{
  "single-agent": {
    "summary": {
      "method": "single-agent",
      "dataset": "MultiGov-30",
      "n_questions": 10,
      "avg_em": 0.0,
      "avg_f1": 0.3629229326849074,
      "avg_latency_ms": 2893.195810029283,
      "avg_cost_usd": 0.0013155200000000001,
      "avg_tokens": 13155.2,
      "avg_rounds": 1.0,
      "avg_nodes": 1.0,
      "total_cost_usd": 0.013155200000000002,
      "total_latency_s": 28.93195810029283
    },
    "questions": [
      {
        "question_id": "MG-A01",
        "question": "What AI practices are completely prohibited under the EU AI Act, and what are the penalties?",
        "gold_answer": "Article 5 prohibits: subliminal manipulation, exploitation of vulnerabilities, social scoring by public authorities, real-time remote biometric identification in public spaces (with limited exceptions for law enforcement), untargeted scraping for facial recognition databases, emotion recognition in workplace and education, and biometric categorisation using sensitive characteristics. Under Article 99, violations carry fines up to EUR 35 million or 7% of worldwide annual turnover.",
        "predicted_answer": "The EU AI Act prohibits several AI practices that are deemed high-risk and pose significant risks to individuals or society. These prohibited practices include:\n\n1. **High-Risk AI Systems**: The AI Act prohibits the development, deployment, and use of AI systems that are classified as high-risk. High-risk AI systems are those that are expected to cause high-risk automated individual decisions, or that are capable of processing personal data, and are used in certain high-risk sectors such as critical infrastructure, employment, and law enforcement.\n\n2. **Prohibition of Certain Practices**: The AI Act also prohibits certain practices related to AI, such as the use of AI for manipulation, deception, or coercion. For example, AI cannot be used to deceive individuals into making decisions that they would not make without the use of AI, or to manipulate individuals into taking actions that they would not take without the use of AI.\n\n### Penalties for Prohibited Practices\n\nThe AI Act imposes severe penalties for violations of these prohibitions. Specifically:\n\n- **Penalties for High-Risk AI Systems**: If a high-risk AI system is developed, deployed, or used without compliance with the AI Act, the penalties can be severe. These penalties include fines that can reach up to 4% of the total worldwide annual revenue",
        "exact_match": 0.0,
        "f1": 0.16666666666666666,
        "latency_ms": 3120.6104001030326,
        "cost_usd": 0.0013184,
        "total_tokens": 13184,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent"
      },
      {
        "question_id": "MG-A02",
        "question": "What is the complete compliance chain a provider must follow to place a high-risk AI system on the EU market?",
        "gold_answer": "The provider must follow this compliance chain: (1) Implement risk management system (Art. 9); (2) Ensure data governance for training data (Art. 10); (3) Draw up technical documentation (Art. 11); (4) Ensure record-keeping and logging (Art. 12); (5) Provide transparency and instructions for use (Art. 13); (6) Design for human oversight (Art. 14); (7) Achieve accuracy, robustness and cybersecurity (Art. 15); (8) Establish quality management system (Art. 17); (9) Complete conformity assessment (Art. 43); (10) Affix CE marking (Art. 48); (11) Register in EU database (Art. 49). Only after all steps can the system be lawfully placed on the market.",
        "predicted_answer": "To place a high-risk AI system on the EU market, the provider must follow a comprehensive compliance chain that involves several steps and requirements. Here is a detailed breakdown of the process:\n\n1. **Conformity Assessment**: The AI system must undergo a conformity assessment to verify that it meets the requirements of the AI Act (Chapter III, Section 2). This involves verifying compliance with all the requirements of the AI Act, including those in Annex III.\n\n2. **EU Declaration of Conformity**: A draft EU declaration of conformity must be prepared. This document contains information about the provider, the system, applicable harmonized standards, and details of the notified body (if applicable). The declaration must be signed by an authorized person.\n\n3. **Registration in the EU Database**: The AI system must be registered in the EU database for high-risk AI systems. This registration provides public transparency about the high-risk AI systems in use and contains details such as the system description, intended purpose, conformity status, and Member States where placed on the market.\n\n4. **Affixing CE Marking**: The CE marking must be affixed visibly, legibly, and indelibly to the high-risk AI system or, if possible, to its packaging or accompanying documents. The CE marking indicates",
        "exact_match": 0.0,
        "f1": 0.6,
        "latency_ms": 3171.861100010574,
        "cost_usd": 0.0013183000000000001,
        "total_tokens": 13183,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent"
      },
      {
        "question_id": "MG-A03",
        "question": "What are the obligations of deployers of high-risk AI systems regarding human oversight?",
        "gold_answer": "Under Article 26, deployers must assign human oversight to natural persons with necessary competence, training and authority. Under Article 14, the system must be designed to enable individuals to: fully understand system capacities and limitations, monitor operation, remain aware of automation bias, correctly interpret outputs, decide not to use the system, override or reverse outputs, and intervene through a stop button. For biometric identification (Annex III point 1(a)), at least two natural persons must verify and confirm any action.",
        "predicted_answer": "Deployers of high-risk AI systems have specific obligations regarding human oversight, as outlined in the AI Act. These obligations include ensuring that human oversight is in place and that the AI system is used in a manner consistent with the intended purpose. Human oversight is a crucial aspect of ensuring that AI systems are used responsibly and ethically, especially in high-risk categories such as those involving critical infrastructure, employment, and essential services.\n\nHere are the key obligations of deployers regarding human oversight:\n\n1. **Human Oversight**: Deployers must ensure that human oversight is in place. This means that there should be human intervention or review to ensure that the AI system is used appropriately and that its outputs are consistent with the intended purpose. Human oversight can be in the form of human decision-makers who review the AI system's decisions or outputs, or it can involve human intervention to correct or modify the AI system's actions.\n\n2. **Monitoring and Reporting**: Deployers are required to monitor the performance of the AI system and report any incidents or failures to the relevant authorities. This includes incidents where the AI system's outputs are not consistent with the intended purpose or where there are concerns about the fairness, reliability, or accuracy of the AI system's outputs.\n\n3. **Incident Reporting**: Deployers must report",
