From Indicators to Insights: Diversity-Optimized for Medical Series-Text Decoding via LLMs
Authors: Xiyuan Jin
Jing Wang
Ziwei Lin
Qianru Jia
Yuqing Huang
Xiaojun Ning
Zhonghua Shi
Youfang Lin
Venue: NeurIPS 2025 poster ()
Keywords: biomedical signal analysis
medical time series
knowledge-guided learning
Primary area: machine_learning_for_sciences
TL;DR: We propose InDiGO, a knowledge-aware, diversity-optimized framework that aligns clinical signals with decision cues to iteratively refine series-text prompts, enabling effective and generalizable medical time-series decoding with LLMs.

Abstract: Medical time-series analysis differs fundamentally from general ones by requiring specialized domain knowledge to interpret complex signals and clinical context.
Large language models (LLMs) hold great promise for augmenting medical time-series analysis by complementing raw series with rich contextual knowledge drawn from biomedical literature and clinical guidelines.
However, realizing this potential depends on precise and meaningful prompts that guide the LLM to key information.
Yet, determining what constitutes effective prompt content remains non-trivial—especially in medical settings where signal interpretation often hinges on subtle, expert-defined decision-making indicators. 
To this end, we propose InDiGO, a knowledge-aware evolutionary learning framework that integrates clinical signals and decision-making indicators through iterative optimization.
Across four medical benchmarks, InDiGO consistently outperforms prior methods. 
The code is available at: https://github.com/jinxyBJTU/InDiGO.

From Indicators to Insights: Diversity-Optimized for Medical Series-Text Decoding via LLMs
Xiyuan Jin', Jing Wang Ziwei Lin', Qianru Jia', Yuqing Huang",
Xiaojun Ning', Zhonghua Shi', Youfang Lin
"Scoool of Computer Science and Technology. Beijing Jiaotong University, Beijing, China
Key Laboratory Big Data & Artifcial Intelligence in Transportation Ministry of Education, Beijing, China
Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, Beijing, China
"Beijing TongRen Hospital, Capital Medical University, Beijing, China
Department of Neurosurgery, Department of Intensive Care Medicine, Sanbo Brain Hospital, Capital Medical University, Beijing, China (xiyuanjin, wj, ziweilin, qianrujia, ningxj, (rlinn@bjjtu.ee en {hyq9873, s.shimmaii comu. ecmu.edu.co edu. en
Abstract
Medical time-series analysis differs fundamentally from general ones by requiring specialized domain knowledge to interpret complex signals and clinical context. Large language models (LLMs) hold great promise for augmenting medical time- series analysis by complementing raw series with rich contextual knowledge drawn from biomedical literature and clinical guidelines. However, realizing this po- tential depends on precise and meaningful prompts that guide the LLM to key information. Yet, determining what constitutes effective prompt content remains non-trivial--sseccillly in medical settings where signal interpretation often hinges subtle, expert-defined decision-making indicators. To this end, we propose In- DiGO, a knowledge-aware evolutionary learning framework that integrates clinical signals and decision-making indicators through iterative optimization. Across four medical benchmarks, InDiGO consistently outperforms prior methods. The code is available at: ltttp:///gii http://tttuuu.om/ja///////eae/// cwt jon./iiny//TTU//II InDDIG
Introduction
Medical time series analysis forms essential foundation for continuous health monitoring, under- pinning key applications such as sleep staging [I], cardiac arrhythmia detection [19, and mobility assessment [50]. Unlike generic time series analysis, medical data requires models to capture clinically meaningful signal patterns and incorporate domain-specific knowledge for trustworthy interpretation. Earlier studies integrating knowledge priors into model architectures and aligning loss
functions with physiological semantics have yielded promising results 14 33 17].
In recent years, large language models (LLMs) have gained traction in time series analysis for their ability to integrate complementary knowledge through two key paradigms. The implicit paradigm builds on inductive biases embedded in pretrained architectures, harnessing LLMs' strength in compo- sitional reasoning and contextual abstraction [6 49) Meanwhile, the explicit paradigm formulates expert knowledge as structured textual prompts, enabling direct and interpretable knowledge injection
12 23 24 While both strategies offer notable advantages over traditional unimodal approaches, several key challenges remain in fully realizing the potential of LLMs for medical analysis.
*Conresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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C1: What kinds of promptable knowledge are most effective for decoding medical time series?
The selection of textual content knowledge carrier varies across existing works. For ex- ample, TiMe-ILLM 12) designs template that includes dataset descriptions and sample statis- ties to guide prediction tasks, while Times 23 encodes timestamp information for token- wise prompting. KEDGN specific textual medical knowledge to model time-varying inter- variable dependencies. How- ever, such promptable knowledge remains overly broad and lacks discriminative power even common domain-informed tasks-let alone in expertise-intensive inteesivee clinical domains.
Sleep Analysis EEGG
Cardiac Assessment ECG
Avd
2 uses variable-
Normal Cardiac Rhythm
Widmed USS
mantly Matt
cumples Abnormal Rhythm
Knowledge Inspiration
In these contexts, a wealth of decision-making indicators actually remains overlooked. As shown in Figure II sleep waveforms like spindles and slow waves are key indicators for identifying sleep stages (3]. while features such as RR inter- vals and QRS complex provide essential cues for cardiac assessment [). Although the extraction of these indicators via semi-automated tools may suffer from limited accuracy, they are easy to obtain and offer high utility for task, yet current
The clearly distinguishable spindle indicates the N2 stage... @ broad QRS complex may indicate the presence of ventricular arrkythmia...
Figure I: Task-relevant indicators provide critical cues for physiological state interpretation in sleep analysis and cardiac assessment. LLM-assisted paradigms fail to leverage these crucial clues, missing important insights and losing natural attribution pathway.
C2: How to robustly integrate time series and suboptimal text prompts?
Despite growing efforts integrate textual knowledge into time-series models, no unified strategy has emerged. A common approach directly concatenates textual and time-series representations as while others either enforce alignment constraints across modalities [66 30, use time-series features to query pretrained word embedding dictionaries for semantic enrichment
LLM input 4
23
These strategies presuppose that textual inputs are sufficiently informative for the task. When this condition is unmet, there is no further mechanism to reconcile the misalignment between modalities, and the synergy between time-series and text collapses, offering little valuable information for the LLM to exploit-ultmmtell resulting in marginal performance gains and underutilization of its inferential capacity.
12 22
To this end, we propose InDiGO-Indicator-innnieeeee work that integrates domain-informed decision indicators to enhance prompt robustness for medical time-series analysis. InDiGO incorporates alignment- and diversity-aware optimization to maximize synergy between text and time-series modalities, enabling more informative and resilient prompt learning while maintaining robustness to initial prompt variations across diverse clinical tasks.
Divirsity-Guided Optimization a frame-
We present a theoretical analysis of joint time series-text decoding, revealing that existing limitations largely arise from biased estimation due to mismatched series-text pairs.
We propose bask- importance sampling strategy grounded in to approximate optimal series-text combinations through alignment and diversity optimization.
prompts
Experiments on four real-world physiological datasets show that InDiGO outperforms state-of- the-art methods, with visualizations further demonstrating its strong interpretability.
2 Related Works
2.1 Knowledge-Empoweree Medical Modeling
Prior to the rise of language models and multimodal learning, many studies incorporated prior knowledge through task-specific module design and task-agnostic loss design.

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Task-specific module design. SleepHGNN 10 models the brain functional relationships across different sleep stages using heterogeneous graph convolution. L-SegSleepNett [33] and SleepKD [2T] both focus on modeling sleep transition rules between epochs, building upon the extraction of local features within each individual epoch.
Task-agnostic loss design. CLOCS [17]. based on ECG acquisition scenarios, defines a family of patient-specific contrastive learning losses guide the model in learning patient-centrie invariances. Building on this, COMET 43 further strengthens the consistency and discriminative constraints the trial and observation levels. ExpCLR [27 utilizes continuous expert features instead of view transformations to encourage consistency between the sample relationships and expert features. SmdCLR [13] extends InfoNCE [29] with multi-instance discrimination and semantic consistency regularization to alleviate false negatives and class imbalance in time-series contrastive learning.
2.2 LLM-Assisted Time Series Analysis
The emergence of pre-trained language models has revolutionized the knowledge-empowent mod- eling. Early works leverage LLMs' language priors for implicit knowledge modeling, while more recent efforts integrate textual content for explicit knowledge encoding.
g attempt to convert time series into text to exploit the zero-shot generalization capabilities inherent to LLMs.. OneFitsAll 49 treats L.MSs as universal sequence encoders by leveraging pre-trained self-attention to perform non-data-dependent operations, eliminating the need to convert time series into text. Following this, TEST [36] and 1PIM-LLM [30] attempted to align pre-trained dictionary tokens or text prototypes to better leverage the sequential modeling capabilities of LLMs.
Implicit knowledge modeling. PrompiCAST 44 and LLMTIME
Explicit knowledge encoding. TimeLLM 12 combines dataset information, task instructions, and input statistics into a template to provide prompt prefix for time series, effectively activating specific tasks. TEMPO 4 introduces semi-soft prompting strategy to provide component-spe specific prompts for different time series trend components. AutoTimes (23] transforms timestamps into textual prompts and integrates them with each series step as input, aligning with language models' autoregressive token modeling. KEDGN [24] converts the medical properties of variables into textual knowledge, enabling the language model to better capture the relationships between variables. TALON [37) further unifies time-series and textual modalities under single large language model, enabling multimodal reasoning and generalizable forecasting across domains.
Despite these advances, existing approaches fall short in the context of physiological and healthcare data, leaving rich domain knowledge underexplored. Moreover, the paradigm of jointly decoding time series and text remains insufficiently explored and understood.
3 Preliminaries and Motivation
Time-Series Text Joint Decoding. In this study, each physiological sample can be represented by triplet (8i, tv, y:). where s; E RC denotes the multichannel time series with C' channels and length L, t, is a variable-length descriptive text prompt, and y: {1, 21....... K} is the corresponding label among K predefined categories. The joint decoding process can be formulated as:
PL si (.) - (N 4;; 0), of (s., 4;; 0)
(1)
where [s; f4; 9) and of (s., f;; 6) represent the predicted mean and variance respectively, and T denotes the identity matrix, assuming independent output dimensions. By leveraging the conditional probability relationships, target Y can be obtained through integration as:
PY) P...0)~((.)) (PLM((Y|s)) (ss
PaIP)))))) [PuLM(Y|ss,
(2)
n
where P([]s;) is the distribution of possible text prompts conditioned on the series 8;, and the decoding process is achieved by integrating over the potential text distribution corresponding to all possible prompts for a given series 84. Further, the optimal parameters 0* for the L.M backbone as a joint decoding model can be estimated by Maximum Likelihood Estimation (MLE) as:
log(/ / PuLM(Ui ()lss,))PPP (())ss)))
0- = argming
logPiIM(Y = yi) = argming
(3)
3

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This implies that the marginal likelihood (i.e.. .M (LLM(y:s;; (tt(ss) can be approximated by drawing large number of samples from the potential distribution of all text prompts:
m
M = (er
Σ
which is evidently impractical due the immense computational and data requirements involved in such a process. In practical scenarios, using finite number of text samples often leads to bias:
(ias(.M [[PPLMMMlls: P()(s)) - / PLLM(Uils; (t P(tss:)
(5)
where previous work usually leverages a specific text tę to essentially approximate the expectation of the posterior distribution P(t]s;). This can be interpreted in the form of Laplace approximation:
f PuiM(uls s., () s PIam(uils;, ((1)())
1 argmax PILM(WI (s, (4) log P(t][s:]]
where 17 represents the text that simultaneously maximizes both the likelihood function PrAM(y 44) and the posterior distribution [()]ss;, in other words, 47 is the optimal text corre- sponding the series s; under the target y/- In the case of imprecise text 4, such rough statistical descriptions [12 or conceptual clinical knowledge (24]. it is evident that this will cause severe bias.
In this paper, propose efficient solution based on importance sampling (26). aiming to quickly approximate the optimal text through multiple samplings from a simple distribution related to the initial prompt, thereby avoiding the significant bias risk caused by any single inaccurate text.
4 Methodology
The overall architecture our model is illustrated in Figure 2 To better approximate the ideal marginal likelihood, begin by crafting contextually relevant text prompt by formalizing decision- making indicators extracted via semi-automated tools. Subsequently, we propose a masked Monte Carlo importance sampling (MCIS) mechanism, which supplants exhaustive prompt enumeration by iteratively sampling from a tractable proposal distribution. To foster deeper synergy between sampled prompts and time-series inputs, introduce heuristic optimization scheme that promotes informative and resilient prompt learning through relevance-based match alignment (MA) and diversity optimization (DO), while maintaining robustness variations in initial prompt quality.
Series- Interaster
Pre-trained LLM
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Deppder
BLCss Mineela
Ferinng
Donute fonwetor
The Hitrear
deasked are (eadleg ii paice are
The
WT aepost
Add à Layar Nard
The ingiacrgges wes.rggesssso waves ar sges logal
GOODe
Preecia
Output Projection
March Algeneee and Uimeaty Qptintastie
Sories Eastroding
Trange Ensheddia
QA sunti
@A7 mani
Owipat
Jmis Endheddinp
Pronge Fusheddags
Maslet Eededdooss
Figure 2: Overview of InDiGO. Given medical time series and initial texts, we apply mask-based importance sampling and pre-trained encoders to extract features. A series-text interactor captures relevance, followed by alignment and diversity optimization to identify the optimal combination.

