{"calibration_version":"1.0","decision_id":"f40fb4e3-7715-496d-9ecb-019728bfd66f","episode_id":"f19030cb-3dfb-462d-bb8b-338046c8d949","episode_label":"arena_episode_20260808","judge_calibration":{"episodic_cognition":{"episodes":20,"mean_total":12.1,"reliability":0.3,"score_range":19.0,"variance":52.99},"trust_provenance":{"episodes":20,"mean_total":9.125,"reliability":0.3,"score_range":8.0,"variance":7.7969},"voice_identity":{"episodes":20,"mean_total":12.39,"reliability":0.3531,"score_range":7.0,"variance":5.2219}},"judge_version":"2.0","judges":[{"commentary_a":"This paper applies existing LLM + rule-based validation techniques to a socially valuable but narrow domain (maternal health enrollment) without advancing voice identity, speaker verification, consent architecture, or protection against voice fraud\u2014the core concerns of voice-as-identity research.","commentary_b":"This work addresses audio-visual source localization through self-supervised learning, which is orthogonal to voice identity, speaker verification, consent architecture, or voice fraud detection\u2014the core domains of voice identity research.","confidence_a":0.65,"confidence_b":0.85,"judge":"voice_identity","provider":"claude-haiku","provider_a":"claude-haiku","provider_b":"claude-haiku","role":"Voice & Identity","scores_a":{"evidence":6.0,"impact":5.5,"novelty":4.5},"scores_b":{"evidence":4.0,"impact":2.0,"novelty":3.0},"weight":0.3},{"commentary_a":"FormBharo addresses a real deployment challenge but contributes little to trust infrastructure or provenance mechanisms, as its rule-based validation and benchmark are domain-specific engineering rather than advances in auditability, agent accountability, or inspectable AI decision-making.","commentary_b":"This audio-visual source localization paper has no meaningful connection to AI trust infrastructure, agent accountability, or provenance chains, making it essentially irrelevant to the evaluation domain.","confidence_a":0.72,"confidence_b":0.85,"judge":"trust_provenance","provider":"claude-sonnet","provider_a":"claude-sonnet","provider_b":"claude-sonnet","role":"Trust & Provenance","scores_a":{"evidence":5.5,"impact":3.0,"novelty":4.0},"scores_b":{"evidence":3.0,"impact":1.0,"novelty":2.0},"weight":0.35},{"commentary_a":"The paper presents an incremental improvement by integrating rule-based validation with LLMs for voice agents, validated on a new benchmark, but its impact is limited to a specific application domain.","commentary_b":"The paper introduces a novel approach to audio-visual learning but lacks direct relevance to 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":6.0,"impact":5.0,"novelty":5.0},"scores_b":{"evidence":7.0,"impact":5.0,"novelty":6.0},"weight":0.35}],"keyword":null,"meta_decision":{"agreement_rate":1.0,"avg_confidence":0.2018,"avg_margin":0.1221,"meta_confidence":0.2018,"total_trials":5,"trial_verdicts":["Paper A","Paper A","Paper A","Paper A","Paper A"],"verdict_counts":{"paper_a":5,"paper_b":0,"tie":0},"winner":"Paper A","winning_trials":5},"num_trials":5,"paper_A":{"authors":["Aman Dalmia","Sanskriti Midha","Jigar Doshi"],"id":"http://arxiv.org/abs/2608.06027v1","published":"2026-08-06T13:34:22Z","summary":"# Summary\n\nThe researchers built FormBharo, a voice agent that uses Large Language Models paired with rule-based validation to help illiterate Hindi-speaking mothers in India enroll in maternal and child health programs over phone calls, which is now being piloted with an NGO called ARMMAN. They created FormVoiceAgentBench, a benchmark with 3,760 multi-turn conversation tests, and found that form completion accuracy drops significantly (~41 points) with real speech transcripts versus perfect ones, but rule-based controls help smaller models match larger ones, and end-to-end evaluation is necessary because component performance doesn't predict overall success.","title":"FormBharo: Designing and Evaluating a Voice Agent for Conversational Form Filling in Rural India"},"paper_B":{"authors":["Han Hu","Dongheng Lin","Yuqi Hou"],"id":"http://arxiv.org/abs/2608.05816v1","published":"2026-08-06T09:47:34Z","summary":"# Summary\n\nThe researchers discovered that self-supervised audio-visual learning models naturally focus on