arxiv: 2604.05116 · v1 · submitted 2026-04-06 · 💻 cs.AI

Recognition: 2 theorem links

· Lean Theorem

Uncertainty-Guided Latent Diagnostic Trajectory Learning for Sequential Clinical Diagnosis

Xuyang Shen , Haoran Liu , Dongjin Song , Martin Renqiang Min

Authors on Pith no claims yet

Pith reviewed 2026-05-10 19:19 UTC · model grok-4.3

classification 💻 cs.AI

keywords clinical diagnosissequential decision makinglatent variable modelsLLM agentsuncertaintytrajectory learningMIMIC dataset

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The pith

LLM agents learn latent diagnostic trajectories guided by uncertainty to enable better sequential clinical diagnosis.

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

This paper proposes a way to make large language models handle clinical diagnosis as a sequence of evidence-gathering steps rather than assuming all information is known at once. It introduces the LDTL framework that treats possible diagnostic sequences as latent paths and defines a posterior distribution favoring paths that deliver more diagnostic value. A planning agent is then trained to produce trajectories aligned with this posterior so that uncertainty decreases over time. A sympathetic reader would care because real medical diagnosis involves deciding what test to order next based on current uncertainty, and current systems often skip this modeling. The result is higher accuracy with fewer tests on benchmark data.

Core claim

We introduce Latent Diagnostic Trajectory Learning (LDTL) as a framework consisting of a planning LLM agent and a diagnostic LLM agent. Diagnostic sequences are modeled as latent paths, and a posterior distribution is defined over them to prioritize those that provide greater diagnostic information. Training the planning agent to match this distribution produces coherent paths that progressively reduce uncertainty in the diagnosis process.

What carries the argument

The posterior distribution over latent diagnostic trajectories, which the planning LLM agent is trained to follow in order to reduce uncertainty step by step.

If this is right

Diagnostic accuracy increases compared to prior methods.
The number of diagnostic tests ordered decreases.
Removing trajectory-level alignment reduces the performance gains.
The framework works in settings where only final diagnoses are labeled, not the paths taken.

Where Pith is reading between the lines

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

Similar latent-path training could help in other LLM planning scenarios that lack path supervision.
The uncertainty reduction focus might connect to active learning methods in machine learning.
Deployment in actual hospitals would require checking if the learned trajectories align with medical guidelines.

Load-bearing premise

It is possible to define and utilize a posterior distribution over diagnostic trajectories that effectively guides the planning agent without having explicit supervision for which paths are desirable in the data.

What would settle it

If the proposed LDTL framework does not achieve higher diagnostic accuracy or requires more tests than the best baselines when evaluated on the MIMIC-CDM benchmark, then the benefit of uncertainty-guided latent trajectory learning would not hold.

Figures

Figures reproduced from arXiv: 2604.05116 by Dongjin Song, Haoran Liu, Martin Renqiang Min, Xuyang Shen.

**Figure 2.** Figure 2: The proposed LDTL framework within an sequential diagnosis system. A planning LLM agent sequentially [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Case studies comparing our method LDTL with the variant without latent path regularization (w/o LP). [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

**Figure 4.** Figure 4: Distribution of diagnostic termination steps [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗

**Figure 5.** Figure 5: Distribution of diagnostic termination steps on the test cases. Numbers indicate how many cases terminate [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗

read the original abstract

Clinical diagnosis requires sequential evidence acquisition under uncertainty. However, most Large Language Model (LLM) based diagnostic systems assume fully observed patient information and therefore do not explicitly model how clinical evidence should be sequentially acquired over time. Even when diagnosis is formulated as a sequential decision process, it is still challenging to learn effective diagnostic trajectories. This is because the space of possible evidence-acquisition paths is relatively large, while clinical datasets rarely provide explicit supervision information for desirable diagnostic paths. To this end, we formulate sequential diagnosis as a Latent Diagnostic Trajectory Learning (LDTL) framework based on a planning LLM agent and a diagnostic LLM agent. For the diagnostic LLM agent, diagnostic action sequences are treated as latent paths and we introduce a posterior distribution that prioritizes trajectories providing more diagnostic information. The planning LLM agent is then trained to follow this distribution, encouraging coherent diagnostic trajectories that progressively reduce uncertainty. Experiments on the MIMIC-CDM benchmark demonstrate that our proposed LDTL framework outperforms existing baselines in diagnostic accuracy under a sequential clinical diagnosis setting, while requiring fewer diagnostic tests. Furthermore, ablation studies highlight the critical role of trajectory-level posterior alignment in achieving these improvements.

