Controllable Sequence Editing for Biological and Clinical Trajectories
Authors: Michelle M Li
Kevin Li
Yasha Ektefaie
Ying Jin
Yepeng Huang
Shvat Messica
Tianxi Cai
Marinka Zitnik
Venue: ICLR 2026 Poster (2026)
Keywords: conditional generation
sequence editing
time series forecasting
counterfactual prediction
multivariate sequences
concept-based learning
longitudinal modeling
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
TL;DR: CLEF is a controllable sequence editing model for conditional generation of immediate and delayed effects. We evaluate CLEF on cellular, patient, and sales trajectories, including a real-world case study on patients with type 1 diabetes.

Abstract: Conditional generation models for longitudinal sequences can produce new or modified trajectories given a conditioning input. However, they often lack control over when the condition should take effect (timing) and which variables it should influence (scope). Most methods either operate only on univariate sequences or assume that the condition alters all variables and time steps. In scientific and clinical settings, interventions instead begin at a specific moment, such as the time of drug administration or surgery, and influence only a subset of measurements while the rest of the trajectory remains unchanged. CLEF learns temporal concepts that encode how and when a condition alters future sequence evolution. These concepts allow CLEF to apply targeted edits to the affected time steps and variables while preserving the rest of the sequence. We evaluate CLEF on 8 datasets spanning cellular reprogramming, patient health, and sales, comparing against 9 state-of-the-art baselines. CLEF improves immediate sequence editing accuracy by 16.28% (MAE) on average against their non-CLEF counterparts. Unlike prior models, CLEF enables one-step conditional generation at arbitrary future times, outperforming their non-CLEF counterparts in delayed sequence editing by 26.73% (MAE) on average. We test CLEF under counterfactual inference assumptions and show up to 62.84% (MAE) improvement on zero-shot conditional generation of counterfactual trajectories. In a case study of patients with type 1 diabetes mellitus, CLEF identifies clinical interventions that generate realistic counterfactual trajectories shifted toward healthier outcomes.

Published as a conference paper at ICLR 2026
CONTROLLABLE SEQUENCE EDITING FOR BIOLOGICAL AND CLINICAL TRAJECTORIES
Michelle M. Li Kevin Lif, Yasha Ektefaie', Ying Jin Yepeng Huang', Shvat Messica', Tianx Cai', Marinka Zitnik
'Harvard University, "MIT, UUniversity Pennsylvania, "Co-corresponding authors
michelleli, yashalll,,asaaaaeeeeeeealaaaaala faasa__kkefaiiee yepengeg harvard. edu, 1 likevingmi .ecu.
jpinstat@whartee upenn. cdu, shvat_mess mose ica@ fas. harvarrd edu, tcai Chai@hsph.r harvard edw.. marimkar hms. hwwvard. edu
ABSTRACT
Conditional generation models for longitudinal sequences can produce new or modified trajectories given conditioning input. However, they often lack control over when the condition should take effect (timing) and which variables should influence (scope). Most methods either operate only on univariate sequences assume that the condition alters all variables and time steps. In scientific and clinical settings, interventions instead begin specific moment, such as the time of drug administration or surgery, and influence only subset of measurements while the rest of the trajectory remains unchanged. CLEF learns temporal concepts that encode how and when condition alters future sequence evolution. These concepts allow CLEF to apply targeted edits to the affected time steps and variables while preserving the rest of the sequence. We evaluate CLEF on 8 datasets spanning cellular reprogramming, patient health, and sales, comparing against state-of- the-art baselines. CLEF improves immediate sequence editing accuracy by 16.28% (MAE) on average against their non-C1a ConFCLEF counterparts. Unlike prior models, CLEF enables one-step conditional generation arbitrary future times, outperforming their non-CLEF counterparts in delayed sequence editing by 26.73% (MAE) on average. We test CLEF under counterfactual inference assumptions and show up 62.84% (MAE) improvement on zero-shot conditional generation of counterfactual trajectories. In case study patients with type diabetes mellitus, CL.EF identifies clinical interventions that generate realistic counterfactual trajectories shifted toward healthier outcomes.
INTRODUCTION
Conditional generation of lon- gitudinal sequences grow- ing challenge in machine learn- ing, where the goal is to pro- duce new or modified trajecto- ries based on a conditioning in-
Contolibu secanoo wckiing
Bequance acti
codentss twalh jurny
Dhaw
put, such as an intervention ap- plied to a system. A central task controlling when in the trajec- tory the condition should take ef- feet (timing) and which subset of variables it should influence (scope). In many domains, inter- ventions begin at specific mo- ment and alter only part the system while the rest of the trajectory remains unchanged. A motivating example comes from virtual cell models, which simulate how molecular or cellular states evolve under perturbations and enable large-scale in silico experimentation (Bunne et al. 2024 Li et al. (2025). To successfully build such virtual cells and patients, the models must consider both the type of intervention (e.g.. drug, surgery) and its timing (e.g., when and how frequent). For instance, prescribing medication should alter
Frasrical dul inaveeiin
Figure 1: Illustrative comparison of (a) controllable sequence editing and (b) existing sequence editing. Unlike existing methods, controllable sequence editing generates sequences (dotted lines) guided by condition while preserving historical data model the effects of immediate (c.g., 2 hours) or delayed (c.g., in week) edits.

---

Published as a conference paper at ICLR 2026
patient's trajectory only after the intervention time while preserving the medical history prior to treatment, and only those clinical variables relevant to the intervention should change while unaffected measurements remain stable (Fig. D
Generative language and vision models enable precise editing guided by a description, such as textual
prompts condition tokens (Zhang et al. 2023b; Gao al. 2023 Ravi et al. [024] Gong et al.
2024 Niu et al.
2024 Gu et al. 2025 Zhou et al. (024). They are designed to gain more global
context-preserving and local precise control over the generation of text (Chatzi et al.
2025 Niu
ct al.
2024
Gu et al. 2025 Zhou et al. 2024), images (Zhang et al. |023b] Gao et al. 2023 Ravi
2024). and even molecular structures (Gong et al. [204]] Dauparas et al., [022] Zhang et al. al.
2024b). Their outputs preserve the imput's 's global integrity yet contain precise local edits to satisfy the
desired condition. Analogous to these models' consideration of spatial context to edit images (Zhang
et al.a 2023b) Gao et al. 2023) and protein pockets (Dauparas et al.) [022] Zhang et al. (024h) via in-painting, our work leverages temporal context to edit sequences based on a given condition.
Controllable text generation (CTG), designed specifically to edit natural language sequences, has been extensively studied (Zhang et al. 2023a). They excel in immediate sequence editing: predicting the next token or readout in the sequence under given condition (Niu et al.
2024] Gu et al.) 2025
Zhou et al. 2024; Chatzi et al. 2025 Zhang et al. 2023a Bhattacharjee et al. 2024). However, CTG models unable to perform delayed sequence editing: modifying a trajectory in the far-future. The distinction is important: the focus is on when the edit occurs, not necessarily when its effects manifest. Whereas immediate sequence editing applies a condition now (e.g., administering insulin today), delayed sequence editing schedules a condition for the future (e.g., starting a chemotherapy regimen in six weeks). Existing CTG models cannot effectively utilize the given context to skip ahead to the future; instead, they would need to be run repeatedly to fill in the temporal gap without any guarantee of satisfying the desired condition. As result, CTG models are insufficient for other sequence types (i.e,, not natural language) for which both immediate and delayed sequence editing are necessary, such as cell development and patient health trajectories.
Controllable time series generation (CTsG) (Jing et al. 2024 Bao et al. 2024) utilizes diffusion modeling to generate time series under a given condition. However, these models are limited to univariate sequences and assume that the entire input sequence is affected (Jing et al. [2024] Bao et al. 2024). These methods are thus insufficient in settings where edits are only allowed after time t (i.e., cannot change historical data) and affect only certain sequences (i.e.. preserve unaffected co-occurring sequences). In other words, CTsG methods are unable to make precise local edits while preserving global integrity. Orthogonal to CTsG is the estimation of counterfactual outcomes over time (ECT) (Melnychuk et al. [0022) [Bca et al. 2020 Huang et al.) 2024] Wang et al. (025). Although not generative, ECT autoregressively predicts the potential outcomes (i.e., next readout in the sequence) as a result of different future treatments (i.e., fixed set of conditions) under counterfactual inference assumptions. While ECT preserves bistorical and unaffected co-occurring sequences, counterfactual inference assumptions may not always hold in real-world applications.
Present work. We develop CLEF, novel ControLlable sequence Editing Framework for instance- wise conditional generation. Specifically, CLEF learns temporal concepts that represent the trajectories of the sequences to enable accurate generation guided by a given condition (Def. (.2). We show that the learned temporal concepts help preserve temporal constraints in the generated outputs. CLEF is flexible and can be used with any sequential data encoder and condition tokenizer. We evaluate CLEF on 8 datasets spanning cellular reprogramming, patient health trajectories, and sales, outperforming state-of-the-ar (SOTA) baselines on immediate and delayed sequence editing (Def. 3.T). We also show that any pretrained sequence encoder can gain controllable sequence editing capabilities when finetuned with CLEF. Additionally, we extend CLEF for multi-step ahead counterfactual prediction under counterfactual inference assumptions (Assumption 3.4 Eq. 4.. and demonstrate (on 3 benchmark datasets) performance gains against 5 SOTA methods in settings with high time-varying confounding. Moreover, CLEF enables conditional generation models to outperform baselines in zero-shot generation of counterfactual cellular trajectories on immediate and delayed sequence editing. Further, precise edits via user interaction can be performed directly on CLEF'S learned concepts. We show through wal-world case studies that CLEF, given precise edits on specific temporal concepts, can generate realistic "healthy" trajectories for patients originally with type I diabetes mellitus.
Our contributions are fourfold. (1) We develop CLEF: flexible controllable sequence editing model for conditional generation of longitudinal sequences. (2) CLEF can be integrated into the
2

---

Published as a conference paper at ICLR 2026
(balanced) representation learning architectures of counterfactual prediction models to estimate counterfactual outcomes over time. (3) Beyond achieving SOTA performance in conditional sequence generation and counterfactual outcomes prediction, CLEF excels in zero-shot conditional generation of counterfactual sequences. (4) We release four new benchmark datasets on cell reprogramming and patient immune dynamics for immediate and delayed sequence editing, and evaluate four established benchmark datasets for conditional generation and counterfactual prediction.
2 RELATED WORK (EXTENDED VERSION APPENDIX A
Sequence editing. The sequence editing task has been defined in language and time series modeling via different terms, but share a core idea: Given a sequence and condition (e.g., sentiment, attribute),
generate sequence with the desired properties. Conditional sequence generation is an autoregressive process in language (Chatzi et al. (025) but a diffusion process in time series (Jing et al.) [204]]
Bao et al. 2024). Prompting is often used to guide the generation of a sequence, both textual and temporal, with a desired condition (Zhang et al. 2023a Bhattacharjee et al., (024) Jing et al. 2024]
Bao et al.j 2024). However, existing approaches are unable to generate multivariate sequences, preserve relevant historical data, and ensure time-sensitive edits. They assume that sequences are univariate and conditions affect the entire sequence (Jing et al., 2024 [Bao et al. 2024). Structural causal models can be incorporated to enable counterfactual text generation while preserving certain attributes (Chatzi et al. 2025 Ravfogel et al.) 2025). Estimating counterfactual outcomes over time
often formulated under the potential outcomes framework (Neyman 1923 Rubin 1978).
Estimating counterfactual outcomes over time. Predicting time-varying counterfactual outcomes
entails estimating counterfactual outcomes over possible sequences of interventions (e.g.. timing and ordering of sequential treatments (Melnychuk et al. [0222] [Bia et al. 22200] Huang et al., 2024 Wang
al. 2025)) ). There decades of research on temporal counterfactual outcomes estimation (Robins
ct al. 2000 Lim 2018 Bica et al. 2020 Li et al. 2021 Melnychuk et al.) 2022). Recently, machine learning models that predict varying counterfactual outcomes learn representations that are
predictive of outcomes while mitigating treatment bias via balancing techmiques (Melnychuk et al. 2022. Bica et al., 2020 Huang al. 2024; Wang et al., 2025). On images, conditional generation models (i.e, guided diffusion, conditional variational autoencoder) have been shown to predict counterfactual outcomes without an explicit density estimation (Wu et al. 2024). However, there may be trade-off between prediction accuracy and balanced representations (Huang et al., 2024
Concept-based learning. Concepts are abstract atomic ideas or concrete tokens of text and im- ages (LCM et al.
2024 Lai et al.) (0244). Concept-based learning can explain (e.g.. predict concepts
in the sample) or transform black- box models into more explainable models (c.g., allow user inter-
vention) (Koh et al.
2020 Shin et al. [2023 Ismail al. 2024| Lai al. 2024 Laguna et al. 2024
van Sprang et al. 2024), and mitigate distribution shifts Zarlenga et al.
2025). While concepts are used in sequence generation (LCM al. 2024), they have not been used for conditional generation. Concept-based learning for counterfactual prediction is limited to improving the interpretability of
image classification (Dominici et al. 2025a De Felice al. 2025
Dominici al. 2025b).
3 CLEF
CLEF generates sequences hased on user-specified conditions and temporal coordinates. Given a longitudinal sequence, forecast time step, and condition token, CLEF modifies only the relevant por- tions of the sequence while preserving unaffected elements, ensuring global integrity. Architecturally, CLEF has two novel components: concept encoder E that learns temporal concepts, representing tra- jectory patterns over time, and concept decoder G that applies these concepts to generate sequences (Sec. A.3). Following SOTA conditional sequence generation and time-varying counterfactual predic- tion models, CLEF has sequence encoder F that extracts temporal features from historical sequence data, and a condition adapter H that maps condition tokens to latent representations (Sec.) A.3).
3.1 PROBLEM DEFINITION
(0)( 1 N J==1 for N independent entities (c.g., cells, patients) at discrete time step t. For each entity i at time observe continuous time-varying covariates
Consider an observational dataset D = (t)
3

---

Published as a conference paper at ICLR 2026
CLE architecure
Model capab Byy: Forocast Rt, by wanking Inputs futur time andiorconditton
Bequeree R.L.,
i in
Conditon
C PT
ST
Forecsst sma
Sequenca encoder
edapter
pe CLEF
Concept decoder
b CLEF
Auy, (Bht,
Bty:
Rt, imteveningg
Concips anoodir
leamed temporti concept
h2 O b,?
charge)
Delayad
GELU
What so incwooeed y knvels and decresodd lowek?
inn
in
Temponai concept jontes of changel e
M
Intervened tompors concect (falas of changin
C'Ox
Figure Overview of CLEF's architecture and capabilities. (a) Given a sequence, forceast time, and condition embedding from a frozen pretrained (PT) embedding model, CLEF generates sequence via immediate or delayed sequence editing. (b) CLEF is composed of a sequence encoder, condition adapter, concept encoder, and concept decoder. CLEF has two key capabilities: (c) forecast sequences any future time and under any condition (e.g.. medical codes), and (d) generate sequences intervening CLEF's learned temporal concepts.
(1) Rd (e.g., gene expression, laboratory test measurements) and categorical conditions s, (e.g.. transcription factor activation, clinical intervention). The outcome of the condition is measured by the covariates (e.g., activating a transcription factor affects cell's gene expression, prescribing medication affects patient's 's laboratory test profile). We omit entity index (i) unless needed.
Definition 3.1 (Sequence editing). Sequence editing is the local sample-level modification of se- quence x to autoregressively generate X.t, under condition given at time -j- Time gap indicates that , ; is measured negligible amount of time after applied") for notation, omit unless needed. There are two types of controllable sequence editing (Fig. 2h):
Immediate sequence editing: Given X,tast, and to occur f.- forecast Stt Delayed sequence editing: Given X.tost, and to occur at 1 4. forecast 8
Examples of immediate sequence editing include generating trajectories after perturbing cells now performing surgery patients today (Sec. 5.1). In contrast, delayed sequence editing generates trajectories after perturbing cells in ten days or performing surgery on patients next year (Sec. 5.2).
Definition 3.2 (Temporal concept). Temporal concept с approximates the trajectory (or rate of change of each variable in sequence between a pair of time steps 1, such that X.,, O X,
Definition 3.3 (Controllable sequence editing). Concept encoder E and decoder G can leverage temporal concepts to perform controllable sequence editing if the following are satisfied.
Condition X:,ort, time step 1 learns that accurately forecasts X such that X,,, x For an alternative condition a 8 on f,,t, at fj. the method learns distinct e' + e that forecasts K,, such that x # X and, if known, X = X',"""
Problem Statement 3.1 (CLEF). Given a sequence encoder F, condition adapter H. concept encoder E, and concept decoder G trained on a longitudinal dataset D, CLEF learns temporal concept (X( (s)) to forecast Kx,,, G(x.t c) for any X, DD t. > t, and s.
X = G(X,,, (X.(x,,, (j), (s)))
'We assume that the condition always occurs shortly before the measured covariates at time ty. When is intervention and our problem becomes counterfactual prediction (refer to Sec, 3.3 for more rigorous discussion about the formulation of sequence editing in this context), our assumption is consistent with existing practice the literature of counterfactual prediction.
4

---

Published as a conference paper at ICLR 2026
3.2 CLEF ARCHITECTURE
CLEF'S input are a continuous multivariate sequence X,,toit, E IR with V measured variables, condition 8, and time 1j for which to forecast X.,,, CLEF consists of major components: sequence encoder F, condition adapter H, concept encoder F,, and concept decoder G (Sec. A.3)
Sequence encoder F. The sequence encoder F extracts features fort, such that h = F(x,,tort)) Any sequential data encoder, including pretrained multivariate foundation model, The time encoder in F generates a time positional embedding h, for any time via element-wise summation of the year (sinusoidal), month, date, and hour embeddings. It is also used to compute the time delta embedding At.,t, be, h, of time steps & and Ł, for the concept encoder E.
be used.
Condition adapter H. The condition token, or embedding z, corresponding to the input condition s. is either retrieved from frozen pretrained embedding model (denoted as PT in Fig. 2 or features of condition/nteervetion. The condition adapter H projects z, into a hidden representation h, H()).
Concept encoder E. Given the hidden representations generated by F and H, concept encoder E learns temporal concepts с Ehxx. At..t, h2). First, the time delta embedding Ae,,, is combined via summation with h, to generate time- and condition- specific embedding h = A..,, @ h,. Then, с is learned via an element-wiss multiplication hy and h an optional linear projection (FNN), and GELU activation to approximate the trajectory between and Ey.
GELU @ h)?))
Concept decoder G. The concept decoder forecasts Ki,, by performing element-wise multiplica- tion of the latest time & of the input sequence X,toot; (denoted as xx,,, and the learned concept c
② X
This decoder is less sensitive to covariates with different units of measure (e.g., blood sodium in meq/L. vs. white blood cell in 10%/L), Also, applying in a single step (via @) allows users to directly intervene on c and simulate the effects of the intervention as counterfactual trajectory (Sec. (566).
Objective function L quantifies the reconstruction error of predicted XX,, from ground truth x (via Huber or MSE). may include balancing loss functions for counterfactual prediction only (Sec. D.I.
3.3 CLEF'S CONNECTION TO COUNTERFACTUAL PREDICTION
Let X,t, refer to the outcomes observed at tj after treatment is given. Our problem can be viewed as counterfactual prediction when there is no treatment assigned between f, and ty except s.
Formally, under the potential outcomes framework (Neyman 1923 Rubin 1978) and its extension to varying treatments and outcomes (Robins & Herman 2008). the potential counterfactual outcomes over time identifiable from the observational data D under three standard assumptions: consistency, positivity, and sequential ignorability (Sec. B). Thus, CLEF (via temporal concepts c)
predicts counterfactuals under the additional Assumption 3.4
Assumption 3,4 (Conditional mean function estimation). For time steps tj t,, temporal concepts learned based on the next treatment St,, historical treatments Stott. and historical covariates X.aat, capture (balanced) representations such that the concept decoder c(st, s x @ X,,, approximates the conditional mean function E E[x:,ty<((st, Stort, (tgd, X.
In the following, we elaborate on why it can be reasonable to view CLEF as an accurate counterfactual prediction model by satisfying Assumption 3.4
Estimating counterfactuals. We estimate future counterfactual outcomes over time, formulated as
E E(x,,,,+ (St, Start, 15g X ).t.)
(4)
by learning function g(T, (,, St. Stort, Xtta:t;) G(x,,, , E(F(x (xx roste : (2), H (st,) (ss with projection horizon (t, t; 2 1 for 7-step ahead prediction (Eq. Sec. 3.2 Indeed, the key to reliable counterfactual prediction is the accurate estimation of Eq. to adjust for bias introduced by time-varying confounders (Robins & Hernan 2008). In particular, design of g() estimates
5