        "exact_match": 0.0,
        "f1": 0.2857142857142857,
        "latency_ms": 3139.5898000337183,
        "cost_usd": 0.0013191000000000001,
        "total_tokens": 13191,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent"
      },
      {
        "question_id": "MG-A04",
        "question": "What specific requirements does the EU AI Act impose on general-purpose AI models with systemic risk?",
        "gold_answer": "Under Article 51, a GPAI model is classified as having systemic risk when training computation exceeds 10^25 FLOPs. Article 55 requires such providers to: perform model evaluations with standardised protocols including adversarial testing; assess and mitigate systemic risks at Union level; keep track of and report serious incidents to the AI Office; ensure adequate cybersecurity protection. Article 52 requires all GPAI providers to maintain technical documentation, provide information to downstream providers, and have a copyright policy.",
        "predicted_answer": "The EU AI Act imposes specific requirements on general-purpose AI models with systemic risk, which are addressed in Annex III. These models are categorized as high-risk AI systems and are subject to stringent compliance measures. The key requirements include:\n\n1. **Full High-Risk Requirements (Art. 9-15)**: General-purpose AI models must comply with the full high-risk requirements outlined in the AI Act, including requirements related to safety, robustness, transparency, and explainability.\n\n2. **Conformity Assessment**: The AI model must undergo a conformity assessment to ensure compliance with the AI Act's requirements. This involves verifying that the model meets the specified criteria and requirements.\n\n3. **EU Declaration of Conformity**: A EU declaration of conformity must be drafted, which states that the provider takes responsibility for the compliance of the AI model with the AI Act's requirements.\n\n4. **Registration in the EU Database**: The AI model must be registered in the EU database for high-risk AI systems. This registration provides public transparency about the high-risk AI systems in use and allows for tracking and monitoring of these systems across the EU.\n\nThese requirements are designed to ensure that general-purpose AI models, which have the potential to cause systemic risk, are subject to comprehensive and rigorous oversight and compliance",
        "exact_match": 0.0,
        "f1": 0.16666666666666666,
        "latency_ms": 3127.198100090027,
        "cost_usd": 0.0013180000000000002,
        "total_tokens": 13180,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent"
      },
      {
        "question_id": "MG-A05",
        "question": "What are the GDPR principles for processing personal data and how do they apply to AI training?",
        "gold_answer": "GDPR Article 5 requires personal data to be processed with: lawfulness, fairness and transparency; purpose limitation (collected for specified purposes only); data minimisation (adequate, relevant, limited to necessary); accuracy (kept up to date); storage limitation (not kept longer than necessary); integrity and confidentiality (appropriate security). For AI training data under Article 10 of the AI Act, these principles mean training data must comply with data minimisation and purpose limitation when processing personal data.",
        "predicted_answer": "The General Data Protection Regulation (GDPR) outlines several principles for processing personal data, which are crucial for ensuring the protection of individuals' privacy and personal data. These principles include:\n\n1. **Lawfulness, Fairness, and Transparency**: Personal data must be processed lawfully, fairly, and in a transparent manner. This means that data processing must be based on clear and lawful grounds, such as consent, contractual necessity, or legal obligations.\n\n2. **Purpose Limitation**: Personal data should be collected for specified, explicit, and legitimate purposes and not further processed in a manner that is incompatible with those purposes. This principle ensures that data is used only for the intended purposes and not used for other purposes without additional consent or justification.\n\n3. **Data Minimization**: Personal data should be collected and processed in an adequate, relevant, and limited manner to fulfill the specified purposes. This principle aims to minimize the amount of personal data collected and processed, ensuring that only the necessary data is retained.\n\n4. **Accuracy**: Personal data should be accurate and, where necessary, kept up to date. This means that data should be corrected or updated as soon as possible if it is found to be inaccurate or incomplete.\n\n5. **Storage Limitation**: Personal data should be kept in a",
        "exact_match": 0.0,
        "f1": 0.8333333333333334,
        "latency_ms": 3160.140899941325,
        "cost_usd": 0.0013184,
        "total_tokens": 13184,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent"
      },
      {
        "question_id": "MG-A06",
        "question": "What transparency obligations exist for AI systems that generate deepfakes or synthetic content?",
        "gold_answer": "Article 50 requires providers of AI systems generating synthetic audio, image, video or text content to ensure outputs are marked in a machine-readable format as artificially generated or manipulated. Deployers of AI systems generating deep fakes must disclose that content has been artificially generated or manipulated. For AI systems interacting directly with persons, providers must design them so persons are informed they are interacting with an AI, unless obvious from context. Exception: AI used to detect or prevent criminal offences.",
        "predicted_answer": "For AI systems that generate deepfakes or synthetic content, there are transparency obligations under the AI Act. Specifically, the AI Act requires that the EU declaration of conformity for such systems must contain a description of the AI system's function and purpose, as well as information about how the system generates synthetic content. This information should be provided to users and the public to ensure transparency about the use and capabilities of the AI system.\n\nAdditionally, the AI Act mandates that the EU declaration of conformity should include a description of the AI system's function and purpose, and it should specify how the system generates synthetic content. This transparency is intended to allow users and the public to understand the capabilities and limitations of the AI system, particularly when it comes to generating content that might be misleading or harmful.\n\nIt's important to note that these requirements aim to ensure that users and the public are aware of the potential risks associated with synthetic content generated by AI systems, such as deepfakes, and that they can make informed decisions about the use of such content.",
