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arxiv: 2605.03212 · v2 · submitted 2026-05-04 · 💻 cs.AI · cs.CL· cs.HC· stat.AP· stat.CO

Recognition: 2 theorem links

· Lean Theorem

ADAPTS: Agentic Decomposition for Automated Protocol-agnostic Tracking of Symptoms

Alexandria K. Vail , Marcelo Cicconet , Katie Aafjes-van Doorn , Ryan Maroney , Marc Aafjes
Authors on Pith no claims yet

Pith reviewed 2026-05-08 17:48 UTC · model grok-4.3

classification 💻 cs.AI cs.CLcs.HCstat.APstat.CO
keywords automated psychiatric assessmentLLM agentsdepression severityanxiety ratingclinical interview analysisprotocol-agnostic AImixture-of-agentssymptom tracking
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The pith

A mixture-of-agents LLM decomposes clinical interviews into symptom tasks to rate depression and anxiety closer to experts than human raters on variable cases.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

ADAPTS breaks long clinical interviews into separate reasoning tasks for each symptom of depression and anxiety using multiple LLM agents. This produces ratings plus justifications that stay aligned with the original conversation timing and speakers. The method was tested on two independent sets of interviews with 204 people total and worked without relying on any single fixed protocol. It gave lower error than the original human ratings when interviews had large discrepancies and reached strong expert agreement after adding clinical conventions to the process. A reader would care because this points toward scalable, objective ways to assess psychiatric severity where expert time is limited.

Core claim

The ADAPTS framework decomposes unconstrained clinical interviews into symptom-specific reasoning tasks with a mixture-of-agents LLM architecture, generating auditable justifications while preserving temporal and speaker alignment, and generalizes across two datasets totaling 204 participants to produce ratings with absolute error of 22 versus 26 for original human ratings on high-discrepancy interviews and ICC(2,1) of 0.877 under an extended protocol that adds qualitative clinical conventions.

What carries the argument

Mixture-of-agents LLM architecture that decomposes interviews into symptom-specific reasoning tasks while keeping temporal and speaker alignment intact.

If this is right

  • Ratings stabilize and reach high agreement with experts once qualitative clinical conventions are added to the protocol.
  • The approach generalizes across distinct interview structures without protocol-specific adjustments.
  • Auditable justifications are produced for each symptom rating.
  • The architecture extends readily to multimodal inputs such as acoustic and visual features.
  • It supplies a foundation for objective psychiatric assessment in resource-limited settings.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Continuous symptom tracking could become feasible in digital therapy platforms if combined with real-time transcription.
  • Standardization of ratings in clinical research trials might improve by using this as a consistent reference.
  • Larger and more diverse interview collections would be required to confirm performance outside the tested datasets.

Load-bearing premise

The LLM agents produce clinically valid justifications for each symptom without systematic bias from training data or prompt choices.

What would settle it

Running the system on a fresh set of high-discrepancy interviews rated independently by new expert clinicians and checking whether automated absolute error stays below the human level of 26.

Figures

Figures reproduced from arXiv: 2605.03212 by Alexandria K. Vail, Katie Aafjes-van Doorn, Marc Aafjes, Marcelo Cicconet, Ryan Maroney.

Figure 1
Figure 1. Figure 1: Distribution of expert ground truth total scores across datasets. view at source ↗
Figure 2
Figure 2. Figure 2: Bland-Altman plots evaluating agreement between LLM ratings and expert benchmarks for Claude Sonnet 4.5 and GPT OSS. Solid lines indicate view at source ↗
read the original abstract

Modeling latent clinical constructs from unconstrained clinical interactions is a unique challenge in affective computing. We present ADAPTS (Agentic Decomposition for Automated Protocol-agnostic Tracking of Symptoms), a framework for automated rating of depression and anxiety severity using a mixture-of-agents LLM architecture. This approach decomposes long-form clinical interviews into symptom-specific reasoning tasks, producing auditable justifications while preserving temporal and speaker alignment. Generalization was evaluated across two independent datasets ($N=204$) with distinct interview structures. On high-discrepancy interviews, automated ratings approximated expert benchmarks ($\text{absolute error}=22$) more closely than original human ratings ($\text{absolute error}=26$). Implementing an ``extended'' protocol that incorporates qualitative clinical conventions significantly stabilized ratings, with absolute agreement reaching $\text{ICC(2,1)} = 0.877$. These findings suggest that the ADAPTS framework enables promising evaluations of psychiatric severity. While the current implementation is purely text-based, the underlying architecture is readily extensible to multimodal inputs, including acoustic and visual features. By approximating expert-level precision in a protocol-agnostic manner, this framework provides a foundation for objective and scalable psychiatric assessment, especially in resource-limited settings.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper presents ADAPTS, a mixture-of-agents LLM framework that decomposes long-form clinical interviews into symptom-specific reasoning tasks for automated, auditable rating of depression and anxiety severity. It claims protocol-agnostic generalization, evaluated on two independent datasets (N=204) with distinct structures. On high-discrepancy interviews, automated ratings showed lower absolute error (22) against expert benchmarks than original human ratings (26); with an 'extended' protocol incorporating qualitative clinical conventions, absolute agreement reached ICC(2,1)=0.877. The framework is text-based but extensible to multimodal inputs.

