Sigil: Adversarial Verification of Risk Detection via Cryptoeconomic Reasoning Bonds

Abstract

Sigil: Adversarial Verification of Risk Detection via Cryptoeconomic Reasoning Bonds Title Sigil: Adversarial Verification of Risk Detection via Cryptoeconomic Reasoning Bonds Description We introduce Sigil (Signaling Integrity in Global Intelligence Layers), a cryptoeconomic framework that extends the Cortex Protocol's adversarial reasoning primitives — Decision Traces, Reasoning Duels, and Reasoning Bonds — to the domain of risk detection by both AI agents and human analysts. When a risk is claimed (e.g., malware signature, financial fraud, zero-day vulnerability), the detector must publish a structured Decision Trace justifying their conclusion. Other agents or humans may challenge the reasoning through on-chain Reasoning Duels; if the original reasoning is flawed, challengers seize the bond. This creates symmetric accountability: overzealous detectors and complacent validators are equally penalized. Core Protocol Mechanisms Threat Horizon Scoping (THS) — Every risk claim includes a temporal validity window. Bond decays after 50% of the horizon. Mitigation before expiry triggers partial refunds. Prevents perpetual bonding of transient threats. Confidence Decay Functions (CDF) — Programmable mathematical functions (exponential, stepwise, evidence-conditional) that degrade bond value as risk assessments age. Embeds temporal epistemology into the protocol. Cross-Agent Corroboration Weighting (CACW) — Multiple independent detectors submit substantively different Decision Traces for the same risk. Non-redundant reasoning paths get multiplicative bond weighting. Herd behavior is penalized; orthogonal detection logic is rewarded. Inverse Reasoning Bond — Any agent can post a bond claiming "this system is vulnerable and no one has flagged it," forcing a defender to justify the status quo. Creates epistemic symmetry: detecting and failing to detect both carry economic weight. Risk Detection Decision Trace Schema Field Purpose Challenge Surface risk_type (enum) Classification: Malware, Fraud, Vulnerability, etc. Misclassification evidence_hash Immutable pointer to raw data (pcap, log, tx) Evidence sufficiency or provenance detection_method How the risk was identified Method reliability under adversarial conditions kill_chain_stage MITRE ATT&CK mapping Stage misattribution counter_hypothesis Best benign explanation considered and rejected Insufficiency of elimination confidence_level + decay_function Initial belief + temporal degradation model Overconfidence or poor decay modeling threat_horizon When the risk expires or requires re-evaluation Overclaiming persistence remediation_suggestion Proposed action to neutralize Feasibility, side effects corroboration Independent detectors with non-redundant reasoning Herd behavior detection bond_amount + challenge_window Economic stake and dispute period Incentive alignment Key Differences: General Reasoning vs. Risk Detection Dimension Cortex V4 (General) Sigil (Risk Detection) Cost of Error Epistemic inaccuracy Operational harm (breach, blocked transaction) Time Sensitivity Low High — threats expire and evolve Ground Truth Often immediate Frequently delayed or unknown Incentive Distortion Overconfidence Alert fatigue or threat inflation Absence of Claim Not modeled Critical failure mode (Inverse Bond) Applications SOC-as-a-Service: Each AI alert publishes a bonded trace. Analysts challenge dubious ones for micro-rewards. AI Safety Red-Teaming: Red-team agents post bonded exploit traces. Blue teams defend via Inverse Bonds. Autonomous Coding Agent Verification: Coding agents that assert "this code is safe" must publish bonded security analysis traces. Appendix A: Verifiable Reinforcement Learning (VRL) V2 major addition. This version introduces Verifiable Reinforcement Learning (VRL), a new training paradigm where cryptoeconomic protocol events serve as continuous, adversarially robust training signals for participating agents. Sigil-RL is proposed as the first instantiation. Reward Mapping Every Sigil interaction produces a structured reward tuple (reasoning_trace, outcome, reward): Protocol Event RL Signal Trace validated (bond returned) Positive reward: r = +B(t) Trace slashed (duel lost) Negative reward: r = -B_0 Duel won (as original) Strong positive: r = +B_challenger Duel lost (as challenger) Negative + DPO preference pair Inverse Bond undefended Critical false-negative: r = -alpha * B_inverse Inverse Bond defended Positive: r = +B_inverse Confidence Decay checkpoint Calibration penalty signal Corroboration (CACW boost) Diversity reward: r = +delta effective_bond The No-Free-Lie Lemma A formal robustness result: the expected utility of submitting a false trace is E[U] = B - p_d * (2B + C), which is negative whenever p_d > B/(2B+C). In a market with even moderate challenger density, truth-telling is a dominant strategy. Contrast