arXiv subjects

James Schwoebel

Publications and source records attributed to James Schwoebel.

2 recordsLinked to original sources

arXiv2026

Cross-Domain Transfer of Depression Voice Biomarkers Depends on the Outcome Instrument: Leakage-Controlled Cross-Sectional Evaluation Study

Whether voice biomarkers of depression generalize across clinical settings is largely untested. Generalization is usually framed as a question about populations. It is also a question about the outcome instrument a model is scored against, a dimension confounded in existing studies with all else that differs between them. In a US-nationwide online study, 446 sessions from 390 pregnant participants at 22 weeks' gestation (analytical N=316) each gave four voice recordings, the PHQ-8 and a modified 9-item EPDS (mEPDS-9). Discrimination was assessed under leakage-controlled cross-validation, with feature and classifier selection inside training folds only, gated by a permutation negative-control harness, across a pre-specified 4 task x 4 outcome grid with Benjamini-Hochberg adjustment. The model was applied to DAIC-WOZ (N=189) and E-DAIC (N=219) under held-out inference; an open-source model trained on ~35,000 individuals was applied to all three cohorts without refitting. The pre-registered within-cohort outcome was at chance (AUC 0.494, 95% CI 0.431-0.560) and no grid cell survived adjustment under either modeling paradigm. The prenatal-trained model did not transfer (0.505, 0.478). Transfer in the reverse direction varied with the outcome instrument: the general-population model reached 0.706-0.708 on general-psychiatric speech, 0.510 (0.411-0.609) against the PHQ-8, and 0.645 (0.541-0.744) against the mEPDS-9 in the same pregnant participants; paired difference 0.135 (0.019-0.248), unadjusted post-hoc p=0.021. Item-level analyses suggest an explanation, though only 1 of 17 tests survived adjustment. The mEPDS-9 used a generic response scale, not the published EPDS anchors, so its thresholds are operational, not validated. Validation should specify population, task and instrument together.

q-bio.QM↗

arXiv2026

World-Time Compute with Verified Code World Models

LLMs generalize across a domain only after seeing many real, labeled examples, which most domains lack. We study a way to manufacture it cheaply. When a domain's dynamics can be written as code, one template instantiates into many world models: executable, verifiable programs over symbolic state, each an inexhaustible source of exactly-labeled trajectories. Fine-tuning an LLM on trajectories through many such worlds, which we call world-time compute, a training-time analogue of test-time compute, lifts generalization to held-out worlds it never trained on (synthesized world families). Gains are largest where capability is scarcest: +29 points at 0.5B; the largest model's lift is within noise, consistent with saturation. Labels can be trusted because the worlds are verified code: synthesized-then-checked dynamics are exact over 20-step rollouts and answer 10x out-of-distribution probes exactly (100%), whereas per-step LLM and MLP predictors compound error and collapse. Unlike domain randomization, each world is independently authored and verified; a corrupted-label control shows label exactness, not task variety, drives the gains. On real benchmarks (ARC-AGI grids, List Functions, CLRS) the same lever holds as per-world test-time training. On List Functions the harder cross-world form holds: one adapter trained on 128 disjoint worlds reaches 40% on held-out worlds versus 6% for a corrupted-label control (+34 points, CI [29, 39]). The gain is a saturating regularity, not a law: largest for few-step reasoning and small/weak models, fading for long chains, perception-induced tasks, and saturated tasks; cross-task transfer is weak without shared skill. Worlds are authored and served by OpenWorld, a zero-dependency framework (companion paper). Scope: symbolic state; pixel-native domains remain territory of learned models. All code, recipes, and this manuscript regenerate from one repository.

cs.LG↗