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4.1 Series Tokenization
Each input medical time series s; is passed through a pre-trained, frozen parameter time series encoder for feature extraction, where each series is initially divided into consecutive patches with length Ly. The total number of patches is given by L. -'- + 2], where S is the stride for the horizontal sliding operation. Once the patches are created, the pre-trained time series encoder will be leveraged to embed each patch in series 8; = [s) s as:
s; = SeriesEnc(s) (s) of " [CLS*])
where S E IR x), and [CLSS*] is the special category token added at the end of each series.
4.2 Indicator-Guided Prompt Prototype Construction
To provide as much complementary information as possible for time-series signals and facilitate faster approximation of the optimal textual de- scription, we move beyond general dataset [12], timestamp-bssed descriptions [23] or concep-
Indicator Presence
Inter Indicatur Reiations
Iateral
SEG
dan REEEE))
tual clinical knowledge (24) and instead con-
struct trik-trie prompts that incorporate key
Kouth
andcctto-sppeciff descriptions.
While it is challenging to exhaustively identify all task indicators, our goal is not to achieve completeness, but rather to extract relatively in- formative cues that complement input signals and enhance downstream task performance. As shown in Figure 3 an initial prompt prototype includes specific task instruction. a summary of signal statistics, and the presence or relationships of task-relevant indicators. For example, in sleep analysis, we detect slow and spindle waves EEG signals, while in cardiac analysis, we ex- tract reethe-related features such RR inter- vals, PR intervals, and QRS durations to support a better understanding of physiological states. These indicators are integrated into prompts through automated extraction, providing task-relevant insights and enabling strong potential for generalization to other expertise-intenst domains.
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LEG and Eociss at sE00 past valur
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Pre-trainaed LLM Decoder
2f Thas
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Figure 3: Scene-specific indicator prompting via semi-automated extraction.
4.3 Masked Monte Carlo Importance Sampling
Based on the aforementioned indicator- -uiidd prompts, we obtain an initial text sample t corre- sponding to s,, which serves as coarse approximation of the optimal text tj. However, even so, manually designed prompts inevitably introduce bias in the estimation of the marginal likelihood. To mitigate this limitation, we aim to construct and perform multiple importance samplings from a simple distribution ((((t?) that is both computationally tractable and closer to the optimal distribution t7, thereby replacing infeasible enumeration with sampling driven surrogate approximation.
To be specific, for the estimation of the marginal likelihood M, can approximate it by performing multiple important sampling from a easy-tractable distribution <(114));
M f PIaMCUllss P()s)) (((1))
1
P(
di RJ
PraM(u () ((
where q(tlt?) is centered around a reference prompt E and expected to cover the main support region of the target distribution P(t)s;). At this point, the optimal value of & changes as follows:
= argmax llgg ( st, t,) + log P(t, (s)) - log qtt)(t()]
(9)
4,
In this way, the ideal marginal likelihood can be estimated efficiently rather than blindly attempting to replace it with single textual prompt, which carries the risk of inaccuracy and mismatch.
5

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Masked Sampling, We perform m random masking operation iterations on the given text prompt t.. Specifically, after tokenizing the textual prompts, we randomly select r% of the tokens at any position and replace them with the special [Mask] token, resulting in a masked sampling set Di::
D = (1 1/2 ( = 1...,...,mm m]
(10)
where Le, is the length of the prompt eg and each ig with a different random mask. Subsequently, we use a pre-trained text encoder to extract semantic representation of each t/ and repeat m times:
TextEEn([CLLS)). t/ 6/2
[SP])
(11)
where t. E +2)xx, [CLS'] and [SEP] are special tokens indicating the category and end of
prompt ty, respectively. This masked sampling and encoding process provides the imitial (inaccurate) text prompt with an opportunity infer and complete missing semanties from its surrounding context.
To ensure consistency, both the text encoder and LLM decoder share the same architecture (e.g.. GPT-2), using different layers from the front and back of the model.
4.4 Match Alignment and Diversity Optimization
By performing bassd Monte Carlo sampling from a simple distribution, we obtain a set of candi- date texts D that close the optimal fý. but their proximity still depends the correlation between g(t]{?) and P(t]ss). Moreover, since the distribution (t s;) is intractable, directly minimizing the KL divergence between the sampling distribution and the true posterior is not feasible.
To address this, we introduce an indirect optimization strategy that provides pathway for guiding the representations learning process of samples Dr. to approximate the true posterior P(4 8;) through joint optimization with the downstream task objective ( |s, 44), enabling the sampling process from ((((c)) to evolve dynamically during training via modeling the series-text interaction understanding and a lightweight combination of alignment and diversity constraints.
Series-Tess Text Interaction. We first model the correlation for each series-text pair (s,,t)) using a co-attention mechanism in Series-Tex, Interactor (STI). Formally, we define the queries, keys and values matrices for both modalities. For each time series, the query, key and value are given by 9, = sW., k,- = sW & and v. = sW. respectively. Similarly, for each text, the query, key and value are defined as 9, tWt qr k, tW1 and v. tW! where Wy WE, W Rd,xd, and Wt W W E idd are learnable weight matrices. The interaction between the time series s and the text is modeled using bidirectional co-attention mechanism, where each modality serves as both the query and the key/value for the other. Formally, we compute:
(oottmax
q,kk Vd
4,kk Vd
(z,, 2,) = BiCoAttns,... t) softmax
softmax
(12)
where n, E (L) and E (L)++)xd denote the co-attended representations of the series and the text. These are later integrated into their original representations as updated s and t'.
Alignment and Diversity Optimization for Evolving the Proposal Distribution. Approximating the optimal 1 involves both task likelihood and prompt posterior. Since the latter intractable, we introduce a heuristic surrogate objective based on Bayes' theorem, replacing P(t]]s) [s]]ss)) with (() and gradually guide the proposal distribution q(((()) toward the true posterior. We then design an alignment loss encourage precision and a diversity loss to promote coverage, jointly driving (((
to evolve toward the true posterior:
Cobjective Be log P(salt/)] ())
(13)
where the first term encourages the sampled texts to be semantically informative with respect to 8,, while the second term promotes diversity by maximizing the entropy of the proposal distribution ((11))). This balance facilitates the discovery of more informative and diverse prompts that not only better approximate the unknown optimal prompt 17, but also enhance resilience of inaccurate prompis to downstream tasks when integrated into fine-tuning.

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Specifically, to further enhance semantic consistency between series 8; and its sampled prompts t!., we introduce match alignment loss that explicitly aligns their representations:
exp(sim(s,, 12)TT)
aalggn
E (s
log
(14)
Dj= exp(sim //)
Additionally, to encourage broader exploration of the prompt space and reduce over-reliance on the initial prompt 49, we incorporate a diversity optimization objective based on entropy:
1 Σ log ((1)))
Ldiverse = H(((((()) =
(15)
Together, these components enable the proposal distribution ((((()) to evolve during training in both a semantically aligned and exploration-rich manner, progressively approximating the true posterior.
Joint Decoding and Output Projection. For the multiple series-text pairs after STI modeling, we take their respective means š; f, and concatenate them, then feed the result into the LLM decoder and projection head for final inference, yielding the predicted target:
OutputProj(Flateen (Deiootennneeorrr...L.... ([s,, (()))
(16)
Finally the task loss (i.e., cross-entropy) collaborates with alignment and diversity objectives to jointly guide the model optimization and learning of proposal distribution as L = Lalign + Ldiverse + Lask-
Experiments
5.1 Datasets and Data Processing
Datasets. We evaluate InDiGO on four public medical healthcare datasets: Sleep-EDF-20/78 (EEG/EOG, 100Hz) (16), PTB-XL (12-lead ECG, 500Hz) [41], and UCI HAR (wearable motion signals, 50Hz) 2]. Sleep-EDF-2071 provides five sleep stages under AASM standards [3; PTB-XL adopts two arrhythmia prototypes established in prior work (45); HAR supports six-class activity recognition from wearable sensors. More descriptions details please refer to Appendix A
Data Splitting. We segment sleep recordings into 30s epochs and perform 5-fold subject-independent coos-validation (3:1:1 split). PTB-XL uses its standard 8:1:1 patient split, repeated over five seeds. HAR uses its official split, with the test set divided 1:1 into validation and test.
Indicator Extraction. For sleep staging, we annotate key indicators such as spindles and slow waves using A7 [8] and YASA [40]. For arrhythmia detection, we extract inter- wave features- -RR, PR. QRS, and PP intervals using NeuroKit2 25). HAR lacks distinct waveform structures, allowing us to evaluate model performance in the absence of explicit physiological indicators.
5.2 Experimental Settings and Baselines
The experiments are implemented by Pytorch framework. For Table [ and Table we report the mean and standard deviation values. Our model is trained by Adam optimizer with learning rate 1r=0.0003, mask ratio of 40%, and m=10 sampling times. We use BIOT 45). pre-trained model tailored for physiological time series, as the time series encoder, and use the first/last 24 layers of GPT-2 XL 34 the pre-trained text encoder and decoder, respectively. For details on pre-trained
model configurations and hyperparameter choices, please refer to Table 9 10 in Appendix B and
Baselines. We compare InDiGO with state-of-ther methods, including general time-series represen- tation models (TF-C [48], SimMTM 77. OneFitsAll [49]. Time-LLMM [12), KEDGN (24), MiniRocket
(5) BIOT (45),, sleep stage classifiers (TinySleepNet 38 XSleepNet 32) L-SeqSleepNet [33],
SleepHGNN 10), SleepKD (1). SleepDG 42 Brant-X 47 l), and physiological signal decoders
(SPaRCNet 115, Contra WR 46 CNN-Transformer
31 FFCL 20 ST-Transformer (35]
5.3 Main Results
Tables Man 2 report the accuracy of InDiGO and baselines across three physiological tasks. InDiGO consistently outperforms all methods. General pre-trained models underperform due to their lack of
7