---

Published as a conference paper at ICLR 2026
Eq. well (refer to Sec. 5.4 for empirical results) due the effective learning of temporal concepts (Def. 3.2) and the strong representation power of the encoders (Sec. 3.2).
Balancing representations via CLEF (Sec. D.4). Since the historical covariates and next treat- ment are encoded independently by F and H. the learned representations are (or balanced), following the discussions in existing balanced representation learning architectures (e.g.. CRN (Bica et al. (020). CT (Melnychuk et al.] 2022)). Further, by Assumption [3.4] our designed structure isolates the causal effect of the treatment from other spurious factors, enabling reliable counterfactual estimation (Zhang et al. 2024a).
4 EXPERIMENTAL SETUP
4.1 DATASETS
CLEF is evaluated on 8 biomed- ical and financial datasets on conditional and counterfactual
Bood sockun
1400 mast
Nokent
Rights
generation (Fig. B Sec. C
siglaaa
Tab. D We introduce bench- marking datasets, WOT (con- ditional generation) and WOT- CF (counterfactual generation), of single-cell transcriptomic pro- files of developmental time courses of cells. We also construct two new real-world patient datasets of irregularly- measured routine laboratory tests from eICU (Pollard et al., (2088) and MIMIC-IV (Johnson et al.
Stitt
PSC
3
Figure 3: CLEF is evaluated on 7 datasets of (a) cellular development and (b) patient health trajectories. Illustrations from NIAID NIH BIOART.
2024a 2023 Goldberger et al.
2000).
We evaluate counterfactual outcomes estimation three established benchmarks related
to tumor growth (Geng et al.] (017)) and patient intensive care units (ICU) (Johnson et al. (0166) trajectories for r-step ahead prediction; trajectories with time-varying confounding y are simu- lated (Yang et al. (2023). We evaluate conditional generation on real-world store sales trajectories:
M5 (Makridakis al. 2022 Huang et al., 2024: Wang et al. 2025).
4.2 SETUP
CLEF evaluated on 3 tasks: immediate and delayed sequence editing (Def. and counterfactual prediction (Sec. 3.3 We use standard metrics (MAE, RMSE, R²) to compare ground truth x",, and predicted Experiments are designed to isolate temporal concepts' contribution to predictive performance (e.g., CLEF and non- CLEF differ only in the components needed to learn temporal concepts; Sec. D.2). Refer to Sec. C for experimental setup and implementation details.
Baselines. We evaluate CLEF against SOTA conditional generation and counterfactual predic- tion models, which do not learn temporal concepts; each baseline has a CLEF counterpart. Conditional generation (4): We adopt the SOTA conditional sequence generation setup with 3 sequen-
tial encoders: Transformer (Waswani al.) 2017; Narasimhan et al., 2024 Ting et al. 2024 /Zhang 2023a); xLSTM (Beck et al.) 2024); and time series foundation model, MOMENT (Goswami
et al.
al.
2024). We evaluate against traditional time series model, Vector Autoregression (VAR) (Lütke Counterfactual prediction (5): We adopt the SOTA counterfactual prediction setup
pohl
2005
using 2 architectures (i.c., Counterfactual Recurrent Network (CRN) (Bica et al., (2200) and Causal Transformer (CT) (Melnychuk al.) 2022) with and without balancing loss functions (i.e., gradient reversal (GR) (Bica et al. 2020), counterfactual domain confusion (CDC) (Melnychuk et al. 2022) )
Ablations. SimpleLinear is an ablation in which temporal concepts are all ones (i.e., not learned nor meaningful), inspired by traditional linear models that excel when x,, e x,, (Toner & Darlow 2024 Ahlmann- Eltze et al. 2025). We evaluate CLEFF with and without an FFN layer E (Sec.
LowR

---

Published as a conference paper at ICLR 2026
an ablated decoder (I+ () where W is low-rank (with rank 4, 8, 16). To demonstrate the benefit of single-step generation, we perform an experiment in which we arbitrarily add three intermediate steps between t, and ły before finally predicting the observed X,,, (Tab. 99.
5 RESULTS
We extensively evaluate the impact CLEF'S learned temporal concepts on controllable sequence editing. R1-R3: How well does CLEF perform in (R1) immediate and (R2) delayed sequence editing. and (R3) generalize to unseen/new sequences? R4: How does CLEF perform in counterfactual outcomes estimation? R5: Can CLEF perform zero-shot conditional generation of counterfactual sequences? R6: How can CLEF be leveraged for real-world patient trajectory simulations?
5.1 RI: IMMEDIATE SEQUENCE EDITING ON OBSERVED SEQUENCES
Immediate sequence editing in- volves forecasting the next time step of a trajectory after applying condition. The defining feature that the condition occurs in the present moment, and its effects are reflected in the next observa- tion of the sequence. This set- ting is relevant when the condi- tion has an instantaneous impact (e.g., administering a drug to cell now, performing surgery on patient today).
Immediate sequarcce editing
WOT
CJ
MMII-N
M5
000
CL MOVEE
NOVETE
Smpicr or
NOXECC
O NOKENT
CLE
OLE
0.83
Delayed sequence ediinng
WOT
eCU
MMCI-V
M5
CLEF models consistently per- form competitively against base- line models across all datasets (Fig. 44. 9 Tab. SimpleLinear, which assumes no temporal changes, performs parably in some cases, but CLEF outperforms it on datasets where short-term dynamics are more complex. On WOT, CLEF outper- forms or performs comparably to the time series forecasting model, VAR. This is particularly excit- ing given recent findings that lin- ear models can achieve competitive or better forecasting performance than neural network mod- els (Toner& & Darlow| 2024 Ahllmann Sallmann-Eltze et al. 2025). Further, CLEF models tend to yield less error (MAE) on both preserved and edited variables of sequence than non-CLEF models (Tab. 5 These results highlight CLEF'S ability to accurately edit sequences at the desired times while preserving unaffected portions of the sequence.
Vin
CUES Trantomer
OLER 363
Smoeuree
CLER MOMENT
OLEF fradomer
Tranfomerr
CLES MOMENT
Sancee
15 16
ca
OLEF
Figure 4: Benchmarking CLEF, baselines, and ablations on (a) immedi- ate and (b) delayed sequence editing on observed sequences. Lower MAE is better. Models are trained on 3 seeds using a standard cell-, patient-, store-centric random split; error bars show 95% CI. Not shown for visualization purposes VAR's performance IV: on immediate sequence editing, MAE eICU and MIMIC-IV are 55982.74 74 and 886.05; on delayed sequence editing, MAE for eICU and MIMIC-IV are 3.02 x 30 and 8.62 20
eICU and MIMIC-
Regardless of the sequence encoder used with CLEF, these models tend to outperform or perform be affected by
comparably non-CI.FF models (Fig. 4b). However, CL.EF'S performance the ability of the sequence encoder to capture the temporal dynamics of the input sequences. For instance, models with the MOMENT encoder generally yield the highest MAE in all three biomedical datasets (Fig. 44) Still, CLEN-MOMENT models have lower MAE than their non-CLEB counterparts.
5.2 R2: DELAYED SEQUENCE EDITING ON OBSERVED SEQUENCES
Delayed sequence editing forecasts a trajectory at a specified future time step under a given condition while preserving sequence consistency. Unlike immediate editing, the condition takes effect the
7

---

Published as a conference paper at ICLR 2026
designated future time, requiring models to project forward without introducing compounding errors. Examples include applying a drug to a cell in ten days or scheduling a patient's 's surgery for next year.
CLEF performs competitively against SimpleLinear (ablation) and VAR on eICU, MIMIC-IV, and M5 (Fig. g
Tab. (). CLeF-transformer and CLEF-XLSTM achieve lower MAE than SimpleLinear, whereas non- non-CLEF transformer and MOMENT baselines perform comparably worse. As in imme- diate sequence editing, models with MOMENT as the sequence encoder (i.e., using temporal concepts with MOMENT) yield the highest MAE on the biomedical sequences. However, incorporating CLEF with MOMENT reduces the MAE to levels comparable to the MAE of SimpleLinear and VAR.
On WOT, SimpleL.inear and VAR outperform neural network models in delayed sequence edit- ing (Fig. 4p). This suggests that cellular trajectories exhibit small and possibly noisy changes at cach time step. favoring linear models (Ahlmann-Elin Eltze al., 2025 Toner& & Darlow 2024). Also, given the relatively small number of training trajectories compared to the high-dimensional state space, nonlinear models may overfit to noise more readily than linear models. Still, CLEF significantly reduces the MAE of non-CLEF models, demonstrating its effectiveness short-term noise while preserving long-term trends. Further, as in immediate sequence editing, CLEF better preserves unedited variables than non-CLEF models (Tab. 5)
regularizer that mitigates
benefit of delayed sequence editing is the ability to perform single-step generation of any time 1 in the future. To empirically demonstrate that single-step generation can help avoid compounding autoregressive error, we perform an experiment in which we arbitrarily add three intermediate steps between & and t, before finally predicting the observed X,,, (Tab. (9). On real-world patient datasets, we find that adding intermediate steps leads to compounding autoregressive error. There are a few possible explanations. The patient datasets have irregular and large time intervals (e.g.. hours, days, weeks, months, years). Because MIMIC-IV and eICU capture patients in the intensive care unit, these patients' lab test measurements (e.g., white blood cell count, glucose) can change rapidly, Arbitrarily adding intermediate steps may introduce noise that compounds and degrades predictions at ty.
5.3 R3: GENERALIZATION TO NEW PATIENT TRAJECTORIES VIA CONDITIONAL GENERATION
We assess CLEF'S generalizability to new patient sequences. We create challenging data splits where the test sets have minimal similarity to the training data (Sec. C.2). CLEF models exhibit
stronger generalization than non-CLEF models on both eICU and MIMIC-IV (Fig. I1 12 Tab. 6). For immediate and delayed sequence editing on eICU, Cr.F--transoormrr and CLEF-XLSTM maintain stable and strong performance even as train/test divergence increases. In contrast, their non-CLEB counterparts degrade significantly. Although baseline MOMENT models show relatively stable performance across train/test splits in delayed sequence editing, they generalize poorly compared to CLEF-MOMENT models. Despite similar performance between xLSTM and CLEF-XLSTM in delayed sequence editing on both patient datasets (Fig. Ep). CLEF-XLSTM demonstrates superior generalizability, highlighting the effectiveness of CLEF in adapting to unseen data distributions.
5.4 R4: COUNTERFACTUAL OUTCOMES ESTIMATION
Following the setup of established benchmarks (Bica al. 2020 Melnychuk et al., 2022) (Sec. D.4 we evaluate CLEF on counterfactual outcomes estimation of synthetic tumor growth and semi- synthetic ICU (Fig. 16 trajectories.
On the tumor growth and ICU trajectories, for which we have ground truth counterfactual sequences, CLEF consistently performs better or competitively against non- non-CLEF models in 7-step ahead prediction (Fig. 13 16). With low time- varying confounding (7 < 3). CLEF-CT with CDC loss and CLEF-CRN with GR loss perform comparably to or better than their non-CL.F counterparts. When vare-narying confounding is high (7 2 3), CLEF-CT with CDC loss and CLEFF-RN with GR loss outperform their non-CLEF counterparts. For all levels of confounding bias, CLEF-CRN with CDC loss outperforms their non-CLEF counterparts. Notably, CLEF-CT and CLEF-CRN without any balancing loss (i.e., neither GR nor CDC; violet-red) are the best performing CT/CRN models. While studies have shown trade-off between prediction accuracy and balanced representations (Huang ctt al. 2024 Wang et al., 2025), this finding is consistent with improved balanced representations and empirically supports Assumption 3.4 In other words, CLEF'S strong performance without
8

---

Published as a conference paper at ICLR 2026
any balancing loss suggests that the remporal concepts learn balanced representations that not predictive of the assigned treatment and approximate the conditional mean function (Eq. Sec. 3.3)
Beyond predictive accuracy met- rics, we additionally evaluate the treatment predictabiliity of CLEF and non-CLEF's learned repre- sentations (Tab. 12). Con- cretely, we compute the binary cross entropy (BCE) loss for predicting the treatment from the learned representations of CLEFF and non-CI.FF models. Be- cause "balanced" representations should be treatment-invariint, higher BCE loss is better. For all three datasets of tumor growth and ICU trajectories, we find that CLEF models have compa- rable or higher (i.e., better) BCE loss in predicting the treatment compared to non-CLEF models, These results suggest that CLEF models learn representations that are treatment invariant.
Causal transtormar
b Counterfactul Recurrntt Network
1.3-
1.2-
30SE
4-
1.0
3-
08-
Amount Timevarynn Confounding
Amount Timeroning Confounding
Rasellee w0R Risce fos AL
C. C.ET
CLFFWOR
Figure 5: Counterfactual 7-step ahead prediction on tumor growth (single-sliding treatment) with different amounts time-varying con- founding. Models are trained on 5 seeds; error bars show 95% CI.
5.5 R5: ZERO-SHOT CONDITIONAL GENERATION OF COUNTERFACTUAL TRAJECTORIES
We evaluate CLEF zero- shot conditional generation of counterfactual cellular trajecto-
Transfrrrormmeer vs. Transformer
CLEF-XLBTM vs. XUSTM
CLEF-MOMENT vs. MOMENT
25-
ries (Fig- 6 17). With the WOT- CF dataset, models are trained on the "original" cellular trajec- tories and evaluated on the "coun- terfactual" cellular trajectories in a zero-shot setting.
20-
1.00
1.0-
0.5
0.25
Figure 6: Zero-shot conditional generation of counterfactual cellular trajectories via delayed sequence editing. Shown are MAE of predictions per time step for counterfactual sequences of length 23 (most common sequence length) starting time step 10 (carliest divergence time step of a counterfactual). Models are trained on 3 seeds; error bars show 95% CL.
By learning temporal concepts, CLEF consistently oulperforms non-CI.F in immediate and de- layed sequence editing (Fig. [7). We examine the predictions for trajectories of length 23, the most common sequence length in WOT-CF (Fig. 6). Since t, = 10 is the earliest divergence time step, input the first nine time steps X,0:9, the counterfactual condition, and t, [10, 23]. Comparing the generated and ground truth counterfactual sequences, we find that CLEF outperforms non- CLn-CLEF models after time step 10, which is when the trajectories begin diverge.
5.6 R6: CASE STUDIES USING REAL-WORLD PATIENT DATASETS
Unlike conditional generation methods that rely on condition tokens to guide generation (Narasimhan et al. 2024 Jing et al., 2024 Zhang et al. 2023a). CLEF allows direct edits to the generated outputs via temporal concept intervention produce counterfactual sequences (Sec. C.2.4). Instead relying on predefined conditions, CLEF can precisely modify the values of specific lab tests to explore their longitudinal effects. We conduct case studies on two independent cohorts of patients with type diabetes mellitus (TID) (Quattrin al. 2023) (Sec. C.2.3)
Setup (Sec. C.2.4.. For each patient, we intervene on the temporal concepts corresponding to specific lab tests to simulate the "reversal" or "worsening" of symptoms, thereby generating "healthier" or "more severe" trajectories. CLEF-generated counterfactual patients are compared to the observed sequences from matched healthy individuals, other healthy individuals, and other TID patients. We

---

Published as a conference paper at ICLR 2026
hypothesize that clinically meaningful edits produce "healthier" (i.e., more similar to healthy patients) or "sicker" (i.e., more similar to other TID patients) trajectories.
Results. First, we modify CLEF'S ' concepts to halve glucose levels, aligning them closer to normal physiological ranges. Such counterfactual trajectories exhibit higher Rº similarity with healthy individuals compared to other T1D patients (Fig. 7p), suggesting that CLEF effectively generates traeectoiiss indicative of a healthier state. Next, we simulate a worsening condition by doubling glucose levels. These CLEF-generated trajectories show higher R²² similarity with other TID patients than with healthy individuals (Fig- 7 as expected based on clinical evidence. Further, unlike CLEP, SimpleLinear (ablation) cannot generate trajectories that resemble the trajectories of healthier or sicker patients, depending on the intervention (Tab. L3
Beyond examining the direct effects of the interventions on CLEF'S concepts, we examine the indirect changes in CLEF- generated patients' lab values re- sulting from glucose modifica- tions. In both eICU-TID and MIMIC-IV-TID cohorts, lower- ing glucose also leads to a reduc- tion in white blood cell (WBC) count (Fig. 7b. a This aligns with clinical knowledge, as TID is an autoimmune disorder where immune activity, including WBC levels. plays critical role (Quat- trin et al. 2023). When we inter- vene on CLEF to reduce WBC levels instead of glucose, glu- cose levels also decrease across both cohorts (Fig. [8p.c), rein- forcing the interdependence of these physiological markers. Fi- nally, we show that modifying multiple lab tests simultaneously
Decrease glacose lovels by 0.5x (nwwrse symita))
Diforemccs between observed CL parients duc to decreesed glucose levds
3.-
VINIINNTTT UCU-TID
WBC 1000
BUM-
62-
My-
Increase glucase lovdlo 2x (worse symptom)
Lab
KDW
MC-45
Caldam
Poteles a 1000
03
0.7
05
10
14
potee Tee pilents
Figure 7: CLEF-generated counterfactual patients via intervention on temporal concepts. We intervene on CLEF to (a) halve double TID patient's glucose levels infer "healthier" "sicker" counterfactual patient. Higher R indicates that patient pairs are more similar. (b) Ob- served and CLEF patients from the eICU-TID cohort are compared to quantify the differences between their lab test trajectories as a result of the intervention to halve TID patients' glucose levels. Delta (x*hma/// indicates no di difference between the observed and CL.EF patients. Error hars show 95% CL. Refer to Tables 13 14 for ablation analyses.
can produce compounding effects. When we intervene on CLEF to reduce both glucose and WBC levels, the resulting CLEr-generated patients resemble healthy individuals even more closely than other TID patients, suggesting that CL.EF can capture the joint impact of multiple simultaneous edits on patient (Fig. [8))). Altogether, we demonstrate that direct temporal concept edits via CLEF enables actionable interpretability and tangible in silico hypothesis exploration.
CONCLUSION
CLEF is a flexible approach that learns temporal concepts for conditional sequence generation and potential outcomes prediction under specific conditions. Extensive experiments show that temporal concepts introduced in CLEF contribute to overall model performance. Controlling for model, time, and space complexity, temporal concepts generally yield faster convergence (Sec. D.5 Across 8 biological, medical, and financial datasets, CLEF excels in the conditional generation of Tongitudinal sequences, making precise local edits while preserving global integrity. CLEF also has stronger generalizability to new sequences than nOn-CLEF counterparts. Under counterfactual inference assumptions, CLEF accurately estimates counterfactual outcomes over time, outperforming baselines in settings with high time-varying confounding bias. CL.EF even outperforms SOTA conditional generation models in zero-shot counterfactual generation. Further, we show that interventions directly CLEF'S temporal concepts can generate counterfactual patients such that their trajectories are shifted toward healthier outcomes. This capability has the potential to help discover clinical interventions that could alleviate patient's 's symptoms. Limitations and future directions are discussed in Sec. We believe that CLEF'S 's controllable sequence editing can help realize the promise of virtual cells and patients to facilitate large-scale in silico experimentation of molecules, cells, and tissues.
10

