Can we generate portable representations for clinical time series data using LLMs?
Authors: Zongliang Ji
Yifei Sun
Andre Carlos Kajdacsy-Balla Amaral
Anna Goldenberg
Rahul G Krishnan
Venue: ICLR 2026 Poster (2026)
Keywords: Machine Learning for Healthcare
ICU Time-series
LLMs
Representation Learning
Primary area: learning on time series and dynamical systems
TL;DR: Explore the ability of LLMs to generate portable and transferrable representations for ICU time-series

Abstract: Deploying clinical ML is slow and brittle: models that work at one hospital often degrade under distribution shifts at the next. In this work, we study a simple question -- can large language models (LLMs) create portable patient embeddings i.e. representations of patients enable a downstream predictor built on one hospital to be used elsewhere with minimal-to-no retraining and fine-tuning.
To do so, we map from irregular ICU time series onto concise natural language summaries using a frozen LLM, then embed each summary with a frozen text embedding model to obtain a fixed length vector capable of serving as input to a variety of downstream predictors.
Across three cohorts (MIMIC-IV, HIRID, PPICU), on multiple clinically grounded forecasting and classification tasks, we find that our approach is simple, easy to use and competitive with in-distribution with grid imputation, self-supervised representation learning, and time series foundation models, while exhibiting smaller relative performance drops when transferring to new hospitals.
We study the variation in performance across prompt design, with structured prompts being crucial to reducing the variance of the predictive models without altering mean accuracy. We find that using these portable representations improves few-shot learning and does not increase demographic recoverability of age or sex relative to baselines, suggesting little additional privacy risk.
Our work points to the potential that LLMs hold as tools to enable the scalable deployment of production grade predictive models by reducing the engineering overhead.

Published as a conference paper at ICLR 2026
CAN WE GENERATE PORTABLE REPRESENTATIONS FOR CLINICAL TIME SERIES DATA USING LLMs?
Zongliang Ji Yifei Sun 1.3 1.3 Andre Amaral Anna Goldenberg 1, Rahul G. Krishnan 1.3
University of Toronto, Canada, "Sunnybrook Health Sciences Centre, Canada, 3 Vector Institute, Canada
(jerryji, rahulgk) rahulgkj@es..torotto..edd
ABSTRACT
Deploying clinical ML slow and brittle: models that work one hospital often degrade under distribution shifts at the next. In this work, we study a simple ques- tion can large language models (LLMs) create portable patient embeddings i.e. representations of patients enable downstream predictor built on one hospital to used elsewhere with minimal-to-no retraining and fine-tuning. To so, we map from irregular ICU time series onto concise natural language summaries us- ing frozen LLM,, then embed each summary with a frozen text embedding model obtain fixed length vector capable of serving as input stream predictors. Across three cohorts (MIMIC-IV, HIRID, PPICU), on multiple elinically grounded forecasting and classification tasks, we find that our approach is simple, easy to use and competitive with in-distribution with grid imputation, self-supervised representation learning, and time series foundation models, while exhibiting smaller relative performance drops when transferring to new hospi- tals. We study the variation in performance across prompt design, with structured prompts being crucial to reducing the variance of the predictive models without al- tering mean accuracy. We find that using these portable representations improves few-shot learning and does not increase demographic recoverability of age sex relative to baselines, suggesting little additional privacy risk. Our work points the potential that LLMs hold tools to enable the scalable deployment of pro- duction grade predictive models by reducing the engineering overhead
variety of down-
INTRODUCTION
Clinical machine learning tools continue to be deployed one site at a time. Teams build a model Hospital A, silent trial, tune thresholds and features, and only then attempt deployment. The process is repeated at Hospital B. Each iteration introduces distribution, population, and inci- mix, and disease prevalence change that can
dence shift as laboratory measurement policies, degrade model performance. These changes inflate time to deployment, lengthen validation time and calibration cycles, and slow the delivery of benefit patients.
The status quo in healthcare treats the model as the object that is transferrable across institutions. Interoperability standards such as OMOP and FHIR reduce data wrangling but do not guarantee an input representation that will remain predictive when moved to a new site. Domain adaptation and invariant risk minimization typically adapt the model to the target hospital, which preserves accuracy at the cost of additional data, labels, and calibration. Foundation models trained on electronic health records improve in-distribution performance and reduce task-specific engincering, yet they are of- ten coupled to particular sensor schema or sampling profile, and model transfer across sites still requires tuning. In short, current solutions standardize data formats, sometimes stripping clinically salient context, or recalibrate models.
We adopt a complementary thesis: portable input representations enable portable models. i.. heterogencous electronic medical records can be mapped into semantically aligned, site-agnostic interface, then conventional predictors will require substantially less site-specific adaptation. Rather
Code available at http:aaala.a///ggghhaa//////rrr/
ICI.2222

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Published as a conference paper at ICLR 2026
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Figure 1: Motivation for Record2 Numeric imputation loses clinical semantics and limits porta- bility; human handoff's preserve meaning but are costly and variable. LLMs can create handoff-style summaries that retain semantics and provide portable inputs for forecasting and classification.
than forcing each hospital's 's raw schema into shared syntactic format, aim produce shared semantic representation.
Clinicians already solve analogous problem. When assuming for patient starting a new shift, physicians interpret heterogeneous measurements through structured narrative handoffs that foreground clinically salient context while abstracting away measurement idiosyncrasies. We investigate whether large language models (LLMs) can analogously transform irregular multivari- patient histories into consistent, handoff-style summaries that serve as a portable intermediate representation.
Concretely, we instantiate this idea with summarize-thnn-embee pipeline. For each patient win- dow, frozen LLM converts the irregular time-series record into a concise natural-languees sum- mary. frozen text embedding model then maps that summary into fixed-length vector consumed by standard forecasting and classification models without architectural modification.
This design leverages several properties of language models. First, summarization operates in a se- mantic space rather than a ochema-boung numeric space, allowing normalization of units, resolution of synonyms, and abstraction over site-specific coding artifacts. Second, natural language provides common representational substrate that aligns heterogeneous sampling policies and missingness patterns into comparable clinical concepts. Third, fixed-length embeddings standardize the down- stream interface, simplifying training budgets and enabling zero- few-shot transfer. Freezing both the summarizer and embedder further constrains overfitting to site- eite-specfiic artifacts and improves reproducibility by decoupling representation learning from downstream optimization.
Our contributions are threefold:
We rethink how machine learning in healthcare should operate via a deployment first framing of prediction that focuses on creating portable input representations for healthcare, aiming reduce per-site engineering and calibration.
We present Record2Vec, practical summarize-then-emleee pipeline using frozen language mod- els to transform irregular ICU histories into fixed-length vectors consumed by standard predictors without architectural changes.
We conduct multi-site evaluation across three cohorts and multiple tasks showing that the learned representations are competitive in-distribution, more portable across hospitals, more data-efficient, and match or improve on privacy preservation relative to established pipelines.
2 RELATED WORK
Clinical schema and harmonization. Common data models and interoperability standards such as OMOP and HL7 FHIR have substantially reduced extract-transsform-lladd burden and enabled reuse of EHRs through shared schemas, vocabularies, and APIs (Stang et al. 2010 Bender & Sartipi 2013) Reich et al., 2024] Vorisek et al. 2022 Tabari et al. 2024). While effective for data sharing and governance, these frameworks standardize format rather than learn
task-optimized, site-invariant representations for predictive modeling.
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Patient record
Duta Matrix
Imputed Matrix
Patient record
Data Matrix
TSDE
Embedding
Patient record
Embedding
Data Matrix
Patient record
Embedding
Summary
Figure 2: Methods to generate medical-record representations. Top to bottom: imputation pipeline; self-supervised TS representation (TSDE); TS foundation model (TimesFM); and Record2 LLM summary followed by text embedding.
Domain shift, generalization, and adaptation. large literature addresses distribution shift by adapting model parameters to target environments. Domain adversarial training encourages site-invariant features via domain classifier (Ganin & Lempitsky 2016); correlation align- ment matches second-order statistics across domains (Sun & Saenko (0T6)) Domain generaliza- tion methods such as invariant risk minimization seek predictors stable across environments (Ar- jovsky et al., 2019; Subbaswamy et al., 2002),, and distributionally robust optimization emphasizes worst-case groups to improve reliability under shift (Sagawa et al. 2020). These approaches are model-centric, often requiring environment labels, target-site data, careful tuning. In contrast, we study input level portability by learning a site-agnostic representation that standard predictors can consume without architectural changes.
EHR and self-supervised representation learning. Representation learning on structured EHRs and clinical time series has advanced rapidly. BEHRT pretrains Transformers over longitudinal code sequences to learn patient timelines (Li et al., 2020b; Steinberg et al.) 2020). Recent surveys review concept and patient-level embeddings for EHRs (Kauffman et al.) (025). Time-series foun- dation models pretrained at scale demonstrate strong zero zero-/few-shor forecasting, notably TimesFM (Das al. 2024). Diffusion-style self supervsiiovi for general time-series representation learning, such TSDE, learns versatile embeddings via
masking with dual-encoder Transformers (Senane et al. 2024). These are strong baselines; however, portability and deployment cost across hospitals are rarely treated as first class evaluation endpoints.
LLMs for clinical summarization and structuring. Large language models have been applied to clinical summarization and structuring tasks. Adapted LLMs outperform experts on multiple clinical text summarization tasks (Van Veen et al., (2044). and there is growing guidance on prompt design for medical use (Zaghir al. (024)). Evaluations of medical evidence summarization further characterize strengths and limitations (Tang et al. 2023). Beyond notes, scoping reviews document integrating standardized terminologies like SNOMED CT with LLMs for normalization and coding Chang & Sung, 2024: Luo et al. 2020). While prior work uses LLMs for text summarization or coding, we repurpose them as an information transformation layer for numeric ICU time series, bridging structured and unstructured modalities.
Recent research investigates serializing structured EHR data for LLM processing. Approaches like TabLLLM (Hegselmann et al. 2023) and DeLLiriuM (Contreras et al. 2025) fine-tune L.MMs on text to perform direct prediction. Other studies (Lee et al. 2025| Gao al.) [0224] [Hegselmann) et al. 2025) use frozen models to embed raw data serializations. A critical distinction lies in our deployment philosophy. These prior methods optimize the model pipeline and often require the transfer of site-tuned LLMs. In contrast, our approach focuses preparing transferable input representation X. We use a standard and locally deployed frozen LLM to summarize dense time- series data. This step normalizes site-specific artifacts at the data level. Consequently, hospitals can generate portable representations that allow any lightweight downstream classifier to function effectively without porting entire end-to-end model pipelines.
METHODOLOGY AND SETUP
We design a controlled study to test whether natural language can serve as an information trans- formation layer that yields portable inputs for ICU prediction, formalizing sites, inputs, and tasks
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across four method families, three ICU cohorts, and seven prediction tasks, and presenting Record2Vec pipeline with baselines, data, and evaluation.
PPICU). For stay i at site the irregular ICU record over a 48- -8-h window is R. = {(c, {(te, H=1)) ce c( a dictionary from clinical concepts to time-value pairs (fx, Uk) observed in the window. We consider methods that transform (.()) into either (i) a regular grid X; E R xT with = 48 hours, or (ii) a fixed- length vector 24 E IRd. A downstream predictor fe maps inputs to task labels y, across standard ICU outcomes spanning different label-prevalence regimes.
Problem formalization. Let sites be S E IMIMIC-IV,HRRE
3.1 RECORD2VEC
We treat language as an information transformation layer. Given the 48h irregular record R(() for stay i at site Record2 first produces concise clinical summary and then embeds that summary into a fixed-length vector used by shared decoders.
Summarization. A frozen LLM Is maps each irregular window and prompt т concise summary, i.., text, = go ( x). We compare structured slot-based prompts that elicit vitals, labs, therapies, trajectories, salient events, and gaps with free-form prompts that request short narrative of states and trends. We evaluate three summarizers representing different deployment regimes: large general model (Gemini-2.0 Flash Comaniei et al. (2025)), a clinically tuned model (MedGemma Sellergren et al. ),255). and small open model (Llama-3. Dubey et al. (2024)). All LLMs remain frozen without finetuning.
Embedding, A frozen text encoder he maps the summary to a fixed-length vector used by down- stream predictors, = hes (text;) E Rdd Unless noted, we use Qwen3 text-embedding as he
2
As a no-summarization control, we also embed a canonical serialization of the record directly direct (R,
2
3.2 BASELINES
We compare Record2Vec to three strong families used for ICU time series, plus the direct-embed
control.
Grid imputation pipelines. Irregular records are discretized into hourly grids X E ( x48 then completed via (a) mean fill, (b) right-shift carry-forward, or (c) linear interpolation. Com- pleted grids feed the shared decoders for forecasting, regression, and classification. Per-cohort normalization training-split statistics and is applied only to grid methods.
Self-supervised representation learning (TSDE). Time-Series Diffusion Embedding (TSDE) (Senane al. (024) learns general-purpose embeddings via masked imputa- tion/intepolaaion/ooresssss objectives. We train TSDE on each cohort's training split to obtain one vector per example; vectors are converted to grids by the shared projection in the downstream model and decoded with the same heads other methods. Model selection uses only training/validationn within the source cohort.
Time-series foundation model (TimesFM), TimesFM (Das al. 2024) is a decoder-only atten- tion model pretrained on diverse time series for strong zero-shot forecasting, We use TimesFM as a frozen encoder: after mean imputing each feature form regular per-feature series, we extract hidden representations feature- feature-by-featee by-feature and average across features to obtain a window-level embedding, which is then mapped to grid by the shared projection and decoded. No TimesFM finetuning is performed.
General healthcare predictive framework (GenHPF-variant) divides patient features hierarchically, encodes each medical event into an embedding followed by a step to aggregate them. We create a variant of this method with minor modifications to their textual templates and follow the same downstream learning pipeline.
GenHPF (Hur et al. 2024)
3.3 DATASETS AND PREPROCESSING
We use three ICU cohorts: MIMIC-IV (Johnson et al. 2023a). HiRID YYehe et al. 2021). and PPICU. PPICU is an external dataset from a distinct hospital system. In total, the cohorts include
We perform embedding model ablations; the results are recorded in Appendix Details on how mapping was done are shown in Appendix
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HiRTD
MIMIC
PPICU
Tisks
Forecrua
LeS
Moet
lab
Forecast
LaS
Mort AUROC
Drag
Lab
Forecnst
Le5
Meet
Drup
Lab
Method
MSF.
MAF
AUROC
Recall
Recall
MSR
MAB
Recall
Recall
VIS
MAF
AUROC
Rocall
Rocall
Meon
0,003
0.378
0.914
0.878
0.857
0.035
0.447
0.847
0.838
0.886
0.28
0.525
0.542
0.834
0.847
Right shife
0.61
0.342
0.913
0.884
0.858
0.017
0.409
0.885
0.541
0.884
0031
0.490
0.968
0.81
0.846
Interplation
0.435
0.320
0.918
0.87
0.83
0.103
0.410
0.871
0.544
0.833
0133
0.491
0.857
0.87
0.847
TSDE
D.029
0411
0.913
0.90
0.902
0.000
0.406
0.915
0.588
0.899
0.51
0.485
0.590
0.899
0.87)
TimesFM
B.028
0.440
0.826
0.850
0.925
0.000
0.413
0.791
0.506
0.940
0.036
0.494
0.658
0.923
0.925
GeuHPF
0.836
0.770
0.741
0.776
0.720
0.713
0.750
0.835
0.6733
Voc
0
0.147
0,930
0
0.931
0,027
0.328
0.333
0.903
0.947
0
0.358
0,890
0,937
0,9%
Table I: In-distribution Result (RQ1). Best (per column) in bold; second-best underlined. counts the number of bests per method across all 15 downstream tasks.
Wins
57,212 stays with 60 variables for MIMIC-IV, 32,216 stays with 64 variables for HiRID, and 39,000 stays with 75 variables for PPICU 4 We segment each stay into non-overlapping windows of 48 hours. The variables cover laboratory tests, vital signs, and clinical interventions.
For the grid baselines, we normalize values within each cohort using statistics computed on the corresponding training split, consistent with prior benchmarks where normalization crucial for numeric model stability. For text-based transformations, we keep raw magnitudes and the native clinical units to preserve meaning in the summaries i.e we not perform further modification of the raw data. For language inputs, we create canonical serialization of the window. For each variable present, the serialization lists the name and the full sequence of timestamps and raw values observed during the window. This serialization is the input to the summarizers and also serves the no-summarization control described in Section [.T]] Additional details on concept mappings, unit handling, and filtering rules, which were curated with practicing ICU clinicians to ensure correct harmonization of variables, appear in Appendix
3.4 TASKS AND EVALUATION
We study five predictive tasks and two privacy probes. The tasks are: multivariate forecasting of all variables over the next 24 hours; remaining length of stay at the window end; in-hospital mortality; two treatment indicators for vasopressor and antibiotic use in the next 24 hours; and multi-label outcome for whether ten common blood tests will ordered in the next 24 hours. The privacy probes test demographic recoverability by predicting age (clipped to I 18-90) and sex.
We use three settings. In-distribution trains and validates within one cohort and tests on its holdout split. Cross-site trains on source cohort and tests on distinct target without target labels; we report target accuracy and the drop relative to in-distribution. Few-shot starts from a source-trained model and fine-tunes on 16, 64, or 512 labeled target examples, then tests the target split.
We report MAE for length of stay and age, masked MSE for forecasting, and micro-averaged recall (with precision and F1) for classification. Results average four seeds with mean and standard devi- ation. Budgets, early stopping, capacity, and regularization are matched within input type. TSDE is trained on the training split with validation selection. TimesFM, 9a. and hyp remain frozen. Full hyperparameters appear in Appendix [
4 RESULTS
We organize the evaluation around seven research questions probing in-distribution performance (RQ1), cross-cohort transfer (RQ2), the value of summarization and model choice (RQ3), prompt sensitivity (RQ4), few-shot adaptation (RQ5), privacy (RQ6), and information analysis (RQ7). Un- less noted, results tables report means over seeds with the top performer highlighted. For consis- tency, the results sections report numbers from a single downstream model (PatchTSMixer), ex- cept for mortality, where we report the best performance across all models tested in the mortality benchmark (Yèche et al. (2022); we verified the same trends hold with neural network architectures including multi-layer perceptron (MLP), LSTM (Hochreiter Schmidhuber 1997). TimeMixer Wang et al., (2244b in Appendix O
4.1 How DO THE FOUR METHODS COMPARE IN DISTRIBUTION? (RQ1)
Record2Vec achieves the strongest in-distribution results overall, winning 13 of 15 tasks and ranking second on the remaining two.
Differencee in EHR systems lead to different variable across sites.
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Datuset
HiRID PPICU
MIMIC - PPICU
Wins
Tasks
Forecast MSE
LoS MAR
Mort AUROC
Drug Recall
Lab Recall
Forecust MSE
LoS MAR
Mort AUROC
Drug Recall
Lab Recall
Method
Mean
0.306
1.09
0.50
0.42
0.77
4.839
0.85
0.50
0.30
0.81
0
Right shift
0.417
1.03
0.50
0.53
0.88
6,344
0.76
0.50
0.23
0.82
Interpolation
24.80
0.91
0.50
0.34
0.81
13.41
0.71
0.50
0.21
0.80
TSDE
0.2019
0.73
0.49
0.91
0.91
0.269
0.76
0.51
0.90
0.90
0
TimesFM
0.217
0.64
0.93
0.95
0.284
0.93
0.69
0,95
0,95
GenHPF
0.582
0.699
0.316
0.425
0.442
0.428
Record2Vec Template
0.195
0.98
0.72
0.96
0.96
0.263
0.77
0.71
0.95
0.95
Record2Vec
0.183
0.72
0.97
0.97
0.242
0.49
0.72
0.95
0.95
Table 2: Transfer Learning Result (RQ2). Best per column in bold; second-best underlined. Wins counts tied bests.
Table T summarizes performance within each cohort. A clear pattern emerges: Record2 leads on most outcomes in HiRID, sweeps all tasks in MIMIC, and again dominates in PPICU with one exception. The two columns not led by Record2 Vec are PPICU mortality, where TSDE is best, and HiRID length of stay, where simple right-shift imputation edges ahead. Representation-leare learning baselines (TSDE, TimesFM) are consistently competitive and often place second, while classic grid imputations rarely win and generally trail across cohorts and endpoints. These results indicate that a Linguage-mediate input yields robust urility across forecasting, regression, and classification with- out tailoring model architectures to site.
We hypothesize three drivers. First, summarization captures clinically salient semantics- states, trajectories, interventions, salient events-while aligning heterogeneous names, units, and sampling policies into shared space. This reduces reliance on ad hoc imputations and preserves mean- ing when measurements are sparse or irregular. Second, the fixed-length embedding offered by Record2 Vec stabilizes the downstream interface, limiting sensitivity to missingness patterns and lo- cal measurement habits that degrade grid-based inputs. Third, compared with TSDE and TimesFM, which emphasize correlations and trends in numeric streams, Record2Vec adds a layer of clinical context that appears to aid discrimination on classification endpoints while remaining competitive on regression. Overall, the findings support the view that LLM-driven transformation produces more informative and portable inputs than either imputation or purely numeric representation learning.
4.2 DOES THE LLM BASED REPRESENTATION IMPROVE TRANSFERABILITY ACROSS ICUS? (RQ2)
Record2Vec transfers best across ICUs, winning 10 of 10 columns in Table 2 with two ties by TimesFM.
When models are trained on HiRID or MIMIC and evaluated on PPICU, Record2 consistently ranks first across forecasting, length of stay, mortality, treatment, and lab prediction. contrast, grid imputations degrade sharply under shift, and several classification scores collapse toward chance. TSDE and TimesFM are stronger than imputation, yet they still trail Record2Vec in most settings. TimesFM ties for the top position in two columns and, together with Record2 benefits from the larger MIMIC source when predicting common interventions and labs at PPICU. The pattern is clear: methods that rely on site-specific numeric regularization or self-supervision the source cohort do not maintain accuracy when confronted with new variable sets, sampling habits, and missingness regimes.
We attribute these results to three factors. First, natural-language summaries align heterogeneous coding choices, units, and documentation styles into a shared clinical space before embedding. This reduces the burden on downstream decoders to re-learn site-specific conventions and helps preserve signal for tasks like mortality and treatment prediction. Second, the fixed-length embedding pro- duced by Record2 Vec offers a stable input interface that is less sensitive to irregular sampling and missingness patterns than grids, which explains the large gap from imputation under distribution shift. Third, compared with TSDE and TimesFM, Record2Vec injects semantic context about states, trends, and salient events extracted by the summarizer. To better understand the gains from summa- rization, we compared against the strategy used by related work (Hur et al.) 2024 Gao et al. 2024 Hegselman Iann et al. 2025) to map patient data onto a fixed template. When comparing Record2 Vec with TSDE, and TFM, against fixed-template baselines, that can be attributed to summarization. We hypothesize these gains primarily come due to the stan- dardization of heterogeneous patient profiles across sites while preserving biologically relevant in-
observe additional performance gains

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medgemmm
2
geminn G-flash
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Figure 3: Rank distributions for No-summary vs. three LLM variants across 15 in-distribution tasks (left) and 30 cross-site transfer tasks (right). Methods are ranked based on performance across five downstream tasks: Forecast (MSE), LoS (MAE), Mortality (AUROC), Drug (Recall), and Lab (Recall). See the Appendix M for the detailed values.
formation for making predictions. Foundatton pretraining helps TimesFM transfer better than TSDE, but it remains tied to numeric trends alone, whereas Vec retains both numerical and clinical meaning. Together, these properties yield robust portability across hospitals for a broad set of ICU prediction tasks.
4.3 Do SUMMARIES HELP, WHICH SUMMARIZER WORKS BEST? (RQ3)
ceemaarzee-ten-ennee improves cross-site transfer while staying competitive in-distribution, and among summarizers Gemini-2.0 Flash and MedGemma perform best, with Llama-3.1 worse than both.
Figure 3 shows rank distributions for four variants: no-summary (directly embed the raw serial- ization), and three LLM summarizers (Llama-3.1, MedGemma, Gemini-2.0 Flash). On the left (in-distribution; 15 tasks), no-summary often attains the top rank, with the LLM-based variants close behind. Within single site, feeding the embedding model the full, unsummarized content ap- pears advantageous and does not require normalization into prose. On the right (cross-site transfer; 30 tasks), the pattern reverses. No-summary concentrates in the worst ranks overall, while the three summarize-theny then-embed variants dominate first and second ranks. Comparing the LLMs, Gemini-2.0 Flash and MedGemma account for most of the top positions both settings; their gap narrows under transfer. 1lama-3.1 accumulates the most last-place ranks among summarizers in both settings.
We think there are three reasons for these outcomes. First, summarization imposes shared clin- ical language over heterogeneous variable names, units, and sampling practices, which removes site-specific idiosyncrasies before embedding. This harmonization matters most under shift and explains why Record2Vec transfers better than direct embedding of raw, site-specific streams. Sec- ond, within-site, direct embeddings preserve all numeric detail and local conventions, which can make representations highly distinctive and strong on held-out splits from the same cohort; however, precisely those site-specific details impair portability to other hospitals. Third, the choice of sum- marizer influences both fidelity and standardization. MedGemma's medical pretraining promotes clinically faithful phrasing that is stable across institutions, supporting transfer. Gemini-2.0 Flash's 's instruction following and planning yield concise, information-dense summaries that are marginally stronger in-distribution and remain competitive under transfer, leading to small gap between the two. Llama-3.1, with smaller capacity and less domain specialization, tends to produce shorter or less standardized summaries, which hurts both within-sit utility and cross-site robustness. A major practical gain efficiency: without summarization the raw serialization passes, on average, ~55 more tokens to the embedder than the summarized version, cutting inference cost pro- portionally while improving transfer.
4.4 HOW SENSITIVE IS PERFORMANCE TO PROMPT DESIGN? (RQ4)
Across four prompting strategies, performance is broadly similar in-distribution and under transfer, with a slight edge for ICD-style prompts in transfer. Figure plots rank distributions for zero-shot, chain-of-thought. trend-focused, and ICD-focused prompts using Gemini-2.0 Flash. The bars span ranks fairly evenly on both the in-distribution side and the cross-site side, indicating limited sensitivity to prompt choice at the aggregate level. We observe a small shift in transfer where ICD-style prompts collect more top ranks, while the other three strategies remain closely clustered. Within cohorts, no single prompt dominates consistently across forecasting, regression, classification.

