{"calibration_version":"1.0","decision_id":"bfce27c6-9580-48bc-91f0-641397855383","episode_id":null,"episode_label":"arena_episode_20260524","judge_calibration":{"episodic_cognition":{"episodes":20,"mean_total":9.2,"reliability":0.3,"score_range":18.0,"variance":40.66},"trust_provenance":{"episodes":20,"mean_total":8.425,"reliability":0.401,"score_range":7.0,"variance":4.2569},"voice_identity":{"episodes":20,"mean_total":8.38,"reliability":0.3,"score_range":15.1,"variance":17.1386}},"judge_version":"2.0","judges":[{"commentary_a":"This paper analyzes AI policy consultation letters using standard topic modeling but has no connection to voice identity, speaker verification, synthetic voice detection, voice cloning, biometric rights, or consent architecture for voice data\u2014the core domains of voice identity research.","commentary_b":"This paper addresses behavioral cloning and multimodal policy learning in reinforcement learning, which is entirely outside the domain of voice identity, speaker verification, voice authentication, synthetic voice detection, voice cloning, biometric rights, or consent architecture for voice data.","confidence_a":0.95,"confidence_b":0.95,"judge":"voice_identity","provider":"claude-haiku","provider_a":"claude-haiku","provider_b":"claude-haiku","role":"Voice & Identity","scores_a":{"evidence":4.0,"impact":1.0,"novelty":2.0},"scores_b":{"evidence":2.0,"impact":0.0,"novelty":1.0},"weight":0.3},{"commentary_a":"This is a policy discourse analysis with no mechanism, protocol, or framework for trust or provenance in AI systems, making it largely irrelevant to trustworthy AI infrastructure despite touching on AI governance themes.","commentary_b":"This paper addresses imitation learning multimodality, which has no meaningful connection to trust infrastructure, provenance chains, agent accountability, or AI auditability, making it essentially irrelevant to the evaluation domain.","confidence_a":0.82,"confidence_b":0.82,"judge":"trust_provenance","provider":"claude-sonnet","provider_a":"claude-sonnet","provider_b":"claude-sonnet","role":"Trust & Provenance","scores_a":{"evidence":3.5,"impact":2.0,"novelty":2.5},"scores_b":{"evidence":2.0,"impact":1.0,"novelty":2.0},"weight":0.35},{"commentary_a":"The paper offers a sociopolitical analysis rather than advancing memory or cognition in AI systems.","commentary_b":"The paper provides an incremental improvement in understanding multimodal policy parameterizations but lacks strong empirical validation across diverse tasks.","confidence_a":0.9,"confidence_b":0.8,"judge":"episodic_cognition","provider":"openai-4o","provider_a":"openai-4o","provider_b":"openai-4o","role":"Episodic Cognition","scores_a":{"evidence":3.0,"impact":2.0,"novelty":2.0},"scores_b":{"evidence":5.0,"impact":5.0,"novelty":6.0},"weight":0.35}],"keyword":null,"meta_decision":{"agreement_rate":0.6,"avg_confidence":0.1203,"avg_margin":0.0752,"meta_confidence":0.0722,"total_trials":5,"trial_verdicts":["Paper A","Paper B","Paper B","Paper A","Paper B"],"verdict_counts":{"paper_a":2,"paper_b":3,"tie":0},"winner":"Paper B","winning_trials":3},"num_trials":5,"paper_A":{"authors":["Alina Karakanta","Alex Christiansen","Tom\u00e1s Dodds"],"id":"http://arxiv.org/abs/2605.22650v1","published":"2026-05-21T15:54:00Z","summary":"This study analyzed public letters submitted during the Trump Administration's US AI Action Plan consultation to understand how different stakeholders perceive AI's role in society, using topic modeling and frequency analysis. The findings revealed that individuals expressed strong concerns about AI's impact on daily life, while the AI Action Plan itself primarily reflected private sector priorities around security and development rather than the broader public's concerns.","title":"Whose Voice Counts? Mapping Stakeholder Perspectives on AI Through Public Submissions to the U.S. Government"},"paper_B":{"authors":["Lorenzo Mazza","Massimiliano Datres","Ariel Rodriguez"],"id":"http://arxiv.org/abs/2605.22493v1","published":"2026-05-21T13:45:28Z","summary":"# Summary\n\nThis paper investigates why behavioral cloning fails when multiple valid actions exist for the same observation, analyzing how different multimodal policy parameterizations (latent-variable and action-space generative