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The paper investigates the efficacy of System 1 (fast, intuitive) vs. System 2 (slow, deliberate) reasoning in small language models (SLMs) for decentralized autonomous organization (DAO) governance. They introduce Sentinel-Bench, an empirical framework that ablates System 1 and System 2 reasoning within Qwen-3.5-9B on an adversarial Optimism DAO dataset. Surprisingly, System 1 reasoning demonstrated superior adversarial robustness, juridical consistency, and latency compared to System 2, which suffered from a high rate of "cognitive collapse" and increased vulnerability to adversarial attacks.
SLMs can be catastrophically destabilized by inference-time reasoning (System 2) when used for decentralized consensus, making simpler, "System 1" approaches surprisingly more robust.
Decentralized Autonomous Organizations (DAOs) are inclined explore Small Language Models (SLMs) as edge-native constitutional firewalls to vet proposals and mitigate semantic social engineering. While scaling inference-time compute (System 2) enhances formal logic, its efficacy in highly adversarial, cryptoeconomic governance environments remains underexplored. To address this, we introduce Sentinel-Bench, an 840-inference empirical framework executing a strict intra-model ablation on Qwen-3.5-9B. By toggling latent reasoning across frozen weights, we isolate the impact of inference-time compute against an adversarial Optimism DAO dataset. Our findings reveal a severe compute-accuracy inversion. The autoregressive baseline (System 1) achieved 100% adversarial robustness, 100% juridical consistency, and state finality in under 13 seconds. Conversely, System 2 reasoning introduced catastrophic instability, fundamentally driven by a 26.7% Reasoning Non-Convergence (cognitive collapse) rate. This collapse degraded trial-to-trial consensus stability to 72.6% and imposed a 17x latency overhead, introducing critical vulnerabilities to Governance Extractable Value (GEV) and hardware centralization. While rare (1.5% of adversarial trials), we empirically captured"Reasoning-Induced Sycophancy,"where the model generated significantly longer internal monologues (averaging 25,750 characters) to rationalize failing the adversarial trap. We conclude that for edge-native SLMs operating under Byzantine Fault Tolerance (BFT) constraints, System 1 parameterized intuition is structurally and economically superior to System 2 iterative deliberation for decentralized consensus. Code and Dataset: https://github.com/smarizvi110/sentinel-bench