the most salient sound source (selective convergence), which they leveraged in a two-stage framework to progressively identify dominant and then remaining sound sources without manual annotations. They achieved competitive performance on dual-source localization benchmarks and identified evaluation inconsistencies in existing benchmarks, proposing pixel-level segmentation masks for more accurate spatial evaluation.","title":"Whence the Voice? Self-supervised Dual-source Audio-Visual Localisation via Selective Convergence"},"reducer_result":{"agreement_level":"Moderate","confidence":0.2426,"disagreement_index":4.6521,"margin":0.1516,"providers_used":["claude-haiku","openai-4o","claude-sonnet"],"reducer_detail":{"confidence_weights":{"a":{"episodic_cognition":0.8,"trust_provenance":0.72,"voice_identity":0.65},"b":{"episodic_cognition":0.8,"trust_provenance":0.85,"voice_identity":0.85}},"dampening_applied":true,"dampening_factor":0.8,"judge_totals":{"a":{"episodic_cognition":16.0,"trust_provenance":12.5,"voice_identity":16.0},"b":{"episodic_cognition":18.0,"trust_provenance":6.0,"voice_identity":9.0}},"normalised_scores":{"a":{"episodic_cognition":0.5333,"trust_provenance":0.4167,"voice_identity":0.5333},"b":{"episodic_cognition":0.6,"trust_provenance":0.2,"voice_identity":0.3}},"pre_dampening_confidence":0.3032,"reliability_weights":{"episodic_cognition":0.3,"trust_provenance":0.3135,"voice_identity":0.5036},"weighted_scores":{"a":{"episodic_cognition":0.128,"trust_provenance":0.094,"voice_identity":0.1746},"b":{"episodic_cognition":0.144,"trust_provenance":0.0533,"voice_identity":0.1284}}},"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":0.5001,"score_b":0.3485,"winner":"Paper A"},"reducer_version":"1.1","timestamp":"2026-08-08T08:59:06.207918+00:00","trials":[{"judges":[{"commentary_a":"This paper applies existing LLM + rule-based validation techniques to a socially valuable but narrow domain (maternal health enrollment) without advancing voice identity, speaker verification, consent architecture, or protection against voice fraud\u2014the core concerns of voice-as-identity research.","commentary_b":"This work addresses audio-visual source localization through self-supervised learning, which is orthogonal to voice identity, speaker verification, consent architecture, or voice fraud detection\u2014the core domains of voice identity research.","confidence_a":0.65,"confidence_b":0.85,"judge":"voice_identity","provider":"claude-haiku","provider_a":"claude-haiku","provider_b":"claude-haiku","role":"Voice & Identity","scores_a":{"evidence":6.0,"impact":5.5,"novelty":4.5},"scores_b":{"evidence":4.0,"impact":2.0,"novelty":3.0},"weight":0.3},{"commentary_a":"FormBharo addresses a real deployment challenge but contributes little to trust infrastructure or provenance mechanisms, as its rule-based validation and benchmark are domain-specific engineering rather than advances in auditability, agent accountability, or inspectable AI decision-making.","commentary_b":"This audio-visual source localization paper has no meaningful connection to AI trust infrastructure, agent accountability, or provenance chains, making it essentially irrelevant to the evaluation domain.","confidence_a":0.72,"confidence_b":0.85,"judge":"trust_provenance","provider":"claude-sonnet","provider_a":"claude-sonnet","provider_b":"claude-sonnet","role":"Trust & Provenance","scores_a":{"evidence":5.5,"impact":3.0,"novelty":4.0},"scores_b":{"evidence":3.0,"impact":1.0,"novelty":2.0},"weight":0.35},{"commentary_a":"The paper presents an incremental improvement by integrating rule-based validation with LLMs for voice agents, validated on a new benchmark, but its impact is limited to a specific application domain.","commentary_b":"The paper introduces a novel approach to audio-visual learning but lacks direct relevance to 