models) struggle with this problem in different ways. The researchers identify key trade-offs: latent-variable policies must balance posterior-prior regularization to ensure reliable sampling while preserving action-conditioned information, while action-space generative policies are fundamentally constrained by the smoothness of their transport maps in covering multiple distinct modes.","title":"Understanding Multimodal Failure in Action-Chunking Behavioral Cloning"},"reducer_result":{"agreement_level":"Moderate","confidence":0.0283,"disagreement_index":4.4572,"margin":0.0177,"providers_used":["claude-sonnet","claude-haiku","openai-4o"],"reducer_detail":{"confidence_weights":{"a":{"episodic_cognition":0.9,"trust_provenance":0.82,"voice_identity":0.95},"b":{"episodic_cognition":0.8,"trust_provenance":0.82,"voice_identity":0.95}},"dampening_applied":true,"dampening_factor":0.8,"judge_totals":{"a":{"episodic_cognition":7.0,"trust_provenance":8.0,"voice_identity":7.0},"b":{"episodic_cognition":16.0,"trust_provenance":5.0,"voice_identity":3.0}},"normalised_scores":{"a":{"episodic_cognition":0.2333,"trust_provenance":0.2667,"voice_identity":0.2333},"b":{"episodic_cognition":0.5333,"trust_provenance":0.1667,"voice_identity":0.1}},"pre_dampening_confidence":0.0353,"reliability_weights":{"episodic_cognition":0.3,"trust_provenance":0.3135,"voice_identity":0.5036},"weighted_scores":{"a":{"episodic_cognition":0.063,"trust_provenance":0.0686,"voice_identity":0.1116},"b":{"episodic_cognition":0.128,"trust_provenance":0.0428,"voice_identity":0.0478}}},"reducer_params":{"dampening_factor_high":0.6,"dampening_factor_moderate":0.8,"dampening_threshold_high":7,"dampening_threshold_moderate":4,"reducer_version":"1.1"},"score_a":0.2419,"score_b":0.2242,"winner":"Paper A"},"reducer_version":"1.1","timestamp":"2026-05-24T08:18:27.658690+00:00","trials":[{"judges":[{"commentary_a":"This paper analyzes AI policy consultation letters using standard topic modeling but has no connection to voice identity, speaker verification, synthetic voice detection, voice cloning, biometric rights, or consent architecture for voice data\u2014the core domains of voice identity research.","commentary_b":"This paper addresses behavioral cloning and multimodal policy learning in reinforcement learning, which is entirely outside the domain of voice identity, speaker verification, voice authentication, synthetic voice detection, voice cloning, biometric rights, or consent architecture for voice data.","confidence_a":0.95,"confidence_b":0.95,"judge":"voice_identity","provider":"claude-haiku","provider_a":"claude-haiku","provider_b":"claude-haiku","role":"Voice & Identity","scores_a":{"evidence":4.0,"impact":1.0,"novelty":2.0},"scores_b":{"evidence":2.0,"impact":0.0,"novelty":1.0},"weight":0.3},{"commentary_a":"This is a policy discourse analysis with no mechanism, protocol, or framework for trust or provenance in AI systems, making it largely irrelevant to trustworthy AI infrastructure despite touching on AI governance themes.","commentary_b":"This paper addresses imitation learning multimodality, which has no meaningful connection to trust infrastructure, provenance chains, agent accountability, or AI auditability, making it essentially irrelevant to the evaluation domain.","confidence_a":0.82,"confidence_b":0.82,"judge":"trust_provenance","provider":"claude-sonnet","provider_a":"claude-sonnet","provider_b":"claude-sonnet","role":"Trust & Provenance","scores_a":{"evidence":3.5,"impact":2.0,"novelty":2.5},"scores_b":{"evidence":2.0,"impact":1.0,"novelty":2.0},"weight":0.35},{"commentary_a":"The paper offers a sociopolitical analysis rather than advancing memory or cognition in AI systems.","commentary_b":"The paper provides an incremental improvement in understanding multimodal policy parameterizations but lacks strong empirical validation across diverse tasks.","confidence_a":0.9,"confidence_b":0.8,"judge":"episodic_cognition","provider":"openai-4o","provider_a":"openai-4o","provider_b":"openai-4o","role":"Episodic