---

Published as a conference paper at ICLR 2026
ETHICS STATEMENT
By introducing flexible and interpretable approach to conditional sequence generation, CLEF bridges the gap between language model-style conditional generation and structured, time-sensitive sequence editing, with implications for decision support in medical and scientific applications. Like all generative Al models, CLEF (and its derivatives) should be used solely for the benefit of society. In this study, we show that CLEF can generate alternative cellular trajectories and simulate the reversal or progression of symptoms to model healthier or sicker patient outcomes. However, this work (and any derivatives) should never be used to induce harmful cellular states (e.g., activating transcription factors to drive a cell toward a pathological state) or negatively impact patient care (c.g., neglecting necessary clinical interventions or recommending harmful treatments). Our goal is to help researchers understand the underlying mechanisms of disease to improve public health. Any misuse this work poses risks to patient well-being. Therefore, the ability to intervene on CLEF'S generated outputs should be leveraged to assess the model's robustness and correctness for ethical and responsible use.
REPRODUCIBILITY STATEMENT
We provide code and instructions to implement CLEF, baselines, and ablations and to reproduce the experiments in this paper: https: //tith/gtttt www.mmmssaaaaaaeeeeeeaeeeeeeaeee haaaasaaaavarraaaaaa
In the Appendix. Sec. Sec. provides details about data construction, data preparation, and experimental setup; and Sec. [ describes the implementation and training of all models. We do not share data or model weights that may contain sensitive patient information.
ACKNOWLFDGMENTS
M.L. and M.Z. are supported by the Berkowitz Family Living Laboratory Harvard Medical School and the Clalit Research Institute. Y.E. is supported by grant T32 HG002295 from the National Human Genome Research Institute and the NDSEG Fellowship. We gratefully acknowledge the support of NIH R01-HD1087994, NSF CAREER 2339524, US DoD FA8702-15-D-0001, ARPA-H BDF program, awards from Chan Zuckerberg Initiative, Bill Melinda Gates Foundation INV- 079038, Amazon Faculty Research, Google Research Scholar Program, AstraZeneca Research, Roche Alliance with Distinguished Scientists, Sanofi iDEA-iTECH. Pfizer Research, John and Virginia Kaneb Fellowship at Harvard Medical School, Biswas Computational Biology Initiative in partnership with the Milken Institute, Harvard Medical School Dean's Innovation Fund for the Use of Artificial Intelligence, Harvard Data Science Initiative, and Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funders. We thank Ruth Johnson, Boyang Fu, and Grey Kuling for their helpful feedback.
REFERENCES
Constantin Ahlmann- Eltze, Wolfgang Huber, and Simon Anders. Deep-learning-basee gene per- turbation effect prediction does not yet outperform simple lincar baselines. Nature Methods, 2025.
Jong-Hoon Ahn and Akshay Vashist. Gaussian mixture counterfactual generator. International Conference on Learning Representations, 2025.
Akash Awasthi, Anh Mai Vu, Ngan Le, Zhigang Deng, Supratik Maulik, Rishi Agrawal, Carol Wu, and Hien Van Nguyen. Modeling radiologists' cognitive processes using digital gaze twin to enhance radiology training. Scientific Reports, 15(I):13685, 2025.
Yifan Bao, Yihao Ang, Qiang Huang, Anthony KH Tung, and Zhiyong Huang. Towards controllable time series generation. wwXiv:2403.03690 2024.
Maximilian Beck, Korbinian Pöppel. Markus Spanring, Andreas Auer, Oleksandra Prudnikova, Michael Kopp. Günter Klambauer, Johannes Brandstetter, and Sepp Hochreiter. xLSTM:: Extended long short-term memory. Advances in Neural Information Processing Systems, 2024.

---

Published as a conference paper at ICLR 2026
Amrita Bhattacharjee, Raha Moraffah, Joshua Garland, and Huan Liu. Zero-shot LLM-guided coun- terfactual generation: A case study on NLP model evaluation. In IEEE International Conference on Big Data, pp. 1243-1248. IEEE, 2024.
Ioana Bica, Ahmed M Alaa, James Jordon, and Mihaela van der Schaar. Estimating counterfactual treatment outcomes over time through adversarially balanced representations. International Conference on Learning Representations, 2020.
Charlotte Bunne, Yusuf Roohani, Yanay Rosen, Ankit Gupta, Xikun Zhang, Marcel Roed, Theo Alexandrov, Mohammed AlQuraishi, Patricia Brennan, Daniel B Burkhardt, et al. How to build the virtual cell with artificial intelligence: Priorities and opportunities. Cell, 187(25):7045-7063 2024.
Defu Cao, Furong Jia, Serean O Arik, Tomas Pfister, Yixiang Zheng, Wen Ye, and Yan Liu. TEMPO: Prompt-based generative pre-trained transformer for time series forecasting. International Confer- ence on Learning Reprexentations, 2024.
Ivi Chatzi, Nina Corvelo Benz, Eleni Straitouri, Stratis Tsirtsis, and Manuel Gomez-Rodriguez. Counterfactual token generation in large language models. CLeaR, 2025.
Lenaic Chizat, Gabriel Peyré, Bernhard Schmitzer, and Srançois-Xavies Vialard. Scaling algorithms for unbalanced transport problems. Mathematics of Computation, 2018.
Justas Dauparas, Ivan Anishchenko, Nathaniel Bennett, Hua Bai, Robert J Ragotte, Lukas F Milles, Basile IM Wicky, Alexis Courbet, Rob de Haas, Neville Bethel, et al. Robust deep learning- protein sequence design using ProteinMPNN. Science, 378(6615))) 15):6995): 2022.
Giovanni De Felice, Arianna Casanova Flores, Francesco De Santis, Silvia Santini, Johannes Schnei- der, Pietro Barbiero, and Alberto Termine. Causally reliable concept bottleneck models. Advances in Neural Information Processing Systems, 2025.
Gabriele Dominici, Pietro Barbiero, Francesco Giannini, Martin Gjoreski, Giuseppe Marra, and Marc Langheinrich. Counterfactual concept bottleneck models. International Conference Learning Representations, 2025a.
Gabriele Dominici, Pietro Barbiero, Mateo Espinosa Zarlenga, Alberto Termine, Martin Gjoreski, Giuseppe Marra, and Marc Langheinrich. Causal concept graph models: Beyond causal opacity in deep learning. International Conference on Learning Representations, 2025b.
Yasha Ektefaie, Andrew Shen, Daria Bykova, Maximillian G Marin, Marinka Zitnik, and Maha Farhat. Evaluating generalizability of artificial intelligence models for molecular datasets. Nature Machine Intelligence, 6(12):1512-1524 2024.
Mouad Bouchattaoui, Myriam Tami, Benoit Lepetit, and Paul-Henry Cournède. Causal contrastive learning for counterfactual regression over time. Advances in Neural Information Processing Systems, 37:1333- 2024.
Shanghua Gao, Zhijie Lin, Xingyu Xie, Pan Zhou, Ming-Ming Cheng, and Shuicheng Yan. EditAny- thing: Empowering unparalleled flexibility in image editing and generation. Proceedings of the 31st ACM International Conference on Multimedia, Demo track, 2023.
Changran Geng, Harald Paganetti, and Clemens Grassberger. Prediction of treatment response for combined chemo-and radiation therapy for non-small cell lung cancer patients using a bio- mathematical model. Scientific Reports, 7(1):13542, 2017.
Ary L Goldberger, Luis AN Amaral, Leon Glass, Jeffrey M Hausdorff, Plamen Ch Ivanov, Roger G Mark. Joseph E Mietus, George B Moody, Chung Kang Peng, and H Eugene Stanley. PhysioBank, PoysioToolkit, and PhysioNet: Components of new research resource for complex physiologic signals. Circulation, 101 (213)))2155e220) 2000.
Haisong Gong, Qiang Liu, Shu Wu, and Liang Wang. Text-guided molecule generation with diffusion language model. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pp. 109-117, 2024.
12

---

Published as a conference paper at ICLR 2026
Mononito Goswami, Konrad Szafer, Arjun Choudhry, Yifu Cai, Shuo Li, and Artur Dubrawski. MOMENT: A family of open time-series foundation models. International Conference on Machine Learning, 2024.
Jihao Gu, Yingyao Wang, Meng Cao, Pi Bu, Jun Song, Yancheng He, Shilong Li, and Bo Zheng. Token preference optimization with self-calibrated visual-anchored rewards for hallucination mitigation. Findings of the Association for Computational Linguistics: EMNLP, 2025.
Jianing Hao, Qing Shi, Yilin Ye, and Wei Zeng. Timetuner: Diagnosing time representations for time-series forecasting with counterfactual explanations. IEEE Transactions on Visualization and Computer Graphics, 10(1):1 2023.
Konstantin Hess, Valentyn Melnychuk, Dennis Frauen, and Stefan Feuerriegel. Bayesian neural controlled differential equations for treatment effect estimation. International Conference on Learning Representations, 2024.
Samuel Holt, Tennison Liu, and Mihaela van der Schaar. Automatically learning hybrid digital twins of dynamical systems. Advances in Neural Information Processing Systems. 37:72170-722188 2024.
Qiang Huang, Chuizheng Meng, Defu Cao, Biwei Huang, Yi Chang, and Yan Liu. An empirical examination of balancing strategy for counterfactual estimation on time series. International Conference on Machine Learning, 2024.
Kosuke Imai and David A Van Dyk. Causal inference with general treatment regimes: Generalizing the propensity score. Journal of the American Statistical Association, 99(467):854-666 2004.
Aya Abdelsalam Ismail, Julius Adebayo, Hector Corrada Bravo, Stephen Ra, and Kyunghyun Cho. Concept bottleneck generative models. International Conference on Learning Representations, 2024.
Moksh Jain, Sharath Chandra Raparthy, Alex Hernández-Garciaa Jarrid Rector-Brooks Yoshua Bengio, Santiago Miret, and Emmanuel Bengio. Multi-objective GFlowNets. In International Conference on Machine Learning, pp. 14631-14653 PMLR, 2023.
Song Jiang, Zijie Huang, Xiao Luo, and Yizhou Sun. Cf-gode: Continuous-ting causal inference for multi-agent dynamical systems. In Proceedings of the 29th ACM SIGKDD Conference Knowledge Discovery and Data Mining, pp. 997-1009, 2023.
Baoyu Jing, Shuqi Gu, Tianyu Chen, Zhiyu Yang, Dongsheng Li, Jingrui He, and Kan Ren. Towards editing time series. Advances in Neural Information Processing Systems, 2024.
Alistair Johnson, Lucas Bulgarelli, Tom Pollard, Brian Gow, Benjamin Moody, Steven Horng, Leo Celi, and Roger Mark. MIMIC-IV (version 3.1). PhysioNet, 2024a.
Alistair EW Johnson, Tom J Pollard, Lu Shen, Li-wei H Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. Mimic-iii, freely accessible critical care database. Scientific Data, 3(1):1-9, 2016.
Alistair EW Johnson, Lucas Bulgarelli, Lu Shen, Alvin Gayles, Ayad Shammout, Steven Horng, Tom J Pollard, Sicheng Hao, Benjamin Moody, Brian Gow, al. MIMIC-IV, freely accessible electronic health record dataset. Scientific Data, 10(1):1, 2023.
Ruth Johnson, Uri Gottlieb, Galit Shaham, Lihi Eisen, Jacob Waxman, Stav Devons-Sherro Curtis R Ginder, Peter Hong. Raheel Sayeed, Ben Y Reis, et al. Unified clinical vocabulary embeddings for advancing precision. medRxiv, pp. 2024-12, 2024b.
Krzysztof Kacprzyk, Tennison Liu, and Mihaela van der Schaar. Towards transparent time series forecasting. International Conference on Learning Representations, 2024.
Pang Wei Koh, Thao Nguyen, Yew Siang Tang. Stephen Mussmann, Emma Pierson, Been Kim, and Percy Liang. Concept bottleneck models. In International Conference on Machine Learning, pp. $338-5345 PMLR, 2020.
13

---

Published as a conference paper at ICLR 2026
Boris P Kovatchev, Patricio Colmegna, Jacopo Pavan, Jenny L Diaz Castañeda, Maria F Villa- Vila-Tamayo, Chaitanya LK Koravi, Giulio Santini, Carlene Alix, Meaghan Stumpf. and Sue A Brown. Human- machine co-adaptation automated insulin delivery: randomised clinical trial using digital twin technology. npj Digital Medicine, 8(1):1-7, 2025.
Keying Kuang, Frances Dean, Jack B Jedlicki, David Ouyang, Anthony Philippakis, David Sontag, and Ahmed M Alaa. Med-real2sim: Non-invasive medical digital twins using physics-informed self-supervised learning. Advances in Neural Information Processing Systems, 37:5757-5788, 2024.
Sonia Laguna, Ričards Marcinkevičs, Moritz Vandenhirtz, and Julia E Vogt. Beyond concept bottleneck models: How to make black boxes intervenable? Advances in Neural Information Processing Systems, 2024.
Songning Lai, Lijie Hu, Junxiao Wang, Laure Berti-Equille, and Di Wang. Faithful vision- Laaiuuee in- terpretation via concept bottleneck models. International Conference on Learning Representations, 2024.
LCM. Loic Barrault, Paul-Ambroise Duquenne, Maha Elbayad, Artyom Kozhevnikov, Belen Alas- truey, Pierre Andrews, Mariano Coria, Guillaume Couairon, Marta R Costa-jussă, et al. Large concept models: Language modeling in sentence representation space. arXiv:2412.0882, 2024.
Rui Li. Stephanie Hu, Mingyu Lu, Yuria Utsumi, Prithwish Chakraborty, Daby M Sow, Piyush Madan, Jun Li, Mohamed Ghalwash, Zach Shahn, et al. G-net: a recurrent network approach to g-computation for counterfactual prediction under a dynamic treatment regime. In Machine Learning for Health, pp. 282-299 282-299. PMLR, 2021.
Xinxiu Li, Joseph Loscalzo, AKM Firoj Mahmud, Dina Mansour Aly, Andrey Rzhetsky, Marinka Zitnik, and Mikael Benson. Digital twins as global learning health and disease models for preventive and personalized medicine. Genome Medicine, 17(1):11, 2025.
Bryan Lim. Forecasting treatment responses over time using recurrent marginal structural networks. Advances in Neural Information Processing Systems, 31, 2018
Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science, 37966637)
2023.
Mingzhou Liu, Ching- Wen Lee, Xinwei Sun, Xueqing Yu, Yu Qiao, and Yizhou Wang. Learning causal alignment for reliable disease diagnosis. International Conference on Learning Representa- tions, 2025.
Mohammad Lotfollahi, Anna Klimovskaia Susmelj, Carlo De Donno, Leon Hetzel, Yuge Ji, Ignacio Ibarra, Sanjay R Srivatsan, Mohsen Naghipourfar, Riza M Daza, Beth Martin, et al. Predicting cellular responses to complex perturbations in high-throughput screens. Molecular Systems Biology, 19(6)ee1517,, 2023.
Christos Louizos, Uri Shalit, Joris M Mooij. David Sontag, Richard Zemel, and Max Welling, Causal effect inference with deep sicent-varibble models. Advances in Neural Information Processing Systems, 30, 2017.
Helmut Lütkepohl. New introduction to multiple time series analysis. Springers Science & Business Media, 2005.
Pouya M Ghari, Alex Tseng, Gokcen Eraslan, Romain Lopez, Tommaso Biancalani, Gabriele Scalia, and Ehsan Hajiramezanali. GFlownet assisted biological sequence editing. Advances in Neural Information Processing Systems, 177:106841110666 2024.
Nikita Makarov, Maria Bordukova, Papichaya Quengdaeng, Daniel Garger, Raul Rodriguez Rodriguez-Estebaan Fabian Schmich, and Michael P Menden. Large language models forecast patient health trajectories enabling digital twins. npj Digital Medicine, 8(1):588, 2025.
Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos. M5 accuracy competition: Results, findings, and conclusions. International Journal of Forecasting, 1344111346-136, 2022.
14