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Rank
Pank
Rance
GaT 4
3
4
ICD
Figure 4: Rank distributions for four prompt variants with Gemini 2.0-flash across 15 in-distribution tasks (left) and 30 cross-site transfer tasks (right). Lower ranks indicate better performance, Meth- ods are ranked based on performance across five downstream tasks: Forecast (MSE), LoS (MAE), Mortality (AUROC), Drug (Recall), and Lab (Recall). See the Appendix fo the detailed values.
interp
interp
interp mean right TFM -
mean
mean
right
right
TFM
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TSDE
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Record2Vec
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0.5 0.6
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Forecast (masked _
LOS (mae)
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interp mean right TFM TSDE Record2Vec
interp mean right
interp mean right TFM
+
TFM
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Record2Vec
0.0
0.2 Forecast (masked mse)
0.4
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0.8
LOS (mae)
Mortality (auroc)
Figure 5: Few-shot finetuning with 16 labeled target samples for mortality prediction (RQ5) shown across transfer settings. All tasks are reported the same metric previous section: Forecast: masked mse, LOS: mae, Mortality: AUROC. The first row is the result of Hirid + Ppicu and the second row Mimic + Ppicu. Reference lines: blue best in-distribution upper bound, orange best pre-finetune result, black best finetuned baseline. Record2 surpassed baselines with large gap, reaching comparable performance to in-distribution.
We think two factors explain this pattern. First, the summarizer is strong enough that high-level clinical content is retained under four prompts, producing broadly comparable embeddings. Sec- ond, the ICD framing may aid transfer by nudging summaries toward standardized terminology that travels better across sites. Overall, these results suggest that prompt choice not the primary driver of utility in setup, and that targeted prompt engineering could be promising avenue for future improvement. Detailed prompt templates appear Appendix
4.5 DOES THE LLM BASED REPRESENTATION IMPROVE FEW-SHOT DOWNSTREAM LEARNING? (RQ5)
Record2Vec needs fewer samples to achieve generalization small clinical datasets. Hospitals that are not part of large medical centers often lack sufficient patient data to train higg-performing models from scratch. Record2Vec models that are pre-trained on HiRID or MIMIC and then fine-tuned on only 16 ran- domly selected PPICU samples. Beyond the gains from in-distribution training, Record2Vec deliv- ers substantial improvements over its transfer variants and outperforms all competing methods under the same supervision budget. In multiple settings, the adapted Record2Ves models approach the distribution reference model trained on 36,019 samples, indicating that very limited supervision can under distribution shift.
Appendix [ compare models trained on 1,000 PPICU samples with
recover most of the performance
These gains likely arise because Record2Vec produces compact representation that is already semantically aligned across hospitals, reducing the burden on the downstream head to learn site- specific conventions from scratch. Finetuning can therefore act mainly as light calibration of task boundaries rather than a wholesale re- re-learning of measurement scales, missingness patterns, and naming differences. In effect, the summary abstracts away local differences while preserving clin- ically salient
states, trends, and recent interventions that are informative for mortality, length of stay, and near-term actions. This combination of cross-site alignment and retained clinical
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HIRID Gender
MIMIC Gender
Gender
0.750
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AUROC
0.500
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0.200
0.000
80000
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Riter www.ppaaiion
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No- Summary 3.1
Mles Comtom Fass
Mean shift
pite interonatton,
IS0E TimesFid
No- Summarry Marr 3-1
Elet Geneema Gemini 9-Plash
Mean shift Piter Pntorpoatiioo
1SD TimesR
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Redtanmun Gemini G-Fuish
3-3
Mean
Interpolation
TimnesFM M
Liama 3.1
MedGemia
Gerini 2 2:0-Flesh
Right shift
TSDE
No-Summary
Figure 6: In-distribution privacy prediction on Gender across three ICU datasets. Each bar is different method. The dashed horizontal line in each panel marks the least leaked classical baseline among {TimesFM, TSDE, Mean, Interpolation, Right shift} for that metric/dataset. Our method reached comparable and mostly reduced results terms of privacy leakage. Age results have less than 0.5% gap and can be inferred from Table 4
content makes Record2 especially data-efficient in shifted settings, enabling effective few-shot learning when labeled target data are scarce.
4.6 DOES THE PORTABLE REPRESENTATION INCREASE PRIVACY RISKS? (RQ6)
We find no evidence that Record2 increases demographic leakage risk: gender prediction collapses to a constant baseline for all methods, and Record2Vec's age error is similar to or higher than baselines. Record2Vec's MAE is generally on par with competing approaches across cohorts, suggesting that the portable representation does not make age easier infer. Figure O visu- alizes demographic recoverability from learned representations. For gender (binary), models trained on embeddings from grids, TSDE, TimesFM, and Record2Vec all degenerate predicting a single class on held-out data, indicating near-chance information about gender regardless of method. We additionally examined performance across minority diagnostic subgroups and found no disparate impact, with Record2Vec tracking overall population trends without exhibiting worst-group perfor- mance degradation relative to baselines. Detailed numbers appear in Appendix O
We think this outcome reflects how the pipeline shapes information. Record2Vec emphasizes clin- ical states, trends, and recent interventions needed for downstream tasks, rather than demographic markers. Because the summarizer and text encoder are frozen and not trained to predict demograph- ics, they do not amplify demographic signal beyond what is already present in the record windows. In practice, the summaries in our setup rarely include explicit age sex mentions, and the embed- ding stage is not optimized to capture them, which helps keep demographic recoverability low while preserving clinical utility. However, we emphasize that these results are specific to demographic leakage and do not preclude other privacy risks such as membership inference or embedding inver- sion.
4.7 WHAT INFORMATION IS OBTAINED OR LOST IN EACH EMBEDDING? (RQ7)
Record2Vec captures semantically meaningful structure that improves in-distribution perfor- mance and, importantly, encodes cross-site invariant patterns, while attenuating demographic attributes. We compare the Record2 ambelding-MeeGGemminn codes against imputation- ure 7). Unless stated otherwise, "gain" denotes the relative improvement (A%) of metric aligned with task direction (e.g., lower for error; higher for AUROC/recall) with respect to the imputation baseline.
summaries prompted with ICD vectors to quantify task-specific information gain or loss (Fig-
For in-distribution, the largest gains are observed for forecasting and mortality prediction, indicating that LLM-derived semantic abstractions, such as ICD-anchored summarization, improve outcome modeling and strengthen associations with significant temporal features. For cross-site transfer, We observe consistent improvements for lab, drug, forecast, LOS, and mortality, with the most pronounced effect on future drug-use prediction. This implies that Record2 Vec captures patterns that generalize across institutions and reduces sensitivity to site-specific artifacts. For demographic attenuation, signals tied to age and gender are generally weakened unchanged. This suggests
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A vs mean (%):
In-distribution (top 3) + Cross- (bottom
150
HIRID-HIRID
+0.43%
-14.62%
+7.32%
+1.04%
+41.88%
-11.52%
+69.90%
100
MIMIC-MIMIC
-0.16%
-10.67%
+6.71%
+2.81%
+19.33%
+10.57%
+52.72%
550
mean (%)
PPICU-PPICU
-7.28%
-8.80%
+9.97%
+9.85%
+36.35%
+30.27%
+12.84%
HIRID-PPICU
-0.94%
+0.70%
+23.50%
122.58%%
+33.32%
+ +38.33% +17.24%
-50
MIMIC-PPPICU
-2.99%
+0.61%
+17.49%
210.35%
+94.70%
+28.47%
+36.96%
age
gender
lab
drug
forecast
los
mort
Figure 7: Information gained vs. lost with Record2Vec (heatmaps; in-distribution and cross- site transfer). Each cell shows the task-alignt aligned relative change (%) of Record2Vec over representa- tion vectors. Overall, Record2 yields the largest gains for forecast, and mortality, including cross-site transfer, while demographic signals (age, gender) are attenuated or unchanged.
Record2Vec does not amplify, and mostly reduces, demographic attribute leakage relative to raw time series, which is another significant advantage under healthcare settings.
LIMITATIONS
Our framework requires sending patient records LLM services. Although all experiments were conducted in secure, IRB-compliant setting with strict controls, some patients and families may be uncomfortable with external processing. present, this confines the approach to research use un- less on-prem or fully compliant deployments are available. Generating LLM-basee representations can be issurrce-ntensiiv. Many hospitals may lack the compute or budget support large-scale or continuous inference without additional investment (see Appendix H for detailed analysis of token counts, latency, and costs). Finally, API-based or large local LLMs introduce non-trivial la- tency compared to lighter models, which can limit real-time applicability even if offline use remains practical. We acknowledge that our study primarily empirical; we believe developing the theory for when and where this methodology works would significantly improve the reach of our work. We rely on downstream task performance as a proxy for representation quality and do not currently uti- lize theoretical tools (such causal diagrams) formally derive the conditions for site-invariance. Furthermore, we do not directly quantify end-to-end information loss (c.g., via mutual information or per-feature fidelity checks). While our downstream accuracy suggests that our method preserves important signals, a limitation of current work, it is challenging to characterize exactly what infor- mation is retained lost during summarization.
CONCLUSION
Transforming irregular ICU records by first summarizing with an LLM and then embedding of- fers a promising route to portable, task-ready representations. Our results indicate competitive in- distribution performance, strong transferability, and improved few-shot learning without increased demographic leakage. We hypothesize that L.MSs can leverage their learned priors to produce sum- maries that are standardized across sites, rich in biological information relevant to downstream deci- sion making and close (in distributional sense) across sites, enabling the portability of downstream predictive models. Nevertheless, deployment risks remain, particularly around privacy, cost, and latency. Future work should focus creating compliant on-prem solutions, and prospective eval- uations within clinical workflows. Assessing feature-level fidelity and developing theory to better understand and control the properties of L.M--bsedd standardization is important direction to understand how far this approach can be extended.
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REPRODUCIBILITY STATEMENT
We take several steps to enable independent verification of our results. Data preprocessing (filter- ing, resampling, normalization, masking, and label construction) is documented in Appendix (C.2 HiRID and MIMIC-IV are publicly accessible via PhysioNet; PPICU is privately maintained dataset that will be shared upon reasonable request to the authors once the paper is public and de-anonymized, in accordance with institutional and data-use policies. Training details, includ- ing hardware requirements, software environment with pinned versions, all hyperparameters, and carly-stopping criteria, are provided in Appendix [D] To assess robustness, we tune key hyperparam- eters and repeat training/evaluation across multiple random seeds; the resulting analyses indicate our conclusions are stable to such variations. The code to reproduce our results is available at Rtw00////e.coo//ee-CCcc--- ICLR2022
We take several steps enable independent verification of our results. Data preprocessing (filtering, resampling, normalization, masking, and label construction) is documented in Appendix C.2 HiRID and MIMIC-IV are publicly accessible via PhysioNet; PPICU is a privately maintained dataset that will be shared upon reasonable request to the authors once the paper is public and de-anonymized, in accordance with institutional and data-uss fata-use policies. Training details- including hardware require- ments, software environment with pinned versions, all hyperparameters, and early-stopping crite- ria-are provided in Appendix [D] To assess robustness, we tune key hyperparameters and repeat training/evaluation across multiple random seeds; the resulting analyses indicate our conclusions are stable to such variations.
ETHICS STATEMENT
Scope and oversight. Our work is in machine learning for healthcare and involves processing de-identified patient records under institutional oversight. All activities complied with applicable regulations and institutional policies (including HIPAA in the United States and the policies of our institutional review/ethics board). Data use agreements were honored throughout.
Training and authorization. All authors completed required human-subjects and privacy training and are certified/authorizeed to handle clinical data in secure research environments.
Data protection and privacy. We followed nata-mininization and peast-privilege principles; access to protected health information (PHI) was restricted to approved personnel and audit-logged. Data were stored on cccess-controlled servers with encryption in transit and at rest; exports outside secure infrastructure were prohibited. When sharing intermediate artifacts (e.g., for internal review), we ensured le-identification and suppression of quasi- craasiiidetiiiers..
Risk assessment and mitigation. We considered potential harms (privacy leakage, representational bias, inequitable performance) and mitigated them via dataset curation checks, stratified evaluation where applicable, and manual review of outputs used in the paper. No clinical decisions were made using the research system.
LLM usage. We employed LLMs in strictly limited ways (see Section A (i) locally hosted models on instiuuiion-managed GPUs within secure, access-controlled servers; and (ii) third-party services (Gemini and ChatGPT families) only for grammar/wording polish of author- author-written written text and for generating small iconography within figures (not full figures and not scientific content). No methods, ideas, analyses, code, experiments were generated by LLMs, and all suggestions were manually reviewed.
Gemini 2.0 Flash configuration. Under dedicated research agreement, we used sandboxed deployment of the Gemini 2.0 Flash family strictly for research purposes. For any interactions related to healthcare data:
prompts and inputs excluded PHI whenever possible; when unavoidable for evaluation, only de-identified data were used;
inference occurred in an access-controlled environment with logging;
data retentieeton was disabled per our agreement; the model does not retain or use our prompts outputs for provider-side training service improvement.
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Author responsibility. Beyond copy-editing and minor figure iconography, LLMs not used. All ideas, study design, algorithm development, experiments, analyses, and substantive writing were performed by the authors, who take full responsibility for the work and its ethical conduct.
ACKNOWLEDGMENTS
RGK is supported by Canada CIFAR AI Chair and Canada Research Chair Tier II in Com- putational Medicine (CRC-2022-00049) This research was supported by an NFRF Special Call NFRFR2022-00526. Resources used in preparing this research were provided, in part, by the Province of Ontario, the Government of Canada through CIFAR, and companies sponsoring the Vector Institute.
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A USAGE OF LARGE LANGUAGE MODELS
We used large language models (LLMS) in two strictly limited ways:
1. Locally hosted models. For development convenience, we ran LLMs on institution- managed GPUs within access-controlled, offline (or firewall-restricted) servers. These models were used only for debugging assistance and rapid prototyping under our secure computing environment; no research data were exported outside these servers.
2. Third-party services for copy-editing and minor figure elements. We used the Gemini and ChatGPT families solely to polish grammar and wording of author-written text and to generate small iconography embedded within figures (e.g., simple symbols), not full figures and not scientific content. We did not use these services to generate methods, ideas, analyses, code, experiments. No sensitive identifying data were included in prompts. All suggested edits were manually reviewed by the authors.
Beyond the above, LLMs were not involved in the conception of ideas, study design, algorithm de- velopment, experiments, analyses, or writing of substantive content. All cirrributionscoonccpppe methodological, experimental, and explanatory entirely by the authors.
B EXTENDED RELATED WORK
Privacy and attribute inference in embeddings. Learned representations may inadvertently re- veal sensitive attributes. Recent analyses show that embeddings can leak information via inversion, membership, or attribute-inference attacks, even when protected features are not explicit (Song & Raghunathan 2020 Zhu al.) 2024). Emerging auditing frameworks evaluate demographic at- tribute inference from model outputs and intermediate representations (Panda et al.] (025). We therefore adopt demographic recoverability (age and sex) from learned embeddings practical proxy for privacy risk, situating our evaluation alongside this auditing literature.
ICU Time Series and Clinical Benchmarks Public ICU datasets drove clinical ML, notably MIMIC and eICU (Johnson et al.) 2016 Pollard et al.] [018 Johnson et al.) 2023b). Early pipelines were bespoke (Harutyunyan et al. [2099] McDermott al. 2021), prompting standardized bench-
marks: canonical MIMIC tasks (Harutyunyan et al.] 2019). 9) the HiRID ICU benchmark with high
frequency signals and evaluation code (Yèche et al. (202). and YAIB harmonizing MIMIC, eICU, HiRID, and Amsterdam UMCdb (van de Water et al. 2024 Thoral et al. (022). Aggregations such as BlendedICU broaden multi center evaluation (Oliver et al. 2023), and foundation pretraining over heterogeneous ICU series is emerging (Burger et al. 2024). Deep sequential models improve early warning and trajectory modeling (Hyland al..2200) Tomasev al., 2019 Di et al. 2024 2025),
and EHR sequence models reach expert level outcomes (Rajkomar et al.] (0018). 8). Persistent chal- lenges include explainability, irregular sampling, and missingness (Morid et al. 202T), addressed by interpretable modeling and transformer forecasting in clinical settings (Tan et al. [202]] Hartvigsen ct al. 2022: Zhang et al.] 2024a). Surveys stress the influence of curation, task design, and pre-
processing (Nńñz Reiz et al.] 2019 Shickel et al. 2018, (Suresh et al. 2017). Recent pipelines underscore standardization needs (Guupta et al. 2022 He & Chiang 2025)
Imputation and Representation Learning for Time Series. Irregular sampling and missingness are central in clinical time series. Model based imputers encode absence patterns or learn imputa-
tion with prediction (Che al. [2018] Cao al. 2018), while generative methods improve accuracy
through diffusion and realistic dynamics (Tahhroo et al.) 2021] Yoon et al., 2019). Continuous time and structure aware encoders address nonuniform sampling and uncertainty (Fortuin et al. 2020
Rubanova et al. 2019; De Brouwer et al. 2019 Kidger et al., 2020; Shukka & Marlin| (221)) l). Self supervised objectives and transformer encoders learn general purpose representations (Eldele al, 2021 Yue et al.) 2022, Woo et al.) 2022 Zerveas et al.) 2021 Franceschi et al.) 2019). Diffusion style self supervision couples imputation, interpolation, and forecasting masks for transferable em- beddings (Senane et al. (2024). Decoder only foundation pretraining yields strong forecasting across domains (Das et al. 2004). Earlier work spans dimensionality reduction and deep recurrent networks
(Li 2019 Balamurali, 2023 Siami-Namini et al. 2019). Recent ideas explore discrete tokenization and approximating LLM embedding spaces (Talukder et al.] [025) Sunn et al.] (024)). In healthcare, pattern based embeddings and semantic grouping improve interpretability (Feremans et al. 2022
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Kuznetsova al. 2023). shift (Trirat et al.
Surveys advocate universal representations robust to noise, sparsity, and
2024).
Large Language Models in Healthcare and Time Series. Large language models have advanced clinical NLP and representation learning. Domain pretrained transformers on coded EHRs improve disease prediction (Li et al. 2020a Rasmy al.. 2021), clinical scale models achieve strong
traction and inference (Yang et al. 2022] Peng et al. 2023), and general medical systems perform
well on exams and challenges (Singhal et al. 2023 2024 Nori al. 2023). Early adaptations
demonstrated clinical question answering and discharge summarization (Agrawal et al. 2022 Lang
al.) 2022; Van Veen et al.] 2023). For patient level prediction. audits report underperformance
and safety concerns (Brown et al. 2025; Wu et al., [025] Griot et al.) 2025] [Moor et al. (023)) Bridging modalities includes in modality pretraining, cross modality transfer and reprogramming. prompt tuning, cross modal fine tuning, autoregressive prediction, and alignment with language space (Kambale et al. 2023: Zhou al. 2023) Chang et al. 2025 [Ti al. 2024] Cao et al. 2024
Liu et 2024a bj 2025). Textualization strategies often trail specialized forecasters (Ansari al.
2024 Gruver et al. 2023 Tan et al. 2024). Few shot models show gains, and surveys emphasize
formatting, scaling, and evaluation (Liu et al., [023] Zhang et al. 2024b; Liventsev & Fritz 2024).
Advances in embeddings and domain specific language models motivate LLM derived representa-
tions for ICU data (Pennington al. 2014 Mikolov al. 2013 Reimers & Gurevych 2019 Su
ct al.
2022 Muennighoff et al. |022 Lee et al. 2024 BeehnmGGaader et al. 2024
Wang et al.
2024a
Luo et al. 2022] Alsentzer
2019).
Recent studies have also investigated serializing structured EHR data for LLM processing. Ap- proaches like TabLLM (Hegselmann et al. 2023) and DeLLiriuM (Contreras et al. 2025) fine-tune LLMs on serialized records for tabular classification or specific risk predictions. Others, such as Lee al. 2025), Gao al. (2024), and [Hegselmann al.] (2025), explore using frozen LLMs to embed raw data serializations directly. While these methods demonstrate the utility of LLMs for medical data, they primarily focus on sparse longitudinal records or single-site benchmarks. Our work distinguishes itself by addressing dense, irregular ICU time-series, where we find that direct scrialization yields excessive sequence lengths and brittleness to distribution shifts, necessitating a summarization step for effective cross-site portability.
Additionally, domain-specific foundation models like Med-BERT (Rasmy et al. 2021 and GenHPF (Hur et al. 2024) apply Transformer based pretraining structured EHR codes. While effective for longitudinal disease prediction within a specific health system, these models rely on fixed vocabular- ies that be sensitive schema variations and distribution shifts across institutions. Furthermore, they often struggle to capture the high-frequency numeric dynamics of intensive care data compared to generalist time-series models or semantic LLM representations, which offer greater flexibility and transferability in diverse deployment settings.
C DATASET AND PROCESSING
C.1 GENERAL INFORMATION
MIMIC (80,749 samples). The processed ICU time series comprises 60 features: ALP, ALT, Bicarbonate, Bilirubin, BloodUzeaNitroeen, Calcium, Creatinine, Creatininekinase, Hemoglobin, INR, Lactate, Magnesium Paco2, Pa02, Phopphate,Plattllet Potassium, Sodium, Troponin, WBC, ph, AirwayPressure, Diastolic3lloodresssuee Fic2, GCS, HeartRate, ICDSC, MeamBloodPressure,
MinuteWenttilation. PEEP, ReapiratoryRate, SAS, $ystollcBBooodressseee Temperature, TidalVolume, UrineOutput, Analgesia, Antiarrhythmics, Antibiotics, Anticoagulants, Antiepileptics, Ant.hyyertenssive, Antipsychotics, CTScan, CaReplacement, Dialysis, Diuretics, EnteralNutrition, ICPMoniton KReplacements MRI, MgReplacement, PPT, Paralysis, TPN, Transfusions, UltraSound, Vasopressors, Ventilation,
HiRID (36,019 samples). The processed ICU time series comprises 64 features: ALP, ALT, AST, Bicarbonate, Bilirubin, BloodUreaNitrogen. Calcium, Chloride, CreatineKinase, Creatinine, Glucose, Hemoglobin, INR, Lactate, Magnesium, Paco2, Pa02, Phosphate, Platelets, Potassium Sodium, Troponin, WBC, ph,
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AirwayPressure, AirwayPressureeakk DiastolicGloodPPesssus Fi02, FluidBalance, GCS, HeartRate, MeamBloodPressure, PEEP, ReapiratoryRate, SAS, Saturation, SystolicHloodPzesuun UrineOutput, analgesia Ant.irrhythhiccs, Antibiotics, Ant anticoaguaants, Antiepileptics, ant.ihyperennsive, Aspirin, CaReplacement, Dialysis, Diuretics, ICPMonitor, Insulin, KReplacement, LiverToxicDrug, MgReplacement, Neuroleptics, Paralysis, Saline, Sedation, Steroids, TPN, Transfusions, Vasopressors, Ventilation.
Temperature, TidalVolume,
PPICU (47,119 samples). The processed ICU time series comprises features: ALP, ALT, AST, Bicarbonate, Bilirubin, BloodUreaNitroger trogen, Calcium, Chloride, Creatinine, CreatinineKinase, GGT. Glucose, Hemoglobin, INR, Lactate, Magnesium, PacO2, Pa02, Phosphate, Platelets, Potassium, Sodium, Troponin, WBC, ph, AirwayPressure, AirwayPressurelf, AirwayrressurePe.,
Fi02, FluidInput, FluidOutput, GCS, HeartRate, ICDSC, hor Mit.ddderrreeeeeeetetteeetteee.e MintrraeMnnteeenilaa.....o PAVSupport, PC, PEEP, PlateauPressure, RespiratoryRate, SAS, Saturation, SystolichloodPPesssii
DiastolictloodPrsssses
Temperature, TidalVolume, UrineOutput, Analgesia, Antiarzhytamic, Antiarrhythmics, Antibiotics, Anticoagulants, Antiepileptics, Antihypertensives, Aspirin, Dialysis, Diuretics, EKG, EVD, ICPMonitor, InhaledWasodilatoor, Insulin, Isoproterenol, MgReplacement, Neuroleptics, OsmoticTherapy, PPI, PS, Paralysis, Sedation, Steroids, TPN, Transfusions, Vasopressors.
C.2 DATA PREPROCESSING DETAILS
We provide reproducible preprocessing pipelines for all ICU datasets considered in this work. This appendix summarizes the steps at a high level, without referencing specific files or directories.
Common setup. We organize and processed data under central data directory. Access to each dataset follows the relevant data use agreements. Unless stated otherwise, per-stay sequences constructed hourly resolution, and features are normalized using statisties computed on the training split. All clinical features are selected in discussion with experienced practicing ICU clini- cians.
HIRID
We first obtain authorized access and download the raw HiRID release. Because HiRID associates each patient with single ICU stay, we group all clinical and medication records by patient identifier and construct a sparse, event-centric representation per patient. We then curate clinically meaningful features and extend the feature dictionary to include start/end times and summary statistics for qual- ity control and normalization. Using this curated set, we produce patient-level targets and serialize time indices, value indices, and per-event counts for downstream modeling. Finally, we standardize signals and combine them on a per-feature basis ensure consistent scaling across patients and over time.
MIMIC-IV
We begin by obtaining credentialed access and downloading the raw MIMIC-IV data, then convert the raw tables into columnar layout to improve I/O efficiency. Next, we filter ICU stays that exceed a minimum duration threshold, retaining this subset for further processing. In collaboration with clinicians, we develop a mapping from measurement identifiers study feature set; this mapping is intentionally decoupled so it can be revised without reprocessing earlier stages. We restrict event tables to the curated features and carry forward only necessary metadata into a project- specific processed area. Core administrative and ICU stay tables are merged to derive age, gender, mortality, and length-of-stay labels, excluding pediatric subjects for adult-only cohorts. We then materialize subject-centric layout (diagnoses, events, procedures, stays), preferring numeric event values where available, and write feature- secture-e storage to enable efficient access and validation across tasks.
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PPICU
For PPICU, we convert all raw sources into uniform, analysis-friendly tabular format and sepa- rate multi-signal streams into per-feature tables for efficient access; laboratory measurements are processed analogously. We merge demographic and outcome information construct a source stay table with identifiers and key labels (age, gender, length of stay. mortality). The feature space restricted to the analysis cohort, with per-feature summary statisties computed for clinician re- view; clinician-verified filters are then applied to remove implausible or out-of-range values. From the merged feature data, we generate per-stay tables sorted by measurement time and align feature names to a harmonized schema. A global time index keyed by stay identifier is built with arrays of time points at the chosen resolution. Each stay is finally converted into both a sparse dictionary- backed representation and dense, zero-filled discrete representation, and we emit the resulting artifacts alongside train/validation/ess splits.
D TRAINING DETAILS
D.1 SYSTEM CONFIGURATION
NVIDIA L40S GPUs. Unless noted, training and inference can be paused and fully completed on a single L40S (24 GB); large-scale reference runs used 4 L40S for throughput.
All experiments
D.2 TSDE EMBEDDING EXTRACTION
The TSDE model is trained in a self-supervised manner on the same training split downstream tasks. We mask the final of each sequence and train model predict masked segment. After convergence, we extract the hidden representation the TSDE embedding for each record.
D.3 TFM EMBEDDING EXTRACTION
We use the time-series foundation model the full HİRID/MIMIC/PPICU datasets and extract representations from the last two transformer blocks. The penultimate layer yields consistently better downstream performance and is used in all main results.
fm-1 0-2000m. We run inference on
D.4 HYPERPARAMETER DETAILS
For baselines, we tune batch size and learning rate with early stopping and report the best configu- ration per method:
right shift: batch size 512, learning rate 1 10-5 5
mean: batch size 512, learning rate x 10
interpolation: batch size 256, learning rate 5 10
Record2Vec models are trained under common setting: batch size 512, learning rate 10-5, All methods are trained with seeds {42, 84, 1005, 2025}; we report the mean and standard deviation across seeds.
D.5 FEW-SHOT FINETUNING (16-SAMPLE SETTING)
We study extreme low-data regime with only 16 labeled target samples and no held-out validation set. Finetuning is performed with AdamW and a warmup cosine schedule; the learning rate scales with the effective batch size:
batch size
LRgaaled = BASELRXX REF.FINETUNE.BS
BASE 1x REF DEF_FINETUNE .BS = 16.
Weight decay is 0.01 with 31 = 0.9, 82 = 0.999. Let S be the total number of optimization steps (S num epochs steps per epoch) and S., max 10, [J) the warmup steps. In the final
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experiment, S 100 16. The per-step multiplier is
++1
t < Sw:
Sw
A(t)
1 + casl т
f-8m
(1 MIN LINLLRRAN LAARIOO
5-
t 2 Sw:
with MIN SINN_L_RAATOO RATIO 0.10. Unless otherwise specified, weight updates apply to both the back- bone and the task decoder.
Concrete finetuning setup (Shared across all methods).
Batch size: batch_ size = 16, LRecaled = 1 x 10-6.
Optimizer: Adam (lamm(1r (Ir LRaealed, weight 0.01, = (0.9, 0.999)).
Scheduler: Warmup (first max steps) then cosine decay 0.1 x the peak LR.
E PROMPTS USED FOR LLM SUMMARIZATION AND A CASE STUDY
In RQ5, we discussed the effect of prompts the quality of embedding. The patient data is sum- marized on a split-by-feature manner, raw text as following: ALP has value 66 in hour
31. ALT has value 22 in hour 31. AST has value 17 in hour 31. Bilirubin has value 4 in hour 31. Blood Urea Nitrogen has value 5.6 in hour 31. Calcium has value 1.99 in hour 31. Chloride has value 115 in hour 31. Creatinine has value 103 in hour 31. Creatinine Kinase has value 316 in hour 31. Hemoglobin has value 134 in hour 31. INR has value 0.99 in hour 31. Lactate has value 1.4 in hour 31. Magnesium has value 1.03 in hour 31. Phosphate has value 0.78 in hour 31. Platelets has value 303 in hour 31.
Potassium has value 3.4 in hour 31. Sodium has value 150 in hour 31. Troponin has value 8 in hour 31. WBC has value 8.4 in hour
31. Airway Pressure has value 7.36 in hour 31, value 7.21 in hour
32, value 7.13 in hour 33, value 7.1 in hour 34, value 7.36 in hour 35, value 6.29 in hour 36, value 5.08 in hour 37, value 5.3 in hour 38. Diastolic Blood Pressure has value 59 in hour 31, value 56 in hour 32, value 77 in hour 33, value 80 in hour 34,
value 75 in hour 35, value 82 in hour 36, value 75 in hour 37,
value 78 in hour 38, value 69 in hour 39, value 68 in hour 40,
value 59 in hour 41, value in hour 42, value 65 in hour 44.
Fi02 has value 30 in hour 31, value 30 in hour 32, value 30 in
hour 33, value 30 in hour 34, value 30 in hour 35, value 30 in
hour 36, value 30 in hour 37, value 30 in hour 38, value 21 in
hour 39, value 21 in hour 40, value 21 in hour 41, value 21 in
hour 42, value 21 in hour 43, value 21 in hour 44. FluidInput has value 80 in hour 34, value 160 in hour 39. FluidOutput has value 100 in hour 39. GCS has value B in hour 32, value 8 in hour 33, value 8 in hour 34, value 8 in hour 35, value 7 in hour 36, value in hour 37, value 14 in hour 39, value 14 in hour 40, value 14 in hour 44. Heart Rate has value 97 in hour 31, value 101 in hour 32, value 94 in hour 33, value 93 in hour 34, value 99 in hour 35, value 104.5 in hour 36, value 105 in hour 37, value 102 in
hour 38, value 109 in hour 39, value 107 in hour 40, value 106 in hour 41, value 102 in hour 42, value 105 in hour 43. Mean Blood
Pressure has value 66 in hour 31, value 62 in hour 32, value 83
in hour 33, value 85 in hour 34, value 81 in hour 35, value 87
in hour 36, value 82 in hour 37, value 85 in hour 38, value 80
in hour 39, value 75 in hour 40, value 68 in hour 41, value 74 in
hour 42, value 72 in hour 44. Minute Ventilation has value 4 in hour 31, value 3.7 in hour 32, value 3.84 in hour 33, value 4.05
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in hour 34, value 6.54 in hour 35, value 5.51 in hour 36, value 4.56 in hour 37, value 4.4 in hour 38. PC has value 0 in hour 31, value 0 in hour 32, value 0 in hour 33, value 0 in hour 34, value 0 in hour 35, value 0 in hour 36, value 0 in hour 37, value in hour 38. PEEP has value 5.88 in hour 31, value 5.88 in hour 32, value 5.88 in hour 33, value 5.88 in hour 34, value 5.88 in hour 35, value 4.78 in hour 36, value 3.68 in hour 37, value 3.68 in hour 38. Respiratory Rate has value 12.5 in hour 31, value in hour 32, value 7 in hour 33, value 6.5 in hour 54, value 8 in hour 35, value 8.38 in hour 36, value 7.5 in hour 37, value
in hour 38, value 20 in hour 39, value 21 in hour 40, value
16 in hour 41, value 15 in hour 42, value 22 in hour 43, value
26 in hour 44, value 20 in hour 47. SAS has value 5 in hour 32, value 5 in hour 33, value 5 in hour 34, value 5 in hour 35, value in hour 36, value 4 in hour 37, value 4 in hour 39, value 4 in hour 40, value 4 in hour 44. Saturation has value 96 in hour 31, value 95 in hour 32, value 99 in hour 33, value 100 in hour 34,
value 99 in hour 35, value 99.5 in hour 36, value 100 in hour
37, value 100 in hour 38, value 96 in hour 39, value 93 in hour
40, value 94 in hour 41, value 97 in hour 42, value 96 in hour
43, value 96 in hour 44, value 95 in hour 45, value 94 in hour
46, value 94 in hour 47, Systolic Blood Pressure has value 94
in hour 31, value 84 in hour 32, value 102 in hour 33, value 106
in hour 34, value 106 in hour 35, value 105 in hour 36, value 108
in hour 37, value 109 in hour 38, value 110 in hour 39, value 102
in hour 40, value 101 in hour 41, value 97 in hour 42, value 98
in hour 44. Temperature has value 36.4 in hour 33, value 35.9
in hour 37. Tidal Volume has value 230 in hour 31, value 215 in
hour 32, value 235 in hour 33, value 267.5 in hour 34, value 115
in hour 35, value 300 in hour 36, value 255 in hour 37, value 480
in hour 38. Urine Output has value 350 in hour 31, value 100 in
hour 32, value 35 in hour 33, value 60 in hour 34, value 55 in
hour 35, value 60 in hour 36, value 60 in hour 37, value 300 in
hour 38, value 100 in hour 39, value 100 in hour 40, value 100 in
hour 44. Patient receives Analgesia at hour 32, 33, 34, 35, 36, 37, 38, 39. Patient receives Anticoagulants at hour 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47. Patient receives Neuroleptics at hour 38, 39, 40, 41, 42, 43, 44, 45, 46, 47. Patient receives PS at hour 31, 32, 33, 34, 35, 36, 37, 38. Patient receives Sedation at hour 32, 33, 34, 35, 36, 37, 38, 39. Patient receives TPN at hour 32.
The following is the detailed prompts and samples of summarization
ZERO-SHOT
You are clinical agent that analyze and then provide the most concise summarization ICU time series data for forecasting.
war.uumatys--aaaaaaaaei exhibits fluctuating hemodynamics and respiratery status, Key observations: **Hencdysamicss=> Initisi hypotension (MBP 68, SBP 94) improved, then showed variability (MB2 62-87, S3P 84-110). DB2 increased significan:ly Irom hour 32-36 (56-82) then decressed. Heart rate is consistently elevated 197-109) Ane v*Respizetory: Airway pressure decreased (7.36 -> 5.08) then increased. PEEP decressed (5.88 -> 2.68). Reapi rotory rste varied signi generally stable (95-100), with cip (103). Grine output is variable 555-350... BUK is elightly elevated (5.6).Am Gus initially
17-261. Fin2 decreased from 30 to 21, Saturation
hour 40, An- **Renal:Av Creatinine is high
*>Beurologic:..
19), improved to 14, then decreased.Ani other Labs (ALP, ALI, ASI, Bilirabin, CK, Tropenin, NBC) normal limits.An. **Interventionss naurolaptica zdministered. TPS given hour 32, ArAn==-veroll:: Patient shaws signa of initial instabl lity with subsequent fluctuations in hemodynamicn ia, resorratory parameters, and neurological status, nigh sodium and creatinine are notable,"
v*Labs:** Sedium is high (150). relatively stable within Continueus enticosgulation, snalgesia, sedation, and
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ICD
You are clinical analysis agent. Summarize ICU time-series patient data for forecasting using this structure:
Trend overall direction of vitals, labs, therapies, and organ support.
Scasonality - repeating cycles (e-g., circadian).
Irregularities acute deviations events.
Map each diagnosis to its affected organ system (cardiac, respiratory, hepatic, renal, neurologic, etc.). For every system, assign severity score from 1 (least affected) 10 (most severe) based on data patterns and level of support required. Output only the summary in clear clinical prose, concluding with semicolon-separated list of organ systems and scores (e.g.. "Cardiovascular 7/10; Respiratory 8/10; Hepatic 3/10"). Do not explain your reasoning.
'0verall, the patient exhibits a cemplex trajectory with perieds of relative stability punetuated acute dateriorstions and impravements. Trenda show initial hemodynamic Instaot lity with low blood presaute, followed by a period of hypertension and tachyoardla, pressures. Reapirstory status marked in sirway pressure and PEEP, alengside variable respiratery rates and tidal volumes, suggesting evolving ventilation needs, keurologic status initially improves significantly before potential decline. Renal function shows initial eliguris followed by periods of increased output, but creatinine remains elevated. Liver function tests relativaly stable. There is clear cireadian pattern evident the provided data. Trregularit include a sharp drop in reapl rate and tidal volume azound hour 35, subsequent increase Lidal 39, and fluctuations in fluid balance, The patient received multiple therapies including analgesis, ontisoagulants, neurolopticn, pressera (PS1, and sedaticn, alangaide TPk. oaaeeuriiva 7/10; Respiratory 8/10; Repatic 3/10; Renal 6/10; Neurologic 6/10'
then a return towards
aignificant decrease
depressed 6cs,
houz 36, a significant imprevement in around hour
TREND
Examine the data closely and describe the trend changes step by step over time. For example: from [start] to imidpoint), what happened? Then from [midpoint] to [end], what happened? After describing each phase, conclude with overall summary in natural language. Summarize many feature possible starting from the most significant ones in concise words. Only include your description and summarization.
"vyPhase 1: Hour 31 to Hour 38xxAn)nEzom hour 31 to hour 38, the patient showed signs of initial Instability followed by some improvement. Airway Pressure decreased steadily from 7.36 5.38, then slightly incressed 5.3. Diastolic slood Pressure initially decreased from 59 to 56, then signt canntly increased to 80, Fluctuating between 75 and 78 before rising agair 62. Fi02 remained stable
39, remained before improving to 14 by hour 39. Beart Rate increased from 97 to 105, then slightly decreased to
until
B,
dropped
62, then increased significantly 87, fluctuating between 82 and 85. Minute Vantilation increased from 4 to 6.54, then decreased to 4.56 and 4.4, FEEP decreased from 5.88 3.68. Reapiratory Rate decreased aignificantly from 12.5 4, then increased shorply 20, Saturation improved from 96 to 102, then decreased to 96. Systolic Blood Presaure decreased from 94 to 84, then increased sign: ficantly 125, then
Mean slood Fressure decreased from
Lo 109. Temperature decreased from
to 35.9. Tidal Volume decreased incressed dramatically to 480. Urine Output decreased signi from 350 to 35, then increased to 300. The patient received analgesia, enticoagulants, FS, and sadation during this perlod. AraavaPaase 2: Hour 39 to Hour 47sx\n)/From hour 39 to hour 47, the patientt's condition showed further fluctuatione and potential deterioration. Alzway Pressure decreased from 5.3 to 6.29, then decreased to 5.08. piastolia Blond Pressura decreased from 67, 65. F102 decressed from 21 to 21, GCS zemained at 14. fleazt Rate increased from 102."
69, then 68, 59,
CoT
You are healtheare agent summarizes ICU patients' time series status future time series forecasting. Analyze this step by step.
Analyze time series data to identify key trends.
Based on the identified trends, determine potential clinical implications.
3. Summarize the findings and suggest possible interventions.
Summarize many feature possible starting from the most significant ones in concise words and only respond with your summarization.
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Dataset
Embedding
Silhouette
HiRID
Raw time-series
0.372
HiRID
CoT embedding
0.452
HiRID
ICD embedding
0.408
HiRID
zero_shot embedding
0.638
HiRID
Trend embedding
0,540
MIMIC
Raw time-series
0.332
MIMIC
CoT embedding
0.618
MIMIC
ICD embedding
0.322
MIMIC
zero_shot embedding
0.472
MIMIC
Trend embedding
0.681
PPICU
Raw time-series
0,486
PPICU
CoT embedding
0.573
PPICU
ICD embedding
0.385
PPICU
zero shot embedding
0.375
PPICU
Trend embedding 0.485
Table 3: Silhouette scores for different embeddings across datasets (higher is better).
SvrSuaryyy of ICU Patient Status (8ouz :eehala (SBP 94, 66) improved ficantly (SSP 110, MBP **Hemddpmmmess: initis" hypotension fluctuations,
shows recens instability (DRP decrease in hour 40-41). cachycardia IaR 97-109) persists. Airway pressure dacreased signific:nt (7.36 to 5.08) then increased slightly, suggesting potential improvement in lung stralegy. \ay svRespiratory ux Respicatory zate is vaciable (initially low 7 6.5, thea increased 20-26). Pic2 decreased from oxygonation. Saturation ganerally stable (96-1001 but decreased recently (93-94). ridal volume shows significant fluctuations (230-480) .n. An. AvRenal Punction:Av Creatinine elevated (103), BUN la borderline high (5.6). Drine output is highly variable (350 35, then wttttaaoli//lllerrrrall
followed possia detertoration
change in vattin
2-, indicating potential improvement
100-300) An- An- Hypocalcemia Neeuroooggcal Status:AN 6C8 initially low (8), improved to 14, then decressed again. AvLiver Fonction:as Liver enzymes (ALP, ALT, AST) are mildly elevated. silirubin borderline high (4) ivCoggulationza INS is normal (0.99).Az.
Hypophosphatemia Hypernatremia (Na 150). Rypokalenta (K 3.41.
0.78), Lactate borderline
(1.4)
AvOther:xy Platelets are normal
(3031. MBC normal (8.4). Troponin is elevated (8), ArAnssLLinnial Implicat fon Iemodynamic instability requires close monitoring and potential vesopcesso: adjustment, Variaole respi atatua suggeats ongoing lung Injury or changing ventilation needa, An+ Renal dystunction televated crealinine, vaslable urine output) warzants monitoring
pstential intervention. Ans Electrolyte imbalances (hypernstremie, hypckalemie, hypocalcemie, hypopharphatemia) cerrection.\n-
F EMBEDDING QUALITY ASSESSMENT
We assess representation quality by applying the unsupervised dimenssionllty--rduction method UMAP to the raw time series and four gemini-2..0-fushh-baeeed summaries (CoT, ICD, zero_shot, Trend). Although UMAP is fit without labels, we color points by mortality status (0/1) and quantify cluster separation using the Silhouette score (higher is better), as proxy for discriminability on the downstream mortality classification task. Silhouette scores are reported in Table 3 Results are averaged over target embedding dimensions of 128, 256, and 512.
G PRIVACY RESULT TABLE
Table referenced in RQ6 is shown in Table
H TOKEN COUNT COMPARISON (QWEN3-EMBEDDING-B0
We report tokenization statistics for two PPICU text corpora using the Owen3-Embeddiin--B tokenizer (special tokens excluded) across raw text and summarization text. Table 5 summarizes per-string token counts. Average tokens for raw text is 6.656.5 and average tokens (Summarization from ICD) is 234.0. Token reduction: 5,872.48 (2,509.3% B).
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Dataset
HiRID
MIMIC
PPICU
Tasks
Age
Gender
Age
Gender
Age
Gender
Method 4
MAE
AUROC
MAE
AUROC
MAE
AUROC
Mcan
0,805017
0.754615
0.803168
0.678225
0.76247
0,707425
Right shift
0. 0,803392
0.742055
0.798042
0.694878
0.767785
0.726552
Interpolation
0,800455
0.726648
0.799755
0.670795
0.76095
0.720293
TSDE
0.798725
0.797682
0.733638
0.76362
0.764377
TimesFM
0.79849S
0.64247
0.795795
0.566465
0.759935
0.63862
No- Summary
0,801658
0.653393
0.803828
0.640195
0.812675
0.666798
Llama 3.1
0,801658
0,64383
0.805292
0.623775
0.81619
0.643475
MedGemmna
0,801207
0.645748
0,803142
0.61944
0.814385
0.652317
Gemini 2.0-Flash
0,801828
0.650965
0.805535
0.605605
0.8133188
0.646813
Dataset
HIRID
PPICU
MIMIC
PPICU
PPICU
PPICU
Tasks -
Age
Gender
Age
Gender
Age
Gender
Method +
MAE
AUROC
MAE
AUROC
MAE
AUROC
Mean
0,805353
0.63862
0,78551
0.48239
0.00
0.000
Right shift
0,791562
0.63101
0,77174
0.63016
0.00
0.000
Interpolation
0.78547
0.778125
0.63008
TSDE
0,7615
0.452603
0.755202
0,457027
0.00
0.000
TimesMM
0,760583
0.63862
0.75289
0.6113
0.00
0.000
No- Summary
0,81 1862
0.643015
0,80723
0.635738
0.00
0.000
Llama 3.1
0,81223
0.63101
0,80953
0.63016
0.00
0.000
MedGemmaa
0,812695
0.64306
0.805072
0.633265
0.00
0.000
Gemini 2.0-Flash
0.813743
0.642868
0.809338
0.63083
0.00
0.000
Table 4: RQ6 (Privacy): Age and gender prediction. AUROC.
Age evaluated with MAE; Gender with
Table 5: Per-string tokenization summary
-BB special tokens).
count
total
mean
std
File A (Raw Text)
46,818
285,894,402
6,106.51
3,328.77
13,233.00
File B (ICD Surmarization)
47,119
11,027,274
234.03
60.52
567.00
We add more detailed report of latency, GPU time, token usage and approximate costs across all of methods in Record2 Vec in Table 6 We report inference embedding latency, GPU time per batch, token counts and costs per patient. Specifically, the language models are served on NVIDIA L40S GPUs using vL.LM inference; the embedding model served on L.40S for the summarization- based methods, while the no-summarization baseline uses L40S. Additionally, we compare the inference time using deterministic template (no summarization). Balancing the results and costs, we find Gemini-2.0- -laf results the least latency/costs while maintaining high performance, with cost of 0.7 dollar per 1000 samples and 0.26s latency per patient.
I 16-SAMPLE FEW-SHOT FINETUNE RESULTS
In this section, we report the detailed mtantsstd results under 16-sample few-shot finetuning scenario as Table This is complementary to Figure 5.
J 1000 SAMPLES TRAINING RESULTS
In the section, we record the comparison between results of pre-training using 1000 in-distribution samples and 16 16-sample few-shot finetuning result of Record2Vec. This is simulation in real world settings for smaller local hospitals, where they have less sample to train a robust model. The 1000 samples chosen random and shared across methods ensure fair comparison.
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Model
Latency (per sample)
GPL time (per batch)
Token counts
Cost (per IK samples)
MedGemma Qwen
0.784 + 0.020a
3959.41 + 119.61 ms
234.03 tks/sample
Llama Qwen
0.25-406 0, 0,023s
1299.4 117.24 ms
254.17 dks/sample
Gemini Qwen
0.28 0.036
293.41 44 dks/sample
0.7 W/lk samples
Qwen (no suminarization)
2.9675
23608. ms
6106.56 Iks/sumple
Table 6: Latency, GPU time, token usage, and approximate cost of different summariza- tion/inference pipelines.
Dataset
HIRID PPICU
MIMIC PPICU
Wins
Task
Forecast MSFJ
LoS MAE]
Mortality AUROC1
Forecast MSFJ
Lo$
Mortality AUROC1
Method
MAEJ
Mean
0.133
0.001
2.678 1 0.229
0.600
0.007
0.183 1. 0.003
1.171
0.284
0.565 1. 0.050
Right
0.002
1.803 0.041
0.003
0.606 00.036
Interpolation
0.139 1 0.001
1.999 1 0.123
0.605
0.033
0.183 1. 0.083
0.972
0.080
0.563 1. 0.028
TFM
0.005
0.896 0.042
0.003
0.001
1.067
0.683
TSDE
0.302 0.002
0.795 + 0 0.036
0.507 0.017
0.392 = 0.016
0.613 = 0,170
0.586 + 0.025
Record2Vee
0.000
0.500 0.005
0.792
0.0215
0.000
0.448
0.003
0.750 0.008
Table 7: Transfer performance from HIRID/MIMC PPICU. Values are mean Best per
column in bold; second-best underlined. Wins count the number of best results per method.
K STRONG MORTALITY TRANSFER RESULTS
In this section, report transfer learning results for strong mortality baselines: LSTM, GRU, TCN, Transformer as mentioned in Yēehe et al. (0022). We observe that none surpass our simple downstream classifier, Mateh which achieves an AUROC of 0.72. (Table This suggests that there still a need to develop models better suited to transfer-learning settings.
L EMBEDDER ABLATIONS
We performed additional ablations on the choice of embedding model and pooling / normalization strategy. Overall, we observe three main trends: (i) stronger embedding models generally yield better performance, although the gains are modest across current SOTA models; (ii) weaker, non- SOTA embedder degrades performance but still remains above our non-summarization baseline; and (iii) our method is largely robust changes in pooling and normalization, with the recommended configuration from the model documentation giving the most consistent performance across bench- marks.
On our base embedder Doven--Embedding--e we tested the following pooling + normalization pairs: mean L2 (our default), mean + none, CLS + L2, last L2, and max L2. In addition, we replaced Qwen3 with the current MTEB leader nvidia/ ama -ettaa--eedddddooe ron-8'o and the prior SOTA gte-Qwen2 2-----een27---ns--- ruct. The in-distribution ablation results on PPICU are reported in Table 10] and the HiRID- transfer ablations are shown in Table [I]
M DETAILED RESULTS FOR COMPARING PROMPTING STRATEGIES AND SUMMARIZATION METHODS
In this section, we provide the detailed performance values used to generate the rank distribution plots in Figure 3 and Figure 4 These figures provide an intuitive visualization of how different summarization and prompting strategies affect relative performance across our benchmark.
The rankings are determined by comparing the performance of the competing methods on five spe- cific downstream tasks, using the same metrics reported in the main results (Tables [ and [n. We utilize Mean Squared Error (MSE) for forecasting, Mean Absolute Error (MAE) for length-of-stay prediction, AUROC for mortality prediction, and Recall for both treatment planning (Drug) and mea- surement ordering (Lab). For the summarization analysis in Figure ranks are computed across four methods: No-summary, Llama 3.1, MedGemma, and Gemini 2.0 Flash. For the prompting analysis in Figure 4 we compare four variants: Zero-shot, In-Context Learning (ICD), Chain-of- of-Thuugh (CoTT) and Trend.
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Task
Small PPICU
Few-shot Finetune (Record2Vec)
Mortality
0.6843
0.72
LoS
1.03
0.49
Forecast
0.62
0.183
Feature
0,901
0.97
Lab
0.94
0.97
Table 8: Performance comparison between models trained on small PPICU subset vs. few-shot
finetuned models.
LSTM
GRU
Transformer
TCN
+P Baseline
0.640
0.687
0.579
0.719
H +P Record2 Vec
0.69788
0.6838
0.679
0.684
M P Baseline
0.5235
0.5476
0.564
0.719
M +P Record2Vec
0.722
0.685
0.682
0.695
Table 9: Model performance for HIRID-PPICU (H-+P) and MIMIC-PPICCU (M-P), comparing baseline vs. Record2 representations.
The rank counts are aggregated across two distinct experimental settings. The "In-Distributton" results (shown the left subfigures) aggregate rankings over 15 total tasks, corresponding five tasks evaluated across three source datasets. The "Transfer Learning" results (shown in the right subfigures) aggregate rankings over 30 total tasks, covering five tasks across six distinct transfer directions. For the Figure 4 we only considered three sets of transfers (total 15 numbers). Table Table [4 Table and Table present the exact performance numbers used construct these rank distributions, allowing for direct inspection of the underlying values.
12
N
GENHPF MODIFICATION AND REPLICATION DETAILS
ICU data contain far greater number of features observed over a much shorter time horizon, with multiple events often occurring simultancously. When converting ICU data into a textual represen- tation following GenHPF (Hur et al. 2024), adopt simple hierarchy: features are first divided into laboratory, vital, and binary (therapy intervention) groups, and each feature concatenated with its group prefix. For example, the feature Hemoglobe n is encoded as lab. Hab.Hemog obbi.. The feature groupings used for the three datasets are summarized Table 17
O DETAILED RESULTS
We provide detailed results and tables in this section for a comprehensive comparison between LLM--baed summarize-then-embee pipeline with the three baseline methods.
31

