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Takeda

Industry researchasia · jp
Official website
Research library6linked papers
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Selected work

Representative Papers

Coherence-Driven Belief Formation and Population Dynamics of Contagion in LLM Agents

Oct 01, 2026

This study investigates the belief adoption mechanisms and collective propagation dynamics of large language model (LLM) agents. Through multi-agent simulations, statistical modeling, and network dynamics analysis, we empirically demonstrate that the probability of belief adoption among LLM agents follows a sigmoidal distribution. We further propose "belief coherence" as a unifying construct to explain the sensitivity of adoption thresholds. Our findings reveal bifurcation cascade windows and self-sustaining hysteresis consensus phenomena within AI systems. Moreover, we establish that clustered networks are significantly more effective than random networks in driving belief diffusion, yielding consensus states characterized by high stability. These results provide critical insights into the emergent collective behaviors of LLM-based multi-agent systems.

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Multilevel regression trees with application to wildfires in the American west

Sep 28, 2026

This study addresses the limited model interpretability and insufficient cross-ecoregion information utilization in wildfire prediction across the western United States by proposing a multi-level Bayesian regression tree model. Methodologically, it integrates data from multiple ecoregions through shared hyperparameters and introduces a novel sampling algorithm based on parallel tempering to optimize conditional mixing at posterior temperatures, thereby overcoming the MCMC sampling bottleneck inherent in traditional Bayesian CART models. Results demonstrate that the proposed approach significantly enhances tree structure similarity and out-of-sample predictive performance. Furthermore, it precisely identifies potential evapotranspiration, temperature, and evergreen forest cover as critical hazard-inducing factors, achieving simultaneous improvements in both predictive accuracy and interpretability.

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Adaptive Sampling of Costly Outcomes in Randomized Clinical Trials

Sep 28, 2026

This study addresses the high cost and prolonged duration associated with measuring primary outcomes in randomized trials by proposing a blinded adaptive sampling design. The method leverages auxiliary variables to dynamically adjust sampling probabilities while ensuring statisticians remain blinded to treatment assignments. Treatment effects are estimated by combining augmented inverse probability weighting (AIPW) with residual variance modeling, and variance loss upper bounds along with valid confidence intervals are derived based on the martingale central limit theorem. Compared with simple random sampling, the proposed approach improves efficiency by 12%–34% and reduces required sample sizes by 10%–26%. In a reanalysis of an antifungal trial, the method achieved a 39% reduction in variance, substantially enhancing statistical power.

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Information Borrowing for Cox Regression with an Auxiliary Outcome

Sep 27, 2026

This study addresses how to effectively leverage auxiliary outcome information to enhance the estimation efficiency of Cox regression in survival analysis. To this end, it proposes a joint-likelihood-free semiparametric framework built upon the Cox proportional hazards and partial linear models. This approach facilitates cross-source information borrowing by linking baseline covariate effects through a quadratic penalty, while an adaptive penalization mechanism is designed to optimize the bias–variance trade-off. The asymptotic properties of the proposed estimator are rigorously derived. Both simulation studies and real-data applications demonstrate that the method substantially improves estimation efficiency while preserving unbiasedness.

0 citationsRead paper
Recent publications

Latest Papers

Coherence-Driven Belief Formation and Population Dynamics of Contagion in LLM Agents

Oct 01, 2026

This study investigates the belief adoption mechanisms and collective propagation dynamics of large language model (LLM) agents. Through multi-agent simulations, statistical modeling, and network dynamics analysis, we empirically demonstrate that the probability of belief adoption among LLM agents follows a sigmoidal distribution. We further propose "belief coherence" as a unifying construct to explain the sensitivity of adoption thresholds. Our findings reveal bifurcation cascade windows and self-sustaining hysteresis consensus phenomena within AI systems. Moreover, we establish that clustered networks are significantly more effective than random networks in driving belief diffusion, yielding consensus states characterized by high stability. These results provide critical insights into the emergent collective behaviors of LLM-based multi-agent systems.

0 citationsRead paper

Multilevel regression trees with application to wildfires in the American west

Sep 28, 2026

This study addresses the limited model interpretability and insufficient cross-ecoregion information utilization in wildfire prediction across the western United States by proposing a multi-level Bayesian regression tree model. Methodologically, it integrates data from multiple ecoregions through shared hyperparameters and introduces a novel sampling algorithm based on parallel tempering to optimize conditional mixing at posterior temperatures, thereby overcoming the MCMC sampling bottleneck inherent in traditional Bayesian CART models. Results demonstrate that the proposed approach significantly enhances tree structure similarity and out-of-sample predictive performance. Furthermore, it precisely identifies potential evapotranspiration, temperature, and evergreen forest cover as critical hazard-inducing factors, achieving simultaneous improvements in both predictive accuracy and interpretability.

0 citationsRead paper

Adaptive Sampling of Costly Outcomes in Randomized Clinical Trials

Sep 28, 2026

This study addresses the high cost and prolonged duration associated with measuring primary outcomes in randomized trials by proposing a blinded adaptive sampling design. The method leverages auxiliary variables to dynamically adjust sampling probabilities while ensuring statisticians remain blinded to treatment assignments. Treatment effects are estimated by combining augmented inverse probability weighting (AIPW) with residual variance modeling, and variance loss upper bounds along with valid confidence intervals are derived based on the martingale central limit theorem. Compared with simple random sampling, the proposed approach improves efficiency by 12%–34% and reduces required sample sizes by 10%–26%. In a reanalysis of an antifungal trial, the method achieved a 39% reduction in variance, substantially enhancing statistical power.

0 citationsRead paper

Information Borrowing for Cox Regression with an Auxiliary Outcome

Sep 27, 2026

This study addresses how to effectively leverage auxiliary outcome information to enhance the estimation efficiency of Cox regression in survival analysis. To this end, it proposes a joint-likelihood-free semiparametric framework built upon the Cox proportional hazards and partial linear models. This approach facilitates cross-source information borrowing by linking baseline covariate effects through a quadratic penalty, while an adaptive penalization mechanism is designed to optimize the bias–variance trade-off. The asymptotic properties of the proposed estimator are rigorously derived. Both simulation studies and real-data applications demonstrate that the method substantially improves estimation efficiency while preserving unbiasedness.

0 citationsRead paper