TimeOmni-1: Incentivizing Complex Reasoning with Time Series in Large Language Models
Authors: Tong Guan
Zijie Meng
Dianqi Li
Shiyu Wang
Chao-Han Huck Yang
Qingsong Wen
Zuozhu Liu
Sabato Marco Siniscalchi
Ming Jin
Shirui Pan
Venue: ICLR 2026 Poster (2026)
Keywords: Time series reasoning
multimodal time series
time series models
time series
Primary area: learning on time series and dynamical systems

Abstract: Recent advances in multimodal time series learning underscore a paradigm shift from analytics centered on basic patterns toward advanced time series understanding and reasoning. However, existing multimodal time series datasets mostly remain at the level of surface alignment and question answering, without reaching the depth of genuine reasoning. The absence of well-defined tasks that genuinely require time series reasoning, along with the scarcity of high-quality data, has limited progress in building practical time series reasoning models (TSRMs). To this end, we introduce Time Series Reasoning Suite (TSR-Suite), which formalizes four atomic tasks that span three fundamental capabilities for reasoning with time series: (1) perception, acquired through scenario understanding and causality discovery; (2) extrapolation, realized via event-aware forecasting; and (3) decision-making, developed through deliberation over perception and extrapolation. TSR-Suite is the first comprehensive time series reasoning suite that supports not only thorough evaluation but also the data pipeline and training of TSRMs. It contains more than 23K samples, of which 2.3K are carefully curated through a human-guided hierarchical annotation process. Building on this foundation, we introduce TimeOmni-1, the first unified reasoning model designed to address diverse real-world problems demanding time series reasoning. The model is trained in multiple stages, integrating a mixture of task scenarios, novel reward functions, and tailored optimizations. Experiments show that TimeOmni-1 delivers strong out-of-distribution generalization across all tasks and achieves a high rate of valid responses. It significantly improves causality discovery accuracy (64.0% vs. 35.9% with GPT-4.1) and raises the valid response rate by over 6% compared to GPT-4.1 on the event-aware forecasting task.

Published as a conference paper at ICLR 2026
TIMEOMNI-1: INCENTIVIZING COMPLEX REASON- ING WITH TIME SERIES IN LARGE LANGUAGE MODELS
Tong Guan 1.2
Zijie Meng²
Dianqi Li
Shiyu Wang Chao-Han Huck Yang²
Qingsong Wen"
Zuozhu Liu?
Sabato Marco Siniscalchi
Ming Jin'
Shirui Pan 1+
Griffith University University of Palermo
Zhejiang University
NVIDIA SSquirel Ai Learning
Norwegian University of Science and Technology
ABSTRACT
Recent advances in multimodal time series learning underscore paradigm shift from analytics centered on basic patterns toward advanced time series understand- ing and reasoning. However, existing multimodal time series datasets mostly remain at the level of surface alignment and question answering, without reaching the depth of genuine reasoning. The absence of well-defined tasks that genuinely require time series reasoning, along with the scarcity of high-quality data, has limited progress in building practical time series reasoning models (TSRMs). To this end, we introduce Time Series Reasoning Suite (TSR-SUITE), which for- malizes four atomic tasks that span three fundamental capabilities for reasoning with time series: (I) perception, acquired through scenario understanding and causality discovery; (2) extrapolation, realized via event-aware forecasting; and (3) decision-making, developed through deliberation over perception and extrapolation. TSR-SUITE is the first comprehensive time series reasoning suite that supports not only thorough evaluation but also the data pipeline and training of TSRMs. It contains more than 23K samples, of which 2.3K are carefully curated through a guunad-guided hicrarchical annotation process. Building on this foundation, we introduce TIME OMNI-1, the first unified reasoning model designed to address diverse real-world problems demanding time series reasoning. The model is trained in multiple stages, integrating a mixture of task scenarios, novel reward functions, and tailored optimizations. Experiments show that TIMEOMNI- I delivers strong cittr--distribution generalization across all tasks and achieves high rate of valid responses. It significantly improves causality discovery accuracy (64.0% vs. 35.9% with GPT-4.1) and raises the valid response rate by over 6% compared to GPT-4.1 on the event-aware forecasting task. Code and checkpoints are publicly available.
INTRODUCTION
Time series data underpin a wide range of real-world systems, including energy, transportation, finance, and healthcare (Lu & Xu 2024 Liu et al. 2023b) Guan et al., 2023 Lan et al., 2025). Comprehending real-world time series extends beyond mere pattern recognition, it necessitates multi- step and multi-hop reasoning to identify external factors driving temporal changes and to support downstream tasks that inherently build upon upstream pattern understanding and extrapolation (Kong) ct al. 2026). For instance, effectively scheduling energy demand requires integrating external knowledge such as extreme weather events, inferring causal mechanisms, anticipating event-driven variations, and ultimately supporting downstream decisions (MacKinlay) 1997, Liang et al. (025).. However, most existing time series approaches remain centered on basic pattern analytics and fall short in addressing such complex reasoning requirements, restricting their effectiveness in scenarios that demand a deeper understanding of context and robust decision maaking support.
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Large language models (LLMs) have recently demonstrated impressive multi-step reasoning abilities across text, code, and mathematics (Wei et al.] 2023] Shao et al. (024). This potential for time series reasoning, however, remains largely untapped. The primary obstacle is the scarcity of large-scale multimodal time series alignment, instruction, and labeled chain-of-thoughtt data during pretraining, which hinders the development of corresponding time series reasoning abilities. This capability gap is further evidenced on even leading LLMs (e.g., 1PT-4.1) by recent benchmark (Merrill et al.) 2024; Chow al. 2024; Jin al. 2024b). Furthermore, time series specific architectures such as Time-MoE (Shi et al., 2024) and Moirai (Woo et al., 2024) remain largely confined to forecasting tasks and lack the generalized reasoning capabilities required for broader applications. These gaps underscore the urgent need for dedicated time series reasoning models (TSRMs) that advance time series understanding, strengthen reasoning, and facilitate temporal analytics and knowledge generation, paving the way toward general-purpost time series intelligence.