---

Published as a conference paper at ICLR 2026
Ablations
Forecast
LoS
Mort
Drug
Lab
Qwen3-mean-12
0.021
0.347
0.90
0.911
0.931
Qwen3-mean-none
0.027
0.371
0.88
0.871
0.919
Qwen3-cls-12
0.024
0.530
0.89
0,900
0,940
Qwen3-last-12
0.022
0,423
0.89
0,920
0,923
gte-Qwen2-instruet
0.028
0,480
0,88
0.886
0.918
llama-embed- nemorron
0.021
0.379
0.89
0.9097
0.929
Baseline
0.040
0.378
0.52
0.878
0.857
Table 10: Ablations PPICU (in-distribution).
Ablations
Forecast
LoS
Mort
Drug
Lab
Qwen3-mean-12
0.183
0.69
0.72
0.97
0.97
Qwen3-mean-nome
0.209
0.66
0.71
0.94
0.96
Qwen3-cls-12
0.230
0.78
0.50
0.995
0.98
Qwen3-last-12
0.190
0.78
0.73
0.94
0.96
gle-Qwen2-inntrruc
0.250
0.82
0.50
0.93
0.96
llama-embed-nemoorro
0.190
0.72
0.713
0.96
0.998
Baseline
0.306
1.09
0.50
0.42
0.77
Table 11: Ablations for HiRID transfer.
Table 12: Summarization methods performance comparison across 15 in-distribution tasks (Figure
left subfigure)
HIRID
MIMIC
PPICTI
Model
Fomocast
1OS
Moet
Drig
Lab
Forezeet
L.08
Mort
Drug
Tab
Fonecast
LOS
Miee
Drug
1abb
0.021
0,3538
0.399
0.931
0.027
0.328
0.82
0.885
0.947
0.0211
0.376
0.54
0.9368
0.923
llama 3.1
0,0244
0,381
0.399
0.8131
0.856
0.938
0.029
0.3752
0.63
0.9271
medgemma
0,0237
0,3682
0.911
0.925
0.0299
0.4005
0.7788
0.8946
0,9432
0.029
0.3626
0.6346
0.925
0.936
0.0064
Table 13: Summarization methods performance comparison across 30 transfer learning tasks (Part 1: Transfer Pairs 1-3)
H
M M
H
Model
Foroesst
LOS
Moxt
Drug
Lah
Forceast
LOS
Mort
Drag
Lab
Forecast
LOS
Mort
Drag
Lab
0.21
0.98
0.66
0.95
0.263
0.77
0.7
0.92
0.92
0.1284
0.693
0.73
0.581
0.832
Huma 3.1
0.1908
0.735
03718
0.249
0.7097
03285
0.134
01378
0.851
medgemma
0.21
0.230
0.5627
0.7145
0.0922
gemini
Table 14: Summarization methods performance comparison across 30 transfer learning tasks (Part 2: Transfer Pairs 4-6)
P-
M I]
P-
Model
Forecast
LOS
Moxt
Forecast
LOS
Mort
Lab
summaryy
0.15971
0.518
015805
0.816
0,7895
Bama
0.141
0.1741
0.033
0.7677
0.734
medgemma
gemini
0.127
0.167
0.1
Table 15: Prompting methods performance comparison across 15 in-distribution tasks (Figure 4 left subfigure)
Drug
Dxug
Drag
0.911
Col'
0,0208
0.381
0.8311
0.9078
0.925
0.0294
0.328
0.8101
0.836
0.33
0.029
0.358
0.6387
0.937
0.935
ICD
0.021
0.3517
0.83
0.899
0.9305
0.027
0.9844
0.8018
0.901
0.977
0J0284
0.3645
0.64
0.925
0.9234
Trend
0.028
0.347
0.9
0.9012
0.9263
0.03
0.406
0.82
0.888]
0.9469
0.0269
0.3713
0.63
0.9144
0.9308
32

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Published as a conference paper at ICLR 2026
Table 16: Prompting methods performance comparison across 15 transfer learning tasks (Figure right subfigure)
Drug
Forecast
LOs
LOS
Drug
0.9399
0.6467
0.2461
0.98
0.1927
0.71
097
0.66
0.5995
0.1248
0,7629
0.847
Dataset
Lab features
Vital features
Binary features
HiRID
ALP, ALT, AST, Bicarhonate, AirwayPressure, AirwayPres, Analgesia, Bilirubin, BlotoUreaNitrogenn, surePeak, DiastolicBlood- Antibiotics, Anticoagulants, Calcium, Chloride, CreatineK- Pressure, FiO2. FluidBalance, Antiepileptics, Antihyperten- inase, Creatinine. Glucose, GCS. HeartRate, Mean- sives, Aspirin, CaReplacement, Hemoglobin, INR, Lactate, BloodPressure, PEEP, Res- Dialysis, Diuretics, ICPMon- Magnesium, PaCO2, PaO2, piratoryRate, SAS, Satura- itor, Insulin, KReplacement, Phosphate, Platelets, Potas- tion, Byodoeic LiverToxicDrug. MgReplace- sium, Sodium, Troponin, Temperature, Tidul ment, Neurolepties, Paralysis, WBC, ph
Antiarrhythmics,
UrineOutput, ICPMonitor, Saline, Sedation, Steroids, Ventilation
TPN, Transfusions, Vasopres- sors, Ventilation
MIMIC
ALP, ALT, Bicarbonate, AirwayPreesure, Bilirubin, BlordUreeNitrogen, tolieBBloodreessure,
Dias- Analgesia, Antiarrhythmics,
FiO2, Antibiatics, Anticoagulants,
Calcium, Creatinine. Crea- GCS, HeartRate, tinineKinase. Hemoglobin, MeanBloodPressure, INR. Lactate. Magnesium, ute Ventilaaan, PEEP. Res- CaReplacement,
ICDSC. Antiepileptics. Antihyperten-
Min- sives, Antipsychotics, CTScan,
Dialysis, PaCO2, PaO2, Phosphate, piratoryRate, SAS, Sys- Diuretics, EnteralNutrition, Platelets, Potassium, Sodium, tolicBloodPressuree, Tempera- ICPMonitor, KReplacement, ture, Volulm, UrineOut- MRI, MgReplacement, PPI, put
Troponin, WBC, ph
Paralysis. TPN. Transfusions, UltrSSundd Vasopressors,
Ventilation, Xray
PPICU
ALP, ALT, AST, Bicarbonate, AirwayPressure, AirwayPres- Analgesia, Antiarryytmmcc Bilirubin, BloodUreeNitrogenn, surelP. AirwayPressurePeak, Antiarrhythmics, Antibiotics,
Calcium, Chloride, Creati- DiastolicBloodPressure. FiO2, Anticoagulants, Antiepileptics, nine, CreatinineKinase, GGT, FluidInput,
FluidOutput. Antihypertensives. Aspirin, Glucose, Hemoglobin, INR, GCS. HeartRate, ICDSC, Dialysis. Diureties, EKG. Lactate. Magnesium, PaCO2, MeanBloodPressur. PaO2, Phosphate, Platelets, uteneenilatiin, PAVSupport, Vasodilator, Insulin, Isopro- MgReplacement,
Min- EVD. ICPMonitor, Inhaled-
Potassium, Sodium, Troponin, PC, PEEP, PlateauPressure, terenol, RespiratoryRate, SAS, Satu- Neurolepties, Osmotic/Terapy, ration, Systolic PPI. PS. Paralysis. Sedation, Temperature. TidalVolume, Steroids, TPN, Transfusions. UrineOutput
WBC, ph
Vasopressors
Feature groupings for converting ICU data into hierarchical textual representations. Each feature is prefixed with its group label (e.g., lab Hemoglobing vital Hea Rate, sanoVassor)ssorr) before tokenization.
Table 17:
33

---

Published as a conference paper at ICLR 2026
Miched
Birst
ppice
Larra-A:1 Coll +IM har
0,299
11.0112
Lamaa 1.1 Orl VIFM pacttimin
01566
1.500
3.736
0.290
4.0034
8.1200
L
7.344
La Cal +IMM unmounee
11.
1.300
2.0000
012912
2.200
1. 311 G/T
0.501
1
3.531
1. CIT partmniioo
0.500
1.000
0.803
3.8%
1017
7.515
1.maa31 CXT Hiwwiisrr
08.
0.807
6.301
1. XCD -TFM
0.5
0.007
1:2000
6.300
1.5
1. XDD -TFM pactrenivrr
0.50
1220
011
6
0.807
1:315
1.00
3.07
0.515
1.8770
1.013
1.000
1. XTD
Invertion
0.710
100
1.mma1111 ICD
0.501
66200
7:03fe-
1.704m
7.74%
Lant-311 3.1 K.D peldesssace
0.5
4.550-
1.007
2:975
0
1.955:
1.000
1.0000
0.511
5.361
LINGS
L.
0.000
0.000
3.1
Lasta311 3.1 Tredd ITEM salp
0.5
7.54
1.000
+ B.056
0,992
2.108:
0.536
0 1586
1.00 = To
Lant 3.1 Tread ITEM paoiossnis
0.905 1.46
1.035-
+ 1.85
0,800
1:222
1.008
2.000
0.516
2.077:
1,00 =
Laneusies
31 bess
0.750
Lant-311 3.1 Treed sly
0.501
1.200
+ 5.540
0,992
1.25%
081
Lint 3.1 Treed prodtaasiaer
0,504
1.00T
2.855
1.247
1.005
0813
Lama-111
1115 hem
215000
Lama-111 szm_atee 1155 msp
(L710) 1,000
7.707
LIBE
L.aa-111 zem_atee 1114 sactemmeer
2:318
1.011
Larta-11 smm
1L7541
1
Larta-J11 sam mip
[1511 0.508
1.003
3.377
LJB12
3,507
Larra-1: smm pachieniarr
0.512
5.1670
1.005-2
0,001
2.033
TFM
0.75% 1.222
0.085
2.330
0,201
0,075
012314
7.755s
TFM wrg
0.716
1100::0--
0:555
2.211o
0.078
TFM
0.766
1:000
0:581
0.7M6
180
1.777
TSDE fors
0.791
0:981
+ 2-651e-1
0.00
0.07
01031
3.5
nly
0.295
0.955
0.70
0:581
13317--1
0.20
1:9
0
1.000
grarri Debbhl CoT -TM Irm
0.716
7.1009
1.005
0.007
1-15
03.99
3.751
gemes-2 b-t.hbbcoooo CoT map
jouus-2Dda Col TPM Ganeuiee
0.5 1.567c
8.596:
0,092
1.253:
0.500
5.100
2alu--2--lah.coo CoT san
0.5
7.466-
125
0,902
1.735
0.500
0 2850
geaus-2 jemui-20:lal.Ca alp
0.501
1.001
6.025
5.326
4.703.
-.BalalC palterriie
2osus-2 D-lu--2 CoT Gascarooe
0,799 4.21:
0.502
1.17
0.500
1.100
gemux-2.b 2odul-2--lel-11C ICD ITM laus
0.706 6.000
8.20
0,902
1.356
0.000
genus-2
clp
paichsenies
0.506
5.015
gomue- 2.dal- ICD 1TMM tinerierr
0.700 1.1
T13
0.903
7.31
1.00
gemm-2 11:1
somn-20dabbl ILD paicoomis
3,005
1.3
086
L1877
jemm--2--boh- R.D inseusser
01 1.000
2:122
2,700
1.505
semm-22-dlah Iren
0L08 1230
08022
1:::2
umm--2ddaa Trend -1IM paccturiiarr
2070
4.500
0
4.703
scmm--2b-llah Tead +1FM tnammtrr
0500
21003
2:200
02012
02001
semu--20dla fsced lats
05
31001
0,302
grmni--.brbe brbbh Trand polrteettrr
0.504 0.5 575
1.000-1
0.011
1.53
10000
0.515
1017
gemri-3 Trund
05 1.1500
055
0.907
0.5
pesri-- D-l.h mm. -TEM
1
1.177-
0.00
grmri-2.bdes (-bbb)))).. mmn +TEM pasttontio
0.5
1724e-3
0890
191h
1.000
0.517
1.000
genrn-2. nmn,
05:
1.00
gemra-2 D-dlh non
0.801
1/001
3.115a-1
01011
1.001
153
gemrz-2.b (-bbbb nonidvr modd
2.1477
1.005
3-52te-3
0.09
1.245
1.005
01813
1:7.0
1.905
1.131e
genen-2 b-tte non dur immonise
1.000
03811
1:7110-
5.41.
4.056
ol
0.756
7.500k
0/266 + 6.276
0.701
= 3,807
0
7.000c
0.704
01000
2.00
sropp peldraation
0.5 2.100
1.035-2 + 3.555
= 3.233
0.389
5.734
0.76)
7.600
0.377
8.631
areep
0.756 008
sxan rily
0.706 0400
701
= 1.01:
0.08
0.754
1.100-
0.011
1.00%
sssn pebdroanioe
0.50 2.5
1015
1.26
0.00
0.760
0.0%5
0.71-
0:980
T TTMM
Sajam, Cell Oat +1 mlp
Qa 1:12 -1
1001
1181
Col
3102
0.00
-3
1002 1 5.47
LI1L3
Sa,am, Caf tezs
(L714) 7.105
1.00
09912
L173e
Au.um Crl mis
0512 1100
10003 4: 5:2500
02912
1.111
LINES
7
jaahimmise
110
0
23736-
1106
2.300
So am XDD IED +1MM lann
(L757 4555
0.000 2: 2.130
019922
L0525
0,000
1.55
No sm XCD ICD +1FM mdp
1U. 5
11003 +: 5.050
0,902
1,72:
2.2770
XCD 11FM pucciaminzz
0.508
1:221
0,201
7:225
NogamnDD (ET)
Nop kum
0S. E00h
1:001 + 1354e-11
0.807
030
0.500
2 2.000
Nojam,KD mlg
0.501
1/201
0,807
5.576
3.332- 5
+-3300
Nigam JDD tmmeriur
(1714
2000
6.181e
01887
1610
5:221 6
01895
1.33
13300-1
Neuam Thost xTMM lom
(1714)
0:599
0.007
0.000
19985-
0.007
5.031e
0.995
5.1.0
Nayam,7rrdd (mmM mip
01007
13177
1991
Noaam,Tree Thord WTM tinertzer
05. 1.55
4.506
3.2716 6
0,500
1.9500
1.815-11
Table 18: In-distribution results Age. (part 1/2)
34

---

Published as a conference paper at ICLR 2026
Methed
hiril
minni
miini
Neger, Theal lmm
0.799
1170
2004
0.808
5.185
0.505 + 0:130
Thrsl rlp
0901
30000
0:41
Tors inmmbor
0.5
600
asdgamt Cdf 1TE3 bit
0,715
5.0000
0,902
0.000
3.400
0 0,004
Libee
2-7096
aslgama
mb
0,01
molgama Off ITEM taneito
0.8 1.543c
1
0
5/12
0.70
01500
1.854s
1,001
1,227
antymma
0.799
5.00--5
5.77
avlgmmaa GVT posteenioee
0.001
2.015
1.005
1:334
3:957
0.155
3-797
1.700
RU0TT
avlymma GXT fivention
0.4
5.0116
2000
0
1-:0-a--
100
0.500
1:400
1.001
1.000
aslgamu
0,799
2.70
0.550
uelganes XCD TPM packooniis
0 3.277cc
1,01
4:25
0,857
1.882:
1.01
0.510
5.800
1.010
5.54
assgontt XCD TPM Gincesact
0.799 10700
8.544
0,902
1:292
0.774
0,500
31150
2,59
avdiume ICDD
03812 8.100
3.957
2:10
1.05
1.001
6.70
awigemra ICD gachroniser
0.912 1132000-
1J007
2338
11
1.005
9-317
0535
1:557 -1
1.01
1236 -
ixttomma ermmere
0.78
5,477
0.00
0.12%c
5.751
selganss Tics (TM ulp
0.8 L,003-
1/001
L002:
0.855
1.53
021
1.001
1.47
asslantt Ticel (TM putcltatiss Gssoniacr
0,905 = 1990
10015
1.50
500
1.001 + 4.34
0.50 + 5.1
1.00
relgares
Tread
0.293 5,0040
31000
0,805
aedgennr (tead mp
0.01 11104
1J003
1:30
1.705
1-312
1.141
4555
ixdiemea (izes pachimite
0.00 2.002
7.189
1,03
03337
2.5000
1.148-
2.307H
zelganta zemalkt +TMM laus
0,799
0.002-
8.20
0.850
A1163
1.000
0.507 + 1.850
2 721
aedgarne semalit TTM nly
0.5 2 6.308
6.301
0.802
10070
7.75
011
1:500--3
1.000
1.450
relgereea zemaaht fireiise
1:971--3
1.01G
0.793
3
setiamnra zem 2en4 ler:
1L700 1.30e -1
5.11
0
1114
2043
3.3280
1: 1.30
rxdjemea zem mlp
5.370
0
1.112
0,511
1.44
0 7.2240
1,200e
19955
12200
3.128e
(ecerreenn0nn 200 Jun TTM
2-81o- 0.799
0.802
5053
0.500
0.507
1:251-
0.000
Nogien
pastecsioer
0.903
1.016
1.452+
1.815e
0.87
30.3.
1.006
5.576
Nugaer Juin chatt aes Jinn 4TFM tiventtrr
0.8
6:200
1.5e-4
0.507
1220-1
0.500
1-136-
1.001
T.100
No serz zan absi Lita
5,003
1.0
01:00
4.00k
peacromzace
0
2.270
1/015
2,072
0
1/112
1.204
1,81
01515 : 4.55
1.06
2.500
SO- 2E10 kadl lisaiin
0.2 2 0.100
0.002
1,30:
1800
0.41
5100
1.00
1.25
right
0.755
8880
0.80
5.00
1.01a
6:977
0751
3.119
0
TIN pocheerisrr
0901
3 9916-
1.015
0.706
3151a-3
0.348
1.913-
0.706
1.337-
BB47
PÇN Invnior
0.755
0.753
5.30-
0.97%
7.087
0.755
1.111-
8305
7.600
Table 19: In-distribution results Age. (part 2/2)
35

---

Published as a conference paper at ICLR 2026
Method
hirid-
ppics
mimic
ppice
mooe
mase
mac
mie
1.mo-11 1.1 Carl +TVM lemn
0.806
1.3006 -3
1.300
0.800)
1 1.000e
6.00h
LJams 3.1 CoT +TEM mip
0.900
2:
7.301e
1,001
6.5000
0.806
1.407c
0.215o
Liama 3.1 Col +TEM posttsmier
0.52
2.642c
1,019
4.362
0.83
2.585e
1.01 1: 0.56e
Limmo 31 CHI +TMM timemnee
0.800
1.01.4
2.
210500
0.800
1.0800c
Le 5
lem
2.850
0.274
0.800
1.07
2.04ku
1lam-31 3.1 slp
0.811
1,001
3.563a
0.800
2.10
4.340e
1. jonditemioor
5.17
11mo-111
0.600
1.193
Liano
-TIM mlp
0151 7,5le
1,003
6.313e
0.800
18855c
3.785e
Liuma -TIM patchismierr
2.40
1,015
5.208
0811
3.9330
3,0380
Llano-3 I/D -TIM cimeriser
0,800 4.005k
0.80
2.5176-33
Llan=-31 3.1 ICD 1stm
0.800 2: 8.811e
2 5.636
0.806
2 2.156
1,2080 5
Llans 3.1 ICD mlp
0,811 2: 2.089
1,001
4.871e
0.806
6000
1: 9,2870 -
11mm-111 1.1 1CD paicheamiscr
0.812 + 3.07 5e
1,007
1.5200
0.800)
2-11e--3
1.005 + 5.351
11mm-331 1 ICD fimeniser
0.806 + 1.573 -
1.95
0.60
1.755
1.15
1.1 Tacrel +TFM lean
0,806 + 7.
=
688
0.606
1.850
2.123
Linmo 3.1 Ieend +1MM mlp
0.51 5,0066
1,002
2. 7.1150
0.800
2:3500
1. 1
LJamo-11 1.1 Ineed +11M ponchtsmiser
0.17 1:741l
1,015
0.81
1,357o
1.005
11am- Tered +IFM timemizer
0.900
1.081
5.053
0800
5.77
1iams Tased bon
6,381c
19916
0.800
5.0150
1.20le
Tesod mip
1,001
6.115e
5,56
Liamo Teend puschtsntioe
5.117c
2.975
0.60
11ann-311
1,10f
0.606
2.300
6.245e
11 sem_shot +IFM
0,900
1.107e
1/001
5:95te
0.800
0,157e
11am-11 bemmushtt #TFM puachtemiuer
0.818
3.166
1
6.47
0.81
5.066
1.011
4.00
11ano-31 1.1 bommushett =TFM timemisez
0.800 + 2.105
5.508
0.00
5.776
5.77
Lians 3.1
15mm stot lam
0 2: 6.52
2: 19919c
0.806
1.746c
1: 3.00 5
Liama 3.1
1.mmmm. chot mlp
0.811 1: 6, 5,0000
1,001
2 3.304e
0.006
2:150c
1 1.04le
Liamo-3: 3.1 semm shott parchienizer
0,812 2,08c
1/005
1.3130
0.8
2.7570
1.006
2: 2.8
Lluea-3 3.1 acro shot Ganemixer
0.800
2: 5.900e
2 2.117
0.606
2.086
= 000
TEM lean
0.752 2: 5.624
0.505
1848
0.75
4: 1.713e
0.035
4: 2.170k
TFM mlp
0.755 1.07
0.335
1050
6.75
0.91
0.805
2.13
TFM paachtemiace
0.761 3.67
0.917
5
0.753
1.02
0.915
1.58
TFM limenisex
0.758 8.853
0.384
1060
0.75
2.007
0.034
4,3233
0,500
137e
3.054c-
mip
1,831e
limeniser
5.21
1.345e
0.75
0,504
4.646
10 fuch CrT -IMM
1.003:
0.806
1.400c
0,050
5om-i-160-faeh OiT -FM mlp
0,81
1.06
1
5,023
0.00
2.875
9.07
2omini222-faah CVT -TM pacctonnscr
0,814 :: 3.800
1,016
6.303
0.000
1.011 + 5. 5.063
scmin--22--aeh CJT JTFM tineniser
0,500
1.55e 3
2 5.384
0.806
1.955
1 1.525e
5
2ommn-10-ffoc ONT lean
0.800 3: 6.2033
2.150
0.000
3.01Me
+ 4.5-
55min-1.0-BBbhh CxT mlp
0.51 5,835t
1,001
1:900
0.800
1.153
5:min--/--Bech OXT paccttemiser
0.813
7.855M -3
1,00
5:030
0.86
1.750
1.003 + 7.008
zemini 2.0 fluhh CoT timemiser
0.808 2: 3.555c
2: 6.397c
0.806
1.7820
1 5.774e
gemini 1.0 foxh JCD -TPM Jom
0,808 1: 4 1.13
1: 7:350
0.806
1:33
gomini-J..-Babb -TFM mlp
1.635
3
1,001
0.00
Jomisi-2.--fahh ICD -TFM puachtunixer
8.87
0,000
1.245
1.000
acmisi-20--Ba ICD -TFM tincmiser
4.363:
9.000
0.800
mlp
0.600
1.051n
jomiis--..--ich ICD paldhoeniser
0.500
2.0 fl-ffuhhh ICD
0,800
5.427e
2:155e
0.006
8.105e
pemmni-1-ffuhh Trend +11.
6, 750M
4.3550
27850-
1225
3omini-10-fsot Trand +TFM
SA077e
1.001
3.152
0.000
1.9550
3.781e
jemini 2.0 fuih Trend xTFM paicktmmerr
0 2: 1.446c
1.02
1. 0,573e
3
0.800
1.505c
1.012 d: 4. 4,2500
jemini 1.0 fush Trend -ITM timenieer
0,51 5.006C
1,001
2 2.24
0.806
1558%c
1::: 1.155e
5ominn-1/-BBel Trand loo
0.800 + 1.811e
1,001 5.003
0.806
2.15
1++ 1.88%
gimini--/-BBahhiee Trand nip
0,81 7.457
1.001
5.1e 4
0.606
2.720
1: 4.573 -
gomiii----Baah Trand pacitsniser
0.814 =: 5,006
1,000
6.200m -
0.807
1.3
1.000 +: 2.961e
gomini-10-ftsh Trend fimerisorr
0 1.0006
+: 2.575e
0
1.40
4: 3e
5omini-1.-BBuh zeno,bet pbet
lean
0
1.11 1e
5.05e
0200
1-8255
5omini-).-BBah vom -TFM mip
1.831e
5.1:3a
0800
177
+
1emiti 20 fush 2ero
0.806 d: 2.724c
1,015
L.41c
1,00 = 5,198
zomini zero
timemiser
2:3000
+ 5-774e
jcminii-.0--Badh
0.606
semini--... 2.0--fulh
6.791e
0.806
9.600
7.5
acmiii-1.0 2.0
puschsnnserr
3.521e
0.800
5,301c
1,000
gomini-10-ftch
timemier
0.500
6.176
1.910n
0.00
2.076e
8.10%
lam
0.751
8 8.070-
0.955
1
0.751
1.27%
0,505
7.00%
interp mlp
0.754 # 6. 615
0.959
3.273
0.752
1.927+
0,008
inter pachienierr
0.765 2,19
0.305
200e
2
0.778
:: 6.000
0,076
1886
inter timemiser
0,733 7.087e
01301
8 1.15
0/75
T 1:30
0.1015
sccui 34m
0 1,66s
0.384
3.463e
0.75
+ 1.59
0.504
8.56
scen mlp
0.752 L.887c
0.503
1 1.557e
0.751
+: 1.80
0,836
1.0470
meen guchtsmiser
0.806 4: 7.824c
1,222
1 1.104e
0.786
1.5800
2
0,051
2.36%
mzan Limmissrr
0.755
0.951
0.75
0.031
Na.amm, -TFM him
3.112
6.20
0
7.47%
No.sum
mlp
4.007e
1,001
6.205a
0
5.881
4.072
No.ommJ''T
goicheonisrr
1.013
CoT
1.5750
0.806
1.9920
1.ttte
CoT
1.001
8.160e
2.83e
7.50
LoT putchsmiser
3.378
0.00
1199
2
Nayam OcT linemiarr
0.808 : 7.857
= 1.182
0.806
1,650e
1: 0.57
Na.sum JCD ICD -TMM Iin
0.807 2: 8.2124
2: 6.876
0.806
1.550
0.500
1.57
No sum ICD ICD xTEM mlp
0,51 1.655
3
2 1.733e
0
4.00e
1.001
7.3550
No.emnJCI ICD -TM ponctteniser
0150 5,800
1 + 2.22
0.80
1.1
1.000 + 3.275
NC.emnJCD ICD fimemiuu
0.800 + 1.005= -
+ 5.55 -
0.800)
7.500
5
5.77th 6
No JCD kim
0,800 + 1.518 5
1,001
4.507h
0.606
1.021e
4
+ 1,452r
No.ommJCD mip
0S1 T.661c
4
1,001
1: 1.45
0.8066
1,575c-
4
1. 8.421
5
NC pochtsmiser
0S1 3300le
3
1000 d: 2.300
0
2 27000-3
3
1,000 4: 2.10
ICD linemises
::
6,88
0.806
7,8%
1 6.77
Tread Tread -TFM
4.00k
0,909
1.105e
0.806
5.000
8.969
Trend Tread slp
5.788
1,001
0.00
3
1 3.315e
NoyeonTread -TFM potciteniscr
Noyem,Trend Treal -TFM
1.39
0.00
1.501e
1.16f
Table 20: Cross-site transfer results Age. (part 1/2)
36

