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arxiv: 2605.13046 · v1 · pith:YV5QITBXnew · submitted 2026-05-13 · 💻 cs.AI

An Agentic LLM-Based Framework for Population-Scale Mental Health Screening

Giuliano Lorenzoni , Paulo Alencar , Donald Cowan This is my paper

Pith reviewed 2026-05-14 19:37 UTC · model grok-4.3

classification 💻 cs.AI
keywords agentic frameworkLLM pipelinemental health screeningdepression detectionproxy evaluationorchestrator agentfreeze mechanismclinical transcripts
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The pith

An agentic LLM framework builds stable pipelines for population-scale mental health screening by locking validated stages after proxy evaluation.

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

The paper proposes an agentic framework in which each stage of an LLM pipeline for clinical data processing is handled by a dedicated LangChain agent. Agents follow explicit policies and are tuned through proxy-guided metrics until a configuration proves stable, at which point the stage is frozen to block future regressions. An Orchestrator Agent then manages the full sequence of preprocessing, retrieval, selection, diversity, threshold optimization, and decoding. A proof-of-concept on transcript-based depression detection shows the system settling on cosine similarity, dynamic Top-k retrieval, and a 0.75 threshold while keeping evaluation costs in check. The approach aims to support reliable screening across large clinical datasets without repeated human re-tuning.

Core claim

The central claim is that an agentic framework, progressing from feature-level exploration through proxy-based tuning and freeze/rollback mechanisms to full orchestration, can produce stable LLM pipelines for mental health tasks. In the transcript-based depression detection proof-of-concept, the Orchestrator Agent coordinates preprocessing, retrieval, selection, diversity, threshold optimization, and decoding, converging on configurations such as cosine similarity, dynamic Top-k, and threshold 0.75 while controlling costs and avoiding regressions.

What carries the argument

The Orchestrator Agent, which coordinates multiple stage-specific agents for preprocessing, retrieval, selection, diversity, threshold optimization, and decoding under policies and proxy-guided evaluation with incremental locking.

If this is right

  • Large volumes of unstructured clinical data can be processed with reduced risk of overwriting stable configurations.
  • Population-level mental health screening becomes feasible through controlled, reproducible LLM pipelines.
  • Evaluation costs remain bounded because only improved configurations trigger re-evaluation after stages are locked.
  • Trustworthiness and adaptability in healthcare AI are addressed by requiring demonstrated gains before any rollback or update.

Where Pith is reading between the lines

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

  • The locking approach could transfer to other high-stakes LLM pipelines such as legal review or diagnostic support where regressions carry high costs.
  • Proxy metrics might need periodic recalibration against real clinical outcomes to maintain alignment over time.
  • The framework's convergence behavior suggests that similar orchestration could reduce manual hyperparameter search in other domain-specific LLM applications.

Load-bearing premise

Proxy-guided evaluation metrics reliably predict actual clinical performance, and locking validated stages will prevent regressions without blocking necessary future adaptations to new patient data or clinical contexts.

What would settle it

A head-to-head comparison in which a locked pipeline configuration shows high proxy scores yet lower accuracy on fresh clinical transcripts than an unlocked alternative, or where a beneficial adaptation is blocked by the freeze mechanism.

Figures

Figures reproduced from arXiv: 2605.13046 by Donald Cowan, Giuliano Lorenzoni, Paulo Alencar.

Figure 1
Figure 1. Figure 1: System view of the agentic RAG pipeline for depres [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The orchestrator (Step 0) coordinates specialized [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 2
Figure 2. Figure 2: Agentic workflow for transcript-based depression [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Orchestration overview: Step 0 (LangChain) coordi [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
read the original abstract

Mental health disorders affect millions worldwide, and healthcare systems are increasingly overwhelmed by the volume of clinical data generated from electronic records, telemedicine platforms, and population-level screening programs. At the same time, the emergence of novel AI-based approaches in healthcare calls for intelligent frameworks capable of processing domain-specific unstructured clinical information while adapting to patient-specific needs. This paper proposes an agentic framework for building robust LLM-based pipelines, where each stage is encapsulated as a LangChain agent governed by explicit policies and proxy-guided evaluation. Stages are incrementally locked once validated, ensuring that later adaptations cannot overwrite configurations without demonstrated improvement. The proposed framework evolves from feature-level exploration, through proxy-based tuning and freeze/rollback mechanisms, to full orchestration by an Orchestrator Agent that coordinates preprocessing, retrieval, selection, diversity, threshold optimization, and decoding. A proof-of-concept in transcript-based depression detection demonstrates that the framework converges to stable configurations, such as cosine similarity, dynamic Top-k, and threshold 0.75, while controlling evaluation costs and avoiding regressions. These results highlight the potential of agentic AI to enable population-level mental health screening over large clinical datasets, addressing critical challenges in trustworthiness, reproducibility, and adaptability required in healthcare environments.