However, two key limitations hinder the development of TSRMs: (1) The scarcity of high-quality data to support general-purpor reasoning over time series. Early efforts, such as constructing TSQA datasets (Kong et al., 2025), remain largely at the level of surface time series question answering and suffer from insufficient input context. Moreover, the formulation of time series reasoning tasks in existing multimodal datasets has not been systematically studied, leaving them unable capture genuine reasoning depth with time series data. (2) The lack of validated and feasible pathway for effective time series reasoning across tasks. It remains unclear which tasks genuinely demand reasoning capabilities over time series, as this question has not been systematically studied. This gap, combined with data scarcity. has confined existing research to narrow, task-specific settings. Many current approaches are trained independently for each task or even each dataset; for example, TimeMaster (Zhang et al. 2025b) employs six distinct models for six datasets. Such fragmentation hinders the transfer of reasoning capabilities across tasks and leaves the development of general-purpose time series reasoning open challenge. These challenges naturally raise pivotal question: How can we take a solid step toward fully incentivizing reasoning capabilities in LLMs over time series, so they can tackle complex real- wolld problems that inherently demand such reasoning?
Answering this question first drives us to tackle the challenge of data scarcity. Based on the limitations of existing time series QA datasets, we argue that time series reasoning tasks should adhere to two key principles. First, they should reward genuine reasoning rather than superficial pattern matching by systematically incorporating multi-step reasoning tasks and complete reasoning chains. Second, they should ensure context sufficiency to enable unambiguous answering or response generation, thereby strengthening the model's reasoning capacity and generalization across diverse scenarios. Guided by these principles, we formalize four atomic tasks that genuinely require reasoning with time series and introduce TSR-SUITE, which covers three fundamental time series reasoning capabilities: (1) perception, acquired through scenario understanding and causal discovery, reveals key temporal patterns; (2) extrapolation, realized via event-aware forecasting, predicts future trends and anomalies; and (3) decision- decision-making, developed through perception and extrapolation, supports informed, adaptive actions. Building on this foundation, we present TIMEOMNI-1, the first generalized reasoning model for time series. The central premise is that effective time series reasoning requires internalizing fundamental temporal priors. To this end, TIMEOMNI-D first injects the above three capabilities identified by TSR-SUITE into LLMs through supervised fine- fine-tuning (SFT) priors. We then design novel time series task- moos-gro rewards to cultivate genuine reasoning from these priors via policy optimization. Finally, to validate that these capabilities represent complementary facets of general time series reasoning, we unify all task capabilities within a single model with joint training.
Our contributions lie in three aspects:
New Datasets and Testbed. We introduce TSR-SUITE, the first comprehensive time series rea- soning suite that formalizes four core tasks spanning three capabilities; perception, extrapolation, and decision-makee making. It contains more than 23K samples, of which 2.3K are carefully curated through a human- -uuan- hierarchical annotation process. The suite serves not only as testbed for thorough evaluation but also as a foundational data pipeline for training TSRMs.
2. New Models. We present TIMEOMNI-1, the first generalized reasoning model on time series data. It unifies diverse reasoning tasks within a two-stage curriculum: Stage I employs supervised fine-tuning with human-guided guided reasoning traces to inject temporal priors across the three key capabilities: perception, extrapolation, and decision making, learning with novel task -roundedd rewards go beyond mimicking priors to robust reasoning.
becisinn-mkinng Stage 2 leverages reinforcement
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3. Comprehensive Evaluation and Key Insights. TIMEOMNI-1 achieves Top-2 performance under both in-distribution (ID) and GPT-4.1 by 40.6% (ID) and 28.1% (OOD) in causal discovery accuracy while maintaining high valid-response rates across all tasks. Further experiments provide the first evidence that joint training across diverse time series reasoning tasks yields mutual gains across capabilities.
(OOD) testbeds. Notably, it surpasses
2 RELATED WORK
Large Time Series Models. Early efforts primarily aimed to endow time series models with zero- shot capability, mitigating domain-specific limitations when large-scale time series data were scarce. Time-LLM (Jin et al., 2024a) sought transfer the generalization ability of LLMs into the time series domain. Prior developments of time series models are discussed in Appendix [B] With the increasing availability of large-scale time series datasets, training time series foundation models (TSFMs) from scratch emerged as the mainstream approach (Woo et al. 2024 Ansari et al. 2024 Shi et al. 2024, Yao et al. 2025). These models demonstrated promising zero-shot performance but still fell short of supporting multi-task and multimodal capability. With the advent of multimodal models (Alayrac et al. 2022; OpenAl et al. 2024a) and reasoning menstring-centric models (OpenAl et al. 2024b: Guo et al. 2025). ) the intersection of time series and LLMs has re-emerged at the forefront. Broadly, existing approaches can be categorized into two main groups. The first are time series language models (TSLMss), which primarily adapt the language modeling paradigm temporal data, focusing on supervised pattern fitting and QA without reasoning (Xie et al. [025]] Wang et al., 2025a Kong et al., [2025] Zhang al. 2025a] Yang et al. 22255 Waag et al. (025c). The second are time series reasoning models (TSRMs), distinguished by their attempt to employ reinforcement learning (RL) cultivate genuine reasoning ability rather than pattern matching, and by their use
of explicit reasoning to improve accuracy while providing interpretable. step-by- by-step explanations that enhance trustworthiness (Wang et al., 2025b; Tan et al. 20256; Luo et al.] (025).. However, this line of research is still in its infancy: the definition and necessity of reasoning remain vague, existing QA datasets are of limited quality, and most current works restrict themselves to single-task experiments under the RI -paardiggm (Guo et al. 2025), without yet establishing veneral-purposs reasoning framework for time scries analogous to LLMs.