Significance. If the results hold with full methodological transparency, this work could advance scalable psychiatric assessment in resource-limited settings by providing explainable, expert-approximating ratings via agentic decomposition. Strengths include use of independent datasets, direct expert benchmark comparisons, and production of auditable justifications. The approach addresses a real challenge in affective computing for unconstrained interactions. However, significance is limited by the apparent need for an extended protocol, which undercuts the protocol-agnostic positioning, and by missing evaluation details that prevent assessing clinical validity.

major comments (3)
  1. [Abstract] Abstract: The absolute error comparison (22 vs. 26) on high-discrepancy interviews lacks any description of the rating scale, exact computation method (e.g., per-symptom or total score), selection criteria for high-discrepancy cases, or statistical tests for the difference. This is load-bearing for the central performance claim that automated ratings approximate experts more closely than humans.
  2. [Abstract] Abstract: The headline result ICC(2,1)=0.877 is explicitly tied to an 'extended' protocol that adds qualitative clinical conventions, yet the framework is positioned as protocol-agnostic whose core is the mixture-of-agents decomposition. No results are shown for the base decomposition alone across the two datasets, undermining the generalization claim.
  3. [Evaluation] Evaluation (implied in abstract and methods): No details are given on inter-rater reliability of the expert benchmarks, how absolute error was computed or normalized, data handling/preprocessing for the N=204 interviews, or any statistical validation. These omissions make it impossible to assess the soundness of the reported metrics.
minor comments (2)
  1. [Abstract] The abstract refers to 'two independent datasets' without naming them or citing their sources/references, which reduces traceability.
  2. [Abstract] Notation for ICC(2,1) and absolute error should be defined or referenced to standard clinical statistics literature for clarity.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed comments on our manuscript. These have prompted us to enhance the transparency of our methods and clarify the positioning of our framework. We provide point-by-point responses to the major comments below and have revised the manuscript to incorporate additional details and results.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The absolute error comparison (22 vs. 26) on high-discrepancy interviews lacks any description of the rating scale, exact computation method (e.g., per-symptom or total score), selection criteria for high-discrepancy cases, or statistical tests for the difference. This is load-bearing for the central performance claim that automated ratings approximate experts more closely than humans.

    Authors: We agree that additional details are needed in the abstract for this central claim. We have revised the abstract to briefly describe the rating scale used, the method for computing absolute error (as the average absolute difference in total scores), the criteria for selecting high-discrepancy cases, and the statistical test applied to compare the errors. Expanded explanations are provided in the Methods section. revision: yes

  2. Referee: [Abstract] Abstract: The headline result ICC(2,1)=0.877 is explicitly tied to an 'extended' protocol that adds qualitative clinical conventions, yet the framework is positioned as protocol-agnostic whose core is the mixture-of-agents decomposition. No results are shown for the base decomposition alone across the two datasets, undermining the generalization claim.

    Authors: The mixture-of-agents decomposition is the core of ADAPTS and is protocol-agnostic in that it does not rely on specific interview protocols but instead breaks down the content into symptom-specific reasoning regardless of structure. The extended protocol is an augmentation that incorporates additional clinical conventions for improved performance. To strengthen the generalization claim, we have added results for the base decomposition on both datasets in the Results section, demonstrating its standalone effectiveness while showing the benefits of the extension. revision: yes

  3. Referee: [Evaluation] Evaluation (implied in abstract and methods): No details are given on inter-rater reliability of the expert benchmarks, how absolute error was computed or normalized, data handling/preprocessing for the N=204 interviews, or any statistical validation. These omissions make it impossible to assess the soundness of the reported metrics.