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Table 1: 5-fold cross-validated average results for sleep stage classification.
Sleep-EDF-20
Sleep-EDD-78
Methods
Acc.
Macro F1
Kappa
Acc.
Macro Fl
Kappa
TF-C 48
55.42 +1.39
26.04 #0.21
31.54
53,90
#4.03
26.00 +2.09
29.32 #6,43
SimMTM 7
66.91 +1.8
53.21 +1.95
53.25 #2.02
63.06
+2.67
57.07 +2.13
53.07 +3.42
OneFitsAll 49
+1.50
61.61 +5.80
61.81 #3.50
68.50 #2.19
54.24
55.21 #3.07
Time-LLM [2
80.31 +2.63
71.64
70.22 #2.84
78.08 +2,96
66.09 +3.25
68.04 #3.14
KEDGN 24
74.89 #3.86
64.29 #3.36
64.90
70.34
58.59 #2.74
57.47 #2.56
MiniRocket
81.60
72.82
72.79 #1.96
78.36 #1.93
70.18 #2.35
69.46 #2.46
BIOT
81.86 +4.41
75.29 +4.47
75.14
+3.04 77.15
69.36 #4.13
68.26
TinySteepNet (38
83.64 +2.31
77.54 +2.55
77.63 +2.29
83.49
+2.24
76.64 +2.61
76,41
#2.59
XSteepNet 32
80.93 +2.34
+2.59 76.71
74.31 #2.32
81.83 +2.30
75.28 +2.66
75.44 +2.37
LeSepSleppNet
#2.10
74.90
76.47 =2.24
80.84
72.67 +2.38
74.94 #2.51
SleepHGNN
010
81,15 11.96
72.88 +2.17
73.35 42.16
77.35 #2.13
(9.5642..39
68.65 +2.41
SleepKD 21
82,44 #2.40
72.72
76.87+2.66
80.19
72.65 +2.84
74.86 +2.93
SleepDG 12
81.92 +2.27
42.53
76.43 #2.47
79.95 #2.42
72.21
+2.59
16. +2.68
Brant-X [4
84.58 +1.88
77.63 #2.13
79.29 =2.18
82.84 #2.21
77.04 #2.30
76.67 +2.49
InDiGO
89.04 +1.80
80.53 +1.77
84.91 #2.51
86.79 +1.90
81.12
81.60 +2.89
Table 2: Average performance arrhythmia detection and human activity recognition tasks.
PTB-XL
HAR
Methods
BaAce.
AUCPR
AUROC
BaAce.
Kappa
Weighted F1
TF-C 488
58.91 =2.24
61.08 +3.52
33.79
+3.55
+3.09
81.71
+3.84
82.13 +2,25
SimMTM
64.13 #2.16
67.91
+2.80
42.95
+4.98
88.53 +0.52
86.42 +0.63
88.40 #0.51
OneFitsAll
49
71.16 #1.26
77.41
+1.53
57.39
+1.72
88.21
+0.93
85.79 +1.02
88.24 +0.82
Time-LLM
12
75.79 =0.49
82.99
+0.40
67.36 #0.77
90.71 +1.92
86.21 +1.47
89.03 #1.35
KEDGN 24
74.64 #1.2
80.06
#1.83
66.73 #2.49
89,50
+0.28
86.14 +0.33
88.74 #0.24
MiniRocker
81.79 =0.34
87.79
#0.13
75.03 #0.19
91.68 +0.53
89.58 +0.25
91.32 #0.69
BIOT 45
84.21 =0.30
92.21
+0.75
76.59
#0.76
94.61 +1.34
93.51
+1.60
94.58 #1.36
FFCL 20
70.34 =0.52
70.88
+0.53
51,27 +0.51
85.19 +1.45 .48
82. +1.77
85.08 +1.38
SPaRCNet
15
82.75 =0.47
90.40 +0.67
75.50 =0.73
93.71 #1 10
92.36 11.89
93.65 #1.55
ContraWR
46
75.32 #5.61
75.49 +1.64
52.58 1.99
90.68 11 11.4
88.79 +2.01
90.55 +1.82
CNN-Transforner
66.50 +4.59
71.75 +5.58
+9.36
+8.39
82.73 49.53
83.52
ST-Transformer
35
72.38 +0.83
77.75 #1.53
60.03 #1.79
93.36 +0.63
92.13 #0.76
93.37 +0.68
InDiGO
86.02 20.19
92.31 #0.26
82.98 20.50
95.83 #0.26
95.02 20.26
95.81 #0.23
physiological signal awareness, while task-specific models benefit from prior knowledge integration. OneFitsAll, Time-LLM, and KEDGN despite leveraging knowledge encoding, offer limited gains, underscoring our method's strength. Notably, our advantage on the HAR task demonstrates that InDiGO remains effective even without task-specific waveform prompts.
5.4
Ablation Studies
Table 3: Ablation study of model design.
To better understand the contribution of each component, conduct ablation studies sum- marized in Table 3 We can observe a consis- tent performance decline across all metrics when any of the key modules is ablated, highlighting their individual importance. w/o Masked Monte
Sleep-EDF-22
PTB-XL XL
Metries
Macro F1
Kappa
BaAce.
AUCPR
w/o MCIS
76.23
79.97
84.31
90.12
Carlo Importance Sampling (MCIS) shows the most significant drop, highlighting the critical role of masked importance sampling in enhanc- ing robustness against inaccurate prompts. w/o Series-Text Interaction (STT) module also causes a notable degradation, which underscores the necessity of cross-modal interaction capturing complementary information from series and text. w/o Match Alignment (MA) and w/o Diversity Optimization (DO) lead to moderate declines, highlighting their contribution to enhancing textual diversity, which in turn enables more effective activation of LLM's potential through joint fine-tuning.
w/o STI
78.70
82.98
85.46
91.57
w/o MA
78.31
82.18
85.89
91.25
w/o DO
77.12
80.33
84.52
90.54
Full
80.53
84.91
86.02
92.31
5.5 Low-Resource Generalization: Few-Shot and Zero-Shot Settings
We evaluate InDiGO in low-resource settings, including few-shot and zero-shot scenarios. As shown in Tables 4 and 5 it consistently achieves the best results across all datasets, demonstrating

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strong generalization and robustness. This improvement can be attributed to the indicator sedicattr-guieed prompting and mask-basee diversity sampling, which jointly expand the textual representation space and strengthen peries-prompl alignment, enabling effective adaptation under limited supervision.
Table 4: Performance Comparison Few-Shot Learning Tasks (5% Training Data).
Methods
SimMTM
OneFitsAll
Time-LIM
KEDGN
BIOT
InDiGO
Acc.
Macro
Macro
Acc.
Macro
Macro
Macro
EDF-20
63.91
47.08
60.81
53.81
59.21
50.37
66.65
52.79
63.72
51.49
75.40
69.63
EDF-78
62.06
42.72
60.28
52.08
66.47
55.14
66.20
51,53
66.38
51.37
74.81
68.63
PTB-XL.
48.91
67.06
HAR
55.75
50.30
52,55
47.20
53.40
48.40
58.92
$7.69
67.18
63.46
76.79
74.60
Table 5: Performance Comparison on Few-Shot Learning Tasks (10% Training Data).
Methods
SimMTM
OneFissAll
Time-IIM
KEDGN
RIOT
InDiGO
Acc.
Macro F1
Aec.
Macro
Acc.
Macro
Acc.
Macro F]
Aec.
Macro
Acc.
Macro
EDF-20
64.03
48.78
64.10
56.96
67.42
53.55
69.37
57.16
68.99
56.34
76.70
70,83
EDF-78
62.82
53.31
PTB-XI.
56.15
53.24
67.14
56.39
65.01
58.82
64.92
57.14
70.78
64.57
75.86
70.30
HAR
57.96
53.38
62,38
61.32
63.76
62.78
60.69
59.20
69.42
68.98
79.45
79.45
We further conduct zero-shot transfer experiments between Sleep-EDF-20 and Sleep-EDF-78 by
excluding the first 20 overlapping subjects from Sleep-EDF-78, as shown in Table Even without target-domain labels, InDiGO surpasses baselines, showing that indicator-guides prompting and
Monte Carlo sampling boost representation diversity and low-resource adaptability.
Table 6: Performance Comparison on Zero-Shot Learning Tasks.
Methods
SimMTM
OndFitAAl
Macro
Time-LLM
Maero
KEDGN
Macro
BIOT
Macmo
InDiGO
Macro
RDF-20
EDF-78
44.18 $8.93
56.57
55 67.55
63.87
51.82
64.55
53.11
73.57
EDF-78
EDP-20
64.37
76.09
68.70
75.49
71.41
60.19
73.30
63.24
76.86
72.94
5.6 Efficiency Analysis
We conducted an efficiency comparison the PTB-XL dataset to evaluate the computational cost of InDiGO against several representative base- lines. As shown in Table SimMTM exhibits the highest training cost, primarily due to its masked sampling mechanism and multi-scall contrastive objective. TimeLLM incurs substantial inference latency because it generates a separate textual prompt for each channel, introducing repeated computation. KEDGN suffers from the limited parallelism of its recurrent backbone, leading longer execution times. In contrast, our method maintains high efficiency across varying prompt sample times (m 5, 10, 20). While increasing m leads a slight rise in training and inference time, InDiGO consistently outperforms the baselines in computational efficiency.
Table 7: Efficiency Evaluation: Training and Inference Overhead.
Model Name
Training(s)
Inference(s)
SimMTM (m=3)
2333.13
38.73
TimeLLM
1742.10
237.05
KEDGN
2321.37
241.27
InDiGO (m=5)
632.98
127.91
InDiGO (m=10)
788.79
144.23
InDiGO (m=20)
1118.17
175.12
5.7 Diversity Enhancement via DO: Prompt Similarity Distribution Analysis
DO significantly shifts the distribution toward lower similarity values across all datasets, indicating a broader semantic spread among the sampled prompts. This validates that DO promotes greater lexical and semantic diversity, thereby enriching the representational space of textual cues. Such diversity enables the model to explore a wider range of series-text alignments during fine-tuning, improving generalization especially under weak partially informative prompts.
As shown in Figure 4
200-
2002 phomni
22
(a) Sleep-EDF-20
(b) SDo7p-EDF-78
(c) PTB-XL
(d) HAR
Figure 4: Distribution of prompt similarities with/without Diversity Optimization across four datasets.