        "exact_match": 0.0,
        "f1": 0.4000000000000001,
        "latency_ms": 2826.085700187832,
        "cost_usd": 0.0013159,
        "total_tokens": 13159,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent"
      },
      {
        "question_id": "MG-A07",
        "question": "How does DORA define ICT risk management requirements for financial entities?",
        "gold_answer": "DORA Articles 5-16 require financial entities to have an internal governance and control framework ensuring effective and prudent management of ICT risk. The framework must include strategies, policies, procedures, ICT protocols and tools to protect all information assets and ICT assets including software, hardware and servers. Financial entities must identify, classify and adequately document all ICT-supported business functions.",
        "predicted_answer": "DORA (Directive on Information and Communication Technologies) defines ICT risk management requirements for financial entities through several articles, particularly in Articles 28-30. Here are the key points:\n\n1. **Article 28**: This article requires financial entities to implement ICT risk management measures to ensure the security and integrity of their ICT systems. These measures include, but are not limited to, risk assessments, risk mitigation strategies, and incident response plans.\n\n2. **Article 29**: This article mandates that financial entities conduct regular ICT risk assessments to identify potential risks and vulnerabilities within their ICT systems. These assessments should be documented and reviewed periodically to ensure ongoing risk management.\n\n3. **Article 30**: This article stipulates that financial entities must have contractual safeguards in place with third-party ICT service providers. These safeguards should include provisions for access to audit the ICT systems, incident notification, and data localization. Additionally, financial entities must have exit strategies in place to handle the transfer of ICT systems to new providers or the discontinuation of services.\n\nIn summary, DORA requires financial entities to implement comprehensive ICT risk management measures, conduct regular risk assessments, and ensure contractual safeguards with third-party providers, all while having clear exit strategies.",
        "exact_match": 0.0,
        "f1": 0.5,
        "latency_ms": 3068.315400276333,
        "cost_usd": 0.0013168,
        "total_tokens": 13168,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent"
      },
      {
        "question_id": "MG-A08",
        "question": "What incident notification timelines does NIS2 require for essential entities?",
        "gold_answer": "NIS2 Article 23 requires essential entities to notify CSIRT or competent authority: (a) within 24 hours \u2014 early warning indicating whether incident is suspected of being caused by unlawful or malicious acts; (b) within 72 hours \u2014 incident notification with assessment of severity and impact; (c) upon request \u2014 intermediate report; (d) within one month \u2014 final report with detailed description, root cause, and mitigation measures.",
        "predicted_answer": "NIS2 (as a broader framework that includes NIS Directive requirements) mandates specific incident notification timelines for essential entities. For essential entities, NIS2 requires that incidents be reported within a specified timeframe. While the exact timelines can vary based on the specific incident and the nature of the threat, generally, essential entities are required to notify relevant authorities of significant cybersecurity incidents within a few hours or at most 24 hours. The exact timeframes are typically defined by national laws or regulations that implement NIS2, and these can differ between countries. For precise and up-to-date information, it's important to refer to the national laws or regulations of the relevant countries or regions.",
        "exact_match": 0.0,
        "f1": 0.25301204819277107,
        "latency_ms": 2272.6864996366203,
        "cost_usd": 0.0013082,
        "total_tokens": 13082,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent"
      },
      {
        "question_id": "MG-A09",
        "question": "What high-risk AI use cases does Annex III define for employment and workers management?",
        "gold_answer": "Annex III point 4 classifies as high-risk: AI systems for recruitment or selection of natural persons, particularly for placing targeted job advertisements, screening or filtering applications, evaluating candidates in interviews or tests. Also: AI for making decisions affecting terms of work-related relationships, promotion or termination, task allocation based on individual behaviour or personal traits, and monitoring and evaluating performance and behaviour of persons in work relationships.",
        "predicted_answer": "Annex III defines high-risk AI use cases for employment and workers management in Category 4. Specifically, it covers:\n\n- Employment\n- Workers management\n- Access to self-employment\n\nThese use cases are considered high-risk because they involve automated decision-making processes that can significantly impact individuals' rights and opportunities, particularly in areas such as employment eligibility, access to self-employment, and management practices.",
        "exact_match": 0.0,
        "f1": 0.2113821138211382,
        "latency_ms": 1884.430699981749,
        "cost_usd": 0.0013035000000000002,
        "total_tokens": 13035,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent"
      },
      {
        "question_id": "MG-A10",
        "question": "What is the role and scope of AI regulatory sandboxes under the EU AI Act?",
        "gold_answer": "Articles 57-58 require Member States to ensure at least one AI regulatory sandbox is operational at national level by August 2026. Sandboxes provide a controlled environment for development, testing and validation of innovative AI systems under strict oversight. They include real-world testing conditions. Personal data may be processed in sandboxes for developing AI in the public interest, with specific safeguards. SMEs and startups have priority access. Sandbox results are considered by market surveillance authorities.",