---

Published as a conference paper at ICLR 2026
Valentyn Melnychuk, Dennis Frauen, and Stefan Feuerriegel. Causal transformer for estimating counterfactual outcomes. In International Conference on Machine Learning, pp. 15293-15322 PMLR. 2022.
Sai Shankar Narasimhan, Shubhankar Agarwal, Oguzhan Akcin, Sujay Sanghavi, and Sandeep Chinchali. Time Weaver: A conditional time series generation model. International Conference on Machine Learning, 2024.
Jerzy Neyman. On the application of probability theory agricultural experiments. Annals of Agricultural Sciences, pp. 1-51, 1923.
Mengjia Niu, Hao Li, Jie Shi, Hamed Haddadi, and Fan Mo. Mitigating hallucinations in large language models via wwff-rfiinementeenanneeee knowledge retrieval. In The Second Workshop on Generative Information Retrieval, 2024.
International Classification of Disease (ICD). who.
classi Fic.ationooooo-oooooo seases
Junhyuk Oh, Greg Farquhar, lurii Kemaev, Dan Calian, Matteo Hessel, Luisa Zintgraf, Satinder Singh, Hado van Hasselt, and David Silver. Discovering state-of-the-arr reinforcement learning algorithms. Nature, pp. 1-2, 2025.
Judea Pearl. Causal inference in statistics: An overview. Statistics Surveys, 3, 2009.
PhysioNet. credenttaleee health data license version 1.5.0. URL http//oonteett/mmmmlcl////eeerrr
Tom Pollard, Alistair EW Johnson, Jesse Raffa, Leo A Celi, Roger G Mark, and Omar Badawi. The eICU collaborative research database, a freely available multi-center database for critical care research. Scientific Data, 5(1):1-13, 2018.
Shuang Qian, Devran Ugurlu, Elliot Fairweather, Laura Dal Toso, Yu Deng, Marina Strocchi. Ludovica Cicci, Richard E Jones, Hassan Zaidi, Sanjay Prasad, et al. Developing cardiac digital twin populations powered by machine learning provides electrophysiologica insights conduction and repolarization. Nature Cardiovascular Research, pp. 1-13, 2025.
Teresa Quattrin, Lucy D Mastrandrea, and Lucy SK Walker. Type diabetes. The Lancet, 401 (10394):2149-211)) 2023.
Shauli Ravfogel, Anej Svete, Vésteinn Snæebjarnarson, and Ryan Cotterell. Gumbel counterfactual generation from language models. International Conference on Learning Representations, 2025.
Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, Eric Mintun, Junting Pan, Kalyan Vasudev Alwala, Nicolas Carion, Chao- Yuan Wu, Ross Girshick, Piotr Dollár, and Christoph Feichten- hofer. SAM 2: Segment anything in images and videos. International Conference on Learning Representations, 2024.
James Robins and Miguel Hernan. Estimation of the causal effects of time-varying exposures. Chapman & Hall/CRC Handbooks of Modern Statistical Methods, pp. 553-599, 2008
James M Robins, Miguel Angel Hernan, and Babette Brumback. Marginal structural models and causal inference epidemiology. Epidemiology. 11(5):550-560, 2000.
Donald B Rubin. Bayesian inference for causal effects: The role of randomization. The Annals of statistics, pp. 34-58, 1978.
Geoffrey Schiebinger, Jian Shu, Marcin Tabaka, Brian Cleary, Vidya Subramanian, Aryeh Solomon, Joshua Gould, Siyan Liu, Stacie Lin, Peter Berube, Lia Lee, Jenny Chen, Justin Brumbaugh, Philippe Rigollet, Konrad Hochedlinger, Rudolf Jaenisch, Aviv Regev, and Eric S Lander. Optimal- transport analysis of single-cell gene expression identifies developmental trajectories in reprogram- ming. Cell, 1 176(4):928 942.e22, 2019.
15

---

Published as a conference paper at ICLR 2026
Sungbin Shin, Yohan Jo, Sungsoo Ahn, and Namhoon Lee. A closer look at the intervention procedure of concept bottleneck models. In International Conference on Machine Learning, pp. 11504-31520 PMLR. 2023.
Samuel Stanton, Wesley Maddox, Nate Gruver, Phillip Maffettone, Emily Delaney, Peyton Greenside, and Andrew Gordon Wilson, Accelerating bayesian optimization for biological sequence design with denoising autoencoders. In International Conference on Machine Learning, pp. (0459-20478 PMLR, 2022.
William Toner and Luke Darlow. An analysis of linear time series forecasting models. International Conference Machine Learning, 2024.
Dani Valevski, Yaniv Leviathan, Moab Arar, and Shlomi Fruchter. game engines. International Conference on Learning Representations, 2025.
Diffusion models are real-time
Angela van Sprang, Erman Acar, and Willem Zuidema. Enforcing interpretability in time series transformers: A concept bottleneck framework. arXiv:2410.06070 2024.
Haotian Wang, Haoxuan Li, Hao Zou, Haoang Chi, Long Lan, Wanrong Huang, and Wenjing Yang. Effective and efficient time-varying counterfactual prediction with state-space models. International Conference on Learning Representations, 2025.
Shirly Wang, Matthew BA McDermott, Geeticka Chauhan, Marzyeh Ghassemi, Michael C Hughes, and Tristan Naumann. MIMIC-extract: A data extraction, preprocessing, and representation pipeline for MIMIC-III. In Proceedings of the ACM conference on Health, Inference, and Learning, pp. 222-222222222222222222 235, 2020.
Zhendong Wang, Ioanna Miliou, Isak Samsten, and Panagiotis Papapetrou. Counterfactual explana- tions for time series forecasting. In 2023 IEEE International Conference on Data Mining (ICDM), pp. 1391-1396. IEEE, 2023.
A Waswani, N Shazeer, N Parmar, Uszkoreit, L Jones, A Gomez, L Kaiser, and I Polosukhin. Attention is all you need. Advances in Neural Information Processing Systems, 2017.
Alexander Wolf, Philipp Angerer, and Fabian Theis. SCANPY: large-scale single-cell gene
expression data analysis. Genome Biology, 19(1), 2018.
Shenghao Wu, Wenbin Zhou, Minshuo Chen, and Shixiang Zhu. Counterfactual generative models for time-varying treatments. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 3402 10002-3413,2220 2024.
Yulun Wu, Louie McConnell, and Claudia Iriondo. Counterfactual generative modeling with varia- tional causal inference. International Conference on Learning Representations, 2025.
Jingquan Yan and Hao Wang. Self-interpretable time series prediction with counterfactual explana- tions. In International Conference on Machine Learning, pp. 391 10-39125. PMLR, 2023.
Zhichao Yang. Avijit Mitra, Weisong Liu, Dan Berlowitz, and Hong Yu. TransformEHR: transformer- based encoder-decoder generative model enhance prediction of disease outcomes using electronic health records. Nature Communications, 14(1):7857, 2023.
Andrew Ying, Zhichen Zhao, and Ronghui Xu. Incremental causal effect for time to treatment initialization. International Conference on Learning Representations, 2025.
Jinsung Yoon, James Jordon, and Mihaela Van Der Schaar. Ganite: Estimation of individualized treatment effects using generative adversarial nets. International Conference on Learning Repre- sentations, 2018
Mateo Espinosa Zarlenga, Gabriele Dominiei, Pietro Barbiero, Zohreh Shams, and Mateja Jam- nik. Avoiding leakage poisoning: Concept interventions under distribution shifts. International Conference Machine Learning, 2025.
Hanqing Zhang, Haolin Song, Shaoyu Li, Ming Zhou, and Dawei Song. A survey of controllable text generation using transformer-base pre-trained language models. ACM Computing Surveys, 56(3): 1-37, 2023a.
16

---

Published as a conference paper at ICLR 2026
Jiaqi Zhang, Joel Jennings, Agrin Hilmkil, Nick Pawlowski, Cheng Zhang, and Chao Ma. Towards causal foundation model: on duality between optimal balancing and attention. International Conference Machine Learning, 2024a.
Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control text-to-image diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3836-3847, 2023b.
Zaixi Zhang, Wan Xiang Shen, Qi Liu, and Marinka Zitnik. Efficient generation of protein pockets with PocketGen. Nature Machine Intelligence, pp. 1-14, 2024b.
Yuqi Zhou, Lin Lu, Ryan Sun, Pan Zhou, and Lichao Sun. Virtual context enhancing jailbreak attacks with special token injection. In Findings of the Association for Computational Linguistics: EMNLP, pp. 11843-11855 2024.
17

---

Published as a conference paper at ICLR 2026
APPENDIX
A EXTENDED RELATED WORK
A.1 LEVERAGING TRAJECTORIES AS INDUCTIVE BIASES
Understanding sequential data as trajectories (c.g., increasing, decreasing, constant) is more natural for human interpretation than individual values (Kacprzyk et al. 2024). Many models temporal data extract dynamic motifs as inductive biases to improve their interpretability (Kacprzyk et al.) 2024| Goswami al. 2024 Cao al., 2024). Such temporal patterns can be used for prompting large pretrained models to perform time series forecasting (Cao et al. 2024), suggesting that trajectories can capture more universal and transferrable insights about the temporal dynamics in time series data. Trajectories have yet to be adopted for conditional or counterfactual sequence generation.
Relevance to CLEF. Temporal concepts с (Def. 3.2) represent trajectories (or rates of change). The concept decoder G leverages temporal concepts e and covariates at the latest time step x,,: to generate the remainder of the sequence (Sec. 3.2). To understand how temporal concepts enable CLEF models to preserve global consistency: One can think of the latest covariates x,,, as set of reference values for each covariate, and these values are modified based on the desired forecast time I and condition token 2,. Such modifications are captured by temporal concepts c, which represent the rates of change (or trajectories) for each covariate from time steps t, to ty.
A.2 BUILDING DIGITAL TWINS
Building virtual representations of cells and patients (commonly referred to as virtual cells, virtual patients, or digital twins) has the potential to facilitate preventative and personalized medicine (Li et al., 2025 Bunne et al.) (024). Medical digital twins (e.g., an artificial lung or pancreas, automated
insulin delivery systems, and cardiac twins) have demonstrated clinical utility (Li et al. 2025
Kovatchev et al. [025 Qian et al. (025). There is wide range of methods for building digital twins, such as mechanistic models (c.g., thysics-informer superviid learning approach (Kuang et al. 2024)), neural models (c.g.. finetuned large language and vision models (Makarov al.
2025
Awasthi et al., 2025)), ), and hybrid models (e.g., framework with mechanistic and neural
components (Holt al. ).
Relevance to CLEF. CLEF is a machine learning-bast based neural model. It is a flexible architecture to enable conditional sequence generation (Def. B.1, Problem Statement 3.1 as well as counterfactual prediction (Sec. 3.3 Sec. D.4 of continuous multivariate sequences.
A.3 ADDITIONAL DETAILS
Controllable text generation. Controllable text generation (CTG), designed specifically to edit natural language sequences, has been extensively studied (Zhang et al.) 2023a). They excel in immediate sequence editing: predicting the next token readout in the sequence under given condition (Niu et al. 2024] Gu et al., 2025; Zhou et al.) 2024 Chatzi et al. 2025 Zhang et al.) [203aa] Bhattacharjee et al. 2024). For example, if asked predict the next word in the sentence "Once upon a time, there lived a boy" under the condition that the genre is horror, a CTG model may respond with "alone" convey vulnerability and loneliness. However, CTG models are unable to perform delayed sequence editing: modifying a trajectory in the far-future. The distinction is important: the focus on when the edit occurs, not necessarily when its effects manifest. Whereas immediate sequence editing applies condition now (e.g., administering insulin today), delayed sequence editing schedules a condition for the future (e.g., starting chemotherapy regimen in six weeks). Existing CTG models cannot effectively utilize the given context to skip ahead to the future; instead, they would need to be run repeatedly to fill in the temporal gap without any guarantee of satisfying the desired condition. As result, CTG models are insufficient for other sequence types (i.e., not natural language) for which both immediate and delayed sequence editing are necessary, such as cell development and patient health trajectories.
Delayed sequence editing vs. long-horizon forecasting, While long-horizon forecasting and delayed sequence editing both predict the sequence or covariates future time I,. delayed sequence editing
18

---

Published as a conference paper at ICLR 2026
does not require autoregressive predictions from i, to t,. which can lead to accumulation of error. Instead, delayed sequence editing allows skipping directly to t, from t2 in a single step.
Intuition for CLEF'S conditional sequence generation architecture. We leverage the state-of- the- art conditional sequence generation setup (Waswani et al., 2017, Narasimhan et al. [024 Jing et al. 2024 Zhang et al. 2023a. Beck et al., 2024| Valevski et al. (025). The sequence encoder extracts features from historical covariates to learn hidden representation that captures relevant information for the generation task. Any encoder (e.g.. pretrained multivariate foundation model) can be used with CLEF. The condition adapter projects the condition token shared latent space with the sequence and time representations. Because condition tokens generated by pretrained foundation model (e.g., ES,2, a protein language model that learns on protein sequences), they capture information that allows CLEF generalize to conditions that have not been observed in the training dataset (e.g.. based on shared evolutionary information between protein sequences). The concept encoder (CLEF only) learns a representation (temporal concepts) that captures information about the condition at the next time step. historical conditions, and historical covariates. The final GELU activation layer transforms its input into values that are 2 O, which are interpreted by the concept decoder as the trajectories (or rates of change) of covariates between time steps i; and Ej. The concept decoder (CLEF only) generates a sequence by applying the learned temporal concepts to the covariates at the last time step f, Element wise multiplication between the learned temporal concepts and the covariates at t2 is a suitable operation because it is less sensitive to covariates with different units of measurements, which are commonly observed in clinical sequences (Sec. C.2 C.4). Further, applying the temporal concepts in single step (via element multiplication) allows users directly intervene the temporal concepts and simulate the effects of the intervention as a counterfactual trajectory (Sec. C.2.4) With these components, CLEF can generate sequences based on high-dimensional sequences at any future time point and condition. Without the sequence encoder, it would be computationally challenging to operate directly on the input historical sequences. Without the condition adapter, CLEF and state-of-the-ee conditional generation models cannot generalize well to unseen conditions in the training dataset. Without the concept encoder or decoder, the model may inaccurately generate sequences (refer Sections 5.1 5.3 and 5.5 for empirical results).
Other usage of counterfactuals. (1) There is extensive work on generating counterfactuals for static data (e.g., single time-step perturbation measured via gene expression profiles or images) (Louizos et al. 2017 Yoon et al. 2018 Lotfollahi et al.) 20233 Wu et al. [024 2025). In this work, we focus on longitudinal trajectories. (2) Counterfactual prediction has been used as an additional task to
improve the predictions" interpretability and accuracy (Yan & Wang 2023 Hao et al. 2023 Wang cct al.
2023 Liu et al. 2025), such as leveraging causal alignment to produce reliable diagnoses (Liu
et al. 2025). While CLEF'S temporal concepts can be intervened upon to interpret model outputs, counterfactual prediction is not an auxiliary task to improve CLEF'S s interpretability and performance.
Excluded baselines for estimating counterfactual outcomes over time. Causal CPC EEl Bouchat taoui ct al. 2024), Mamba-CDDi (Wang et al.) 2025). and GMCG (Ahn & Vashist 2025) can estimate counterfactual outcomes over time, but excluded due unavailable code. While BNCDE (Hess) jet al. 2024) can estimate counterfactual outcomes over time, it is designed to forecast outcomes as well as uncertainty (rather than single-point estimates, which is the focus of Sec. (.33. As such, extending BNCDE (Hess et al., (2024) with CLEF is not directly feasible. CF-GODE (Jing et al. 2023) can estimate continuous- time counterfactual outcomes, but excluded due to unavailable code.
Distinction from biological sequence design. Methods for biological sequence design (Stanton al. [0222] M Ghari al. 2024. Jain al.) (023) are not comparable CLEF because there is no temporal aspect in the data. The "sequence editing" by these models focuses on positional changes, not temporal. These methods are more related to text generation, where time is not requirement.
Distinction from reinforcement learning. In contrast to reinforcement learning approaches (Oh) et al. 2025), CLEF is trained via a fundamentally different objective. The objective reinforcement g is to learn policy (i.e., output the optimal next action given the current/history of states) (Oh) al. 2025). On the other hand, CLEE akin to forward transition model (i.e., predict future state given history of states and actions/conditions). CLEF is complementary to reinforcement learning.
learning
19

---

Published as a conference paper at ICLR 2026
B ASSUMPTIONS FOR CAUSAL IDENTIFICATION
Under the potential outcomes framework (Neyman 1923 Rubin 1978 and its extension to time- varying treatments and outcomes (Robins & Hernan 2008), the potential counterfactual outcomes over time (i.e., 7-step ahead, where т = t, tis potential outcome conditioned on history from Eq. D are identifiable from factual observational data under three standard assumptions: consistency, positivity, and sequential ignorability.
Assumption B.1 (Consistency). Let be the given sequence of treatments for patient, consisting of historical treatments Stott, and next treatment St,- The potential outcome is consistent with the observed (factual) outcome X,,, (s) ',,,
Assumption B.2 (Positivity). There is always non-zero probability of receiving (or not) a treatment
for all the history space over time (Imai & Van Dyk, 2004]: If P(stont foott) ) > O, then <
P(si (.pgtt < 1 for all Staid, This assumption is also referred to as (sequential) overlap (Bica
ct al. 2020 Melnychuk et al., 20222..
Assumption B.3 (Sequential ignorability). The current treatment is independent of the potential outcome, conditioning on the observed history: St, Il x,,, (st, )[ X.,t,tt This implies that there no unobserved confounders that affect both treatment and outcome.
While Assumptions B.2 and B.3 are standard across all methods that estimate treatment effects, they may not always be satisfeed in real-world settings (Robins al.) 2000 Pearl 2009 Ying et al. 2025). Corollary B.4 (G-computation). Assumptions B. / B.3 B.3 provide sufficient identifiability conditions
for Eq. (i.e, with G-computation (Li et al. (222 However, it requires estimating conditional distributions of time-varying covariates (Melnychuk et al.) (222). Since this could be challenging given a finite datasel size and high dimensionality of covariates, we refrain from the explicit usage of G-computation (Melnychuk et al. (2022).
Note that the standard setup for counterfactual prediction assumes fixed time grid and normalized covariates (Bica et al. 2020; Melnychuk et al. 2022). As such, the standardized data preprocessing pipeline entails forward and backward filling for missing values and standard normalization of continuous time-vare varying features (Bica et al. 2020; Melnychuk et al.) (022). With the model
architecture shown in Fig. 2 these preprocessing steps are not necessary, thereby better reflecting real-world data. Still, Assumptions
B.1... B.3 hold for our models depicted in Fig.
C DATA & EXPERIMENTAL SETUP
We provide further details about data construction, data preparation, and experimental setup. Sec- tions
C.I C,2 and Tab. | describe the novel conditional sequence generation benchmark datasets. C.4 discuss the standard synthetic and semi-synthetic benchmark datasets for coun- terfactual outcomes estimation. Each section also contains the corresponding experimental setup. We share code and instructions in our GitHub repository to reproduce the experiments in this paper: https: thub. wwt.coommmmmmmme ha haaaarddrLEE
Sections
Overview of novel datasets. To study cellular development, fibroblast cells derived from mice be artificially reprogrammed into various other cell states in vitro. A cell's state is defined by its gene expression. Throughout reprogramming, a cell activates transcription factor (TF) genes at different time points to change its gene expression, thereby influencing its developmental trajectory. In Fig. 3a, a mouse fibroblast is being reprogrammed over the span of 20 days (DO-D20); color and shape represent cell state. On day if the cell activates the 0box6 TF. the cell is on the path toward becoming an induced pluripotent stem cell (iPSC); whereas if it activates the Neurod4 TF, on the path toward becoming a neuron or astrocyte. The health of a human patient often monitored through lab tests (e.g. blood sodium level, white blood cell count). As shown in Fig. Bp. the history of lab results across multiple patient visits (V1-V9) as well as candidate clinical interventions (c.g., medication) can be used to infer the most likely future trajectory of the patient's 's health.
C.1 CELLULAR DEVELOPMENTAL TRAJECTORIES
Here, we describe the process of (1) simulating single-cell transcriptomic profiles of developmental time courses for individual cells and (2) preparing these trajectories for modeling.
20