Cognition","scores_a":{"evidence":3.0,"impact":2.0,"novelty":2.0},"scores_b":{"evidence":5.0,"impact":5.0,"novelty":6.0},"weight":0.35}],"reducer_result":{"agreement_level":"Moderate","confidence":0.0283,"disagreement_index":4.4572,"margin":0.0177,"providers_used":["claude-sonnet","claude-haiku","openai-4o"],"reducer_detail":{"confidence_weights":{"a":{"episodic_cognition":0.9,"trust_provenance":0.82,"voice_identity":0.95},"b":{"episodic_cognition":0.8,"trust_provenance":0.82,"voice_identity":0.95}},"dampening_applied":true,"dampening_factor":0.8,"judge_totals":{"a":{"episodic_cognition":7.0,"trust_provenance":8.0,"voice_identity":7.0},"b":{"episodic_cognition":16.0,"trust_provenance":5.0,"voice_identity":3.0}},"normalised_scores":{"a":{"episodic_cognition":0.2333,"trust_provenance":0.2667,"voice_identity":0.2333},"b":{"episodic_cognition":0.5333,"trust_provenance":0.1667,"voice_identity":0.1}},"pre_dampening_confidence":0.0353,"reliability_weights":{"episodic_cognition":0.3,"trust_provenance":0.3135,"voice_identity":0.5036},"weighted_scores":{"a":{"episodic_cognition":0.063,"trust_provenance":0.0686,"voice_identity":0.1116},"b":{"episodic_cognition":0.128,"trust_provenance":0.0428,"voice_identity":0.0478}}},"reducer_params":{"dampening_factor_high":0.6,"dampening_factor_moderate":0.8,"dampening_threshold_high":7,"dampening_threshold_moderate":4,"reducer_version":"1.1"},"score_a":0.2419,"score_b":0.2242,"winner":"Paper A"},"summary_a":"This study analyzed public letters submitted during the Trump Administration's US AI Action Plan consultation to understand how different stakeholders perceive AI's role in society, using topic modeling and frequency analysis. The findings revealed that individuals expressed strong concerns about AI's impact on daily life, while the AI Action Plan itself primarily reflected private sector priorities around security and development rather than the broader public's concerns.","summary_b":"# Summary\n\nThis paper investigates why behavioral cloning fails when multiple valid actions exist for the same observation, analyzing how different multimodal policy parameterizations (latent-variable and action-space generative models) struggle with this problem in different ways. The researchers identify key trade-offs: latent-variable policies must balance posterior-prior regularization to ensure reliable sampling while preserving action-conditioned information, while action-space generative policies are fundamentally constrained by the smoothness of their transport maps in covering multiple distinct modes.","trial":1},{"judges":[{"commentary_a":"This paper is entirely outside the domain of voice identity, speaker verification, voice authentication, synthetic voice detection, voice cloning, biometric rights, or consent architecture for voice data, and therefore has no relevance to voice-as-identity research.","commentary_b":"This paper addresses behavioral cloning and multimodal policy learning in imitation learning, which has no connection to voice identity, speaker verification, voice authentication, synthetic voice detection, voice cloning, biometric rights, or consent architecture for voice data.","confidence_a":0.95,"confidence_b":0.95,"judge":"voice_identity","provider":"claude-haiku","provider_a":"claude-haiku","provider_b":"claude-haiku","role":"Voice & Identity","scores_a":{"evidence":3.0,"impact":0.0,"novelty":1.0},"scores_b":{"evidence":2.0,"impact":0.5,"novelty":1.0},"weight":0.3},{"commentary_a":"This is a policy discourse analysis study with no mechanism, protocol, or framework for trust or provenance in AI systems, making it largely irrelevant to trustworthy AI infrastructure despite tangential relevance to AI governance discourse.","commentary_b":"This paper analyzes imitation learning failure modes in multimodal action spaces but has no meaningful connection to trust infrastructure, provenance chains, agent accountability, or AI auditability, making it nearly irrelevant to this evaluation domain.","confidence_a":0.82,"confidence_b":0.72,"judge":"trust_provenance","provider":"claude-sonnet","provider_a":"claude-sonnet","provider_b":"claude-sonnet","role":"Trust & Provenance","scores_a":{"evidence":3.5,"impact":2.0,"novelty":2.5},"scores_b":{"evidence":4.0,"impact":1.5,"novelty":3.0},"weight":0.35},{"commentary_a":"The paper does not introduce new memory architectures or cognitive models, lacks empirical validation related to episodic memory, and has minimal impact on AI systems requiring long-horizon context or human oversight.","commentary_b":"The paper