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arxiv: 2606.18068 · v1 · pith:D7EROFD4new · submitted 2026-06-16 · 💻 cs.AI

Agentic AI-based Framework for Mitigating Premature Diagnostic Handoff and Silent Hallucination in Healthcare Applications

Divyansh Srivastava , Shreya Ghosh , Anshul Verma , Rajkumar Buyya This is my paper

Pith reviewed 2026-06-27 01:18 UTC · model grok-4.3

classification 💻 cs.AI
keywords agentic AImedical diagnosishallucination mitigationOLDCARTS protocolsemantic entropymulti-agent systemsdiagnostic precisionuncertainty quantification
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The pith

Multi-agent framework with OLDCARTS and entropy gates lifts diagnostic precision by 11.3 points on simulated cases.

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

The paper develops a multi-agent AI system to reduce two failure modes in medical chatbots: jumping to a diagnosis before enough information is gathered and producing undetected hallucinations. It replaces open-ended LLM routing with fixed deterministic gates that first require full collection of the eight OLDCARTS symptom dimensions and second compute semantic entropy across five independent diagnostic samples to intercept inconsistent outputs. Evaluation on 150 simulated patient cases shows the combined system reaches 49.3 percent diagnostic precision, an absolute gain of 11.3 points over the unconstrained baseline. The authors also report a statistically significant negative correlation between symptom completeness and semantic entropy. This setup matters because it offers a concrete way to make conversational medical agents safer before they deliver advice to real users.

Core claim

Replacing LLM-as-a-judge routing with deterministic orchestration constraints, the framework deploys a neuro-symbolic state-tracking gate that blocks diagnostic transitions until all OLDCARTS dimensions are collected and an epistemic uncertainty gate that flags high semantic entropy across five samples; on 150 simulated cases this yields 49.3 percent diagnostic precision (11.3 points above baseline) together with a negative correlation (r = -0.181) between completeness and entropy.

What carries the argument

Neuro-symbolic state-tracking gate enforcing OLDCARTS completeness combined with semantic entropy quantification gate across K=5 diagnostic samples.

If this is right

  • Diagnostic transitions are blocked until all eight OLDCARTS dimensions have been collected.
  • Outputs showing high semantic entropy across five samples are intercepted before reaching the user.
  • Higher OLDCARTS completeness is associated with lower diagnostic uncertainty.
  • The combined gates produce an 11.3 percentage point gain in diagnostic precision over the baseline.

Where Pith is reading between the lines

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

  • The same deterministic gating approach could be tested in other safety-critical conversational domains that require structured data collection before conclusions.
  • Real-world clinical trials would be needed to determine whether the simulated precision and correlation results persist with human patients and physicians.
  • The observed correlation suggests that simply improving information completeness may itself reduce hallucination risk even without separate entropy modeling.

Load-bearing premise

The 150 test cases generated by simulated patient agents are representative of real clinical conversations and the measured precision gain and correlation will translate to actual patients and clinicians.

What would settle it

Deploying the full framework with real patients and clinicians and measuring whether the 11.3-point precision improvement and the negative correlation between OLDCARTS completeness and semantic entropy remain present.

Figures

Figures reproduced from arXiv: 2606.18068 by Anshul Verma, Divyansh Srivastava, Rajkumar Buyya, Shreya Ghosh.

Figure 1
Figure 1. Figure 1: Illustration of two critical failure modes of unconstrained LLM clinical [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: System architecture of the proposed Neuro-Symbolic Multi-Agent Triage framework. The pipeline operates across three phases: (1) structured history [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Three-phase algorithmic view of the proposed neuro-symbolic multi-agent clinical triage framework. The workflow consists of structured history [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Ablation study across N ∈ {50, 100, 150} test cases. Panels (a)–(c): diagnostic accuracy (solid bars) and OLDCARTS σ-score (hatched bars) with 95% bootstrap CIs for Baseline (B), Ablation A (A), and Full Architecture (FA). Panels (d)–(e): accuracy and σ-score scaling with N (shaded = 95% bootstrap CI). completeness (σ-score) is associated with lower epistemic uncertainty (H). A complete symptom profile mor… view at source ↗
Figure 5
Figure 5. Figure 5: Linear regression analysis of the Full Architecture ( [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
read the original abstract

Recent advances in Large Language Models (LLMs) and multi-agent systems have driven the rise of Agentic AI, showing promise for medical reasoning. However, open-ended conversational agents remain prone to two critical failure modes: premature diagnostic handoff and silent clinical hallucinations that may go undetected before reaching the patient. In this work, we propose a multi-agent framework that addresses both issues by replacing ``LLM-as-a-judge'' routing with deterministic orchestration constraints. The framework incorporates two safety mechanisms. First, a neuro-symbolic state-tracking gate enforces completeness of the OLDCARTS clinical protocol (Onset, Location, Duration, Character, Aggravating/Alleviating factors, Radiation, Timing, and Severity) by blocking diagnostic transitions until all required dimensions are collected. Second, an epistemic uncertainty quantification (UQ) gate computes semantic entropy (H) across K=5 independent diagnostic samples to identify and intercept divergent outputs before delivery. We evaluate the system using simulated patient agents powered by the llama-3.1-70b-instruct model on 150 test cases. The full architecture achieves 49.3% diagnostic precision, representing an absolute improvement of 11.3 percentage points over an unconstrained baseline. Additionally, we observe a statistically significant negative correlation (r = -0.181, p < 0.05) between OLDCARTS completeness (\sigma) and semantic entropy (H), suggesting that structured information gathering is associated with reduced diagnostic uncertainty.

