Verification Cost Asymmetry in Cognitive Warfare: A Complexity-Theoretic Framework

• URL: http://arxiv.org/abs/2507.21258v2
• Date: Fri, 01 Aug 2025 00:52:05 GMT
• Title: Verification Cost Asymmetry in Cognitive Warfare: A Complexity-Theoretic Framework
• Abstract summary: We introduce the Verification Cost Asymmetry coefficient, formalizing it as the ratio of expected verification work between populations under identical claim distributions.<n>We construct dissemination protocols that reduce verification for trusted audiences to constant human effort while imposing superlinear costs on adversarial populations lacking cryptographic infrastructure.<n>The results establish complexity-theoretic foundations for engineering democratic advantage in cognitive warfare, with immediate applications to content authentication, platform governance, and information operations doctrine.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Human verification under adversarial information flow operates as a cost-bounded decision procedure constrained by working memory limits and cognitive biases. We introduce the Verification Cost Asymmetry (VCA) coefficient, formalizing it as the ratio of expected verification work between populations under identical claim distributions. Drawing on probabilistically checkable proofs (PCP) and parameterized complexity theory, we construct dissemination protocols that reduce verification for trusted audiences to constant human effort while imposing superlinear costs on adversarial populations lacking cryptographic infrastructure. We prove theoretical guarantees for this asymmetry, validate the framework through controlled user studies measuring verification effort with and without spot-checkable provenance, and demonstrate practical encoding of real-world information campaigns. The results establish complexity-theoretic foundations for engineering democratic advantage in cognitive warfare, with immediate applications to content authentication, platform governance, and information operations doctrine.

Related papers

• AI Slop and Hallucinations in Vulnerability Assessment: A Survey on Reasoning Failures and Trustworthy Mitigation [48.55515767840701]
  "AI slop" is artifacts, hallucinated vulnerabilities, plausible but incorrect patches, and semantically repackaged bug reports.<n>This paper surveys the empirical evidence, identifies a unifying mechanism, and traces a path toward trustworthy triage.
  arXiv  Detail & Related papers  (2026-08-26T11:48:38Z)
• CLAIM: Leading Open-domain Active Clarification of Large Language Models with Uncertainty Measurement [50.91045324738942]
  CLAIM is an uncertainty-driven framework for active clarification learning in open-domain settings.<n>We propose an entropy-driven synthetic data generation pipeline that integrates uncertainty estimation with semantic clustering and reasoning-based judgments.<n> Experimental results demonstrate that CLAIM can learn stable and generalizable clarification strategies without relying on manually labeled data.
  arXiv  Detail & Related papers  (2026-08-12T04:27:45Z)
• Leveraging System-Level Observations to Inform Bayesian Learning of Model Parameters for Quantitative Verification [5.199549051521146]
  Using inaccurate or uninformative PK can negatively affect quantitative analysis, yielding incorrect verification results.<n>EPIK formulates a twofold problem to derive the distributions of unknown transition parameters and then embeds these distributions to verify new or difficult-to-measure (elusive) properties.<n>The detailed experimental evaluation using multiple variants of real-world case studies and diverse EPIK instantiations shows its effectiveness, flexibility and generality.
  arXiv  Detail & Related papers  (2026-08-04T11:25:44Z)
• Agentic Cognitive Profiling: Realigning Automated Alzheimer's Disease Detection with Clinical Construct Validity [66.94391219005291]
  This paper proposes Agentic Cognitive Profiling (ACP), an agentic framework that realigns automated screening with clinical protocol logic.<n>Central to our design is decoupling semantic understanding from measurement by delegating all quantification to deterministic function calling.<n>ACP achieves 90.5% score match rate in task examination and 85.3% accuracy in AD prediction, surpassing popular baselines.