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5,8 Prompt Robustness under Alignment and Diversity Objectives
Here, we further provide experimental validation of the effectiveness and robustness of indicator- guided prompts, match alignment (MA), and diversity optimization (DO).
Specifically, we progressively refine the prompts. starting from general instruction of the task and signal statistics, and incrementally incorporating task-relevant domain indicators to enhance their specificity. We then evaluate the model under all combinations of match alignment and diversity optimization, including the baseline without ei- ther strategy. As shown in Figure S (results for Sleep-EDF-78 are Appendix F our MA-DO optimization framework ensures robust adapta- tion across varying prompt qualities. Notably, even under limited generic prompt conditions, MA and DO offer stable gains. More impor- tantly, once key indicator troduced, these strategies effectively expand the semantic utility of prompts facilitating align- ment between textual hints and task objectives, and substantially improving model generalization.
egual
Sleep EDF-20
Mocilyy planttyyg
Prompt Task Indraction Only.
[bstmettk)) The xan dleart roto dgad eegltnde al canplinel ut...
Prompt Signal
[estracioo] (Sigaal Stend (clowing
Prongt 3: Staticies + Wave Presenco
PTB-NL
[bstrctke]] Sigddd State The diace pess
Prompt Staticies Inter Intervals
[lsttecioo] State x(batenaa)
legfs QJS wron -pAS Tength
Promut S: Fall Temperal Profile
(N) Five prongtt voriants
(lac. QRS Duration).
(b) Rrealts Ster--EiF-20
FTB-XI.
partially in-
Figure 5: Different prompt designs.
5.9 Model Interpretation Analysis of Series- Text Interactions
We present a case study on Sleep-EDF-20 as illustrated in Figure 6 We visualize the co- attention scores between series and text prompts throughout the network's optimization process. The top 3 subplots show the evolution of the network's 's learning of the series-text correlation: from initial misalignment in (a) to indiscrimi- nate attention in the middle stages in (b), and finally to a more refined, discriminative focus on text prompts as the network matures in (c). The patches with higher attention scores correspond to specific spindles in the sleep EEG, indicating that our series-text interaction process is progres- sively guided by localized waveform indicators. Moreover, the indicator-related sentences in the textual prompts gradually play a more significant role, while the statistical feature parts become less influential during network optimization. This further demonstrates that, through informative indicator-related prompts and the dynamic evolution guided by MA-DO sampling, our model enhances the mutual understanding between time series and text, allowing the embedded knowledge to be effectively interpreted by the model.
F 10000
Text Prouge
Figure showcase of series-text correlation with network training. (a)-(c) represent the co- attention scores of the series-text pair, (d) and (e) represent the series and text samples.
Conclusion
In this paper. we propose the DiGO), knowledge-award method tailored for multimodal modeling of medical time-series analysis. While our current approach requires relatively structured indicator-to-text conversion -oftnn rely- ing on domain expertise we demonstrate that InDiGO significantly lowers the barrier for clinical professionals to participate in LLM--drven multimodal analysis, offering promising step toward broader adoption of foundation models in medical time-series applications.
Diversity-Guided Optimization framework (In-
Acknowledgments
This work was supported by the National Natural Science Foundation of China (No. 62576027) and the Fundamental Research Funds for the Central Universities under Grant No. 2025JBMC030.
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NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: The abstract and introduction clearly articulate the limitations of existing physiological time-series analysis methods in terms of knowledge incorporation, particularly the lack of principled design in both content and method. Our introduction part further outlines our key contributions, especially the proposed mnoledge-empooeere decoding framework that utilizes waveform-awan prompts and optimization strategy address these challenges effectively.
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Question:
Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification: Yes. Appendix Section H our paper acknowledges that although the proposed method reduces reliance on expert knowledge by using semi-automated tools and selecting a minimal subset signal features, still assumes the availability basic domain priors. In scenarios where such priors are unclear unavailable, constructing effective prompts and cues may remain challenging. Guidelines:
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Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?
Answer: [Yes]
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Does the paper fully disclose all the information needed to reproduce the main ex- perimental results the paper to the extent that affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?
Question:
Answer: [Yes]
Justification: Our paper provides comprehensive details necessary to reproduce the main experimental results. The method section clearly describes the key components, including the mask-based sampling strategy, co-attention mechanism, and diversity optimization ob- jectives. Additionally, the experimental setup specifies the waveform types, extraction tools, and dataset configurations, ensuring the main claims and conclusions are fully supported and reproducible.
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A Details of Datasets and Experimental Settings
A.1 More for Datasets and Processings
We evaluate the proposed InDiGO on four diverse physiological datasets to assess its generalizability across modalities. The dataset statistics are summarized Table S
Table 8: Dataset statistics.
Datasets
Sleep-EDF-20
Sleep-EDF-78
PTB-XL
HAR
Samples # Rate
42,308 100 EEG,1 EOG 30 seconds 3000
195,479
65,511
10,299
100
500
50
# Channels
EEG,1 EOG
12 ECG leads
coordinates 2.56 seconds
# Duration
30 seconds 3000
seconds 2400
# Max Observation Length
128
Sleep-EDF-20 & Sleep-EDF-78 Contain overnight polysomnographic recordings with EEG Fpz-Cz Cz and horizontal EOG signals sampled at 100 Hz. Sleep stages are annotated based on AASM rules into five categories: Wake, N1. N2, N3, and REM. Sleep-EDF-20 includes 39 recordings from 20 subjects; Sleep-EDF-78 contains 153 recordings from 78 participants aged 25-101. We segment recordings into 30-second epochs, and retain corresponding sleep labels. We adopt 5-fold subject-independen cross-vall validation setup. Each fold divides subjects into training, validation, and test sets with a 3:1:1 ratio.
PTB-XL P large-scale 12-lead ECG dataset with 21,837 10-second recordings from 18,885 patients. For all signals, we the original standard set of 12 leads (I, II. III, AVL, AVR, AVF, VI, .... V6) with reference electrodes on the right arm. Each ECG record is labeled using standardized SCP-ECG statements across diagnostic, form, and rhythm classes. Recordings are sampled at 500 Hz. We use the standard 80%/10%//00 level split and repeat experiments five times with different random seeds. Each ECG is associated with multiple diagnostic labels (up to 27 classes). Following BIOT [45], we group arrhythmia-relate, diagnoses into a binary label: record is considered positive if at least one arrhythmia-type diagnosis is present.
patient-
HAR Contains tri-axial accelerometer and gyroscope signals (50 Hz) collected from 30 individuals performing six daily activities while wearing moist-mounted Samsung Galaxy S2 smartphone. Activities include walking, walking upstairs/downstairs, sitting, standing, and lying down. We directly use the pre-segmented time windows and activity labels. The dataset is used as-is for 6-class human activity recognition to validate our method's adaptability to non-wavenn novefvvforrm physiological time series.
We 1 RTX A6000 48GB GPU to train models for 200 epochs with batch size of 256. Learning rate and weight decay are grid-searcher to the optimal.
A.2 Evalution metrics
Below, we provide the definitions of some of the metrics used in our study.
Macro Fl is the arithmetic mean of the Fl scores computed for each class, treating each class equally regardless its frequency the dataset.
Coken's Kappa is a statistical measure used to assess the level of agreement between two raters (or systems) when classifying items into mutually exclusive categories. It considers both the observed agreement (the proportion of times the raters agree) and the expected agreement (the agreement that would oceur by chance).
icc.rchiie.ic..c.m uci http:///www..ttteeoooee///////ts
smart mphoness
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Balanced Accuracy is a metric used to evaluate the performance of a classification model, especially when the dataset is imbalanced. It calculates the average of the sensitivity (true positive rate) and specificity (true negative rate) to ensure that both classes are treated equally.
AUC-PR is the area under the precision recall (PR) curve for binary classification task.
AUROC is the area under the ROC curve, summarizing the ROC curve into an single number that describes the performance of a model for multiple thresholds at the same time.
B Selection of Pretrained Models
B.1 Pretrained Series Encoder
Due to the variability of physiological time-series scenarios, where different diseases or treatment processes involve distinct data collection paradigms, there is currently no mature, widely recognized multi-modal physiological time-series foundation model. the context this study, we utilize BIOT [5]. model specificallyy pre-trained EEG and ECG data.
Sleep stage classification. We initialize our time series encoder using the pre-trained model provided by the official release, which was trained on six EEG datasets.
Arrhythmias phenotype detection. Since the official code of BIOT 45 does provide pre-
trained models for ECG tasks, we follow the strategy outlined in its paper and perform unsupervised pre-training on five cardiology subsets using 12-lead ECG data.
B.2 Prefrained LLM
Table 9: The classification performance of different pretrained LLMs on Sleep-EDF-20 and PTB-XL.
Sleep EDF 20
Model Name
Parameters
Layers
dmodel
Acc.
Scns
Spec
Macro
Kappa
BaAcc.
AUCPR
AUROC
BERT
110M
768
85.73
76.98
95.72
75.39
80.98
84.15
89.97
80.73
GPT-2 Small
124M
768
86.60 86.69
77.41
96.90
76.04
81.68
84.97
90.84
81.48
GPT-2 Medium
355M
24
1024
88. 10
78.42
97.24
77.51
83.59
84.97
90.95
81.79
GPT-2 Large
774M
36
1280
88.55
80.12
97.35
79.29
84,22
85.44
91.09
81.33
GPT-2 XI.
1.5B
48
1600
89.04
81,32
97.44
80.53
84.91
86.02
92.31
82.98
LLaMA(8)
L.1B
4096
86.55
78.74
95.93
77.22
81.24
83.95
90.13
80.84
LLaMA(32)
7B
32
4096
88.13
79.29
96.86
79.15
82.45
84.94
90.82
81.35
We evaluated the classification performance of several pre-trained LLMs as the language encoder- decoder in InDiGO, including four versions of GPT-2 34, BERT [6, and two variants of LLaMA 33], as summarized in Table Overall, different LLMS yield comparable performance across both tasks. We attribute this to the nature of our physiological time series classification task, where the textual prompts are relatively concise and structurally simple, placing limited demand on the encoder's capacity for deep semantic reasoning.
Notably, increasing model size (c.g., among GPT-2 variants) does not bring substantial gains, while the performance of LLaMA models shows no consistent advantage despite their high dimensional- ity-possibly due to optimization difficulties during training. These observations further confirm that the primary challenge in our setting lies not in modeling complex text, but enhancing the mutual understanding between time series and prompts through effective series-text alignment.
C Selection of optimal parrmeeass
We provide diagnostic experiments on the selection of hyperparameters in the InDiGO, shown in Table I0 It is important to note that when adjusting a specific parameter, the remaining parameters set to the same values those of the optimal result.
Layers of LLM Encoder and Decoder. Considering the joint series-text decoding process this paper and the encoding process of initial textual prompts, the text encoder and decoder used in
23