---

Published as a conference paper at ICLR 2026
Table I: Dataset statistics for conditional sequence generation benchmarks. We construct three core datasets for benchmarking conditional sequence generation: WOT (cellular developmental trajectories). eICU (patient lab tests), and MIMIC-IV (patient lab tests). We also construct a paired counterfactual cellular trajectories dataset, WOT-CF, N is the number of sequences (i.c., cellular developmental trajectories, patient lab test trajectorics), V is the number of measured variables (i.c., gene expression, lab test), and L is the length of the sequences.
Dataset
N
V
Mean L
Max I.
WOT
3,000
1,480
27.03 +: 6.04
37
WOT-CF
2,560
1,480
= 27.31
37
eICU
108, 346
17
20.27 = 25.23
858
MIMIC-IV
310
16
15.56 24.43
949
C.I.1 SIMULATING TRAJECTORIES
Cellular reprogramming experiments help elucidate cellular development (Schiebinger et al. 2019). In these let-lab experiments, cells are manipulated and allowed to progress for specific period of time before they undergo RNA sequencing (RNA-seq), and we analyze the resulting RNA-seq data to observe their new cellular profiles (Schiebinger et al. 2019). RNA-seq is a destructive process for the cell, meaning that the same cell cannot be sequenced at two different time points. Computational models are thus necessary to infer the trajectory of a cell.
Waddington-OT dataset and model. Waddington-OT (Schiebinger et al. 2019) is popular approach to reconstruct the landscape of cellular reprogramming using optimal transport (OT). There are two components in Waddington-OTT (1) single-cell RNA-seq (scRNA-seq) dataset of mouse cells from a reprogramming experiment, and (2) an OT-haase trajectory inference model fitted on the scRNA-seq dataset. The scRNA-seq dataset consists of 251 251,203 mouse cells profiled from 37 time points (0.5-day intervals) during an 18-day reprogramming experiment starting from mouse embryonic fibroblasts. The trajectory inference model consists of transport matrices Tre with dimensions N M that relate all cells x ,-.,, X"x profiled at time l. all cells -..,: profiled at time 4x+1- An entry at row i and column j of Ttaifk11 corresponds to the probability that X is descendant cell of x as determined using optimal transport (Chizat et al. 2018). Every cell in the scRNA-seq dataset is either pre-labeled as one of the 13 provided cell sets (I.e., induced pluripotent stem, stromal, epithelial, mesenchymal-epithern oligodendrocyte progenitor, neuron, radial glial, spiral artery trophoblast giant, astrocyte, other neural) or unlabeled. We cluster the unlabeled cells using Leiden clustering via scanpy (Wolf al. 2018) at a resolution of 1, and define the resulting 27 unlabeled clusters as unique cell sets. As a result, each cell in the dataset belongs one and only one cell set.
transition. trophoblast, spongiotrophoblast trophoblast progenitor,
Simulating cell state trajectories. We define "cell state" as the transcriptomic profile of a cell. Here, a transcriptomic profile is the log-normalized RNA-seq counts of the top 1, 479 most highly variable genes. To create simulated trajectory of cell states for an individual cell undergoing reprogramming, we randomly and uniformly sample a cell profiled at time step to (Day 0.0) from the Waddington-OT scRNA-seq dataset, and generate via the transport matrix Tro.ts probability
distribution Ir,, over possible descendant cells x1, .....,.... x at time step t1 (Day 0.5). We sample a cell from this distribution, and repeat the process until we reach either Day 18.0 or a terminal state
(i.e., neural, stromal, or induced pluripotent stem cell). After generating trajectory composed of cells from the Waddington-OTT scRNA-seq dataset through this process, we retrieve the transcriptomic profile of each cell to compose X.,tgitr where T is the length of the trajectory.
Inferring conditions. A condition 8t, is defined as the activation of transcription factor (TF) that leads cell to transition from state xt, descendant state xt, To infer such conditions, perform differential expression analysis between cells from the same cell set as xt, (i.e., x" A) and cells from the same cell set as Xt,.. (i.e., x" E B). Using the wot. tmap. di diff_exp function (via the Waddingt on-oT OT library), we identify the top TF that was significantly upregulated in x" e A compared to x B. If no TFs are differentially expressed, then the condition is "None." " We retroactively perform this analysis on all pairs of consecutive cell states in cell state trajectory X,to to obtain the full trajectory containing both cell states and TF conditions: {xt, Stg Xty; St " Str-11 Xt7). In other words, represents simulated trajectory of an
21

---

Published as a conference paper at ICLR 2026
individual cell undergoing the reprogramming process. Condition embeddings Zg RR5220 obtained from the (frozen) pretrained ESM-2 embedding model (Lin et al. 2023).
Generating matched counterfactual trajectories. We additionally create pairs of matched counter- factual trajectories to evaluate a model's performance in zero-shot counterfactual generation. Each pair consists of "original" trajectory Toy and "counterfactual" trajectory Tef- First, we generate Tog using the Waddington-OP model. Then, given divergence time step D, the first D time steps of Tog are carried over to Tef such that the first D cell states and conditions of Tog and Tef are exactly the same. The remaining states and conditions of Tef are sampled independently from Tog. resulting in an alternative future trajectory based on an alternative condition time step D. The paired trajectories are simulated such that the alternative condition is different from the original, and the alternative condition leads to a different cell set at D 1, All trajectories are unique.
Implementation note: Since CLEF learns time embeddings based on the year, month, date, and hour of timestamp, we convert the time steps of each cell into timestamps. We set the starting time to as timestamp 2000/01/00 00 00: CO, and add 10 & hours to the converted timestamp of f:-1-
C.1.2 EXPERIMENTAL SETUP
Generating data splits. There are three cell sets (i.e., groups of cells with the same cell state label) that consist of cells from Day 0.0 in our post-clustering version of the Waddington-Ol dataset. We refer to these cell sets as "start clusters" because all initial cell states are sampled from one of these cell sets, Since the choice of start cluster can influence the likelihood of a cell's trajectory reaching certain terminal fates, we split our cellular trajectories into train, validation, and test sets based on their start cluster. This cell-centric data split allows us to evaluate how well model can generalize to different distributions of trajectories. Start cluster #1 is in the train set, start cluster #3 is in the validation set, and start cluster #2 is in the test set.
Zero-shot counterfactual generation. The data split for zero-shot counterfactual generation is con- structed such that the original trajectories Tog in the train or validation sets, and the counterfactual trajectories Tef are in the test set.
C.2 PATIENT LAB TESTS
Here, describe the process of (1) preprocessing electronic health records to extract longitudinal routine lab tests data and (2) preparing these trajectories for modeling.
C.2.1 CONSTRUCTING ROUTINE LAB TEST TRAJECTORIES
We leverage two publicly available medical datasets: eICU (Pollard et al. 2018) and MIMIC-
IV (Johnson et al., 2024a 2023] Goldberger et al. 2000). Both datasets are under the PhysioNet Credentialed Health Data License 1.5.0 (PhysioNet). The retrieval process includes registering credentialed user on PhysioNet, completing the CITI "Data or Specimens Only Research" " training, and signing the necessary data use agreements.
We process each dataset (i.e., eICU, MIMIC-IV) separately with the following steps. First, we extract the routine lab tests only (annotation available only in MIMIC-IV) and the most commonly ordered lab tests (i.e., lab tests that appear in at least 80% of patients). Next, we keep patients for whom we have least one of each lab test. If there are multiple measurements of lab test at the same time step (i.e., year, month, date, hour, minute, and seconds), we take the mean of its values. We extract patients with more than one visit (or time step).
We define patients' conditions as medical codes, specifically International Classification of Dis- eases (ICD), of their diagnosis. Both eICU and MIMIC-IV use ICD-9 and ICD-10 codes. We extract the medical codes and their timestamps (multiple medical codes at a single time step is possible). Since the timestamps of diagnostic codes and lab tests are not necessarily the same (and there are fewer entries of diagnostic codes than lab orders), we merge them with a tolerance range of 12 hours (eICU) or two days (MIMIC-IV). We obtain (frozen) condition embeddings Z₃ E 1228 (retrieved on December 22, 2024) from an embedding model that has been pretrained clinical knowledge graph (Johnson et al. 2024b). The clinical knowledge graph is constructed by integrating six existing databases of clinical vocabularies used in electronic health records: International Classification of
22

---

Published as a conference paper at ICLR 2026
Diseases (ICD), Anatomical Therapeutic Chemical (ATC) Classification, Systemized Nomenclature of Medicine Clinical Terms (SNOMED CT), Current Procedural Terminology (CPT), Logical Observation Identifiers Names and Codes (LOINC), and phecodes (Johnson et al. 2024b).
C.2.2 GENERATING DATA SPLITS
We generate a standard patient-centric random split for benchmarking model performance (R1-R2), and series of increasingly challenging data splits via SPECTRA (Ektefaie et al.) 2024) to evaluate model generalizability (R3). We describe in detail the process of constructing SPECTRA data splits.
SPECTRA (Ektefaie et al. 2024) creates a series of splits with decreasing cross-split overlap or similarity between the train and test sets. By training and testing models on these splits, we can assess model performance as a function of cross-split overlap (Fig. TO). SPECTRA refers to this relationship as the spectral performance curve, which provides insight into how well a model generalizes less similar data. When new dataset split is encountered, it can be plotted as a point on this curve. The area under the spectral performance curve (AUSPC) serves as a metric of model generalizability and enables comparisons across models (Tab. 66.
To generate a split with SPECTRA, a similarity definition and a SPECTRA parameter (SP) value between 0 and are required. SP controls the level of cross-split overlap (Fig. [0]: values closer to 0 create splits resembling classical random splits, while values closer to produce stricter splits with minimal no overlap between train and test sets. For example, at input of 1, no similar samples are shared between the train and test sets.
For eICU and MIMIC-IV, we define two patients as similar if: (1) they of the same gender, (2) they are born in the same decade, and (3) they share at least one ICD-9 or ICD-10 category. We exclude ICD-9 and ICD-10 codes that are present in more than 50% of patients avoid overly generic features. SPECTRA systematically prunes similar patients to produce splits. For this study, we generate 20 splits with SP values that are evenly spaced between 0 and I (Fig. I0] Given a train and test set, cross-split overlap is defined as the proportion of samples in the train set that are similar at least one sample in the test set (Fig. 10p.
C.2.3 CONSTRUCTING COHORTS OF PATIENTS WITH TYPE 1 DIARETES MELLITUS
We conduct case studies on two independent cohorts of patients with type 1 diabetes mellitus (TID), chronic autoimmune disease in which the immune system attacks proodin-produiin cells in the pancreas (Quattrin et al. 2023). From our processed eICU and MIMIC-IV datasets, we construct two cohorts of TID patients and matched healthy individuals.
Procedure. To define a type diabetes mellitus (TID) patient cohort in eICU and MIMIC-IV, identify patients with TID and matched healthy individuals. A patient has TID if the ICD-10 code E10 (or the equivalent ICD-9 code 250) is present in the electronic health records. Matched healthy patients are defined by three criteria. First, the patient must contain any of the following ICD-10 (and ICD-9 equivalent) codes: 1, 1,1,,11 313, E12, E08, E09, R73, and 024. An initial healthy patient cohort constructed using these filtering codes. Next, we identify frequently co-occurring ICD codes between the initial set of patients and patients with TID to filter out generic ICD codes (threshold 20). Finally, healthy patients are matched with a TID patient if: they are of the same gender, they are born in the same decade, and they share at least 50% of ICD codes.
Data statistics. eICU-TID contains 59 TID patients and 579 matched healthy controls, while MIMIC-IV-TID includes 25 TID patients and 226 matched healthy controls.
C.2.4 EXPERIMENTAL SETUP FOR TYPE DIABETES MELLITUS CASE STUDY
We evaluate CLEF'S ability to simulate counterfactual patient trajectories through temporal concept intervention. This is analogous to intervening on concept bottleneck models by editing concept values and propagating the changes to the final prediction (Koh et al. 2020). This capability is particularly useful when condition tokens are insufficient (e.g., prescribing medication dosage). Editing concept values allow users (e.g., clinicians) simulate potential trajectories
result of the precise edits.
We conduct case studies on two independent patient cohorts with type I diabetes mellitus (TID). For each patient, we intervene on the temporal concepts corresponding specific lab tests to simulate the
23

---

Published as a conference paper at ICLR 2026
"reversal" or "worsening" of symptoms, thereby generating "healthier" or "more severe" trajectories. Formally, given temporal concept с learned from X,1o4, and an optional condition s, we modify c' # c such that at least one element satisfies C₁ * c/- Then, CLEF simulates future trajectories of length T 10. We then compare these counterfactual trajectories (i.e., CLEF-generated patients) against observed sequences from matched healthy individuals, other healthy individuals, and other TID patients. Our hypothesis that clinically meaningful edits will produce "healthier" (i.e., more similar to healthy patients) or "sicker" (i.e., more similar to other TID patients) trajectories.
C.3 SYNTHETIC TUMOR GROWTH TRAIECTORIES
The tumor growth simulation model (Geng et al., 2017) produces trajectories of tumor volume (i.e., one-dimensional outcome) after cancer diagnosis. There are two binary treatments (i.e., radio- therapy AT and chemotherapy A,) at time I, and the possible treatments are: {(Aş 0, A 0), (A; = 0), (A; 0, A 1), (A; 1, A, =1)]. For T-Step ahead prediction, we simulate synthetic tumor growth trajectories under single-sliding treatment (i.c., shift the treatment over window) (Bica et al. 2020, Melnychuk et al. 2022) and random trajectories (i.e., randomly assign treatments) settings (Melnychuk et al. 2022). importantly, the ground-truth counterfactual trajectories are known. We limit the length of trajectories to maximum of 60 time steps. For each setting, we generate trajectories with different amounts of time-varying confounding 7 E [0, 2, 4], each with 10,000 trajectories for training, 1,000 for validation, and 1,000 for testing.
We follow the data simulation process and experimental setup as described in Appendix and GitHub repository of the original Causal Transformer publication (Meinychuk et al. (022).
C.4 SEMI-SYNTHETIC PATIENT TRAJECTORIES
MIMIC-II-CF is semi-synthetic dataset based on patient data from real-world intensive care units (Wang et al. 2020; Johnson et al., 2016). The data are aggregated at hourly levels, with forward and backward filling for missing values and standard normalization of the continuous time-varying
features (Wang et al. 2020 Johnson et al. 2016 Bica et al. 2020 Melnychuk et al.) 2022). Patients have 25 vital signs time- varying covariates and static covariates (gender, ethnicity, age). Untreated trajectories of outcomes are first simulated under endogenous and exogenous dependencies, and then treatments are sequentially applied (Meinychuk et al.) 2022). There are synthetic binary treatments and 2 synthetic outcomes (Melnychuk et al. (022). Importantly, the ground-truth counterfactual trajectories are known. We limit the length of trajectories to maximum of 60 time steps. We generate 1,000 patients into train, validation, and test subsets via a 60%, 20%, and 20% split. For 7-step ahead prediction with Tmax 10, we sample 10 random trajectories for each patient per time step.
We follow the data simulation process and experimental setup described in Appendix K and GitHub repository of the original Causal Transformer publication (Melnychuk et al. (022).
C.5 SALES TRAJECTORIES
Sales trajectories (M5 Forecasting) are obtained from daily transaction data of Walmart stores across three US states Makridaakis al. 2022). Following (Huang al. 2024) and (Wang et al. 2025), the objective of the model is to predict the future unit sales, and the condition is defined by the product price (Huang et al., 2024 Wang et al./ 2025). There are 1,942 time points on 3,049 products from 10 stores (4 in California in Texas, and 3 in Wisconsin). The dataset is split by state: train, validate, and test on California, Texas, and Wisconsin, respectively. As the dataset does not contain any ground truth counterfactual trajectories, M5 is only used for conditional generation of observed sequences.
D IMPLEMENTATION DETAILS
We provide code and instructions to implement CLEF, baselines, and ablations: httpt:://ttttttttte com/ mims ftarvardd, CLEF To implement basclines, we follow the authors' recommendations on model design and hyperparameter selection from the original publications. We do not share data model weights that may contain sensitive patient information.
24

---

Published as a conference paper at ICLR 2026
D.1
OBJECTIVE FUNCTIONS
For conditional generation, we use Huber loss, where a = X,,, Xx,,, and ò 1,
if | S 8 [0.5a? otherwise [6(aa] 0.56);
[(x xx,))
(5)
We use PyTorch's implementation. To briefly explain each component of Huber loss:
ô is used to switch between mean squared error (MSE) and mean absolute error (MAE).
The 0.5a² term (MSE) is quadratic component that penalizes outliers when errors s 8.
The ô([a] 0.56) term (MAE) is a linear component that does not over-penalize large errors when errors are larger than
We also train CLEF via another commonly used objective function for forecasting (i.e., MSE loss) and objective functions designed specifically for counterfactual prediction (i.e., gradient reversal, counterfactual domain confusion; Sec. D.4) (Bica et al. 2020; Melnychuk et al. 2022)..
D.2 EXPERIMENTS TO EVALUATE TEMPORAL CONCEPTS
We intentionally design temporal concepts isolate their contribution predictive performance. In our formulation, temporal concepts are learned from an aggregation of historical data, the future time point, and the desired condition by concept encoder E (Eq. 2}. We evaluate multiple ways of defining E: via the state-of-the ofrt----tt- setups for conditional sequence generation (Sec. (.ec.D.3) and counterfactual outcomes estimation (Sec. D.4 with different sequential encoders. Also, temporal concepts applied directly to the latest time step in the concept decoder F' (Eq. 3) to generate the future state. Alternative model architectural designs for learning temporal concepts and applying conditions (or interventions) to the model may obfuscate the contribution of temporal concepts to predictive performance. For example, feeding the intervention directly to the decoder would bypass the temporal concept mechanism, meaning that the concepts would capture only the passage of time rather than the effect of the intervention. Similarly, feeding the future state directly to the decoder would introduce an additional function applied after the temporal concepts, making it difficult to directly control the edit by the specified concept (because of the add-on decoder).
To further isolate the contribution of temporal concepts to predictive performance, CLEF and non- CLEF models differ only in the components needed to learn temporal concepts (i.e., concept encoder and decoder). In other words, CLEF models share the same sequence encoder and condition adapter as their non-CLEF counterparts but replace the forecasting decoder in SOTA baselines (Sec. A.3 D.3 D.4 with a concept encoder and decoder to leverage temporal concepts.
Altogether, our formulation keeps the architecture minimal and interpretable, ensuring that perfor- mance gains can be attributed directly to temporal concepts.
D.3 BASELINES FOR CONDITIONAL SEQUENCE GENERATION
We benchmark CL.FF against the state-of-tth-art conditional sequence generation setup with sequential data encoders (Fig. 2 Transformer (Waswani et al. 2017; Narasimhan et al.] [024] Jing et al., 2024 Zhang et al. 2023a); xLSTM (Beck et al.) 2024);; and state-of-the-at time series foundation model, MOMENT (Goswami et al. 2024). For MOMENT,
finetune adapter for the 1024-dimensional embeddings from the frozen MOMENT -aeenNT----arree large embedding model.
D.4 CLEF EXTENSIONS AND BASELINES FOR COUNTERFACTUAL OUTCOMES ESTIMATION
Due to its versatility, CLEF can be leveraged by state-of the-art machine learning models designed to estimate counterfactual outcomes (Bica et al. 2020 Melnychuk al. 2022). Counterfactual Recurrent Network (CRN) (Bica et al.) 2020) and Causal Transformer (CT) (Melnychuk et al. 2022) demonstrate sate-of-the offtthe-rr performance in the established benchmarks (Bica et al. 2020 Melnychuk et al. (022). To implement CLEF-CRN and CLEF-CT, the GELU activation layer from the concept encoder E. (Eq. 2 and the concept decoder G (Bq. 3 of CLEF appended outcome
25