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.

Desk Editor's Note private letter to a colleague

LDTL frames sequential diagnosis as latent trajectory learning with an uncertainty-guided posterior, but the abstract leaves the posterior computation and experimental controls too thin to verify the accuracy and efficiency claims.

read the letter

The paper's main move is to cast diagnostic test ordering as latent paths over which a posterior is defined to favor high-information trajectories. A planning LLM is then aligned to that posterior while a separate diagnostic LLM handles the actual predictions. This directly tackles the missing path-level supervision in datasets like MIMIC-CDM by using uncertainty reduction as the guiding signal instead of explicit labels. That formulation is distinct from standard sequential decision or active-learning setups in the abstract, and it gives a concrete mechanism for making LLM agents produce coherent evidence-gathering sequences rather than myopic choices. The reported outcome—higher diagnostic accuracy with fewer tests plus ablations crediting the posterior step—is the kind of practical result that matters for deployment questions. The stress-test worry about uncertainty proxies drifting from true clinical utility is fair to raise, but the abstract does not supply enough detail to confirm or refute it yet. The main soft spot is the lack of any description of baselines, exact metrics, statistical tests, or how the posterior is computed from the diagnostic agent's outputs. Without those, the headline claim cannot be checked against the data. The circularity risk is real if the information-gain signal is derived from the same model being trained. This work is for researchers building agentic LLM systems for medicine who already accept the sequential-diagnosis framing. It is worth sending to peer review because the problem is well-motivated and the latent-trajectory idea is a clear departure from prior agent work, even though the current write-up will need substantial expansion on methods and results before it can be evaluated properly.

Referee Report

2 major / 2 minor

Summary. The paper proposes the Latent Diagnostic Trajectory Learning (LDTL) framework for sequential clinical diagnosis with LLMs. It comprises a diagnostic LLM agent that models diagnostic action sequences as latent paths equipped with a posterior distribution over trajectories that prioritizes those providing more diagnostic information, and a planning LLM agent trained via alignment to this posterior to produce coherent paths that progressively reduce uncertainty. On the MIMIC-CDM benchmark the framework is reported to achieve higher diagnostic accuracy than baselines while using fewer tests; ablations are said to confirm the importance of the trajectory-level posterior alignment.

Significance. If the experimental results and the construction of the uncertainty-guided posterior hold under scrutiny, the work would provide a concrete mechanism for LLMs to handle the sequential, partially observed nature of clinical diagnosis without requiring explicit path-level supervision. This could reduce unnecessary diagnostic tests in clinical decision support while maintaining accuracy, addressing a practical gap in current LLM diagnostic systems. The approach of deriving a posterior from internal uncertainty estimates to guide planning is a potentially reusable idea for other sequential decision tasks lacking direct trajectory labels.

major comments (2)

[§3.2] §3.2 (Posterior over latent trajectories): The posterior is defined to prioritize trajectories that supply more diagnostic information, yet the manuscript does not provide an independent, pre-specified measure of information gain or uncertainty reduction that is computed outside the training objective. Because MIMIC-CDM supplies no path-level labels, any circular dependence between the proxy used to define the posterior and the loss used to train the planning agent would undermine the claim that the learned trajectories are clinically preferable.
[§4] §4 (Experiments on MIMIC-CDM): The headline claim that LDTL outperforms baselines in diagnostic accuracy while requiring fewer tests is not accompanied by the necessary reporting details—specific baseline methods, exact metrics (accuracy, test count, F1, etc.), statistical significance tests, confidence intervals, or the precise ablation isolating the posterior-alignment component. Without these, the central empirical result cannot be verified or reproduced.

minor comments (2)

[Abstract] The abstract introduces the acronym LDTL without spelling it out on first use; this should be corrected for readability.
[§3] Notation for the posterior p(·|·) and the uncertainty proxy should be introduced with explicit definitions and distinguished from the planning policy to avoid reader confusion.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive review. The comments highlight important areas for clarification and improved reporting, which we will address in the revision. Below we respond point-by-point to the major comments.

read point-by-point responses

Referee: [§3.2] §3.2 (Posterior over latent trajectories): The posterior is defined to prioritize trajectories that supply more diagnostic information, yet the manuscript does not provide an independent, pre-specified measure of information gain or uncertainty reduction that is computed outside the training objective. Because MIMIC-CDM supplies no path-level labels, any circular dependence between the proxy used to define the posterior and the loss used to train the planning agent would undermine the claim that the learned trajectories are clinically preferable.