Reasoning with Large Language Models. Generative LLMs offer greater flexibility and general- ization than traditional deep learning models, making them effective in complex tasks that require multi-step reasoning (Wei et al., 2023 Kojima al. (022).. However, reasoning errors can propa- gate and degrade performance in some cases. To address this, Lightman et al. (2023) introduces a step-level reward mechanism into both data construction and model fine-tuning enhance reasoning. DeepSeck-RR 1ansSeck-R1-ZA. -Zero (Guo et al., 2025) shows that RL using only format and final answers can also improve reasoning. And has expanded to math, code, translation, and multimodal tasks (Zhang
et al. 2025d c Feng et al. 2025b Huang et al. 20255] Feng et al. 2025a Zhan et al, [2025] Liu et al.
2025
However, LLM- reasoning for time series remains underexplored due data scarcity.
3
METHODOLOGY
Problem Definition. We define time series reasoning as the process in which reasoning model (RM)
Po first generate a sequence of intermediate rationales R = (f1,... .Kk and then produce a final answer y, conditioned on (1) observed time series inputs X {a Jm=i and (2) auxiliary context C' (e.g., task instructions or external knowledge). Formally:
(R, v) pa X,C) = po (R X, R,X,C).
This formulation covers both discrete-output tasks, where y is categorical option, and sequence- output tasks, where y is a numerical sequence, under a unified reasoning framework. To standardize outputs, RMs must generate rationales R within Sthink></thinn answer y enclosed in an <answer></aa answer>> block. In contrast, non-reasoning models directly predict y ~ p X, C) without rationales, producing only the <answer></ a/seer/>////s/.........
tags, followed by the final
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115
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Figure I: Limitations of existing TSQA dataset (Kong et al.
2025 ). (a) The marginal performance
gap between RMs and NRMs. (b) Reasoning on simple TSQA leads to over wer-thinkinng (c) Insufficient context leads to a performance platcau. (d) Ambiguous options forcing models to guess.
3.1 FORMULATING CRASONING-CRITICAN TIME SERIES TASKS
Limitations of Existing QA Tasks, We use Time-MQA (Kong et al. 2025), the largest existing time series question answering (TSQA) dataset, to highlight two limitations. (I) questions are overly simple and straightforward, where invoking reasoning leads to over-thinking. At the aggregate level, as shown in Figure I(a]. the accuracy gap between stronger models (GPT-4.1) and smaller baselines (Qwen2.5-14B) is marginal, and in some cases reversed, indicating that additional reasoning capacity brings no benefit. Furthermore, all models achieve accuracy above 75%, highlighting that the tasks are not sufficiently challenging. At the instance level. as illustrated by the True/False QA from Time- MQA in Figure [igu the question can be directly answered by non-reasoning models, while reasoning complicates the process. (2) Questions often lack sufficient input information, either in the time series X or the context C, which prevents well well-grounnd grounded answers and introduces ambiguity. As shown in Figure [(c) even advanced models plateau below 65% accuracy and show no gains after SFT. To investigate this, we conducted a human evaluation, which revealed numerous ambiguous cases caused by missing context. As exemplified in Figure[T(C the options are not clearly distinguished (e.g.. no explicit thresholds for high, moderate, and low volatility). forcing the model to guess rather than a reward modeling (Silver & Sutton 2025) oriented reason. Consequently, errors reflect chance rather than insufficient time series reasoning ability (see Appendix for detailed analysis of existing TSQA datasets). To address these issues, we propose two design principles for formulating time series QA tasks that require genuine reasoning.
Principle 1- QA-pairs must reward reasoning. A reasoning model MRM explicitly generates rationales R before producing the answer whereas a non-reasoning baseline MNRM directly outputs y. To determine whether a task requires reasoning. RMs should (significantly) outperform non- reasoning models (NRMs): ((MRM) >> S(MNRM). where S-) denotes the mean score across tasks (c.g., accuracy for categorical prediction tasks or regression metrics for regression tasks).
Principle 2 2 QA-pairs must ensure context sulficiency. Both the time series input X and auxiliary context C' constitute the basis for reasoning. Unlike coding and mathematical problem solving, where a well-posed problem typically admits unique solution (Imani et al.) [203)), time series problem solving especially sensitive to the sufficiency of X and C. Let denote the number of ambiguous options. Even ideal reasoner with infinite reasoning capacity (RC + oo) will be forced to guess X or C is underspecified (c.g., missing thresholds for distinguishing high vs. low variance), while it should substantially exceed random guessing once X and C' are sufficient:
if X or C' is underspecified, K
lim P(correct RC-
| X,C)
(2)
if X and C' are sufficient.
K
Therefore, ensuring context sufficiency is a critical design principle for formulating reasoning-critical time series tasks, it prevents ambiguity and enables reasoning to be applied meaningfully.
Reasoning-Critten Critical Tasks. The two principles motivate us to directly address the unique challenges of time series reasoning (ensuring QA requires reasoning and context sufficiency). To this end, we design a suite of tasks that form a progressive pathway covering three fundamental time series reasoning capabilities: (1) perception, (2) extrapolation, and (3) decision-making.
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Task Scenario Understanding
Task 2: Discovery
Task3: Event- Aware Forecasting
Task4; Decision Making
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Figure 2: Illustrative examples of the four reasoning reasoning-critical critical time series tasks in TSR-SUITE.
As shown in Figure 2 the foundation of time series reasoning capabilities is perception, where the model first recognizes temporal patterns and then uncovers their underlying causes. This includes Task 1: Scenario Understanding, which focuses single-series attribution by linking fluctuations to generative scenarios or external events (c.g., higher temperatures Icading to increased ice-cream sales). It also encompasses Task 2: Causality Discovery, which extends attribution to the multi- series setting, requiring the model to compare trends across sequences and identify causal relations (e.g.. upstream discharge influencing downstream flow). Together, these tasks ensure the model not only observes time series but also interprets them in a context-aware and causal manner. Task 3: Evenf-aware Forecasting requires the model build on its perception ability to extrapolate future trajectories under explicit event perturbations. Accurate extrapolation depends on leveraging intrinsic temporal knowledge to analyze external events and infer their impact on temporal dynamics. Finally, Task 4: Decision Making represents the culmination of this chain. Building on the perception of temporal patterns (Task 1), causal relations (Task 2), and extrapolation (Task 3), the model must integrate these to select actions (Task 4) that maximize downstream utility (e.g., maximizing profits). By following the progressive capabilities of perception, extrapolation, and decision-making in formulating casoning-criitice tasks, ensure that reasoning is intrinsic requirement. Solving these tasks demands explicit reasoning from the outset, unlike conventional analytical tasks such as interpolation, where models often succeed through implicit fitting without reasoning.
