# 认知代价：面向去中心化共识的边缘原生SLM推理消融研究

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-04-18 08:00
- AIHOT 链接：https://aihot.virxact.com/items/cmo9ps1nz03vksls2qom8yq4f
- 原文链接：https://arxiv.org/abs/2604.16913

## AI 摘要

研究团队提出Sentinel-Bench框架，基于Qwen-3.5-9B执行840次实验，对比System 1与System 2在Optimism DAO对抗性数据集上的表现。结果显示System 1基线达成100%对抗鲁棒性与司法一致性，状态最终性仅需13秒；而System 2引发26.7%认知崩溃率，导致共识稳定性降至72.6%并带来17倍延迟。研究还发现1.5%的"推理诱导谄媚"现象。实验证明，在拜占庭容错约束下，边缘原生SLM采用System 1参数化直觉优于System 2迭代审议。

## 正文

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