provides an incremental analysis of existing multimodal policy parameterizations without introducing a new memory architecture or cognitive model.","confidence_a":0.9,"confidence_b":0.8,"judge":"episodic_cognition","provider":"openai-4o","provider_a":"openai-4o","provider_b":"openai-4o","role":"Episodic Cognition","scores_a":{"evidence":3.0,"impact":2.0,"novelty":2.0},"scores_b":{"evidence":6.0,"impact":5.0,"novelty":5.0},"weight":0.35}],"reducer_result":{"agreement_level":"Moderate","confidence":0.109,"disagreement_index":4.5019,"margin":0.0682,"providers_used":["claude-sonnet","claude-haiku","openai-4o"],"reducer_detail":{"confidence_weights":{"a":{"episodic_cognition":0.9,"trust_provenance":0.82,"voice_identity":0.95},"b":{"episodic_cognition":0.8,"trust_provenance":0.72,"voice_identity":0.95}},"dampening_applied":true,"dampening_factor":0.8,"judge_totals":{"a":{"episodic_cognition":7.0,"trust_provenance":8.0,"voice_identity":4.0},"b":{"episodic_cognition":16.0,"trust_provenance":8.5,"voice_identity":3.5}},"normalised_scores":{"a":{"episodic_cognition":0.2333,"trust_provenance":0.2667,"voice_identity":0.1333},"b":{"episodic_cognition":0.5333,"trust_provenance":0.2833,"voice_identity":0.1167}},"pre_dampening_confidence":0.1363,"reliability_weights":{"episodic_cognition":0.3,"trust_provenance":0.3135,"voice_identity":0.5036},"weighted_scores":{"a":{"episodic_cognition":0.063,"trust_provenance":0.0686,"voice_identity":0.0638},"b":{"episodic_cognition":0.128,"trust_provenance":0.064,"voice_identity":0.0558}}},"reducer_params":{"dampening_factor_high":0.6,"dampening_factor_moderate":0.8,"dampening_threshold_high":7,"dampening_threshold_moderate":4,"reducer_version":"1.1"},"score_a":0.1943,"score_b":0.2624,"winner":"Paper B"},"summary_a":"This study analyzed public letters submitted during the Trump Administration's AI Action Plan consultation to understand how different stakeholders perceive AI's role in society, using topic modeling and frequency analysis. The findings revealed that individuals expressed strong concerns about AI's impact on their lives, while the AI Action Plan itself primarily reflected private sector concerns about security and development, with individual concerns underrepresented.","summary_b":"# Summary\n\nThis paper investigates why behavioral cloning fails when multiple valid actions exist for the same observation, analyzing how different multimodal policy parameterizations (latent-variable and action-space generative models) struggle with this problem in different ways. The authors identify key trade-offs: latent-variable policies must balance posterior-prior regularization to ensure reliable sampling while preserving action information, and action-space generative policies are fundamentally constrained by the smoothness of their transport maps, requiring either sharp transitions or off-support regions to capture multiple modes.","trial":2},{"judges":[{"commentary_a":"This paper has no connection to voice identity, speaker verification, synthetic voice detection, voice cloning, biometric rights, or consent architecture for voice data, and therefore falls entirely outside the evaluation domain.","commentary_b":"This paper addresses multimodal action learning in imitation learning, which is orthogonal to voice identity, speaker verification, synthetic voice detection, voice cloning, biometric rights, or consent architecture\u2014the core domains of voice identity research.","confidence_a":0.95,"confidence_b":0.95,"judge":"voice_identity","provider":"claude-haiku","provider_a":"claude-haiku","provider_b":"claude-haiku","role":"Voice & Identity","scores_a":{"evidence":4.0,"impact":1.0,"novelty":1.0},"scores_b":{"evidence":4.0,"impact":1.0,"novelty":2.0},"weight":0.3},{"commentary_a":"This is a policy analysis study using topic modeling on public comments, which offers no new mechanism, protocol, or framework for trust infrastructure, auditability, or provenance in AI systems, making it largely irrelevant to the evaluation domain.","commentary_b":"This work analyzes multimodal imitation learning trade-offs but has no meaningful connection to trust infrastructure, provenance chains, or AI auditability, making it largely irrelevant to the evaluation