3.2 TSR-SUITE
To mitigate the scarcity of data the field, we construct Time Series Reasoning Suite (ISR-SUITE) the first unified dataset suite tailored for time series reasoning. Unlike prior benchmarks designed purely for evaluation, ISR-SUITE is built as a saining-and-evaluattin suite that supports TSRMs development. The dataset spans 10 diverse domains and contains 23,605 curated QA pairs. Among them, 2,339 samples are annotated through a human-guided hierarchical annotation process. Detailed statistics for each task are provided in Appendix D.2 As shown in Figure 3 a). the data organization comprises three components as follows.
Raw Data Collection. Guided by the "perception cettrappllaaioondcciiiooo----oo ing our four tasks, we systematically collect publicly available time series data across 10 domains. Figure a) provides an overview of the domain distribution, see Appendix for data source details.
pathway underly-
Task Formulation. To align with our task design and support RL, we standardize the QA format across tasks. Specifically, Tasks 1, 2, and 4 are framed as discrete-output selection problems, while Task 3 is formulated as a requence-outpll forecasting task, as shown in Figure 2 Each task adopts a customized data construction pipeline and extensible support further scaling with new input series. A key improvement over prior datasets is that our dataset, with over 23K QA pairs, is sufficiently large to support both training and evaluation (e.g., CiK with 355 samples (Williams et a TSAIA with 1, 054 samples (Ye et al. 2025)) rather than serving solely as a testbed.
Hierarchical Chain-of- -hhouughss Existing time series QA datasets typically provide only la- bels (Kong et al. 2025), overlooking the fact that LLMs lack temporal priors for time series reasoning. To fill the gap, we design a hierarchical annotation pipeline involving an LLM Analyzer, Human Reviewers, and an LLM Rewriter (Figure b)). (1) Human-guided solvable annotation. Instead of asking the LLM analyzer to directly solve the problems, we guide it with structured templates to elicit consistent reasoning, and we retain correctly solved samples as Step-/ CoT data. (2) Context
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Data Papeline TSR-Suite
B. Nierarchical CoT Ansotation
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Figure 3: Overview of data and training pipeline. (a) Construction of TSR-SUITE, including domain distribution and sample statistics. (b) Hierarchical CoT annotation pipeline with outputs from each step for tasks. (c) Two-stage training TIMEOMNI-L Stage injects temporal priors via SFT; Stage 2 refines reasoning with task -eokurroddddd reward signals under RL.
sufficiency verification. For questions answered incorrectly in the first step, human experts a customized evaluation interface (see Appendix (D3) to examine whether the provided context is sufficient disambiguate the answer. If a question is solvable by human reviewers, expert-written written reasoning chains are subsequently polished by the rewriter to follow our structured templates, and the resulting samples collected Step-2 CoT data. Task 3 (Event-aware Forecasting) is treated as a special case: unlike tasks with unique answers, forecasting outputs cannot perfectly match the ground truth due to inherent noise in real-world time series data. Human reviewers examine the cases and select 400 samples with relatively low mean absolute error (MAE). As result, annotated predictions in Task may coincide exactly with the ground truth, but they capture plausible and well-justified reasoning. Additional analysis of Task 3 is provided in Appendix D.4
3.3 TIMEOMNI-1
Developing time series reasoning poses unique challenges compared to other domains. Pretrained LLMs lack temporal priors, they are not largely exposed time series data during pretraining. To bridge this gap, we propose a two-stage training paradigm: (1) injecting temporal priors to anchor the model in a temporal knowledge space, and (2) refining these priors for robust reasoning through lask-grounded rewards (Figure B(c)). All experiments in this section use in-distribution (ID) testbeds.
Stage 1: Injecting Time Series Reasoning Priors. Human-guide reasoning priors instruct LLMs on how to decompose time se- ries tasks into meaningful com- ponents. These traces narrow the exploration space to focus on three key capabilities (i.e.. percep- tion, extrapolation, and decision- making) instead of drifting to- ward commonsense heuristics or generic algebraic QA. We inject this knowledge through supervised fine-tuning (SFT). Implementation details of SFT are provided in Ap- pendix
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Figure 5: Human-guided tem-
plates are critical for priors.
Time series reasoning ability need not be innate; it can be effectively cultivated via supervised fine-tuning on small set of high-quality, curated reasoning traces.
Finding 1:
Base models without temporal priors collapse to chance-level accuracy when questions require fundamental temporal understanding (e.g., Task 2: 21.6% 33.3% random guess in Figure

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Injecting reasoning traces, even with < K seeds, boosts Task 2 accuracy by 46. 1 after Stage 1, with comparable gains across other tasks. This prove that time series reasoning not inherent to LLMs but can be systematically established through temporal priors.
Finding 2: Human-guided traces establish decomposition priors critical for time series reasoning.
Without guidance, LLMs tend to produce unstable, generic math-style reasoning traces that incon- sistent across samples and fail to capture temporal dependencies. In contrast, when prompted with human-guie gonnan- templates, the pretrained LLMS generates structured traces that explicitly follow de- composition strategies and achieve substantially higher accuracy. As shown in Figure on GPT-4.1, human-guided templates improve zero-shot consistency accuracy from 28.7% to 71.1% on Task with improvements also observed across all four tasks. These further confirm that pretrained LLMs lack temporal priors and must be enhanced through Stage 1 training.