        "predicted_answer": "AI regulatory sandboxes under the EU AI Act are experimental environments designed to test and evaluate AI systems in a controlled setting. They are crucial for fostering innovation and ensuring that AI technologies are developed and deployed safely and responsibly. The role and scope of AI regulatory sandboxes include:\n\n1. **Testing and Evaluation**: Sandboxes allow AI developers to test their systems under controlled conditions, ensuring they meet the requirements of the AI Act and other relevant regulations. This includes testing for safety, reliability, and compliance with data protection laws.\n\n2. **Risk Assessment**: Developers can conduct risk assessments within a sandbox environment, which helps identify potential risks and vulnerabilities of their AI systems before they are deployed in real-world scenarios. This can include assessing the impact on privacy, security, and fairness.\n\n3. **Feedback and Iteration**: Sandboxes provide opportunities for developers to receive feedback from regulators, industry experts, and end-users. This iterative process helps refine AI systems and aligns them more closely with regulatory expectations and user needs.\n\n4. **Education and Training**: Sandboxes can serve as educational platforms for developers and regulators. They provide a space to learn about the latest AI technologies and how they interact with existing regulations, helping to build expertise and consensus.\n\n5. **Experimentation with Regulations**: Sandboxes allow",
        "exact_match": 0.0,
        "f1": 0.21245421245421248,
        "latency_ms": 3161.0395000316203,
        "cost_usd": 0.0013186,
        "total_tokens": 13186,
        "convergence_rounds": 1,
        "active_nodes": 1,
        "method": "single-agent"
      }
    ]
  },
  "cognigraph-pcst": {
    "summary": {
      "method": "cognigraph-pcst",
      "dataset": "MultiGov-30",
      "n_questions": 10,
      "avg_em": 0.0,
      "avg_f1": 0.3780273454110046,
      "avg_latency_ms": 66400.36685001105,
      "avg_cost_usd": 6.926e-05,
      "avg_tokens": 692.6,
      "avg_rounds": 2.8,
      "avg_nodes": 8.0,
      "total_cost_usd": 0.0006926,
      "total_latency_s": 664.0036685001105
    },
    "questions": [
      {
        "question_id": "MG-A01",
        "question": "What AI practices are completely prohibited under the EU AI Act, and what are the penalties?",
        "gold_answer": "Article 5 prohibits: subliminal manipulation, exploitation of vulnerabilities, social scoring by public authorities, real-time remote biometric identification in public spaces (with limited exceptions for law enforcement), untargeted scraping for facial recognition databases, emotion recognition in workplace and education, and biometric categorisation using sensitive characteristics. Under Article 99, violations carry fines up to EUR 35 million or 7% of worldwide annual turnover.",
        "predicted_answer": "### Synthesized Answer\n\nBased on the analysis from multiple specialized agents, the following AI practices are completely prohibited under the EU AI Act (Article 5), and the associated penalties are as follows:\n\n#### Prohibited AI Practices:\n1. **Subliminal Techniques and Manipulative Techniques**: The use of AI systems that employ subliminal techniques or purposefully manipulative or deceptive techniques to distort a person's decision-making, causing significant harm.\n2. **Exploitation of Vulnerabilities**: AI systems that exploit vulnerabilities of a specific group of persons due to their age, disability, or specific social or economic situation.\n3. **Social Scoring**: AI systems used by public authorities or on their behalf for social scoring.\n4. **Real-Time Remote Biometric Identification**: The use of real-time remote biometric identification by public authorities in publicly accessible spaces for law enforcement purposes, except in cases of targeted search for victims of abduction/trafficking, prevention of imminent threat to life, or genuine terrorist threat.\n5. **Untargeted Scraping of Facial Images**: The scraping of facial images from the internet or CCTV for facial recognition databases without targeted use.\n6. **Emotion Recognition**: The use of emotion recognition in workplace and educational institutions.\n7. **Biometric Categorization**: The use of biometric categorization using sensitive characteristics.\n\n#### Penalties:\nThe enforcement penalty for prohibited AI practices is up to EUR 35 million or 7% of worldwide annual turnover, whichever is higher. Enforcement begins 2 February 2025 (6 months after the entry into force of the AI Act). National market surveillance authorities are responsible for enforcement, with the AI Office coordinating at the EU level.\n\n### Confidence\nThe confidence in the synthesized answer is 100%, as all the information provided is consistent and coherent across the provided evidence chunks.\n\n### Key Evidence Sources\n- Agent: aiact_art5_prohibited | Type: contradiction | Confidence: 100%",
        "exact_match": 0.0,
        "f1": 1.0,
        "latency_ms": 82102.54839994013,
        "cost_usd": 9.480000000000001e-05,
        "total_tokens": 948,
        "convergence_rounds": 3,
        "active_nodes": 8,
        "method": "cognigraph-pcst"
      },
      {
        "question_id": "MG-A02",
        "question": "What is the complete compliance chain a provider must follow to place a high-risk AI system on the EU market?",
        "gold_answer": "The provider must follow this compliance chain: (1) Implement risk management system (Art. 9); (2) Ensure data governance for training data (Art. 10); (3) Draw up technical documentation (Art. 11); (4) Ensure record-keeping and logging (Art. 12); (5) Provide transparency and instructions for use (Art. 13); (6) Design for human oversight (Art. 14); (7) Achieve accuracy, robustness and cybersecurity (Art. 15); (8) Establish quality management system (Art. 17); (9) Complete conformity assessment (Art. 43); (10) Affix CE marking (Art. 48); (11) Register in EU database (Art. 49). Only after all steps can the system be lawfully placed on the market.",
        "predicted_answer": "### Synthesized Answer\n\nTo place a high-risk AI system on the EU market, a provider must follow a comprehensive compliance chain as outlined in Article 5 of the EU AI Act. The key compliance steps are as follows:\n\n1. **Audit Existing AI Systems for Prohibited Practices**:\n   - **Evidence**: [3]\n   - Confidence: 100%\n   - Explanation: Organizations must audit their existing AI systems to identify any prohibited practices such as subliminal techniques, social scoring, real-time remote biometric identification, and emotion recognition in workplace and educational institutions.\n\n2. **Inventory All AI Systems**:\n   - **Evidence**: [3]\n   - Confidence: 100%\n   - Explanation: Providers must create a comprehensive inventory of all AI systems within their organization.\n\n3. **Classify Against Article 5 Categories**:\n   - **Evidence**: [3]\n   - Confidence: 100%\n   - Explanation: After inventorying all AI systems, organizations must classify each system against the categories outlined in Article 5. This classification will help identify which systems are high-risk and require additional scrutiny and compliance measures.\n\n4. **Identify Any Emotion Recognition in Workplace and Educational Institutions**:\n   - **Evidence**: [3]\n   - Confidence: 100%\n   - Explanation: Providers must specifically identify any AI systems that involve emotion recognition in workplace or educational institutions.\n\n5. **Review Biometric Processing Activities**:\n   - **Evidence**: [3]\n   - Confidence: 100%\n   - Explanation: Organizations must review their biometric processing activities to ensure they comply with Article 5, including checking for any biometric categorization using sensitive characteristics.\n\n6. **Check for Dark Patterns and Manipulative AI**:\n   - **Evidence**: [3]\n   - Confidence: 100%\n   - Explanation: Providers must check for any manipulative or deceptive techniques in their AI systems, including those that use subliminal techniques, purposefully manipulate or deceive users, or exploit vulnerabilities of specific groups.\n\n### Overall Confidence\n- **Confidence**: 100%\n\n### Key Evidence Sources\n- [3]\n\nThis comprehensive compliance chain ensures that high-risk AI systems are thoroughly vetted and compliant with Article 5 of the EU AI Act.",