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":6.0,"impact":5.0,"novelty":5.0},"scores_b":{"evidence":7.0,"impact":5.0,"novelty":6.0},"weight":0.35}],"reducer_result":{"agreement_level":"Moderate","confidence":0.2426,"disagreement_index":4.6521,"margin":0.1516,"providers_used":["claude-haiku","openai-4o","claude-sonnet"],"reducer_detail":{"confidence_weights":{"a":{"episodic_cognition":0.8,"trust_provenance":0.72,"voice_identity":0.65},"b":{"episodic_cognition":0.8,"trust_provenance":0.85,"voice_identity":0.85}},"dampening_applied":true,"dampening_factor":0.8,"judge_totals":{"a":{"episodic_cognition":16.0,"trust_provenance":12.5,"voice_identity":16.0},"b":{"episodic_cognition":18.0,"trust_provenance":6.0,"voice_identity":9.0}},"normalised_scores":{"a":{"episodic_cognition":0.5333,"trust_provenance":0.4167,"voice_identity":0.5333},"b":{"episodic_cognition":0.6,"trust_provenance":0.2,"voice_identity":0.3}},"pre_dampening_confidence":0.3032,"reliability_weights":{"episodic_cognition":0.3,"trust_provenance":0.3135,"voice_identity":0.5036},"weighted_scores":{"a":{"episodic_cognition":0.128,"trust_provenance":0.094,"voice_identity":0.1746},"b":{"episodic_cognition":0.144,"trust_provenance":0.0533,"voice_identity":0.1284}}},"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":0.5001,"score_b":0.3485,"winner":"Paper A"},"summary_a":"# Summary\n\nThe researchers built FormBharo, a voice agent that uses Large Language Models paired with rule-based validation to help illiterate Hindi-speaking mothers in India enroll in maternal and child health programs over phone calls, which is now being piloted with an NGO called ARMMAN. They created FormVoiceAgentBench, a benchmark with 3,760 multi-turn conversation tests, and found that form completion accuracy drops significantly (~41 points) with real speech transcripts versus perfect ones, but rule-based controls help smaller models match larger ones, and end-to-end evaluation is necessary because component performance doesn't predict overall success.","summary_b":"# Summary\n\nThe researchers discovered that self-supervised audio-visual learning models naturally focus on the most salient sound source (selective convergence), which they leveraged in a two-stage framework to progressively identify dominant and then remaining sound sources without manual annotations. They achieved competitive performance on dual-source localization benchmarks and identified evaluation inconsistencies in existing benchmarks, proposing pixel-level segmentation masks for more accurate spatial evaluation.","trial":1},{"judges":[{"commentary_a":"FormBharo applies existing LLM+rule-based architectures to a specific accessibility use case rather than advancing voice identity, speaker verification, or consent infrastructure; the benchmark contribution is useful but the work lacks engagement with voice biometrics, authentication, or consent-specific mechanisms that define the voice identity domain.","commentary_b":"This paper addresses audio-visual sound source localization without voice identity, speaker verification, consent architecture, or biometric rights considerations, placing it outside the core domain of voice and identity research.","confidence_a":0.72,"confidence_b":0.85,"judge":"voice_identity","provider":"claude-haiku","provider_a":"claude-haiku","provider_b":"claude-haiku","role":"Voice & Identity","scores_a":{"evidence":6.0,"impact":5.5,"novelty":4.5},"scores_b":{"evidence":6.0,"impact":2.0,"novelty":4.5},"weight":0.3},{"commentary_a":"FormBharo introduces a domain-specific benchmark and hybrid LLM/rule-based architecture for voice form-filling, but contributes negligibly to trust infrastructure, provenance chains, or auditability mechanisms as understood in trustworthy AI research.","commentary_b":"This audio-visual source localization paper has no meaningful connection to AI trust infrastructure, agent auditability, or provenance chains, making it essentially irrelevant to the evaluation domain.","confidence_a":0.72,"confidence_b":0.85,"judge":"trust_provenance","provider":"claude-sonnet","provider_a":"claude-sonnet","provider_b":"claude-sonnet","role":"Trust & Provenance","scores_a":{"evidence":5.5,"impact":3.5,"novelty":4.5},"scores_b":{"evidence":3.0,"impact":1.0,"novelty":2.0},"weight":0.35},{"commentary_a":"The paper presents an incremental improvement in integrating LLMs with rule-based systems for voice agents, validated on a new benchmark with potential impact on AI applications in underserved communities.","commentary_b":"The paper presents an incremental improvement in audio-visual learning with solid validation but limited direct 