Stage 2: Refining Reasoning with Task-grounded Rewards. While Stage 1 provides priors, they remain insufficient for robust reasoning. Stage 2 employs RL through group relative policy optimization (Shao et al. 2024) to turn mimicking priors into stable and generalizable reasoning behaviors (Figure c)). Implementation details of RL stage are provided Appendix
Here we focus designing uuskggruuddd,, outcome-based wards for time series reasoning, with detailed reward design pro- vided in Appendix G Each sample receives reward composed of format verification and task correctness. Rtormat enforces the <think></think>> <ansee>>>>> snswe>>> schema. For cor- rectness, we distinguish task types: for Tasks I, 2, and 4, Ruiserete E {0. 1) denotes exact-match accuracy (1 correct, otherwise). For Task 3, we add a counting bonus Reount = 0.1 if the predicted se- quence length matches the required horizon. This structural reward is essential since LLMs still struggle with counting. For example, our Stage I checkpoint achieves only 55.7% success on sequence length. In addition, we an exponential decay to map the unbounded MAE into a normalized range, which compresses arbitrarily large MAE toward zero ensure higher rewards for smaller MAE
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Finding 3: Reinforcement learning works reliably only once the base model is anchored with fundamental temporal priors, which prevent collapse into spurious exploration.
Applying Stage directly to base model yields only marginal even negative im- provements (as shown in Fig- ure 6 with 5.3% drop Task 4), since the rewards can- not distinguish genuine tem- poral knowledge from explo- ration within the pretraining corpus space. In contrast, when preceded by Stage the same rewards refine temporal priors and progressively de- velop into robust reasoning.
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Figure 7: Joint training results: (a) Zero-shot capability transfer from precursor tasks (CI: Perception, tasks 1, task 2; C2: Ex- trapolation, task 3) to decision-making: (b) Supervised capability supplement by progressively incorporating tasks (C3: Decision- Making); (c) Joint training outperforms single-tase tagle-task training.
Joint Training for Time Se- ries Reasoning. Unlike prior single-task (or single-dataset)
pipelines (Tan al. 2025a;
Zhang et al. 2025b), we investigate whether unifying perception, extrapolation, and decision-making objectives through joint training yields mutual benefits. We design the following two complemen- tary experimental settings to systematically study the synergistic gains among the three reasoning capabilities.
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Finding 4: Joint training turns perception, extrapolation, and Recision-making from silos into complementary capabilities, supporting train-once, use-across-tan across-tasks paradigm for TSRMs.
Progressive Capability Transfer. This evaluates whether precursor reasoning capabilities transfer to downstream decision-making in a zero-shot manner. We evaluate three conditions on the II) decision-making testbed: (1) base model without precursor training,
(2) model trained only on perception tasks, and (3) model trained on both perception and extrapolation tasks. As shown in Figure ((a) accuracy on recision-makingg tasks increases from 25.5% 26.2% and further 31.3%, indicating that precursor capabilities enhance downstream reasoning even without direct supervision.
Progressive Capability Supplement. This assesses supervised joint training by gradually incorporating precursor tasks. We compare: (1) training solely on decision decision-making making, (2) joint training on extrapola- tion and decision-making and (3) full joint training across four tasks covers all three capabilities. Decision-makes makinn accuracy rises from 40.9% to 45.7% and peaks at 47.9%, as shown in Figure 7(b) confirming that progressively supplementing related tasks creates complementary learning benefits.
Scaling to All Tasks. Building on the above complementary settings, we compare single-task training against joint training across all four tasks. As shown in Figure [7c]] joint training consistently outperforms single-task training on the ID testbed. These results support "train-once, use-across- task" paradigm for time series reasoning, where joint training effectively captures intrinsic connections within the temporal reasoning capabilities without task interference.
4 EXPERIMENTS
Evaluation Metries. We observe that different models vary significantly in instruction-follet following ability, sometimes generating repetitive or malformed outputs. To ensure fair comparison, we adopt the standardized system prompt shown in Appendix E.2 and apply regular expressions to extract answers. We report the Success Rate (SR), which the proportion of model outputs that yield a valid and extractable answer. All subsequent evaluation metrics are computed only on these valid cases, ensuring that performance reflects time series reasoning ability rather than -nslrwction-followingg compliance. For discrete-output tasks (Scenario Understanding, Causality Discovery, Decision Making), we use Accuracy (ACC) via exact match. For the sequence- output task (Event-aware Forecasting), we use Mean Absolute Error (MAE) to assess forecasting precision. Higher ACC and lower MAE indicate better performance. The hyperparameters used are provided in Appendix H
Baselines. We compare TIME 1 against up-to-date models in two categories: (1) Time series language models: Time-R1 (Luo et al. 2023) (TSRMs for classical forecasting). Time-MQA (Kong) al. 2025) (fine-tuned 7B-SB models tailored for TSQA), and CharTS (Xie al. 2025) (fine-tuned
Table 1: Model comparison on four tasks, reporting ACC for Tasks 1, 2, and 4, MAE for Task 3, together with SR under ID) and OOD testbeds. All metrics are computed only on valid responses. denotes SR below 10%, where results are omitted for lack of statistical significance and therefore we choose not to report them. Red: the best, Blue: the 2nd best.
Secnarin Understanding
Cansality Discovery
Event-aware Forecasting
Decision Making
Method
Base LLMs
TD
OOD
TD
OOD
ID
0OD
ID
OOD
SR
ACC SR%
ACC
SR
MAR SRS
SRS
ACC
SR
Propeictary Models
GPT-4 1-22---12022-4--1
100.0
87.8
100.0
28.7
99.9
35.9
100.0
13.79
97.4
170.78
76.1
25.5
100,
100,0
GPT-441--aa
66.2
97.5
62.6
98.7
29,8
98.6
28.0
98.4
18.98
92.8
170.78
76.1
28,9
99.5
34.1
97.8
Open-Source Models
1.lma-33 1-700--Instratt
596
100.0
23,4
24.67
238.98
97.0
30.3
96.8
97.4
Mistral Small--.1-240 3.1-34----2-00 Ins
64.8
100.0
69.