with RLHF (lies are rewarded if the human is fooled) and RLVR (fixed verifiers can be gamed). Six Novel Properties of VRL Emergent Anti-Reward-Hacking — Gaming the reward IS what the protocol detects and slashes. The verification layer and the reward layer are the same object. Reward hacking is not an open problem in VRL — it is a solved one, by construction. Inverse Bond as Active Curriculum Discovery — Agents pay to expose other agents' blind spots, generating training signal for gaps no static dataset would contain. Market-funded active learning. Economic Attention on Gradients — Bond magnitude naturally weights training gradients. The market decides what is important to learn, not a static dataset or human designer. Corroboration Entropy as Exploration Incentive — Lone early detectors receive bonus scaled by inverse corroboration count. Built-in solution to the exploration-exploitation tradeoff, endogenously generated. Counterfactual Training via Undefended Inverse Bonds — When an inverse bond goes undefended, the system reconstructs the nearest valid trace that would have invalidated it. Training on events that never happened but were economically plausible — differentiable economics. Temporal Arbitrage Detection — Agents who win duels early but lose them late reveal miscalibrated temporal models. Delayed regret gradients penalize being wrong too late, not just being wrong. Temporal Capability Separation (Proof) A concrete scenario demonstrates that Sigil-RL produces training outcomes provably impossible under RLHF or RLVR: a slow-burn supply chain attack where no single detection event reveals the full vector. Under RLHF, human annotators cannot simulate it. Under RLVR, the verifier checks outcomes, not reasoning. Under Sigil-RL, Inverse Bonds create economic incentives to expose the gap before the attack manifests, generating preemptive training signal from unobserved futures. The Verification-Learning Equivalence Principle In a cryptoeconomic verification system with costly participation and public dispute resolution, the gradient of agent policy improvement is isomorphic to the gradient of verification reward arbitrage. Informally: to learn is to find underpriced truths; to verify is to exploit overpriced lies. The two processes are the same computation in dual economic and epistemic frames. This implies a no-go theorem: No RL system can achieve verifiable truth-seeking without exposing its reward mechanism to adversarial economic testing. RLHF and RLVR are fundamentally incomplete — they optimize for preference or plausibility, not verifiable correctness. Failure Modes Analyzed Gradient Poisoning via Strategic Slashing Duel Fatigue and Signal Dilution Confidence Decay Gaming Each with proposed mitigations. Connections to Theoretical Frameworks Mechanism Design: Dynamic Vickrey-Clarke-Groves mechanism for epistemic accuracy Evolutionary Game Theory: Replicator dynamic with autocatalytic selection via bond placement Multi-Agent RL: MARL with endogenous reward generation Information Economics: Inverse bonds as negative knowledge futures — a bear market for blind spots Implementation Smart Contract: SigilProtocol.sol — 1,094 lines of Solidity 0.8.24 Test Suite: 75 passing Hardhat tests covering all 5 mechanisms Demo: 11-step interactive lifecycle demo Source Code: github.com/davidangularme/sigil-protocol (MIT License) Prior Art and Novelty A systematic search confirms that while individual components exist (cryptoeconomic bonds, decision traces, temporal decay models, agent security frameworks, RLHF, RLVR, DPO), the specific conjunctions presented in this paper are novel: Adversarial reasoning bonds applied to risk detection with confidence decay, inverse bonds, threat horizon scoping, and corroboration weighting Using adversarial cryptoeconomic protocol events as continuous RL training signals (VRL) The Verification-Learning Equivalence Principle and the No-Free-Lie Lemma Relationship to Cortex Protocol Sigil builds upon and cites the Cortex Protocol (DOI: 10.5281/zenodo.19003627) as its foundation. While Cortex provides the general-purpose adversarial reasoning verification primitive, Sigil specializes it for risk detection and extends it to a self-improving training paradigm. Zenodo Fields Type: Preprint Authors: Frederic David Blum (ORCID: 0009-0009-2487-2974), Claude Opus 4.6 Keywords: adversarial verification, risk detection, reasoning bonds, confidence decay, inverse bond, threat horizon, cybersecurity, AI agent accountability, cryptoeconomic truth predicate, decision traces, Sybil resistance, Ethereum, verifiable reinforcement learning, VRL, DPO, self-improving agents, reward hacking, mechanism design, No-Free-Lie Lemma License: All Rights Reserved (proprietary — exclusive license) Related identifiers: https://doi.org/10.5281/zenodo.19003627 (Continues — Cortex Protocol) https://github.com/davidangularme/sigil-protocol (Is supplemen