  arXiv  Detail & Related papers  (2026-03-18T06:15:35Z)
• Pushing the Boundaries of Natural Reasoning: Interleaved Bonus from Formal-Logic Verification [49.506412445511934]
  Large Language Models (LLMs) show remarkable capabilities, yet their next-token prediction creates logical inconsistencies and reward hacking.<n>We introduce a formal logic verification-guided framework that dynamically interleaves formal symbolic verification with the natural language generation process.<n>We operationalize this framework via a novel two-stage training pipeline that synergizes formal logic verification-guided supervised fine-tuning and policy optimization.
  arXiv  Detail & Related papers  (2026-01-30T07:01:25Z)
• 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)
• Towards Comprehensive Stage-wise Benchmarking of Large Language Models in Fact-Checking [64.97768177044355]
  Large Language Models (LLMs) are increasingly deployed in real-world fact-checking systems.<n>We present FactArena, a fully automated arena-style evaluation framework.<n>Our analyses reveal significant discrepancies between static claim-verification accuracy and end-to-end fact-checking competence.
  arXiv  Detail & Related papers  (2026-01-06T02:51:56Z)
• "Show Me You Comply... Without Showing Me Anything": Zero-Knowledge Software Auditing for AI-Enabled Systems [2.2981698355892686]
  This paper introduces ZKMLOps, a novel MLOps verification framework.<n>It operationalizes Zero-Knowledge Proofs (ZKPs)-cryptographic protocols allowing a prover to convince a verifier that a statement is true.<n>We evaluate the framework's practicality through a study of regulatory compliance in financial risk auditing.
  arXiv  Detail & Related papers  (2025-10-30T15:03:32Z)
• VeriLLM: A Lightweight Framework for Publicly Verifiable Decentralized Inference [3.8760740008451156]
  We introduce VeriLLM, a publicly verifiable protocol for decentralized language models (LLMs) inference.<n>VeriLLM combines lightweight empirical rerunning with cryptographic commitments, allowing verifiers to validate results at approximately 1% of the underlying inference cost.<n>We show that VeriLLM achieves reliable public verifiability with minimal overhead.
  arXiv  Detail & Related papers  (2025-09-29T04:07:32Z)
• Entanglement certification by measuring nonlocality [13.128419799503641]
  This paper presents a practical approach to entanglement verification based on Clauser-Horne-Shimony-Holt inequality violation.<n>We develop statistical methods that optimize resource usage while maintaining verification reliability.
  arXiv  Detail & Related papers  (2025-07-24T03:40:25Z)
• Towards Robust Fact-Checking: A Multi-Agent System with Advanced Evidence Retrieval [1.515687944002438]
  The rapid spread of misinformation in the digital era poses significant challenges to public discourse.<n>Traditional human-led fact-checking methods, while credible, struggle with the volume and velocity of online content.<n>This paper proposes a novel multi-agent system for automated fact-checking that enhances accuracy, efficiency, and explainability.
  arXiv  Detail & Related papers  (2025-06-22T02:39:27Z)
• EarthOL: A Proof-of-Human-Contribution Consensus Protocol -- Addressing Fundamental Challenges in Decentralized Value Assessment with Enhanced Verification and Security Mechanisms [0.0]
  This paper introduces EarthOL, a novel consensus protocol that attempts to replace computational waste in blockchain systems with verifiable human contributions.<n>We propose a domain-restricted approach that acknowledges cultural diversity and subjective preferences while maintaining cryptographic security.<n>We present theoretical analysis demonstrating meaningful progress toward incentive-compatible human contribution verification in high-consensus domains.
  arXiv  Detail & Related papers  (2025-05-27T01:29:13Z)
• Engineering Trustworthy Machine-Learning Operations with Zero-Knowledge Proofs [1.7723990552388873]
  Zero-Knowledge Proofs (ZKPs) offer a cryptographic solution that enables provers to demonstrate, through verified computations, adherence to set requirements without revealing sensitive model details or data.<n>We identify five key properties (non-interactivity, transparent setup, standard representations, succinctness, and post-quantum security) critical for their application in AI validation and verification pipelines.
