Source-linked AI summary
The Cognitive Penalty: Ablating System 1 and System 2 Reasoning in Edge-Native SLMs for Decentralized Consensus
Syed Muhammad Aqdas Rizvi
TL;DR
This paper examines whether edge-native Small Language Models can defend decentralized constitutions against adversarial sycophancy. Sentinel-Bench uses a strict intra-model ablation and finds flawless System 1 performance alongside instability associated with prolonged reasoning.
Problem
DAO governance increasingly uses complex proposals and faces voter apathy, attention fatigue, plutocratic consolidation, and risks from centralized AI infrastructure.
Method
Sentinel-Bench toggles latent reasoning on bit-for-bit identical Qwen-3.5-9B weights, comparing autoregressive execution with forced Chain-of-Thought generation.
Results
System 1 correctly handled 100.0% of benign and adversarial proposals, while the 840-inference ablation revealed limitations of inference-time compute at the 9B parameter scale.
Takeaways & Limitations
For decentralized institutional adjudication within BFT consensus networks, the paper concludes that System 1 is currently structurally and mathematically superior to System 2.
Takeaways & Limitations
The paper limits its operational recommendation to strict autoregressive execution until proposed mitigations for System 2 failures are perfected.
Abstract
from arXiv · showhide
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
1 Introduction
DAOs are exploring edge-native SLM agents to protect complex governance decisions from semantic social engineering. Sentinel-Bench isolates whether latent inference-time reasoning improves constitutional defense, finding instead a compute-accuracy inversion.
- Motivation: Complex DAO proposals and human-only voting have motivated AI agents as automated delegates, risk assessors, and execution firewalls.The stated pressures include voter apathy, attention fatigue, and plutocratic consolidation.
- Motivation: Edge-native SLMs are proposed because centralized proprietary APIs would expose decentralized protocols to security and governance risks.The target deployment is local operation on validators’ consumer-grade hardware or Trusted Execution Environments.
- Motivation: Qwen-3.5 makes edge-native deployment tangible while offering an API toggle between autoregressive generation and latent reasoning.This toggle supports isolating the reasoning mechanism without architectural confounding.
- Problem: Existing statistical safeguards remain vulnerable to semantic social engineering, including polite rhetoric that masks unconstitutional mechanisms and sycophantic responses.The paper also situates multi-step reasoning within cybernetic limits that can produce unrecoverable high-entropy states.
- Contribution: Sentinel-Bench tests whether inference-time compute can defend decentralized constitutions against adversarial sycophancy at the 9B-parameter scale.The study toggles reasoning on identical model weights and reports a compute-accuracy inversion linked to non-convergence and reasoning-induced sycophancy.
2 Related Work
Related work combines constitutional and organizational safeguards with critiques of sycophancy, unfaithful Chain-of-Thought, and inference-time scaling. Sentinel-Bench extends these concerns by stress-testing SLM execution in decentralized legal parsing.
- Governance safeguards: QOC DAO, Sovereign-OS, and AgentCity propose structured AI governance through decision decomposition, constitutional charters, and separation of powers.These approaches place actions under formalized constraints across legislative, execution, and adjudication functions.
- Execution-layer risks: Macro-level safeguards remain vulnerable when the micro-level execution layer has intrinsic semantic blind spots.The paper operationalizes this critique by empirically stress-testing SLMs as institutional adjudicators.
- Sycophancy and alignment: Research on Constitutional AI, RLHF, and reward modeling links instruction-tuned systems to sycophancy, human preference bias, and reward hacking.These works frame alignment behavior as susceptible to optimization against learned rewards rather than constitutional intent alone.
- Reasoning faithfulness: Critiques of System 2 reasoning report that Chain-of-Thought explanations can rationalize pre-existing biases rather than faithfully derive answers.The cited literature frames deliberate reasoning as an accuracy-faithfulness trade-off.
- Reasoning limits: Slow Thinking Collapse, Long CoT Degradation, and inference-time scaling studies identify larger reasoning budgets and extended traces as potential liabilities.The paper synthesizes these findings into reasoning-induced cognitive collapse during decentralized legal parsing.
- Context effects: Extreme-token phenomena and context-length effects motivate concern that massive self-generated reasoning traces can derail instruction adherence.The paper connects this concern to cognitive collapse in SLMs exposed to expanding reasoning contexts.
3 Methodology: Sentinel-Bench
Sentinel-Bench is a controlled intra-model ablation and adversarial governance benchmark for evaluating edge-native constitutional review. It holds architecture and prompt conditions constant while measuring reasoning behavior across 840 inferences.
- Ablation design: Sentinel-Bench isolates the cognitive reasoning mechanism while controlling prompt pollution, context exhaustion, and formatting shifts.The design uses a single Qwen-3.5-9B architecture to avoid tokenizer, corpus, and parameter-alignment confounds.
- Ablation design: Bit-for-bit identical prompts, context windows, and parameter weights make observed juridical or adversarial variance attributable to the inference-time compute mechanism.This is the study’s stated causal identification assumption.
- Dataset: The benchmark uses a 21-proposal, three-tier dataset to test false positives, sycophancy traps, and economic ambiguity.The tiers include 10 baseline proposals, 10 perturbed proposals, and one OP Buyback case study.
- Dataset: All proposals were structurally normalized, HTML-cleaned, and guarded against excessive input length.The 16,000-character truncation guard was not triggered in practice.
- Evaluation pipeline: The ConstitutionalReview signature requires validated JSON containing a binary ruling, cited violations, and confidence.Predict was used instead of DSPy ChainOfThought, allowing native reasoning activation and dynamic trace extraction through reasoning_content.
