. Zero-shot, cross-domain evaluations on r/ChangeMyView show that TS-RAG improves the transfer of abstract argumentative logic where standard semantic retrieval degrades (+14.5 win-rate points over an unaugmented persuader for Gemini Flash 3.0, p<0.05). In asymmetric deployments that mix lightweight and frontier models, TS-RAG raises the win rate of a lightweight persuader against a stronger opponent (70.5% \rightarrow 78.5%, p<0.05) and reduces rounds to consensus when the persuader is already dominant. Finally, we introduce trace-level diagnostics via a turn-by-turn Debate State Representation (DSR), and show that strict stance-holding constraints on the opponent are necessary to prevent evaluation collapse caused by default agentic sycophancy.
%U https://aclanthology.org/2026.aacl-long.216/
%P 4881-4896

Markdown (Informal)

[Retrieve the Logic, Not the Topic: Taxonomic Strategy RAG for Cross-Domain Persuasion Agents](https://aclanthology.org/2026.aacl-long.216/) (Ayromlou et al., AACL-IJCNLP 2026)

• Retrieve the Logic, Not the Topic: Taxonomic Strategy RAG for Cross-Domain Persuasion Agents (Ayromlou et al., AACL-IJCNLP 2026)

ACL

• Sana Ayromlou, Purvi Sehgal, and Pradyumna Narayana. 2026. Retrieve the Logic, Not the Topic: Taxonomic Strategy RAG for Cross-Domain Persuasion Agents. In Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4881–4896, Hengqin, China. Association for Computational Linguistics.