---

Published as a conference paper at ICLR 2026
Mathod
Iarid
ppicu
mimic
No.sum Too.uu--Tized lam
0,906
L.0kle
1,850
No.un, Tecal mlp
0.806
Tical paidoaniser
2.5055
1,005
No.um,2hred
2,507
0
1.7564
IIFM
9.810
4.460
0
7.907e
8.2150
modymma OxT -TFM puichsoniszz
0,81
1.653
1.000
1.100
0.800
1.123
1,009
1.3384
acdiemma CrT -TFM timeniser
0.81
1:156
1.001
5.755M
0.806
1.30
5.77 -
indgemma CoT Iam
0,508
+: 1.31c
1
3.2076
0
5,745 -
4.570
madgemma Cil mtp
01311
1:385 -
6,851e
0,906
1.7156
-
1.56
modjenma OUT puachsenissrr
0.12
2.8120 -
1.00
1,500e
0.811
2.366
1,005 2: 2.2574
indgrmma Cel timemmser
0808
1.811
0.806
9.57
modyrmms
0808
1831
0.806
1.150h
indgermma
1.108e
indgeemma -IM paccttmiser
3.5000
2,3330
1,00P
1.351c
atcijemmaa
ICD
0
4:30
ILD
0311 5.0900
5.007e
0.806
3.199e
1.19MM
mndgermaa ICD pulcloonier
0
4.0844
1000
8.122
0
2.4176
1,001
1.1574
mndgerma ICD timeniser
0,208
5.045o
1.08
0
8.6050
1 DED
madgrmma Ternd +TFM lam
0800
1.00 -
+
1850 -
0
1.000h
1.70
madgenmma Trend +TEM mlp
0.81
1.050
1000
0
1.961e
1.2236
medgenim Trend +TEM pacchumikerr
3.809 :: 1.700
DUT
3,2000
0.000
3.377
1.011
madlennm Tranl imemissr
1.500s
mndgemma
iodgrmma mlp
Tiznd tinemiser
7.0030
17777
0
9.574
mailymma sem,ahon plan TTMM latm
0.888
3,00/a
8.80
0.806
1.11f
1.00M
muzlgemma alar TTM mlp
3.51
1.000
1.001
5 5.50
0
1.206a
1.774
iedgenma zem sbot TPM paccesmmser
0.310
2 2,8000 -
5. 5.56le
0
2.8870
1.005 : 2070
medgrmma zem sbot TVM timemieer
01808
10156 -
1
1080 -
0
3.0000
5.77
mndgeniaa zero shot 5tm
0.808
1886 -
1
7.1060
0.505
2.200
8.847
modgrmma pemuuher mlp
8808
0.806
modgymma sem shai hichimmiser indgermma
0815
3.501e
0.505
1100--5
1.005
7.71
1.291c
zero seco
2.007
5.251
0,805
0.201u
posctreminee
3.48To
0.51
1.123e
+IIM timemiser
5.515
1.750e
0.506
2005
1.5086
No acro shot istm
3.51
1.741c
3
1.001
4.545e
0
1.26le
1
5.129k
No suns zero sbot mlp
3.51
1.638c
3
1,001
9.5te
0
1.882
12 1.037e
No.arm Jar, sent paichasmiser
0,812
1.57 5
1.07
2.50te
0.807
6.2:0n-2
1.001
9.251
No.sur .ur.urr zem shot timeritee
08008
5.21
1
1,25de
0
2.181e
1. 1.733k
right Istm
01211
9:325e
00003
1.30e
0.731
1.516o
0.805
5.847e
Rgl mlp
2.27%
0.033
L.5
0.76
6.710v
rul pallesmiser
0.934
1.566e
0.965
night
Table 21: Cross-site transfer results Age. (part 2/2)
37

---

Published as a conference paper at ICLR 2026
Miched
Birsi
ppice
Larra-A: Coll +IM har
EL582
01800
1,350
11.00
11-1.21
Lanaa 1.1 Orl VIFM pacctimin
1L70%
11.08
1.50te
0.212
5.0080
20
0.734
5,8500
01.015
6.322
-3
La Cal +IMM unmounee
0.585
0.000
14458
IUSS
01.644
&1.412)
1. 311 G/T
0.761
0.007
5:377
0.000
0.707
1.046
1. CIT partmniioo
0.607
100
0.575
1.150e
0.038
0.03
1,00
0.713
1.6:00
0.553
1.004
1.maa31 CXT Himwiiser
07.
0:571
0-5076
01087
0.977
00000
0.00
0.513
1. 1.1 XCD -TFM
0.731
1750
0.056
1100
0/717
11:337
1. XDD -TFM pactrenivrr
0.716
5.577H
0.58T
3.050
0.607
5-150
0.587
8100
0.710
7:30
0.553
4.515
1.ma-111 XTD
Invertion
0.716
0.586
01977
0.397
8-7770
0.571
1.mma1111 ICD
0.886
0:757m-3
01615
1.317
0:090
3,012
1.130
Lant-311 3.1 K.D peldesssace
0,022
1.300c
0.560 4 2:154e
0.11:
0.00
2.030
0.0911
L.
0.500
2.14
0.577
2,0711
3.1
Lasta311 3.1 Treed ITEM salp
0.718
1,18k
0.285
1.150-
0.001
0 1.712
0,00
2.000
0.711
1.500
0.687
7.62%
Lant 3.1 Tread ITEM paoissunis
0.708 = 2,000
0.555
2-515-
0.72
+ 0.001
000 = 7.50-
7.80
0.291
9.864.
Lanessiee
0.707
Lint 31 bess
5.000
Lant-31 3.1 Treed sly
0,600 0
0.374
5.351e
0.611
8.35%
0300
1.155
0.510
6.315
Lint 3.1 Treed prodtaasiaer
37
0.271 + 1-556-
0.667
1.00
0100
0.701
5.670
0.517
4.355
Lana-111
1115 hem
01877 5250
0.56
1.00
0.64
1.3000
1.3ile
1.657e
Lama-111 szm_atee 1155 msp
0.799 7009
0.002
0.721
2033 A 1,300
0.343
1.3377
L.aa-111 zem_atee 1114 sactemmeee
0.381
3.5576
0.1
2110
0/4
0.3013
6.005
Larta-11 smm
0U357 25
0.335
5.750
0U8T
1.850
5,30
1.0044
Larta-J11 sam mip
01..5 2.57
0.527
2:37
0.00
1.900
IUSS
1,8220
Larra-1: smm pachieniarr
0.808
0.575
3,000
0,20
4:74%
0.11
6.57
TFM
0,07 1.0022
0.541
4.354
1.350
1.300
TFM wrg
0.671
0.545
0.000
0-661
0.508
TFM
0.511
0.510
3.131e-3
1.:57
TSDE fors
0.661 5:500
0.551
0.819
2.7--
0.87
1575
0.755
1.370
nly
0.791
0.65
2.177
01831
3.731
07767 1:00
0:65
21000
0.99
3333
100
1.500
11:557
grarni Debbhl CoT -TM Iim
[1666 5.3006-
0.561
1-707o-
0.091
1.051
1.303
0.717
1.120
0.001
11-155
gemes-2 b-t.hbbcoooo CoT map
0.716
0:507
0.711
1:310
(1.75
1.31e
11,077
30HuE-2 D-dal- Col TPM Ganeuiee
0.714
735
0.257
1.756c
0.088
5/022:
5.100
0.005
1.225
2omui-2b-laa-Co CoT san
0.700 = 1.6480
0.555
5.750-
0.001
1.933
2100-
0.121
4.350
0.034
6,025
geaus-2 cauus-2D.aal.Ca alp
0.71 4.695:
0.081
8.121
6.7.41
2oaue-2 D-lu--2 CoT Gracnios
0.707 1870
0.354
1.155
0.68
6.923
0607
1.000
0.724
5.300
0.611
9.491
semui-2b-dehh ICD ITM laun
0,688 185
0.977
1.216
0000
211
0.704
1.15
0.501
1.655
genus-2
clp
0.755
1.433
paichseniss
6.855
gomue- D-dal ICD 1TMM tinerieer
0.714 725-
0.286 + 2.750
0.005
0541
0.711
1.150
0.500
1.00
gemm-2 11:1
01116
0:381
0.861
1.4h-
0.011
somn-20dabbl ILD paccooise
1:43
0.365
1.455e-
1
MAST
11800
0.718
5.000
1100
jemm--2b-hoh R.D inseusser
0.000
0.391
1:300
0.00
0111 1 1.15 -3
03922
4.2
semm-22-daa Irend
01777
0.545
030
7-1286
0M0
5,000
umm--2ddaa Trend -1IM pacctoriiorr
0.381
12210
scmm--2b-llah Tead +1FM tnnmmtee
0.707
6200
0.555
3.57
0,83
0.115
0 1.911
semu--20dlah fsced lats
0.574
0371
7.364-
IL506
grmni--.bbbb Drbbh Trand polrteettor
0.000
1
0.575
1:100
0.09
7.111
D.1
0.706
0.555
3.310
gemri-3 Trund
07
1.71%
0.576
1667
3.457
0000
0.531
1.15%
pesra-- D-w.h mon -TEM
1950
1.3576-7
0.011
1.357
1.305
grmri-2.bdes (-bbbbb)).. mmn
pasttention
01716
1226
0.535
1-312
0.707
1.000
0.01
0.712
1.000
0.633
1.3377
genrn--. nmn,
0.23
0/511
1.471e-3
0.675
255
B.06
3.099
0711
1.00
0.931
1.571
gemra-2 D-dlh don
01736
1170
0.131
01611
0.755
1.517
5.500
genrz-2. bebbb nonidvr modd
(1710)
2.100
2:001
0.007
1.951
0.755
1.710
0.098
1.2 22
gemen-2 b-tte non dur immonise
0.753
0.012
2:035-
01531
7-513-
1-100m
11:01
7.554
ol
0.750
2.04
0:054
1.475
2.31:
0.005
2.103
0.631
5 5.047
0.729
+ 1.135.
sropp peldroation
0.568
0.756
1.11l
0.722
1.077
0.71
2.006
0.565
2.760
0.75
areep
4,703
0.755
1.174
0.155
05445
sxan cily
0.557 1,00
0.751 + 1.950
0.721
2236
0166
0.554 +
0,753
sson pebdroanioe
0.571
175
01
411
0.79
6.211
0.756
0.795
T TTMM
01759
Aajam, Cell Oat 41 mlp
[U110 1.520
0.534
2:3
0.76
ILN 1:130
012113
2.80
Col
(L72 3.116
0.74
0.714
Saaam, Caf tezs
0.215
0.525
01714
1
1.500
Au.um Crl mis
[L756 0.8000
B000 1:27
0.743
0.2000
3.500
0,855
1.310
pachimiise
(1755
0664
1:50
0.730
So am XDD IED +1MM lann
(L716
LK
0.204
1.200
0.703
L.31
ILT122
0,771
5,64
0,077
1.31%
No sm XCD ICD +1FM mdp
0.71 = 7272
0.081
2 5.700
0.207
2 7357:
ILTH = 01500
0.197 2 1,100
0,815
1.535
XCD 11FM pacciaminzr
5:055
0.341
NogamnDD (ET)
0.115 7-70
0/155
0.780
3.50
0.77%
3.607
0.673
955m
Nogam kum
0.700
1,201e-3
0/31
4 2.17n-
0.735
+ 7-310
D181
11104-3
0.75
3.35 3
0.61
Nojam,KD mlg
01711
5.111-3
0/566
1341h-3
0.51
1:150
0/988
113te-1
0.511
11355
0.673
6.6 16-3>-
Ni tmmeriur
(1751
1100
0/35
1-356-
1.237
11817e
0.755
3.560
0.695
1.77
Nagam Thost xTTM lom
[1775
1200
0/11
1 53
0.754
B.
2388
2,676e
0.200
/1133 -2
Nayam,7rrdd 4TM mip
01716
0/15
13556
0751
1.777-
1,00
0.74
11135-
01:0
1.155
Naaam,Tree Thosd 4TM tinertoer
2
0.33
1 -3136-3
30.3--3
0.70
31000
0.782
2.516-
0.668
1.3317
Table 22: In-distribution results Gender. (part 1/2)
38

---

Published as a conference paper at ICLR 2026
Methed
hiril
ninic
minii
aagie
sugie
sapee
Neger, Theal lm
0.71
5:53
0:63
3. 150m
0:728
1-41
0.563
1.612
0.778
700
0.061
3.510
Thrsl rlp
075
31770
1038
0.564
1.136-
0.787
6.177
01046
Turs inmmior
800
6.736
255
0.335
1,845
0.28
0.071
asdgamt Cdf 1TE3 bit
0,001
7000
0,382
1.444
6.539
4.732
0.358
4.300
1.724
1:236
2,000
aslgama
mb
0.351
molgana Off ITEM taneito
0.71
0.222
0,211
0.004
0.71
8.500
0.555 + 5.1085
0.720
5,822
01512
5.850
antymma
0716
513
0.011
5075
0.339
0.731
3-3716-3
3.60
avlomma GVT posteenioee
2.300
1.503
0.23
0.15
3,00
avlymma GXT fivention
0.76
0.595
17:10
6.705
500 -
0.012
1-583
0.711
2-6176-2
0.58
1616
aslgamu
1.710c
2.407
0.585
5.50%
uelganes XCD TPM packioniis
0.590
5.120
0.615
0.44
0.081
7.402
0.558
0.721
5.554
0.00
1.000
assgontt XCD TPM Gincesact
0.16
0.233:
0,207
0255
0.00 = 2.00
0.531
2.782:
0.715 + 5-150
BUC
4.06c
avdiumea ICDD
0.17
1655
0.80
9.15
0.311
9:311
0.75
0,50
L
awigemra ICD gachroniser
0.71 1-0036
0.505
15577 -
0.535
6.500
0:21
1 7.206 -3
6.312
ixttomma ermmere
0717 31123
5,537e
-
0.311
3.535a
0.706
1.801
2.600
selganss Ticsd 1TM ulp
0.723
0.772
0.002
1855
0.711
1.25
0:511
1.355
0.700 = 5.111
0.00
4.50
asslantt Ticel (TM putcitatiss Gssoniacr
0.003 - 7.78
0,585
1.7
0.71
0.540
1.247
0716 + 6.85
0,00
1,777
selgaresa
1.764
Tread
161070
0.201
1.353e
aelgentr (tead mp
0107 6.1te-11
0,343
1226 -
0.201
0.335
7.431
1.700
13300
5,072
ixdiemea (izes pactimite
1116 2.00--3
110
9,700
0.335
0:1.00
1077
1.110-
zelganta zemaakt +TMM laus
0.996 2 3.200-
0.578
2.5
6:675
1045
0512
1.69s
0.781
1.30
0.00L
5.04
aedgarne semalit TTM nly
0737
0.00
2.55
0
0.555
1.256
0.700
+ 7.854
0.40
1.100-
relgereea zemaaht fireiise
0.725
0.755
0.50
setiamnra zem 2ho4 ler:
0.717 3 4
0.911
0.588
1.033
1L700 2 1.100
1121
2.00
rxdjemea zem mlp
1U225
1,50
1.00
10080
0.338
1.100
0/821
0,723 2 2,003e
0,291
12086
12200
0.32
1.155s
1L38
3,054
5,700
(ecerreenn0nn 200 Jun TTM
0.222 4.1.1o
0.015
101-
6.712
0.581
1
0.76 =
0.051
1.85
Nogien
pastecsiour
7.075+
1:10
0.71
0.700
2.310
Nugaer Juin chitt aes Jinn 4TFM tiventter
0788
0.433
7.30--3
6:719
135
0.76
1.41---3
0.761
155
B
2.07%
No serz zan absi Lita
1112kc
121.3
6,727
0,30
1.27s
IL754
50511
1.17
peacromzace
0.709 2 5.70x
6/811
4887c
6,242
5.08:
0.52
5.334
IL794 : 1.555x
0,01 1 1.900
SO- 2E10 kbdl lisaiin
0.280 2 1.at3:
0.011
190% 8
0,727
0.506
5.435
0.742 2 2.75
[U22 = 5,11e
right
0.51
6.070-
0.736
1.515-3
0.36
1.657-
0.707
1-300e
TIN pocheerisrr
0.833 60300-
0.801
1876- -3
0.0
200
0.781
0.56
1.115
0.16
3.7786
PÇN Invnior
0773
-3
-
6.737
0.341
1880h-3
0.70
1.888
1.337
Table 23: In-distribution results Gender. (part 2/2)
39

---

Published as a conference paper at ICLR 2026
Method
hirid- ppirs
mimic ppice
aurvc
Duroc
1.mo-11 1.1 Carl +TVM lemn
0.864
+ 6. 6. -3
0.52%
+ 5.5055
0.073
7.83 -5
0,508 1:
4.000
LJams 3.1 CoT +TEM mip
0.073
2: 3.1177
0.535
3.658e
0.674
2.000c
0.54
4.741c
Liama 3.1 Col +TEM positsmier
0
1.007c
0.544
2: 1.277
3
0.676
L15le
0.546
1.1744
Limmo 31 CHI +TMM timemnee
0.07
4: 3.143
0.344
1: 6.208
01675
1.270c
0.542 2:
1,0270
11an- lem
0.074 5.015
0.342
2.121m
0.673
5.016
1lam-31 slp
0.076
8.012
0.543
0.677
2.263k
1 jonditemioor
0.657
1,060
2.507
5.053
Liana -TIM mlp
0.064 3.33.4
0.667
1.799
3.007C
Liuma -TIM patchsmmierr
6.03.6
0.535
122
0.6TI
7.19%0
18845
Llamo-3 I/D -TIM cimeriser
0.000 3.02.6
0.67
0,506
3.141e
Llano-311 3.1 ICD 1stm
0.667 2: 1.773
0.281
1.912
0.663
1.754k
0,534
1.340
Llans 3.1 ICD mlp
0,667
1.75c
0.582
2,488o
0.608
1.353
0.636
1.12
11mm-111 1 1CD paicheamiscr
0.068 2.10/
0.535
1.122 3
0
1.141e-3
0.508
0. 6.138
11mm-331 1 ICD fimeriserr
0.069 7.853
0.535
7.315
0.67
1.223
3
0.500
1,00
3.1 Tacrel .TFM lean
0.682 5.54
0.527
+ 6.505a
0663
5.15
3
0.528
1,80
Limmo 3.1 Ieend +1IM mlp
0.008
2,851c
015331
2: 6.31e
3
0.007
1:
4.110
0
LJmmo-11 Ineed +11M ponchtsmiser
0.67 1.747e
0.241
3:033
3
01672
1.755
0.5IT
11am- Tered +IFM timemizer
0.674
1.897
0.347
06772
1.810
0.508
1iams Tased bon
9.6162
0.534
2.630
0.067
415c
Tesod mip
2.436G
0.547
4.101e
0.007
3.757
Liamo Teend puschtsntioe
7-063k
0,517
8.551e
0.007
0.770
11ann-311
0.656
8.192
11 sem_shot +IFM
0.003
2:10
0.321
0.772
0,500
2:11
11mm-111 bemmushtt =TFM poachtemiuer
0.064
1.155
0.527
1.600
0.700
1.9
0.545
2.00%
11ano-31 1.1 bemmushett =TFM timemisez
0.067
3.
0.531
4.187
0.733
9.505
0,512
2.207
Lians 3.1
15mm stot lam
0.661 2: 1.77
0.322
2 2:
0.672
2 2000
0,539
2.57
Liama 3.1
1.mmmm. shot mlp
0,602 2: 2.248e
0.525
2.711e
3
0.675
1.282e-
0.545
2.4770
Liamo-3: 3.1 semm shot parchienizer
0,002
1.8dle
0.324
0
0.546
7.57%
Lluea-3 3.1 acro shot Camemixer
0.662 2: 1.6466
0.525
2: 1.073e
0.722
6.77%
0.541
1.04f
TEM lean
0.647 2: 1.813)
0.314
4,081e
0.647
3.477
0.513
4.21%
TFM mlp
0.636
1.30
5.05---- 3
2.00
0.571
1.87
TFM paachtemiace
0.061 1.805=
0.527
1.4076 5
0651
1.00
0.572
1.0
TFM limenisex
0.664 2.007
0.529
2.850a
0.058
1.02
1.059
9.017C
mip
3,901e
2
0.571
limeniser
2.7376
3.73te
10 fuch CrT -IMM
3.387c
6.572
2.00
0,653
2.386c
5om-i-160-faeh OiT -FM mlp
0,051
3.500
0.549
4.401m
0688
2 2.008
0.559
3. 5.50
2omin-222-fieh CVT -TM paccttnnscer
0.657
1,2733
0.558
1.275
0.055
+ 2.00a -
0.967
2.025
scmin--22--aeh CJT JTFM tineniser
0.681 + 2,181
0.565
1.812-
0.694
1.233
0.665
1.472
2ommn-10-ffoc ONT lean
0.68 6.530
3
0.552
à 2:200
3
0.055
à 3. 19h
0.508
3.311e
55min-1.0-BBbhhh CxT mlp
0.085 1.100
0.356
4.79%
3
0.88
1.907
0.561
1.28/
5:min--/--Bech OXT paccttemiser
0.081
1.28/3
0.565
+ 109%
5
0.00
+ 1,553 -
0.565
1.00%
zemini 2.0 fluh CoT timemiser
0.670 :: 1.501c
0.502
1: 1.252
0.687
28000c
0.561
4: 1.56
gemini 1.0 flxh JCD -TPM Jom
0,071 2: 6.3e
0.54
1,0000
3
0.56
3.2160
gumini-3..-fibb -TFM mlp
0.651
1.120
0.549
4.585m
3
0:601
Jomisi-2.--fahh ICD -TFM puachtsnixer
2,007e
1.10%
0.002
2.07
2.007e
acmisi-20--Ba ICD -TFM tincmiser
0.03
3.373
mlp
jomiis--..--ich ICD paldhoeniser
0.685
3.261
2.0 f0-ffuhhh ICD
1.155 06
0.352
0.085
1.171e
jemini-1-ffuoh Trend +11.M
0.054
3
U.329
4.386
0.67
0.545
3.571
3omini--0-fsoh Trand +TFM
0.678
6. 097H
0.547
6.900
0681
1.5
0.501
5. 5.155
jemini 2.0 fuih Trend xTFM paicktmmerr
0.651 2: 2.505e
0.252
3.500
0.655
2.15
0,554 = 3.015c
jemini 1.0 fush Trend -ITM timenieer
0,652
5,803k
0.258
2 2.541e
0.655
2 5,00e
0,504
5,5kc
5ominn-1/--Bel Trand loo
0.077 + 1,200
0.517
+ 2.631n
0682
2
0.551 + 2.04f
gimini--/-BBahhiee Trand nip
0.661 + 1.060
0.551
2 160
0681
1.68
0.558 + 1.453
gomiii----Baah Trand pacitsniser
0.68 1.276s
3
0.561
= 1,06
8
0.681
: 8.55
0.664
= 1.770
2omini11--fsch Trand fimerisorr
0.08
1.17de
0.351
8. 78885
0681
751e
0.505
1:20
5omini-1--BBah zeno,bet abot
lean
0.673
3.500
0.5:1
5.30
0081
167
0.536
1,27
5omini-).-BBahh vom -TFM mip
0.838
4.17
0.559
5.3%%
0.564
3.85
1emiti 20 2ero -TUM pucchtsmiier
1,151c
0,559
0.000
2.070c
0.006
2473e
zomini zero
timemiser
0,506
jcminii-.0--fadh
6.
0602
1.65L
semini-3... 2.0--fuhh
0.656
2.853x
0.568
0.60L
1.67%
0.605
acmiii-2.0 2.0
puschtnniser
0.367
8.41le
0.600
1.07Ac
0,066
gomini-10-ftch
timemierr
0.057
5.04
0.555
5.1076
0
1.355-
0.560
lam
0 + 1.16
0.505
1.57f
2
0641
7775
0.901
7.505
interp mlp
0.657
1.27e 3
0.52
9. 6.500=
0.677
4.55
0. 0.809
6.267
inter pachienierr
0.651
3.70le
0.54B
5.572
3
0003
888e
0.534
4. 4:351
inter timemiser
0.650 =: 4.30/9
0.321
8 4b4e
3
0
7 M.Me
0.506
1170%
sccui 34m
0.638
: 1,606e
0,000
1 2.585e
0.645
:: 9.4e
8
0.507
1.4770
scen mlp
0.66 3.843c
0,525
1 3.351e
0.607
+ 5.150
0.549
T.58c
meen guchtsmiser
0.094 4: 6.713
0,507
1 3.063
0.GG
1.7420
2
0.542
2 1.163
mzan Limmissrr
3.805
0-108
18
0661
Nayaun, -TFM him
1,166
0.56
0.711
No.sum
mlp
1.857e
0.563
No.ommJ''T
puichennisrr
CoT
1.58%
1,56le
0.706
3.57%
CoT
0.097
0.576
1,600
LoT putchsmiser
2.251c
0,700
0.582
2.905
Nayamn OcT linemiarr
0.606 :: 1.16Ga
L.52 8 0.57
0.607
3,000
0.577
3.58
Na.sum JCD -TDM lin
0.657 2: 7.413
0.509
0.100
0.711
2
0.667
6.32
No sum ICD ICD xTEM mip
0,708 2: 5,818
0.572 :: 7.28
3
0.712
7.727
0.056
6.08
No.amnJCI ICD -TMM ponctteniser
0.706 1.18
0.573 2.100 -
0714
2.500
0.588
1.01%
NCDemnJCD ICD fimemiuu
0.202 + 1
0.57 + 4.53%
4
0.700
7.859
3
0.565
8. 6.085
No JCD kim
0.666 + 1.83
0.575 + 2,08
-
0.700
1.367
0.561
+ 1.12
No mlp
0.877 :: 2.346c
0.376 1 3.1136
3
0.706
6.114c
0.5e5
1: 0, 0.2550
ND puchttmiser
0,80 2.1e5e
03378 :: 1885
3
0711
1.830
3
0.5e8
1.16te
ICD linemises
0.708
1.62%
0.58
1.191e
0.705
2.900
2.06Ge
Tread Tread -TFM
2.631e
0.308
0.715
0.589
4.91e
Trend Tread mlp
0,038 5,703
0.368
5
0.572
NoyeonTreed -TFM potchtemisor
0.006
Noyam,Tread Trcal -TFF
1.00G
0.509
0.714
1.267e
Table 24: Cross-site transfer results Gender. (part 1/2)
40

---

Published as a conference paper at ICLR 2026
Mathod
Iirid
ppicu
minic
oupee
No.sum Too.uu--Tized lam
0.656
2.715
0.901
3.173e
0.712
2.5000
0.585
2.990C
No.an, Tocall mlp
635
0.565
L.508
0.707
Tical paidoaniserr
0.536
L.500
No.um,2hred ITFM
3,335e
0.542
4,160e
9.923e
IIFM
2,008c
0
modymma OxT -TFM puichsoniszz
3.00
5.3.0
0.556
8.550
0.087
1.400
0.50
1.613
aciiemmm CrT -TFM timeniser
0.85
6.61
0.555
5 5.58f
0.665
8.753
0.559
1.573+
indgemma CoT Iam
0.511
0.003e
0.531
1.301e
0,053
1.40le
0.555
5: 1 1,53
madgemma Orl mtp
0.05
0.525
21000e
0,885
0.900e 5
013661
6.61
modjenma OUT puachsenissrr
0.557 2 7.450k
0.658
8.631e
0.686
6.106s
0.561
8 3.858
indgrmma Cel timemmser
1,000
-5
0.536
1.000
0.881
1.410
0.555
1.100
modyymms
2.908
0.53
0.500
0.675
1.207%
0.5:1
indgermma IFM
1.619
0.548
2.504
0.3030
0.547
2011c
indgeemma -IM puccttmnserr
0.531
1.740e
0.679
atcijemmaa
ICD
2.1990
0.546
1.7MMe
ILD
9:077e
0.501e
0.675
1.9876
0.545
2331e
mnddermaa ICD pulcloonier
0.070
2.647o
0.654
2.541e
0
1.9670
3
0.545
2.0344
mndgerma ICD timenixer
2,65
5.072:
0.534
L,0tle
0.675
6.050
01540
6.74c
madgrmma Ternd +TFM lam
3.07
5.TVI=
0.538
3.115
0
3-100
0.511
madgemmma Trend +TEM mlp
3.811
0.546
1,10
0
3.324
0.554
5.511c
medgenim Trend +TEM pacchumikerr
0155
1.4270
0.522
1,28
0.643
2:2122
0.254
1,831
mndlenna Tranl imemissrr
0.535
4.70
0.084
0.565
andgemma
0.688
1.056
iodgrmma Trend mlp
Tiznd tinemiser
6,851e
1,174e
0.557
mailymma sem,ahon plan TTMM latm
0471
100
(1538
5,555
0.081
2.500
0.552
2.85
muzigemma alan TTM mlp
0.770
6.635
0.548
0.38
0
3.67%
0.565
5.361
iadgenma zem Sbot TPM paccesmmser
D.51
2.1000
01553
4,121e
0
4.088
0.30
0.507e
medgrmma zem sbot TVM timemixrr
B05T 2 1: -
0.50
2. -3
0.084
7/9-76
01505
1.5200
mndgeniaa zero shot 5tm
2.65
1.026
0.653
1.547e
0
8.385e
0,565
2 1.22%
modgrmma pemuther mlp
1.6,50
03555
2.14
0.085
1.0056
0.36
modgymma sem shai nichimmiser
00811 1,001e
0.537
1.105
0.085
1.405n
0.557
zero seco indgermma
3,022
0.526
5.700e
0.525
1.040
0.751u-3
posctreminge
0.567
6.61ee
0,700
T.137o-
0.38
+IIM timemiser
3.098
1.760
0.57
2.010
0.7VM
8.2:36-3
0.581
6.354e
No acro shot istm
0,550
5.802o
0.862
8.527e
0,079
2.18tc
-
0.573
8 8.48
No suns zero sbot mlp
D,558
1.250e
0.924
2,553e
0,701
1.758c
3
0.579
2061
No.arm par,am_ sent paichasniser
0.080
1,27 -
0.564
1,7276
0.668
1.7:76-3
0.575
6.55
No.sura .ur.uur zem shot timeriteer
01583
=: 1.613e
0.533
1,083
0,093
200080
3
0,565
2.346c
right Istm
0.107
0.505
1.2176
0el)
0.5
Rgl mlp
0,511
2.07
1.51e
0.504
rul palcesmissr
0.649
0.519
night
Table 25: Cross-site transfer results
Gender. (part 2/2)
41

---

Published as a conference paper at ICLR 2026
Miched
-irst
-piic
mnirro
recall milroo
El micee
recal micro
EL micro
recall usire
Larra-A:1 Coll +IM har
2:000
Laraa 1.1 Orl VIFM pacttimii
03730
1.57
0.519
1.40me
0,794
7.5577
6.354e
8,510a
La 1.1 Cal +IMM unmounee
0344
0.454
1.535
0.227
BU511
11000
8-2000
03:223
1. 311 GIT
0.238
2.150e
0.00
1.915
T000
7.477
1. CIT partmniioo
0.70
0.00
0.550
14400-
0.033
0
2.70
0.767
2.30
0.837
5:153 -3
1.maa31 CIT nimwiiser
0.78
0.555
01081
33550
0.53
01833
7.58.
1. 1.1 XCD -TFM
0.701
01866
3.576
0.796
7-157
0.515
6.37
0,356
3.553
1. XDD -TMM pactrenivrr
0.77 1:350
0.551
1.011e-3
0.007
1:333
0.51
1.113
0.505
0.917 3:357 -3
1.ma-111 XTD
Inveriion
0.778
0.556
3.577e-3
5-131
B5:66
0.997 1,5
1.mma1111 ICD
0.756
2:015
Lant-311 3.1 K.D peldesssace
0.766 = 0.300C
0,655
4.340
0.710
2.01%
[U521
2.073
0.766
0.334
6.7-71
7.084:
0.500
0.006
5.746
3.1
3.25
Lasta311 3.1 Tredd ITEM salp
0.517 2
0.655 + 1.856
0.707
6.77%
0,53
4.00G
0.566 2 6.340
0.990
1.637L
Lant 3.1 Tread ITEM paoiossnis
0.750
1,50kc
0.5
1ikec
0.791
6:4:3
0.508
0.881 0 1.100
0.895
6.255
Lanessies
0.758
0.655
0.776
0.025
Lint 31 bess
1.57%
4.225
Lant-31 3.1 Treed sly
0.751
420
9.847 + 6.310
0.747
7.358
0516
580-
0.5%6
7.550
0.031
2.54%
Lint 3.1 Treed prodeaasiaer
0.766
163
0.44
1.100-
0.733
6:455
0.00
20
0.684
1.116
-1:5 hem
2.575
0.522
5,85
1.0000
Lama-111 szm_atee 1155 msp
2.000
0.722
1-476-
0.702
3:108
3,450
0.314
L.aa-111 zem_atee 111 sactemmaee
01311
0,80
1:50
0.76
5.60
Larta-11 smm
123
0.72
2.44
Larta-J11 sam mip
0.700
1.100
0
7.454
0
5.00
2.004
IL0G
Larra-1: smm pachieniarr
03722
0.881
BUSI
TFM
0L741
0,545
4,550
0.721
12270-
01022
2.007
TFM wrg
0.217
0:85
0.735
3311e
TFM
0.758
3.17le-
0.715
TSDE ely
0,833
5:15
6.771
0.804
33370
0550
0.511
0.897
11177
lon
0.555
0:900
1.507e-3
7.31
0.57
5009
01901
0.991
1.003
0.592
1.755
grarni Debbh- CoT -TM Irm
7:215m
0.579
2:335e-3
0.00
5.577
0.906
0.556
3.117
0.311
1.740
gemes-2 b-t.hbbcoooo CoT mip
0:355
0.571
3-351e-3
5:121=
03397
jouus-2Dda Col TPM Ganeuvee
0.510 [.00Kc
0.811 + 5.120
0.70
5.06
0.50
1.10
0.550
5.150
0.817
1.465:
2olui-2b-lah.Co CoT san
3,5 8.1.1
0,860
7.750-
0.790
2.007
IL503
6.507
0.442
7.01
0.001
1.855
geaus-2 cauus-2-.aal.Ca
8.07%
0.577
0.7%%
2oaus-2 D-lu--2-daaCCCo CoT Gascaroos
0.78% 5.477
0.865 + 2.15
0.772
6.751
0.501
7000
0.500
1,000
0.014
4.2.5
gemux-2.b.le 2alul-2-1ael-1C ICD ITM laus
0.50 = 0.073
0.865 + 2-660-
0.781
6.971
0,60
0.655
0.002
2.953
genus-2
clp
0:677
0.506
1.556
2omue- D-dal ICD 1TM tinerierr
0.50%
7.580
9.575
1.637
0.71
7.977-
0541
0,518
0.5-
8.10%
gemm-2 11:1
7.877
0.173
0.75
batt
0.013
3.372
somn-20dabbl ILD paccomise
1
0.555
5.300
1.34
1/312
jemm--2b-hoh R.D inseusser
[L70 1200
0.675
3-12
1230
2.7100
0.31.
semm-22-daah Iren
01500
1890
0.800
3.22
0391
7.035
5.343
smm--2ddaa Trend -1IM pacctoriiorr
0.21
1,242
01801
7.5
0,700
0.20
0887 7.000
2.218
scmm--2b-llah Tead +1FM tnammtee
[1756
4.11
0,584
1-924e
0.794 : 1.005:
0,81 1102
0,304
2.20
semu--20daah fsced lats
0.114
01823
grmni--.bbl brbbh Trand polrteettor
0.775
1771
0.555
1-35
0.707
DS16
0.817
1.7 0
0.816
1,510
gemri-3 Trund
0.776
10
0.510
1.170
BS17
0.833
1:13
03911
pesri-- D-l.h mm. -TEM
01500
3-315a
0.0
5:501
7.540
paateentio
0.516
2.215
1:100m
0.877
0.50
0.465
1.507
0.31
3.235
genrn-2. nmn,
0.508
-1
0.505
1-301e-3
088
1.300
03991
gemra-2 D-dlh mon
0556
6.55
5.333
09
1.015
01514
5,737
gemrz-2.-e (ebbbb nonidwr poidemoor
01532
11000
0100
11755
0.33
7:330
0,100
5.000
2.160-3
gemes-2 b-bthb non dur immonise
0.522
110
0:00
1100--
0.612
1:200
B.00
T:177
ol
0.556
0.07k
0,500
2110-
0.901
2.19%
0.44
1.341
0/925
0.887
4.195
sropp peldroation
0.577 = 1.307
0.570
+ 6:754
= 2.234
0.566
1.7000
0.645 + 4.56
0,837
1.623)
areep
2-17
7.55
0.554
2.650
sxan cily
0.501
0.857
+ 2.110-
0.917
+ 1:555
2030
0/550
2 2.300
0.867
1,67%
sson pebdroanioe
0.553 300
0.77%
1
0841
= 11:00
0.44 5:
1.358
0.565
T TTEM
00
5.555
2.915
Aajam, Cell Oii +10 poactomor
1,779 (4)
01500 m4
0,75 t6b
0L913
Sol terr
MALL
0 2100
Su,am, Cal pelchoriie:
1255
5,200
Ao.um Col timensser
0
3.306
0991
2 2:220-
ILK2 = 2000
7996 -
0.313
0:155-2
1.420-3
0200
2344--
1000
2.002
2.137
XDD bim
(LSTU 21686
0.51
2 1222
0.87
L214
1.301
5.990
1.5tic-
No sun XCD sal
(1577
0/905 + 2.100
0371 2 4,07%
0.901
2.22 2 3,521e
0,311
1.00
XDD pactrousioo
0
4:200
0.561
585
0:511
1
38877
0.00
14300
5/500
5. 189
Neaa Thod TTM tomm
0.500
3.76
0-913
4 -3
0.80 + 1.157>
0.00
53333
2.00
0.917
4 3,877 -1
Noyamm,Trad Thord 4TM partunisrr
(44
04)
0.901
0.887
0.801
[1558
1100
0.01
2915- -3
0187
3353
0.00
7.050
5/922
5.06le
03914
1-3,055
Nagam pacdrentoor
[1520
1200
0.909
1 2455--
0.891
7.530 -3
08
1.709-3
0.111
2.500
0.311
3.3855 -3
0.922
0:512
1.530
0,991
1
8.00
138te-3
03811
11330h-
melormma Gol -11M
01515
7
0.965 1 53
0883
0150
2522-
0,550
5,710
03914
1.25577-
Table 26: In-distribution results -rug. (part 1/2)
42