---

Published as a conference paper at ICLR 2026
predictor network (denoted Gy where Y is the outcome of the given treatment in the original publications (Bica et al., 2020 Melnychuk et al. 20222) of CRN and CT. Following the original CRN and CT publications, emor (MSE) (Bica et al. 2020 Melnychuk et al. 2022).
minimize the factual outcome loss (i.e., output of Gy) via mean squared
We evaluate CLEF against their non-CLEF counterparts with and without balancing loss functions (i.e., gradient reversal (GR) (Bica et al.) 2020), counterfactual domain confusion (CDC) loss (Mel nychuk al., 2022)). This results in distinct state-of-the-art baselines: CRN with GR loss (i.e., original CRN implementation) (Bica et al. 2020); CRN with CDC loss (Melnychuk et al.
2022); CRN without balancing loss (Melnychuk al. 2022): CT with CDC loss (i.e., original CT
implementation) (Melnychuk et al.) 2022); and CT without balancing loss (Melnychuk et al. (022)
D.5 MODEL TRAINING
CLEF models do not require any additional resources than non- ConCCLEF models. All CLEF models have comparable number of parameters (Tab. ( and time complexity (Tab, 3) their non-CLER counterparts. In 67% of cases, the CLEF model's best checkpoint occurs earlier than its non- CLEF counterpart, indicating faster convergence (Tab. 4
Models are trained on single GPU (i.e., NVIDIA A100 or H100). For the model with the largest number of parameters (i.e. CLEF-MOMENT with FFN=1 the M5 dataset; Tab. 2.. 24GB of GPU memory is allocated and the maximum utilization is 100%. For the model with the largest number of
trainable parameters (i.e., CLEF-XLSTM with FFN=1 the M5 dataset; Tab. 2b. 12GB of memory is allocated and the maximum utilization is 86%.
Table 2: Model parameters. The number of all (denoted as A) trainable (denoted T) parameters is comparable between CLEF and non-CLES counterparts. FFN refers the optional FFN layer in the concept encoder, the number layers iyeN [0, 1] hyperparameter.
Dataset
Encoder
Baseline (A)
Baseline (T)
CLEF (A)
CLEF (T)
WOT
Transformer
67002560
66947800
FFN=0: 67008480
FFN=0: 66953720
FFN=1: 69200360
FFN=1: 69145600
WOT
XLSTM
66170384
66115624
FFN=0: 66176304
FFN=0: 66121544
FFN=1: 68368184
FFN=1: 68313424
WOT
MOMENT
354933280
13638200
FFN=0: 354939200
FFN=0: 13644120
FFN=1: 357131080
FFN=1: 15836000
eICU
Transformer
52560
37116
FFN=0: 52632
FFN=0: 37188
FFN=1: 52974
FFN=1: 37530
eCCU
xLSTM
78716
63272
FFN-0: 78788
FFN=0: 63344
FFN=1: 79130
FFN=1: 63686
eCCU
MOMENT
341293924
38160
FFN=0: 341293996
FFN=0: 38232
FFN=1: 341294338
FFN=1: 38574
MIMIC
Transformer
48000
32816
FFN=0: 48064
FFN=0: 32880
FFN=1: 48336
FFN=1: 33152
MIMIC
xLSTM
79096
63912
FFN=0: 79160
FFN=0: 63976
FFN=1: 79432
FFN=1: 64248
MIMIC
MOMENT
341290816
35312
FFN=0: 341290880
FFN=0: 35376
FFN=1: 341291152
FFN=1: 35648
M5
Transformer 260954950
260890900
FFN=0: 260967150
FFN=0: 260903100
FFN=1: 270272700
FFN=1: 270208650
M5
xLSTM 280615102
280551052
FFN-0: 280627302
FFN=0: 280563252
FFN=1: 289932852
FFN=1: 289868802
M5
MOMENT 372621770
31317400
FFN=0: 372633970
FFN=0: 31329600
FFN=1: 381939520
FFN=1: 40635150
26

---

Published as a conference paper at ICLR 2026
Table 3: Time complexity. The training and evaluation times are comparable for the CLEF and non CLEF models, Reported times rounded the nearest half hour.
Dataset
Encoder
Baseline
CLEF
WOT
Transformer
1.0 hour
1.0 hour
WOT
xLSTM
hour
1.0 hour
WOT
MOMENT
4.0 hours
4.0 hours
eICU
Transformer
5.0 hours
5.0 hours
eICU
XLSTM
5.0 hours
5.0 hours
eCCU
MOMENT
5.5 hours
5.5 hours
MIMIC
Transformer
hours
7.0 hours
MIMIC
XLSTM
7.5 hours
7.5 hours
MIMIC
MOMENT
7.5 hours
7.5 hours
M5
Transformer
0.5 hour
0.5 hour
M5
XLSTM
0.5 hour
0.5 hour
M5
MOMENT
hour
hour
Table 4: Model convergence. Shown the epochs of the best model checkpoints (index starting 1) for each dataset. FPN refers the optional FFN layer in the concept encoder; the number layers IyIN [0. 1] a hyperparameter. 67% of cases, the CLEF model's best checkpoint occurs earlier than its non-C1.FF counterpart, indicating faster convergence.
Dataset
Encoder
Baseline
CLEF
WOT
Transformer
49
FFN=0: 46
FFN=1: 38
XLSTM
44
FFN=0: 28
FFN=1: 20
MOMENT
5
FFN=0:
FFN=1: 5
eICU Transformer
47
FFN=0: 48
FFN=1: 45
xLSTM
45
FFN=0: 44
FFN=1: 44
MOMENT
4
FFN=0:
FFN=1:
MIMIC
Transformer
48
FFN=0: 42
FFN=1: 42
XLSTM
42
FFN=0: 42
FFN=1: 48
MOMENT
5
FFN=0:
FFN=1:
M5
Transformer
46
PFN=0: 46
FFN=1: 46
XL.
50
FFN=0: 35
FFN=1:
MOMENT
5
FFN=0:
FFN=1:
27

---

Published as a conference paper at ICLR 2026
D.6 HYPERPARAMETER SWEEP
The selection of hyperparameters for the (conditional sequence generation) models trained from scratch are: dropout rate [0.3, 0.4, 0.5, 0.6), learning rate [0.001, 0.0001, 0.00001). and number of layers (or blocks in xLSTM) [4,8]. Because the number of heads must be divisible by the number of features. the number of heads for eICU (18 lab tests) [2, 3, 9] and for others [4, 8]. For xLSTM, the additional hyperparameters are: ID-convolution kernel size E [4.5, 6] and QVK projection layer block size e [4.8].
D.7 BEST HYPERPARAMETERS
MIMIC-IV (patient trajectories) dataset. The best hyperparameters for the (conditional sequence generation) models trained on the MIMIC-IV dataset are: dropout rate = 0.6, learning rate 0.0001, number of layers (blocks in xLSTM) 8, and number of heads 4. For xLSTM models, 1D- convolution kernel size 4 and QVK projection layer block size 4. For CLEF models, the number of FNN in the concept encoder (Fig.
eICU (patient trajectories) dataset. The best hyperparameters for the (conditional sequence generation) models trained on the eICU dataset are: dropout rate 0.6, learning rate 0.0001, number of layers (blocks in xLSTM) = 8, and number of heads = 6. For xLSTM models, the number of heads 2, ID-convolution kernel size 4, and QVK projection layer block size For CLEF models, the number of FNN in the concept encoder = (Fig. 9
WOT (cellular trajectories) dataset. The best hyperparameters for the (conditional sequence generation) models trained on the WOT dataset are: dropout rate = 0.6, learning rate 0.00001, number of layers (or blocks XLSTM) = 4, and number heads = 8. For xLSTM models, convolution kernel size 4 and QVK projection layer block size 8. For CLEF models, the number of FNN in the concept encoder= = (Fig. 9
Synthetic tumor growth and semi-synthetic patient trajectories datasets. For the counterfactual prediction models (including CLEF and non-Cunn models), we follow the best hyperparameters as reported in the original publications of CT (Melnychuk al. 2022) and CRN (Bica et al. 2020).
M5 (store sales trajectories) dataset. The best hyperparameters for the (conditional sequence generation) models trained the M5 dataset are: dropout rate 0.6, learning rate 0.00001, number of layers (or blocks in xLSTM) 4, and number of heads = 5. For xLSTM models, number of heads 2, ID-convolution kernel size 6, and QVK projection layer block size 8. For CLEF models, the number of FNN the concept encoder (Fig. 9
28

---

Published as a conference paper at ICLR 2026
E
ADDITIONAL FIGURES AND TABLES
WOT
e/UU
MIMIC-IV
M5
Immedaate sequence editing
04
30
0.
Delayed sequerce editnng
1.0
05-
Smppll raar
VAR
Tranfformrr
CLEF- Tra anntooomar
XUSTM
CLEF ALEFALLTM
MOMENT ALER-MOMENT
Simole near
Transformer CLEF- Tra anstormmn
#LSTM CLEF-XISST OFF-ISSIT TM
MOMENT QUEF- MOMENI
SmpleL Inaa
Transformer
CLEF-I T'8 natormerr
XLSTM
FF STMM
MOMENT
CLEF- MOMENI
SimpleLi mear
VAR
CLEF- Tranaformer
RLEF-ULSTM M
MOMENT
CLEP- MOMEMM
Raseline
CLEF 0
FRN
Figure 8: Benchmarking the performance of CLEF, baselines, and ablation models on (a) immediate and (b) delayed sequence editing observed trajectories (See. C.I C.2) Performance measured by MAE (lower is better). Models are trained on 3 seeds using standard cell-, patient- or store-centric random splits; error bars eICU and MIMIC-IV datasets: on
show 95% CI. Not shown for visualization purposes are VAR performance immediate sequence editing. MAE for eICU and MIMIC-IV are 55982. 74 and 886.05, respectively: delayed sequence editing, MAE for eICU and MIMIC-IV are 3.02 10 and 8.62 102 respectively.
WOT
eICU
MIMIC-I
Immedaate sequence eciting
1.0
20-
30
Delayed sequence sciting
SirplLLissrr
WAR
CLEF- Transformrr
XLSTM
CLEF-X -LSTM
MOMENT
CLEF MOMENT
SimeeLrraar
Tranfformer CLEF- mennsoomar
XUSIM CLEF- MLSTM
MOMENT CL CLBF-R MOMENT
Simplel Inear
Transformer
FI Innifommr
XLSTM
CLEF MUSTM
MOMENT
CLDT MOMENT
SimpeLuneer
VAR
Tanssforer
CLEF- Tre rentommer
CLEF-VUS ALEFXILSTM
MOMENT
CLEF MOMENT
Reeeling
CI
FFN
Figure 9: Benchmarking the performance of CLEF, baselines, and ablation models on (a) immediate and (b) delayed sequence editing of observed trajectories Ce.. Performance is measured by RMSE (lower is better). Models are trained on 3 seeds using standard cell-, patient-. or store-centric random splits: error bars show 95% CL. Not shown for visualization purposes are VAR performance eICU and MIMIC-IV datasets: on immediate sequence editing, MAE for eICU and MIMIC-IV are 135003.67 and 1793.23, respectively; on delayed sequence editing, MAE for eICU and MIMIC-IV are 5.84 x 1030 and 1.59 x 10 respectively.
29

---

Published as a conference paper at ICLR 2026
Table 5: Performance of CLEF and non- Con-CLEF models preserving unedited variables while editing sequences. Models are evaluated on WOT (cellular) dataset. Generally, CLEF models have less error (MAE) than non-CLEF models. Notably, CLEF models typically have less error (MAE) on "Not Edited" (i.e., preserved) variables than "Edited" variables, whereas the opposite is true for non-CLEF models.
Model
Immediate
Delayed
Edited
Not Edited
Edited
Not Edited
Transformer
0.35860
0.00308
0.36-449 1 0.00370
1.27342 = 0.02205
1.32864 0.03137
CLEF-transformer
0.36150
0.00083
0.34/31 + 0.00094
0.49511 = 0.04567
0.48322 0.04640
XLSTM
0.37160
0.00386
0.37278 # 0.00188
1.59413 = 0.02165
1.65325
0.02717
CLEF-XLSTM
0.00107 0.35000
0.33820 + 0.00114
0.72733 0.01621
0.70962
0.01898
MOMENT
0.38030
0.00231
0.42576 + 0.00080
0.83916 0.01876
0.81810
0.00788
CLEE-MOMENT
0.35099 0.00076
0.38743 0.00025
0.49849 0.00224
0.42301
0.00231
0.8
0.5
0.6
0.2
0.2
0.0
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
Figure 10: Cross-split overlap (CSO) as function of SPECTRA parameter (SP) for eICU and MIMIC-IV datasets (Sec. [222 CSO is defined as the number of samples in the test set that are similar to at least one sample in the train SP is internal parameter used by SPECTRA to control the CSO of generated data splits. CSO decreases as increases. These data splits are used evaluate conditional sequence generation models' generalizability to unseen patient trajectories.
Table 6: Generalizability of CLEF, baselines, and ablations on eICU and MIMIC-IV datasets (Sec. C.2 in immediate and delayed sequencing. Performance is measured by the area under the spectral performance curve (AUSPC) for MAE (Fig. [T] or RMSE (Fig. [2]. Smaller AUSPC values indicate better performance. Models trained on 3 seeds; standard deviation is reported.
Model
Immeddate
Delayed
laumeditee
Delayed
MAB
RMSE
MAE
RMSE
MAB
RMSE
MAE
RMSE
Transfommer
27.06 0 0.98
59.83 1.14
22.59 = 1.31
50.25 = 0.56
40.87 + 0.15
71.77 = 0.21
44.61 0.19
80.38 0.32
CLBF
15.16 1.09
32.95 2.47
14.36 1.07
34.27 2.112
32.79 + 1.41
57.76 3.39
35.65 1.73
45.10
9.25
3.16
36.92
28.47
40.75
+ CLBF
2.11
35.43 6.01
15.32 2.10
34. 7.00
06.006+11 1.13
2..42
33. 88
3..63
CLPF FFN
0..11
28.09 0.08
9.04 2: 18
26.21 a 0.44
21.04 2.32
37.50 4.60
22.63 2.61
42.12.55.00
MOMENT
a 0.03
90.54 0.03
48.83 0.02
82.60 0.00
46.55 à 0.01
77.22 0.01
50.59 0.02
85.72 0.01
* CLEY
47.09 0.33
82.18 0.34
40.10 U.44
72.70 0.46
44.01
0.35
73.83 0.63
46.88 U.38
81.20 1.27
30

---

Published as a conference paper at ICLR 2026
F.-T-ranoomer Transformer
excu
vs. XLSTM
VS. MOMENT
50
50
70
seque nos editnn
40-
40
60
Immediate
30
30
20
20
CLEF =
10-
10
30
CLEF FFN
0.25
0.50
0.75
1.00
0.25
0.50
0.75
1.00
0.25
0.50
0.25
1.00
40
Delayed sequence eciting
35-
45-
40
20-
20
35
30-
0.25
0.50
0.75
1.00
0.25
0.50
0.75
1.00
0.25
0.50
0.75
1.00
MIMIC-IV
70
immodiate sequence editin
60
40-
40-
50-
0.25
0.50
0.75
1.00
0.25
0.50
0.75
1.00
0.25
0.50
0.75
1.00
60-
60-
Delayed sequence e editing
60
55-
30
30
45
20
20
40
10
35-
SPECTRA Parameter
SPECTRA Paramater
SPECTRA Paramotor
High
Low traes spit simiziity
Figure Generalizability CLEF, baselines, and ablation models eICU and (b) MIMIC-IV patient datasets (Sec. C22) in immediate and delayed sequence editing. Performance is measured by MAE (lower is hetter). Models are trained on 3 seeds; enro bars show 9,% CI. As the SPECTRA parameter increases, the train/test split similarity decreases (Fig. 1O). AUSPC evaluation is in Tab. 0
31

---

Published as a conference paper at ICLR 2026
F.T--aanoomerr Transformer
excu
vs. XLSTM
VS. MOMENT
100
100
sequerce ros ediing
W
80
100
Immediats
40
40
CLEF =
CLEF FFN
0.50
0.75
1.00
0.25
0.50
0.75
1.00
0.25
0.50
0.75
Delayed sequence eciting
70-
60
60
SAaE
50
70-
40
80
30
0.25
0.50
0.75
1.00
0.25
0.75
1.00
0.25
0.75
MIMIC-IV
150
150
140
immodiate sequence editin
125
125
120
50
50
25
25
0.25
0.50
0.75
1.00
0.25
0.50
0.75
1.00
0.25
0.50
0.75
1.00
120
120
Delayed requencc e editing
100
100
110
100
80
0.50
SPECTRA Paramrter
SPECTRA Paramate"
SPECTRA Paramotor
High ceraaityyy
Low frae spit simizitty
Figure Generalizability CLEF, baselines, and ablation models eICU and (b) MIMIC-IV patient datasets (Sec. (S2c. in immediate and delayed sequence editing. Performance is measured by RMSE (lower hetter). Models are trained on 3 seeds; enrr bars show 9,5% CI. As the SPECTRA parameter increases, the train/test split similarity decreases (Fig. 1O). AUSPC: evaluation is in Tab. 0
32

---

Published as a conference paper at ICLR 2026
Causal Transformer
Counterfactual Recurrent Network
2.0
1.4-
1.2
RMSE
RMSE
1.4
1.0
1.2
0.8
Amount of Time-varying Confounding
Amount of Time-varying Confounding
Baseline WCDC
Baseline WiGR
Beseline w/ BL
CLEF e/DDC
CLEF WGR
CLEF wio B.
Counterfactual 7-step ahead prediction on tumor growth (random trajectories setting) with different amounts of time-varying confounding (Sec. C.3 GR refers to Gradient Reversal loss (Bica et al. 2020); CDC refers to Counterfactual Domain Confusion loss (Melnychuk al. 2022): BL refers to Balancing Loss (i.c., GR CDC). Models trained seeds; bars show 95% CI.
Figure 13:
a
Causal Transformer
0.55
1.
18-
12
0.78
0.2
12
0.65
0.5
2122
b
Counterfactual Recurrent Network
L.3
2.1
1.0
25.
12.-
3.0
1.0
20
0.0
5.-
0.75
1.0
8.0
5442
we BL
w/CDC
CLEF
Figure 14: Counterfactual 7-step ahead prediction on tumor growth (single-sliding treatment) with different
amounts of time-varying confounding y (Sec. C.3 GR refers Gradient Reversal loss (Bica al. 2020 CDC refers to Counterfactual Domain Confusion loss (MeInychuk al, (2022); BL refers CDC). Models are trained on seeds; error bars show 95%
Balancing Loss (L.e., GR
33