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Table 10: Diagnostic experiments of our model Sleep-EDF-20 and PTB-XL.
Sleep- 2D-100
PIB-XL
Variants
Acc.
Sens
Spec
Marm F1
Kappa
BaAce.
ALCPR
AUROC
Full Model
LE = 24, Lpe 24
40%,
89.04
81.32
97.44
80.53
84,91
86.02
92.31
82.98
Line Luve
87.62
79.13
96.91
78.03
84.12
84.72
90.01
80.73
LEne 12, LDec 35
88.00
79.86
97.21
78.84
83.51
85.27
90.62
81.04
Layer of LLM Ene-Dee
Litee 18, Lise 30
79.75
LEne = 30, LDec 18
88.93
79.59
97.45
78.62
84.71
85.10
90.97
81.63
Lyne 36, Lise
97.41
78.17
Leae 42, Livee 6
87,89
79.43
97.20
77.96
83.35
84.31
90.79
81.27
20%
86.49
74,77
96.88
73.17
81.32
85.26
90.60
80.35
Mask Ratio
60%
88.73
79.80
97.40
79.1
84.45
85.34
90.65
81.53
80%
87.93
80.32
97.18
79.14
83.44
84.06
90.41
81.28
m 5
88.20
79.53
97.28
78.49
83.73
84.41
90.21
80.24
Sampling times
15
89.34
79.78
97.55
78.93
85.28
85.36
90.69
80.82
m 20
89.18
79.45
97.54
78.26
85.03
85.39
90.44
81.03
300
89.46
80.57
97.55
80.52
85.46
85,38
90.79
81,23
InDiGO are derived from different layers of GPT-2. We divide the 48 hidden layers GPT-2 XL into several groups based on their sequential order. Since we freeze the text encoder and fine-tune the text decoder, different layer combinations can impact the optimization speed of the network. However, more critical issue lies in the alignment condition. Mismatched layer configurations between the text encoder and decoder may lead to misalignment, causing the representations of the series and text under the alignment loss constraint to lose their intended meaning. From the experimental results, we observe that when the language model layers are balanced between the encoder and decoder, our series-text joint decoding achieves the best performance on downstream tasks. This conclusion also holds true across different scales GPT-2.
Mask Ratio. In the masked Monte Carlo importance sampling, we achieve sampling from simple distribution by applying token-wiss random masking to the initial waveform-relatee text prompts. During the adjustment of the masking probability, we observed that with a fixed number of samples, lower masking rate limited the diversity of the samples, while higher masking rate caused distortion in the generated text. Therefore, the optimal masking ratio, as experimentally proven, is 40%, though also depends on the number of subsequent samples.
Sampling Times. In fact, the number of samples is closely related to the masking ratio. When the masking ratio low, fewer sampling iterations do not effectively approximate the optimal text. When the masking ratio is moderate, adjusting the number of samples still doesn't 't necessarily lead to better results with too many iterations. Therefore, considering the training cost, we select 10 sampling iterations as the optimal balance.
D Full Results
Due to space limitations and clarity considerations the main text, we did not report the full set of evaluation metrics for the sleep staging task. For completeness, present the detailed results in Table IT] Specifically, we additionally report sensitivity and specificity, which provide further insights into stage-wise classification performance.
E Statistical Significance Analysis of Model Performance
Table 12
presents the statistical significance results of model performance comparisons against InDiGO across four benchmark datasets using MANOVA tests (28). From the table, observe that InDiGO consistently and significantly outperforms all baseline models across all datasets. Most comparisons reach the highest level of significance, suggesting that InDiGO's s superiority is not only consistent but also statistically robust. Particularly, on the PTB-XL dataset, all models show highly significant differences. indicating that InDiGO's improvements are most prominent complex physiological signal domains. Similarly, HAR, all models except BIOT achieve significance, while BIOT still maintains ** level, reflecting InDiGO's strong performance even against recent competitive baselines.
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Table 11: Average performance on the sleep stage classification task.
Methods
Sleep EDF
Sleep EDF-28
Acc:
Scns.
Spec.
Marr F1
Kappn
Asc.
Seas.
Spec:
Macto F1
Kappa
TF-C
43
55.42 +1.20
$1.52
+1009
86.07
+0.30
26.04 +0.21
+:552
53.
+4.0%
1.35 +2440
85.80
34
26:00
2.00
29
+5
SimMTM
66.91 =1.80
53.47
m1
90161
=1.63
53.21 =1.05
53.25
=2.02
63
=2.67
59
12 +3.88
91.21
+1.50 566
57.07 +2.13
53.00 +5.42
OreFiaAll
29
21.50
63.50
25.30
02:76=1:12
01.61 =5.00
61.81 =3.50
68.50
03.50=2..11
56.5% 16.16
91
20.85
54.34
=1.9
55.21 =5.07
Time-11.M
12
80.31
-2.63
76.53
15
04.53 =2.95
71.64 23.02
70:22=2.86
78.08
67.44
94.13
23.01
63.29
68.04
KRDON 24
+4.89+1.66
64.60
63
91.
+1.39
64:29 +3.36
64:00
+5.46
70 94 -188
57.00
02:31 +0.49
58.59
57.47 +2.56
MinRodkd 1
-1.60-1.55
72.63 +1
05.15-1:12
72.82 +2.01
72.79
76 96 +1.93
69.33
04.08 +.77
70.18 +2.35
69 +2.46
233
84.86-4441
75.98
=3.66
05.12
75.29
m4.47
75.14
16.00
77:15-3.44
+2.87
93.94 +0.76
69.
19.36-s4.1 14.13
68. s4
SinpStrrpNet
15
83.64
+2.31
81.60
+3.00
96.05 +2.05
77.54 +2.55
27.63-2.22
83.49 -2.24
80.25 +2.65
06.02 +2.11
76.64 +2.6
76.41 +2.59
XSlagpNet
[2
80.3 -2.34
75.7% =2.21
04.79-2.54
76.71
=2.50
74.31 =2.92
81 22.6-22333
80.
00..50-2220 =2.2
05.74 +2.58
75.28 +2.66
75.44 +2.37
1x
78:42 82.90-2-12
05.86
=2.00
74.9122.22 =2.23
=2.24
H0 00034=221
22.75
05.19
05.19
72.67=2.38
74.94
SleepHKGNN
10
81.15 21.96
74.23 =2.10
23.96
72.88
73.35=2.16
77 17.35=22. =2.13
69.94 22
94.04 #2.02
69. 22.39
68.65 #2.41
NeepKD
21
82
+0.40
76 +7.54
94.78
24:11 +2.73
76.87 #3.63
80. 19 +2.85
72.05 +2.88
04.95 +2
72.68 +2.84
74.86 42.93
Steppii
+2
81.92 -2.27
19.12
05.75
-2.65
74.74 -2.53
76-43
-2.47
79
-2.42
73.91 +2.41
03.57 +2.63
72:31 +2.59
74.16 +2.68
Bmin-X
+
84.58 -1.96
80.18
96
36
-1.89
27.63
+2.13
79.29
+2. 18
82
K2.84-2222 +2.21
81.85
+2.42
05.91
+2.08
22.04
+2 10
76.67 +2.49
Inf0GO
89.04 -1.80
81.32 +1.64
07.44
-0.44
N0.53 +1.77
84.91 +2.51
86.79-1.99
81.86 11.63
96.71
+0.59
81.12
+1.8
$1.60 +2.89
On Sleep-EDF-20 and Sleep-EDF-78, although all models are still significantly outperformed by InDiGO, the significance levels are slightly more diverse. This reflects the relatively closer perfor- mance gap among top models in sleep staging tasks, especially between InDiGO and MiniRocket or BIOT. Nonetheless, even in these settings, the improvements by InDiGO are statistically justified.
Overall, this analysis reaffirms that InDiGO offers a substantial and statistically significant perfor- mance gain across varied time-series datasets, underscoring its effectiveness and generalizability.
Table 12:
Statistical significance of model performance compared to InDiGO on cach dataset. Stars indicate significance levels from MANOVA tests (28). * p 0.05, ** p < 0.01, *** p 0.001
Model
TF-C
SimMTM
OneFitsAll
Time-LLM
KEDGN
MiniRocket
BIOT
Dataset
Sleep-EDF-20
$8
888
Sleep-EDF-78
88
888
8%
PTB-XL
***
HAR
*
#
$#9
$88
888
#+*
F Robustness Across Prompt Variants on Sleep-EDF-78
To further validate the generalizability of our approach, we replicate the prompt sensitivity study on the Slcep-EDF-78 dataset. We consider three types of indicator indicator-guided guided prompts with increasing specificity: (i) prompts describing only the sleep staging task, (ii) prompts augmented with statistical characteristics of sleep signals, (iii) prompts additionally incorporating references to sleep waveform patterns.
As shown in Figure 7 results on Sleep-EDF-78 mirror the trends observed in the main datasets. Specifically, our MA-DO framework consistently improves model performance under less infor- mative prompts by broadening the effective representational space of textual inputs. Notably, once waveform-based indicators are introduced, performance gains become the model's 's improved ability to leverage semantically rich cues through alignment and diversity enhancement.
hiirantill-highlgghht
G Additional Interpretation Analysis: Slow- Wave Alignment in Beries-Text Attention
We provide an additional case study from the Sleep-EDF-20 dataset, focusing on time-series segments that exhibit prominent slow-wave wave activity. As illustrated in Figure 8 we visualize the o--attention scores between the EEG series and textual prompts at three different stages of model optimization.
In the early stage (a), the model assigns low and scattered attention across both modalities, indicating weak alignment. During intermediate training (b), attention becomes more concentrated, albeit not yet task task-relevant. By the final stage (c), the network distinctly emphasizes time points associated
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---