Authors: We appreciate this observation on the construction of the posterior. In the LDTL framework, the posterior over latent diagnostic trajectories is defined using a fixed, pre-specified uncertainty measure: the Shannon entropy of the diagnostic LLM agent's predictive distribution over diagnoses, conditioned on the accumulated evidence at each step. This entropy is computed directly from the diagnostic agent's output logits and serves as an independent proxy for information gain; it does not depend on the planning agent's parameters or loss. The planning agent is subsequently trained via a separate alignment objective (KL divergence to the posterior) that encourages selection of trajectories reducing this entropy. While the current manuscript describes this process at a high level, we acknowledge that an explicit mathematical separation between the entropy computation and the alignment loss would strengthen the presentation and remove any perception of circularity. We will revise §3.2 to include the formal definition of the entropy-based information gain, a step-by-step derivation showing its independence from the planning loss, and a schematic diagram of the two-agent pipeline. This revision will also emphasize that the measure is clinically motivated (progressive uncertainty reduction) and fixed prior to training the planner. revision: yes
Referee: [§4] §4 (Experiments on MIMIC-CDM): The headline claim that LDTL outperforms baselines in diagnostic accuracy while requiring fewer tests is not accompanied by the necessary reporting details—specific baseline methods, exact metrics (accuracy, test count, F1, etc.), statistical significance tests, confidence intervals, or the precise ablation isolating the posterior-alignment component. Without these, the central empirical result cannot be verified or reproduced.

Authors: We agree that the experimental reporting in §4 must be expanded for reproducibility and verifiability. The revised manuscript will include: (i) an exhaustive list of all baseline methods with citations and implementation details; (ii) complete numerical results for accuracy, average diagnostic test count, F1-score, and any auxiliary metrics, reported as mean ± standard deviation over multiple random seeds; (iii) statistical significance tests (paired t-tests or Wilcoxon signed-rank tests) with p-values comparing LDTL against each baseline; (iv) 95% confidence intervals; and (v) a dedicated ablation table that isolates the trajectory-level posterior alignment component by comparing the full model against variants that omit the posterior or replace it with uniform sampling. We will also release the full codebase, hyperparameter settings, and evaluation scripts as supplementary material. These additions directly address the concerns about verification and will allow independent reproduction of the headline results. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected in LDTL framework

full rationale

The paper defines a new LDTL framework by treating diagnostic sequences as latent paths and introducing a posterior over trajectories that prioritizes those providing more diagnostic information, then trains the planning agent to match this posterior. This is a constructive modeling choice to handle the absence of path-level supervision rather than a derivation that reduces by construction to its own inputs. No equations, fitted parameters renamed as predictions, or self-citation chains are shown that would force the central result. Performance claims rest on independent MIMIC-CDM benchmark experiments, which serve as external validation outside the framework definition itself.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The framework rests on the modeling assumption that diagnostic sequences can be usefully represented as latent variables whose posterior can be aligned without explicit path supervision.

axioms (1)

domain assumption LLM agents can be trained to act as planners and diagnosticians over latent trajectories
Invoked when the planning agent is trained to follow the posterior distribution

invented entities (1)

Latent Diagnostic Trajectory no independent evidence
purpose: To represent sequences of diagnostic actions as hidden variables that can be aligned via posterior
New modeling construct introduced to handle lack of explicit supervision on desirable paths

pith-pipeline@v0.9.0 · 5503 in / 1190 out tokens · 41627 ms · 2026-05-10T19:19:58.580218+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

we introduce a posterior distribution that prioritizes trajectories providing more diagnostic information... q(z|h0,y)=exp(βS(z))/∑exp(βS(z′))... approximated... IG(ht,a)=logp(y|ht+1)−logp(y|ht)... Lplanner=∑KL(q(a|ht,y)∥πθ(a|ht))
IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

Experiments on the MIMIC-CDM benchmark demonstrate that our proposed LDTL framework outperforms existing baselines in diagnostic accuracy... while requiring fewer diagnostic tests.

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

4 extracted references · 3 canonical work pages · 1 internal anchor

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work page internal anchor Pith review arXiv 2023