{"id":"c76e4a04-f878-4634-a611-c147585aff01","arxiv_id":"2507.21188","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"high","formal_verification":"none","parameter_count":3,"one_line_summary":"LDFR is a proposed diagnostic signal that flags LLM diagnostic instability when embeddings cross a PCA-based boundary under small clinical edits, tested on synthetic and 90 real notes.","lead":"This paper proposes a metric called LDFR that counts how often small edits to clinical notes push a language model's internal representation across a decision boundary in a compressed PCA space. The authors claim this latent fragility escapes standard text similarity scores and matters for auditing clinical AI.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"LDFR as reported cannot be the flip rate defined in Eq. 4.3: it is 0.91 at zero perturbation and decreases with perturbation, so the central fragility signal is either miscomputed or measures agreement rather than boundary crossings.","rationale":"The reader's rejection is well-founded, and the strongest supporting reason is the internal inconsistency between Eq. 4.3 and the reported LDFR values. The reader's stated weakest assumption concerned the validity of the logistic-regression boundary as a proxy for clinically meaningful diagnostic regions; that is a genuine external-validity concern, and Section 8 explicitly concedes that clinicians never adjudicated the perturbation-induced flips, so the boundary's clinical meaning is unverified. However, the metric-definition mismatch is more load-bearing because it undermines the quantitative basis of every headline claim before any question of clinical interpretation arises. I therefore partially agree with the reader's weakest_assumption but identify the definition/table mismatch as the primary concern. A single recomputation of LDFR(0) and the threshold trend would settle whether the reported metric is a flip rate at all. The paper does contain useful scaffolding — a reproducible pipeline is claimed, perturbation axes are clinically motivated, and real-note evaluation is attempted — but the central numeric artifact is not trustworthy as written. The verdict should remain REJECT pending a corrected definition or recomputed results.","tokens_in":18422,"tokens_out":3284,"duration_ms":34900,"concrete_test":"Run the released pipeline (github.com/unni12345/geometric_diagnostics) on the synthetic note set and recompute LDFR exactly per Eq. 4.3 at thresholds 0, 0.25, 0.5, and 1.0 for masked entities, using the provided ClinicalBERT embeddings and logistic probe. Check whether LDFR(0) = 0 and whether LDFR increases with threshold. If LDFR(0) > 0 or the trend is decreasing, Tables 1 and 2 do not measure the defined flip rate; if the reported column is instead an agreement rate, the central fragility claim is not supported by the corrected numbers.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim that LDFR exposes hidden latent fragility rests on the quantitative behavior of LDFR in Tables 1 and 2 and Sections 6.1 and 7. Those numbers are incompatible with the definition in Eq. 4.3. LDFR(t) = (1/N) * sum_i I[d0_i != dt_i]. At t=0, the perturbed note is identical to the unperturbed note, so d0_i = dt_i for every i and LDFR(0) = 0. Table 1 reports LDFR = 0.9125 at t=0 for all four perturbation types, and Table 2 reports roughly 0.69. Moreover, if LDFR is a flip rate, fragility should make LDFR increase with perturbation intensity, because more representations cross the diagnostic boundary; instead the tables show LDFR monotonically decreasing (e.g., masking from 0.9125 to 0.3525). The text interprets this decrease as 'latent agreement breaks down' (Section 6.1) and 'the latent classifier no longer follows the LLM's diagnosis reliably' (Section 7), which describes an agreement or alignment rate, not a flip rate. Thus the operationalized metric is not the metric defined in Eq. 4.3. Since every headline result — masking and negation trigger large LDFR changes, surface metrics hide instability, LDFR generalizes to real notes — is stated in terms of this number, the empirical support for the central claim is invalid as reported. If the column is actually an agreement rate, then the abstract's claim that 'latent fragility emerges' is unsupported, because a decreasing agreement rate under stronger perturbations is the expected baseline behavior, not evidence of boundary crossings. If the column is meant to be a flip rate, the tables would need to start near zero and rise with perturbation intensity, changing the sign and magnitude of all reported effects.