  arXiv  Detail & Related papers  (2025-05-26T15:39:11Z)
• Efficient Conformal Prediction under Data Heterogeneity [79.35418041861327]
  Conformal Prediction (CP) stands out as a robust framework for uncertainty quantification. Existing approaches for tackling non-exchangeability lead to methods that are not computable beyond the simplest examples. This work introduces a new efficient approach to CP that produces provably valid confidence sets for fairly general non-exchangeable data distributions.
  arXiv  Detail & Related papers  (2023-12-25T20:02:51Z)
• Zero-Knowledge Proof of Traffic: A Deterministic and Privacy-Preserving Cross Verification Mechanism for Cooperative Perception Data [7.919995199824006]
  This study proposes a novel approach called zero-knowledge Proof of Traffic (zk-PoT) Multiple independent proofs regarding the same vehicle can be deterministically cross-verified by any receivers without relying on ground truth, probabilistic, or plausibility evaluations. The proposed approach can be incorporated into existing works, including the European Telecommunications Standards Institute (ETSI) and the International Organization for Standardization (ISO) ITS standards.
  arXiv  Detail & Related papers  (2023-12-13T07:53:06Z)
• From Chaos to Clarity: Claim Normalization to Empower Fact-Checking [57.024192702939736]
  Claim Normalization (aka ClaimNorm) aims to decompose complex and noisy social media posts into more straightforward and understandable forms. We propose CACN, a pioneering approach that leverages chain-of-thought and claim check-worthiness estimation. Our experiments demonstrate that CACN outperforms several baselines across various evaluation measures.
  arXiv  Detail & Related papers  (2023-10-22T16:07:06Z)
• Prototype-based Aleatoric Uncertainty Quantification for Cross-modal Retrieval [139.21955930418815]
  Cross-modal Retrieval methods build similarity relations between vision and language modalities by jointly learning a common representation space. However, the predictions are often unreliable due to the Aleatoric uncertainty, which is induced by low-quality data, e.g., corrupt images, fast-paced videos, and non-detailed texts. We propose a novel Prototype-based Aleatoric Uncertainty Quantification (PAU) framework to provide trustworthy predictions by quantifying the uncertainty arisen from the inherent data ambiguity.
  arXiv  Detail & Related papers  (2023-09-29T09:41:19Z)
• Advancing Counterfactual Inference through Nonlinear Quantile Regression [77.28323341329461]
  We propose a framework for efficient and effective counterfactual inference implemented with neural networks. The proposed approach enhances the capacity to generalize estimated counterfactual outcomes to unseen data. Empirical results conducted on multiple datasets offer compelling support for our theoretical assertions.
  arXiv  Detail & Related papers  (2023-06-09T08:30:51Z)
• Robust and efficient verification of graph states in blind measurement-based quantum computation [52.70359447203418]
  Blind quantum computation (BQC) is a secure quantum computation method that protects the privacy of clients. It is crucial to verify whether the resource graph states are accurately prepared in the adversarial scenario. Here, we propose a robust and efficient protocol for verifying arbitrary graph states with any prime local dimension.
  arXiv  Detail & Related papers  (2023-05-18T06:24:45Z)
• Modeling Multiple Views via Implicitly Preserving Global Consistency and Local Complementarity [61.05259660910437]
  We propose a global consistency and complementarity network (CoCoNet) to learn representations from multiple views. On the global stage, we reckon that the crucial knowledge is implicitly shared among views, and enhancing the encoder to capture such knowledge can improve the discriminability of the learned representations. Lastly on the local stage, we propose a complementarity-factor, which joints cross-view discriminative knowledge, and it guides the encoders to learn not only view-wise discriminability but also cross-view complementary information.
  arXiv  Detail & Related papers  (2022-09-16T09:24:00Z)

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.