---

Published as a conference paper at ICLR 2026
a Causal Transformer
y=2
y=3
y=4
0.50
0.
0.70
1.4
12-
67-
0.62
0.
0.7
0.8
Step
b
Counterfactual Recurrent Network
0.0
3.5
8.00
2.0
0.70
1.0
6.5
S11E
9121
500P
Baseline
we
Figure 15: Counterfactual 7-step ahead prediction on tumor growth (random trajectories setting) with different amounts of time-varying confounding (Sec. C.3 GR refers to Gradient Reversal loss (Bica et al. 2020) CDC refers to Counterfactual Domain Confusion loss (Melnychuk al. 2022 BL refers Balancing (i.c., GR CDC). Models are trained seeds; bars show 95% CI.
Causal Transformer
Base wCOC
Dace in wGGR
Base wlo BL
wiD0
CLEF WGGR
CLEF BL
0.50
0.25
0.00
Step
Counterfactual Recurrent Network
3.5
2.0
0.5
0.0
Figure 16: Counterfactual r-step ahead prediction semi-synthetic patient ICU trajectories (Sec. C.4 GR refers Gradient Reversal loss (Bica al. 2020): CDC refers to Counterfactual Domain Confusion loss (Melnychuk al. 2022;; BI refers Balancing Loss (i.e., GR CDC). Models trained on 5 seeds; error bars show 95% CI.
34

---

Published as a conference paper at ICLR 2026
Immediate sequence editing
b
Delayed sequence editing
0.4
2
Basolin
M4E
0.2
MAE
FIN.
0.0
1.0
RMSE
RMSE
0.5
Trandooer
CLEF Transformor
XLSTM
CLEF XLSTM
MOMENT
CLEF-MOMENT
Transformar
CLEF Transormmr
XLSTM
CLEF-XLSTM
MOMENT
BLEE-MOMENT
Figure 17: Benchmarking the performance of CLEF, baselines, and ablation models on zero-shot (a) immediate and (b) delayed counterfactual generation of cellular developmental trajectories (Sec. C.I). Performance is
measured by MAE (top row) and RMSE (bottom row). Models trained on 3 seeds; error bars show 95% CI.
35

---

Published as a conference paper at ICLR 2026
Differences between observed and CLEF patients due to decreased glucose levels (MIMIC-VV-TDD)
b Differences between observed and CLEF patients due to decreased WBC levels MMMIC-IV-T1D
Glurose
White Blood
Nitrogen
Glacase-
Hemoglobin
Red Bleedd Cells
Hemeglobin
Bicerbonate
Red Blood Cells
MCHC
MCMC
He metotrcnt
Hematocrit
MCH
Lab
Bicarbonate
Lab
MCV
MCH
Chio 'dc
Potassium
Sodium
Nitrogen
Patelet Count
Platolet
(atharne (UT)
Datta
C Differences between observed and CLEF patients due decreased WBC levels
d
Decrease WBC levels 0.5x
(reverse symptom)
(eICU-TID)
1.0
MMCC-N
WBC 1000
0.6
Bicarbonate
RBC
0.2
Flotclets
Potassium
Lab Test
Decrease glunose WBC levels by 0.5x (reverse symptom)
MCHC
ROW
-
1.0
0.6
Sodium
Chorie-
Calcium
0.4
Creatiinn
0.2
SUN
0.4
0.6
Data
0.8
1.0
1.2
0.0 CLBF match
C.LE vs. other healthy patlenss
CLEF vs. other T1C palkents
healthy patiants
Observed and CLEF Figure 18: CLEF-generated patients via intervention on temporal concepts (Sec. C.2
patients are compared to quantify the differences between their lab test trajectories as a result of the intervention to halve the (a) glucose levels in TID patients from MIMIC-IV-TID, (b) WBC levels in TID patients from MIMIC-IV-TID, and (c) WBC levels TID patients from eICU-TID. (d) After intervening CLEF halve WBC levels, observe whether resulting CLEF patients' trajectories are "healthier" or "sicker" compared to other patients in the real-world cohort (top). Further, we investigate whether the intervention effects are compounded when simultaneously reducing glucose and WBC levels by half (bottom). Error bars show 95% CL.
36

---

Published as a conference paper at ICLR 2026
Table 7: As an ablation on the concept decnder, we implement LowR, a decoder (I (c where W low-rank (with rank 4,8, 16). We evaluate the models on immediate sequence editing of observed trajectories. Models are trained on 3 seeds using standard cell-, patiente, store-centric random splits.
Dataset
Model
MAR
MIMIC-IV
Tranformer
7.284 = 0.005
Lowx Kranttratttrmme (Kank
6.674 0.974
Low3 transformer (Rank= 8)
6.748 0.067
tramffrrtffrrmer
CLEP transformer (PPN O
6.577 = D.8ds
CLEF transformer (FPN
6.562 0.046
MIMIC-IV
sLSTM
7.561 = 0.354
LowR-sLSTM (Rank =
7.018 0.162
LuuR-xLSTM (Rank
7.031 = 0.058
CLEF xLSIM (PHN (0)
7.493 0.722
CLBP xLSTM (PTN=
6.942 0.052
MIMIC-IV
MOMENT
14.804 0 036
LouR-MOMENT (Rank
10.671 0.290
LooE-MOMENT
10.603 0.083
LooR-MOMENT (Rank (6)
10.685 0.204
C1Rs-MOMEN (FFN =
9.779 0.148
aCU
Transformer
9.439
LowB-traffemmrr (Rank
8.135
Low8--randormme (Rank -k)
8. 8.265 0.109
Low LowR-iranfoemer (Rank
8.426 0.162
Cist-tonndormer (FPN
CLu--tnneformer (PFN
8.338 0.064
Low XLSTM (Rank
7.731 0.054
LowR-xLSTM (Rank -8]
8.018 0.062
Loug-xLSIM (Kank
8.001
CTR
7.815
CLP-XISTM (PN
7.761 0.050
eICU
MOMENT
13.376 0.089
Low MOMENT (Rank =
10.405 0.099
Lo4R-MOMENT (Rank
10.256 0,252
LouR-MOMENT
11.729
CLEF MOMENT (FFN
9. 3..95 0.164
CLas ALa--MOMENT (PFN 1)
10.111 0.329
WOT Transformer
0.380 0.903
LowR transfommer (Rank 5)
0.365 0.001
LowR-tramformer
Low transformer (Rank 16)
0.386 = 0.002
CLEF inmoformer (FFN =0)
0.360 = 0.001
CLEF-tmnndormeer (FFN
0.344 0.001
WOT
ST
0.373 0.004
LowR-xLSTM
LowR-xISTM (Rank =
0.363 0.001
IowR-x1 (Rank 16)
0.365 0.001
CLEP-ASTM =PFN
0.350 0.001
CLEF-1157M
WOT
MOMENT
0.386
LooE-MOMENT
0.370
LouR- MORR-MMMNNT.OO (Rank
0.372 0.001
LooR-MOMENT (Rark 16)
0.381 0.001
CLU-MOMENT
0.356
CLEF-MOMENT (FFN
0.356 0.001
Tranffrmer
1.203
Lows-transoomme (Rank
1.137 D.013
Low& S-rnsonsormmrr (Rank =
1.136 0.015
Loo--tranforme (Rank
1.151
CLEs CLaa--raneforree (FFN
1.089
CLEP mannformme (FTN
1.086 0.000
XLSTM
1.306 = 0.032
Lowal-L XLSMMM (Kank
1.029 0.011
Low3 xLSTM (Rank =)
1.039
LowR-xLLTM
CLEF-XLSIM (MN
1.025 0.00s
CLEF XLSTM (FNN
0.545 0.006
MS
MOMENT
1.156 = 0.043
LmoR-MOMENT (Rank
0.835 0.019
Low LowR-MMMENT (Rank
0.875 0.023
CLEF MOMENT (FFN 0
0. 0.786 0.005
CLEP MOMENT =
0.778 0.015
37

---

Published as a conference paper at ICLR 2026
Table 8: As ablation on the concept decnder, we implement LowR, decoder (I (c x) where W low-rank (with rank 4, 8, 16). We evaluate the models on delayed sequence editing of observed trajectories. Models are trained on 3 seeds using standard cell-, patient-, store-centric random splits.
Dataset
Model
MAR
MIMIC-IV
Tranformer
8.346 0.129
LowX Kranttratttrmee (Kank
7.518 0.945
Low3 transformer (Rank=
7.943 0.041
CLEP transformer (PPN u
7.915 0.846
CLEF transformer (FPN
7.563 = 0.065
MIMIC-IV
sLSTM
7.678 = 0.029
LowR-sLSTM (Rank
7.742 0.075
7.853
LuuR-xLSTM (Rank
7.823 0.025
CLEF xLSIM (PHN -0)
8.420 0.092
CLBP xLSTM (PTN=
7.794 0.042
MIMIC-IV
MOMENT
10.807 0.054
LouR-MOMENT (Rank
LooE-MOMENT
8.926 0.221
LowR-MOMENT (Rank (6)
9. 0.001 0.493
C1Rs-MOMEN (FFN =
8.688 0.074
aCU
Transformer
8.377
LowB-trafeemmrr (Rank
7.465
Low8--randormme (Rank -k)
7.567 0.070
Low LowR-iranfoemer (Rank
7.788 128
Cist-tonndormer (FPN
CLu--tnneformee (PFN
7.566 0.840
7.086
Low XLSTM (Rank
7.253 0.023
LowR-XLSTM (Rank -8)
7.375 0.094
Loug-xLSIM (Kank
STR
7.324 0.048
CLP-XISTM (PN
7.241 0.082
eICU
MOMENT
10.289 a.000.... 0.086
Low MOMENT (Rank =
8.573 0.157
LO4R-MOMENT (Rank
9.074
LouR-MOMENT
CLEF MOMENT (FFN
8.773 0.120
CLas ALa--MMMENT (PFN 1)
8.472 0.239
WOT Transformer
1.273 0.022
LowR transfommer (Rank 5)
0.552 0.049
LowR-tranformer
Low transformer (Rank 16)
0.525 = 0.057
CLEF inmoformer (UFN =0)
0. 194 = 0.045
CLEF-tmnndormeer (FFN
0.748 0.035
WOT
ST
1.592 0.022
LowR-xLSTM
LowR-xISTM (Rank =
0.854 0.047
1owR-x1 (Rank 16)
0.882 : 0.048
CLEP-ASTM =PFN
0.724 0.016
CLEF-11.7M
1.028
WOT
MOMENT
0.836
LooE-MOMENT
0.534
Lou LoR-MMMENT (Rank
0.542 0.005
LooR-MMMETT (Rark 16)
0.576 0.003
CLU-MOMENT
CLEF-MOMENT (FFN
0.512 0.009
Tranffrmer
1.214
Low--tranfoommr (Rank
1.133 0.036
Low& S-rnsfasorrmrr (Rank =
1.137 0.010
Loo--tranforme (Rank
1.149
CLEa CLaa--randforret (FFN
1.090
CLEP mannformme (FTN
1.086 0.000
XLSTM
1.345 = 0.004
Lowal-L XLSMMM (Kank
1.067 0.014
Lowi xLSTM (Rank =)
1.075 0.013
LowR-xLLTM
CLEF-XLSIM (MN
1.066 0.009
CLEF XLSTM (FNN
0.567 0.008
MS
MOMENT
1.162 = 0.041
LovR-MOMENT (Rank
0.858 0.015
LowR MOM.NNT (Rank
0.907 0.026
CLEF MOMENT (FFN 0
0.619 0.004
CLEF MOMENT =F
0.507 0.011
38

---

Published as a conference paper at ICLR 2026
Table 9: To show the henefit of single-step generation, we arbitrarily add three intermediate steps between i;2 and hefore finally predicting the observed x,,,, Because only have the ground truth for x,,t, we evaluate only the predicted x,y Models trained on seeds using standard cell-, patient-, store-centric random splits.
Dataset
Model
MAE
MIMIC-IV
Transformer
8.346 0.129
Transformerrintemmddaatt
8.627 0.365
7.915 0.046
A.LE-traasformerr(ttemdddddi
8.279 0.135
MIMIC-IV
xLSTM
7.678 0.029
(LSTMMintermcciate)
8.281 0.414
CLEF-xLSTM
8,420 0.692
CLEB-xLSTTMintemmediitt))
8.684 0. 7.767
MIMIC-VV
MOMENT
10.807 = 0.054
(ntemci(ne
10.673 0.059
CLEF-MOMENT
8.688 0.074
CLE--MOMENT((nemmedaae)
9.096 0.137
cICU
Transformer
8.377 0.047
Transformer(nnemmmddatt)
9.707 1. ,6446
tLan--ransformer
7.643 0.083
CLEE-tramsforrmeriinememcaaaa
8.144 0.217
eICU
XLSTM
7.086 0.066
XLSTMintermediitt))
8.015 = 0.566
CLEF-xLSTM
7.324 0.048
8.121 0.371
eICU
MOMENT
10.289 0.086
MOMENTintermediate)
10,233 0.069
CLEF-MOMENT
8.773 + 0.120
CLE--MOMENTinnemeddan
9.591 0.198
WOT
Transformer
1.273 0.022
Transfomer((ntermediatt)
1.068 0.053
CLEF-transformer
0.494 0.045
CLEF--tamsforrmerriiteemmdaaaaa
0.411 0.026
WOT
XLSTM
1.592 0.022
XLSTMMintermmciate)
1.444 = 0.043
CLEF-XISSTM
0.724 + 0.016
CLEF=xlSTTMinteemdddd
0.395 0.010
WOT
MOMENT
0.836 = 0.018
MOMENT(ntemmediae))
0.764 0.015
CLEF-MOMENT
0.493 + 0.002
CLaF-MMOMENTintemcccian
0.463 0.003
M5
Transformer
1.214 0.016
(ransformerrintermeddaaa
1,205 0.016
CLEF-transformeet
1.090 a: 0.001
TLEE-rranfoormerittemdddaa
1.075 0.001
M5
XLSTM
1.345 0.034
XLSSMMintermediate)
1.271 a 0.030
CLEF-XLSTM
1.066 0.009
(LEFxXLSTMMinemmmddate))
1.121 0.011
M5
MOMENT
1.162 0.044
MOMENT((ntermediaa
1.137 0.043
CLEF-MOMENT
0.819 0.004
Caar-MOMENTinemmeddae
0.799 0.005
39

---

Published as a conference paper at ICLR 2026
Table Binary cross entropy (BCE) loss predicting the treatment from learned representations CLEF non-CL.F models on tumor growth (single-sliding treatment) with different amounts of time-varying confounding y (Sec. C.3 Models are trained on 5 seeds.
Model
BCE Loss
CT w/CDC
1.499 0.099
CLEF-CT w/CDC
1.506 0.096
CRN w/GR
1.193 0.169
CLEF-CRN w/GR
1.196 0.167
CRN w/CDC
1.695 0.652
CLEF-CRN w/CDC
1.597 0.187
Table 11: Binary cross entropy (BCE) loss for predicting the treatment from the learned representations of CLEF and non- CLE- models tumor growth (random trajectories setting) with different amounts of time-varying confounding 7 (Sec. C.3 Models are trained seeds.
Model
BCE Loss
CT w/CDC
1.497 0.089 1.509 0.098
CLEF-CT w/CDC
CRN w/GR
1.194 + 0,168
CLEF-CRN w/GR
1.196 0.167
CRN w/CDC
1.583 0.178
CLEF-CRN w/CDC
1.598 0.188
Table 12: Binary cross entropy (BCE) loss predicting the treatment from learned representations of CLEF and non- non-CLEF models on semi-synthetic patient ICU trajectories (Sec. C.4.. Models are trained on seeds.
Model
BCE Loss
CT w/CD CLEF-CT w/CDC
1.656 0.631 1.300 0.375
CRN w/GR
0.330 0.149
CLEF-CRN w/GR
0.364 0.151
CRN w/CDC
1.706 0,662
CLEF-CRN w/CDC
1.650 0.640
Table 13: Generated counterfactual cICU-TID patients via intervention (i.c., decrease glucose levels by 0.5x, increase glucose levels by 2x) temporal concepts from CLEF and SimpleLinear (ablation). We compare the generated trajectories against observed trajectories of matched healthy, other healthy, and other TID patients. Higher R² indicates that patient pairs are more similar. CLEF'S generated trajectories are more similar those of the other TID patients (H² 0.348 0. 0.149) than matched healthy (H2 0.147 + 0.111) and other healthy (R²2 0.175 0.106) patients. On the other hand, SimpleLinear" generated trajectories have consistent and low similarity to those of matched healthy (R²² 0.287 0.14)., other healthy (At² 0.263 0.159), and other TID (R² 0.240 0.087) patients. Also, the R² between SimpleL generated trajectories and the trajectories of the matched healthy, other healthy, and other T1D patients are comparable. On the other hand, the R² between CLEF'S generated trajectories and hcalthy trajectorics are significantly lower than the R² between CLEF'S generated trajectories and other TID patients'. Unlike CLEF, SimpleLincar cannot generate trajectories that resemble the trajectories of healthier sicker patients, depending on the intervention.
Model
Intervention
Matched Healthy
Other Healthy
Other T1D
CLEF
0.5x glucose
0.757 = 0.227
0.737 = 0.126
0.600 = 0.122 0.288 + 0.109
Simplel.inea
0.5x glucose
0.403 = 0.226
0.390 = 0.233
CLEF
2x glucose
0.147 + 0.111
0.175 = 0.106
0.348 0.149
SimpleLinear
2x glucose
0.287 = 0.141
0.263 = 0.159
0.240 + 0.087
40

---

Published as a conference paper at ICLR 2026
Generated counterfactual MIMIC-IV-TID patients intervention (i.c., decrease glucose levels by 0,5x, increase glucose levels by 2x) on temporal concepts from CLEF and SimpleLinear (ablation). We compare the generated trajectories against observed trajectories of matched healthy, other healthy. and other TID patients. Higher It indicates that patient pairs similar to those of other TID patients (R² 0.472 0.134) than matched healthy (It" 0.364 0.126) other healthy (R² 0.344 + 0. 0.129) patients, which is expected. On the other hand, SimpleLinear" 's trajectories are more similar to those of the matched healthy (R² = 0. 790 0.110) and other healthy (Ai² 0.70 0.051) patients than other TID patients (R² 0.541 0.056), which is unexpected. Unlike CLEF, Simple cannot generate trajectories that resemble the trajectories of healthier sicker patients, depending the intervention.
Table 14:
similar. CLEF'S generated trajectories
Model
Intervention
Matched Healthy
Other Healthy
Other T1D
CLEF
0.5x glucose
0.838 = 0.140
0.814 0.076
0.692 0.071 0.564 a 0.082
SimpleLinear
0.5x glucose
0.713 = 0.166
0.689 = 0.113
CLEF
2x glucose
0.364 = 0.126
0.344 = 0.129
0.472 + 0.134
SimpleLinear
2x glucose
0.790 = 0.110
0.709 = 0.051
0.541 a 0.056
Table 15: We provide qualitative trajectories for a patient. CL.FF seems to generate lab test values that are closer to the observed lab test values than non- CLEF models.
Lab
Time
Observed
CL.FE
Non-CLET
Hot
1900-01-03 (9:
28.200
31.664
32.547
1900-01-04
10:09:00
26,700
32,022
32,805
190--11-05
1900-01-66
1900-01-07 10:00000
27.500
31.779
32.357
1900-01-99 09:20.000
29.500
31.084
32.169
1900-01-10 10:04:00
30,000
1900-01-03 09:58:00
9,700
10.193
10.558
10:09:00
1900-01-05 (9::51
8:700
10.230
10.341
1900-01-00 06-001-06000 09:19:00
9.200
10.295
10.605
1900-01-07 10:00:00
9.300
10.182
10:472
9,700
9.938
1900-01-11 10:04:00
9,500
9,804
10.192
MCI
1900-01-03 09:55:00
31,700
29.5.18
1900-01-04 10:09:00
31 31,400
29 904
874
1900 01
09:31:00
31,000
29.626
27.617
1900-01-07 10:00:00
467
1900-01-00 09:29:00
30,700
29.574
28,
1900-01-10 10:01:00
30.100
29.315
27.55
MCV
1900-01-03 09 09:55:00
92.300
93.786
89.05
1900-01-04 10:09.00
1900-01-05 09:31:00
92.500
93.173
88.
1900-01-06 19:00
91.700
93. 508
88.65
1900-01-00 07-07 10:00:00
93.300
93.270
$8.68
1900-01-09
1900-01-10 10:04:00
93.000
93.824
190001103
1900-01-04 10:09:00
157.000
157.157
141.779
1900-01-05 09:31:00
138 000
136.605
141 348
1900-11-06
1900-01-07 10:00:00
142.
1900-01-09 09:29.00
037.000
778
142. 912
1900-11-10 10:34.00
137.000
138.031
141.562
41