    Authors: We have revised the Methods section to include the inter-rater reliability of the expert benchmarks, the exact computation and normalization of absolute error, details on data handling and preprocessing for the N=204 interviews, and the statistical validations performed. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical claims rest on independent datasets and external benchmarks

full rationale

The paper describes an LLM-based framework evaluated across two independent datasets (N=204) with distinct structures, reporting direct absolute error comparisons to expert benchmarks (22 vs 26) and ICC(2,1)=0.877 under an extended protocol. No equations, parameter fits, predictions derived from inputs, or self-citations are present in the text that would make any result equivalent to its own construction. The protocol-agnostic claim is supported by cross-dataset generalization rather than internal redefinition, making the derivation chain self-contained against external references.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework rests on the untested premise that current LLMs can perform reliable clinical symptom decomposition; no free parameters or invented entities are stated in the abstract.

axioms (1)
  • domain assumption Large language models can decompose unconstrained clinical dialogue into symptom-specific assessments that align with expert clinical judgment
    This assumption underpins the entire mixture-of-agents architecture and the claim that automated ratings approximate expert benchmarks.

pith-pipeline@v0.9.0 · 5538 in / 1275 out tokens · 60586 ms · 2026-05-08T17:48:59.848551+00:00 · methodology

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Reference graph

Works this paper leans on

32 extracted references

  1. [1]

    A RATING SCALE FOR DEPRESSION,

    M. Hamilton, “A RATING SCALE FOR DEPRESSION,”Journal of Neurology, Neurosurgery & Psychiatry, vol. 23, no. 1, pp. 56–62, Feb. 1960

    1960

  2. [2]

    THE ASSESSMENT OF ANXIETY STATES BY RATING,

    ——, “THE ASSESSMENT OF ANXIETY STATES BY RATING,” British Journal of Medical Psychology, vol. 32, no. 1, pp. 50–55, Mar. 1959

    1959

  3. [3]

    A New Depression Scale Designed to be Sensitive to Change,

    S. A. Montgomery and M. ˚Asberg, “A New Depression Scale Designed to be Sensitive to Change,”British Journal of Psychiatry, vol. 134, no. 4, pp. 382–389, Apr. 1979

    1979

  4. [4]

    Major Depressive Disorder: De- veloping Drugs for Treatment; Draft Guidance for Industry,

    U.S. Food and Drug Administration, “Major Depressive Disorder: De- veloping Drugs for Treatment; Draft Guidance for Industry,” Center for Drug Evaluation and Research (CDER), Silver Spring, MD, Tech. Rep. FDA-2018-D-1919, Jun. 2018

    2018

  5. [5]

    Trends in (not) using scales in major depression: A categorization and clinical orientation,

    K. Demyttenaere and L. Jaspers, “Trends in (not) using scales in major depression: A categorization and clinical orientation,”European Psychiatry, vol. 63, no. 1, p. e91, 2020

    2020

  6. [6]

    Rater Training in Multicenter Clinical Trials: Issues and Recommendations,

    K. A. Kobak, N. Engelhardt, J. B. Williams, and J. D. Lipsitz, “Rater Training in Multicenter Clinical Trials: Issues and Recommendations,” Journal of Clinical Psychopharmacology, vol. 24, no. 2, pp. 113–117, Apr. 2004

    2004

  7. [7]

    Effects of Interrater Reliability of Psychopathologic Assessment on Power and Sample Size Calculations in Clinical Trials:,

    M. J. M ¨uller and A. Szegedi, “Effects of Interrater Reliability of Psychopathologic Assessment on Power and Sample Size Calculations in Clinical Trials:,”Journal of Clinical Psychopharmacology, vol. 22, no. 3, pp. 318–325, Jun. 2002

    2002

  8. [8]

    Self-reported versus clinician-rated symptoms of depression as outcome measures in psychotherapy research on depression: A meta-analysis,

    P. Cuijpers, J. Li, S. G. Hofmann, and G. Andersson, “Self-reported versus clinician-rated symptoms of depression as outcome measures in psychotherapy research on depression: A meta-analysis,”Clinical Psychology Review, vol. 30, no. 6, pp. 768–778, Aug. 2010

    2010

  9. [9]

    Leveraging Prompt Engineering and Large Language Models for Automating MADRS Score Computation for Depression Severity Assessment

    A. Raganato, F. Bartoli, C. Crocamo, D. Cavaleri, G. Carr `a, G. Pasi, and M. Viviani, “Leveraging Prompt Engineering and Large Language Models for Automating MADRS Score Computation for Depression Severity Assessment.”