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 / 1 minor

Summary. The manuscript proposes an agentic LLM framework for population-scale mental health screening in which pipeline stages are implemented as LangChain agents governed by explicit policies and proxy-guided evaluation. Validated stages are incrementally locked with freeze/rollback mechanisms to prevent regressions. A proof-of-concept on transcript-based depression detection is reported to converge to stable configurations (cosine similarity, dynamic Top-k, threshold 0.75) while controlling evaluation costs.

Significance. If the proxy metrics used for stage locking can be shown to correlate with clinical diagnostic performance and the framework is validated on held-out expert-labeled data, the approach could offer a reproducible method for building trustworthy, adaptable LLM pipelines suitable for large-scale mental health screening.

major comments (3)
  1. [Proof-of-Concept] Proof-of-Concept section: the claim that the framework 'converges to stable configurations' and 'avoids regressions' is unsupported because no quantitative performance numbers (accuracy, F1, AUC), baseline comparisons to non-agentic retrieval, error bars, or details on how regressions were measured against clinical labels are supplied.
  2. [Framework Description] Framework and evaluation description: the central assumption that proxy-guided metrics (cosine similarity, threshold 0.75) reliably predict actual clinical performance is not tested; no correlation analysis with DSM-5 or PHQ-9 expert annotations on held-out transcripts is presented.
  3. [Orchestrator Agent] Orchestrator Agent section: the description of full orchestration coordinating preprocessing, retrieval, selection, diversity, threshold optimization, and decoding lacks any ablation showing that the agentic coordination itself improves stability or cost control over simpler scripted pipelines.
minor comments (1)
  1. [Abstract] Abstract: the statement that the framework 'controls evaluation costs' is not accompanied by any concrete cost figures or comparisons.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive comments, which identify key areas where additional empirical support will strengthen the manuscript. We address each major comment below and will incorporate the requested analyses in the revision.

read point-by-point responses

  1. Referee: [Proof-of-Concept] Proof-of-Concept section: the claim that the framework 'converges to stable configurations' and 'avoids regressions' is unsupported because no quantitative performance numbers (accuracy, F1, AUC), baseline comparisons to non-agentic retrieval, error bars, or details on how regressions were measured against clinical labels are supplied.

    Authors: We agree that the proof-of-concept claims require explicit quantitative backing. In the revised manuscript we will report accuracy, F1, and AUC for the converged configuration (cosine similarity, dynamic Top-k, threshold 0.75), add comparisons to non-agentic retrieval baselines, include error bars from repeated runs, and detail how regressions were quantified against the available clinical labels. revision: yes

  2. Referee: [Framework Description] Framework and evaluation description: the central assumption that proxy-guided metrics (cosine similarity, threshold 0.75) reliably predict actual clinical performance is not tested; no correlation analysis with DSM-5 or PHQ-9 expert annotations on held-out transcripts is presented.

    Authors: We acknowledge that the current version does not include a direct correlation analysis between the proxy metrics and clinical annotations. The framework uses proxies for efficient stage locking during iterative development; we will add a correlation study between cosine similarity / threshold values and DSM-5/PHQ-9 expert labels on held-out transcripts to the revised evaluation section. revision: yes

  3. Referee: [Orchestrator Agent] Orchestrator Agent section: the description of full orchestration coordinating preprocessing, retrieval, selection, diversity, threshold optimization, and decoding lacks any ablation showing that the agentic coordination itself improves stability or cost control over simpler scripted pipelines.

    Authors: We will add an ablation study to the Orchestrator Agent section that directly compares the full agentic orchestration against simpler scripted pipelines. The study will quantify gains in stability (variance reduction across runs) and cost control (evaluation token usage) attributable to the agentic coordination. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical POC outcomes independent of inputs

full rationale

The paper presents a descriptive agentic framework for LLM pipelines with incremental locking of stages based on proxy-guided evaluation. The POC reports observed convergence to configurations such as cosine similarity, dynamic Top-k, and threshold 0.75 as empirical results from transcript-based depression detection, without any equations, derivations, or self-referential definitions that reduce predictions to fitted inputs by construction. No load-bearing self-citations, uniqueness theorems, or ansatzes are invoked in the provided text. The central claims rest on reported internal convergence and cost control rather than tautological reductions, qualifying as self-contained with no circular steps.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 1 invented entities

The framework depends on the assumption that proxy metrics can stand in for clinical outcomes and that LLM agents can be reliably governed by policies; one threshold value is reported from the POC.

free parameters (1)
  • threshold = 0.75
    Reported as the stable value reached in the depression detection proof-of-concept.
axioms (1)
  • domain assumption Proxy-guided evaluation metrics accurately reflect downstream clinical performance in mental health detection tasks.
    Invoked to justify tuning and locking of agent stages.
invented entities (1)
  • Orchestrator Agent no independent evidence
    purpose: Coordinates preprocessing, retrieval, selection, diversity, threshold optimization, and decoding stages.
    Introduced as the top-level coordinator in the proposed framework.

pith-pipeline@v0.9.0 · 5509 in / 1403 out tokens · 29647 ms · 2026-05-14T19:37:59.538593+00:00 · methodology

discussion (0)

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