domain.","confidence_a":0.82,"confidence_b":0.72,"judge":"trust_provenance","provider":"claude-sonnet","provider_a":"claude-sonnet","provider_b":"claude-sonnet","role":"Trust & Provenance","scores_a":{"evidence":4.0,"impact":2.0,"novelty":2.5},"scores_b":{"evidence":5.0,"impact":2.0,"novelty":4.0},"weight":0.35},{"commentary_a":"The paper offers a sociopolitical analysis of AI perceptions rather than advancing AI memory or cognition architectures.","commentary_b":"The paper provides an incremental improvement in understanding multimodal policy parameterizations with solid validation on synthetic tasks but limited direct impact on episodic memory or long-horizon reasoning.","confidence_a":0.9,"confidence_b":0.8,"judge":"episodic_cognition","provider":"openai-4o","provider_a":"openai-4o","provider_b":"openai-4o","role":"Episodic Cognition","scores_a":{"evidence":3.0,"impact":2.0,"novelty":2.0},"scores_b":{"evidence":7.0,"impact":5.0,"novelty":6.0},"weight":0.35}],"reducer_result":{"agreement_level":"Moderate","confidence":0.2051,"disagreement_index":4.4768,"margin":0.1282,"providers_used":["claude-sonnet","claude-haiku","openai-4o"],"reducer_detail":{"confidence_weights":{"a":{"episodic_cognition":0.9,"trust_provenance":0.82,"voice_identity":0.95},"b":{"episodic_cognition":0.8,"trust_provenance":0.72,"voice_identity":0.95}},"dampening_applied":true,"dampening_factor":0.8,"judge_totals":{"a":{"episodic_cognition":7.0,"trust_provenance":8.5,"voice_identity":6.0},"b":{"episodic_cognition":18.0,"trust_provenance":11.0,"voice_identity":7.0}},"normalised_scores":{"a":{"episodic_cognition":0.2333,"trust_provenance":0.2833,"voice_identity":0.2},"b":{"episodic_cognition":0.6,"trust_provenance":0.3667,"voice_identity":0.2333}},"pre_dampening_confidence":0.2563,"reliability_weights":{"episodic_cognition":0.3,"trust_provenance":0.3135,"voice_identity":0.5036},"weighted_scores":{"a":{"episodic_cognition":0.063,"trust_provenance":0.0728,"voice_identity":0.0957},"b":{"episodic_cognition":0.144,"trust_provenance":0.0828,"voice_identity":0.1116}}},"reducer_params":{"dampening_factor_high":0.6,"dampening_factor_moderate":0.8,"dampening_threshold_high":7,"dampening_threshold_moderate":4,"reducer_version":"1.1"},"score_a":0.2303,"score_b":0.3584,"winner":"Paper B"},"summary_a":"The researchers analyzed public letters submitted during the Trump Administration's AI Action Plan consultation using topic modeling to understand how different stakeholders perceive AI's role in society. They found that individuals expressed strong concerns about AI's impact on their lives, while the final AI Action Plan primarily reflected private sector priorities around security and development, leaving individual concerns underrepresented.","summary_b":"# Summary\n\nThe researchers investigated how different multimodal policy parameterizations handle behavioral cloning when multiple valid actions exist for the same observation, analyzing the trade-offs between mode coverage and deployment reliability in both latent-variable and action-space generative policies. Their experiments on synthetic tasks and robotic simulations revealed that latent-variable policies struggle with balancing posterior-prior regularization, while action-space generative policies are fundamentally constrained by the smoothness of their transport maps in covering multiple distinct action modes.","trial":3},{"judges":[{"commentary_a":"This paper addresses AI policy discourse but contains no methodology, validation, or findings relevant to voice identity, speaker verification, synthetic voice detection, voice cloning, biometric rights, or consent architecture for voice data.","commentary_b":"This paper concerns behavioral cloning and multimodal policy learning in reinforcement learning\u2014entirely outside the domain of voice identity, speaker verification, voice authentication, synthetic voice detection, voice cloning, biometric rights, or consent architecture for voice data.","confidence_a":0.85,"confidence_b":0.95,"judge":"voice_identity","provider":"claude-haiku","provider_a":"claude-haiku","provider_b":"claude-haiku","role":"Voice & Identity","scores_a":{"evidence":4.0,"impact":1.0,"novelty":2.0},"scores_b":{"evidence":0.0,"impact":0.0,"novelty":0.0},"weight":0.3},{"commentary_a":"This is a policy discourse analysis with no mechanism, protocol, or framework for