100.0
24.6
100.0
25.8
100,0
17.28
72.0
120.97
43.1
44.2
100.0
429
100,0
Llama-3.. 3.1. 5B- Instruct
32.1
46.8
27.68
196.0
29.8
7.4
28.7
Mistral-BB-vi
40.5
92.2
34.7
87.6
29.0
86.0
26.9
52.6
5.3
0.0
24.3
94.2
16.7
96.7
Qwen2. 5-Instnai-77
100.0
42.8
100.0
31.6
26
100.0
23.38
146 12
25.5
100.0
100,0
Time Series Language Models
Time-MQA
L-8m13-88
32.2
29.5
25.1
32.6
30.1
44.3
31.2
53.2
1.4
0.4
12.0
13.3
11.6
15.8
TimeMQAA
Mistral7B-
Time MQA
Qwen2.5 5 -TB
25.0
14.0
37.5
22.7
29.5
33.0
30.5
32.0
19.76
12.2
6.5
23.8
58.0
26.4
44.3
CharTS
69
18.2
30.1
26.7
0.0
5.8
27.1
27.1
Time Series Reasoning Models
Time- R1
Qwee2. 5- Instruct-79
3099
94.0 34.0 92.5 30.2 53.8
31.4 48.9 17.61 38.7
6.3
27.8 95.7
32.1
93.1
TIMEOMNI-1
Qwen2.5 Listract-79 7B 98.7
97.5
87.7 98.3 69.3 99.8 64.0 99.8 14,30
93.8
165.53
8233
47.9
100 589 100
8

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Published as a conference paper at ICLR 2026
14B model for time series understanding). (2) General-purpose LLMs: two GPT-4.1 variants as proprietary representatives, and five open-souree LLMs (7B to 70B) for comprehensive evaluation.
4.1
MAIN RESULTS
As shown in Table TIMEOMNI-1 consistently ranks among the top-2 models across all time series reasoning tasks. Notably, it exceeds GPT-4.1 by 40.6% (ID) and 28.1% % (OOD) causal discovery. While achieving comparable accuracy on scenario understanding. TIMEOMNI-I surpasses GPT-4.1 by wide margin on tasks requiring deeper temporal priors (e.g., decision-making). Existing time series specialized models, however, exhibit weaker instruction-followiin ability than general LLMs (consistently lower SR). For example, ChatTS achieves 0% SR on the event-aware forecasting task; upon inspection, we found it fails to produce the required numeric sequences, generating only free-form text. This highlights critical limitation of existing time series task-specific models: over-specialization compromises generalization ability.
4.2
MORE ANALYSIS
General Reasoning Capability. We evaluate whether our time se-
80
ries specialization diminishes general reasoning ability. We compare the base model, Stage SFT model, and TIMEOMNI-1 on three gen- eral reasoning benchmarks: DROP (Dua et 2019 GPQA (Rein) et al. 2024), and ReClor (Yu et al.) 2020), which focus respectively on numerical reasoning. graduate- leeel knowledge reasoning, and
Base
logical reasoning. As shown in Figure 8 TIMEOMNI-L improves average accuracy by 16.5% over the base model and 1.3% over the Stage model. This indicates our approach not only maintains but also enhances general reasoning capabilities while specializing in time series tasks, avoiding the instruction-followiii degradation observed in other specialized models.
DROP
GIMAA
Necdoo
Figure 8: Performance on gen- eral reasoning benchmarks.
Ablation on Training Stage.
We evaluate configurations: (1)
Stage models, including NRMs via answer-only fine-tuning (ANS-
SFT) and RMs via CoT fine-tuning (CoT-SFT): (2) Stage 1+ Stage models (CoT-SFT+RL), which first activates reasoning via CoT-SFT and then applies RL. We analyze performance under multi- task joint training. As shown in Table 2 complete two-stage training (CoT-SFT+RL) delivers the most balanced performance, ranking Top-2. In causal discovery, CoT-SFT reaches 67.7% accuracy compared to only 30.5% for ANS-SFT, showing that answer-only supervision merely fits answer distributions without fostering reasoning. On decision-
the CoT-SFT vs. ANS-SFT gap narrows from 10.1% (ID) to 5.5% (OOD), further confirming that ANS-SFT fails to foster reasoning, whereas CoT-SFT establishes transferable reasoning skills that are consolidated by RL in Stage II.
Ablation on Training Strategy. We compare single-task fine-tuning against multi-task joint training under identical training budgets. As shown in Table 2, multi-task joint training often enhances performance across all tasks and training stages. On the ID testbed, the jointly trained CoT-SFT+RL model (TIMEOMNI- 1) achieves accuracy gains of 8.2%. 1.8%, 2.46 (MAE), and 7.0% across the four tasks compared to single-task training. Together with Figure 7 which demonstrates progressive capability transfer and supplement, these results validate that joint training effectively integrates temporal reasoning capabilities, reinforcing the "train-once, use-across-tasks paradigm.
Table 2: Comparison across training stages and training strategies. Red: the best, Blue: the 2nd best.
Scenario Understanding
Causality Discovery
Event-aware Forecusting
Decision Muking
Training Stuge
Training Strategy
OOD
OOD
OOD
ACC SR%
ACC
SR%
ACC
SR%
ACC
SR%
MAE
SR%
MAE
SR%
ACC
SR%
ACC
SR%
Base Model
Qwen2.55 S-sttrecct
48.5
100.0
42.8
100.0
21.6
99.8
26.3
100.0
23,28
53.1
146.12
55.5
25,5
100.0
24.9
100.0
ANS-SFT
Single-task
77.5
100.0
73.9
100.0
35.7
100.0
53.8
100.0
23.87
39.7
15042
0.6
20.2
100.0
24.2 100.0
Mukti task Joint
80.5
100.0
76.6
100.0
30.5
100.0
34.6
100.0
13.23
48.3
156.78
38.9
51.0
100.0
58.2
100.0
C-T-SFT
Single task
73.9
100.0
85.6
83.9
66.3
96.0
61.8
92.4
15.10
64.6
157.21
34,5
39.4
98.40
47.3
94.77
Multi-task Joint
89.8
88.0
85.8
88.2
67.7
98.8
61.1
97.0
15.53
71.8
151.1
55.7
40.9
100.0
52.7
100.0
Single-task
82.5
100.0
87.0
98.7
67.5
99.6
61.7
99.0
16.76
79.2
169.88
66.0
40.9
100.0
63.6
99.6
COT-SFT-RI.