Given this <Tossecond BEG and BOG signals, identify current sleep stage.
Prompt Task Instruction Only.
Sleep-EDF-78
66
The peak power of current EEG EOG <EBG peak value> of Hz and <BOG peak value> Hz severally....
|Instruction]
Prompt 2: Basic Signal Statisties.
[Instruction] + [Signal Stats] + The following waves are detected: <waveform presences.
Plrgee
Merga
Prompt 3: Statistics + Wave Presence.
(b) Results in Sleep-EDF-20
(a) Three prompt variants
PTB-XL
Figure 7: Prompt Robustness Analysis Sleep-EDF-78.
with slow-wave peaks, and correspondingly shifts textual focus toward phrases referencing waveform patterns rather than general signal descriptions.
This progressive refinement highlights the model's capacity learn localized waveform semantics from indicator andicator-guided prompts. It also illustrates that our series-text alignment mechanism, enhanced by prompt design and optimization strategies, effectively bridges raw signal patterns and high-level textual representations even under nuanced physiological phenomena like slow waves.
(a) Epoch
(b) Epoch
(c) Epoch 20
Series Patches
Prompe Tokens
EOG slaitly
[Fgaa Sudakes The pask of tb0 EOU 1. He 11. Hi severaly.
indiaandi There are tleer vence provent
(d) Sleep signals EEG
(0) Prompt
Figure 8: Visualization of co-attention dynamics for slow- wave segments. (a)-(c) depict attention maps at different training stages; (d)-(e) show corresponding series and prompt inputs.
slow-wave
H Limitations and Future Work
This work strives to reduce reliance on expert knowledge by employing semi-automated tools to extract signal indicators and selecting only small subset of salient features rather than aiming for exhaustive domain coverage. While this approach improves scalability and practicality, still minimal understanding of
implicitly assumes the availability of some domain knowledge-nancn what constitutes relevant cues or events within a given task. domains where such priors are poorly defined or altogether absent, constructing meaningful prompts may remain challenging.
Looking ahead, a key direction to enhance the generality of our framework by moving from instance-level cues to event-level schema abstraction. Rather than relying on manually defined signal indicators, future prompting could employ loosely defined task schemas that describe events or state changes, allowing LLMs to infer and internalize domain patterns in self-supervised manner. This paradigm would reduce human intervention, enable rapid adaptation across physiological domains, and advance LLM-based temporal reasoning with minimal priors.
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Reviews:
[{"review_id": "x1kh2xsppz", "reviewer": "Reviewer_cguB", "summary": "- The work tackles medical time‑series classification by injecting decision‑making indicators (e.g., sleep spindles, RR intervals) into prompt design, a type of knowledge current LLM approaches largely ignore.  \n- The proposed method creates an indicator‑aware prompt prototype, then uses mask‑based Monte Carlo importance sampling plus additional training objectives to pick robust series‑text combinations.  \n- Experiments on four public datasets show consistent gains over existing baselines.  \n- Ablations confirm each module – sampling, series‑text interaction, alignment, diversity – contributes to achieving best performacne", "questions": "1. What concrete criteria did you consider for choosing indicators per task? Were any indicators left out during part of the development process?\n2. Is co‑attention strictly necessary, or could a single cross‑attention layer achieve similar gains? In many Perceiver-like setups, it is often enough with only one type of cross-attention between feature vectors.\n3. Can you please report results for m=1 to determine the benefit of prompt diversity.  \n4. Does the GPT‑2 encoder half still use causal masks? If so, why is autoregressive masking desirable for encoding?", "limitations": "The paper does admit reliance on manual indicator extraction but does not measure robustness to noisy or missing indicators. Please consider adding experiments with perturbed indicators, and maybe discussing ethical risks of wrong prompts.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "## Strengths\n- *Quality* - Paper has wide empirical coverage: multiple datasets, extensive set of baselines, plus methodological ablations.  \n- *Significance* - Shows that lightweight indicator prompts can outperform heavier multimodal models, potentially easing clinical adoption.  \n- *Originality* - In my opinion, the paper presents a novel combination of indicator‑guided prompt construction, smartly combining LLM prompts with tokenized time series.\n- *Clarity* - The mathematical treatment of sampling and losses is rigorous and detailed. \n\n\n## Weaknesses\n- *Quality* - Indicators must be **hand‑specified per task**, limiting out‑of‑the‑box generalization; authors themselves note dependence on structured indicator extraction.  \n- *Complexity* - Pipeline stitches multiple pretrained blocks and requires repeated prompt sampling; compute overhead vs. simpler fusion models is not fully quantified.  \n- *Presentation* - Presentation can be improved. There are some typos such as “ralated” and “RR_intervael”. Additionally, Figure 2 is densely packed and hard to correctly parse.  \n- *Experiments* - No single‑prompt baseline (m=1) nor variant with simpler cross‑attention variants.", "was_revised": true, "final_justification": "The work is technically sound but methodologically contrived in my opinion. A valuable contribution, but not a groundbreaking one, hence my score.", "rebuttal": "We would like to sincerely thank Reviewer cguB for providing a detailed review and insightful suggestions.\n\n> **W1: Quality - Indicators must be hand specified per task, limiting out of the box generalization; authors themselves note dependence on structured indicator extraction.**\n\nOur approach indeed relies on domain-informed indicators, but we would like to clarify that these are not task-specific handcrafted rules; rather, they are essentially modality-level physiological primitives that are widely shared across applications within the same modality.\n\nFor instance, in EEG-based analysis, spindles and slow waves are not unique to sleep staging—they are also key features in seizure detection and anesthesia depth monitoring. Similarly, for ECG signals, RR intervals, PR intervals, and QRS durations are universally used in arrhythmia detection, QT interval abnormality screening, and heart failure risk assessment. Because these indicators reflect underlying physiological structures (e.g., PQRST complexes for ECG, characteristic EEG waveforms), the same extraction pipeline can be reused without major redesign when moving across tasks or datasets within a modality.\n\n| Modality        | Core Indicators                     | Example Tasks (Sharing Same Indicators) |\n|-----------------|------------------------------------|-----------------------------------------|\n| EEG             | Spindles, Slow Waves, K-complexes   | Sleep Staging; Seizure Detection; Anesthesia Depth Monitoring |\n| ECG             | RR Interval, PR Interval, QRS Duration | Arrhythmia Detection; QT Abnormality; Heart Failure Risk Prediction |\n\nWe agree that some dependence on structured extraction remains, and we acknowledge this as a current limitation. However, the modality-level nature of these indicators provides a degree of “out-of-the-box” generalization while maintaining clinical interpretability, which we see as an important trade-off for physiological and medical analysis.\n\n> **W2: Complexity - Pipeline stitches multiple pretrained blocks and requires repeated prompt sampling; compute overhead vs. simpler fusion models is not fully quantified.**\n\nOur design combines a pre-trained series encoder and LLM to address the prompt–signal mismatch observed in medical tasks. The masked Monte Carlo sampling is included to reduce prompt bias rather than to add complexity; ablation results show that removing MCIS causes the largest performance drop (Table 3).\n\nRegarding overhead, Table 4 provides quantitative comparisons: despite using multiple pretrained components, InDiGO(m=10) achieves lower inference latency than TimeLLM and similar training cost to KEDGN, while offering significantly higher accuracy. This suggests the additional sampling yields a favorable performance–efficiency trade-off.\n\nWe acknowledge the reviewer’s point that direct comparisons to simpler fusion models would further quantify the performance–efficiency trade-off. Our current study primarily focuses on domain-informed prompting and thus does not include plain series–text concatenation or vanilla feature fusion baselines. While such architectures may be computationally lighter, they lack mechanisms to integrate medical knowledge and, as evidenced by TF-C[44] and BIOT[41], do not achieve competitive performance on clinically grounded tasks, which is the core motivation behind InDiGO’s design.\n\nWe acknowledge the importance of simplifying the pipeline and are exploring adaptive sampling and model distillation to further reduce compute while retaining robustness.\n\n> **Q1: What concrete criteria did you consider for choosing indicators per task? Were any indicators left out during part of the development process?**\n\nWe based indicator selection on three concrete criteria: (1) physiological relevance, i.e., well-established links between the indicator and the underlying clinical state (e.g., spindles and slow waves for sleep stages, RR/QRS intervals for cardiac rhythm); (2) cross-task generality, prioritizing indicators that appear across multiple tasks within the same modality; and (3) automated extractability, focusing on features that can be reliably obtained via semi-automated tools to avoid manual annotation bottlenecks.\n\nDuring early development we experimented with a broader set of candidates (e.g., EEG α-band spectral power, ECG T-wave alternans). Some were left out because they were highly redundant with existing indicators or exhibited unstable extraction quality that risked introducing noise. We also note that our current experimental paradigm does not yet incorporate every potentially useful indicator; for instance, K-complexes, which are known to play an important role in sleep-stage classification, are not included in the present version. Our aim was not to exhaustively enumerate all possible features, but to identify a core, transferable subset that balances clinical interpretability with robustness and practical extraction.\n\n> **Q2 & W4: Necessity of Bidirectional Co-Attention and Impact of Simpler Cross‑Attention Variants**\n\nOur choice of bidirectional co-attention stems from the joint series–text decoding objective: both modalities carry complementary cues, and allowing each to query and update the other provides a stronger pathway for mutual alignment. As shown in Figure 5 of the paper, co-attention enables the series encoder to gradually focus on indicator-related prompt tokens, while the text branch refines attention toward task-relevant waveform patches.\n\nWe agree that a single cross-attention layer is more lightweight. We experimented with Perceiver-style setups using unidirectional cross-attention, with either series→text or text→series as the query. While both variants reduced computation, we observed a consistent performance gap compared to co-attention. For example, on Sleep-EDF-20 and PTB-XL:\n\n| Attention Type             | Sleep-EDF-20 (Macro F1) | PTB-XL (BaAcc.) |\n|:--------------------------:|:-----------------------:|:---------------:|\n| Series → Text Cross-Attn   | 78.12                   | 85.28            |\n| Text → Series Cross-Attn   | 77.64                   | 84.71            |\n| **Bidirectional Co-Attn (ours)** | **80.53**               | **86.02**        | \n\nThis suggests that while cross-attention achieves reasonable alignment, having only one modality serve as the query weakens the contribution of the other and underutilizes complementary information. We see co-attention as a trade-off between computational overhead and robust bidirectional fusion, especially for clinical signals where modality synergy is critical.\n\n> **Q3 & W4: No single‑prompt baseline (m=1) nor variant with simpler cross‑attention variants.**\n\nWe conducted an additional experiment with m=1 to evaluate the impact of prompt diversity. The results are summarized below along with the w/o MCIS variant (no sampling). Compared to the full model (m=10), using a single prompt yields a noticeable drop, indicating that prompt diversity plays a critical role in reducing bias from any single textual description.\n| Variant        | Sleep-EDF-20 (Macro F1) | Δ vs Full | PTB-XL (BaAcc.) | Δ vs Full |\n|:----------------:|:-------------------------:|:-----------:|:-----------------:|:-----------:|\n| w/o MCIS       | 76.23                   | (-5.3%)   | 84.31            | (-2.0%)   |\n| m = 1          | 78.71                  | (-2.3%)   | 85.12            | (-1.0%)   |\n| m = 10 (Full)  | **80.53**               | —         | **86.02**        | —         |\n\n> **Q4: Does the GPT‑2 encoder half still use causal masks? If so, why is autoregressive masking desirable for encoding?**\n\nIn our implementation the GPT 2 “encoder half” is used purely as a feature extractor, and we do not apply causal (autoregressive) masks during this stage. This allows the text encoder to attend bidirectionally over the entire prompt, which we found more appropriate for capturing the full contextual semantics of the indicator-guided descriptions."}, {"review_id": "8g9SN8Xm3w", "reviewer": "Reviewer_U1ZG", "summary": "Compared to general-domain time-series analysis, the medical domain requires more domain-specific knowledge – for example, understanding what QRS complexes and R-R intervals are, and how they influence diagnosis or prognosis. Therefore, researchers explore knowledge integration into time-series analysis in different forms, such as designing task-specific architectures and explicitly feeding necessary knowledge within an LLM’s context. Nonetheless, current explicit medical knowledge integration approaches remain limited in performance, mainly due to a lack of specificity in the prompts and only conceptual integration of knowledge, ignoring the interdependent nature of medical phenomena. To this end, the authors propose InDiGO, which synergises time-series and domain knowledge prior to pre-trained LLM integration. InDiGO incorporates three important mechanisms that boost performance noticeably: constructing prompt prototypes guided by task-relevant indicators; using masked Monte Carlo sampling to reduce existing bias in manually designed prompts; and finally combining match alignment and diversity optimisation to boost performance and promote the diversity of selected prompts. The authors demonstrate leading performance with their method across multiple tasks and datasets, perform extensive ablation studies to identify how each component contributes to the final performance, and include visualisations illustrating the qualitative effects of their work.", "questions": "N/A", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "### **Strengths**\n\n1. The experimental setup is strong. The authors used various prevalent datasets for different tasks. The ablations in the paper were necessary and on point. Including case studies such as low-resource generalisation provides a broader picture of InDiGO's impact.\n2. Although the work combines the recent advances in time-series analysis with LLMs and medical analysis by injecting related 'indicators' dynamically, and using latest improvements in time-series analysis with LLMs, the product is original and can be leveraged in other domains that require external knowledge such as financial analysis.\n1. InDiGO performs the best compared to baselines across the board. Although the most similar framework to InDiGO is KEDGN in terms of domain and recency, InDiGO significantly outperforms it while introducing training and inference efficiency.\n3. The mathematical formulation was clear and understandable, and equations follow to the best of my capabilities\n5. The flow of the paper is nice, clear examples \n\n### **Weaknesses**\n1. Although using masked Monte Carlo sampling can reduce the existing bias in manually designed prompts, the results are still extremely dependent on the expert-curated prompt prototypes. Nonetheless, the authors do not present an analysis regarding the relationship between these prototypes and InDiGO's performance.\n2. While the masked Monte Carlo sampling reduces bias (shown contributions mathematically and empirically), it is not clear to me why the authors have not utilised multiple prototype candidates for a single prototype, such that the bias would be even smaller.\n3. Figure 2 is bloated and it is hard to follow the flow and the steps depending on the figure alone. Although the text helps clairfying it to a certain degree, the figure can be improved for easier and quicker understanding.", "was_revised": true, "final_justification": "The authors showed clear responses from their paper regarding the first weakness and performed additional experiments to shed light into my concern over the lack of multiple prototypes. Therefore, there are not any issues from my front and I would suggest the acceptance of this paper.", "rebuttal": "We thank the Reviewer U1ZG for the careful reading and constructive feedback.\n> **W1: Although using masked Monte Carlo sampling can reduce the existing bias in manually designed prompts, the results are still extremely dependent on the expert-curated prompt prototypes. Nonetheless, the authors do not present an analysis regarding the relationship between these prototypes and InDiGO's performance.**\n\nWe acknowledge that InDiGO leverages expert-curated prompt prototypes as entry points for domain knowledge. However, our goal is not to exhaustively enumerate all possible task-relevant indicators, but to demonstrate that even a small set of known, valuable indicators can be rapidly transformed into effective prompts—a non-trivial step in medical time-series analysis.\n\nIndeed, our current experiments do not cover all potential indicators: for example, K-complexes and eye movements in sleep staging or QT/PP intervals in arrhythmia detection. In such cases, the proposed Match Alignment (MA) and Diversity Optimization (DO) mechanisms play a critical role in compensating for missing or imperfect prototypes by dynamically aligning and enriching the prompt distribution.\n\nImportantly, this aspect has been explicitly analyzed in the main paper. In Figure 4 and Section 5.7, we investigated how InDiGO responds to progressively richer prototypes, ranging from minimal task instructions to detailed waveform relations, and compared these against strong baselines. This analysis was designed to ensure our evaluation of prototype sensitivity is systematic and complete. The summarized results are as follows:\n| Method / Prompt Type          | Sleep-EDF-20 Acc.                |\n| ----------------------------- | ------------------- |\n| Time-LLM                      | 80.31               |\n| BIOT                          | 81.86               |\n| Brant-X                       | 84.58               |\n| **InDiGO – Instruction only** | **87.53** (*86.32*) |\n| **InDiGO – Statistics**       | **87.95** (*86.82*) |\n| **InDiGO – Wave Presence**    | **89.04** (*87.38*) |\n*Values in parentheses indicate performance without MA/DO.*\n\n| Method / Prompt Type          | PTB-XL Acc.                |\n| ----------------------------- | ------------------- |\n| Time-LLM                      | 75.79               |\n| BIOT                          | 84.21               |\n| SPaRCNet                      | 82.75               |\n| **InDiGO – Instruction only** | **83.27** (*82.34*) |\n| **InDiGO – Statistics**       | **83.51** (*82.55*) |\n| **InDiGO – Wave Presence**    | **84.60** (*82.74*) |\n| **InDiGO – Wave Relation**    | **86.02** (*84.12*) |\n\nThese results demonstrate three key findings:\n\n**(1)** InDiGO achieves significant gains over baselines even with only minimal task instructions, showing that it does not require an exhaustive set of handcrafted indicators to deliver strong performance.\n\n**(2)** Performance scales with richer domain cues, confirming the effectiveness of indicator-guided prompts and their ability to inject clinical semantics into the model.\n\n**(3)** The consistent gap between the full model and the version without MA/DO shows that these modules are essential in mitigating prototype dependency, dynamically enriching the prompt distribution and ensuring robustness under imperfect or sparse indicators.\n\nBeyond these points, we would like to emphasize that this experiment also reflects **a practical design philosophy**: in real clinical workflows, obtaining a comprehensive set of perfect indicators is rarely feasible due to noisy semi-automated extraction and heterogeneous protocols. By showing that InDiGO performs well even with a single, partially informative prototype and automatically compensates for missing cues, we argue that our approach strikes a balance between **robustness, generalizability, and real-world applicability**. Furthermore, the architecture is fully compatible with multiple prototypes if domain experts can curate them, and we expect this to yield additional improvements in specialized settings, which we plan to explore in future work.\n\n>**W2: While the masked Monte Carlo sampling reduces bias (shown contributions mathematically and empirically), it is not clear to me why the authors have not utilised multiple prototype candidates for a single prototype, such that the bias would be even smaller.**\n\nWe thank the reviewer for the constructive suggestion. We agree that designing multiple prototype candidates for each task could further reduce prompt bias, and we initially considered this option. However, we observed two major limitations in practice.\n\n**(1) Reduced generality across tasks:** In medical time-series analysis, a single framework must often cover diverse tasks (e.g., sleep staging, arrhythmia detection, wearable activity recognition). Maintaining multiple prototype sets per task would compromise portability and require manual adjustment when transferring to new domains.\n\n**(2) Increased error accumulation:** Multiple prototypes also amplify the error rate introduced by automated indicator extraction tools, whose accuracy is inherently imperfect in clinical settings.\n\nIn light of these trade-offs, we deliberately chose to work with a single prototype and rely on our masked Monte Carlo sampling combined with diversity optimization to approximate a richer prompt distribution at low cost, achieving strong cross-task robustness without extensive manual curation.\n\nThat said, for domain experts with reliable indicator pipelines, integrating multiple handcrafted prototypes into InDiGO is straightforward and we expect it would indeed yield additional gains, which we plan to explore as future work.\n\n> **W3: Figure 2 is bloated and it is hard to follow the flow and the steps depending on the figure alone. Although the text helps clairfying it to a certain degree, the figure can be improved for easier and quicker understanding.**\n\nWe appreciate the reviewer’s feedback on Figure 2. We acknowledge that the current version is visually dense and may be difficult to follow without additional context. In future revisions, we are happy to revise the figure by streamlining the flow, abstracting low-level details, and enhancing visual hierarchy to improve readability. We believe these changes will make the framework easier to understand at a glance, even without relying on the text."}, {"review_id": "3nYNPFtXN0", "reviewer": "Reviewer_yNdq", "summary": "In this paper, the authors propose a new method for integrating medical time series data with large language models. The proposed method, called InDiGO, is derived using masked importance sampling. The method seems to have strong performance in numerical experiments.", "questions": "1. How does your InDiGO method compare methodologically to existing methods for integrating time series data and LLM's in the literature?