trust infrastructure \u2014 it observes a representation gap in consultation processes but offers no technical advance in auditability, provenance, or agent accountability.","commentary_b":"This paper analyzes imitation learning failure modes in multimodal action spaces but contributes nothing to trust infrastructure, provenance chains, auditability, or agent accountability, making it largely irrelevant to the evaluation domain.","confidence_a":0.72,"confidence_b":0.72,"judge":"trust_provenance","provider":"claude-sonnet","provider_a":"claude-sonnet","provider_b":"claude-sonnet","role":"Trust & Provenance","scores_a":{"evidence":4.0,"impact":3.5,"novelty":3.0},"scores_b":{"evidence":4.0,"impact":2.5,"novelty":3.5},"weight":0.35},{"commentary_a":"The paper does not introduce new memory architectures or cognitive models, lacks empirical validation, and has limited relevance to AI memory or cognition systems.","commentary_b":"The paper provides an incremental understanding of multimodal policy parameterizations but lacks strong empirical validation and significant impact on long-horizon memory systems.","confidence_a":0.9,"confidence_b":0.8,"judge":"episodic_cognition","provider":"openai-4o","provider_a":"openai-4o","provider_b":"openai-4o","role":"Episodic Cognition","scores_a":{"evidence":3.0,"impact":2.0,"novelty":2.0},"scores_b":{"evidence":5.0,"impact":5.0,"novelty":6.0},"weight":0.35}],"reducer_result":{"agreement_level":"Low","confidence":0.0745,"disagreement_index":5.2765,"margin":0.0466,"providers_used":["claude-sonnet","claude-haiku","openai-4o"],"reducer_detail":{"confidence_weights":{"a":{"episodic_cognition":0.9,"trust_provenance":0.72,"voice_identity":0.85},"b":{"episodic_cognition":0.8,"trust_provenance":0.72,"voice_identity":0.95}},"dampening_applied":true,"dampening_factor":0.8,"judge_totals":{"a":{"episodic_cognition":7.0,"trust_provenance":10.5,"voice_identity":7.0},"b":{"episodic_cognition":16.0,"trust_provenance":10.0,"voice_identity":0.0}},"normalised_scores":{"a":{"episodic_cognition":0.2333,"trust_provenance":0.35,"voice_identity":0.2333},"b":{"episodic_cognition":0.5333,"trust_provenance":0.3333,"voice_identity":0.0}},"pre_dampening_confidence":0.0932,"reliability_weights":{"episodic_cognition":0.3,"trust_provenance":0.3135,"voice_identity":0.5036},"weighted_scores":{"a":{"episodic_cognition":0.063,"trust_provenance":0.079,"voice_identity":0.0999},"b":{"episodic_cognition":0.128,"trust_provenance":0.0752,"voice_identity":0.0}}},"reducer_params":{"dampening_factor_high":0.6,"dampening_factor_moderate":0.8,"dampening_threshold_high":7,"dampening_threshold_moderate":4,"reducer_version":"1.1"},"score_a":0.2618,"score_b":0.2153,"winner":"Paper A"},"summary_a":"This study analyzed public letters submitted during a US government AI consultation to understand how different stakeholders perceive AI's role in society. The findings revealed that individuals prioritize concerns about AI's impact on daily life, while the official AI Action Plan predominantly reflects private sector priorities around security and development, leaving individual concerns underrepresented.","summary_b":"# Summary\n\nThe researchers investigated why behavioral cloning struggles when multiple valid actions exist for the same observation, analyzing how different multimodal policy parameterizations (latent-variable and action-space generative models) fail to capture multiple action modes. They found that latent-variable policies face a trade-off between regularization stability and mode preservation, while action-space generative policies are fundamentally limited by the smoothness of their transport maps, requiring either sharp transitions or off-support regions to represent multiple well-separated modes.","trial":4},{"judges":[{"commentary_a":"This paper addresses policy consultation analysis rather than voice identity, speaker verification, synthetic voice detection, or consent architecture\u2014falling entirely outside the evaluation domain and thus scoring near zero across all criteria.","commentary_b":"This paper addresses behavioral cloning and multimodal policy learning in robotics\u2014a domain entirely orthogonal to voice identity, speaker verification, voice authentication, synthetic voice detection, voice cloning, biometric rights, or