Multi-task Joint
90.7
97.5
87.7
98.3
69.3
99.8
64.0
14.30
93.8
145.53
82.3
47.9
100.0
58.9
1000

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Published as a conference paper at ICLR 2026
5 CONCLUSION
In this paper, we introduce TSR-SUITE, which addresses the scarcity of reasoning-criticn critical time series data. It formalizes four tasks across three fundamental capabilities for time series reasoning: perception, extrapolation, and decision-making. On this basis, we present TIMEOMNI-I, the first generalized, unified model for time series reasoning. It first injects temporal priors through supervised fine-tuning. Then, reinforcement learning with task-grounded rewards guides the model beyond mimicking priors toward robust reasoning. Experiments show that TIMEOMNI-1 achieves top-tier performance while preserving the general reasoning ability of the base model. Finally, we demonstrate that joint training across diverse reasoning tasks yields mutual gains, supporting "train-once, use-across-tasks" paradigm for future time series reasoning models.
ETHICS STATEMENT
Our work focuses solely on scientific challenges and does not involve human subjects, animals, or environmentally sensitive materials. We foresee ethical risks conflicts of interest. We committed to upholding the highest standards of scientific integrity and ethical conduct to ensure the validity and reliability of our findings.
REPRODUCIBILITY STATEMENT
To ensure reproducibility, we provide: Detailed hyperparameters for multiple training stages (Ap- pendix H); All system prompts used in annotation, training, and evaluation (Appendix Eb. Our code and model checkpoints are publicly available.
ACKNOWLEDGMENT
S. Pan was partially supported by Australian Research Council (ARC) under grants FT210100097 and DP240101547 and the CSIRO - National Science Foundation (US) AI Research Collaboration Program. This work was also supported by the NVIDIA Academic Grant in Higher Education and Developer program.
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A
THE USE OF LARGE LANGUAGE MODELS
During the preparation of this manuscript, we only employed large language models as auxiliary tools for non-substantive tasks. Their applications were limited to assisting in code debugging, checking grammar and formatting consistency, and improving the fluency of written text. The research design, experimental analysis, and conceptual contribution were independent of the LLMs output. All scientific insights and conclusions presented in this work are solely attributable to the authors.
B FURTHER RELATED WORK
Time series analysis has underpinned applications in finance, energy, transportation. healthcare, among others, over the past decade (Lu & Xu 2024, Liu et al. 2023b; Guan et al.) 2023 Lan et al. 2025). Most existing studies still concentrate on single specific task, such as forecasting, classification, anomaly detection, and imputation (Guan et al. Zhao et al,j Zheng et al. Jiaheng ct al.). These systems are typically task-specific and lack generality across tasks. Attempts such as TimesNet (Wu et al. 2023) and UNITS (Gao et al.) replace the output layer and loss to reuse common backbone across tasks, but the resulting models still exhibit limited out-of-distributiin (OOD) robustness. Transfer-leamning approaches (Ismail Fawaz et al.) Wang et al.) and pre-trained models (Li et al.) 2023) seek mitigate OOD shifts; however, empirical evaluations typically remain domain-specific (e.g., different districts of one city) rather than achieving genuine transfer from domain A domain A growing line of work further argues that a core bottleneck in time series analytics lies in the lack of integration with supplementary textual knowledge (Williams et al.) Ge al. 2025), yet current models remain modality-locked, being unable to ingest such event information in textual form. In addition, most existing models adopt fixed output formats and depend on black-box computation, providing limited interpretability. even though some efforts rely on attention map (Liu et al. (2024), causal inference (Wang et al. (2024)). or visualization of hidden representations (Wang et al. 2024b Cai et al. 2024a] Yi et al. 2024) to offer implicit explanations. Such latent interpretabillity, however, often difficult for non-experts understand or trust.
In contrast to these models, SUITS mitigates domain-specific brittleness by curating cross- domain, reasoning-criticaa time series data and expanding the task space beyond surface QA. And TIMEOMNI-1 improves reasoning accuracy and OOD generalization at scale across diverse domains through multi-task joint training. Finally, TIMEOMNI-1 yields step-by-step rationales that decompose temporal priors, event effects, and decision criteria, turning black-box predictions into transparent, reproducible reasoning.
C LIMITATIONS OF EXISTING TIME SERIES REASONING DATASETS
As supplement to the Section 3.1 we also conducted additional experiments existing datasets to assess how these limitations affect reasoning performance (shown in Figure 9): (1) Reasoning necessity. Across most tasks, Time-MQA, pre- pre-trained LLMs, and fine-tuned TS-LLMs do not exhibit clear performance separation, indicating that the difficulty distribution lacks sufficient granularity to discriminate model capabilities. Moreover, CoT-based SFT does not outperform ANS-based SFT, suggesting that explicit reasoning is not required to solve these tasks. In fact, when tasks can be addressed through surface-level pattern matching, extra reasoning capacity yields only marginal gains. Therefore, we should construct dataset with calibrated difficulty gradients that genuinely require reasoning to solve. (2) Context sufficiency. For anomaly detection, current datasets lack relevant contextual information, forcing models to decide solely from the input sequences. As result, both Time-MQA and pre-trained LLMss achieve only 50-60% accuracy, barely above random selection. After training, performance of ANS-based SFT improves by more than 20% on the ID subset, whereas CoT-based gains are less than half of that, indicating that improvements primarily come from directly mapping encoded sequence features to anomalous outcomes, rather than reasoning about the causes of anomalies. Consequently, for TSR with LLMs, we should prioritize providing sufficient context to support reasoning, rather than pushing models to overfit of time-series signals.
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Multi-choice
Anomaly Detection
100
77.2
71.0
Accuracy (%)
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Accuracy (%6
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Pane
Owenk 3.
$2.