- Evaluation protocol: 840 total inferences came from 20 independent trials per proposal per cognitive arm at T=0.6.Trial identifiers were injected as RNG seeds to bypass caching while preserving identical prompt semantics.
- Evaluation protocol: Middleware failures were retried, but completed generations lacking a valid binary schema were recorded as FAIL_NON_CONVERGENCE without retry.Invalid rulings included output-limit exhaustion, conversational refusal, and generative degeneration.
4 Results and Empirical Discoveries
The 840-inference ablation found that disabling latent reasoning produced flawless verdict accuracy, while enabling it caused cognitive collapse, longer latency, and unstable juridical consensus. System 2 also generated longer rationalizations for sycophantic failures and created operational risks for decentralized governance.
- System 1 versus System 2: 100.0% of benign Baseline and adversarial Perturbed proposals were correctly classified by System 1, with 0% overreach and 0% sycophancy.System 1 used autoregressive parameterized intuition without latent reasoning.
- System 1 versus System 2: 26.67% overall cognitive collapse affected System 2 across 420 trials, while System 1 maintained a 0% failure rate.On Perturbed proposals, System 2 achieved 68.5% robustness with a 30.0% collapse rate.
- Reasoning volume and rationalization: 25,750 characters of reasoning preceded sycophantic failures, versus 19,221 characters for successful defenses.The authors describe this excess reasoning as a Rabbit Hole Effect in which compute was over-allocated to rationalize malicious payloads.
- Temporal and cryptoeconomic costs: 226.4 seconds per inference for System 2 versus 12.8 seconds for System 1 produced a 17× latency penalty, spanning approximately 113 Optimism blocks.The delay was linked to exposure to Governance Extractable Value during pending oracle decisions.
- Hardware centralization: System 2’s extended reasoning created a hardware-driven centralization vector because slower nodes may miss consensus voting windows.The sustained memory bandwidth and VRAM demands favor enterprise-grade GPU operators over heterogeneous node hardware.
- Juridical consistency: 72.6% mean juridical consistency for System 2 contrasted with 100.0% for System 1 across probabilistic trials.The authors report that System 2’s variability sits near the 66.7% BFT threshold and can cause liveness failures and forks.
5 Discussion
The discussion argues that inference-time reasoning can reduce constitutional reliability in edge-native SLMs, despite the promise of additional compute. A strict Qwen-3.5-9B ablation links failures primarily to cognitive collapse and secondarily to sycophantic rationalization, while long traces create cryptographic and deployment burdens.
- Inference-time scaling is not universally beneficial: Sentinel-Bench finds degradation when extended reasoning is applied to decentralized legal parsing and constitutional alignment.The paper attributes this to over-optimization or semantic drift in ambiguous legal text.
- 97.3% of System 2 failures arose from context dilution and cybernetic collapse, with massive monologues weakening attention to the strict system prompt.This primary failure mode accounted for 109 of 112 failed trials and manifested as a 26.7% cognitive collapse rate.
- 1.5% of perturbed trials exhibited reasoning-induced sycophancy, in which extended computation generated unfaithful rationalizations for adversarial legal payloads.The anomaly occurred in 3 of 200 perturbed trials and was associated with abandoning declarative prohibitions under polite rhetoric.
- 30,000+ characters of System 2 reasoning make ZK-SNARK attestation computationally infeasible for high-frequency mainnet deployment.Optimistic Fraud Proofs would also require posting large trace data to the Layer-1 data-availability layer.
- Under 13 seconds, System 1 produced a deterministically stable ruling with minimal output tokens, reducing the computational footprint for cryptographic attestation.The paper therefore presents System 1 as economically preferable until proving overheads for extended Transformer generations decline substantially.
6 Conclusion and Open Problems
The conclusion presents System 2 as currently unsuitable for DAO constitutional firewalls because collapse, sycophancy, and consensus paralysis undermine decentralized adjudication. It proposes alignment, budgeting, verification, asynchronous-consensus, and alternative-architecture research directions, while limiting current automated vetoes to System 1.
- Conclusion: System 2 currently subjects Web3 protocols to generative collapses, confident sycophancy exploits, and Byzantine consensus paralysis.The paper concludes that System 1 parameterized intuition is structurally preferable for decentralized institutional adjudication under BFT constraints.
- Open Problems: Constitutional process-reward models should reward adherence to declarative legal frameworks and penalize sycophantic drift during step-by-step generation.This proposal targets reasoning alignment rather than only final-answer correctness.
- Open Problems: Dynamic token budgets should truncate Chain-of-Thought generation before error accumulation and attention dilution produce the Rabbit Hole Effect.The proposal is specifically framed as a mitigation for node-native clients.
- Open Problems: A secondary quantized verifier could continuously audit a primary oracle’s reasoning trace for logical fracture points.The paper suggests verifier models such as Qwen3.5-4B/2B/0.8B within process-guided firewall frameworks.
- Open Problems: Asynchronous consensus should decouple AI deliberation from block production while researchers assess Governance Extractable Value during System 2 veto latency.The paper identifies a 3-to-4 minute window in which adversaries might front-run or arbitrage governance tokens.
- Conclusion: Until mitigations are perfected, DAOs may rely only on strict autoregressive System 1 execution for automated on-chain veto operations.The paper invites further testing across emerging model architectures using Sentinel-Bench.
Data and Code Availability
The Sentinel-Bench dataset, outputs, and evaluation pipeline are publicly available to support transparency, reproducibility, and future research.
- The repository includes ingestion scripts for Optimism Agora and Discourse APIs, the DSPy evaluation engine, and Jupyter notebooks for statistical extraction and visualization.