ECO/CPO-DAG: A Contradiction-Based Accountability Layer for Adversarial Supply Chains
- URL: http://arxiv.org/abs/2607.06804v1
- Date: Tue, 07 Jul 2026 21:03:26 GMT
- Title: ECO/CPO-DAG: A Contradiction-Based Accountability Layer for Adversarial Supply Chains
- Abstract summary: ECO/CPO-DAG is a domain-specific accountability protocol for adversarial supply chains.<n>It formalizes contradiction detection as a supplemental validation layer.<n>The protocol's boundary is explicit: it detects provable contradictions, not consistent lies.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present ECO/CPO-DAG, a domain-specific accountability protocol for adversarial supply chains that formalizes contradiction detection as a supplemental validation layer rather than a consensus or truth-establishing mechanism. Participants publish signed Event Claim Objects (ECOs) into a causally ordered, append-only directed acyclic graph (DAG) whose edges encode happened-before relations. When two claims about the same subject violate a domain constraint, any observer can compile a Contradiction Proof Object (CPO), a self-verifying object binding the two signed claims and the violated rule, which, on public verification, triggers economic slashing of a determinately blamed party. We map constraints to GS1 EPCIS 2.0 event semantics (spatial uniqueness, temporal monotonicity, quantity conservation, quality monotonicity, regulatory validity), so detection targets inconsistencies that are meaningful in practice. Selective disclosure via commitment schemes and, optionally, zero-knowledge contradiction proofs lets parties withhold claim contents until a challenge forces the minimal opening. We give an analytical treatment: an independent-observer detection model $1-(1-p_{\min})^h$, a deterrence condition $S>g(1-p)/(kp)$ under $k$-party collusion, and a storage estimate of order 1 GB per participant per year under stated assumptions. The protocol's boundary is explicit: it detects provable contradictions, not consistent lies; a party that never contradicts itself is invisible to it, so the layer complements, and does not replace, source verification and oracle aggregation. A single-machine reference implementation corroborates the detection model, with the predicted coverage band overlapping the measured 95% confidence interval at every observer count, and records zero false accusations; the fully zero-knowledge CPO, multi-party propagation, and adaptive-adversary evasion remain analytical.
Related papers
- CABAL: Multi-Agent Simulacra for Tracing the Effects of Collusive Bidding in Peer Review [20.449658717520876]
alg is an end-to-end multi-agent simulacra framework for studying reviewer assignment integrity.<n>We develop an affinity-guided collusive bidding strategy that uses mutual reviewer-paper affinities to construct collusion rings.
arXiv Detail & Related papers (2026-09-04T14:55:33Z) - One Gate Is Not Enough: Composing Stateful Pre-Action Controls for Agentic AI [0.0]
We show that a remediation applied by one control can change the action, evidence, or context another control evaluates, invalidating that control's earlier judgment.<n>We formalize this coupling and give a remediate-and-regate protocol that restores per-action soundness in the current bounded, idempotent setting.<n>This is a mechanism demonstration on open payload data with a synthetic metadata layer, not a claim about production prevalence.
arXiv Detail & Related papers (2026-08-18T22:24:44Z) - Repeated-Game Security for Restaking-Based Verifiable Inference [2.9661360665913574]
This paper shows that a one-round slashing condition can overstate security when inference is supplied repeatedly under the same stake.<n>We propose a deployable mechanism combining history-dependent challenges, reputation-weighted slashing, and stake vesting.
arXiv Detail & Related papers (2026-08-10T03:03:56Z) - The SIGReg Objective as Variational Free Energy: A Theoretical Active-Inference Account of JEPA World Models [47.471225029744424]
Joint-Embedding Predictive Architectures (JEPAs) are the dominant design for latent world models.<n>We show that the choice of anti-collapse regulariser determines whether a JEPA's training objective, a prediction loss plus a weighted embedding regulariser, is a valid Active Inference (AIF) variational free energy.