consent architecture for voice data.","confidence_a":0.95,"confidence_b":0.95,"judge":"voice_identity","provider":"claude-haiku","provider_a":"claude-haiku","provider_b":"claude-haiku","role":"Voice & Identity","scores_a":{"evidence":4.0,"impact":1.0,"novelty":2.0},"scores_b":{"evidence":2.0,"impact":0.0,"novelty":1.0},"weight":0.3},{"commentary_a":"This is a policy discourse analysis with no mechanism, protocol, or framework for trust or provenance in AI systems, making it largely irrelevant to trustworthy AI infrastructure despite touching on AI governance themes.","commentary_b":"This paper addresses imitation learning multimodality, which has no meaningful connection to trust infrastructure, provenance chains, agent accountability, or AI auditability, making it essentially irrelevant to the evaluation domain.","confidence_a":0.82,"confidence_b":0.82,"judge":"trust_provenance","provider":"claude-sonnet","provider_a":"claude-sonnet","provider_b":"claude-sonnet","role":"Trust & Provenance","scores_a":{"evidence":3.5,"impact":2.0,"novelty":2.5},"scores_b":{"evidence":4.0,"impact":1.5,"novelty":3.0},"weight":0.35},{"commentary_a":"The paper provides a sociopolitical analysis of AI perceptions rather than advancing memory or cognition in AI systems.","commentary_b":"The paper provides an incremental improvement in understanding multimodal policy parameterizations with solid validation but limited impact on episodic memory or long-horizon reasoning.","confidence_a":0.8,"confidence_b":0.8,"judge":"episodic_cognition","provider":"openai-4o","provider_a":"openai-4o","provider_b":"openai-4o","role":"Episodic Cognition","scores_a":{"evidence":3.0,"impact":2.0,"novelty":2.0},"scores_b":{"evidence":7.0,"impact":5.0,"novelty":6.0},"weight":0.35}],"reducer_result":{"agreement_level":"Low","confidence":0.0467,"disagreement_index":5.0042,"margin":0.0292,"providers_used":["claude-sonnet","claude-haiku","openai-4o"],"reducer_detail":{"confidence_weights":{"a":{"episodic_cognition":0.8,"trust_provenance":0.82,"voice_identity":0.95},"b":{"episodic_cognition":0.8,"trust_provenance":0.82,"voice_identity":0.95}},"dampening_applied":true,"dampening_factor":0.8,"judge_totals":{"a":{"episodic_cognition":7.0,"trust_provenance":8.0,"voice_identity":7.0},"b":{"episodic_cognition":18.0,"trust_provenance":8.5,"voice_identity":3.0}},"normalised_scores":{"a":{"episodic_cognition":0.2333,"trust_provenance":0.2667,"voice_identity":0.2333},"b":{"episodic_cognition":0.6,"trust_provenance":0.2833,"voice_identity":0.1}},"pre_dampening_confidence":0.0584,"reliability_weights":{"episodic_cognition":0.3,"trust_provenance":0.3135,"voice_identity":0.5036},"weighted_scores":{"a":{"episodic_cognition":0.056,"trust_provenance":0.0686,"voice_identity":0.1116},"b":{"episodic_cognition":0.144,"trust_provenance":0.0728,"voice_identity":0.0478}}},"reducer_params":{"dampening_factor_high":0.6,"dampening_factor_moderate":0.8,"dampening_threshold_high":7,"dampening_threshold_moderate":4,"reducer_version":"1.1"},"score_a":0.2421,"score_b":0.2713,"winner":"Paper B"},"summary_a":"This study analyzed public letters submitted during the Trump Administration's AI Action Plan consultation to understand how different stakeholders perceive AI's role in society, using topic modeling and frequency analysis. The findings revealed that individuals expressed strong concerns about AI's impact on daily life, while the AI Action Plan itself primarily reflected private sector priorities around security and development, with individual concerns significantly underrepresented.","summary_b":"# Summary\n\nThis paper investigates why behavioral cloning fails when multiple valid actions exist for the same observation, analyzing how different multimodal policy parameterizations (latent-variable and action-space generative models) struggle with this problem in different ways. The researchers identify key trade-offs: latent-variable policies must balance posterior-prior regularization to enable reliable sampling while preserving action information, and action-space generative policies are constrained by the smoothness of their transport maps, requiring either sharp transitions or off-support regions to cover multiple modes\u2014findings validated through synthetic and robotic experiments.","trial":5}]}