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":6.0,"impact":6.0,"novelty":5.0},"scores_b":{"evidence":7.0,"impact":5.0,"novelty":6.0},"weight":0.35}],"reducer_result":{"agreement_level":"Moderate","confidence":0.1893,"disagreement_index":4.3665,"margin":0.1183,"providers_used":["claude-haiku","openai-4o","claude-sonnet"],"reducer_detail":{"confidence_weights":{"a":{"episodic_cognition":0.8,"trust_provenance":0.72,"voice_identity":0.72},"b":{"episodic_cognition":0.8,"trust_provenance":0.85,"voice_identity":0.85}},"dampening_applied":true,"dampening_factor":0.8,"judge_totals":{"a":{"episodic_cognition":17.0,"trust_provenance":13.5,"voice_identity":16.0},"b":{"episodic_cognition":18.0,"trust_provenance":6.0,"voice_identity":12.5}},"normalised_scores":{"a":{"episodic_cognition":0.5667,"trust_provenance":0.45,"voice_identity":0.5333},"b":{"episodic_cognition":0.6,"trust_provenance":0.2,"voice_identity":0.4167}},"pre_dampening_confidence":0.2366,"reliability_weights":{"episodic_cognition":0.3,"trust_provenance":0.3135,"voice_identity":0.5036},"weighted_scores":{"a":{"episodic_cognition":0.136,"trust_provenance":0.1016,"voice_identity":0.1934},"b":{"episodic_cognition":0.144,"trust_provenance":0.0533,"voice_identity":0.1784}}},"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":0.5203,"score_b":0.402,"winner":"Paper A"},"summary_a":"# Summary\n\nResearchers developed FormBharo, a voice agent that uses Large Language Models paired with rule-based controls to help illiterate Hindi-speaking mothers in India enroll in maternal and child health programs by filling forms over phone calls. They created FormVoiceAgentBench, a benchmark with 3,760 multi-turn conversation tests, and found that form completion accuracy drops significantly with real speech transcripts, but rule-based controls help smaller models match larger ones, with optimal model selection requiring end-to-end evaluation rather than component-level metrics.","summary_b":"# Summary\n\nThe researchers discovered that self-supervised audio-visual learning models naturally focus on the most salient sound source (selective convergence), which they exploited in a two-stage framework to progressively identify multiple sound sources without manual annotations. They achieved state-of-the-art self-supervised performance on dual-source localization benchmarks and revealed evaluation inconsistencies in existing benchmarks by introducing pixel-level segmentation masks for more accurate spatial assessment.","trial":2},{"judges":[{"commentary_a":"FormBharo applies existing LLM and rule-based techniques to a socially valuable but narrow use case (maternal health enrollment) without introducing novel voice identity, speaker verification, or consent mechanisms; the benchmark contribution is useful but the work is primarily an application study rather than an advance in voice-as-identity research.","commentary_b":"This work on audio-visual sound source localization is technically competent but orthogonal to voice identity, speaker verification, consent architecture, or voice authentication\u2014the core domains of voice-as-identity research\u2014making it unsuitable for evaluation under the stated criteria.","confidence_a":0.62,"confidence_b":0.75,"judge":"voice_identity","provider":"claude-haiku","provider_a":"claude-haiku","provider_b":"claude-haiku","role":"Voice & Identity","scores_a":{"evidence":6.1,"impact":5.3,"novelty":4.2},"scores_b":{"evidence":6.0,"impact":2.0,"novelty":4.5},"weight":0.3},{"commentary_a":"FormBharo applies LLMs to a socially valuable voice-form task but contributes little to trust infrastructure or provenance \u2014 rule-based validation is a well-known technique, and the benchmark tests accuracy rather than auditability, adversarial robustness, or accountability mechanisms.","commentary_b":"This audio-visual localization paper has no meaningful connection to AI trust, provenance, auditability, or agent accountability infrastructure, making it essentially irrelevant to the evaluation domain regardless of its technical merits.","confidence_a":0.72,"confidence_b":0.82,"judge":"trust_provenance","provider":"claude-sonnet","provider_a":"claude-sonnet","provider_b":"claude-sonnet","role":"Trust & Provenance","scores_a":{"evidence":5.5,"impact":3.0,"novelty":4.5},"scores_b":{"evidence":3.5,"impact":1.5,"novelty":2.5},"weight":0.35},{"commentary_a":"The paper presents an incremental improvement in applying language models for voice-based form filling with limited validation on a specific benchmark, impacting a niche application rather than advancing general memory or cognition in AI.","commentary_b":"The paper introduces an incremental improvement in audio-visual learning with solid evidence but limited direct impact on episodic memory or cognition in AI.","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":6.0,"impact":5.0,"novelty":5.0},"scores_b":{"evidence":7.0,"impact":5.0,"novelty":6.0},"weight":0.35}],"reducer_result":{"agreement_level":"Moderate","confidence":0.162,"disagreement_index":3.6806,"margin":0.081,"providers_used":["claude-haiku","openai-4o","claude-sonnet"],"reducer_detail":{"confidence_weights":{"a":{"episodic_cognition":0.8,"trust_provenance":0.72,"voice_identity":0.62},"b":{"episodic_cognition":0.8,"trust_provenance":0.82,"voice_identity":0.75}},"dampening_applied":false,"dampening_factor":1.0,"judge_totals":{"a":{"episodic_cognition":16.0,"trust_provenance":13.0,"voice_identity":15.6},"b":{"episodic_cognition":18.0,"trust_provenance":7.5,"voice_identity":12.5}},"normalised_scores":{"a":{"episodic_cognition":0.5333,"trust_provenance":0.4333,"voice_identity":0.52},"b":{"episodic_cognition":0.6,"trust_provenance":0.25,"voice_identity":0.4167}},"pre_dampening_confidence":0.162,"reliability_weights":{"episodic_cognition":0.3,"trust_provenance":0.3135,"voice_identity":0.5036},"weighted_scores":{"a":{"episodic_cognition":0.128,"trust_provenance":0.0978,"voice_identity":0.1624},"b":{"episodic_cognition":0.144,"trust_provenance":0.0643,"voice_identity":0.1574}}},"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":0.499,"score_b":0.418,"winner":"Paper A"},"summary_a":"# Summary\n\nResearchers developed FormBharo, a voice agent that uses Large Language Models paired with rule-based controls to help illiterate Hindi-speaking mothers in India enroll in maternal and child health programs by filling forms over phone calls. They created FormVoiceAgentBench, a benchmark with 3,760 multi-turn conversation tests, and found that form completion accuracy drops significantly with real speech transcripts, but rule-based validation helps smaller models match larger ones, and end-to-end evaluation is necessary to select optimal model configurations balancing accuracy, cost, and latency.","summary_b":"# Summary\n\nThe researchers discovered that self-supervised audio-visual learning models naturally focus on the most salient sound source (selective convergence), which they exploited in a two-stage framework to progressively identify and localize multiple sound sources without manual annotations. They achieved competitive performance on dual-source benchmarks and identified a fundamental evaluation bias in existing benchmarks by introducing pixel-level segmentation masks for more accurate spatial assessment.","trial":3},{"judges":[{"commentary_a":"FormBharo is a competent application of LLM+rule-based validation to a real-world health enrollment use case, but introduces no novel voice identity, speaker verification, consent, or biometric methods\u2014it is a voice UI system for form completion that happens to use Hindi audio, not a voice identity or rights-respecting consent architecture contribution.","commentary_b":"This work addresses audio-visual source localization, not voice identity, speaker verification, consent architecture, or voice authentication\u2014falling entirely outside the evaluation domain of voice as identity and biometric rights.","confidence_a":0.62,"confidence_b":0.85,"judge":"voice_identity","provider":"claude-haiku","provider_a":"claude-haiku","provider_b":"claude-haiku","role":"Voice & Identity","scores_a":{"evidence":6.1,"impact":3.8,"novelty":4.2},"scores_b":{"evidence":4.0,"impact":2.0,"novelty":3.0},"weight":0.3},{"commentary_a":"FormBharo addresses a real deployment problem but contributes negligibly to trust infrastructure, auditability, or provenance\u2014its benchmark and rule-based validation are useful for voice agent reliability but do not advance