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Earthquake rescue priorities are often determined from incomplete evidence that is partly shared across information channels. Under such conditions, an apparently plausible mean score may conceal substantial directional conflict. This study investigates whether that conflict can be carried forward into the action boundaries themselves. We develop the Conflict-Driven Action Boundary Generation Model (CABGM), which links composite-conflict diagnosis to a constructed association proxy, normalized action propensities, and feedback-sensitive three-way decision boundaries. The structural coefficients are screened against prespecified sign, normalization, boundedness, and boundary-feasibility constraints and are treated as theory-constrained operating settings rather than estimates fitted to rescue outcomes. For the retrospective empirical application, we reconstructed ten named settlement-scale units from public records using a prespecified five-level documentary coding protocol, a fixed source hierarchy, and a fixed evidence cutoff before applying the frozen model specification. We also implemented a scalar-score adaptation of adaptive-consensus logic and a reliability-informed Dirichlet comparator for channel-weight uncertainty. Both comparators are transparent adaptations to the present $10\times 4$ score matrix rather than exact reproductions of the original linguistic or event-network models. In the public-record audit, all five methods yielded identical rank-concordance statistics: a Spearman correlation of 0.912, Kendall’s tau-b of 0.839, a p-value of 0.0016 from an exact two-sided permutation test, and 100% top-four overlap. The fixed-threshold conflict gate, opinion-distance update, adaptive-consensus adaptation, and CABGM each produced an acceptance/deferment/rejection split of 6/4/0, whereas the Bayesian weight-uncertainty comparator produced a 4/6/0 split. CABGM offers a new methodological option for emergency group decision-making by integrating composite-conflict diagnosis, a constructed association proxy, normalized action propensities, and feedback-sensitive three-way decision boundaries within a unified framework. Compared with fixed-boundary and consensus-contraction approaches, the model makes the transmission of diagnosed conflict into boundary adjustment explicit and traceable, while distinguishing boundary adaptation from score smoothing. It therefore provides a transparent mechanism for preserving unresolved or threshold-adjacent alternatives for further verification before definitive action is taken. Full article

The deployment of large language models as autonomous retrieval agents over unstructured knowledge bases gives rise to a persistent structural conflict between probabilistic neural generation and deterministic physical execution. While agentic paradigms facilitate complex multi-hop retrieval, their unconstrained generative nature frequently violates strict syntactic requirements. This systemic vulnerability directly triggers execution hallucinations, such as fabricated API parameters or malformed schemas. Consequently, these syntax-driven failures force systems into redundant trial-and-error recovery loops, resulting in severe computational inflation that degrades both token efficiency and inference latency. To resolve this reliability–efficiency dilemma, this paper proposes RAG-CoT-MCP, a neuro-symbolic architecture that orthogonally decouples probabilistic cognitive planning from deterministic tool execution. By integrating the Model Context Protocol (MCP) as a strict system-level validation boundary, the framework ensures that latent reasoning trajectories manifest exclusively as syntactically valid operations. Exhaustive empirical evaluations across four disparate datasets—incorporating a multi-dimensional LLM-as-a-Judge framework, rigorous ablation studies, and granular cost tracking—validate the proposed approach. The findings demonstrate that RAG-CoT-MCP compresses network-level execution error rates from 45.2% (in unconstrained baselines) to a mere 6.0%, yielding substantial enhancements in semantic comprehensiveness and logical coherence compared to existing baselines. Counterintuitively, by proactively intercepting malformed actions and redirecting computational resources from reactive error handling to valid causal deduction, the framework drastically reduces redundant token consumption and achieves the lowest overall inference latency. Ultimately, this study establishes that deterministic execution constraints do not hinder agentic flexibility; rather, they serve as a fundamental prerequisite for deploying robust, high-speed, and cost-effective knowledge retrieval systems. Full article