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The manuscript proposes LAPD, a geometry-aware framework for probing the latent robustness of clinical LLMs under four structured perturbation types (masking, negation, synonym replacement, and numerical variation). The central contribution is the Latent Diagnosis Flip Rate (LDFR), defined as the proportion of perturbed samples whose embeddings cross a PCA-based diagnostic decision boundary. Experiments on synthetic notes (DDXPlus) and 90 real notes (DiReCT/MIMIC-IV) claim that LDFR reveals fragility hidden by surface metrics such as BERTScore, and that masking and negation cause large latent shifts even when surface similarity remains high.","tokens_in":18731,"tokens_out":4737,"duration_ms":43821,"significance":"If valid, LDFR would offer a genuinely useful auditing signal for clinical LLMs, going beyond surface-level agreement to probe internal representation stability. The paper includes several positive elements: a concrete, reproducible pipeline; evaluation on both synthetic and real clinical notes; and an explicit acknowledgment of limitations in Section 8. However, the central metric is internally inconsistent as defined and as reported, and its validation is circular. These issues undermine the main claim, so the positive elements cannot currently be credited as supporting a sound contribution.","major_comments":[{"comment":"The definition LDFR(t) = (1/N) Σ_i I[d0_i ≠ dt_i] forces LDFR(0) = 0, because at threshold 0 the perturbed note is identical to the unperturbed note, making d0_i = dt_i for every i. Table 1 reports LDFR = 0.9125 at threshold 0.00 for all four perturbation types, and Table 2 reports values around 0.69. These numbers are impossible for the quantity defined in Eq. (1). The tables must be reporting a different metric, such as an agreement rate between the PCA classifier and the LLM, but this is not stated. This direct contradiction invalidates the quantitative foundation of the paper.","section":"Section 4.3, Eq. (1) vs. Table 1"},{"comment":"If LDFR were a flip rate, one would expect it to increase with perturbation intensity under fragility, because stronger perturbations should drive more embeddings across the decision boundary. Tables 1 and 2 show the opposite: LDFR decreases monotonically (e.g., masking from 0.9125 to 0.3525). The text interprets this decrease as 'latent agreement breaks down' (Section 6.1) and states that 'the latent classifier no longer follows the LLM’s diagnosis reliably' (Section 7). That interpretation describes an agreement or alignment rate (1 − flip rate), not a flip rate. The reversed interpretation is load-bearing: the paper's headline claim that masking and negation trigger latent fragility is based on a quantity that behaves opposite to the definition.","section":"Section 6.1, Section 7, Tables 1–2"},{"comment":"The PCA-logistic probe is trained on unperturbed embeddings with ground-truth diagnosis labels, not on the target LLM's internal decision regions. Section 8 explicitly admits that clinician reviewers did not adjudicate whether the perturbation-induced flips are clinically warranted. The validation of LDFR is therefore circular: LDFR is validated by correlating it with the same LLM's output flips, but the LLM is the system under audit, and there is no external ground truth for whether the flips should occur. LDFR may be measuring probe–LLM misalignment rather than genuine latent fragility, and the manuscript offers no test to distinguish these alternatives.","section":"Section 4.2 and Section 8"},{"comment":"The assumption that a logistic regression classifier on PCA-reduced embeddings faithfully represents the LLM's diagnostic decision boundary is untested. The probe is a separate model trained on dataset labels, so its boundary could reflect dataset statistics rather than the audited LLM's reasoning. A concrete test would be to compare the probe's boundary to per-model decision regions (e.g., by training probes on each model's own outputs) or to define LDFR using the LLM's own embeddings. Without such a check, the central 'fragility' signal may be an artifact of the probe rather than a property of the LLM.","section":"Section 4.2"}],"minor_comments":[{"comment":"Section 4.2 reports 10-fold cross-validation for the latent classifier, while Section 5.4 reports 5-fold