---

Published as a conference paper at ICLR 2026
Methed
hird
minic minic
pokcs ppics
Belleonma Off 4TEM padteasarr
0885 = 1,000
0,575
0,000
0:511
5:321s
0,561
3.315 3
1L555 2 1.353--
IL312
5,550
axogarma tanemior
411236
2.033
0.21
0,864
2,08
0,765
1/136s
0.540
4.008s
[154]
5.244
0.505
5,27
aetyyma ExT postremiee
0316
0100
0.773
0.11%
1
0.55%
1330
3.50
avlgmma ICT -TMM
0831
5.176-
0.813
6.17
0.461
0.55
1.310-
0.06
3.35m
avlymma XCD-IIMM wip
0.33
11030 -3
1150
1-300--3
0:431
1000 -1
0.077
0:200
1.700
B.11
2377
packianiis
0.81
aelganes JCD San
0.14
8,007
0,868
0.70
= 1,521
0.569-
2.855s
01564
5.25%
0,00
5.50
assgantta XCD sly
0812 = 7.31:
0,821
2.433
0.707
= 0.1
0.365
5.230
0,556
7.954
0.04 + 4.1:
avdiumer ICID mibormm
0.911
2:0m
5.6
0.86
2.05
0.3
0.066
1.800
avigentra Theat -11M am
0.817
3:333
0,874
1011 -
6.791
0.556
1.750
0.3069
0105
5,121
ixttomma Trand -11M mtp
0.233 51772
1807
8,12
1/432
0,33
0:81
1.00
selganes Tiesd (TPM Grsoniicr
0.007
11000
0,872
1.53-
6.776
1095:
0.535
2.688
0,318
9.00
0.0G
2.65+
asslannt Ticel ban
0.299
0,872
4700
6,717
5.00
0.18
0.7
0.518 + 4.857
0.00
1.16
reogareza Treel
pectimite
0.001
6.31.6
6:21
1:504e
neltennr (test ancemser
0.781
5-2256
11ST
0.221e
6.726
5-173
1-370-
0xt
1:200
BUST
1.00
iecierea zem, +11M
0353
2702
7:33
0.91
0,00
5.52-
relgames semalit 4TM peldrssioor
0.5
7.045
0.297
1.14
0.341
7903:
0.558
5.477
0.57
7000
0.001
1.770
aeslgrnea temalket +TM frreriee
0,37
0.703
0.993
1.300
0.542
1.29
0.506
1.40
0,500
7.855
0.00
6.606
resgereea
zemahitull oll
5.177e
0.5%
aesijenrra zem 2h14 pachomtorr
03223
2.1000 -5
0.807
11139
1111
[L8 2. 5-907
ILAT
6.0m
excleenra zem abot linersser
0.225
0,88
4:208
ILSEK
5.30
Ne Chil Tars
#2FM paidrannour
0.555
11500
0122
09144
0.881 3.87
2856
0.87%
1,377
0.00
0.921
1.250
0.11
3.66l
ntly
0.873
1115--3
0.007
3.57
1
6.955
0.15
1.210-
1.10
Nrgeer JA tireniser
0.961 4 10000
0.813
035 -
0.857
105
0330
2.75
0.506
1.403
0.15
3.175
rghh bin
2:236
0
1.11f
5.100
0 2.880c
0,884
0.431e
0.34
1944:
0.41 2 1.430
01548 : 5.87c
IUSI
2.44e
198h Innaiiis
0.9 2.0-2:2 1.41 8
0,915
4.012
8
0,503
2. 1723
0.80
2.00
0.500 2 4.000
ILS4) 5 1,300e
Table 27: In-distribution results - Drug. (part 2/2)
43

---

Published as a conference paper at ICLR 2026
Method
hirid-
ppics
mimic
ppice
FL micro
recall micro
1I micro
recall maloro
1.mo-11 1.1 Carl +TVM lemn
0.704
1.5ete -3
0.951
1:8t -
0.777
7.00 -
0.001
1.503e -3
LJams 3.1 CoT +TEM nip
0.763
2:
1,471c
0.503
1.309e
0.774
1.628
0.535
2: 4.5770c
Liama 3.1 Col +TEM positsmier
0.75
27781c
0.345
1: 4.113c
0.707
2.5890
0.532
2: 1.9077
Liamo 31 CHI +TMM timemnee
0.764
3.20cc
0.307
1: 5.708e
0.772
1.65cc
0.803
2: 0.05 0,00.0
11an- lem
3.77
0.341
8.270
0.706
7.11Ge
1lam-31 slp
2.066e
0.341
7.002s
0.761
1.7576
6.212
1 jonditemioor
Liana -TIM mlp
1.884c
1:354e
Liuma -TIM patchssmierr
1,309
0.774
2.245c
2
Llamo-1 I/D -TIM cimeriser
5.022le
0.554
4.85e-3
0.77M
Llano-311 3.1 ICD 1stm
0.773 1.46s 0
0.947
8.975e
8
0.776
1.883
0.918
8.713
Llans 3.1 ICD mlp
0.774 1.2277
0.342
6,2430
0.77G
2.145
0.947
3.50
11mm-111 1 1CD paicheamiscr
0.771 + 2.85
0.911
4.757
5
0.771
2,85
0.052
3.5 5.87
11mm-331 1 ICD fimeniser
0.778
2. 2.125
0.915
2 2.7
5
0.776
2.913
3
0.35
2.51
3.1 Tacrel .TFM lean
0.759 + 1.706
0.925
2.22222 2.2:1n
0.799
1.162
3
0.072
+ 3.260
Linmo 3.1 Ieend +1MM mlp
0.786 4: 3,093
0.301
1: 1.3850
0.79
2.030
0,90
3.100
LJmmo-11 Ineed +11M ponchtsmiser
0.771 2.4754
0.225
3.235e
3
0.781
1.077-
0,126
11am- Tered +IFM timemizer
0.755
0.302
1.801 5
718
1.215
0.971
1iams Tased bon
1.77%k
0.305
0.785
1.248c
1,778
Tesod mip
1.81
0.307
0.784
Liamo Teend puschtsntioe
0,509
0.77)
L8100
2,61
0.502
11ann-311
-TEM
2.100
0.94
2.710
0.779
0.013
11 sem_shot +IFM
0.767
2:087e
0.942
0.778
1.44le-
1.035
11am-11 bemmushtt =TFM puachtemiurr
0.747
1.155e
0.941
3.005
5
0.771
2.058
0,478
1.055
11ano-31 1.1 bemmushett =TFM timemisez
0.765 3.653
0.911
1.5:1e
0.773
1.115e-=
0,001
3.100
Lians 3.1
15mm stot lam
0,774 4: 4.573k
0
2: 6,489e
0.770
3.300c
0.50
3,991c
Lians 3.1
1.mmmm. shot mlp
0,773 6: 3,3315
0.95 4.000
3
0.774
2:277cc
0.55
2.5086
Liamo-3: 3.1 semm shott parchienizer
0.772 5: 4.10 de
0.302 2 200
3
0.774
2,935
0,040
4.17%
Lluea-3 3.1 acro shot Camemixer
0.776 4: 3.18%
0
2: 2.07
0.776
2.505
0.858
2
1.217
TEM lean
0,792
:: 6,626e
0.504
1: 3.325e
0.788
2.000%
0.511
2.7720
TFM mlp
0.70
1,000
0.302
1.30
5
07883
1,500
0.811
2.947
TFM paachtemiace
0.779 1.083
0.385
2.77
0.781
1.16l=
0.917
TFM limmnisex
0.785 1.22
0.943
3:055
0.757
9.5%
0.911
mip
5.085
0,915
1.303
0.731
1.478c-
1,451
5:005e
0.917
prccttemiser
5.027
1.507e
0.754
1.91cc
3.655e
10 fuch CrT -IMM
2.001c
0.797
1.453c
0,019
4.10%
5om-i-160-faeh OiT -FM mlp
0,702
9.
0.927
4.151
0.700
2.000
0,020
1.965
2omin-222-fiah CVT -TM pacctonnscr
0.77
6.655
0.912
3.66
S
0.776
6.061
0,010
4.5.0
scmini-2.--aeh CJT JTFM tineniser
0,779
+: 2,736
0.921
2,20
0,798
1.60
0,021
2.555
2ommn-10-ffuch ONT lean
0.70 3.17de
3
0.95
1.000e
3
714
0.004
4.77
55min-1.0-BBbhh CxT mlp
0.751 2. 2.876
0:55
9. T:5
3
1856
0.401
+ 0. 0.755
5:min-----Bech OXT paccttemiser
0.758
2. 08.5
0.95
10
3
0.79
1.700-
0.982
5.13
zemini 2.0 fluhh CoT timemiser
0.703 1,80c
3
0.53
1: 2 2.004
3
0.700
2.2422
0.529
1.58%
gemini 1.0 flxh JCD -TPM Jom
0.76
1.12
3
0.229
1: 221e
0.774
1.577e-
0.106
2,818
gumini-3..-fabb -TFM mlp
1.258
0.967
5u
0.777
0.915
Jomisi-2.--fahh ICD -TFM puachtsnixer
2.042
0.368
2.10
7.881
acmisi-20--Ba ICD -TFM tincmiser
0.758
4.107e
0,347
6.715e
0.773
5.022
mlp
jomiis--..--ich ICD paldhoeniser
2.187-
2.0 fl-ffahhh ICD
1,102
0923
3.166
0.78
2.7650
4.2
pemmni-1-ffudh Trend +11.
0.781
0.781
6.712
1.71de
2ommn-1---fuo Trand +TFM
0.777
2.135e
0.951
6.784
1.15
0,005
2.50
jemini 2.0 fuih Trend xTFM paicktmmerr
0,759 2 5.442
0.525
6.722
3
0.774
2.64lc
0
2.477
jemini 1.0 fush Trend -ITM timenieer
0,774 2: 3.093
0.504
3.535e
0.783
9.19%
0.028
0,546c
5ominn-1/-BBel Trand loo
0.786 - 1.8
0.955
4.227
5
0.785
2.57%
0.911
1.07%
gamini--/-BBehhhae Trand nip
0.766 2: 2.858
0.265
+ 4,567
-
0.787
1.800
0.91
1-550
gomiii----Baah Trand pacitsniser
0.758 2: 2.250
0.269
+: 5.06le
3
0.786
1.849
0.041
=: 3.64le
2omini11--fsch Trend fimerisorr
0.758 1.185e
0:341
1.366e
3
07877
2:000
0.811
1.005
5omini-1.-BBsh zemo,bet pbot
lemn
0.865
4.81
0.931
2.36
5
0800
0.0011
1.000
5omini-).-BBahh vom -TFM mip
1.003
0.957
5.0 3
0800
1,830
0.805
+ 1,759
1emiti 20 fush zero
puccttsmiier
4.705e
0.917
0.79
2188cc
= 1,202
zomini zero
timemiser
3.123k
0.927
1.550e
0200
1,244c-
1.003
jcminii-.0--Badh
semini--... 2.0--fulh
2.503
0.806
3.16te
acmini-2. 2.0
puschsnnierr
L.B15e
0,525
1.81e
gomini-10-ftch
timemierr
0.906
1, 1,100
0.917 + 3.00- 3
0.66
5
0,308
2.110
lam
0.712 7.00 3
0.57
5.905
2
0.516
1.06
1.318
interp mlp
0.728 5.267
0.534
3.155
2
0.621
1,50
0.554 = 2.115
inter pachtenierr
0
3,25 de
0.345
3.30
0-125
22
0,213
2: 11300
interp timemiser
0.646
1,600
0.201
a 3.18%
03161
1:277
0.227
2 111//5
sccui 34m
0,020 : 9.90
0.529
: 1.40%
2
0.682
2.055
0.803
22 0.50
aecu mlp
0.711 +: 9.823:
0.712
1: 2.225
0.662
7.92%
0.621
1.500
meen guchtsmiver
0.511 4: 5,515
0.422
+: 7.283c
0.424
3.0000
0.000
2: 1,00%0
mzan immmisrr
07 6.65
0.719
1.3055
0.197
7.00
0.581
1.%
Na,aun, -TFM
0.831
8,552
0.363
2.600 8
0.8
1/88 8
0.91
1,000
No.sum
.7FM puchieniser
0.505
0.90
6,770
0,022
No
0.901
1.730
patchomiser
1.65%
1.4.3e
0,838
6,050
CoT timeniser
1,14
0.365
0.824
3.000
0.040
2.703k
JCD ICD +TIM Irm
0,828
1,855e
3.560
0.800
0,016
1.220
Nayamn JCD ICD .TFM pachitaniser
081 :: Gen
0.031
6.781 + 0-0
0.524 0-0
No sam JCD han
0,536 7,286 4
0.305
4.500
0.82 # 1.116e-
0.55 =: 2.01e 5
No sum ICD mlp
0.506 :: 9.5c
0.363
2 2.515e
3
0.17
1 1.02
0.014
2: 2.906
NC1 penchtemiser
0.K36 + 1,27 -
03305
+ 1.21
5
(L818
1.81 -
0.5100 + 2.005
JCD limeminez
0.836 + 1.503 5
0.971
+ 9.225 -
0.255
2.560
3
0,051
+ 2.26 1-
Nassmn-Tead Trcad -TFM kom
0.53 5:000 3
0.962 + 5.102
5
6. 700
3
0.029 + 2.760
Drend Trend -IIM parchimnier
11.000 Be0
0.125
0.751 4: Dell
0.523
180
No.omm_Lhend Istm
[1836
3
0:361
4.45% 3
0,8U5
1: 1. 3
0.848
1.11
Tread mlp
8,87%
0.363
3.464e
0.821
5.00
0.945
2.646
sum Tread paitlommssrr
0.536 1.50l
0.962
2.571e
0.82
1,400e
3.025e
Trend
=: 1.247c
0.97
1.908
13355 0:820
2.029k
mralyama -TFM
0.005
molyymmu .TFM mlp
1.408
0.706
2.986
Table 28: Cross-site transfer results - Drug. (part 1/2)
44

---

Published as a conference paper at ICLR 2026
Method
ppice
ppicu
2.678
0.907
0.315
IIFM tineniser
0.718
modyyma OrT kam
0.788
9.30
01023
1.78
0.797
0.500m
0.522
5 5.905
acdiemma CrT
0.79
6.558
0.021
3.700
0.755
0.553
0.525
7 1.525+
indgeemma Col poschemiieer
0.91
1,58
01122
8 8.002
0.713
1.714
0.521
3.78k
madgemma Oil timemsser
077
1:221
(11.5
6.075
0.710
1
0.927
5 41-d -1
mndjenma ICD -TFM latn
3,60
1.000
-
0.056
1.36le
0.805
1.341
0.927
2.8004
indgrmma
mlp
080
2.0075
0.M
3:347
0.800
6.066e
0.807
3,00
modiymma
0741 6.01
0.794
2.007
0.10
indgermma
7.214c
inddgemma
patchesmer
01130
4.802
IUD timeniser
3.000
2.170e
mndieemaa Tecndd .TFM lum
0.772
9.801c
0,04
1.07le
0.779
2,408
0.925
2.055
mndgerma Teend -TEM ulp
0.788
4.23
0.945
0.205e
0.781
1.800o
3
0,501
18827
madgymma Ternd +TFM gatchsuiierr
0.75
6.003
0.955
1:210
0.760
1.507
0.827
167
madgenmma Trend +TEM Caneniter
0.764
2.7H
0.54
5.22
3
0.776
1.255e
0,314
2.315c
medgenima Trend Iam
2.75
2.071c
0.455
4.016e
0.722
1.714e 3
0.9
3.075
madgennm Traul mlp
0.000
0.782
mndgemma puchommiser
modgrmma
1,815e
mailemma TFM puiclanniser
0.55 1.415+
0.014
0,07
0.794
2.001
0.917
5.710
muzigemma alar -TMM linemiar
0.70 5.500
0.018
5.30
0.855
2.323
0.919
1.728
iedgenma zem sbot lstm madgrmma zem sbot mlp
0850 9.6396 -
(112
1,301e
0.M.7
7.601
0.927
1.444c
0887 7:139 -
(1331
7:173e
0.M07
1.000
0.925
1.200
madgeniaa zero shot paclionnisrr
B806 2 1.8840
0.051
2.164e-
0
2.071e
0.925
8.550
modgrmma zem shon timenixer
080
7.505m-
0.055
1,200e
0.865
1,0000
0.925
5.0
jo.jumm.emm.hur shre +TFM lem
2.291
0.065
5:5
0.160
2.00a
0.909
7:19
zero zero. -TM pusctremiie
(k0
iam
2.352.
paictesmoerr
1.153e
01158
3.000
0.510
1.81e-3
0.945
No timemisur
1.1335
01955
7.500
0.871
2.119
0.917
6008 -3
rigl lus
0.720
2.01
0.750
9.362
0,526
1.080
2
0.41
:: 1.30c 2
HAD mlp
0.720
S,533e
0.822
0. 0,38ke
0.501
0.53e
0,517
5.03c
righ palchsmiser
3.575
+ 2.50
0.533
5.01
0.546
2.55
0.231
2151+
-2
right timeniser
00055
6 9,987e
0.722
3: 5.000e
0.37
d: 5.214e
2
0,256
d: 4.133c
Table 29: Cross-site transfer results Drug. (part 2/2)
45

---

Published as a conference paper at ICLR 2026
Meched
-irsst
inicro
recall milero
El micee
recall micrr
EL micro
recall nskeo
Larra-A:1 Coll +IM
IL522
2.10ie-33
Lamaa 1.1 Orl VIFM pactiming
EL557
11 13100
0.019
5.320e
0.292
1
IL555
7:00e 0
1.383a
L. Cal IIM unmounee
0,518
5/125
0.125
3.150e-33
0.ST
1,800
IL000
0.511
5,45
013129
1,3831
1. 311 G/T
0-0
0:911
11 CIT partmniio
0.545 0-0
0-0
0.553
0-0
0:917
533
040
0.93
1.maa31 CIT tiwwiisrr
0.511 0-0
0-0
0.515
1. 111 XTD -TMM
0557 1270
0:576
7:130-
09893
7.1 15
9,086
0.615
7.047
1. XDD -TMM pactrenivr
0.56 1100
0
1 1:304-
0.885-
0.001
0.592
1070
0.913
1.710
1.ma-111 XTD
Inveniion
0.531
122
0.575
5:000
0.861
7.0
6.000
0.53
7.57
1. 1.1 ICD
(44
01001
0:915
0.53
(040
Lant-311 3.1 K.D pebdesssace
0.541 0-0
0500 041
0.5T6 0.D
0.345
0850 2 041
0U5G0 = 10D
3.1
0,578
0:125
6.24.
2,07
5/070
Lasta311 3.1 Teedd ITEM silp
0,58 1,2171
0 0252
0.25
0.9
1.097c
0.94
+ 7.85
0
199
0.922
1/6.4
Lant 3.1 Tread ITEM paoiosanis
0.566 = 2.002
0
2 211
0.201
2,170:
0L501
1000
D.54
+ 6.57
0.01
6.315)
Laneuises
0.720 +
0.875
4023:
0:117
D.500
1.736
31 bes
Lant-311 3.1 Treed sly
0.951 0:0
0,552
0.0
0.941
0567 040
0.059 0-0
Lint 3.1 Treed prodesasiaer
0.445 0-0
0 00
0.556
0.0
0.342 96
0,838
0,009
0.0
Lana-1.1
-1:5 hen
2
0:121
1-386---
0.80
12220
1.00
1.33
Lama-111 szm_atee 1155 ms
0,01 1,388 -1
0:225
1.33--4
5.15 4
0100 1 2.7 -
7.235
L.aa-111 zem_atee 111 sactemmaee
0412 1 Jo3le-
7:4576
1.30
Larta-11 1.1 smm
0,554 063
160
Larta-J11 sam shot mip
02007 943
(022) 1M
C.501
0.344
11877 2 1901
Larra-1: smm pachieniarr
0345
01811 2
TFM
055 K.50
0/124
1,750e
0.77
L138s
ILSCO
9.000
TFM wrg
0.50 11700-3
0:531 + 1413--3
0.871
5:356-
0.531
7.677
TFM
0.51 100
0:53T
1-131e
7.105
0.041
0.506
TSNE fors
0.50
0:971
+ 3-150e-3
1.00
0:51
3.75%
0.65T
5.5700
0.971
7.50T -3
nly
0.575 100
3.67
1.027
1.00
1.0000
0.00
5.500
0.703
0TT1) 3:206
0:913
1.65%--
0.760
0.11
1.35
7:17A
3.3
grarri Debbhl CoT -TM Irm
0,85 1.88 3
0:971
<-100--
0800
135--5
1000
2 10055
0.013
1.55EP
genen-2 b-t.hbbhoo CxT map
0.566
0:97%
1-359-
3.00
1-610
0.91
7:270
jauu--2Dda Col TPM Ganeuiee
0.550
0.124
+ 5.140
0,39
0,500
0.504
2 5.000
(L5C0
6.000
2olui-2b-lab-Co CoT san
0,554 = 0.222
0,223
2350
+ 1941:
0.564
- 1,000
035 = 0072
ILOOS
6.7%
gesus-2 cauu-22-daal.Ca mly
6,56-
0,444 +
1864
=
0,501
2osus-2 D-lu--2 CoT Guscaroos
0.344 0.0
0 123
04
6.75 5 0.0
0.947 + 060
1250
0,009
0
2emui-2b-deh ICD ITMM laus
0,552 1000
0:225 + 1.116
0,50 = 1:000
1007
0.012
genus-2 D.-i--2--.llIICDD
clp
1:556
6.03-
0.551
500
2omue-2 D.dal ICD 1TM tinerierr
0.564
101e
1186
0.905
1.371
0,506
1.000
0.554
0.035
gemm-2 11:1
01511
0425
72
736
somn-20dabbl ILD paccooise
01547 0e)
03945
0881
ELIGS
jemm--2b-hoh R.D inseusse
0341
semm-22-daah Iren
0,23 1
0:22
1-334-
Mille
BaST 3.57
smm--20dal Trend -1IM pacctoniierr
[L362 680
0:15
scmm--2b-llah Tead +1FM tnnmetr
[1564
0/122
244
1085:
D.ST 2 5.547
0
semus-20-aak fsced lats
0.550
0012
2401e-3
02007
001
1.531
Du56 5 7.807
IL5G1
18500
grmri---brbe D-bb- Trand polrtenttor
0:551-1 0-0
033 060
0,50
105
091 + 960
096
0.00
D-0
gemri-3 wonn--b--bhh-. Trund
0:517 0-0
04
0.551
100
08-5 040
pesmi-- D-fah mm. -TEM
0.536
0 2000
0:5TT
0.995
5.37
1.000
3rmmi-2.bds (-bbb)))).. mmn +TEM prattontion
0.67
5.905 -3
0.515 1 1-330-
0.097
11351 -3
1000
0.45
3.00
0.315
9.7
genrn--. cmn,
0.561
1.51 1:11i-33
0:511
3-510e-3
0.875
26
5.0%
2-171
03915
6.335
gemra-2 D.dlh mon
0.821 (m4)
(44)
(e0
0.912
0166
(34)
0.505
gemre-2.bxb Debb- nonidvr poidemorr
0.855 (44
144)
0.504
1-0
0.913
1997
11875
(54)
EUECC 160
gemen-2 b-btbb non dur immonisr
0.833
144
6:611
1-0
0:411
0.41
0.512
ol
0.564 = 2.577
0.857
2.54e
0,506
1,303
Lo7le
D.972
6.680
0.871 + 2.106
sropp peldraation
0.86 4.166
0.555
3-554e
0,201
1954:
7070
0,500 = 5.00
0,847
1
areep
1.55
1.771
0.007
0.560
1/0TT
0.557
0.550
sxan cily
0.552 7.300c
0.555
1.5
0.982
+ 1.3.1:
0.504
5000
0.546
1.500
0.841
5.319
sssn pebdroanioe
0.80 1,873
0.557
1.115-
0.50
5,500
0,286
0.15
+ 3.78
0.547
1.7
0.560
0.57
T TTMM
0.500 1555
Aajam, Cell Oat +1 mlp
[LV6 1.8009-
0-435
19911 -3
3.31
03133
6.0-1
Col
0,81 4.2336
04125
1.000-
1.157
Sa,am, Caf tess
[L535 31503e
01523
0.5
0300
019222
330
2.100
7,555
Ao.um Crl mis
11:05 (4)
0L914 IND
2 (041
0L005
jaahimmise
0.577
5.155
03300
18:22
0.000
1.755
So am XDD IED +1MM lann
(L550) 11.122
0:131
L
1.710
1.52%
No sm XCD ICD +1FM mdp
0.500
172
0/135
+ 1.40le
0304
1.131
0.555
0,312
1.212
XCD 11FM puccimiinzr
0.582
470
0.005
1.32%
NogamnDD (ET)
0.975
1310--3
0.885
1.777
0.550
Nop kum
0.ST
0.1
7 7:450
0.8G
L.1
BA61
0.571
2.500
01801
1.131e
Nojam,KD mlg
0501
640
0.911 6.0
0.113
Nigam JDD tmmeriur
[1580
7:13
0:322
13300-1
03
338
1139--3
0,462
1.177
11.00
Seaa Thost xTMM lom
[1557
2. 2.17
033
81056 -4
0913
1 9.77>
B307
1135%
0
1.300
0.301
1.113 -3
Nayam,7rrdd hhod-TMMM mip
0301
51055
0:31
13
0M17
1973-
01887
013611
1.377
Noaan,Tree Thosd WTM tinertzrr
0555
63
05311
22336
0.80 3240
6.36
0,56
L. 1.960x
11677e
Table 30: In-distribution results- Lab. (part 1/2)
46

---

Published as a conference paper at ICLR 2026
Methed
heid
hiril
minic
minie
ppice
Neger, Theal lem
0,878
cod
0,935
0,906
2.
0.913 5.67
0.55 1.3e-
0.000
1.315
Thrrl rlp
0.500
0-0
Turs inmmior
0.873
0/515
0.00%
aslgamt Cdf 1TE3 bit
0.500
6.15e
0,925
0.304
12240
0.338
1.230
[L661
2.350
IL000
1,212
aslgama
mb
7.617
molgama Off ITEM taneito
0.55
2.115
0.023
1,20,2
0,553
7.346
0.355
1/845
01555 2: 2,474
0.025
6.250
antymma
0.953
8.00%
1255
0.311
3-430--3
0.501
0.855
avlomma GVT posteeniore
0.566
0.585
0.55
(v0
040
avlymma GXT fiventior
0100 (44)
0.997
(40)
D.1
0197
040
uelganes XCD TPM packooniis
0 1000
0.318
0.354
1085:
6.32
3.734:
0,422
L10:
5200
assgontt XCD TPM Gincesact
0,902
2.770
0.021
0
5:295
0.355
27665
0.11
+ 1.706
ILOCT
avdiume ICDD
0.565
060
088
05555
awigemra ICD gachroniser
0.552 Cw0
042
(44
640
040
ixttomma ermmerr
01:5
0L922
140
selgonss Ticsd (TM ulp
0.855 1000
0L000
2
6.05
1574
0.04
0.560
1.60%
445
asslantt Ticel (TM putcitatiss Gssonixer
1990 0.664
0,556
2000
0.30
1.772
0.500
1.0
6
relgares
Tread
01811
6.64
1.00
200
0.55
1.35
aelgenta (tead mp
0.571 0e0
100
160
ixdiemea (izes pachimite
8,555
zelganta zemaaktt (TMM laus
0.885 01000
233
0:933
25
0:35
7.55c
0.550
= 1:100-5
0.05
aedgarne semalit TTM nly
0.911 4
0.15
6.171
0.55
5.55
DOL
relgereea zemaaht fireiise
0,877
setjemnra zem 2he4 ler:
03977
1:0140--0
187 -
222
IUST
BLOOS
rxdjemea zem mlp
0.55
0.522
0L124 +
HUST
Nogserizens 2en 200 Jun TTM
0.999 4
0.831
1
6.9
2235
0.91T
3.154
0.554
1750
1.19
Nogien
pastecsiour
0.883
0:541
4.99
0.555
5.055
Nrgaer Juin chit aes Jinn 4TFM tiventtrr
0.871
0.936
12077 -
6
1515
0.341
7.452
0.550
1920
0-033
1.55
No serz za1 absi Lita
BUST
1:25
12805
0.342
1,500
01535
1.500e
peacromzace
0,877 0.503e
IL5G
4.500e
6.055
20002
6.345
5:238s
(LST4 = 5.300c
IL500
1.37he
SO- 2E10 kadl lisaiin
0,903 = 5.0Ge
0,312
5.055
0,415
0.342
4.338s
0.90
7:34
0.13
1.172:
right
0.873
1172
8.901--3
8.11-11
1.03
0.56
1.100
0:570
1.391e--
TIN pocheerisrr
0.91
4 -
0.838
1655 -
0.80
2.500
0.581
0.55
2.551--3
0.566
1.10%,
PÇN Invnior
0.871
3.00-
0.559
6.57
0.911
1.388
0.50
1,50
B.90%
2.718e
Table 31: In-distribution results Lab. (part 2/2)
47

