Loading
Life sciences · Preprint
arXiv · September 3, 2026
Early or partial results. Treat as a signal, not a conclusion.
LongCounsel-8 is a computationally generated benchmark dataset of 7,749 simulated five-session counseling trajectories designed to enable research on longitudinal depression tracking from multi-session dialogues. Algorithm evaluation on this synthetic data reveals that single-session score accuracy does not predict trend identification, methods underperform on worsening trajectories, and additional session history may reduce trend prediction reliability. This is a resource paper establishing a tool for machine learning development; it does not validate clinical utility or performance on real patients.
Computational benchmark dataset with simulated multi-session counseling dialogues and algorithm evaluation. Simulated client profiles and depression trajectories, not real patients or counselors.. Intervention: Three independently generated benchmark datasets with controlled depression states embedded in simulated counseling dialogues. Compared with: Existing depression tracking methods evaluated on the benchmark. n = 7,749.
Benchmark comprises three independently generated datasets totaling 7,749 five-session counseling trajectories Simulated self-reports closely recovered the controlled depression states, supporting label fidelity Finding 1: lower single-session score error does not guarantee accurate identification of depression trend (improvement or worsening)
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
This dataset and benchmark may support development of machine learning methods for longitudinal depression assessment, but clinical validation on real patients and counseling sessions is required before deployment. The findings suggest current methods may miss clinically meaningful worsening patterns.
This is a computational benchmark dataset paper with simulated counseling dialogues and no clinical validation or patient outcomes; it establishes a resource for algorithm development but does not yet demonstrate clinical utility or real-world performance.
As stated by the source record.
Quoted from the source exactly as published.
This dataset and benchmark may support development of machine learning methods for longitudinal depression assessment, but clinical validation on real patients and counseling sessions is required before deployment. The findings suggest current methods may miss clinically meaningful worsening patterns.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
Tracking depression from multi-session counseling dialogues requires estimating both current symptom severity and how it changes across sessions. Yet progress on this task is constrained by the scarcity of longitudinal counseling data with standardized session-level depression labels. Existing resources typically provide either multi-session conversations without depression labels or labeled interviews in a single session. Building such a benchmark poses three challenges: maintaining longitudinal consistency and diversity, grounding symptom progression in empirical patterns, and expressing controlled depression states naturally without exposing target labels. To address these challenges, we introduce LongCounsel-8, a benchmark suite of three independently generated datasets totaling 7,749 five-session counseling trajectories, grounded in real-world client profiles, depression trajectories, symptom compositions, and counseling patterns. We combine profile-grounded simulation, empirically informed state construction, and indirect behavioral realization to address these challenges. Across the benchmark, simulated self-reports closely recover the controlled states, supporting label fidelity. Experiments on existing depression tracking methods reveal three key findings: (1) lower single-session score error does not guarantee accurate identification of trend, i.e., improvement or worsening; (2) existing methods are consistently less reliable on worsening trajectories; and (3) additional session history may reduce the accuracy of trend prediction. Together, these findings establish LongCounsel-8 as a foundation for advancing depression assessment from static, single-session prediction toward reliable longitudinal tracking of mental-health change.
Taken from the source record, never inferred. Follow any of these and new work involving them reaches your briefing.