cross-validation. These statements should be reconciled.","section":"Section 4.2 vs. Section 5.4"},{"comment":"The solver is listed as 'lgfbs'; this appears to be a typo for 'lbfgs'.","section":"Appendix A.6.3"},{"comment":"The phrase 'another direction illucidated' contains a typo ('illucidated' should be 'elucidated').","section":"Section 8"},{"comment":"The caption claims the table shows 'Boundary crossings in latent space predict real diagnostic flips', but the table reports Pearson/Spearman correlations and LDFR values; the operational link between these columns and 'real diagnostic flips' is never defined.","section":"Table 1 caption"},{"comment":"The term 'model-agnostic' is misleading: LDFR depends on the selected encoder (ClinicalBERT or Clinical-Longformer), the PCA threshold, and a classifier trained on a specific dataset, so the metric is not agnostic to these choices.","section":"Abstract"}],"recommendation":"reject","confidential_remarks":"The reviewer agrees with the reader's assessment. The mismatch between the defined LDFR and the reported numbers is not a presentation issue; it undermines every quantitative result in Tables 1 and 2 and the conclusions drawn from them. Even if the authors corrected the definition or the tables, the validation remains circular because the only ground truth for 'fragility' is the LLM under audit, with no clinician adjudication. The paper would need a substantial redesign of both the metric and its validation to be publishable. The workshop-ready structure and real-data evaluation are positive, but they cannot compensate for the load-bearing inconsistency."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Two things you should know about arXiv:2507.21188. First, the central metric, LDFR, is not computed as defined. Eq 4.3 defines it as a flip rate between the probe classifier's predictions on original and perturbed notes; at zero perturbation that rate must be zero. Table 1 reports 0.9125 at threshold zero for all four perturbation types, and the values decrease as perturbations increase. The text reads the decrease as 'latent agreement breaks down,' which is an agreement rate, not a flip rate. So the column labeled LDFR appears to be an agreement rate between the probe and the LLM, and the actual boundary-crossing metric is never reported. This is load-bearing, because every headline claim about latent fragility is expressed in terms of LDFR.\n\nWhat the paper does well: the evaluation pipeline is thoughtfully assembled. It generates synthetic clinical notes from DDXPlus with forward and backward reasoning, applies four clinically motivated perturbation axes (masking, negation, synonym, numeric), tests five models, and includes 90 real DiReCT notes. The idea that surface metrics like BERTScore stay high while representations cross diagnostic boundaries is worth testing, and the variance decomposition analysis is a reasonable way to look for low-dimensional instability. The synthetic note generation is creative, and the human evaluation, while tiny, is a good instinct.\n\nWhere it falls down beyond the metric issue: the probe's decision boundary is only validated against the same LLMs under audit. Section 8 admits no clinician adjudicated whether perturbation-induced flips are clinically warranted, so the 'boundary' may be an artifact of the probe. The real-note evidence is thin: 90 notes, correlations as low as 0.16. There are also small internal inconsistencies (10-fold vs 5-fold cross-validation, 13.2 vs 83.5 entities per DiReCT note) that undermine confidence.\n\nBottom line: the direction is salvageable but the paper as written does not support its claims. I would not cite it, and I would not publish it in this form. If it comes to you as a referee, my recommendation is reject with constructive comments: redefine LDFR, recompute the tables, and add an external validation anchor. It might then become a useful workshop paper.","headline":"The paper's central metric LDFR is not computed as defined: at zero perturbation it should be zero, not 0.91, and the reported trend is backwards for a flip rate, so the main claim of latent fragility is unsupported as written.","tokens_in":19335,"tokens_out":5661,"would_cite":false,"duration_ms":55286,"reading_group":"maybe","serious_thinker":"no","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"This paper proposes LDFR, a geometry-aware metric that exposes latent diagnostic fragility in clinical LLMs: small