\n\n2. To what extent does the performance of InDiGO depend on the normality assumption in equation (1)? Can this be modified or relaxed?", "limitations": "Yes", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 1, "significance": 1, "originality": 2, "strengths_and_weaknesses": "Strengths: The work is well-motivated by important real world scientific applications of AI. The visual aids (in particular Figures 2 and 3) are very well-made and \n\nWeakness: The weaknesses of the paper lie in its presentation of the proposed InDiGO method. First of all, the assumption that the class-label probabilities follow a normal distribution in equation (1) does not make any sense. Since the rest of the method follows from this assumption, I am concerned about the theoretical validity of the proposed algorithm. Additionally, the authors spend a lot of space in Section 4 detailed how the importance sampling method is derived; however, they do not provide sufficient context for how their method compares to previous works in the literature. As such, I have no sense at all of the methodological innovation of InDiGO over existing methods.\n\nAdditionally, the experimentation section is unclear and poorly written. It is not apparent at all from the main body of the text what the specific tasks on which the proposed method and its competitors are evaluated. While additional details are given in the appendix, as the paper is currently written it is difficult to parse the numerical results and evaluate them critically.", "was_revised": true, "final_justification": "The authors clarified some of my concerns regarding the conceptual setting of the paper. However, I still maintain reservations about the clarity of the work and the presentation of the method, which detract from the overall quality of the paper.", "rebuttal": "We thank the reviewer yNdq for the feedback and appreciate the opportunity to address several concerns, many of which appear to stem from a misunderstanding of our formulation and experimental setup.\n>**Reviewer Concern (W1 & Q2):**\n\n> **W1:  The assumption that the class-label probabilities follow a normal distribution in equation (1) does not make any sense. Since the rest of the method follows from this assumption, I am concerned about the theoretical validity of the proposed algorithm**\n\n>**Q2: To what extent does the performance of InDiGO depend on the normality assumption in equation (1)? Can this be modified or relaxed?**\n\n**Response:**\n\nOur assumption in Eq. (1) does ***not*** imply that the true class labels are normally distributed. Instead, it follows a common formulation in probabilistic deep learning and uncertainty quantification for classification tasks, where the *model’s predictive distribution* over class logits is modeled as a Gaussian around the estimated mean $ \\mu_i(s_i,t_i;\\theta)\\) $ with variance $ \\sigma^2_i(s_i,t_i;\\theta) $. This is a standard surrogate likelihood for discrete outputs in the context of differentiable optimization, used in time-series modeling and Bayesian deep classifiers (e.g., Gal & Ghahramani, 2016 [1]; Kendall & Gal, 2017 [2]).\n\nImportantly, the subsequent derivations in Sections 3–4 do not rely on a strict normality assumption. The key requirement is that the predictive distribution $ P_{\\text{LLM}}(Y|s,t) $ can be parameterized and integrated over the prompt distribution $ P(t|s) $. Eq. (1) simply adopts an isotropic Gaussian as a tractable approximation to express that the network’s prediction is a *distributional output* dependent on both data and learnable parameters, rather than a point estimate. Other distributions (e.g., Dirichlet or softmax-based categorical likelihoods) could be substituted without affecting the core importance sampling and match-alignment objectives. \n\nTo further address Q2: We conducted a sensitivity check by replacing the Gaussian with a temperature-scaled softmax categorical distribution and observed <1% variation in key evaluation metrics across Sleep-EDF-20 and PTB-XL, indicating that InDiGO’s performance is largely agnostic to the exact parametric form of Eq. (1). This supports our claim that the framework’s effectiveness stems from indicator-guided prompt optimization and not from a strong normality prior. The results are summarized below:\n\n| Dataset       |  Likelihood Form   | Acc. | Macro-F1 |\n|:-------------:|:------------------------------------:|:--------:|:-------------:|\n| Sleep-EDF-20  | Gaussian predictive distribution (Eq.1) |  89.04  |  80.53         |\n| Sleep-EDF-20  | Temperature-scaled Softmax categorical  | 88.97    |     80.51     |\n\n| Dataset       | Likelihood Form                       | AUCPR | AUROC |\n|:-------------:|:------------------------------------:|:--------:|:-------------:|\n| PTB-XL        | Gaussian predictive distribution (Eq.1) | 92.31    | 82.98         |\n| PTB-XL        | Temperature-scaled Softmax categorical  | 92.28    | 82.99         |\n\n>**Reviewer Concern (W2 & Q1):**\n\n>**W2:  Additionally, the authors spend a lot of space in Section 4 detailed how the importance sampling method is derived; however, they do not provide sufficient context for how their method compares to previous works in the literature. As such, I have no sense at all of the methodological innovation of InDiGO over existing methods.**\n\n>**Q1: How does your InDiGO method compare methodologically to existing methods for integrating time series data and LLM's in the literature?**\n\n**Response:**  \nWe agree that Section 4 focused heavily on the derivation of the masked Monte Carlo importance sampling (MCIS) mechanism, while the broader methodological context may not have been sufficiently emphasized. Below we clarify how InDiGO differs from and advances beyond existing series–text methods:\n\n- **Existing series–text works rely on static or single-point prompts.**  \n  Prior approaches such as Time-LLM [12] and AutoTimes [21] concatenate a fixed prompt template with the series input, approximating $ P(y|s,t) $ with a *single* handcrafted $ t $. TEST [33] and KEDGN [22] attempt prompt alignment, but still depend on pre-defined prototypes without modeling the underlying distribution $ P(t|s) $.\n\n- **No prior work explicitly approximates the marginal likelihood over prompts.**  \n  Current methods do not consider $ M = \\int P(y|s,t)P(t|s)dt $ in their formulation. InDiGO is, to our knowledge, the first to treat prompt generation as a stochastic process and to approximate the marginal decoding objective via importance sampling rather than deterministic concatenation.\n\n- **Our MCIS is not generic importance sampling.**  \n  InDiGO leverages *indicator-informed proposal distributions* $ q(t|t_0) $ and combines masked perturbations with match-alignment and diversity optimization to dynamically evolve $ q(t|t_0) $ during training. This differs fundamentally from naive prompt ensembling or static uncertainty sampling: the proposal distribution itself becomes task-aware through series–text co-attention and indicator-guided cues.\n\n- **Integration with domain indicators.**  \n  Existing works either use dataset-level statistics (Time-LLM) or generic textual descriptions (KEDGN), which lack discriminative power in clinical tasks. InDiGO is the first to embed semi-automatically extracted physiological indicators into the proposal distribution, ensuring that the sampling space is grounded in domain knowledge.\n\nTo further make this distinction clear, we added a control experiment replacing MCIS with naive prompt ensembling (uniform random perturbations without indicator-informed $ q(t|t_0) $):\n\n| Dataset       |  Method   | Acc. | Macro-F1 |\n|:-------------:|:------------------------------------:|:--------:|:-------------:|\n| Sleep-EDF-20  | InDiGO (full, indicator-guided MCIS) |  89.04  |  80.53         |\n| Sleep-EDF-20  | Naive Prompt Ensembling (uniform mask)   | 85.41    |     77.55     |\n\n| Dataset       |    Method                    | AUCPR | AUROC |\n|:-------------:|:------------------------------------:|:--------:|:-------------:|\n| PTB-XL        | InDiGO (full, indicator-guided MCIS) | 92.31    | 82.98         |\n| PTB-XL        | Naive Prompt Ensembling (uniform mask)  | 87.73    | 78.82         |\n\nPerformance consistently degrades by 3–5% across key evaluation metrics when indicator-informed MCIS is replaced with naive ensembling, demonstrating that the gains of InDiGO stem not merely from sampling, but from the *indicator-guided, dynamically optimized* proposal mechanism.\n\nWe will revise Section 4 to explicitly connect these methodological differences with the existing literature to better highlight the innovation of InDiGO.\n\n>**Reviewer Concern (W3):**\n\n>**W3: Additionally, the experimentation section is unclear and poorly written. It is not apparent at all from the main body of the text what the specific tasks on which the proposed method and its competitors are evaluated. While additional details are given in the appendix, as the paper is currently written it is difficult to parse the numerical results and evaluate them critically.**\n\n**Response:**  \nWe respectfully disagree with the reviewer’s assessment and would like to clarify that the main body already provides explicit descriptions of the evaluation tasks at multiple points:\n﻿\n- **Section 5.1 (Datasets and Data Processing):**  \nWe clearly state the mapping between datasets and tasks:  \n• **Sleep-EDF-20/78** — 5-class sleep staging under AASM standards using EEG/EOG signals.  \n• **PTB-XL** — binary arrhythmia phenotype detection from 12-lead ECG.  \n• **UCI HAR** — 6-class wearable motion recognition from accelerometer/gyroscope data.  \nThe modalities, sampling rates, and label spaces for each dataset are specified in this section.\n﻿\n- **Section 5.3 (Main Results):**  \nBefore presenting Tables 1 and 2, we explicitly link each dataset to its corresponding task to make the reported metrics interpretable in context. For example, Table 1 is introduced as “5-fold cross-validated average results for *sleep stage classification*,” and Table 2 as “average performance on *arrhythmia detection* and *human activity recognition*.”\n﻿\n- **Section 5.4–5.7 (Ablations, Few-shot, Prompt Robustness):**  \nAll ablations and low-resource evaluations are performed on the same clearly defined tasks, and we explicitly name the datasets and task settings (e.g., “Macro-F1 on *sleep staging*,” “Balanced Accuracy on *arrhythmia detection*”) in each subsection.\n﻿\n- **Appendix A (Additional Dataset Details):**  \nProvides segmentation strategy, subject splits, and preprocessing parameters to complement the main text.\n﻿\nThis structure follows the same level of description as representative works in the domain such as **BIOT [41]** and **Brant-X [43]**, which adopt the same datasets and provide comparable task descriptions in their main sections. We therefore believe the experimental setup is already presented with sufficient clarity for interpreting and critically evaluating the results directly from the main text.\n﻿\n\n>**[1] Gal et al. (2016). Dropout as a bayesian approximation: Representing model uncertainty in deep learning. ICML.**\n\n>**[2] Kendall et al. (2017). What uncertainties do we need in bayesian deep learning for computer vision? NeurIPS.**"}, {"review_id": "D8dOFoYsAt", "reviewer": "Reviewer_Cvyn", "summary": "This paper emphasizes the importance of prompt design in extracting contextual information from LLMs. Motivated by this, the authors propose a knowledge-informed evolutionary learning framework, InDiGO, which incorporates expert-defined clinical indicators into the prompt optimization process.", "questions": "1. The authors should provide further explanation for why randomly masking tokens in the prompt leads to better model performance. What exactly is meant by “inaccurate prompts” in this context, and why are masked variants considered more “accurate”? A clearer justification, both empirically and conceptually, is needed.\n2. Could the authors clarify the efficiency evaluation setup in Section 5.5? What are the batch sizes and memory usage for each model? Were the computational costs of extracting indicators (e.g., RR intervals) included in the runtime? In real-world deployment, these indicators would need to be computed online. Please provide details on the tools used and their computational overhead.\n3. It would be helpful to report statistics on the label distribution for each dataset. In medical classification tasks, label imbalance is common and could significantly affect performance metrics. In such cases, metrics like AUC might be more appropriate than accuracy.", "limitations": "The authors adequately addressed the limitations of their work.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "## Strengths\n\n1. The motivation is well-grounded, addressing a meaningful gap in combining expert knowledge with LLM-based time-series interpretation.\n2. The idea of using indicator-guided prompt construction is novel and intuitive, leading to prompts that better align with clinical reasoning.\n\n\n## Weaknesses\n\n1. The method requires external tools to extract clinical indicators (e.g., RR intervals, QRS durations) for each input sequence, which are then integrated into prompts. This introduces additional computational overhead and may hinder real-time inference. However, the authors do not report the cost of computing these indicators.\n2. The proposed prompt optimization strategy is based on randomly masking parts of the initial prompt. It is unclear why masking informative parts of the prompt would lead to better performance, as complete prompts presumably contain richer semantics. Compared to this, prior work such as [1] employs LLMs to iteratively generate semantically different prompts, which may offer more principled and interpretable variations.\n3. Several key experimental details remain insufficiently reported, making it difficult to fully assess the method’s efficiency and fairness. See the Questions section for specific concerns.\n\n[1] Guo et al. (2024). Connecting Large Language Models with Evolutionary Algorithms Yields Powerful Prompt Optimizers. ICLR.", "was_revised": true, "final_justification": "This paper is generally well-written and explores a relatively under-explored area—prompt optimization for medical time-series modeling with LLMs. While some of the design claims appear somewhat overstated (e.g., the reported efficiency gains are largely due to offline preprocessing rather than inherent architectural advantages; the masking strategy does not fully convince me that it effectively addresses semantic mismatch), the proposed approach remains reasonable and shows empirical value.\n\nGiven the novelty of the direction and the practical utility of the method, I have slightly raised my score.", "rebuttal": "We sincerely thank the reviewer Cvyn for carefully reading our manuscript and providing thoughtful evaluations and suggestions.\n> **Reviewer Concern (W1 & Q2):**  \n\n>**W1: The proposed method relies on external tools to extract clinical indicators (e.g., RR intervals, QRS durations) for each input sequence, which may introduce computational overhead and hinder real-time deployment. The paper does not report the cost of computing these indicators.**\n\n>**Q2: In Section 5.5, could the authors clarify the efficiency evaluation setup, including batch sizes, memory usage, and whether the reported runtime accounts for indicator extraction? Since these indicators would need to be computed online in practical scenarios, please also specify the tools used and their computational overhead.**\n\n**Response:**  \nWe have performed additional profiling experiments to quantify the indicator extraction cost and clarify the efficiency setup:\n\n1. **Indicator computation is extremely lightweight.**  \n   We use widely adopted open-source signal processing libraries (*YASA* for EEG, *NeuroKit2* for ECG). These operations are simple peak detection and morphology-based rules with $ O(L) $ complexity.  \n   - On Sleep-EDF (EEG, 30 s), spindle and slow-wave detection takes **~3.4 ms/sample**.  \n   - On PTB-XL (12-lead ECG, 5 s), extracting RR/PR/QRS intervals takes **~2.1 ms/sample**.  \n\n2. **Overhead is comparable or lower than other prompt-based methods.**  \n   We benchmarked InDiGO against Time-LLM [12] with indicator or prompt generation included.  \n   | Model         | Indicator/Prompt Time (per sample) | 1 Epoch Train Time (Sleep-EDF-20 / PTB-XL) | \n   |---------------|-----------------------------------|------------------------------------|\n   | **InDiGO**    | 3.4 ms (EEG) / 2.1 ms (ECG)        | 66.2 s / 788.8  s                             | \n   | Time-LLM | 2 ms (EEG) / 9ms (ECG)      | 156.3 s / 1742.1 s                            |\n\nEven when accounting for online extraction, the overall training and inference overhead remains comparable to or lower than other prompt-based methods, and does not constitute a bottleneck under our batch size (128) and memory configuration (~27 GB for GPT-2 XL).\n\n3. **Clinical indicators yield substantial gains that justify their small cost.**  \n   Ablation (Figure 4) shows removing indicator-guided prompts leads to an **average 1.6–3.1% Acc drop**. In medical tasks where interpretability and accuracy are critical, this small computational overhead is a worthwhile trade-off.\n\n> **Reviewer Concern (W2 & Q1):**  \n\n> **W2: The proposed prompt optimization strategy is based on randomly masking parts of the initial prompt. It is unclear why masking informative parts of the prompt would lead to better performance, as complete prompts presumably contain richer semantics. Compared to this, prior work such as [1] employs LLMs to iteratively generate semantically different prompts, which may offer more principled and interpretable variations.**\n\n>**Q1: The authors should provide further explanation for why randomly masking tokens in the prompt leads to better model performance. What exactly is meant by “inaccurate prompts” in this context, and why are masked variants considered more “accurate”? A clearer justification, both empirically and conceptually, is needed.**\n\n**Response:**  \nWe agree that the rationale behind our masking-based prompt optimization needs further clarification.\n\n1. **Motivation and “inaccurate prompts”.**  \n   Our overarching goal is to provide a *knowledge-enriched series–text joint decoding framework*. However, high-quality series–text paired datasets with rich clinical descriptions are extremely scarce. As in many recent works, we must manually or semi-automatically inject domain knowledge into prompts. This paradigm inevitably introduces *inaccurate prompts*, which we define in two ways:\n   - **Semantic mismatch:** Textual content such as generic dataset statistics (Time-LLM [12]), variable-relationship hints (KEDGN [22]), or timestamp descriptors (AutoTimes [21]) often lacks direct relevance to the current sample in medical classification tasks, leading to prompts that are *textually correct but semantically unaligned*.  \n   - **Detection noise:** Methods that rely on semi-automated tools for indicator extraction, including our approach and several recent works (e.g., Time-LLM [12], AutoTimes [21], PromptCast[40], ), obtain domain cues via external rule-based or automated detectors (e.g., RR/QRS intervals or sleep spindles). These tools are imperfect and may flag a waveform that does not actually exist in the current segment. Such erroneous cues can actively mislead the joint decoder.\n\n   Under both conditions, “complete” prompts are not necessarily optimal; they may contain noise or irrelevant content that harms alignment between series and text.\n\n2. **Why masking improves performance.**  \n   Our masking strategy is not intended to randomly discard useful information, but to expose the model to *multiple variants of the same prompt* under joint training with the downstream task objective. Combined with our Match Alignment (MA) and Diversity Optimization (DO), this achieves following effects:\n   - By sampling different masked variants and jointly optimizing with the series–text interaction module, the model learns to focus on *semantically consistent regions* between the series and text.  \n   - Variants where masking removes noisy or misleading tokens (e.g., falsely detected waveforms) tend to yield higher task likelihood, and the model adapts to give higher weight to these “clean” regions.  \n   - Conversely, masking genuinely informative tokens does *not* improve performance, so their contribution is preserved during optimization.  \n\n   Empirically, this results in higher robustness to imperfect prompts and better generalization when prompts are partially incorrect—a common situation in medical deployments.\n\n3. **On iterative prompt generation vs. masking.**  \n   We agree that works such as [1] (Guo et al. (2024)) explore LLM-driven iterative prompt generation. However, in medical series–text decoding this is fundamentally challenging: generating diverse, semantically rich prompts requires an LLM already capable of *understanding the time series*. Training such a cross-modal LLM in turn requires *large-scale, high-quality* series–text pairs—a resource that is currently unavailable in our domain. For practical medical classification tasks, the masking+MA+DO design provides a lightweight yet effective solution to the “imperfect prompt” problem without depending on unattainable data scale.\n\n4. **Empirical support.**  \n   Table 3 and Figure 4-5 show that without masking, the model suffers a 2–4% drop on Sleep-EDF and PTB-XL. More importantly, prompts with partial masking consistently outperform “complete” but noisy prompts, demonstrating that our framework successfully filters out misleading textual content and enhances series–prompt alignment.\n\nIn summary, the masking strategy is not a substitute for richer semantics but a robustness mechanism against *unavoidable prompt imperfections* in knowledge-injection paradigms for medical time series. This design choice is both empirically validated and conceptually aligned with the realities of scarce, noisy series–text data in clinical domains.\n\n\n> **Reviewer Concern (W3 & Q3):**\n\n> **Several key experimental details remain insufficiently reported, making it difficult to fully assess the method’s efficiency and fairness. See the Questions section for specific concerns.**\n\n> **It would be helpful to report statistics on the label distribution for each dataset. In medical classification tasks, label imbalance is common and could significantly affect performance metrics. In such cases, metrics like AUC might be more appropriate than accuracy.**\n\n**Response:**  \n\n1. **Label distribution statistics.**  \n   We have added the class distribution for each dataset:  \n   - **Sleep-EDF-20:** Wake 19.58 %, N1 6.63 %, N2 42.07 %, N3 13.48 %, REM 18.24 %.  \n   - **Sleep-EDF-78:** Wake 33.74 %, N1 11.01 %, N2 35.37 %, N3 6.67 %, REM 13.22 %.  \n   - **PTB-XL:**  Arrhythmia 75.75 %, Normal 24.25 %.  \n   - **HAR:** 16.83 %, 15.99 %, 14.25 %, 16.66 %, 18.05 %, 18.22 %.  \n\n   We agree that label imbalance is common in medical classification tasks, which is precisely why our main results include **Balanced Accuracy** and **Weighted F1**, both of which explicitly account for class distribution.\n\n2. **Evaluation protocol and metrics.**  \n   Our work follows established experimental setups and evaluation metrics from prior studies for each dataset:  \n   - **Sleep staging:** Brant-X [43], TinySleepNet [34], Brant -2.  \n   - **PTB-XL:** BIOT [41], NeuroLM [22].  \n   - **HAR:** Official benchmark splits.  \n\n   These works also adopt accuracy, Balanced Accuracy, and macro/weighted F1 as primary metrics. Following this convention ensures comparability with existing literature.\n\n3. **On AUC and additional metrics.**  \n   We appreciate the suggestion to include AUC-based metrics. For PTB-XL, we have already reported **AUCPR** and **AUROC** in Table 2. For the sleep datasets and HAR, AUC is less commonly used due to the multi-class nature, but we are willing to include per-class ROC/AUC curves and micro/macro AUC scores in the camera-ready version to further strengthen the evaluation.\n\nIn summary, we have now reported class distributions, clarified that our metrics account for imbalance, and will incorporate additional AUC-based evaluations in the final version as suggested."}]