mechanisms for AI accountability or inspectable decision chains.","commentary_b":"This audio-visual localization paper has no meaningful connection to AI trust infrastructure, agent accountability, provenance chains, or auditability \u2014 it is a perception/representation learning contribution evaluated entirely outside the trustworthy AI deployment domain.","confidence_a":0.62,"confidence_b":0.82,"judge":"trust_provenance","provider":"claude-sonnet","provider_a":"claude-sonnet","provider_b":"claude-sonnet","role":"Trust & Provenance","scores_a":{"evidence":5.5,"impact":2.5,"novelty":3.5},"scores_b":{"evidence":4.0,"impact":1.5,"novelty":3.0},"weight":0.35},{"commentary_a":"The paper introduces a novel application of LLMs with rule-based validation in a specific context but lacks groundbreaking architectural advances in memory or cognition.","commentary_b":"The paper introduces an incremental improvement in audio-visual learning with solid empirical validation, but its impact on episodic memory and long-horizon reasoning is limited.","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":7.0,"impact":6.0,"novelty":6.0},"scores_b":{"evidence":7.0,"impact":5.0,"novelty":6.0},"weight":0.35}],"reducer_result":{"agreement_level":"Moderate","confidence":0.2028,"disagreement_index":4.4716,"margin":0.1267,"providers_used":["claude-haiku","openai-4o","claude-sonnet"],"reducer_detail":{"confidence_weights":{"a":{"episodic_cognition":0.8,"trust_provenance":0.62,"voice_identity":0.62},"b":{"episodic_cognition":0.8,"trust_provenance":0.82,"voice_identity":0.85}},"dampening_applied":true,"dampening_factor":0.8,"judge_totals":{"a":{"episodic_cognition":19.0,"trust_provenance":11.5,"voice_identity":14.1},"b":{"episodic_cognition":18.0,"trust_provenance":8.5,"voice_identity":9.0}},"normalised_scores":{"a":{"episodic_cognition":0.6333,"trust_provenance":0.3833,"voice_identity":0.47},"b":{"episodic_cognition":0.6,"trust_provenance":0.2833,"voice_identity":0.3}},"pre_dampening_confidence":0.2535,"reliability_weights":{"episodic_cognition":0.3,"trust_provenance":0.3135,"voice_identity":0.5036},"weighted_scores":{"a":{"episodic_cognition":0.152,"trust_provenance":0.0745,"voice_identity":0.1467},"b":{"episodic_cognition":0.144,"trust_provenance":0.0728,"voice_identity":0.1284}}},"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":0.4999,"score_b":0.3732,"winner":"Paper A"},"summary_a":"# Summary\n\n**What was done:** Researchers built FormBharo, a voice agent that uses Large Language Models paired with rule-based validation to help low-income, Hindi-speaking mothers enroll in maternal and child health programs in India over phone calls. They created FormVoiceAgentBench, a benchmark with 3,760 multi-turn conversation tests using real Hindi audio to evaluate the agent's components and overall form completion performance.\n\n**What was found:** Form completion accuracy dropped significantly (~41 points) when using real speech transcripts instead of perfect ones, but rule-based controls helped smaller, cheaper models match or exceed frontier models; importantly, component-level performance did not predict end-to-","summary_b":"# Summary\n\nThe researchers discovered that self-supervised audio-visual learning models naturally focus on the most salient sound source (selective convergence), which they exploited in a two-stage framework to first identify dominant sources and then locate remaining sources without manual annotations. They achieved state-of-the-art self-supervised performance on dual-source localization benchmarks and revealed evaluation inconsistencies in existing benchmarks by introducing pixel-level segmentation masks for more accurate spatial assessment.","trial":4},{"judges":[{"commentary_a":"FormBharo applies existing LLM and rule-based validation techniques to a specific accessibility use case rather than advancing voice identity, speaker verification, or consent architecture; the benchmark contribution is useful but narrow, and the work lacks any evaluation of voice authentication, synthetic voice risks, biometric consent, or voice rights\u2014the core domains of voice identity research.","commentary_b":"This work addresses audio-visual source localization via self-supervised learning