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Gobasco AI Labs

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Selected work

Representative Papers

Generation Provenance Before Behavior Attribution: Auditing Synthetic Speech Research Objects

Oct 01, 2026

This study addresses the difficulty of attributing model behaviors to their origins due to the lack of generative provenance in synthetic speech data. We propose a compact provenance contract and auditing protocol, formally establishing provenance as a necessary but insufficient condition for behavioral attribution. Methodologically, we construct synthetic research objects that bind source specifications to content within a Japanese nursing care scenario, implementing audits through immutable manifests, disjoint versioning of scenario seeds, and multimodal asset linkage. Experimentally, we audit 1.55 hours of speech, revealing impediments to precise upstream attribution and establishing a candidate causal graph framework. This work provides a novel paradigm for enhancing the traceability and causal analysis of synthetic data.

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When Aggregate Alignment Misleads: Auditing Policy Repair Without Per-State Expert Actions

Jul 03, 2026

This work proposes a policy repair evaluation paradigm that operates without access to expert action labels or reward signals at the state level. Focusing on a hotel pricing simulation environment, the approach leverages only region-level diagnostic feedback—summarizing discrepancies between the current and baseline policies across temporal, inventory, and market dimensions of price distributions. A multi-agent LLM architecture integrates diagnostic projection, a tree editor, and a non-semantic proposer to perform constrained policy editing. Evaluated over 5,000 test episodes, the method achieves a RevPAR of 108.47 (approaching the baseline’s 108.75) and significantly reduces episode composition distance to 0.609, outperforming multiple baselines. The study further reveals that aggregate alignment can mislead repair outcomes, underscoring the necessity of translating diagnostic feedback into reliable closed-loop evaluation metrics.

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When Outcome Looks Right But Discipline Fails: Trace-Based Evaluation Under Hidden Competitor State

May 18, 2026

This work addresses the limitation of evaluating agents solely through reward metrics—such as revenue—which often overlooks strategic discipline, particularly in environments with hidden competitor states. To remedy this, the authors propose a “discipline stability” evaluation paradigm that comprehensively assesses behavioral alignment by defining baseline behaviors, constraining observations, conducting trajectory diagnostics, and performing ablation and transfer experiments. A new benchmark task is introduced, featuring dual-hotel pricing with hidden budget-based bidding, within a multi-agent reinforcement learning framework. The study analyzes trajectory alignment using PPO variants, behavioral cloning, Trace-Prior, and history correction strategies. Results show that pure behavioral cloning achieves near-ideal alignment under symmetric conditions, while Trace-Prior RL enables bounded adaptation under capacity asymmetry; furthermore, hidden states reduce label uncertainty, and reward-only optimization fails to ensure trajectory consistency.

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Latest Papers

Generation Provenance Before Behavior Attribution: Auditing Synthetic Speech Research Objects

Oct 01, 2026

This study addresses the difficulty of attributing model behaviors to their origins due to the lack of generative provenance in synthetic speech data. We propose a compact provenance contract and auditing protocol, formally establishing provenance as a necessary but insufficient condition for behavioral attribution. Methodologically, we construct synthetic research objects that bind source specifications to content within a Japanese nursing care scenario, implementing audits through immutable manifests, disjoint versioning of scenario seeds, and multimodal asset linkage. Experimentally, we audit 1.55 hours of speech, revealing impediments to precise upstream attribution and establishing a candidate causal graph framework. This work provides a novel paradigm for enhancing the traceability and causal analysis of synthetic data.

0 citationsRead paper

When Aggregate Alignment Misleads: Auditing Policy Repair Without Per-State Expert Actions

Jul 03, 2026

This work proposes a policy repair evaluation paradigm that operates without access to expert action labels or reward signals at the state level. Focusing on a hotel pricing simulation environment, the approach leverages only region-level diagnostic feedback—summarizing discrepancies between the current and baseline policies across temporal, inventory, and market dimensions of price distributions. A multi-agent LLM architecture integrates diagnostic projection, a tree editor, and a non-semantic proposer to perform constrained policy editing. Evaluated over 5,000 test episodes, the method achieves a RevPAR of 108.47 (approaching the baseline’s 108.75) and significantly reduces episode composition distance to 0.609, outperforming multiple baselines. The study further reveals that aggregate alignment can mislead repair outcomes, underscoring the necessity of translating diagnostic feedback into reliable closed-loop evaluation metrics.

0 citationsRead paper

When Outcome Looks Right But Discipline Fails: Trace-Based Evaluation Under Hidden Competitor State

May 18, 2026

This work addresses the limitation of evaluating agents solely through reward metrics—such as revenue—which often overlooks strategic discipline, particularly in environments with hidden competitor states. To remedy this, the authors propose a “discipline stability” evaluation paradigm that comprehensively assesses behavioral alignment by defining baseline behaviors, constraining observations, conducting trajectory diagnostics, and performing ablation and transfer experiments. A new benchmark task is introduced, featuring dual-hotel pricing with hidden budget-based bidding, within a multi-agent reinforcement learning framework. The study analyzes trajectory alignment using PPO variants, behavioral cloning, Trace-Prior, and history correction strategies. Results show that pure behavioral cloning achieves near-ideal alignment under symmetric conditions, while Trace-Prior RL enables bounded adaptation under capacity asymmetry; furthermore, hidden states reduce label uncertainty, and reward-only optimization fails to ensure trajectory consistency.

0 citationsRead paper