  10. [10]

    LlaMADRS: Prompting Large Language Models for Interview-Based Depression Assessment,

    G. Y . Kebe, J. M. Girard, E. Liebenthal, J. Baker, F. De la Torre, and L.-P. Morency, “LlaMADRS: Prompting Large Language Models for Interview-Based Depression Assessment,” 2025

    2025

  11. [11]

    Using a fine-tuned large language model for symptom-based depression evaluation,

    S. Weber, N. Deperrois, R. Heun, L. Fr ¨uhsch¨utz, A. Monn, S. Homan, A. H¨afliger, E. Seifritz, T. Kowatsch, MULTICAST consortium, L. J¨ager, K. Schultebraucks, S. Gershov, J. Mocellin, B. Kleim, and S. Olbrich, “Using a fine-tuned large language model for symptom-based depression evaluation,”npj Digital Medicine, vol. 8, no. 1, p. 598, Oct. 2025

    2025

  12. [12]

    Lost in the Middle: How Language Models Use Long Con- texts,

    N. F. Liu, K. Lin, J. Hewitt, A. Paranjape, M. Bevilacqua, F. Petroni, and P. Liang, “Lost in the Middle: How Language Models Use Long Con- texts,”Transactions of the Association for Computational Linguistics, vol. 12, pp. 157–173, Feb. 2024

    2024

  13. [13]

    A VEC 2013: The continuous audio/visual emotion and depression recognition challenge,

    M. Valstar, B. Schuller, K. Smith, F. Eyben, B. Jiang, S. Bilakhia, S. Schnieder, R. Cowie, and M. Pantic, “A VEC 2013: The continuous audio/visual emotion and depression recognition challenge,” inProceed- ings of the 3rd ACM International Workshop on Audio/Visual Emotion Challenge. Barcelona Spain: ACM, Oct. 2013, pp. 3–10

    2013

  14. [14]

    SimSensei kiosk: A virtual human interviewer for healthcare decision support,

    D. DeVault, R. Artstein, G. Benn, T. Dey, E. Fast, A. Gainer, K. Georgila, J. Gratch, A. Hartholt, M. Lhommet, G. Lucas, S. Marsella, F. Morbini, A. Nazarian, S. Scherer, G. Stratou, A. Suri, D. Traum, R. Wood, Y . Xu, A. Rizzo, and L.-P. Morency, “SimSensei kiosk: A virtual human interviewer for healthcare decision support,” inProceedings of the 2014 Int...

    2014

  15. [15]

    ReAct: Synergizing Reasoning and Acting in Language Models,

    S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. R. Narasimhan, and Y . Cao, “ReAct: Synergizing Reasoning and Acting in Language Models,” inThe Eleventh International Conference on Learning Representations, Sep. 2022

    2022

  16. [16]

    A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research,

    T. K. Koo and M. Y . Li, “A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research,”Journal of Chiropractic Medicine, vol. 15, no. 2, pp. 155–163, Jun. 2016

    2016

  17. [17]

    Robust Speech Recognition via Large-Scale Weak Su- pervision,

    A. Radford, J. W. Kim, T. Xu, G. Brockman, C. McLeavey, and I. Sutskever, “Robust Speech Recognition via Large-Scale Weak Su- pervision,” Jun. 2023

    2023

  18. [18]

    WhisperX: Time-Accurate Speech Transcription of Long-Form Audio,

    M. Bain, J. Huh, T. Han, and A. Zisserman, “WhisperX: Time-Accurate Speech Transcription of Long-Form Audio,” inINTERSPEECH 2023. ISCA, Aug. 2023, pp. 4489–4493

    2023

  19. [19]

    Pyannote.audio 2.1 speaker diarization pipeline: Principle, benchmark, and recipe,

    H. Bredin, “Pyannote.audio 2.1 speaker diarization pipeline: Principle, benchmark, and recipe,” inINTERSPEECH 2023. ISCA, Aug. 2023, pp. 1983–1987

    2023

  20. [20]

    DAIC-WOZ: On the Validity of Using the Therapist’s prompts in Automatic Depression Detection from Clinical Interviews,

    S. Burdisso, E. Reyes-Ram ´ırez, E. Villatoro-tello, F. S ´anchez-Vega, A. Lopez Monroy, and P. Motlicek, “DAIC-WOZ: On the Validity of Using the Therapist’s prompts in Automatic Depression Detection from Clinical Interviews,” inProceedings of the 6th Clinical Natural Language Processing Workshop. Mexico City, Mexico: Association for Computational Linguis...