        "exact_match": 0.0,
        "f1": 0.15591397849462363,
        "latency_ms": 70409.76980002597,
        "cost_usd": 7.390000000000001e-05,
        "total_tokens": 739,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst"
      },
      {
        "question_id": "MG-A03",
        "question": "What are the obligations of deployers of high-risk AI systems regarding human oversight?",
        "gold_answer": "Under Article 26, deployers must assign human oversight to natural persons with necessary competence, training and authority. Under Article 14, the system must be designed to enable individuals to: fully understand system capacities and limitations, monitor operation, remain aware of automation bias, correctly interpret outputs, decide not to use the system, override or reverse outputs, and intervene through a stop button. For biometric identification (Annex III point 1(a)), at least two natural persons must verify and confirm any action.",
        "predicted_answer": "### Synthesized Answer\n\nBased on the analysis of the provided evidence and related provisions, the obligations of deployers of high-risk AI systems regarding human oversight are not explicitly addressed in the given information. However, the principles and provisions related to human oversight and data protection suggest that deployers of high-risk AI systems would need to ensure that their systems are designed and used in a manner that complies with the EU AI Act and related GDPR provisions. This would likely include mechanisms for human oversight to ensure that the AI systems do not violate the prohibited practices and that they are used in a manner that respects human dignity and privacy.\n\n#### Key Evidence Sources:\n1. **Article 5(1)(c) of the EU AI Act**: This article prohibits social scoring by public authorities or on their behalf, which implies requirements for human oversight to ensure that the AI systems are not used in a manner that violates this prohibition.\n2. **Cross-reference to GDPR Articles 22 and 9**: This cross-reference indicates that there are requirements for human oversight and data protection, which are relevant to high-risk AI systems. Article 22 of the GDPR requires that automated decision-making, including profiling, does not have a significant impact on an individual's rights, freedoms, or legitimate interests, suggesting that there are likely requirements for human oversight in high-risk AI systems.\n\n#### Confidence:\nThe overall confidence in synthesizing the answer is 80%.\n\n### Summary\nWhile the specific obligations regarding human oversight for high-risk AI systems are not directly addressed in the provided evidence, the principles and provisions related to human oversight and data protection suggest that deployers of high-risk AI systems would need to ensure that their systems are designed and used in a manner that complies with the EU AI Act and related GDPR provisions. This would likely include mechanisms for human oversight to ensure that the AI systems do not violate the prohibited practices and that they are used in a manner that respects human dignity and privacy.",
        "exact_match": 0.0,
        "f1": 0.2857142857142857,
        "latency_ms": 79144.3256996572,
        "cost_usd": 8.520000000000001e-05,
        "total_tokens": 852,
        "convergence_rounds": 3,
        "active_nodes": 8,
        "method": "cognigraph-pcst"
      },
      {
        "question_id": "MG-A04",
        "question": "What specific requirements does the EU AI Act impose on general-purpose AI models with systemic risk?",
        "gold_answer": "Under Article 51, a GPAI model is classified as having systemic risk when training computation exceeds 10^25 FLOPs. Article 55 requires such providers to: perform model evaluations with standardised protocols including adversarial testing; assess and mitigate systemic risks at Union level; keep track of and report serious incidents to the AI Office; ensure adequate cybersecurity protection. Article 52 requires all GPAI providers to maintain technical documentation, provide information to downstream providers, and have a copyright policy.",
        "predicted_answer": "### Synthesized Answer\n\nBased on the analysis provided by the specialized agents, the EU AI Act imposes specific requirements on general-purpose AI models with systemic risk, although these requirements are not explicitly addressed by Article 5. The key areas of agreement across the agents are:\n\n1. **Article 5 Prohibition**: Article 5 of the EU AI Act prohibits certain AI practices, such as subliminal techniques, social scoring, real-time remote biometric identification, and others. While these practices are not directly related to systemic risk, they set a framework for the implementation of the AI Act, including penalties, enforcement, and compliance steps.\n\n2. **Systemic Risk Mitigation**: Organizations must ensure their AI systems do not engage in prohibited practices, which can help mitigate systemic risks. For example, if an AI system is found to be using subliminal techniques, it would be in violation of Article 5, and this could indicate a systemic risk in the design or implementation of the AI system.\n\nThe agents agree that Article 5 does not explicitly address systemic risk but provides a framework that can indirectly influence the design and risk management of general-purpose AI models. The specific requirements for general-purpose AI models with systemic risk are not addressed by Article 5, but the prohibition of certain AI practices can help mitigate systemic risks.\n\n### Confidence\nThe overall confidence in this synthesis is 80%.\n\n### Key Evidence Sources\n1. **EU AI Act (Article 5)**: Provides specific prohibited AI practices, including subliminal techniques, social scoring, real-time remote biometric identification, and others.\n2. **Article 5 Implementation**: Sets a framework for the implementation of the AI Act, including penalties, enforcement, and compliance steps.\n3. **Systemic Risk Mitigation**: Ensures AI systems do not engage in prohibited practices, which can help mitigate systemic risks.\n\n### Final Answer\nThe EU AI Act imposes specific requirements on general-purpose AI models with systemic risk through the prohibition of certain AI practices outlined in Article 5. While these practices are not directly related to systemic risk, they set a framework for the implementation of the AI Act, including penalties, enforcement, and compliance steps. Organizations must ensure their AI systems do not engage in prohibited practices, which can help mitigate systemic risks. Therefore, the specific requirements for general-purpose AI models with systemic risk are not addressed by Article 5, but the prohibition of certain AI practices can help mitigate systemic risks.",
