Diagnosing and Mitigating Compounding Failures in Agentic Persuasion via Taxonomic Strategy Retrieval

• URL: http://arxiv.org/abs/2606.24976v2
• Date: Tue, 30 Jun 2026 19:54:43 GMT
• Title: Diagnosing and Mitigating Compounding Failures in Agentic Persuasion via Taxonomic Strategy Retrieval
• Abstract summary: Taxonomic Strategy RAG (TS-RAG) is a systems intervention that routes strategies through a discrete categorical bottleneck to decouple argumentative structure from topical content.<n>TS-RAG significantly improves the transfer of abstract logic where standard semantic retrieval collapses.<n>It acts as a "capability bridge" in asymmetric deployments, empowering lightweight persuaders to consistently defeat superior opponents.
• Score: 2.5607169168631696
• License: http://creativecommons.org/licenses/by/4.0/
• 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, agents in subjective tasks like persuasion experience severe problem drift and sycophantic conformity. We identify semantic leakage in standard Retrieval-Augmented Generation (RAG) as a reproducible trigger for these failures, as standard RAG prioritizes vocabulary overlap over logical necessity. To eliminate this leakage, we introduce Taxonomic Strategy RAG (TS-RAG), a systems intervention that routes strategies through a discrete categorical bottleneck to decouple argumentative structure from topical content. Zero-shot, cross-domain evaluations demonstrate that TS-RAG significantly improves the transfer of abstract logic where standard semantic retrieval collapses. Crucially, TS-RAG acts as a "capability bridge" in asymmetric deployments, empowering lightweight persuaders to consistently defeat parametrically superior opponents (improving win rates from 70.5 to 78.5) and accelerating argumentative efficiency. Finally, we introduce trace-level diagnostics via a turn-by-turn Debate State Representation (DSR), demonstrating the necessity of strict constraints to prevent evaluation collapse via default agentic sycophancy.

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