# TRACE

Generalizing randomized-trial findings to a broader target population can yield distorted estimates when the trial sample is misaligned with that population. TRACE decomposes the asymptotic variance of the efficient generalizability estimator into a baseline-mismatch channel and a censoring channel, then uses a tree search to localize underrepresented subgroups and attribute each to its mechanism. In a case study generalizing the JTPA Study to U.S. JTPA-eligible adults, restricting to reliably generalizable subgroups narrows the 95% confidence interval for the refined estimand by 60%.

Generalizing randomized-trial findings to a broader target population can yield distorted estimates when the trial sample is misaligned with that population. TRACE decomposes the asymptotic variance of the efficient generalizability estimator into a baseline-mismatch channel and a censoring channel, then uses a tree search to localize underrepresented subgroups and attribute each to its mechanism. In a case study generalizing the JTPA Study to U.S. JTPA-eligible adults, restricting to reliably generalizable subgroups narrows the 95% confidence interval for the refined estimand by 60%.

TRACE maps longitudinal visit records to interpretable progression phenotypes and lets the number of phenotypes exceed the number of diagnostic classes. In the label-matched setting on the ADNI cohort it achieves the highest purity, NMI, ARI, silhouette index, accuracy and macro-F1 at the current, 12-month and 24-month horizons; at 24 months it exceeds the strongest baseline by 3.1 and 5.8 percentage points in purity and silhouette index. With the phenotype count freed, it splits a new phenotype out of the cognitively-normal- and MCI-dominant groups, one whose cognitive-scale trajectories sit in between while its hippocampal-volume trajectory coincides with the MCI group.

TRACE maps longitudinal visit records to interpretable progression phenotypes and lets the number of phenotypes exceed the number of diagnostic classes. In the label-matched setting on the ADNI cohort it achieves the highest purity, NMI, ARI, silhouette index, accuracy and macro-F1 at the current, 12-month and 24-month horizons; at 24 months it exceeds the strongest baseline by 3.1 and 5.8 percentage points in purity and silhouette index. With the phenotype count freed, it splits a new phenotype out of the cognitively-normal- and MCI-dominant groups, one whose cognitive-scale trajectories sit in between while its hippocampal-volume trajectory coincides with the MCI group.

For behavioral classes that arrive over time, TRACE stores no historical samples and never expands a shared classifier; it feeds a frozen temporal-spectral representation to a small autoencoder per class and labels by reconstruction error. Across five multivariate time-series class-incremental benchmarks it reaches 88.85% five-dataset average final accuracy with 9.17 MiB of retained memory, about 98.26% less memory than TS-ACL, the strongest analytical baseline by average accuracy, and an approximately 33.43% relative accuracy gain on GrabMyo.

For behavioral classes that arrive over time, TRACE stores no historical samples and never expands a shared classifier; it feeds a frozen temporal-spectral representation to a small autoencoder per class and labels by reconstruction error. Across five multivariate time-series class-incremental benchmarks it reaches 88.85% five-dataset average final accuracy with 9.17 MiB of retained memory, about 98.26% less memory than TS-ACL, the strongest analytical baseline by average accuracy, and an approximately 33.43% relative accuracy gain on GrabMyo.

TRACE introduces a condition-aware benchmark and evaluation framework for streaming video understanding that makes temporal validity, execution conditions, and operational outcomes explicit, evaluating eight publicly available models or systems in eight configurations on 1,240 records from 517 videos, and finds that nearly identical QA accuracy can mask substantial differences in completion, answer validity, and generation workload, while proactive performance separates into response quality, response delay, false alarms, and missed target windows.

TRACE introduces a condition-aware benchmark and evaluation framework for streaming video understanding that makes temporal validity, execution conditions, and operational outcomes explicit, evaluating eight publicly available models or systems in eight configurations on 1,240 records from 517 videos, and finds that nearly identical QA accuracy can mask substantial differences in completion, answer validity, and generation workload, while proactive performance separates into response quality, response delay, false alarms, and missed target windows.