        "exact_match": 0.0,
        "f1": 0.16666666666666666,
        "latency_ms": 62843.18070020527,
        "cost_usd": 5.61e-05,
        "total_tokens": 561,
        "convergence_rounds": 2,
        "active_nodes": 8,
        "method": "cognigraph-pcst"
      },
      {
        "question_id": "MG-A05",
        "question": "What are the GDPR principles for processing personal data and how do they apply to AI training?",
        "gold_answer": "GDPR Article 5 requires personal data to be processed with: lawfulness, fairness and transparency; purpose limitation (collected for specified purposes only); data minimisation (adequate, relevant, limited to necessary); accuracy (kept up to date); storage limitation (not kept longer than necessary); integrity and confidentiality (appropriate security). For AI training data under Article 10 of the AI Act, these principles mean training data must comply with data minimisation and purpose limitation when processing personal data.",
        "predicted_answer": "### Synthesized Answer\n\n#### GDPR Principles for Processing Personal Data\n\nThe General Data Protection Regulation (GDPR) outlines several principles for processing personal data, which are designed to ensure the protection of individuals' rights and freedoms. These principles include:\n\n1. **Lawfulness, Fairness, and Transparency**: Personal data must be processed lawfully, fairly, and in a transparent manner in relation to the data subject.\n2. **Purpose Limitation**: Personal data should be collected for specified, explicit, and legitimate purposes and not further processed in a manner that is incompatible with those purposes.\n3. **Data Minimization**: Personal data should be adequate, relevant, and limited to what is necessary in relation to the purposes for which they are processed.\n4. **Accuracy**: Personal data should be accurate and, where necessary, kept up to date; every reasonable step must be taken to ensure that personal data that is inaccurate, having regard to the purposes for which they are processed, is erased or rectified without delay.\n5. **Storage Limitation**: Personal data should be kept in a form that permits identification of data subjects for no longer than is necessary for the purposes for which the personal data are processed.\n6. **Integrity and Confidentiality**: Personal data should be processed in a manner that ensures appropriate security of the data, including protection against unauthorized or unlawful processing and against accidental loss, destruction, or damage.\n7. **Accountability**: Organizations must be responsible for compliance with the GDPR and must be able to demonstrate compliance.\n\n#### Application to AI Training\n\nWhile the focus of the provided evidence is on the Prohibited AI Practices of the AI Act, we can still discuss how these principles apply to AI training, which is a related but distinct area of data processing.\n\n- **Lawfulness, Fairness, and Transparency**: AI training involves the use of personal data to train machine learning models. This process must be transparent to the data subjects, and the purposes of data collection and processing must be clearly communicated.\n- **Purpose Limitation**: The purposes for which personal data is collected for AI training should be specified, explicit, and limited to the purposes of training the machine learning models. The training process should not be used for purposes that were not initially intended.\n- **Data Minimization**: Only the necessary personal data should be used for training the machine learning models to ensure data minimization.\n- **Accuracy**: The accuracy of the personal data used for training should be maintained to ensure the quality and reliability of the machine learning models.\n- **Storage Limitation**: Personal data used for training should be retained for no longer than necessary for the purposes of training the machine learning models.\n- **Integrity and Confidentiality**: The integrity and confidentiality of the personal data used for training should be maintained to prevent unauthorized access and ensure the security of the data.\n- **Accountability**: Organizations must be responsible for ensuring compliance with the GDPR and must be able to demonstrate compliance with the principles of GDPR, including those related to AI training.\n\n### Overall Confidence\nBased on the provided evidence and the analysis of the agents' outputs, the overall confidence in synthesizing a coherent answer is 80%. The primary focus of the provided evidence is on the Prohibited AI Practices of the AI Act, but the principles of GDPR are still relevant and applicable to AI training. The agents' outputs indicate a high confidence (50%) in the contradiction regarding the Prohibited AI Practices, which is a key area of focus, but the principles of GDPR are still applicable and relevant to AI training.\n\n### Key Evidence Sources\n1. **General Data Protection Regulation (GDPR)**: The principles of GDPR are outlined in the GDPR itself, which provides a comprehensive framework for data protection.\n2. **AI Act**: The Prohibited AI Practices of the AI Act provide context for the application of GDPR principles to AI training, although the primary focus is on the AI Act.\n\n### Final Answer\nThe General Data Protection Regulation (GDPR) outlines several principles for processing personal data, including lawfulness, fairness, and transparency, purpose limitation, data minimization, accuracy, storage limitation, integrity and confidentiality, and accountability. These principles are applicable to AI training, where the purposes for which personal data is collected should be specified, explicit, and limited to the purposes of training machine learning models. The principles of GDPR ensure that the use of personal data for AI training is transparent, secure, and in compliance with the rights of data subjects.",
        "exact_match": 0.0,
        "f1": 0.8333333333333334,
        "latency_ms": 101537.87099989131,
        "cost_usd": 0.0001254,
        "total_tokens": 1254,
        "convergence_rounds": 3,
        "active_nodes": 8,
        "method": "cognigraph-pcst"
      },
      {
        "question_id": "MG-A06",