    2024

  21. [21]

    Development and reliability of the HAM-D/MADRS Interview: An integrated depression symptom rating scale,

    R. W. Iannuzzo, J. Jaeger, J. F. Goldberg, V . Kafantaris, and M. E. Sub- lette, “Development and reliability of the HAM-D/MADRS Interview: An integrated depression symptom rating scale,”Psychiatry Research, vol. 145, no. 1, pp. 21–37, Nov. 2006

    2006

  22. [22]

    Psychomotor symptoms of depression,

    C. Sobin and H. A. Sackeim, “Psychomotor symptoms of depression,” American Journal of Psychiatry, vol. 154, no. 1, pp. 4–17, Jan. 1997

    1997

  23. [23]

    Investigation of Whisper ASR Hallucinations Induced by Non-Speech Audio,

    M. Bara ´nski, J. Jasi´nski, J. Bartolewska, S. Kacprzak, M. Witkowski, and K. Kowalczyk, “Investigation of Whisper ASR Hallucinations Induced by Non-Speech Audio,” inICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Hyderabad, India: IEEE, Apr. 2025, pp. 1–5

    2025

  24. [24]

    Pyannote.Audio: Neural Building Blocks for Speaker Diarization,

    H. Bredin, R. Yin, J. M. Coria, G. Gelly, P. Korshunov, M. Lavechin, D. Fustes, H. Titeux, W. Bouaziz, and M.-P. Gill, “Pyannote.Audio: Neural Building Blocks for Speaker Diarization,” inICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Barcelona, Spain: IEEE, May 2020, pp. 7124– 7128. 12

    2020

  25. [25]

    Fairseq S2T: Fast Speech-to-Text Modeling with Fairseq,

    C. Wang, Y . Tang, X. Ma, A. Wu, D. Okhonko, and J. Pino, “Fairseq S2T: Fast Speech-to-Text Modeling with Fairseq,” inProceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: System Demonstrations. Suzhou, China: Association fo...

    2020

  26. [26]

    A Structured Interview Guide for the Hamilton Depression Rating Scale,

    J. B. W. Williams, “A Structured Interview Guide for the Hamilton Depression Rating Scale,”Archives of General Psychiatry, vol. 45, no. 8, p. 742, Aug. 1988

    1988

  27. [27]

    Chain-of-thought prompting elicits reasoning in large language models,

    J. Wei, X. Wang, D. Schuurmans, M. Bosma, B. Ichter, F. Xia, E. H. Chi, Q. V . Le, and D. Zhou, “Chain-of-thought prompting elicits reasoning in large language models,” inProceedings of the 36th International Conference on Neural Information Processing Systems, ser. NIPS ’22. Red Hook, NY , USA: Curran Associates Inc., Nov. 2022, pp. 24 824– 24 837

    2022

  28. [28]

    The GRID-HAMD: Standardization of the Hamilton Depression Rating Scale:,

    J. B. Williams, K. A. Kobak, P. Bech, N. Engelhardt, K. Evans, J. Lipsitz, J. Olin, J. Pearson, and A. Kalali, “The GRID-HAMD: Standardization of the Hamilton Depression Rating Scale:,”International Clinical Psy- chopharmacology, vol. 23, no. 3, pp. 120–129, May 2008

    2008

  29. [29]

    Correlation Coefficients: Appropriate Use and Interpretation,

    P. Schober, C. Boer, and L. A. Schwarte, “Correlation Coefficients: Appropriate Use and Interpretation,”Anesthesia & Analgesia, vol. 126, no. 5, pp. 1763–1768, May 2018

    2018

  30. [30]

    Comparison of Values of Pearson’s and Spearman’s Correlation Coefficients on the Same Sets of Data,

    J. Hauke and T. Kossowski, “Comparison of Values of Pearson’s and Spearman’s Correlation Coefficients on the Same Sets of Data,” QUAGEO, vol. 30, no. 2, pp. 87–93, Jun. 2011

    2011

  31. [31]

    Individual Comparisons by Ranking Methods,

    F. Wilcoxon, “Individual Comparisons by Ranking Methods,”Biometrics Bulletin, vol. 1, no. 6, p. 80, Dec. 1945

    1945

  32. [32]

    Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing,

    Y . Benjamini and Y . Hochberg, “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing,”Journal of the Royal Statistical Society. Series B (Methodological), vol. 57, no. 1, pp. 289–300, 1995

    1995