---

Published as a conference paper at ICLR 2026
Method
hirid-
ppics
mimic
ppice
FL micro
recall micro
1I micro
recall maloro
1.mo-11 1.1 Carl +TVM lemn
0.774 +
0.007e
0.959
3.07% -
0.700
1-933
0.50
5.050
LJams 3.1 CoT +TEM nip
0.78
2.150c
3
0.305
2.175e
0.761
1.30%c
0.536 2: 1.442)
Liama 3.1 Col +TEM posttsmmer
0.777
4: 2.3030
0.309
4.087e
0.703
1.600e
0
=: 2.004
Limmo 31 CHI +TMM timemnee
0.770
1.3040
0.302
3.385o
0.775
44760
0.99
lem
0.7%6
0.054
6.775
0,802
1lam-31 1.1 slp
0.781 0e0
0.955
6.705
0.501
1. jonditemioor
6.77
1.35
Liano
-TIM mlp
2.1355
0.761
1.713e
0.855
Liuna -TIM patchismiser
2,201
0.76 1.61
0.036
3. 3,091e
Llano-3 I/D -TIM cimeriser
7,14
0.302
0.773
1,00l -3
1.107e
Llan=-31 3.1 ICD 1stm
0.775 060
0.916
6.770
4 000
0.947
Llans 3.1 ICD mlp
0.775 060
0.947
000
0.77 Oxd
0.545
11mm-111 1 1CD paicheamiscr
0.789 D-)
0351
(40
0.176 + 1e0
0.55 + 0-0
11mm-331 1 ICD timeniser
0.787 D-0
0.951
040
0.777 + 0e0
0.447
040
1.1 Tacrel .TFM lean
0.773 1.50 3
0.202
6.30
0.771
2 2-454
0.01
= 1.418
Limmo 3.1 Ieend +1MM mlp
0.778 1,860 J
0.96
1 1.11 3
0.77
4 2.5000 3
0,048
= 122
LJmmo-11 1.1 Ineed +11M ponchtsmixer
0.778 8: 1.735e
03077
+ 7:5000
2000e-
0106
11am- Tered +IFM timemizer
0.774 5.0%6
0.35P
6.21
0771
0.945
1iams Tasod bon
0.70 0:0
0.052 = 04
0.77 0xn
Tesod mip
0.788 Be0
0.90
6,708 +
11877
Liamo Teend puschtsntior
0.787 Be0
0116
05553
1lann-311
0.202
1.780
4.00%
11 sem_shot +IFM
0.770 5.07Te
1.50fe
888e
0.15T
3.00
11am-311 bemmushtt =TFM poachtemiser
0.78
1.05
0.951
1.310
7133
18857
0.055
6.07
11ano-31 1.1 bemmushett =TFM timemiser
0.78 2.000 3
0.957
3.383 -
0.70
2.573
0.911
2.4
Lians 3.1
15mm stot lam
0.791 DC0
0.010 +: O4)
0,781 4: 0c0
11545
100
Lians 3.1
1.mmmm. sbot mlp
0.791 De0
0.006 (40
0,775 1: Gc0
11545
Liamo-3 3.1 semm shott parchienizer
0.710 DeO
(US3 (4)
0.77
cet
U.54
De0
Lluea-3 3.1 acro shot Ganemixer
0.794 DxO
0.910
0.0
0,781 +: 0cD
0.545
TEM lean
0.730 2.24 3
0.94 +: 2,729e a
0.787
1.78le
0.034
2:
1.136e
TFM mlp
0.756 5.3/6
0.912
5
799
0.001
TFM paachtemiace
0.791 1.727
0.945
8/150s
07778
1.30
0.945
1.54
TFM limenisex
3.07
0,943
2.703m
0.911
6.27
1.001e
07788
6:300
mip
07311
LUSTe-3
0.900
limeniser
0,850 000
4.70
10 fuch CrT -IMM
1,15
1.505e
1.50%
5om-i-160-faah CiT -PM mlp
0.781
2.
0.955
5 5.21
5
0,965-
1.55
0.961
1.06
2omin-222-faah CVT .TMM packtoniscr
0,761 + 1. 1,646
0.96
237
8
0.756
7.211+
0.061
jcmini-2.--aah CJT .TFM tineniser
0,778 7,45e
0,968
# 1.47% 8
0.77L
6.74%
0.015
2omm--10-ffuh ONT lean
0.790 0: 1.5g de -3
0.915
8 3.807 3
0.773
5:300
0.011
55min-2.0-BBbbh CxTT mlp
0.703 D-)
0.00 + (4)
0.172 + 0-0
0.955
5:min--/--Bech OXT paccttemiser
0.791 D-3
0,95
0.774 + 0e0
0.5.5
zemini 2.0 fluhh CoT timemiser
0.799 Bk0
0.55
100
0.77T +. Kell
0.545
160
gemini 1.0 flxh JCD -TPM Jom
0.775 4: 3.041c
0.96
4.06
0.77
+ 2,000e
0.845 2:
gumini-J..-fibb -TFM mlp
0.777
0.96
L
8
1077
3
Jomisi-2.--fahh ICD -TFM puachtnnixer
5.120
0,365
0.700
acmisi-20--aa ICD -TFM tincmiser
0.771
0,365
0.772
7.007
0.910
mlp
jomiis--..--ich ICD palchoenisur
040
0:563
2.0 (1-ffuhhh ICD
0,772 De0
0.075
040
0,77
U5499
jemmni-1-ffulh Trend +11.M
0.764 4.197e
0:942
7.12 3
07776
5
3omini--0-fsoh Trand +TFM
0.750
2. 121e
0:917
400m
077
6.571e
0,015
505
jemini 2.0 fuih Trend xTFM paicktmmerr
0,769 4: 1.35%
0.344
1.347e
0.703
2
0,051
1.50
jemini 1.0 fush Trend -ITM timenieer
0,781 2: 1.0022
0.354
: 2.575e
3
0.770
1.480
0.046
1.000
5ominn-1/-BBel Trand loo
0.801 + 3.11 -3
0.955 + 3.15%
0.781
2.7
0.005
+ 0. 8.
gamini--/-BBehhhae Trand nip
0.705 0m0
0.939
04)
0.777
+ 060
0.811
gomiii----Baah Trand pacitsniser
0,502 0x0
0.946 000
0.788
4: 6e0
3.94
:: 0.0
gomini-10-ftsh Trend fimerisorr
0.8 (K0
0.12 (40
0. 785
5omini-1--BBah zeno,bec pbok
lean
0.778 2.641e
0.952
3.300 5
6.77 + 1:00
5omini-).-BBahh vom +TFM mip
0.778 1.000
0.967
766
3
0.000
3.29
1emiti 20 flshh zero -TUM puccttsmiier
0.775
1,757c
0,569
0.002
4.923
zomini zero
timemiser
0.774
3.55e
0778
3
0,032
jcminni-3.--faah
semini--... 2.0--ful
0.912
040
0.762
0.967
acmiii-2.0 2.0
puschtnniser
0.795
0.551 040
0,767
0.554
gomini-10-ffch
timemierr
0.767 De)
0.557
0.0
0.767 + 0-0
0.577
040
interp lam
0.708 + 1.009
0.585
6:2:3 5
0.657
9.101
0.813
2.645
isterp mlp
0.738 +: 1.945
0.955
8.40
7.500
0,911
2.10 -
inter pachienierr
0,702 =: 9.013e
0.505 : 13
2
0.778
7.866-
0.5
1 2022
interp timemiser
0.611 2: 8,843
0,854 : 7000
0002
33676
013
K 6.54
sccui 34m
0.640 : 1.08%
0.8 : 1.13 2
0.22
5.661
0.726
= 1,50fe
scen mlp
0,601 1.4%c
3
0.585 1 6.06le
0.622
19442
0.772
+: 2.006
meen guchtsmiser
0.636 :: 1.273:
0.775
1: 2.775c
0,679
+: 7.150
0.51 1.600c
mzan Limmissrr
0.067 5.50T
0.708
1.185
0619
0.754
1.35%
Na.amm, -TFM him
0.756
1.06f
0.9%
0.775
1.180
0.915
2.769
No.sum
mlp
0.758
3.16%
0.951
-
0.771
0.051
3.640
No.ommJ''T
goicheonixrr
1:203e
0,937
CoT
0.7%6
5.680
5.541c
CoT
01.003
0.949 000
LoT putchsmiser
0.55
1,30le
0.781
4993le
Nayamn OcT linemiarr
0.807 :: 9.066
0.945
6.28
0.706
1.00
0,008 : 4.04le
Na.uum JCD ICD -TMM lin
0,758 :: 3.210v
0.587 = 1.66
0.783
7 701
0,018 : 1.560
No sum ICD ICD xTEM mip
0,758 2: 6,315e
0 : 1.20
0.77L
2 263
0.561 2,03 -3
No.amnJCI ICD -TMM ponctteniser
0.757 + 1.885
0.35 + 2.14le 5
6.773
+ 8.6
0.011
1.80
No.emnJCD ICD fimemiuu
0.752 + 3.11
0.315 + 1.12 -
0.777
1.567 3
0,111 + 5.870
No JCD kim
0.806 + 3.556
0.912 + 6.255u -
0.79
1,25 5
0,911 + 2,000
No.ommJCD mlp
11,002 BK0
0.782 4: tell
(1543 Beo
NC puchtsmiser
0.800 1: 9, 0050
0.953 :: 5113-
4
07833
1888
3
0.841 1: 7.3000
ICD linemises
0.811 7.36G
0.942
8.1666
4
0.794
3.833
=: 7.
Tread Tread -TFM
7.995e
0.905
0.776
29936
6.04%
Trend Tread slp
:: 2.368c
0.961
1,200
0/771
4.85
NoyeonTreed -TFM prcchtemisor
1.707
Noyam,Tread Treal -TFF
0.777
4.916
6.11fa
Table 32: Cross-site transfer results LLab. (part 1/2)
48

---

Published as a conference paper at ICLR 2026
Mathod
lirid
ppice
mimic
ppicu
fl flmierro
recatlanicro
fl.mkrre
recallmicre
No.sum Too.uu-TTized lam
0880
5.2190
0.344 # 1.77le
0.78
3.98
0,863 # 5.442c
No.an, Tecal mlp
Oxl)
0.77)
Tical paidoaniser
7.472u
2.502
1.084
No.um,2rred
0.955
6.m.3e
0.776
1.662
IIFM
0.057
7.516e
0.771
1.34le
0.949
modymma OxT -TFM puichsonizzz
0.788 4.118 5
0.858
9.58
0.196
2,886-3
0.952
aciiemma CrT -TFM timeniser
0.78 1884h
0.950
3.550
0.772
5.851a-3
0,911
8.06l
indgemma CoT Iam
0.797 2,7000 3
01966
:: 1,017e
0.774
2,3050
0.501 1 0.214
madgemma Cil mtp
0.707 (M0)
0:947
(4)
0.774
040
0.00 2. D29
modjenma OUT puachsenissrr
0.700 040
0.364 (4)
0.774
0.0
0,91
0rD
indgrmma Cel timemsser
0.706 IxO
0:301
0.770
0.16
0e0
modyymms
0.96
7.587h
0.911
2011+
indgermma
0.533
0.367
1034:
indgeemma -IM puccttnnier
atcijemmaa
6.620
ICD
0.791 3.800
0106
1.00le
0,945
ICD
0.789 M40
CHU
mnddermaa ICD pulcloonier
0,785 040
0,357
0,700
0.0
0,032
060
mndgerma ICD timenixer
0,786 Dx0
0:368 = 1M
0.776
0,910
020
madgrmma Ternd +TFM lam
0.79 5.745 -
0.55 + 12007
0.715
0000
2.83
madgenmma Trend +TEM mlp
3.78 1.0578
3
0.088
1,407
0.773
1.7677 3
medgenim Trend +TIM pacchumiker
0.54 0.50
0.61
+ 1:0
0.704
1,0050
0.802
madlennm Tranl imemiser
0.963
1.000
0.77
mndgemma
0.012
2.29
0.385
iodgrmma mlp
6x0
Tiznd tinemiser
0,796 0x0
D00
0.57
mailymma sem,ahon plan +TM latm
0.222 2.638
0.951
5.331
0.775
3.98
0.912
5711
muzlgemma alar TTM mlp
0.277
9.00 5.0074
0,002
1:15-
0.209
1.01
0
1.05
medgenma zem sbot TPM paccesmiser
0.773 1.
(153
108e-
0.701
1:36
0.96
2.905
medgrmma zem sbot TVM timemixrr
0.774 6 3.37 -
(191
0.708
3.000
0.902
2:31 -1
mndgenina zero shot 5tm
0.703 2,070a -
0.947 + 7,561e
0.777
= 8.1
0.343
1 8.026
modgrmma pemuuher mlp
0.701 (+0
0773
(44)
0.00
modlymma sem shai mncchmmiierr indgermma
0.791 (+0
0.700
0,051
0e0
0x0
0.75
zero seco
0.954
2.008
posctremine
3,300
7
0,705
0.947
+IIM timemiser
4.51.te
01911
3
0.774
2981e
0,942
No acro shot istm
0,508
9.6736
0.930 : 1.36l-
0.787
4,9030
0.90
8.082
No suns zero sbot mlp
0.8
0cf1
0.347
Dv)
0,770
000
0.946
020
No.arm Sampam_ sent paichasmiser
0,800 188k
5
0.98
5.175
0.781
1.305 -
0.916
1996
No.sura .ur. zem shot timenitee
01807
9. 7070
0341
1,385e
01720
5.1e
0.343
1.54%c
right Istm
0111
0100
1.471e
0.715
7.8576 3
0.853
7.735
Rgl mlp
0.938
2.507
0.665
1.00
rul pallesmiser
L.003
0.523
night
Table 33: Cross-site transfer results L.ab. (part 2/2)
49

---

Published as a conference paper at ICLR 2026
Miched
ppicc
kodked
masked
usasked
srasked
mankked
kedkked
Larra-A:1 Coll +IM har
Lamaa 1.1 Orl WIFM pacctimin
11120
1.850
0.225
3,531e
01121
0.455
1.31le
01.331
La Cal +IMM unmounee
1:422
0.033
2:158
01011
1L008
2.4000
0.00
0.200
03822)
22/055
1. 311 GIT
0.317
0.015
0.031
50033
0.000
1150
1.355v
1. CIT partmniioo
0.11
1.00l-3
0.711
1.500
0.131
1855
0.01
7.351
00
0.031
1.355
1.maa31 CXT nimwiiser
0.0m
01033
0.77
5.067
0.018
1. 111 XCD -TFM
0.070
1:000
3.75m
0.097
1.75
0.000
0.015
1. XDD -TMM pactrenivrr
0.155
2.6
0.075
0.11
1.800
0.001
0:533
9.85
013315
1.337
1.ma-111 XTD
Invertion
0.077
0:05
01883
7,630
0.008
2.00
0-10-
01:111
31130
1.mma1111 ICD
0.07 6.5:00
3.03fe
01991
5,205
1.033
1.000
Lant-311 3.1 K.D peldesssace
0110 0.215c
0.044
2.723c
0.15
1.740-
0,01
6.383
= 5.450-
[L0C1 = 1.400
L.
0,079
2.147
1.3766
1.237
3.1
0,000
Lasta311 3.1 Treed (TEM salp
0,079 T00K
0.234
+ 2.344
0.00
= 1,772
0,000
1.120
0.88
1.489
4.9.7
Lant 3.1 Tread ITEM paoifssnis
0J156 = 1.500-
0.025
+ 4.55
0.14 = 1,2:
0,044 + 5.4%
0.20
1.295-
0.275
1.954.
Laneusies
0,577
2.128
0.018
0.008
Lint 31 bess
0.014
2.965
Lant-311 3.1 Treed sly
0,070 1,77
0:034
+ 5.17
0.09
2.90%
0.000
6.600
0.01G
1.73
Lint 3. Treed prodtaasiaer
0.314 1000
0.224
5.70
0.15
0.360
0.000
+-7.71
Lama-111
1115 hem
[L072 7.802
0
0181
1.358
Lama-111 szm_atee 1155 msp
1.800
0.02
1-351-
BUITT
200
DCTT
5.500
0J814
3.133
L.aa-111 zem_atee 1114 sactemmaer
01125 0.100
0
2.17
0.34
1.00
3.40
0.276
1.287
0.1.11
Larta-11 smm
IL002 2..00
00044
6.130
1.160
ILIOG
2.500
Larta-J11 sam mip
0 1586
0
1:5tl
1055-
Larra-1: smm pachieniarr
0.114
0022
5.300
0.122
IL/C
5.255
TFM
0.000 0.530
0.215
1.350
0.2001
6.710
6.710
TFM wrg
0.007
0.015
5.931e-3
0
3035-
0.00
D/C76
0.014
TFM
0.076 6.576
0.033
1551
0.000
1..00
TSDE fors
0.06 3-300--
0.032
7-05T-5
0.07
11:00
0.000
30000
0/022
0.0%5
3.3 72
nly
0.877
122
00011
0173
7.138
0.09
0.005
7.75
TSDE lmmeiizer
0.500 6.
0/012
7-410m-3
0103
1.50
0.009
1.533
grarne Debbhl CoT -TMM Irm
0.07 1.1077
0.022
1-310--
0101
5.000
B.0IT
1.007
0/072
2.0773
0.014
gemen-2 b-t.hbhhoo CoT
map
0.076
1-350p-3
0185
1:310
0.08
7-36
0.011
acauE-2 D-daal Col TPM Ganeuiee
0,075 4.50
0:012
1.650c
0.02
2 5,055:
0
0.007 2 5.110-
2olui-2b-lab-Co CoT san
0 0.473
0.015
1-550-
0,001
2.50
0L008
= 8.50
5.34
0.014
2.80&
geaus-2 cauus-2-.aal.Ca alp
0
0:015
2.32%
0.012
-.Baal:C palterriie
2oaue-2 D-lu--2 CoT Gascaroos
0.000
1.572
0:034
4.550-
0.m1
5.356
0.018
1.91e
0/064 2 1.100e
0.015
2.074.
gemux-2.b.le 2olul-2--lel-1CC ICD ITM laun
0,072 = 6.045
0.015
0m0
1-11:
0,017
8.003-
0.014
6.655
genus-2
clp
0,07%
0:015
2533
0/07
7.56
paichseniss
0.120
2.553
gomue-2 D.dal- ICD 1TMM tinerieer
0,874 180-
0:015 + 2.100
0.03
7.475
0.1T
5.74
0:007 +
0.021
5.1447
gemm-2 11:1
01022
0u35
0.113
somn-20dabb ILD paccoomss
0118 6.100
0.125
1.250
0.121
1.676
11000e
1.65
jemm--2b-boh- R.D insensser
0/05 2:138 -1
0.04
1.130
0009
6.10
7,54
semm-22.-ee Irend
0L002
0.877
0003
2.355
14::2
0.114
2mnn- Trend -1IM paccturniorr
1980
0022
0.121
2.177
01531
1,590
semm--2b-llah Tear +1FM trammtee
0,06%
1.170
0.022
53000
ILITT
3,300
0.821
semus-20daak fsced lats
0U302
0.244
4.314
IL00
grmni--.brbe brbbh Trand polrteettor
0.114
1.000
0.011
4 1-113
0.150
1.00
0.000
0.017
gemri-3 Trund
0,077
186
0:011
5.515
01911
5
0.08
0.014
pesra-- D-w.h mmn -TEM
7.003-
0.012
3.350-
0000
0UST
1.40
6.500
0.011
1.05%
pradeontion
0.125
1-56
0.776
1130e-
01138
1.433
B.00
6.27
0.316
0.167
genrn-2. cmn,
0.055
0.03
1-10p-1
0191
0.165
gemra-2 D-dlh don
0.505
1155
0/135
13300-
0101
1.000
0.00
7,163
gemrz-2.be bebbb non dwn moid
11112
0-125
1-377--1
0.12
1 5.033
B1008
2.0000
1.0000
0.817
1.3337
gemen-2 b-tth nono dur immonise
0/014
1,33
0/033
3-307e-3
1-500-
000
0101 1
01.05
3331a
ol
0.06
2.003:
0.000
2.850
0.01
1,655
0.01
2.00
D/054 = 5.7.0
0.003
4.24%
sropp peldroation
0.154 = 4.418
0.415 + 2.756
0.142
2.953
0J00
7.50k
0.117 + 1.13:
0.131
4.655
areep
0.551
01111
1.54le
4.704
0.000
0.310
5,500-
4.150
sxan cily
0.004 0.000
0.305 + 3.650
0.07
6.137
0812
D/511
T.14le
0.038
1.4:4
sson pebdroanioe
0.100 0.00%
0.0
2222
0.106
2:447
0,005
0.035
2.353:
0.100
519-
T TTMM
0.11
0.063
Aajam, Cell Oat +1 mlp
000
mU 136
0191
1100
20070
Col
0.128
1,000
DU2T
5:420
0.15
1.22
Sa,am, Caf tezs
[L388 1.00
0002
2.557
1.330
1
7.127
1,336
Au.um Col mip
0J05 4.33
01011
2 1.034
2 2:280
IL006
12200
DuDES 2 2,377
0JI13
jaahimmise
116
0002
2.535
0.121
2.000
1.877
1,003
So am XDD IED +1MM lann
(L164 1100k
BOLL 2.50
0
1.4.5
0015
1.68c
0,250
1.724
No sm XCD ICD +1FM mdp
0
<.10k
0M0
1000
0J86-
2.000
ILOGS = 11800e
1,201
01011
XCD 11FM pucciaminzr
+ 37200
0.123
NogamnDD (ET)
0.066
1855
001
1.100--
0014
3/000
1.05x-
0.015
1,551
Nogam,JT) kum
0.066
6.200
0012
à 2.10e-1
0095
1.070
0.06
4.700
0/06
5.300-
0.013
1.177
Nojam,KD mlg
0.068
0/12
1151e-1
0187
5007>
B.06
5/665
6.376
00.03
1.50-
Nigam XDD tmmeriur
01511
1.305-1
0/112
1.56--
0187
0.06
2513-
5/70-
7.150-
010144
1:3355e
Nagam Thost xTTM lom
0951
3--3
0.0
7 72226-
0007
1200-
0.055
1.73
3061
1.600
01011
-100--
Nagam,7rrdd Thent mip
01867
1338p
010
338
0151
2505
5/87
1877
0.81
11.35
Naaam,Tree Thord WTM tinertoer
(L560)
11135-
066
1 1.45
0184
0.003
1.818
0.1
133
3.11-22
Table 34: In-distribution results -Forecast. (part 1/2)
50

---

Published as a conference paper at ICLR 2026
Methed
hard
hiril
minic
minni
polic
Negee, Tieal lem
0.05
0.013
0,055
1.437
0.016
3:175
D.266
1:157
0.013
1,550
Thrrl rlp
0.05
2000
0.011
1777
0.016
3.784
0:07
6.000
Niven.Tiral
Turs inmmior
1.805
1115
0.316
0:000
1:150
0.011
asdgami Cdf 1TE3 bit
0,00
2287.
0.012
10000
0J085
5/35
0.000
0
1.be
BL005
5.317
aslgama
mb
105-
0:21
0L0TT
0.005
molgana Off ITEM taneito
0,02
1.1080
0,011
2.105s
01:00
1.181e
0:318
2/8185
0.300 0 1,841
0.02
4.7690
antymma
8100 6-00e-1
1:515---
6
1.355-
0.317
1.708
0.07
0.011
avlgmmaa GVT posteeniiee
0.112 1-5--3
1.83
5,07
0.09%
1.305
0.062
1.577-
0.005
avlymma GXT fivention
0103 2.100
0.013
0
0:317
2-700-
0:655
6.3110-
0.013
3.3
aslgamu
ban
0
0.20
0:31
0.004
uelganus XCD TPM packioniis
0.125
1.200
50125
1.60
0.119
1.28
0:355
2.250
0.410
8.00
0.275 + 4.63
assgontt XCD TPM Gincesact
0.74
1.77lc
0.012
3.113
6
7.448:
0.311
2.255
0.307 = 5.854
0
1
avdiumea ICDD
0153 800
5.597
0.01%
1.811
0:001
1:210
0.055
awigemra ICD gachroniser
0.13
08:00
0
1.57
6.17%
3:503
0:009
2/537
0:05
1-312-
0,005
1.39
ixttomma ermmese
0108
2.87--4
GJ9S1
0.01%
3.153
0:055
9-787
B.06
1.800
4.703
selganss Ticsd 1TM ulp
0,05 0.000-
0
1.298
6,052
5.178:
0.31
1.563:
0.05
1.407
0.014
4.77A
asslantt Ticel (TM putcitatiss Gssoniaer
0.22
0.005
1.00
0.134
0
4.390
0.316
4.14%
0.588
4.5
seogaresa
4,30%
1.277
Tread
0161
1.550
nedgentr (tead mp
0102 3 71128
0
GJ022
3:202
0.01
7-228
1.177
BL005
1.1s
ixdiemea (izes pachimite
0113
11124
0.128
1:113
0:029
4,307
00005
4:230
zelganta zemalkt +TMM laus
0,09 2 2.100
0.012
T005 D
0.00
2%
001T
5.75
0.011
+ 2.455
0.014
6.550
aedgarne semalit TTM nly
0 1.30e
0.013
2734-
0,053
5.475-
0207
2:255
0.755
+ 155
0.01
1.45
relgereea zemuaht fireiise
7.075
0.306
setiamnra zem 20d4 ler:
BU0 34114 4
GJIL3
07711
11:01
1:00
0.03
1.85
1UTT 2 1.20
BLUSS
1.504e
rxdjemea zem mlp
0172 2220
1:00
CJ93
5:230
1-2377
0J64 2 2.111e
G
4233e
0:33
1.672-
0JI44
BL06 1.558e
No.eerizenn0 Jen 201 Jun TTM
0.61
1
0.011
2.00e---
0,083
1.500
0.01
1577-
0.66
1.10
0.01
2.540
Nogien
pastecsiuur
0.111
1116--3
0.007
5.10e
1.315-
0:25T
1.460
0.256
1.450
1.17f
Nugaer Juin Chiit aes Jiin 4TFM tiventtrr
0.05
0.01
L.38p-11
6.055
1-5"--3
0015
1.15
01
7.863
0.015 2.61
No perz zan absi Lita
117736
2:413
1122s
5.134
0L005 2.00
peacromzace
0.31
L.588e
G/022
288.2
6.125
0.25
1.756
0L099
5.758
ILUT 2.35
SO- 2E10 kadl lisaiin
0302 2 2.770
0J012
122%
0J085
0.16 2 1255:
0J8
IL01S = 3,521
right
01056
0.008
169
1900-
0/112
0358
1.05%
0.005 11550x
TIN pocheerisrr
0.103 17136
0.011
33772
0.11
5,076-
6/37
1.377-
0263
9-354
B101 6.36in
PÇN Invnior
01156
-3
0.311 7337-
0/06
0.27
1.61
8:44
Table 35: In-distribution results - -Forecast. (part 2/2)
51

---

Published as a conference paper at ICLR 2026
Method
hirid-
ppics
mimic
ppice
masked moc
masked msee
masked moc
masked moe
1.mo-11 1.1 Carl +TVM lemn
0.2333 + 1.15
0.12
180 -3
0.151
2200 be
LJams 3.1 CoT +TEM mip
0.257
2: 1.2550
0.138
2,02
0.206
5.077
0.155
2: 1.5773
Liama 3.1 Col +TEM positsmier
0.305
1,0422
0.201
2:272
0.30
4: 6,790e
0.267
1.66140
Limmo 31 CHI +TMM timemnee
0.244
5,006e
0.341
4,2030
0.27
4: 1,080e
0.155
1.74le
11an- lem
0.210
3.68
0.34
4.00%
0.263
1.10
1.277k
1lam-31 slp
1.17p
0.364
1.125e
0.266
1.86
5,530
1 jonditemioor
0.350
6.223
Liana -TIM mlp
0.355
7.5355
8.615e
0.262
7.835c
Liuna -TIM patchsmmierr
0.302
7.701
1,52%
0.368
1.02le-
0.279
2.00T
Llamo-1 I/D -TIM cimeriser
0.258
1.905e
0.168
0.134
6. M0To
Llano-31 3.1 ICD 1stm
0.218 2: 6.055k
0.34
9. 2012
0.261
5.688
0.36
1.050e
Llans 3.1 ICD mlp
0.218
8 5.808
0.14
1: 9.503e
0.267
9.823
0.161
0.503
11mm-11 1 1CD paicheamiscr
0.250
+ 1.57%
0.191
1.787
0.399
5.122
0.259
1.50
11mm-331 1 ICD fimeniserr
0.254 1.
0.164
1.757 -
0.272
1.19
0.165
1.51
1.1 Tacrel .TFM lean
0.211 9.20
0.127
8.55
0.263
2.777
0.154
4.277
Linmo 3.1 Ieend +1MM mlp
0.255
2.103k
0.336
1: 3o04e
3
0.284
1.307e
0.132
1,500
LJmmo-11 1.1 Ineed +11M ponchtsmiser
0.300
2.04
0.212
1.075
0:35
6:284
0.244
1:2470
11am- Tered +IFM timemizer
0.255
3.131
0.135
1.995
3
0:27
7:50
0.135
1iams Tased bon
2.013:
0.241
2,458
02163
9.153c
Tesod mip
6.3202
0.342
7.00le
0
1.008c
6.153
Liamo Teend puschtsntior
7.225k
1.733e
6.5250
0.233
0.365
11ann-311
-TEM
9.661e
0.263
11 sem_shot +IFM
0.355
9.077
0.250
1.050--3
0.140
3.
11am-11 bemmushtt =TFM poachtemiurr
0.507
1.2215
0.205
5.3036
0.39)
1.016-22
0.250
1.057
11ano-31 1.1 bemmushett =TFM timemisez
0.258
1.540
0.155
3.902
0.27
2:725 -
0.155
3.77
Lians 3.1
15mm stot lam
0.248 :: 2.1860
0.139
3.125e
0.203
S.148c
0
5.000
Liama 3.1
1.mmmm. shot mlp
0.252 1.0113
0.143
1.042
3
0.206
3.
0.36
4.70G
Liamo-3: 3.1 semm shott parchienizer
0.250 3: 3.144
0.385 2: 5.3600-
0
1.817
0.254
1.16
Lluna-3 3.1 acro shot Gamemixer
0.254 : 1.51
0.145 : 1.546e
0.274
5.347c
0.166
2 2.725
TEM lean
0.256 1.1870
0.148 1: 8.207e
0.271
2.313
0.165
1.201e
TFM mlp
0.259 2.50
0.112
3.36
0
0.167
TFM paachtemiace
0.913 5,07
0.217
1.900
03K55
TFM limenisex
1,853
0.384
2.33
0:272
0.165
5,703e
0.281
7.19
0J17
7.M54c-
0.172
mip
3.614e
0,313
limeniser
4.67%
0.300
1980c
10 fuch CrT -IMM
0.345
8.157e
0,264
2om-i-160-faeh OiT -TM mlp
0.256
2.56
0.157
6.790m
0:271
5.31.
0.158
1.91
2omin-222-fieh CVT -TM pacctonnscrr
0.963
5.025
0.394
1.585
2
0.85
1.45f
0,210
1.510
scmin--22--aeh CJT JTFM tineniser
0.262
4.526e
0.14
L.102
8
0.271
4.35%
0,152
3.102
2ommn-10-ffoc ONT lean
0.245
2.001e
0.137
2.332
3
0.900
6-47/e-
0.16
1.000
50min-1.0-BBbhh CxT mlp
0.251
1.175
0.352 + 1.15%
0.967
210
0.10
8.07
5:min--/--Bech OXT paccttemiser
0.257
1.00
0.191 + 6.100h
5
0.315
+ 8-73 -
0.215
1.871
zemini 2.0 fluhh CoT timemiser
0.250
1.743:
0.147 + 1.713c
0.272
3.14
0.163
=: 3,351c
gemini 1.0 flxh JCD -TPM Jom
0.212
1,8855
0.128 1: 1.2070
0
5.30
0.148
1: 0,053
gomini-J..-fabb -TFM mlp
0.381
1.706e
0.100
Jomisi-2.--fahh ICD -TFM puachtsnixer
2.810
0.202
0.350
10.4
acmisi-20--aa ICD -TFM tincmiser
0.253
1.01%
0.126
0.257
5.34
mlp
1.155
1,50
0.159
8.60
jomiis--..--ich ICD paldhoeniser
0.215
2.0 f1-ffuhhh ICD
0.250
2.31
2.325e
0,272
0,164
3.513
pemmni---ffuoh Trend +11.M
0.223
2.10
0.312
187
03288
5:100
0,144
6.115e
2ommn-1---fuot Trand +TFM
0.255
7.55
0.152
1856
0.163
6.800
1.065e-
jemini 2.0 fuih Trend xTFM paicktomerr
0,304
4.473k
0.295
9.200e
0,362 :: 0.5c
3
0.267
1.865
jemini 1.0 fush Trend -ITM timenieer
0.239 2: 2.829
0.341 : 5.123e
0.71
2:00
0,152
4.531c
5ominn-1/--Bel Trand loo
0.216
2.30 1-
0.155 + 2.78 -
0.261
6.463
0.158
8.87
gimini--/-BBahhiee Trand nip
0.218 2: 0.52 6z
0.16 + 9.55
0.266
2.05
0.161 + 1.068
gomiii----Baah Trand pacitsniser
0.304
2: 2.008
0.216 : 1.305e
0.847
1.16
0.214
: 1.002
2omini11--fsch Trand fimerisorr
0.234
1.007e
0.301 a 1:70 3
0377
422e
0.164
1.78le
5omini-1--BBsh zemo,bec pbot
lemm
0.227
683-
0.125
0.90
A55
0.137
8.83e
5omini-).-BBahh vom -TFM mip
3.51
0.36
3.00%
1.155
0.158
1.19
1emiti 20 fush 2ero -TUM pucchtsmiier
1.65%
0.217
1,00%
0.14
2.87cc
zomini zero
timemiser
2i1e
0.152
1,07
jcminii-.0--Badh
2,000
semini--... 2.0--fulh
0.258
1,153
8.367e
0.168
acmini-2. 2.0
puschtnnierr
2.072
1.87e
0,251
gomini-10-ftch
timemierr
0.256
6. 6.955+
0.116
01172
1880
0.165
1.051
lam
0.536 7.265+
0.217
5.95%
0.333
1.215
0,270
2.745-
interp mlp
0.500 5.545
0.466
9.105 2
0.507
1.317
0.427 : 1.915-
inter Nachtenierr
0,001
4 1.003e
24.8
7.0500
0.83
13,311
3,41360
inter timemiser
0.50 22228
0.511
A 1888
0.55
1.332e-
0.561 8: 4.00
sccui 34m
0.207
7.53c
3
0.107
: 1.58
8
0.35
: 9.415e
0,261 : 1.09le
scen mlp
0,412 + 9,945c
0.292
1 0.2000
0.402
1.35%
2
0,242
+: 1.5860
meen guchtsmiser
0.255
4.115e
0.305
1 1,00
0,448
1: 1,35e
4,800
1.34500
mzan Limmissrr
0.575
5.54
0.541
1000
0,557
0.51
1.04
Nayaun, -TFM him
0,211
4.077
0.125
5.11 11
0.3
2.576
No.sum
mlp
3666
0.144
0:271
0.156
4.36%
No.ommJ''T
goicheonixrr
0.31
1
0,21
2.12:
3.417c
CoT
0,202 0.53c
0.145
6.56
2.47e
0.174
LoT putchsmiser
0,302
0.214
188t
0:151
0.25
3.000
Nayamn OcT linemiarr
0.361
2.56
3
0.141
2 2.155
0.74
1.66
3
0.168
5.06
Na.sum JCD ICD .TEM Iin
0.215 2: 2.003
0.182 : 2.88
0.265
1619
3
0.152
5 1,220
No sum ICD ICD xTEM mlp
0.264 2: 6.78
0.145 : 7.287o
0,277
:: 2,85e
-
0.163
2.006
No.amnJCI ICD -TMM ponctteniser
0.311 + 9.201 -
(132 + 3. 1536
2
0.353
0 2,50
0.267
1.88%
Nc.emnJCD ICD fimemiuu
0.258 + 3.285 -3
0.159 + 5.700
3
0.272
5,07
3
0.155
8.070
No JCD kim
0:343 = 10x
0.155 + 1.57 -
0.222
1.3%%
-
0.165
+ 0.515
No.ommJCD mlp
0.218 :: 1,423e
0.141 1 1.3630
0.776
2100
3
0.168
:: 1.711e
ND puchttmiserr
5x31e-3 3
0.215
0336
1
3
0,003
1.785e-
ICD linemises
2.058
0.16
L.50te
0.25
4.026
3.804s
Tread Tread -TFM
0.243 2,00c
0.129
0206
1.700k
3
6.05
Trend Tread mlp
0.201 2,61e
0.140
3.7ile
0377
NoyeonTreed -TFM poaciteniscr
Tread Treal -TFF
2.008
1.17
Table 36: Cross-site transfer results - Forecast. (part 1/2)
52