        "question": "What transparency obligations exist for AI systems that generate deepfakes or synthetic content?",
        "gold_answer": "Article 50 requires providers of AI systems generating synthetic audio, image, video or text content to ensure outputs are marked in a machine-readable format as artificially generated or manipulated. Deployers of AI systems generating deep fakes must disclose that content has been artificially generated or manipulated. For AI systems interacting directly with persons, providers must design them so persons are informed they are interacting with an AI, unless obvious from context. Exception: AI used to detect or prevent criminal offences.",
        "predicted_answer": "### Synthesized Answer\n\n#### Areas of Agreement\n1. **No Direct Provisions in Article 5**: Multiple agents agree that Article 5 of the Prohibited AI Practices (Article) does not explicitly address transparency obligations for AI systems that generate deepfakes or synthetic content.\n2. **Intersection with GDPR**: The agents agree that the General Data Protection Regulation (GDPR) intersects with Article 5 of the Prohibited AI Practices (Article) and has provisions related to transparency and the use of personal data.\n3. **Transparency Obligations via GDPR**: The agents concur that transparency obligations for AI systems generating deepfakes or synthetic content would be governed by the GDPR, with specific requirements outlined in Articles 13 and 14.\n\n#### Contradictions\n1. **Article 5 Focus**: One agent (aiact_art_art5_prohibited) suggests that Article 5 focuses on specific prohibited AI practices such as subliminal techniques, social scoring, real-time remote biometric identification, and manipulation of AI systems, which does not directly address deepfakes or synthetic content.\n\n#### Synthesized Answer\nBased on the analysis of the provided evidence, there are no direct provisions in Article 5 of the Prohibited AI Practices (Article) that address the transparency obligations for AI systems that generate deepfakes or synthetic content. However, Article 5 intersects with other regulations such as the General Data Protection Regulation (GDPR), which does have provisions related to transparency and the use of personal data.\n\nThe intersection with the GDPR suggests that transparency obligations for AI systems generating deepfakes or synthetic content would be governed by the GDPR, with specific requirements outlined in Articles 13 and 14. These articles require that data controllers provide individuals with information about the processing of their personal data, including the purposes of the processing, the types of personal data involved, and the recipients or categories of recipients to whom the data are disclosed.\n\nTherefore, the transparency obligations for AI systems generating deepfakes or synthetic content would be governed by the GDPR, with the specific requirements outlined in Articles 13 and 14.\n\n#### Confidence\nThe confidence in this synthesized answer is 80%, as it is based on the agreement of multiple agents and the intersection of regulations such as the GDPR.\n\n#### Key Evidence Sources\n1. **Article 5 of the Prohibited AI Practices (Article)**\n2. **General Data Protection Regulation (GDPR)**\n3. **Charter of Fundamental Rights**\n\n#### Overall Confidence\nThe overall confidence in this synthesized answer is 80%.",
        "exact_match": 0.0,
        "f1": 0.6,
        "latency_ms": 69560.89480035007,
        "cost_usd": 8.300000000000001e-05,
        "total_tokens": 830,
        "convergence_rounds": 3,
        "active_nodes": 8,
        "method": "cognigraph-pcst"
      },
      {
        "question_id": "MG-A07",
        "question": "How does DORA define ICT risk management requirements for financial entities?",
        "gold_answer": "DORA Articles 5-16 require financial entities to have an internal governance and control framework ensuring effective and prudent management of ICT risk. The framework must include strategies, policies, procedures, ICT protocols and tools to protect all information assets and ICT assets including software, hardware and servers. Financial entities must identify, classify and adequately document all ICT-supported business functions.",
        "predicted_answer": "### Synthesis of Agent Outputs\n\n**Agent 1:** aiact_art5_prohibited\n- **Type:** synthesis\n- **Confidence:** 0%\n\n**Agent 2:** aiact_art5_prohibited\n- **Type:** synthesis\n- **Confidence:** 0%\n\n### Areas of Agreement\n- Both agents indicate that the query about DORA defining ICT risk management requirements for financial entities is not aligned with the provided knowledge, specifically the EU AI Act (Article 5 Prohibited AI Practices).\n\n### Contradictions\n- There are no contradictions between the agents' outputs.\n\n### Synthesized Answer\nThe query about DORA defining ICT risk management requirements for financial entities is not aligned with the provided knowledge, which is specifically focused on the EU AI Act (Article 5 Prohibited AI Practices). There is no information provided about DORA or its definitions of ICT risk management requirements for financial entities. Therefore, based on the given evidence and properties, it is not possible to provide a response related to the query.\n\n### Overall Confidence\n- The confidence is 0% due to the lack of relevant information and the alignment with the provided knowledge.\n\n### Key Evidence Sources\n- The provided knowledge is specifically about the EU AI Act (Article 5 Prohibited AI Practices).\n\n### Final Response\nThe query about DORA defining ICT risk management requirements for financial entities is not aligned with the provided knowledge, which is focused on the EU AI Act (Article 5 Prohibited AI Practices). Therefore, there is no information available to address the specific query.",
        "exact_match": 0.0,
        "f1": 0.3333333333333333,
        "latency_ms": 33335.60180012137,
        "cost_usd": 2.15e-05,
        "total_tokens": 215,
        "convergence_rounds": 3,
        "active_nodes": 8,
        "method": "cognigraph-pcst"
      },
      {
        "question_id": "MG-A08",
        "question": "What incident notification timelines does NIS2 require for essential entities?",
        "gold_answer": "NIS2 Article 23 requires essential entities to notify CSIRT or competent authority: (a) within 24 hours \u2014 early warning indicating whether incident is suspected of being caused by unlawful or malicious acts; (b) within 72 hours \u2014 incident notification with assessment of severity and impact; (c) upon request \u2014 intermediate report; (d) within one month \u2014 final report with detailed description, root cause, and mitigation measures.",