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 proposes a multi-agent framework for LLM-based medical diagnostic agents that replaces open-ended routing with two deterministic safety mechanisms: a neuro-symbolic gate enforcing completeness of the OLDCARTS clinical protocol before allowing diagnostic handoff, and an epistemic UQ gate that computes semantic entropy across K=5 diagnostic samples to intercept divergent outputs. Evaluated exclusively on 150 test cases generated by simulated patient agents powered by llama-3.1-70b-instruct, the full architecture reports 49.3% diagnostic precision (absolute gain of 11.3 pp over an unconstrained baseline) together with a statistically significant negative correlation (r = -0.181, p < 0.05) between OLDCARTS completeness (σ) and semantic entropy (H).

Significance. If the simulation faithfully reproduces real clinical dialogue distributions, the neuro-symbolic OLDCARTS gate and semantic-entropy gate would constitute a concrete, reproducible method for adding verifiable safety constraints to agentic medical systems, directly addressing two failure modes that current LLM-as-a-judge approaches leave unmitigated. The reported correlation between structured information gathering and reduced output divergence is a falsifiable empirical observation that could guide future hybrid neuro-symbolic designs.

major comments (3)
  1. [Evaluation] Evaluation paragraph: the 11.3 pp precision gain is reported relative to an 'unconstrained baseline' whose precise architecture, prompting strategy, and use (or non-use) of any gates is never defined, so it is impossible to determine which component of the proposed framework produces the measured improvement.
  2. [Evaluation] Evaluation paragraph: all 150 test cases are generated by patient agents powered by the identical llama-3.1-70b-instruct model used for the diagnostic agents, with no description of case construction, symptom-distribution statistics, human validation of realism, or cross-model checks; this untested modeling assumption is load-bearing for any claim that the observed precision or correlation will translate to interactions with actual patients and clinicians.
  3. [Abstract/Evaluation] Abstract and Evaluation: the semantic entropy H is computed across K=5 samples, yet the manuscript supplies neither the exact procedure for determining semantic equivalence/divergence among samples nor any variance or error bars on the 49.3 % precision figure, rendering the central numeric claims non-reproducible from the given description.
minor comments (2)
  1. [Abstract] Abstract: the symbols σ (OLDCARTS completeness) and H (semantic entropy) are used before any definition is supplied.
  2. [Abstract] Abstract: the phrase 'statistically significant' is attached to r = -0.181 without stating the exact test, degrees of freedom, or any correction for multiple comparisons.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback on the evaluation methodology. We address each major comment below and commit to revisions that improve clarity, reproducibility, and acknowledgment of limitations without overstating the simulation results.

read point-by-point responses

  1. Referee: [Evaluation] Evaluation paragraph: the 11.3 pp precision gain is reported relative to an 'unconstrained baseline' whose precise architecture, prompting strategy, and use (or non-use) of any gates is never defined, so it is impossible to determine which component of the proposed framework produces the measured improvement.

    Authors: We agree the baseline description is insufficient. In the revised manuscript we will explicitly define the unconstrained baseline as a single diagnostic agent using the identical llama-3.1-70b-instruct model and standard prompting, with neither the OLDCARTS neuro-symbolic gate nor the semantic-entropy gate applied. This will allow readers to attribute the 11.3 pp gain specifically to the two proposed safety mechanisms. revision: yes

  2. Referee: [Evaluation] Evaluation paragraph: all 150 test cases are generated by patient agents powered by the identical llama-3.1-70b-instruct model used for the diagnostic agents, with no description of case construction, symptom-distribution statistics, human validation of realism, or cross-model checks; this untested modeling assumption is load-bearing for any claim that the observed precision or correlation will translate to interactions with actual patients and clinicians.

    Authors: The study is deliberately conducted in a controlled simulation to isolate the effect of the deterministic gates. We will add a detailed description of case construction and the symptom-distribution statistics used to generate the 150 cases. Because human validation of realism and cross-model checks were not performed, we will insert an explicit limitations paragraph stating that generalizability to real patients and clinicians remains untested and requires future work with human subjects and heterogeneous models. We will also moderate any language implying direct clinical translation. revision: partial

  3. Referee: [Abstract/Evaluation] Abstract and Evaluation: the semantic entropy H is computed across K=5 samples, yet the manuscript supplies neither the exact procedure for determining semantic equivalence/divergence among samples nor any variance or error bars on the 49.3 % precision figure, rendering the central numeric claims non-reproducible from the given description.

    Authors: We accept that the semantic-equivalence procedure and statistical reporting are underspecified. The revised manuscript will include the precise algorithm used to classify semantic equivalence or divergence across the K=5 samples (including the embedding model and similarity threshold) and will report standard error or bootstrap confidence intervals around the 49.3 % precision figure computed over the 150 cases. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical measurements are direct observations

full rationale

The paper presents a multi-agent framework evaluated via direct measurement on 150 simulated cases, reporting precision (49.3%), improvement over baseline (+11.3 pp), and an observed correlation (r = -0.181). No equations, fitted parameters, or self-citations are shown that reduce these quantities to inputs by construction. The OLDCARTS gate and semantic entropy gate are described as deterministic mechanisms whose effects are measured externally rather than defined in terms of the reported outcomes. This is a standard empirical evaluation without self-definitional or load-bearing circular steps.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The framework rests on the domain-standard OLDCARTS protocol and treats K=5 as a fixed design parameter; no new physical entities are postulated.

free parameters (1)
  • K = 5
    Number of independent diagnostic samples used to compute semantic entropy
axioms (1)
  • domain assumption OLDCARTS supplies a complete and sufficient set of dimensions for clinical history taking
    Invoked as the target state that the neuro-symbolic gate must reach before allowing diagnostic transition

pith-pipeline@v0.9.1-grok · 5810 in / 1309 out tokens · 52048 ms · 2026-06-27T01:18:29.906201+00:00 · methodology

discussion (0)

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