clinically plausible edits can flip the embedded diagnosis while surface similarity stays high.","keywords":["clinical LLM evaluation","latent space robustness","diagnostic fragility","perturbation testing","embedding decision boundaries","PCA probing","BERTScore gap","LDFR"],"falsifier":"Have clinicians score a sample of perturbed notes whose embeddings crossed the boundary. If the majority of flips are judged clinically unwarranted, or if retraining the boundary on the LLM's own final-layer hidden states makes the flips disappear, then the fragility the paper reports is an artifact of the linear PCA probe rather than a property of the LLM.","tokens_in":18136,"feed_emoji":"🩺","tokens_out":10478,"duration_ms":99591,"temperature":0.7,"pith_summary":"The paper tries to establish that clinical large language models can be latently fragile even when they look robust on standard text metrics. To show this, it introduces LAPD, a perturbation-and-probing pipeline, and LDFR, a metric that counts how often a small clinically grounded edit—masking a symptom, negating a finding, swapping a synonym, or nudging a numeric value—moves the model's embedding across a diagnostic boundary. The measured gap is stark: at 25% entity masking, BERTScore stays above 0.89 while the latent diagnostic signal falls from about 91% to about 55%, and the pattern holds on 90 real discharge summaries from MIMIC-IV as well as on synthetic notes. If the paper is right, high benchmark scores and high surface similarity are not evidence that a clinical assistant will reason stably, and geometry-aware auditing should be part of pre-deployment checks.","feed_headline":"Masking a symptom flips LLM diagnoses that surface scores miss","feed_subtitle":"A new geometry-aware metric, LDFR, catches the flips by watching embeddings cross diagnosis boundaries in PCA space.","key_machinery":"The load-bearing object is the LDFR probe: embeddings are extracted from frozen encoders (ClinicalBERT for synthetic notes, Clinical-Longformer for real ones), reduced by PCA to the 90%-variance subspace, and a logistic-regression classifier trained on the unperturbed projections marks a linear diagnostic boundary. LDFR counts, per perturbation threshold, the fraction of perturbed embeddings whose boundary assignment differs from the unperturbed assignment, and the paper reports it as an agreement signal between probe and LLM. The complementary mechanism is dimensional collapse: under masking and negation, one or two PCA dimensions absorb up to 20–30% of total variance, so the embeddings compress into a narrow subspace where small edits cross the boundary. The structured perturbations themselves—masking, negation, synonym replacement, and numeric variation, applied at controlled intensities—are the stress tests that drive the geometry.","core_discovery":"The paper's central claim is that robustness in clinical LLMs has a latent, geometric component that output-level metrics do not see. It defines LDFR as the proportion of perturbed samples whose latent representations cross a diagnostic decision boundary, with that boundary estimated by a logistic-regression probe trained on PCA-reduced embeddings of unperturbed notes. In the reported measurements, this probe tracks the LLM's own behavior: under entity masking the probe's agreement with the model's diagnoses falls from roughly 91% to 55% at only 25% perturbation intensity, while BERTScore remains above 0.89; negation produces a smaller but consistent decline; synonym substitution is model-dependent; and numerical edits leave both surface and latent metrics nearly flat. The paper reads the last result as evidence that these models lean on explicitly stated symptoms and underuse quantitative cues. The same fragility pattern appears on 90 real discharge notes from a MIMIC-IV-based benchmark, which the paper takes as evidence that LDFR captures a real deployment-relevant weakness rather than a synthetic-data artifact; the paper's own limitations section concedes that no clinician adjudicated whether the perturbation-induced flips are clinically warranted.","pith_inferences":["Editorial extension: the paper's own limitation section concedes that no clinician adjudicated whether the perturbation-induced flips are clinically appropriate; if most flips turn out to be warranted, LDFR measures useful sensitivity rather than failure, which would change the recommended audit response from suppressing the model to flagging cases for human