Author rebuttal:

Meta-review: This paper introduces InDiGO, a framework for integrating clinical indicators into prompt-based modeling of medical time series with LLMs. The method combines indicator-aware prompt prototypes with masked Monte Carlo importance sampling and alignment/diversity objectives, yielding consistent improvements across multiple public datasets. Ablations demonstrate that each module contributes to performance gains. The central contribution lies in leveraging structured physiological indicators as prompts, bridging signal-based modeling with domain knowledge in a novel and interpretable way.

Overall, reviewers appreciated the strong empirical results, broad evaluation, and clear clinical motivation. Strengths include the originality of indicator-guided prompts, robustness demonstrated via ablations, and practical gains over heavier multimodal baselines. Weaknesses noted include reliance on hand-specified indicators, presentation issues (dense figures, some unclear explanations), and limited exploration of efficiency trade-offs or alternative baselines. During rebuttal, the authors provided additional experiments (e.g., m=1 baseline, cross-attention variants, runtime profiling) and clarifications on the role of indicators, masking, and efficiency. These responses resolved most concerns, with multiple reviewers raising their scores after discussion.

Given its technical soundness, solid experimental support, and novelty in integrating clinical knowledge with prompt optimization, I recommend acceptance as a poster. While not a groundbreaking contribution warranting spotlight, the work makes a meaningful step toward clinically grounded series–text modeling and will likely inspire follow-up research in knowledge-informed medical AI.
Decision: Accept (poster)