---

Published as a conference paper at ICLR 2026
Mathod
lirid
ppice
mimic
ppica
mate
masked.mse
marked, mame
marked mmme
No.sum To.zu lam
200200
0.143
2.8170
0.271
1.11le
0.363
1.11ic
No.an, Tocall mlp
2.00
Tical paidoaniserr
0.243
No.um,2rred ITFM
6.816
0.125
0.369
7.4650
8.317
IIFM
3,00%
0.157
9.543e
0.258
1.119
0.364
modymma OxT -TFM puichsonizzz
0.315
1,02
(1195
2.000e
0.955
1.45%
0.281
5 5.50
aciiemmm CrT -TFM timeniser
0.262
3.61TH
(1138
2.565e
0.367
3.583
0.15
5.676
indgemma CoT Iam
0.200
7.6680
0.141
4.02
0.350
1.2250
3
0.36
1.675e
madgemma Orl mtp
0.251
2:3730
(1142
1
0.367
1.201
-3
0.36
modjenma OUT puachsenissrr
0330 5.200k
0.2
6.400
0.858
0.822
0.261
1.3086
indgrmma Cel timemmser
0358
2.108
0.147
7.61
0.971
1.301p
0.163
1.800
modyymms
3.515
5
0.127
0.966
2.7:3n
0.156
indgermma IFM
0.137
1.203e
0.155
6.705c
indgeemma -IM paccttmiser
0.334
1.313e
atcijemmaa
5.273
ICD
0.138
3.500e
0.359
1.3150
ICD
0.10/5
01141
0,005
0.007
1.3136
0.361
1,3700
mnddermaa ICD pulcloonier
0.201
5,0890
0,204
2.2.2220 2,70
0,851
1
0,207
2.044
mndgerma ICD timenixer
0.255
2.00%
0.145
2.06le
0.272
2.2800
0
0.164
2.240
madgrmma Ternd +TFM lam
0:215
1,5000 -
01129
5.57
0.366
6.775e
0.157
5 5.123
madgemmma Trend +TEM mlp
0.254
4.18%c
0.133
1.285e
0.367
1,400c
0,354
1.471c
medgenim Trend +TFM parchumikerr
0-112 3,30%
0.221
1.173e
0,302
1,225e
0,253
1.52
mndlenna Tranl imemissr
1,105e
4041
andgemma
1.1e
L.01le
iodgrmma Trend mlp
Tiznd tinemiser
1.800
1,07le
0,164
mailymma sem plan TTM latm
0.55
2.700
(1139
1,65
0.361
9.350
0.151
9.904
mnzigemma alar TTM mlp
0.257
1,000
(.158
2.685
0.268
9.30
0.165
1.066
iadgenma zem sbot TPM paccesmiser
0.310 2 5.3/80 -
0.257
L.bile
0
2,022
0.26
2.805e
medgrmma zem sbot TVM timemierr
03250 4.90 -3
(1130)
7.1dbe
0.271
3.100e 3
0.151
4.70
mndgeniaa zero shot 5tm
0.288
1.558a -
0.141
1.112
0,267
1.2766
0.16
8.217
modgrmma pemuther mlp
01111
5.505e
0.307
0.16
1:277e--
modgymma sem shai nichimmiser
4.016e 5
0.192
1.80
0.349
7/000
0.217
1.300
zero seco indgermma
0.163
2.29
6.715e
posctreminer
2.445e
(.19)
9.183e
0.336
0,265
1.122
summermmshe No acro shot istm
+IIM timemiser
9:800
(113:
3. 3.100=
0.275
3,0000
0.156
4.317
0.266
1.023e
3
0.130
4.6ile
0.27
1.7%
3
0.162
2.87c
No suns zero sbot mlp
0.251
2.339e
0.143
1.50le
0,252
2.933
3
0.173
2.77%
No.arm par, sent paichasniser
[1200
1.002 -
0.195
105e
0.351
3.353
0.259
+ 2.072
.ur.uur zem shot timeritee
01200
3,272
0.14 = 3,473
0.28
2.901e
0,369
:: 1,83
right Istm
9.12 700e
3
0.212
5.90
33
0.379
= 3.1e
I127
2:00e
Rgli mlp
0.317
3.616e
rul palcesmissr
0,900
1.20
0.417
4.725e
8.4856
6,944
1.676e0
night
Table 37: Cross-site transfer results - Forecast. (part 2/2)
53

---

Published as a conference paper at ICLR 2026
Miched
ppice
ppicc
Larra-A: Coll +IM har
0.171-4
01:222
11.077
Laraa.. 1.1 Orl VIFM pacttimii
01326
21005
0.693
3,471e
00.43
2.80
1,2200
0.415-2
0.250k
1.5356 -3
La 1.1 Cal +IMM unmounee
03340
0.208
0.308
1.54
IL501
10000
0.357
8.000
013224
7:2346
1. 311 GIT
0.75
0.337
7.855
0.355
7:1:4e
1. CIT partmniioo
0.43 9.311 -3
0.001
117
0.035
1977
0.776
1.34
0.371
8.101e
0.813
1.50% -3
1.maa31 CXT Hiwwiisrr
0.188 3
0.005
1,077
0.715
0.315
1.00
0.935
3.853
1. 111 XCD -TFM
0.211
1750
0.335
0.307
1.131
0.519
0.335
1.750
1. XDD -TFM pactrenivr
0.67
0. 6.100
0.510
1-335o-1
0.43
3.370
0.007
1800
T-301
0.993
9..05 -3
1.ma-111 XTD
Iiveniion
0.337
0.153
3.155n-3
033
5-515
0-330
5.713
01.01
1.mma1111 ICD
0375
0.110
01395
1.000
0/062
7.001
Lant-311 3.1 K.D peldesssace
0.44
1,958
0.267
2.370-
0.414
1.35%
1.000
0.365
5-100
0.311
7.557%
L.
0.415
0.385
7-0775
0.76
0.346
5.8.5.
3.1
Lasta311 3.1 Tredd ITEM silp
0.291
1,000-
0.342
2.353
0.50% 90
0.500
0.554 + 6..10
0.915
2.627
Lant 3.1 Tread ITEM paoifssnis
0.488
0
0.20
0.201
2.456
0.724
7.000
0.414
1.1%
0.093
4.57%
Laneusies
Lint bess
0,517
1/346
Lant-311 3.1 Treed sly
0.18 1.513
9.411 + 2.107
0,811
1.95%
0.79
120c
0.151
5,510-
0.038
1.15%
Lint 3.1 Treed profeaasiaer
0.558
0.473 + 1.75e
0.300
1.10-
0.16
700-
0.374
0.311
Lana-1.1
1115 hen
0.31
9.545e
0.35%
2,5
0.32
1.355
0.312
1.50k
Lama-111 szm_atee 1155 msp
0.317 0:4
0.153
266tn-2
0,31
7:62 -3
0.315
5.500
03312
1,005e
L.aa-111 zem_atee 111 sactemmaee
0.00
2.700
0.515
5-550
0:3
L
Larta-11 smm
01355 7.61.
0.45
1.217
0.301
21000
UL340
Larta-J11 sam mip
0.30 1.277 -2
0.435
0.377
1
0
03344
Larra-1: smm pachieniarr
0,421 1500
0.311
1.345-
0.209
1.75
0.3617
TFM
0.315 6.
0.415 2 1.433
0,522
4,797
1,212
TFM rrg
0.310 0.5176
0.110
0.336
5.977
TFM
0.315
1070
TSDE fors
0.500
7.301
0
170
0.335
0.11
0.300
0.895
-1.15
nly
0:255
5:49
0.17%
1-304e-3
0.3
510
01899
0131
1:386
033
11.00
1.205
grarni Debbhl CoT -TM Imm
03111
7100
0.901
1455h-
0.00
0.316
1.300
0.301
5.7
gemes-2 b-t.abbcooo CrT mpp
0.25
0.311
1.200
050
1000
1.00
0.311
1:310
jouus-20da Col TPM Ganeuiee
0.342
2.5..c
7.827c
0,553
1.326:
05507
8.000
0.155
3:45
01500
8.00
2olui-2b-lab-Co CoT san
0.364
1:154e
0,264
5.205:
B141
0105
0.345
0.314
5,005
celus-2D.lal.Ca alp
0.570
1.43%
5.431.
0.365
2oaus-2 D-lu--2 CoT Gascarocs
0.558 2.311e
0.115
5:550
0.995
1,01%
0.01
8300
0.84
7.13
0.041
1.715
gemux-2.b 2olul-2--lel-1CC ICD ITM laun
0.14 = 165
0
2.50
0.211
1333
0000
0.147
0.333
1.6-7:
genus-2
clp
7.655
0,016
0.400
0.400
5.950
gomue-2 Dadal- ICD 1TM tinerierr
0.501 8.0-
0.300 + 1,677
0.312
1.331
0368
000-
0.131
1.45-
0.017
1.413
gemm-2 11:1
03377
0.310
031
somn-20dabbll ILD paccooise
0,425 1.85e
0.154
1:200
0.331
134t
0.30
1.755
1.300
jemm--2--boh- R.D inseusser
0.122 4.38e -3
0.575
14000
0.20
1
1.7000
0.33T
1.00
033433
4.17
semm-22-dlah Iren
01301
0.184
0337
umm--20daa Trend -1IM pacctoniiarr
6/6
0.211
1.3.3
0,399
7.724
scmm--2b-llah Tead +1FM tnnmmtrr
[1530
1,2070
0.315
2:110
01277
7:226
0.34
0.358
0,325
semu--20daah fsced lats
01521
013-
0.435
0.224
IL507
grmni--.bbbb D/bbh Trand polrteettrr
0:379-2 3.77
0:170
0.37
5.50%
D.15
0.366
0.811
gemri-3 Trund
0:35 8.000
0.471
033
0665
0.376
0.911
pesri-- D-w.h mm. -TEM
0.200
715+
0.151
1.570-3
0.377
7.635
3.351
grmri-2.bdes (-bbb)))).. mmn +TMM paateontio
0556
--
0/13p
0/04
1336
B135
1.900
3.107
1.0
0.975
1.331
genrn-2. cmn,
0.330
6.36--3
0.151
3.317e-2
0:311
13377
B561
0.35
5.300
03311
1.20
gemra-2 D-dlh don
03377
0.450
1.100
01331
5.335
1.10-
5.181e
genrz-2. (ebbbb nonidve modd
01111
1 1
0.500
2-031e-3
03374
1:281
2.30
0.361
03323
1.511 -3
gemen-2 b-tte non dur immonise
0.376
1.115
2:203
0.25
1.314e-
1100l
013335
5.721
1.340
ol
0.501
7 7.700
0.366 + 5.806
0,522
7.015
0.455
1.77
0.70
1.807:
sropp peldroation
0.27
1,346
0:40
1004
0.45
1.107
00014
1.00
0.466 2 5.57
0,891
2.726
areep
0.357
0.436
1.905
1.25%
sxan cily
0.500 7 7.46k
0.357 + 2:257
0.211
7.51%
0.502
61177
0.166 + 2.300
0.201
1.153
sson pebdroanioe
0.38 4.33
0.456
1.506-
0.547
97177
0.008
0.555
0.951
1.275
T TTMM
0.25
0.360
1.77
1.250
Aajam, Cell Oat 41 mlp
0.131 1:2120
01353
01311
1:210
01:411
2.313e
0.525
1:227e
0.44
1.277
1.01
Sa,am, Caf tezs
[L301 1.11
6v4 1.152
02244
1.3.1-
7.754
Au.um Crl mis
0.219 3
0.100 2. 7:233n
0.28
3.340
IUST
0.312
4.577
03317
1.411
jaacimmise
01355
1286
0.432
0.222
7.345s
03522
0:251
So am XDD IED +1MM lann
0,218 1.25
0.254
2.42
0.244
0.404
0.305
2.221
No sm XCD ICD +1FM mdp
01200)
012080
0.300 2: 1.730
0.200
1.20
0
0.151
01.40
2,311
XCD 11FM pucciaminzr
0.500
0:555
4910c
1100kc
NogamnDD (ET)
1.000
0311
0550
1.335
11057
Nogam,JT) kum
0.267
52210
0.145 à 1-31ee-
1:31
B556
11500
0.336
1.01le-3
03313
3,705
Nojam,KD mlg
0.300
-3
0.111
1-451e-3
0:9
BM
7555
0334
01815
Nigam XDD tmmeriur
(1300
6.550
1.280-7
0.3
1,855
0.521
5.311
1.330e
1.675
Nagam Thost xTMM temm
(1271)
0.113
1 1250e-3
[30
1500 -3
0.33
0.33
1.007e
0.305
1187%
Nayam,Trrdd Thend 4TM mip
01271
0.10T
1d1te-1
0187
350h
0.001
2389-
-33---3
01815
1-57
Noaan,Tree Thord WTM tinertoer
0.321
0.310
1.536-
0.3 490
0550
1-709-2
0311
7.000-
1.355
Table 38: In-distribution results- Los. (part 1/2)
54

---

Published as a conference paper at ICLR 2026
Methed
Iarid hiril
minic
minii
BOC
Neger, Tieal lm
0.301
0.200
77m
0:353
1:45
0.335
7-885
0.330
1-357---
0:07
Thrrd rlp
0000
0.336
1.410
0.345
1:103-
0.009
Niven.Tiral
Turs inmmbor
0.33
0.335
0.355
5.510
0.066
6.500v
aslgamt Cdi 1TE3 bit
0211
00000
0.013
2:234
0:312
1:24
0.589
1.544:
0.340
3.118
IL501
6.100
aslgama
mb
molgama Off ITEM taneito
0.441
0.176c
0.015
LoV1s
0,319
1904:
6.542
1.27%s
0.22
0.20
0156
1,600
antymma
0311
1013
0:33
0.863
0317
1.110
0.09
aelomma GJT posteenioee
0.05
1:41
1597
0.547
0.350
1.959
0.09
6.5
avlymma GXT fiventior
0337
108
0.519
1.00 --
0:317
0.515
1.399
0.33
7.499---
0:91
2709c
5.127s
2400
6.341
uelganes XCD TPM packioniis
0,684
4.2666
0.087
0
0.750
5.441
0410
5.771:
1,02
0.00
assgontt XCD TPM Gincesact
0.540
6.35c
0,015
20744
0.343
1,29
0.345
1.688
0.344
= 5.05%
5.08
avdlumea ICDD
0333
1.175
0.38
5.505
avigemra ICD gacheoniser
0.221
1.1336
0.51
1559
0:309
2133
0.35
3.127
IL366
1:3100
0-11
ixttomma ermmere
0.365
0.33
1/414--3
0:31
selganss Ticsd (TM ulp
0.277
1.000
0.346
2044
6,351
5:77:
0.474
1.746s
0.344
1.65%
0,00
1.51
asslantt Ticel (TM putcitatisn Gssonixcr
0.012
3107
0,677
27
0,456
1.571
0.73
0:11
3.7
0.000
2.50
reogares
Tread
0316
2101
12235
0:301
5.85
60
aelgenta (izad mp
0.221
2.335
0.40
1.00
0.322
0.300
1.108
0.31
9.3
T.8TT
indierea (izes pactimite
0333
8
1:11
0.33
13110
0:3
1133
1.117
relganta zemaakt (TMM laus
0.005
1,33
0.211
1777
0.1
0.54x
0.556
1.412
0.345
7.55%
0,500
1.700-
aelgarne semalitt TTM mly
0.91
0.200
0.311
6.344
1.14
0.33
35700
0.345
1.10
00000
2.00
relgerera zemahht fireiise
0.35
0.355
setjemnra zem ler
0340 81077
0
0.345
133-2
0
11330
0.311
4.7
-.1--
rxdjemea zem mlp
100.0
1294
0.388
IL341
5:5080
0:17
0.291 0.1999
1004
0532
1.354
IL355
1.355
BL50
1.10
No.jerizenn0 Jen 201 Jun TTM
0.977
57766-
0.373
0.383
1110
0.81
0.357
0.000
2.50
Nogien
pastecsioer
0511
6.00
0.115
1.71
Nrgaer Niyerr.em_nm Juin cait aes Jinn 4TFM tiventtrr
0.211
-3
0.315
705
0.1
15
0.2
177
0.355
1.51h--3
0.07
1.55
No serz zan Lita
0311
5.313
1:0e
0,334
IL3ST
1.322
5.80
0.244 1.18ie
IL465
5 1.2e
0,311
125
6.32
1.148
I1388
5:355
1.3
SO- 2E10 kbdl lisaiin
0398 2 1.8iec
6,411
0:113
1200
0.36
5750
0.124
5:111
6.10
right
0.026
1001
1885
6.755
0.06
1-736-
0776
1.000x-
TUN pocheerisrr
030 31156
0.06
6788
0.359
1038
-
0.657
1480--3
0.33
1006-
B915
6.367-
PÇN Invnior
0.59
5,700
3.09
0.333
1.50- - 0.55
7.215-
0.196
-
8581
1.760
Table 39: In-distribution results - Loss (part 2/2)
55

---

Published as a conference paper at ICLR 2026
Method
hirid-
ppics
mimic
ppice
mooe
mac
mnse
1.mo-11 1.1 Carl +TVM lemn
0.513 + 66.773 -3
1/317
6.16te
5
0.317
1.165e-- 3
0.506
2.54le
LJams 3.1 CoT +TEM mip
0.370
4: 3.781c
1.047
2 0.3490
0.351
1: 2,050
1.011
2:
L.4544
Liama 3.1 Col +TEM positsmier
0.516
1.646c
1.113
2: 1.323e
0,489
4: 1.70
2
1.187]
1.059
Liamo 31 CHI +TMM timemnee
0.425
6,000
1,043
30000
0399
2-02ee
2
1.0155
1,253
lem
0.705 3,91e
1.341
5.505e
0.658
2.000
1.267
1lam-31 1.1 slp
0.738
9.831e
1.522
2
1.633e
1.209
3.200
1. jonditemioor
1.175e-3
0.355
2:310a
0.972
Liana
-TIM mlp
0.356
1.282
0.347
0.963
Liuma -TIM patchssmierr
0.516 1.217c
5.50te
0.535
3.187e
1.154
1.11%
Llamo-3 I/D -TIM cimeriser
7.748
6,571
0-425
7,4400
1.016
4.5et
Llano-31 3.1 ICD 1stm
0.624 6: 2.6
1.715
4.504e-
0.683
2.7784
2
1.474 2: 7.6570
Llans 3.1 ICD mlp
0 4: 2,00.0
1,682
0,8950
0.677
1.607e
1.412
1.5350
11mm-111 1 1CD paicheamiscr
0.307 2:30
1.585
4:903
0.645
2.376
1:29
1.103-
11mm-331 1 ICD fimeniserr
0.756 1.100
1.515 2.635 -
0.577
7.890
1,208
1.813
1.1 Tacrel .TFM lean
0.566
1.5070
1,004 +: 6.776l
0.363
1.113
0.077
+ 1.14%
Linmo 3.1 Ieend +1IM mlp
0.380
3.8766
1/007 1: 1.4530
0.778
0.865
1:128
LJmmo-11 Ineed +11M ponchtsmiser
0.565 1.0006
1,152 1: 1.453o
0.633
3.777e-
1.125
11am- Tered +IFM timemizer
0.063
1.085
1/319
1-21
0.441
1.775
1iams Tased
2,203
5.925e
0.077
1,451
Tesod mip
2,021c
2.328
0.021
1.279
1,701
Liamo Teend puschtsntior
0,585
2.71c
1.574
6.313e
2.0380
11ann-311
0.355
3.006
4.357
11 sem_shot +IFM
0.353
5.133
1
7:93te
038
6.00
0.302
3,00
11mm-111 bemmushtt =TFM puachtemiser
0.317 1.50
1.107
8:00t
0.487
1:310
1.071
1.00
11ano-31 1.1 botmushent =TFM timemisez
0.118 9..51
1,225
8.00mm
(.929
6.63
1.015
7.565+
Lians 3.1
15mm stot lam
0.834 2: 1.07740
1,515
4.315e
0.722
150kc
1.446
6.50%
Lians 3.1
1.mmmm. shot mlp
0,872 2 2,250
1,573
3.503
0.721
5.735c
1.477
1.211e
Liamo-3: 3.1 semm shott parchienizer
0.824 3: 9.807
1,309 2 2.5050
0
3.927
1:314
6,500
Lluea-3 3.1 acro shot Camemixer
0.776 2: 2.656e
1.465 = 5.00-
2
0.700
4.272
2
1.351
5. 3.066e
TEM lean
0.008 4.184:
1,525
1: 0.286e
2
1,052
:: 3.700
2.178
11,570
TFM mlp
0.855 1.546
3:30
088
1.8k3e
7.100
2.00
TFM paachtemiace
0.797 6.515
1.205
1-133
0.939
1.585
3.215
TFM limenisex
1,250
2,451e
0
7.457
1.911
3.06
2.003e
0.706
2.1300
mip
3,135
2,30/m
limeniser
2,350
1.750
10 fuch CrT -IMM
1,015
4.572
0,086
5omii-160-fach OiT -TM mlp
0.555
5.003
1.07
1.150
0.366
1.200
0,908
1.57 50
2omin-222-faah CVT -TM pacctonnscr
0.524 :: 7.045
1.119
4.755M
0.606
1.762
1.008
1.268
scmin--22--aeh CJT JTFM tineniser
0,413
6.6.0e
1,084
8.50
0.418
3.945
1,025
2.60
2ommn-10-ffoc ONT lean
1.031 1.0015
2/006
8 3.130 -2
0.5)
1.35te
1.107
1.850
50min-1.0-BBbh CxT mlp
1.055 3.36/
2/053
7.10%
2
0.525
2:210
1.178
2.30-
5:min-----Bech OXT paccttemiser
0.061 1.81
1.82-
1.017
0,537
2.000
2
1.138
+ 1.755
zemini 2.0 fluhh CoT timemiser
1.040-
3.713c
2,001 1 7.003e
0.52
+: 2.384e
1.36
3.1966
gemini 1.0 flxh JCD -TPM Jom
0.320
2.20.4
1,15 1 100850-
04381
2:107e
0.070
4.003e
gumini-3..-fabb -TFM mlp
5.665
1.002
2.08
2
0.348
4.53
Jomisi-2.--fahh ICD -TFM puachtsnixer
1.868k
1,182
2.11%
0.101
7.453
acmisi-20--Ba ICD -TFM tincmiser
0.464
1.75e
1,072
1.003
0.097
2.800
mlp
0.502
jomiis--..--ich ICD paldhoeniser
0.545
1.761
2.547
2.0 (f-ff ICD
3.7.kk
1.324
0.506
2
1,113
3.115e
jemmni-1-ffuoh Trend +11.M
0.366
8.430m
0.366
3.000e
0,002
0.
3omini-10-fsot Trand +TFM
0.561
1.00 6m
5.305
0366
1.5000
0.867
4.500
jemini 2.0 fuih Trend xTFM paicktmmerr
0.527 2 2.353
1.12 1. 9.3170
3
0.400
:: 1.71e
1.076
1.707e
jemini 1.0 fush Trend -ITM timenieer
11,45 2: 1.2
1.042
2 5.70
0.433
1.004c
1.01G 2 7.300
5ominn-1/--Bel Trand loo
1,075
2.50
2
2.200
+ 7.425
2
0.52
+ 5,120
1.167
0.75
gimini--/-BBehhiee Trand nip
1.115 2 3.56.
2.265
+ 9.705
2 0.645
2.363
2
1.187
3.60
gomini----Baah Trand pacitsniser
1,058 2: 4.256
2,341
:: 1.14
0.676
2.366
-2
1.165
+ 3.00G
2ommni1-0-fssh Trand fimerisorr
1.053
3.3014
2.152 8 1/707
1.1.0e
-2
1.141
1.200
5omini-1.-BBsh zemo,bec pbec
lemn
0.561
5.897
1/317
1.433
5
0:333
1.7%
0.971
3.500
5omini-).-BBahh vom -TFM mip
0.575
3.2234
1.051
0.366
1.055
0.589
1.185
1emiti 20 fush 2ero
pucchtsmiier
1,187c
1.144
2583
0.11
2.272
2
1,092
zomini zero
timemiser
5,0033
5.355e
jcmini--.0--fadh
0.476
2.417
semini-1.. 2.0--ful
1,18
3.46
0.481
14714
1.067
acmiii-2.0 2.0
puschsnnierr
1.613
3,502
0.49L
1.925
1.046
1.57%
gomini-10-ftch
timemierr
0.858
3.18
1.400
9.533m 2
0.451
2.025
1.065
2.581
lam
2.515 1.515+
6.411
9.515
1.01
1.025
2.53
11667
interp mlp
2.558 6. 1.00
T.075
5.313
1.465
1.1
3,839
6.10%
inter pachienierr
0,008 5,771e
1 2: 1.359e
0.7:
4: 5.4530
1,210 2 5.500
inter timemiser
0.845 + 3.56
1,063 2 4.57t
2
0.506
5. 5.530
1,122 4 4.2 4.270
sccui 34m
3.38 2.200
30.611
:: 1.47300
1.298
1: 1.00
5,012
= 1.45200
scen mlp
2,746 4: 6,051e
8.177 1 3.525e
2:144
1.500%
6,225 1: 0,756
meen guchtsmiser
1,052 6,823k
1,522 1 1.541c
0,54D +. 5.7c
1,303 0.50l
mzan Limmissrr
0.582
1,66
10
0u61
175
0.878
Nayaun, -TFM him
9.269
0.391
3.007
0.367
1.619
1.021
1,770
No.sum
mlp
1,019
0.455
1,002
5.57
No.ommJ''T
goicheonixrr
2.17
1.170
CoT
5.067e
1.20
0.967
3.653c
CoT
4.334c
1.00le
0,915
1,851
LoT putchsmiser
0,050
4.675e
0,84
Nayamn OcT linemiarr
0.017 : 2.54
1,568 = 6.20- 2
0,51
5.0
1.778 2: 2.512
Na.sum JCD ICD .TEM Iin
0.967 :: 1.16Ge
0.969 : 7.202
0.887
1.40
2
0.962 :: 0.855
No sum ICD ICD xTEM mip
0,414 :: 2.747c
1,524
2 2.565
2
0.374
6.777
1.005
:: 2.132
No.amnJCI ICD -TMM ponctteniser
0.05 + 1-83
1.205 + 5.10
2
0.585
2550
1.13
3.813-
NC.emnJCD ICD fimemiuu
0.533 1,22
-2
1.112 + 1.01
2
0.506
2,530
1.065
+ 2.50
No JCD kim
0.586
1.50
-2
1 + 4.57
2
0.97
1.666
-
1.715 + 1.36
No.smmJCD mip
0.880 :: 2.503k
2/224
1: 7.106o
2
0877
3.1122
-
1,675
d: 0.145
ND puchtsmiserr
0.026 2: 3.175e
1,501
2
01877
3,2786-
-
1,1510
1: 6.001e
ICD linemises
0.756 2: 5,07
1,502
1.723>
0.716
5.49%
2
1.416
Tread Tread -TFM
1.126e
0.978
2.836
0.452
2.963
2
3,527e
Trend Tread slp
2,15
0422
1998
2
1,013
NoyeonTreed -TFM potchteniscr
2.53
Tread Treal -TFF
6.8
2.55
3.060r
Table 40: Cross-site transfer results - Los. (part 1/2)
56

---

Published as a conference paper at ICLR 2026
Mathod
lirid
ppicu
mimic
Iise
No.sum Tozed lam
0.214
1.300
1.58le
0.742
1.461
No.an, Tecal mlp
1.0055
1.505
6.087
Tical paidoaniser
6.786e
1,465
No.um,2hred
1.007
6.533
0.852
6.705e
8.0534
IIFM
1.042
1.305e
0.845
0,576e
0.985
modymma OxT -TFM puichsoniszz
0.538
1:000
1.130
3.50
0.46
0.00000000 0.505
1.07
aciiemma CrT -TFM timeniser
0.128
5,55f
1,005
9.5
0.616
1,003
1
6,001e
indgemma CoT Iam
1116
2.19220
2,313
8:630
0,535
0.2077
1.383
3.17lc
madgemma Orl mtp
1881
2:121
8.1122
0,539
225300
1,153
3
modjenma OUT puachsenissrr
1:
5.09
1,878
1.56le
0.554
2:224
-
1.118
2.70
indgrmma Cel timemmser
10018
5.01
3.40
7.175
0.536
1.47le
1.105
8333
modyymms
0367
19/9
1.505e
0.531
8.387h
0.972
indgermma IFM
6.505e
indgeemma -IM pacctsmiser
1.10
1,201
atcijemmaa
1.227
6.85
ICD
3,3090
8.443e
0,007
3,00
ICD
0.20
1.855
1.217
mnddermaa ICD pulcloonier
0.004 2: 3.21
1.681
6.104e
0
3,3470
2
1,213
8 0.66
mndgerma ICD timenixer
0,252
2.0000
1.7L
4.050
0,585
7.2022
1.21
4 1ice
2
madgrmma Ternd +TFM lam
0.361
5.000 -
0.995
8 8.700
0.57
2.100
0.95
madgenmma Trend +TEM mlp
0.155
1.4120
1,00
= 5.04lc
0.549
2,807
0.99
7.0lc
5
medgenim Trend +TEM pacchumikerr
8.55
3212
1.149
1,421e
0,002
1.755p
3
1,282
3.95c
madlennm Tranl imemissrr
1.038
6.163
0.624
1,011
mndgemma
1.075
L.007e
0
L.522
1,351
iodgrmma Trend mlp
Tiznd tinemiser
1885
1,271
mailymma sem,ahon plan TTMM latm
0.351
6. 67
1.025
0.78
0.542
0.250
0.965
6.758
muzigemma alar TTM mlp
0.355
90
1.077
1.12fe
0.545
6.275
0.979
1.358
iadgemma zem Sbot TPM paccesmmser
0.533 2 5,2010
1,131
2 1.56
0,503
2.225
1.003
medgrmma zem sbot TVM timemieer
0.133 2 0.21 -
1/345
+ 2.5--3
0.301 = 1.211
0.987
0.77
mndgeniaa zero shot 5tm
1004
4.891c
2:256
1,103e
0.51
8,5770
1.187
1.3014
modgrmma pemuuher mlp
10075
1.5715
2.132
0.641e
0.518
4.130
modgymma sem shai nichimmiser
7:100pm
1.50
0.50
1.533
1.087
1.37%
zero seco mndgermma
1,902
1.005e
7.534:
1.00
1.162r
posctremingr
1:555
1.13M
1.325e
0.576
3.066e
1.344
4.07
+IIM timemiser
[823e
1.30
0.56
3.0000
1.15
5.300e
No acro shot istm
0,007
2.510
1.867
0,505e
0,973
3,3730
2
2.007
0.612
No suns zero sbot mlp
0.778
2:4120
2.132
7.555e
0,579
2.3530
2,971
7.347c
No.arm Jar, sent paichasmiser
03353
5.615
1,896
1.315e
0
0.180
1,890
9.50
No.sur .ur.urr zem shot timeriteer
5,0340
1.620
1.205e
0,873
3,800
2
1.763
5.177
right Istm
2.116 4.7%c
0.35
2.000
01002
6.10-
1.542
2.375
Rgl mlp
2.011
5,65
7,54ke
8.253
rul pallesmiser
6.3716
2
1.643
1.106e
6,882)
night
Table 41: Cross-site transfer results - Los. (part 2/2)
57

---

Published as a conference paper at ICLR 2026
Miched
ppice
ppicc
Larra-A:1 Coll +IM har
2:30le
1.2344
Laraa.. 1.1 Orl VIFM pacttimii
11351
1,500
2,500e
0.412
4.790
1800
0.267
1.3100
01,008
1.355e
L. Cal +IMM unmounee
01345
1000
0.395
01.017
3.1.3
0.705
1.7733
1. 311 GIT
0.36
7.400
7.156
1,356
1.1171
1. CIT partmniioo
0.71
0.770
1:30-
0.97
5.350
0.671
6.0
0.254
1.300e
0.515
1.515 -7
1.maa31 CXT Himwiiser
0.36
1.955--3
0.338
0.501
0.005
2000
7:3
1. 1.1 XCD -TFM
0.511
1:200
1.817e-3
1:517
0.531
2.55
0.716
1.500
1. XDD -TFM pactrenivrr
0337
1:150
0.85
0.011
1.61
0.261
1.01
0.752
3.000
01515
13230
1.ma-111 XTD
Inveriion
0316
0.057
5.836
B536
5-703
1.mma1111 ICD
0:35
1.
1.233
1.520
Lant-3. 3.1 K.D peldesssace
0.184 1/02:
0.065
1,050x
0.261
6.344:
IL072
0.276
2.000
11.0
5.181c
2
L.
0.575
0:410
2.8155
7.665:
3.1
Lasta311 3.1 Tredd ITEM salp
0.468
1.445
0.907 + 1.450
0.5
1.74k:
0.545
3,000
0.00
1
0.788
9.126
Lant 3.1 Tread ITEM paoiossnis
0,357 = 2.300-
0.271
1:145e
0.222 +
7.002-
0.790
0.40
1.000-
0.507
1.715
Lanesiies
0.553
6.871
Lint bess
0,500
Lant-31 3.1 Treed sly
0.553 1.000-
0:50
0.637
3,77:
0.12
0.484
1.700-
0.748
1.345
Lint 3.1 Treed prodtaasiaer
0.22 6.831:
0.81T
5:205
0.265
1,11
0.000
1.00e-
0.566
0.035
2.965
1115 hem
0L51M 1.445
0:964
2.150
0.5T
1.710
0.364
1.51
7.777e
Lama-111 szm_atee 1155 msp
0.00
7:200
0-907
1:55te
0.30
1
U.5
3:30 2
0.243
L.aa-111 zem_atee 111 sactemmaee
0.320
1996
0.685
1.4b6-
ILTW
2:000
0.N11
Larta-111 smm
IU356 2
0.555
1.156
5.452
2.250
0.728
1.223
Larta-111 sam mip
0.007 3.1406-
0
1.320
0.301
8-485-
2.377
0.773
Larra-1: smm pachieniarr
0.338
0:525
1.2300
1.310
TFM
0.35
8,200
0.555
1,650
1.312
11000
11.40
TFM rlg
0.361
100
0.551
00001
0.635
TFM
0.65
1.9100
1-33
7.151
TSDE fors
0558
1.
0.0
0.708
1.800
0.13
0.755
01855
nly
0.55
0:911
2.150--3
0.715
1.83
BR0
3:70--9
0:900
2:5500
11.00
0.96
3.331
grarni Debahl CoT -TM Irm
0.555
1
5 5:101 -5
0.5T
3.000 -3
0.50
3.667
2.300
0.80
1.001
genrn-2 b-t.hbbcoooo CoT map
0.555
0:913
0.500
1:230
2-801
32HuE-2 D.li-2 Col TPM Ganeuiee
0.468
2,577c
0.857 + 7.653
0,995
1,988
0.565
6.000c
0.311
1.688
7.057
2omui-2b-laa-Co CoT san
0,488 = 1.515c
0,855
5.800e
0.051
1.125:
0.500
0250
0600
0,813
1.31%
cauus-2D.aal.Ca alp
0.400
0.543
4.301:
0.50
0.355
1,103
2osus-2 D-lu--2 CoT Gascarocs
0111 1.00
0.887 + 1.754
0.627
5.431:
0.44
1.856
0.585 +
4.300
0,780
2.4:
2ouu--2-dah--Bl ICD ITM laun
0.542 = 1.500
0.00
0.30
0.071
1112:
0,500
0.017
1,6
0,815
5.1.6
genus-2
clp
0.500
0:917
0.56
pacchseniss
1.55%
gomue-2 D.dal- ICD 1TM tineriser
0.414 2.500
0.854
0.001
1.35
0.50
0.002
2.53
0.817
1.271
gemm-2 11:1
0.01 0.209
0:665
1916-
01811
somn-20dabbl ILD paccooiss
01322 1.700
0.785
1.253
0.344
1
9.00
0.385
7.000
2.7-4
jemm--2b-hohhi R.D insensser
0U5 11100
LVB
3.01%
0.00
1-111
01002
2,50
1.302
semm-22.dea Irend
0.506
0:011
0.577
1.31
2.170
0,801
2odn-2 Trend -1IM paccturniarr
(1380
31.000
0.585
0.554
1.8.1
01:05
scmm--2b-llh Tear +1FM trammtee
0
0.555
0.001
1.000-
ILSA
0.300
5,500
0.203
semu--2-daa fsced lats
0.455
0,902
6.903
grmni--.brle brbbh Trand golrteettor
0340 1,
0.516
7.350
03311
0.701
0.116
0.511
1.300
gemri-3 Trund
0.M 7-:30
0.541
0:995
5.07
BS3
pesri-- D-l.h mmn -TEM
01566
1.51
0:576
-435e-3
1.355-
0.3103
grmri-2.bdbe (-bbb)))) mmn
pradeention
0:519 3-477
0:913
73113-3
0.281
-3333--
DS18
03555
0.001
1.5
genrn-2. cmn,
0.152
0.58T
1.11e-7
0555
1-137--2
BS50
1100
0.815
gesra-2 D-dlh don
01511
0:905
13350-
0.55
K.106
1.000
015
1.053
gemrz-2.-e bebbb non dwi poidemoor
01502
11 11100
0:963
1.030e-3
0:367
1.50h
1.00
0.152
1.3200
0:515
gemen-2 b-tth non dur immonise
0.31
01001
1114-3
5,381
3.083
0
4.157
ol
0.502 = 0.013:
1000
1.20
0.681
0.00.
0,81
2.156
0/710 2 7.560-
0.863
4.551.
sropp peldroation
0.00 D.D
6,161 0.0
0.1
2 0.0
0.223
0.5 0-0
areep
0.673
D:34
0.502 1,500-
0.50
1.553
sean rily
01540 = 2.50G
0:515 + 5.77
0,581 > 1.434
0563
2.300
0.683 + 0.08
0,872
1.154.
ason pebdesanior
0.00 7.00
6.161 0.0
0.0
0.233
0x0
0.70 476
0:187
No Talt
1847:
Su,am, Cel mlp
[UG 1.3000
0-125
unt
6
2
0.800
puachimise
04227
0.529
u.a
XDD bim
0/011
0/124
UALL
1.202
200
1,811
Ao au.. XDD sal
[L567 K.000
01222
1953
0U522
1.30e
0
5.153
03.44
sm pactrannisr
1.027s
0
0.303
1111:
01,022
Trena bit
(L529 111
0/125
1.555e
GALL
12.72
221
8457 2 0.500
1.43%
No sun Lised mb
0L50% = 7.5077
0100
1250
2
1.3.%
ILS00
0 2 1.43
01851
Tread paiteanniour
11385
7.053
01511
0.000 1000
0:531
2.750-
0.015
1.855
mnfgemne CcT -TFM Iotn
0.502
1.700
0:937
4 -31
0.01
677
0.89
6.07 -3
0.08
01853
1.515
awotyrmm CcT -TBM mxp
05. 3.00%
0:579
1-350-
0.583
1.117
1.475
5435
1.51.p
01833
77555
avdlymmo CoT -TMM finemnur
[1555
1
0.11
2.000
01568
1.200
8.000-
0/115
2.000
01.37
11000
mebermo CaT lemn
0.556
14
0117
1.3-8- -3
0377
151
3/001
1.500-
0.818
11005e -
nettemma mip
0.522
3.073
0332
5411e-
1.125-
8578
0.500
1.350
nelormm Gol dimeroer
0.555
0.966 1 3316e-3
0.57
1.36
0.96
0.30%
1.366e-
17.657
Table 42: In-distribution results Morr. (part 1/2)
58