arXiv Detail & Related papers (2026-07-15T08:59:36Z) - Anytime PAC-Bayes for Constrained Density-Ratio Networks under Covariate Shift [0.0]
PAC-Bayes is instantiated on the weighted risk in a fixed-time regime that yields Bernoulli-KL bounds.<n>A patch test against analytic ground truth with a real-data deployment validates the framework.<n>A single fixed-time coverage failure is recorded, with per-split coverage aligning one-to-one with the magnitude of the label shift.
arXiv Detail & Related papers (2026-05-17T01:07:17Z) - IMPACT-CYCLE: A Contract-Based Multi-Agent System for Claim-Level Supervisory Correction of Long-Video Semantic Memory [73.22944697933603]
Existing pipelines produce opaque, end-to-end outputs that expose no intermediate state for inspection.<n>We present IMPACT-Cycle, a supervisory multi-agent system that reformulates long-video understanding as a multimodal iterative claim-level maintenance.
arXiv Detail & Related papers (2026-04-22T03:03:33Z) - Preregistered Belief Revision Contracts [2.28438857884398]
We introduce PBRC (Preregistered Belief Revision Contracts), a protocol-level mechanism that separates open communication from admissible change.<n>A PBRC contract publicly fixes first-order evidence triggers, admissible revision operators, a priority rule, and a fallback policy.<n>We show that auditable trigger protocols admit evidential PBRC normal forms that preserve belief trajectories and canonicalized audit traces.
arXiv Detail & Related papers (2026-04-16T22:22:54Z) - Neural Uncertainty Principle: A Unified View of Adversarial Fragility and LLM Hallucination [60.197429875410286]
Adversarial vulnerability in vision and hallucination in large language models are conventionally viewed as separate problems.<n>Formalizing a Neural Uncertainty Principle (NUP) under a loss-induced state, we find that in near-bound regimes, further compression must be accompanied by increased sensitivity dispersion.<n>In vision, masking highly coupled components improves robustness without costly adversarial training.<n>In language, the same prefill-stage probe detects hallucination risk before generating any answer tokens.
arXiv Detail & Related papers (2026-03-20T02:07:10Z) - Preventing the Collapse of Peer Review Requires Verification-First AI [49.995126139461085]
We propose truth-coupling, i.e. how tightly venue scores track latent scientific truth.<n>We formalize two forces that drive a phase transition toward proxy-sovereign evaluation.
arXiv Detail & Related papers (2026-01-23T17:17:32Z) - Audit the Whisper: Detecting Steganographic Collusion in Multi-Agent LLMs [0.0]
Audit the Whisper is a conference-grade research artifact that spans theory, benchmark design, detection, and validation.<n>Our contributions are: (i) a channel-capacity analysis showing how interventions such as paraphrase, rate limiting, and role permutation impose quantifiable capacity penalties-operationalised via paired-run Kullback-Leibler diagnostics.<n>We release anonymized regeneration scripts, anonymized manifests, and documentation so external auditors can reproduce every figure, satisfy double-blind requirements, and extend the framework with minimal effort.
arXiv Detail & Related papers (2025-10-05T17:51:52Z) - Quantum Proofs of Deletion for Learning with Errors [91.3755431537592]
We construct the first fully homomorphic encryption scheme with certified deletion.
Our main technical ingredient is an interactive protocol by which a quantum prover can convince a classical verifier that a sample from the Learning with Errors distribution in the form of a quantum state was deleted.
arXiv Detail & Related papers (2022-03-03T10:07:32Z) - ADC: Adversarial attacks against object Detection that evade Context
consistency checks [55.8459119462263]
We show that even context consistency checks can be brittle to properly crafted adversarial examples.
We propose an adaptive framework to generate examples that subvert such defenses.
Our results suggest that how to robustly model context and check its consistency, is still an open problem.
arXiv Detail & Related papers (2021-10-24T00:25:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.