        "predicted_answer": "### Synthesized Answer\n\nBased on the provided knowledge and evidence chunks, the query about incident notification timelines for NIS2 (Network and Information Systems) essential entities is not addressed. The provided information focuses on the Prohibited AI Practices (Article 5) of the AI Act and related enforcement, implementation, and cross-regulation aspects. There is no mention of NIS2 or its incident notification timelines. Therefore, I cannot provide a response related to the NIS2 incident notification timelines based on the given information.\n\n### Confidence\n0%\n\n### Key Evidence Sources\n- aiact_art5_prohibited: This evidence chunk discusses the Prohibited AI Practices (Article 5) of the AI Act and related aspects, but does not mention NIS2 or its incident notification timelines.\n\n### Flagged Contradictions\n- None of the provided evidence contradicts the given information.\n\n### Overall Confidence\n0%\n\n### Synthesis\nThe provided information does not cover the query about NIS2 incident notification timelines. The evidence focuses on the Prohibited AI Practices of the AI Act, which is unrelated to NIS2. Therefore, no reliable information can be synthesized to answer the query about NIS2 incident notification timelines.",
        "exact_match": 0.0,
        "f1": 0.136986301369863,
        "latency_ms": 44981.552799697965,
        "cost_usd": 2.85e-05,
        "total_tokens": 285,
        "convergence_rounds": 3,
        "active_nodes": 8,
        "method": "cognigraph-pcst"
      },
      {
        "question_id": "MG-A09",
        "question": "What high-risk AI use cases does Annex III define for employment and workers management?",
        "gold_answer": "Annex III point 4 classifies as high-risk: AI systems for recruitment or selection of natural persons, particularly for placing targeted job advertisements, screening or filtering applications, evaluating candidates in interviews or tests. Also: AI for making decisions affecting terms of work-related relationships, promotion or termination, task allocation based on individual behaviour or personal traits, and monitoring and evaluating performance and behaviour of persons in work relationships.",
        "predicted_answer": "### Synthesized Answer\n\nBased on the analysis of the provided information, the high-risk AI use cases for employment and workers management, as per the EU AI Act Annex III, are:\n\n1. **Social Scoring**: Any form of social scoring by private companies would be considered high-risk, as it could violate GDPR Art. 22 and Art. 5.\n2. **Emotion Recognition**: This is explicitly prohibited in Article 5(5), making it a high-risk use case.\n\n### Areas of Agreement:\n- Both agents agree that social scoring is a high-risk use case for employment and workers management.\n- Both agents agree that emotion recognition is a high-risk use case for employment and workers management.\n\n### Contradictions:\n- Agent `aiact_art5_prohibited` states that Annex III does not define high-risk AI use cases for employment and workers management, which contradicts the reasoning based on Article 5 of the AI Act.\n\n### Synthesis:\nThe provided evidence from Agent `aiact_art5_prohibited` is based on a contradiction, which suggests that Annex III does not explicitly define high-risk AI use cases for employment and workers management. However, based on Article 5 of the AI Act, we can infer some high-risk use cases. Specifically, Article 5(1)(c) prohibits social scoring by public authorities or on their behalf, and Article 5(5) prohibits emotion recognition in workplace and educational institutions. Therefore, the high-risk use cases identified are:\n1. **Social Scoring**: Any form of social scoring by private companies would be considered high-risk, as it could violate GDPR Art. 22 and Art. 5.\n2. **Emotion Recognition**: This is explicitly prohibited in Article 5(5), making it a high-risk use case.\n\n### Confidence Level:\nThe overall confidence in this synthesis is **95%**. The reasoning is based on the explicit prohibition in Article 5(5) and the potential violation of GDPR Art. 22 and Art. 5 for social scoring.\n\n### Key Evidence Sources:\n- **EU AI Act Annex III**: The general absence of high-risk use cases for employment and workers management.\n- **EU AI Act Article 5**: Prohibitions on social scoring and emotion recognition in workplace and educational institutions.\n\n### Final Answer:\nThe high-risk AI use cases for employment and workers management, as per the EU AI Act, are:\n1. **Social Scoring**: Any form of social scoring by private companies.\n2. **Emotion Recognition**: In workplace and educational institutions.\n\nThe overall confidence in this synthesis is **95%**.",
        "exact_match": 0.0,
        "f1": 0.10096153846153845,
        "latency_ms": 78667.85650001839,
        "cost_usd": 8.570000000000001e-05,
        "total_tokens": 857,
        "convergence_rounds": 3,
        "active_nodes": 8,
        "method": "cognigraph-pcst"
      },
      {
        "question_id": "MG-A10",
        "question": "What is the role and scope of AI regulatory sandboxes under the EU AI Act?",
        "gold_answer": "Articles 57-58 require Member States to ensure at least one AI regulatory sandbox is operational at national level by August 2026. Sandboxes provide a controlled environment for development, testing and validation of innovative AI systems under strict oversight. They include real-world testing conditions. Personal data may be processed in sandboxes for developing AI in the public interest, with specific safeguards. SMEs and startups have priority access. Sandbox results are considered by market surveillance authorities.",
        "predicted_answer": "### Synthesized Answer:\nBased on the analysis provided by the multiple specialized agents, there is no specific mention of AI regulatory sandboxes in the context of the EU AI Act. The evidence and information primarily focus on the prohibited AI practices and their enforcement, as well as the cross-regulation with other data protection laws. Therefore, it is not possible to provide a focused reasoning response regarding the role and scope of AI regulatory sandboxes under the EU AI Act based on the given information.\n\n### Key Evidence Sources:\n1. The provided evidence does not contain any information regarding AI regulatory sandboxes.\n\n### Confidence:\n0%\n\n### Explanation:\nThe agents' outputs indicate that the focus of the EU AI Act is on prohibited AI practices and their enforcement, as well as the integration with other data protection laws. There is no mention of AI regulatory sandboxes, which suggests that the current scope of the EU AI Act does not include these specific regulatory mechanisms. Therefore, the confidence in providing a focused answer on AI regulatory sandboxes under the EU AI Act is 0%, as the provided evidence does not support any information on this topic.",
        "exact_match": 0.0,
        "f1": 0.1673640167364017,
        "latency_ms": 41420.067000202835,
        "cost_usd": 3.85e-05,
        "total_tokens": 385,
        "convergence_rounds": 3,
        "active_nodes": 8,
        "method": "cognigraph-pcst"
      }
    ]
  }
}