---

Published as a conference paper at ICLR 2026
ppico
ppCCO
motesmera K.D -TT lans
0.822
6:35we-3
2.808
0ay2
1223
0L87
0.02+
0,05
2380 -2
1.551
1,200e
madgsmmaa K.D
1.300
4.50
4.1820
01854
nnlgrema
KDD TTM
0.55
0.005
0.991
7000
1:47e-
7.34
0.000
1.756
mafgrema KD tern
0.065
1.013
0.000
7.309
0.547
1::40
0.556
7.377e-
0.581
5,807
0.51
0
nadgrmma mlp
8.007
1:8000
0.001
1.575
5,275
0.586
8:007
modastura XCD pascttssio
0.7216
1.61
molaastusa Tread ITFM bis
0.824
0.32
(LOIL
1.00
0.540
1.522
0,864
0.620
0.643
3,370
0.827
1.110c
motgymera Thaad -TM mlp
0.513
1.7100
11904
6 6-387
0.332
13778
0180
7:30 -
0.546
1
0.513
9.100
msdgrmma ITM quactioniser
0.933
3
0:35
5,30
mateymma Thand Iemn
2.5886 -
0.841
5.003
0.301
6.507
0.516
1.181e-3 -3
0.505
1.00
0.788
1.100
nolistinaa Tread mip
0.454
2.115c
0.895
0.085:
0,852
1.114c
0.548
0.100
0.502
2.800
0.707
1.356
nolgemiaa Tread pactisniie
0.50 2 4.301o
0.852
8.19
0.10
0,710
0.20
0.204
1.100
0,006
1.0k
solqeturaa fincenierr
0.500
0.021
1.558
1.105
1.758
modesmira ano -133 mlp
1,102e
(U92)
5.109
2,007
0
0,85
2,65
1.56
molezmira amo -134 palchtrnisrr
0.115
6.312
1L88
2.3066
0,218
2102
0.518
14770
0.572
1.152
0.79 2:
1:214
modestirss axo -IF imennser
6:40
1882
0.811
nalgremm
05555
0.013
096
6.35
2.50
0.595
1.00-
1.05
matgreerra meo.tht padreniir
0.107
7.510
0187
1.00 -3
0.453
6205
0811
5.3 -2
05
1.707-
0.207
1.000
matgrmma man dos
0.315
6.03
0.8
1100
6.31m-
0.565
0:001
zero
IL:05:
9,87c
No. ze1o Just palcttaniaer
0.402 1,255
(1822
0.065
0.479
1207c
0.559
7.115c
0.20
1.186
0.54
4644:
No sati zero sbot limerter
0.87
1.445c
ILO17
1,00
0,579
214i:
0,571
= 0.234c
0.61
: 1.74c
0.55 = 1.107c
nch
0.372
6.200
0375
1.50
4109%-3
0.728
REN gachomiser
0100 1 0e0
1.13--4
01161
060
0.3 060
(4)
[1.
figh Smentinr
0.54 2.07
0.077
1.66% 2 0.089 #: 1.30e
0.507 3.513e 3
0.648 :: 5,786
3
0.501 2.060
Table 43: In-distribution results Mort. (part 2/2)
59

---

Published as a conference paper at ICLR 2026
Method
hirid-
ppies
mimic pptce
auroc
1.mo-11 1.1 Carl +TVM lemn
0.365
+ 5.0773 -3
0.556
2.309
-3 0.31
2-421e- 3
0.481
1.be
LJams 3.1 CoT +TEM nip
0.373
2: 8.213k
01.564
1.307e
0.383
7.766k
0.67
6.7902
Liama 3.1 Col +TEM positsmier
0.542
2.198c
0,522
1: 1.415e
0.361
LG54c
0.630
2: 5.433e
Limmo 31 CHI +TMM timemnee
0.380
4,2210
0,5022
2: 3.853e
0.376
2.80c
0.065
2: 228813
lem
3.56%
0.553
1.08%
1.063
1lam-31 3.1 slp
1.063x
0.65
4.10f
0.365
2.77%
2.356a
1. jonditemioor
0.389
1.147e
Liana
-TIM mlp
3.315e
0.383
3.627e
Liuna -TIM patchsmmierr
0.347 9
0.532
2.703
0.351
2.176
0.61T
Llamo-3 I/D -TIM comeriser
0.308
2.00
0.861
0.888
1.47lc-
1.001e
Llano-311 3.1 ICD 1stm
0,550
1,40
0.607
2 9.470
0.385
4.72%
3
0.074
2: 2.0030
Llans 3.1 ICD mlp
0 2.116
0.507
2: 7.300e
0.386
1.05%k
0.074
4.826e
11mm-111 1 1CD paicheamiscr
0.291 1.38
0.505
5.78%
2
0:31
5.565
0.001
+ 3.30
11mm-331 1 ICD fimeriserr
0.962 1.8-6
0.655
+ 1.707
0.388
5: 1:25
3
0.072 + 1,055
1.1 Tacrel .TFM lean
0.556 + 3.70
0.655
+ 9.36
0.386
7.460k
0
5.30
Limmo 3.1 Ieend +1MM mlp
0.26 4.777
0.66
1. 3.
3
0.3M
7100
0.677
4. 4.70%
LJmmo-11 1.1 Ineed +1IM ponchtsmiser
0.311
3:72
0/021
6-719
3
03773
2.730
0.602
1.0000
11am- Tered +IFM timemizer
0.59-
4.57
0.005
1:51 1m 5
03333
3.67
0.672
1.115
1iams Tased bon
0.306
2.842:
0.503
4.979
0.885
7.211c
Tesod mip
1.0562
0.367
2.202
0.884
Liamo Teend puschtsntioe
01542
6.61
0.365
11ann-311
6,10
0.206
5.006
1.057e
11 sem_shot +IFM
0.548
1.00 te
9600
0
2
0,086
2.440
11am-311 bemmushtt =TFM poachtemiuer
0.535
1.88 6-
0.612
5.95te
5
0338
2.800
0.052
2.5 2.907
11ano-31 1.1 bemmushett =TFM timemisez
0.561
1.530=
0.605
1.209
5
0.397
1.268+
0.685
5.83
Lians 3.1
15mm stot lam
0.362 2: 2.10/3
0.554
5,515e
0.39
9.319
0,571
1.57c -3
Liama 3.1
1.mmmm. shot mlp
0.302 1.83.
0.554
2: 1.9850-
3
0.29
+: 2.984e
0.672
1.831e
Liamo-3: 3.1 semm shott parchienizer
0.336 2: 3.
0.552
2 4.1320
01121
L61Te-
0
4.226
Lluea-3 3.1 acro shot Gamemixer
0.361 2 2.158k
0.507
2: 1.588
0.880
2.366
0.673
2: 2.800
TEM lean
0.405 1,686
3
0,686
2: 1.311e
0.200
3.64%
0
=: 1,007e
TFM mlp
0.000 1.15f
0,675
3.000 5
0.10
1.36
0.001
TFM paachtemiace
0.575 1.75e
0.657
0:115
9.513
0.088
3.05%
TFM limenisex
1.763-
0,502
1-213a- 5
1.115
1.147
0.313
0,321 2: 1.100
0351
2.09
2
6.70
mip
1,145e
1.21%
1M
limeniser
1,87%
3.505e
0.276
6.000
10 fuch CrT -IMM
5.234
1.302-
0.48L
0.743
1.26
5om-i-160-faeh OiT -PM mlp
0,915 + 8.223
0.714
6.110
0.178
8,200
0.712
3.50
2omin-222-faah CVT -TM paccttnnscer
0.004 :: 2.505
0.672
8 8.257
S
0.495
2 8
0.009
7.007
scmin--22--aeh CJT JTFM tineniser
0,439 :: 3.651e
0.717
1.775e
8
0.466
LG766
0,736
8.50
2ommn-10-ffoc ONT lean
0.617 2. 2.14
0.715
8: 1.98%
3
0.4M1
0.111
3.773
55min-1.0-BBbhhh CxT mlp
0.455 1.20/
0.715
1:205
089
167
3
0.739
1.877
5:min-----Bech OXT paccttemiser
0.915 1.39
0.711
1.123
5
0
966
0.082
1.01
zemini 2.0 fluhh CoT timemiser
0.455
2.492c
0.714
= 5.7
0.450
2.83
0.741
=: 1:451
gemini 1.0 foxh JCD -TPM Jom
0.372
1.
0.202
1 4300
0.454
1 3.400
0.710
=: 1.642
gomini-J..-fibb -TFM mlp
0.573
5.705
0,665
2.080
0.479
4.44l
0.735
Jomisi-2.--fahh ICD -TFM puachtunixer
0.35
4.7%
0.601
8.77ke-
0.431
5.018
4.100
acmisi- ICD -TFM tincmiser
1.423k
0.575
8.4
0.47
0.658e
2776
mlp
jomiis--..--ich ICD paldhoeniser
2.0 (1-ffuhhh ICD
1.500
0.476
1.100
jemmni-1-ffudh Trend +11.M
0.806
0.436
0,708
3.505
3omini-10-fsot Trend +TFM
0.800
2.57
0.00
1.100-33 3
0.431
1
0.000
2.111
jemini 2.0 fuih Trend xTFM paicktmmerr
0,576 2: 1.885e
0,559
2 2.212
3
0.412
7070
0,674
1.57
zemini 1.0 fush Trend -ITM timenieer
0 2: 2,103
0,202
7.545
0-422
7.13kc
0,005
5.20
5ominn-1/--Bel Trand loo
0.1 5.055
0.867
5.0%
5
0.305
8.00
0.002
03 0.700
gimini-2/-BBahhiee Trand nip
0 + 1.207
0,505 + 4.08k
0-412
1651e
3
0.606
=: 1.007+
gomiii----Baah Trand pacitsniser
0.878 2: 7.002
0.562
+ 7.51%
0.826
1.66
-2
0.613
= 5.77m
2omini11--fsch Trand fimerisorr
0.000
2.10%
0.001
2.300
3
0386
7.758
0.620
3
5omini-1--BBsh zemo,bec pbot
lean
0.46
2,500t
3
0.721
1/30l
5
0.490
1000
0.75
2.013
5omini-).-BBahh vom +TFM mip
0.455 1.00
0.721
la 5
0.19)
6.00)
0.717
1emiti 20 fush 2ero
pucchtsmiier
2.520e
2.01
0,453
5.03e
4.403
zomini zero
timemiser
2,155e
0.716
0.40)
0,742
jcmini--.0--Badh
7.37
6.055
semini-1.0. 2.0--fulh
0.657
1.85%k
3.000
0.745
acmini-200 2.0
puschsnnserr
0,454
2.01%
1.138c
gomini-10-ffch
timemierr
0.965
1.00 Se
0.722
0.300
1000
0.711
6.70
lam
0.530 2.580
0.612
501e 2
0.327
5.000-
0.557
3.16 fo
interp mlp
0 + 8.073
0.575
22525
2
0283
5,807
0.415 + 1.575
inter2 pachienieer
0.273 1.45c
0.5 0.50
0,273 4: Ge0
0.5 1: 040
interp timemiser
0,250 2. 2eletee
0,530 + 4.000
0,272 à 1800
0.898
sscui 34m
0.35 8756
3
DuG 2 9.412
0.856
6.78%
2
0.078
= 4.55
secu mlp
0.841 :: 2,137e
0,388
+: 2:905e
2
0a1a
3.2
2
0.005
21 5.16
mecnn guchtsmiser
112733 : 1.70
0.5 2.00
0,273
1 1.450
05
= 9.50
mzan immmisr
0.915 1.00
0.615
0351
0.510
Na.amm, Istm
7,255
0.767
0.477
5,8%
0.736
No.sum CoT mlp
0.76
7.20
0.440
No paccemmmerr
9.707e
0.166
No.orm_'nn
ICD
4.305e
0.492
6.297
ICD
0.504 7,53
2.55
0.512
1.189c
JCD poncttsmiser
7,306c
0.7 7.015e
0.190
0,055
Nayamn JCD limeminas
0.652 : 5 182
0.747
1.10l
0.483
1.00
0.730
5.26
No sum. Tread latn
0.510 :: 2.18
0.768 = 1226
3
0.480
8.001
0.702
4.13 -3
No Tread mlp
0.531 =: 1.711e
0.772 +: 3.500
0.480
1.7TLe
0,720
1 2
No.oim_ Trand paichsanisrr
0.061 7.005
0.723 + 180
2
0.37)
5.5
0.672
0..00
Ssyeen_Trand fimeniser
0.525
0.50 3
0.750 + 5.27
3
0-133
1.791e
2
0.775
+ 1.06 1-
malganm: CoT -TFM kom
0.661 + 2.005
0.721 + 6.18%
4
0-196
7.900-
3
0.737
+ 1.26
medgemmo Cal -TIM mlp
0.46 2
-3
0.722
1 5,9720
-
0.491
1: 3,20
-3
0.727
4: 3.273
medgamno Cal -TMM patchtimminrr
0.4101 4: 3.118e
0
1 6.807
0
1
3
0.89%
4: 7.701e
medeemna =TPM Cincmiser
2.07le
0.721
0.481
2.074
3.010
medeemna bun
1.615e
0.781
0,501
6.132
medremma mlp
:: 2.855
0.78
1,21
6,07e
maalyamn pacchsaiizz
0.30
5.20
mollmmmu
0.173
0.166
0.735
2.753
Table 44: Cross-site transfer results - Mort. (part 1/2)
60

---

Published as a conference paper at ICLR 2026
Method
harld
ppicu
miimie
ppócu
sugre
amroc
supee
medgemmo ICO TFM
0.459
2.10
0.215
1.8750
0.480
4.20e
0.TAL
2,5000
medgemmo ICD
mip
7.500e
3.011h
0.084
0.5770
0.730
medgemma JCD 1TMM paschomiser
0.408
2.124e
0.075
2.087e
0.440
2.890e
0.7
400e
medgemma JCD TTM timenizer
0.435
1
0.712
4.350e
0,482
3,808s
0.733
6.0012
medgemma JCD letm
0.45
7:277e
0.715
1.2286
0.49
4.8.5e
0.741
21.300e
medgemmo ICD mip
0.46
2.622n
0.117
1.0176-3
0.085
2.73de
-
0.74
1.130
medgemma ICD posctmmmierr
0.116
5.818e
1.581
0.000
7.541e
0.038
medggemm
2.04
7.770
0.485
3,375
0.737
medgemma Trend =IIM Ist
0,411
3.80e
0.694
2.313e
3
0,422
7,350
0.698
4.2284c
medgemma Trend -1IM mlp
0.407
2820
0.691
7.451e
0,423
4.594e
0.697
121c
medgemma Trend -TTM patchtsmiter
0.363
2.000
0.641
5.80
0.38
2.03
0.633
3.541e
medgemmo -TFM fimemieer
1.04e
0.000
0.690
9.073
medggmmm
5.2833
2.208
0.300
8.377
0.001
medgemma Trend mlp
0.425
1.544e-
0.705
1.227e
0,200
4.620c
0
1.470
meigemma Trend pitchtsmiser
0.41:
1.825e
0.7
1.00
0,373
4.873e
0.600
4.005c
medgemma Trend timemieer
0.419
0.201
6.000
2.12221111 2.712
0,613
1.813
medgemma serouhott -TFM
0.463
0-483
0.730
1.181
medgemma scmu -TFM mlp
4.85
0.726
7.523m
0.49
0,737
medgemma zero sbot -TTM pacctsmiser
0.424
L.622
2
0,681
9.891e 3
0,446
7.6766
0,698
7,8044
meigemma zeno sbot -TV timemier
0.846
2,230
0.72
1.1e
0,478
1.054e
0.728
medgemma zenouchot lam medgemma seroudutt mlp
0.455
2.704e
0.217
2.657e
000
5.50
0.74
2.099e
0.715
2,225e
0.735
1.473e
medgemma sem Juutt peachtmmiser
3.011
0.710
medgemma zito timemberr
0.454 1 1,164e
0.713
1.44le
0,485
8.083e
0.731
4.914c
No zero shot Istm
0.54
1,200e
2
0.764
6,374e
0,477
3.833e
0.734
4.113c
Nousum_zem sumuzzeo...e shot mlp
0.563
6.61le
0.17
3.377
0.46
0.718
133e
No.ummuzmm.th shot palchtemixer
0.354
6,858e
5.228e
0.44
0.710
3.200
Minuummrrmmhhe limemitzr
1.57%
0.75
3
0.718
1.801e
right Istm
0.377 1. 3.521e
0.632
3.20le
0,320
4.221e
0,808
4.497c
night mip
0.35
1.78.c
0.646
2.1926
0,216
0.500e
0.383
2.54e
nihh: patchismiser
0.273
1,85e
1.0c
0,273
1. 0e0
0.5
0m0
nigh fimemieer
0.332
2.
0.09
3.82/e
0.251
4.167e
0.424
1.200
Table 45; Cross-site transfer results Mort. (part 2/2)
61


Reviews:
[{"review_id": "LzK4FpT9cs", "reviewer": "Reviewer_t4Cv", "summary": "This paper proposes a method for transfer learning clinical machine learning models to different hospitals without a harmonization process, addressing the problem of data format differences that arise during deployment. The authors designed the 'Record2Vec', which utilizes LLMs to generate 'portable' patient representations. This methodology transforms irregular ICU time-series data into a natural language summary (a clinical handoff-style summary) using LLM, and then extracts text embeddings from this summary to be used for training predictive models.\n\nThe authors claim that this representation vector overcomes the heterogeneity of data formats between hospitals, allowing a downstream predictive model trained at one hospital to operate at another with minimal-to-no retraining. Experiments conducted on multiple prediction tasks across three ICU cohorts (MIMIC-IV, HIRID, PPICU) showed that the proposed method achieves in-distribution performance comparable to existing methods, while exhibiting less performance degradation in cross-site transfer and greater efficiency in few-shot learning.", "questions": "1. Lack of Explanation for Performance Improvement: There is an insufficient explanation of 'how' standardizing the input format into a unified summary leads to performance benefits in prediction. An analysis is needed on how clinically important information is better preserved or extracted during the summarization process—beyond simply matching formats—and how this positively impacts downstream tasks.\n\n2. Lack of Justification for Classifier Choice: The paper does not provide a reason or justification for choosing PatchTSMixer as the downstream predictive model for comparing the different representations. An explanation is needed as to whether this model is suitable for the task, or if the same conclusions would hold if a different classifier were used.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "(1) Well motivaed and novel approach: It is timely and important that the paper directly addresses the practical bottleneck of inter-hospital portability in clinical ML model deployment. Unlike traditional model-centric domain adaptation or schema standardization (OMOP/FHIR), the approach of using an LLM as an 'information transformation layer' to generate semantically aligned input representations is novel and interesting.\n\n(2) Comprehensive Experimental Design: The authors attempted to validate the core claim (portability) of the proposed methodology in various scenarios, including In-distribution, cross-site transfer, and few-shot learning, using three different ICU cohorts.\n\n(3) Practicality and Privacy Considerations: The 'Record2Vec' pipeline is conceptually simple. Furthermore, the evaluation of privacy risks—specifically, the recoverability of sensitive information (age, sex) from the embeddings—and the finding that it does not introduce additional risk is a commendable aspect for a study utilizing clinical data.", "weaknesses": "1. Inadequate Baseline Comparison: To demonstrate the superiority of the proposed representation, the paper compared the performance of various representation methods by feeding them into the same downstream classifier (PatchTSMixer). However, this approach omits comparisons with key baselines in the field of EHR prediction.\n(a) Traditional EHR prediction models that directly utilize medical code sequences, such as BEHRT (Li et al., 2020) or MedBERT (Ramsy et al., 2021).\n(b) Models like GenHPF (Hur et al., 2023), which are trained end-to-end by converting EHR time-series inputs into text.\n\nA direct performance comparison with these end-to-end models is necessary. Merely comparing input representations while keeping the PatchTSMixer classifier head fixed is insufficient to demonstrate the practical performance advantages of the proposed method.\n\n2. Practicality of Transfer Learning: In real clinical settings, hospitals will likely prioritize achieving the highest accuracy by training models optimized for their own in-distribution datasets, even if it requires significant cost and time. For the portability offered by the proposed transfer learning approach to be meaningful, the performance of the model transferred from a source to a target (transfer learning performance) must be superior to, or at least comparable to, the performance of a model trained directly on the target hospital's data (in-distribution performance). The current results do not provide a clear incentive for hospitals to choose portability at the expense of accuracy.\n\n3. Overstatement on Resolving Distribution Shift: While it is true that LLM summarization standardizes heterogeneous data formats into a common text input, the claim that this resolves the 'core' issue of cross-site distribution shift is an overstatement. The primary cause of distribution shift is not merely that the same patient data is recorded in different formats, but that the patient populations themselves are fundamentally different between hospitals (e.g., differences in severity, prevalence rates). Generating summaries can alleviate input format differences to some extent, but it seems that author overclaimed it.\n\n4. Unclear Role of LLM Summarization and Limitations of Experimental Setup: The purpose of LLM summarization in this study is ambiguous. While reducing the input length is a general advantage of converting time-series data to text, this experiment was conducted using 'pre-selected' common features (60-75) across datasets. This presupposes that the key shared variables between datasets are already aligned. It is questionable whether performing this pre-alignment step before summarization is an appropriate experimental setup to validate the purpose of LLM summarization (without aligning process EHRs).\n\nIf the role of the LLM is 'generating a standardized format,' a comparison should have been made with recent studies that use LLMs to convert EHRs to a standard data model (CDM) like OMOP and then apply models such as BEHRT (e.g., Adams et al., 2025).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "WjuEHbqIYY", "reviewer": "Reviewer_xPBC", "summary": "This paper proposes Record2Vec a pipeline that first asks an LLM to write a brief handoff of an ICU patient’s irregular time‑series record and then feeds that text to a text embedder to obtain a fixed‑length vector for standard predictors. Across three ICU cohorts and seven tasks, the authors report competitive in‑distribution results, improved cross‑site transfer, better few‑shot adaptation, and no increase in demographic leakage compared to strong baselines.", "questions": "- Please position results against task-specific SOTA (e.g., strong ICU mortality baselines) so readers can judge absolute utility, not only portability. You do include mortality among your tasks; a direct SOTA comparison table would help. \n\n\n- How sensitive are outcomes to the choice and dimensionality of the frozen text embedder? You mainly note Qwen3, an embedder ablation (dim, normalization, pooling) would clarify it. \n\n\n- The normalization choice differs across baselines (grids normalized with train-split stats, language keeps raw magnitudes/units). Can you justify fairness here or add a control where text summaries are unit-standardized too? \n\n\n- Figures 3–4 rely on “rank distributions.” Please report the exact metric used to compute ranks in the captions and include a small table of corresponding absolute numbers per method/prompt.   \n\n\n- Few-shot: what was the selection protocol for the 16 examples (random vs stratified), and how variable are results across seeds? A CI/SD per setting would help.\n\n\nMinor:\n- In line 21, “surprisingly competitive with in-distribution with grid imputation …” consider “competitive in-distribution with grid imputation …”. \n\n\n- In line 1088 the sentence is slightly off: “All clinical features are selected in discussion with real clinician have prominent experience in ICU units.\n\n\n- In Figure 6 caption, “Age results have less than 0.5% gap and can be inferred from appendix 4”, should be table 4? \n\n\n- Figure captions especially figures 4 and 5 could explicitly state the evaluation metric used for ranking/few-shot plots, right now the captions don’t name a metric.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Clear, deployment‑motivated idea summarize irregular numeric streams into natural language, then embed once to standardize inputs across hospitals, figure 2 nicely explains the study.\n- Extensive benchmarking across three datasets and seven various outcomes, with record2vec winning most in‑distribution tasks and most cross‑site transfer columns.\n- Systematic ablations on summarizer choice and prompt design, good findings: summarization generally boosts portability and structured prompts modestly reduce variance.\n- Few‑shot study shows that with only 16 labeled target examples, finetuning on record2vec closes much of the cross‑site gap for mortality. \n- Privacy and representation diagnostics are included: demographic recoverability stays comparable or lower than baselines, and UMAP/silhouette analyses provide an embedding‑quality proxy.", "weaknesses": "-The paper does not quantify information loss end‑to‑end or analyze the embedding geometry needed for downstream tasks, proxies like silhouette and task‑relative gains are informative but do not measure retained mutual information or per‑feature fidelity, and both the summarizer and embedder remain frozen rather than being optimized for downstream geometry. This makes it unclear whether key clinical signals are systematically discarded or distorted during the summarize-embed pipeline.\n\n- Absolute performance on mortality is mixed: in‑distribution AUROC reaches about 0.90 on HiRID but is lower on MIMIC and PPICU, and cross‑site mortality AUROC is ~0.72, which may be below what many deployments would select.\n\n- The embedder is used off‑the‑shelf and frozen, the paper does not test task‑aware metric learning, embedder finetuning, or geometry‑regularized objectives that could improve linear probe performance and calibration.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "UZOnBrKfbY", "reviewer": "Reviewer_xPpy", "summary": "This paper introduces Record2Vec, a novel \"summarize-then-embed\" pipeline designed to create portable representations of clinical time-series data from the Intensive Care Unit (ICU). The central claim is that by first using a frozen Large Language Model (LLM) to generate a natural language summary of serialized patient data, and then embedding this summary into a fixed-length vector, the resulting representation enables downstream predictive models to generalize across different hospital systems with minimal retraining. The authors evaluate this approach on three distinct ICU cohorts (MIMIC-IV, HiRID, and PPICU) across a range of forecasting and classification tasks. They demonstrate that their method achieves competitive in-distribution performance while significantly outperforming baselines in cross-site transfer and few-shot learning settings, without introducing additional privacy risks related to demographic recoverability.", "questions": "Recent work, such as Hegselmann et al. (2025), has shown that a direct \"serialize-then-embed\" approach can also achieve strong generalization across different cohorts using off-the-shelf embedding models. Your \"no-summary\" baseline, however, shows poor transfer performance. Could you please discuss this discrepancy? \n\n--- \n\nIn general I think this is a well motivated and nicely executed work, but I'm not sure how much novelty is left once other recent works are properly taken into account. Can the authors please clearly state how their work goes beyond the current state of the art?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 2, "strengths": "Important and Practical Problem: The paper tackles the problem of model portability in clinical machine learning. The degradation of model performance across different hospital systems is a major barrier to the real-world deployment of AI in healthcare.\n\nNovel \"Summarize-then-Embed\" Architecture: The main contribution is the two-stage process that uses an LLM for summarization as an intermediate step. The paper provides compelling evidence (Figure 3) that this summarization step is crucial for achieving portability, acting as an intelligent compression and harmonization layer that abstracts away site-specific noise. \n\nRigorous Evaluation of Portability: The experimental design is strong, with a clear focus on testing cross-site transfer as the primary outcome.", "weaknesses": "The primary weakness of the submission is its failure to properly contextualize itself within the rapidly evolving literature on using LLMs for structured EHR data. Several highly relevant, recent works (see below) that explore similar \"textualization\" and embedding-based approaches are not cited or discussed. This significantly overstates the novelty of the paper's general premise and misses a critical opportunity to highlight its more specific, architectural contribution. This prior works also also demonstrates cross-site generalization with a direct embedding approach, which directly challenges some of the implicit claims in this paper. By not engaging with several recent papers, the authors overstate the novelty of their core idea and fail to properly frame their specific contributions. A substantial revision of the manuscript is necessary to accurately position this work in the current landscape. \n\nThe paper's core narrative sometimes feels repetitive, and the most novel and important findings, specifically, the crucial role of the summarization step for portability and efficiency, can get buried. Additionally, several figures in the submission (e.g., Figures 3 and 4) are low-resolution.\n\nSimon A. Lee, Sujay Jain, Alex Chen, Kyoka Ono, Arabdha Biswas, Akos Rudas, Jennifer Fang, and Jeffrey N. Chiang. \"Clinical decision support using pseudo-notes from multiple streams of EHR data\". npj Digital Medicine, 8(1):394, 2025.\n\nStefan Hegselmann, Georg von Arnim, Tillmann Rheude, Noel Kronenberg, David Sontag, Gerhard Hindricks, Roland Eils, and Benjamin Wild. \"Large Language Models are Powerful EHR Encoders\". arXiv preprint arXiv:2502.17403, 2025.\n\nYanjun Gao, Skatje Myers, Shan Chen, Dmitriy Dligach, Timothy A. Miller, Danielle Bitterman, Matthew Churpek, and Majid Afshar. \"When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications?\". In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 5414–5428, 2024.\n\nContreras, M., Kapoor, S., Zhang, J., Davidson, A., Ren, Y., Guan, Z., Ozrazgat-Baslanti, T., Nerella, S., Bihorac, A., Rashidi, P.: DeLLiriuM: A large language model for delirium prediction in the ICU using structured EHR. arXiv. arXiv:2410.17363 [cs] (2024).\n\nStefan Hegselmann, Alejandro Buendia, Hunter Lang, Monica Agrawal, Xiaoyi Jiang, and David Sontag. \"TabLLM: Few-shot Classification of Tabular Data with Large Language Models\". In Proceedings of The 26th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 206:5549-5581, 2023.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Dey3BIvP2L", "reviewer": "Reviewer_rGkM", "summary": "This paper asks a crisp, deployment-relevant question: Can frozen large language models (LLMs) produce portable patient representations from irregular ICU time series that permit downstream predictors trained on one hospital to generalize to another with minimal re-training?\n\nTo address this, the authors propose Record2Vec, a novel \"summarize-then-embed\" pipeline. This approach first maps a 48-hour irregular patient record to a concise natural-language summary by using a frozen LLM. This summary is then converted into a fixed-length vector using a frozen text embedder. The authors evaluate Record2Vec across three distinct cohorts (MIMIC-IV, HiRID, and PPICU) and seven clinical tasks (including forecasting, length of stay (LOS), mortality, and predictions of treatment or lab values). The method is compared against multiple established baselines, including classical grid imputation, a time-series diffusion embedding (TSDE), and a time-series foundation model (TimesFM). Record2Vec is reported to be competitive with these baselines for in-distribution performance. Crucially, the approach demonstrates substantially greater robustness under cross-site transfer. Furthermore, the authors show that it is highly data-efficient in few-shot settings and, importantly, does not increase demographic recoverability relative to the baselines. The full set of empirical claims, the experimental setup, and the detailed results are documented in the submitted draft.", "questions": "- How much of Record2Vec’s transfer advantage remains if you replace the LLM summarizer with a deterministic template based approach that normalizes variable names, units, and missingness and emits a short structured template? This would clarify whether LLM semantic abstraction is essential or merely convenient and be a direct comparison with previous literature (https://www.nature.com/articles/s41746-025-01777-x)\n- Why is TSDE excluded from few-shot adaptation? Could you adapt TimesFM and TSDE with the same small labeled target budgets (or simulate a light adaptation procedure) to make the few-shot comparison symmetric?\n- Can you report concrete latency and cost numbers (tokens, GPU time, ms per example) for Gemini-2.0, MedGemma, Llama-3.1 summarizers and for Qwen3 embeddings, so practitioners can weigh the tradeoffs?\n- Have you looked at worst-group performance (e.g., by race/ethnicity, rare diagnoses, or admission types)? The aggregate AUROC can mask severe degradation for clinically important subpopulations.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Strengths:\n- treat language as a harmonizing interface between heterogeneous EHR encodings and generic predictors. Though some other works have described similar trends. Please cite (https://www.nature.com/articles/s41746-025-01777-x). Also cite MEDS which is trying to build transferable data schema in tabular domain (https://openreview.net/forum?id=IsHy2ebjIG) \n- The multi-site empirical evaluation and transfer (public and private cohorts) is a major asset so the cross-hospital experiments carry real weight. \n- The authors also do a good job exploring multiple LLM variants, prompt styles, and downstream tasks; the finding that summaries reduce token costs by ~25× (page 6) while improving transfer is practically important. \n- Methodologically, the pipeline is simple, engineering-light, and compatible with frozen models, which is attractive for real systems where re-training large models is infeasible. \n- The inclusion of privacy probes and sanity checks about demographic leakage shows the authors are thinking beyond pure accuracy", "weaknesses": "- A number of ablations and diagnostics could strenghten the work that would make the story rigorous rather than suggestive. The first is a controlled ablation separating the effects of (a) canonicalization of names/units, (b) summarization (compression of time series), and (c) the choice of text embedder. Without this, one cannot conclude whether “language” per se is necessary, or whether a structured canonicalizer plus a shallow encoder would suffice.\n\n- the privacy evaluation is narrowly scoped to demographic inference for age/sex. This is insufficient to claim \"no additional privacy risk.\" Embedding inversion, membership inference against the summarizer+embedder pipeline, and probing for rare/highly identifying events (e.g., unusual lab values, timestamps, or free-text tokens) are missing. Additionally, the LLM summarizer itself could hallucinate or introduce artifacts; there is no human evaluation of summary faithfulness to the numeric inputs, nor an automated fidelity metric (e.g., check that numeric extrema referenced in summaries match the underlying series).\n\n- operational costs and latency are discussed qualitatively but lack quantitative benchmarks. Reporting inference latency (ms per window), token counts, embedding dimensionality, and cost per 1k patients would give readers a concrete sense of real-world viability.\n\n- figures can be of higher quality.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)