---

Published as a conference paper at ICLR 2026
Table 16: We provide qualitative trajectorics for a patient. CLEF seems to generate lab test values that are closer to the observed lab test values than non CLEF models.
Time
Observed
CL.EF
Non-CLEF
1900-01-07
32.783
1901-01-08
10.29.00
33,500
32.530
31,575
1900-01-09
11:58:00
34.500
32.110
31,828
1900-01-10 18:05.00
32.788
1900-01-11 11:24:00
33.300
32:224
31,802
1900-01-12 11:28:00
35,100
32.135 138
31,045
1900-01-13 10:21:00
36,400
32.361
415
1900-01-14
1900-01-15 15:0255000
31,684
100--01-16 10:14:00
31,400
$1,706
786
1900-01-17 09:56:00
35,500
31.111
30.515
1900-01-18 10:13:00
1900-01-15 09:00-05000
32,300
31.057
30.230
1900-01-20 10:47:00
31,100
31.115
30.727
1900-01-21 11:07:00
31,300
30,864
29.925
Hgh
190--01-07 12:31.00
11,300
10.967
10.607
190-411-88 10.29.00
190-001-99 11:58
11,500
190--91-10 18:05.00
12,200
10.736
10.296
1900-01-11 11:24:00
11,100
10.685
10.388
1900-01-12 11:25.00
1900-11-3
12.300
1900-01-14 11:10:00
10,900
10,605
10.253
1900-01-15 09:00:2500
10.000
10.527
10.008
190-91116
1900-01-17 (7.0955::00
11,500
10,425
190--01-18 10:13:00
11.000
10.405
10. 108
1900-01-19 09:25:00
10,400
10.290
9,748
1900-01-20 10:47:00
190-011-11
9,500
10.198
190-01-077
1900-01-08 10:20:00
30.000
29 252
27.722
190--01-09 11:58.00
30.700
20 300
27 925
190-011-0
1900-01-11 11:24:00
27. 440
190--91-12 11:25.00
29.500
29.160
27 412
190-011-13 10:21:00
30.300
29.183
27.374
190-01-144
190-00-15500
190-001-66 10:14:00
30.200
29.197
27 0007
190--01-17 (709956:00
29,000
29.024
76 864
190-01-18 10:13.00
1900-01-19
29.044
190--01-20 10:47:00
20 900
20
16-
190-011-11 11:07
29,500
28.978
26,834
MCV
1900-01-07 12:51:00
91,700
87.581
85.512
1900-01-08 10:20:00
1900-11-99
1900011-00 18:05.00
91.700
831097
84.901
1900-91-11 11:24:00
90.500
83.038
84.469
190--11-12
190-01-13 10:21:00
100--11-14 11:10:00
92.500
87.738
83.119
190-001-15 09:25.00
93.200
87.634
82.869
1900-01-16
1900-1-17 1000566000
93.000
190-01118 10:13:00
95.500
87.828
82. 801
1900-01-19 09:25:00
93,400
87.831
$3.391
190--11-20
87.924
1900-11-11 11:37:00
95.400
88,055
12:31:00
1900-01-08 10:29.00
137.000
135.000
199.291
1900-11-99 11:58.00
136000
137.241
141,514
1900-01-10 18:08:00
136.000
1900-1--1 11:24:00
1900-11-22 11:25.00
155 000
138.312
143.
1900-11-33 10:21:00
133,000
138.811
142.580
11:10.0
138.777
190-01-15.00
100--91-16 10:14:00
134.000
150.366
141.228
190-011-17 (9.56.00
135,000
138.747
139,891
10:13:00
157,000
100001-19
1900-11-20 10.47.00
135.000
138.093
199.
1900-01-11 11:07:00
138,000
138.965
138,745
42

---

Published as a conference paper at ICLR 2026
F LIMITATIONS & FUTURE DIRECTIONS
There are a few key limitations of CLEF. Firstly, we define temporal concepts such that each element represents unique measured variable in the sequence (e.g., gene expression, lab test). Instead, may be beneficial to learn higher-order relationships between the measured variables or across time as abstract hierarchical concepts (LCM al. 2024, Kacprzyk et al., 2024). Secondly, while CLEF able to generate counterfactual sequences for any condition, including those it may not have seen during training, CLEF could potentially improve with additional guidance from real-world causal model for the system domain of interest (Chatzi et al. 2025). Since defining such a real-world causal graph is major challenge, one promising future direction could to enable user interventions, such those performed in our TID case studies, to finetune CLEF. There many opportunities to improve CLEF. While elegant, the multiplicative decoder may impose a linearity assumption. Also, in the concept decoder, it be limitation to use the last time step x,,, for decoding the next time step if there are missing or zero values. Straightforward solutions are to take the mean of the historical context impute the missing values in the historical data before forecasting Lj- In addition to temporal editing of trajectories, future work may extend CLEF for spatial and positional editing.
43


Reviews:
[{"review_id": "pgCxlxJGAY", "reviewer": "Reviewer_goGg", "summary": "This paper proposes CLEF, a novel framework for controllable sequence editing, designed to address the lack of granular control over timing and scope in the conditional generation of longitudinal data. The core idea is to learn \"temporal concepts,\" which represent the rate of change for each variable, and apply them via a simple multiplicative decoder to generate future states in a single, non-autoregressive step. This approach is designed to handle both immediate and delayed edits—modifying a sequence at a distant future time point without generating intermediate steps. The authors extend this framework to counterfactual outcome estimation, arguing that the architecture provides implicit balancing that outperforms state-of-the-art methods, even without explicit balancing losses. The method's effectiveness is demonstrated through an extensive empirical evaluation on 8 datasets spanning biology, clinical medicine, and finance, where CLEF-augmented models show significant improvements over numerous baselines, particularly in the novel delayed editing task and in zero-shot counterfactual generation. A case study on generating \"healthier\" trajectories for diabetic patients further highlights the model's practical utility and interpretability.", "questions": "* On the \"Implicit Balancing\" Claim: The paper's causal claims rely on the idea of \"implicit balancing,\" which is currently supported by downstream predictive accuracy. To substantiate this claim more directly, could you provide a more targeted diagnostic for balance? For instance, could you report on either (a) the predictability of treatment assignment from the learned representations (where a lower AUC would indicate better balance) or (b) an IPM-style distance like MMD between the representation distributions for treated versus control groups?  \n\n* On the Limitations of the Multiplicative Decoder: The diagonal multiplicative decoder, x^=c⊙x, is elegant but seems to impose a strong linearity assumption. Could you please add a discussion on its potential limitations in systems with known non-linear couplings, compositional constraints (e.g., variables that must sum to a constant), or saturation effects? Clarifying the boundaries of this design choice would help readers understand its ideal application scope.  \n\n* On the Scope of Endpoint vs. Trajectory Generation: The one-step forecast is a key design choice to avoid compounding error. Could you clarify the model's intended scope (i.e., is it primarily for endpoint editing rather than full trajectory simulation)? To help illustrate this, could you include a short-horizon chaining test (e.g., 3–5 steps) to show how error accumulates when the model is applied autoregressively compared to the one-step approach?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "*   **Clear, useful problem framing for controllable sequence editing.** In my opinion, the split between immediate vs. delayed editing—and the choice to define delayed editing as a single-step, non-autoregressive jump—cleanly targets a real pain point (compounding error) in scientific/clinical forecasting. This makes the task definition itself a contribution.\n\n*   **Broad, careful empirical evaluation with new benchmarks.** I think the scope (8 datasets, 4 contributed benchmarks, 9 baselines, plus generalization and zero-shot counterfactual tests) is a major strength. It shows the idea isn’t brittle and gives the community reusable testbeds. The writing is also notably clear about how this fits vs. related work.\n\n*   **Simple, modular architecture that travels across encoders.** In my view, the “temporal concept” (multiplicative edit) is easy to implement, computationally light, and integrates with varied sequence encoders. That portability lowers the barrier to adoption and facilitates ablations/reuse.\n\n*   **Actionable interpretability via direct concept edits.** I like that domain experts can intervene on the learned concept vector and see resulting counterfactuals; the Type 1 Diabetes case study makes this tangible for in-silico hypothesis exploration. It’s not interpretability in the semantic-concept sense, but it is a practical, controllable handle on predictions.\n\n*   **Good engineering trade-offs for stability vs. flexibility.** In my opinion, the “one-step to $t_j$” design is a pragmatic choice: it sacrifices full trajectory modeling to reduce error accumulation and make edits predictable. Given many operational needs are “endpoint-centric,” that’s an appealing trade-off.", "weaknesses": "*   **“Implicit balancing” feels asserted more than demonstrated.** In my opinion, the paper leans on architectural intuition and empirical accuracy to suggest that the learned representation is balanced, but it doesn’t directly test balance. It might read more cautiously if the claim were framed as “consistent with improved balance,” and, if feasible, paired with light diagnostics (e.g., predicting treatment from the learned representation and reporting an IPM-style distance such as MMD/HSIC between treated vs. control in the rep space).\n\n*   **Decoder family may be too restrictive for coupled or constrained systems.** I read the diagonal, multiplicative generator $\\hat{x} = c \\odot x$ as elegant but narrow. In my view, this per-variable scaling risks missing cross-variable couplings, conservation/compositional constraints, or saturation effects. A short limitations note—and, if bandwidth allows, a small ablation with a lightly coupled decoder $(I+W)(c \\odot x)$ where $W$ is sparse or low-rank—could clarify where the current choice shines and where it struggles.\n\n*   **One-step “jump” to $t_j$ trades off path information for stability.** Personally, I see the single-step design as a smart way to avoid compounding autoregressive error, but it does mean path-dependent phenomena (transients, intermediate interventions, accumulation) aren’t modeled explicitly. A brief scope statement that CLEF targets endpoint editing—and a short-horizon chaining stress test (3–5 steps) to show how rollouts behave—would, in my opinion, set expectations more cleanly.\n\n*   **Evaluation blends predictive strength with causal validity.** My sense is that strong MAE/RMSE/AUC is being read as support for the causal story, but predictive accuracy alone doesn’t confirm deconfounding. It may help to report a couple of lightweight causal diagnostics alongside accuracy—(i) treatment predictability from the learned reps, (ii) a balance/divergence metric, and (iii) a small sensitivity analysis—to separate “good predictions” from “good balancing.”", "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": "sjzNY0ftyc", "reviewer": "Reviewer_QAjY", "summary": "The authors propose CLEF, a new framework for Controllable Sequence Editing, designed to modify longitudinal sequences (e.g., patient health trajectories, cellular development) based on a specified condition. [cite_start]The key contributions are twofold: 1) a new problem formulation, \"delayed sequence editing,\" which involves applying a condition at a future time and generating the output in a single step, and 2) the CLEF model, which learns \"temporal concepts\" to represent the change from the last observed time to the future time.\n\nThe core mechanism of CLEF is to predict a future state as an element-wise product of the last observed state and the learned temporal concept. This concept $c$ is generated by a \"concept encoder\" that takes the encoded history ($h_x$), the encoded condition ($h_s$), and the time delta ($\\Delta_{t_i, t_j}$) as input.", "questions": "- See weaknesses\n- Also I'd argue that c is more of a ratio than a rate of change", "limitations": "", "rating": 4, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The formalization of \"delayed sequence editing\"  as a one-step generation task is interesting contribution. This distinguishes the problem from standard auto-regressive forecasting\n- Good empirical results CLEF-based models demonstrate consistent and often large improvements over their non-CLEF counterparts across all primary tasks: immediate editing , delayed editing , and zero-shot counterfactual generation", "weaknesses": "- Does the model assume that the entire, complex dynamic evolution of each specific variable (e.g., a single lab test) over an arbitrary time $\\Delta t$ can be modeled as a single multiplicative scaling factor $c$ for that variable? This still seems dynamically and biologically implausible, as it ignores the coupled, differential nature of these systems.\n- Btw if that is the case, then if any single variable in the last observed state, $x_{k, t_i}$, is 0, then the predicted value for that variable, $\\hat{x}_{k, t_j}^s$, must also be 0 (since $c_k \\odot 0 = 0$). This is a severe limitation for any data that is sparse or has variables that can cross zero.", "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": "7RncRgLajM", "reviewer": "Reviewer_v6m5", "summary": "The paper studies the problem of sequence editing and forecasting. The paper proposes a new method to achieve this goal. The proposed method is based on encoder decoder solution. Given a condition, the proposed method changes the sequence to meet the given condition. The paper splits the problem into two subproblems: immediate sequence editing and delayed sequence editing. Experiments are conducted on variety of biomedical and financial datasets.", "questions": "- After reading the paper, it is not entirely clear how the proposed solution and results differ between immediate sequence editing and delayed sequence editing. Could you elaborate on this? My understanding is that immediate sequence editing can be viewed as a special case of delayed sequence editing.\n- There are existing approaches originally proposed for biological sequence design that could be applied to this problem. For example, Bayesian optimization such as the one introduced in “Accelerating Bayesian optimization for biological sequence design with denoising\nautoencoders” (Stanton et al., 2022) could be considered. Another possible direction is to model the problem using reinforcement learning, where $x$ represents the state and $s$ denotes the value of that state. Note that the term state in this paper may differ from its usage in reinforcement learning. Furthermore, GFlowNets have been widely applied in biological sequence design and specifically in sequence editing as introduced by “GFlowNet-Assisted Biological Sequence Editing” (Ghari et al., 2024) and multi-objective generation as introduced by \" Multi-objective GflowNets\" (Jain et al., 2023). Although direct comparison with these methods is not necessary for this paper, I recommend including a discussion of such approaches to motivate future research.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper addresses an interesting problem with significant practical applications.\n- The paper is well-written and the proposed method is clearly motivated.\n- Experimental section is thorough and convincing.", "weaknesses": "- The related work section could be expanded. Certain areas, such as reinforcement learning, are closely related to this problem but are not discussed in the current version.\n- The paper could also benefit from including more baselines by adapting other existing and closely related methods to the experimental setup. For example, some methods proposed for biological, protein, and DNA sequence editing could be applied to the problem studied in this paper, even though they were originally designed to address slightly different tasks in other biological domains. However, this is not necessary for this paper whereas it can improve its strengths'.", "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": "B24Nn5W62j", "reviewer": "Reviewer_hatS", "summary": "The paper studies controllable time‑series editing and proposes CLEF, a framework for conditional generation of time‑series given an intervention time and value. The model encodes past history, time difference, and the intervention into a temporal context embedding that modulates the extent of output change. The method is evaluated on conditional and counterfactual generation tasks across eight datasets from multiple domains, including cellular dynamics, healthcare, and sales.", "questions": "* Question: Have the authors considered/experimented with other concept encoder and concept decoder architectures?\n* Suggestion: Lack of qualitative trajectory examples. The paper (and related counterfactual outcome estimation work [Bica+20; Seedat+22; Melnychuk+22]) does not provide example estimation trajectories. Including sample time‑series from each dataset with baseline and proposed model estimates would illustrate data characteristics and model behavior.\n* Suggestion: The paragraph about \"controllable text generation\" in the Introduction (starting ln. 59) does not seem directly relevant to the problem at hand. The authors could reconsider removing it.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "* The work addresses counterfactual outcome estimation, an important problem in computational biology and healthcare.\n* The introduction motivates the problem well, and Fig. 1 is informative. The writing is generally clear. Figures are descriptive and polished, although they are currently too small to read without zooming, so they should be enlarged.\n* The model design is simple and clean.\n* The method is evaluated on a substantial number of datasets from three domains; however, the experimental setup is difficult to follow from the main text at times (see Weaknesses).", "weaknesses": "The paper exhibits several weaknesses: the proposed method shows unexpectedly poor performance relative to a simple linear baseline, the experimental setup lacks clarity in several places, and the results as presented in the Experiments section are not fully aligned with the abstract. I am willing to reconsider my score if these concerns are addressed convincingly.\n\n**(i) Performance relative to simple baselines**\n\nThis is the main weakness. In R1 and R2, performance is unexpectedly poor relative to a simple linear baseline: the linear baseline appears better on average than non‑CLEF models and comparable to CLEF models. This raises concerns about applicability and practicality. If these models do not outperform simply using the previous time point, the value of training and deploying them is unclear.\n\n**(ii) Ambiguity in experimental setup and model variants**\n\nIt is unclear how CLEF and non‑CLEF models differ. Lines 297–298 state that “CLEF and non‑CLEF differ only in the components needed to learn temporal concepts,” which is ambiguous, and Appendix D is difficult to follow. Please clarify whether non‑CLEF models (e.g., Transformer, xLSTM) are implemented exactly as in their original papers or adapted in some way. It is surprising that these models underperform the simple baseline on average in Fig. 4.\n\n**(iii) Abstract–experiment misalignment and selective reporting**\n\nThe abstract’s presentation of results does not accurately reflect the evidence shown in the Experiments section. It is unclear where the abstract’s claims are reported in the main results. Reporting “accuracy gain up to” an arbitrary or worst baseline is not a fair summary. In Fig. 4, the simple baseline appears on par with the proposed complex models. Examples:\n* Lines 21–22: “… immediate sequence editing accuracy by up to 36.74% (MAE).”\n* Lines 22–23: “… delayed sequence editing by up to 65.71% (MAE).”\n\n**(iv) Task definition: R5 vs. R4**\n\nThe definition of R5 (zero‑shot conditional generation of counterfactual trajectories) is not clear. The distinction from counterfactual outcome estimation in R4 should be specified.\n\n**(v) Intervention mechanics in R6**\n\nFor R6, it is unclear how interventions on temporal concepts are performed to decrease or increase glucose levels. As a sanity check, can authors construct a simple baseline also for this task?", "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)