review.","Editorial extension: a natural check is to fit the boundary not with PCA plus logistic regression but with the LLM's own hidden states or a nonlinear classifier; if the flips vanish, the linear probe manufactured the apparent fragility.","Editorial extension: the dimensional-collapse result suggests a monitoring heuristic—track per-dimension variance of embeddings during deployment and alert when variance concentrates into a few axes—which could catch fragile inputs in real time.","Editorial extension: LDFR is label-agnostic, so the same boundary-crossing logic could be applied to other high-stakes text tasks whose outcome space is discrete, such as legal classification or radiology triage."],"forward_implications":["High BERTScore, ROUGE-L, or entity F1 cannot certify a clinical LLM; the paper's measurements show surface similarity and diagnostic stability coming apart under masking and negation.","Entity masking is the most effective stressor, so an audit for clinical deployment should include symptom-omission edits before trusting a model's diagnosis.","LDFR can be computed from frozen embeddings without access to model weights or output logits, making it a practical model-agnostic screen across proprietary and open models.","Numerical perturbations leave latent structure intact, suggesting current clinical LLMs underuse vitals and lab values; training that forces quantitative grounding might close the gap.","The same fragility pattern on real discharge summaries implies that LDFR-based screening can be run on real documentation, not only on controlled synthetic notes."],"supporting_citations":[{"why":"Supplies the structured diagnostic dialogues (DDXPlus) from which the agentic pipeline generates the synthetic clinical notes used in all synthetic experiments.","marker":"[23]"},{"why":"Supplies the DiReCT benchmark of 90 MIMIC-IV discharge summaries used to test whether LDFR fragility generalizes beyond synthetic notes.","marker":"[24]"},{"why":"MedFuzz established that clinically grounded input fuzzing makes high-performing medical LLMs change answers, the phenomenon LDFR is designed to measure in latent space.","marker":"[15]"},{"why":"BERTScore is the surface metric whose high values above 0.89 define the gap the paper claims LDFR exposes.","marker":"[29]"},{"why":"Provides prior evidence that internal embedding dynamics, not surface labels, can act as a signal of reasoning instability, supporting the latent-probe premise.","marker":"[26]"},{"why":"Shows a latent probing approach for logical consistency properties such as transitivity and negation that LDFR extends to concrete clinical diagnosis decisions.","marker":"[12]"}],"fun_headline_variants":["Latent flips in clinical LLMs evade surface metrics","Small edits flip diagnoses that BERTScore misses","LDFR exposes hidden instability in clinical LLMs","Geometry-aware metric catches diagnosis flips","Clinical LLM robustness has a hidden fragility"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The whole signal depends on a fitted line in embedding space standing in for the model's true diagnostic reasoning, and the paper concedes that no clinician adjudicated whether the measured flips are clinically warranted—if that line is misplaced, the flips describe the probe, not the model.","fun_headline_variants_meta":{"raw":{"variants":["Latent flips in clinical LLMs evade surface metrics","Small edits flip diagnoses that BERTScore misses","LDFR exposes hidden instability in clinical LLMs","Geometry-aware metric catches diagnosis flips","Clinical LLM robustness has a hidden fragility"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000173,"raw_usage":{"total_tokens":1315,"prompt_tokens":1021,"completion_tokens":294,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":637,"completion_tokens_details":{"reasoning_tokens":223}},"tokens_in":637,"tokens_out":294,"duration_ms":3202,"temperature":1.0,"reasoning_tokens":223,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-15T17:45:15.576737+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Have clinicians score a sample of perturbed notes whose embeddings crossed the boundary. If the majority of flips are judged clinically unwarranted, or if retraining the boundary on the LLM's own final-layer hidden states makes the flips disappear, then the fragility the paper reports is an artifact of the linear PCA probe rather than a property of the LLM.","supporting_citations":[],"review_version":2}