but lacks any connection to voice identity, speaker verification, consent architecture, or biometric rights\u2014the core domains of voice identity research\u2014making it out of scope for evaluation in this framework.","confidence_a":0.72,"confidence_b":0.85,"judge":"voice_identity","provider":"claude-haiku","provider_a":"claude-haiku","provider_b":"claude-haiku","role":"Voice & Identity","scores_a":{"evidence":6.0,"impact":3.5,"novelty":4.5},"scores_b":{"evidence":4.0,"impact":2.0,"novelty":3.0},"weight":0.3},{"commentary_a":"FormBharo applies LLMs to a socially valuable form-filling task but contributes little to trust infrastructure, auditability, or provenance mechanisms \u2014 the rule-based validation is a standard guardrail, not a novel accountability framework, and there is no adversarial testing of trust properties.","commentary_b":"This audio-visual localization paper has no meaningful connection to AI trust, provenance, auditability, or agent accountability infrastructure, making it essentially irrelevant to the evaluation domain regardless of its technical merits.","confidence_a":0.72,"confidence_b":0.82,"judge":"trust_provenance","provider":"claude-sonnet","provider_a":"claude-sonnet","provider_b":"claude-sonnet","role":"Trust & Provenance","scores_a":{"evidence":5.5,"impact":3.0,"novelty":4.5},"scores_b":{"evidence":4.0,"impact":1.5,"novelty":3.0},"weight":0.35},{"commentary_a":"The paper introduces a novel application of LLMs with rule-based validation for voice agents, validated on a new benchmark, but its impact on episodic memory and long-horizon reasoning is limited.","commentary_b":"The paper presents an incremental improvement in audio-visual learning with solid empirical validation but limited direct 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":7.0,"impact":6.0,"novelty":6.0},"scores_b":{"evidence":7.0,"impact":5.0,"novelty":6.0},"weight":0.35}],"reducer_result":{"agreement_level":"Moderate","confidence":0.2123,"disagreement_index":4.3865,"margin":0.1327,"providers_used":["claude-haiku","openai-4o","claude-sonnet"],"reducer_detail":{"confidence_weights":{"a":{"episodic_cognition":0.8,"trust_provenance":0.72,"voice_identity":0.72},"b":{"episodic_cognition":0.8,"trust_provenance":0.82,"voice_identity":0.85}},"dampening_applied":true,"dampening_factor":0.8,"judge_totals":{"a":{"episodic_cognition":19.0,"trust_provenance":13.0,"voice_identity":14.0},"b":{"episodic_cognition":18.0,"trust_provenance":8.5,"voice_identity":9.0}},"normalised_scores":{"a":{"episodic_cognition":0.6333,"trust_provenance":0.4333,"voice_identity":0.4667},"b":{"episodic_cognition":0.6,"trust_provenance":0.2833,"voice_identity":0.3}},"pre_dampening_confidence":0.2654,"reliability_weights":{"episodic_cognition":0.3,"trust_provenance":0.3135,"voice_identity":0.5036},"weighted_scores":{"a":{"episodic_cognition":0.152,"trust_provenance":0.0978,"voice_identity":0.1692},"b":{"episodic_cognition":0.144,"trust_provenance":0.0728,"voice_identity":0.1284}}},"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":0.5059,"score_b":0.3732,"winner":"Paper A"},"summary_a":"# Summary\n\nResearchers developed FormBharo, a voice agent that uses Large Language Models paired with rule-based validation to help illiterate Hindi-speaking mothers in India enroll in maternal and child health programs over phone calls. They created FormVoiceAgentBench, a benchmark with 3,760 multi-turn conversation tests, and found that form completion accuracy drops significantly with real speech transcripts, but rule-based controls help smaller models match larger ones, with end-to-end performance requiring different model choices than component-level metrics suggest.","summary_b":"# Summary\n\nThe researchers discovered that self-supervised audio-visual learning models naturally focus on the most salient sound source (selective convergence), which they exploited in a two-stage framework to first identify dominant sources and then locate remaining sources without manual annotations. They achieved competitive performance on dual-source localization benchmarks and identified a fundamental evaluation flaw in existing benchmarks, proposing pixel-level segmentation masks for more accurate spatial evaluation.","trial":5}]}