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Model Performanc
Performance
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Time-MQA (Max)
10
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Finc Anc-tuned LLMs
Modal scale
scale
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Meto Lan-Lamme-13 1-7788
GPP4-1
Geen2.5
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GPM.11
open2.5.1 148
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Anomaly Detection QA 00D Performance
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NOA
Lann 708
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Meza Liema 2770
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Moniotal 2430
garmer aamma-2-4.00 2-6.00
AMS-SPT
consm
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Conspaan
Figure 9: Evidence for the necessity of reasoming and the sufficiency of context on Time-MQA dataset: multiple-choice and true/false tasks saturate in inddistribution settings, while the anomaly- task exhibits apparent guessing under out-of -ut-of-distributtin shift.
D DATASET DETAILS
D. RAW DATA SOURCE
Task 1: Scenario Understanding. We collect diverse time series data across different scenarios
from the work of Merrill et al. (2024). The out-of-distribution (OOD) test set is constructed based on the original domains of the time series data: specifically, samples from the Agricultural, Education and Healthcare domains are used as OOD test data.
Task 2: Causality Discovery. We perform causal discovery on river discharge time series from the CausalRivers dataset (Stein et al. (202)), aiming to uncover causal relationships from observational data. Ground-truth causal directions are determined according to river flow: the amount of water measured at an upstream station directly influences the amount measured downstream at later time, and we thus consider such relations causal; two rivers are not connected, we consider them non-causal. For the OOD test set, split by geographical regions: training and in-distribution (ID) test data are taken from Eastern Germany, while the OOD test set is sourced from Bavaria.
Task 3: Event-aware Forecasting. We use human mobility dataset the primary training scenario, specifically taxi drop-off data near the Barclays Center in New York City. We collect the raw time series together with aligned events from Liang al. (2024)
Ge et al. 2025). For the OOD test set,
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adopt electricity load time series paired with weather events from the EWELD dataset (Luu et al. 2023a).
Task 4: Decision Making. To evaluate decision-making with counterfactual reasoning ability (i.c., reasoning about the outcomes of unobserved actions), we adopt a sandbox environment based on real building load data. Specifically, building load profiles and battery charge/discharge operations under a dynamic pricing scheme. Given 48 hours of historical building load and peak-valley pricing information, models are required to determine charge/discharge strategies for the next 24 hours. For the OOD test set, we select two buildings whose load patterns differ significantly from those in the training and ID sets.
use the CityLearn dataset (Nweye al. 2023 which provides
For all four tasks, we construct clear data pipelines, as detailed in Section 3.2 thereby facilitating future dataset expansions and task extensions.
D.2 DATA STATISTICS
This section provides the detailed quantitative breakdown of TSR-SUITE, complementing the high- level overview in Section 3.2 Table TubleBlists lists the number of samples available for each reasoning task, stratified by their use in the two-stage training (Stage SFT and Stage 2 RL) well in-distribution (ID) and -uitt--diittiuution (OOD) testbed. The data confirms substantial scale for SFT (Stage 1) and even larger set for RL (Stage 2), ensuring robust learning and generalization evaluation for each task.
Task
#Stage Train
#Stage 2 Train
#ID Test
#OOD Test
Scenario Understanding
609
5104
200
899
Causality Discovery
778
6044
800
800
Event-aware Forecasting
400
2780
418
476
Decision Making
552
3284
188
273
Table 3: Detailed sample count statistics for the four time series reasoning tasks in TSR-SUITE across training stages and testhed.
D.3 STATISTICS ON SEQUENCE LENGTH AND TOKEN BUDGET
In this section, we provide statistics for the actual sequence lengths used in TSR-SUITE. in Table as well as the corresponding token budgets computed using the tokenizer of our base model (Qwen2.5- Instruct- 7B) in Table 5, These results clarify that our tasks involve substantially longer sequences than the illustrative examples Figure The average total token budget (1, 106 tokens) remains far below the maximum supported input length of S.ven2.5-Instruct-ie (32,768 tokens).
Table 4: Maximum and average time series lengths across four tasks.
Scenario Understanding Causality Discovery
Event-aware Forecasting
Decision Making
MAX/AVG length
316.3
124/
96/78.3
48 / 48.0
Table Average token budgets computed using the - tokenizer.
Scenario Understanding Causality Discovery
Event-aware Forecasting Decision Making
Overall
AVG tokens of series X
1701
698
357
281
860
AVG tokens context
261
I60
216
408
246
AVG total tokens
1962
573
1106
D.4
TASK 3 (EVENT-AWARE FORECASTING) SPECIAL NOTES
Unlike multiple-choice tasks, where correct answers are explicitly listed among options, Task requires forecasting future sequence within fixed output window. This open-ended formulation
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Table 6: MAE(U) of CoT-SFT with different chain construction on Task 3.
ID
OOD
LLM Analyzer Generated
15.10
157.2
Ground Truth Guided
24.53
395.56
significantly increases the difficulty of constructing coherent reasoning chains and prevents the LLM Analyzer from perfectly predicting results that are fully aligned with the ground truth. However, instead of guiding the generation of reasoning chains using the ground truth, allowed the LLM Analyzer to generate predictions based on its own understanding. In our experiments, reasoning chains generated with ground-truth hints consistently resulted in worse CoT based SFT performance compared to those produced directly by the LLM Analyzer as shown in Table This result also aligns with recent findings (Zhao al. 2025 Gao al. 2025 Li al. 2025). which suggests that the most effective training data are instances slightly beyond model's current ability but not prohibitively difficult. Furthermore, ground- trrtthh-tuthhh-e guided chains tend to obscure the task's inherent difficulty and deviate from the base model's ' natural data distribution. Therefore, we examine the cases and select 400 samples with relatively low MAE chains generated by the LLM Analyzer without relying on ground-truth hints as supervision for Stage I training to balance difficulty and quality.
D.5 HUMAN EVALUATION INTERFACE
As described in Figure when the LLM Analyzer (GPT-4.1 in our case) fails to solve a sample in Step the instance proceeds to Step 2. In this step, human reviewers the interface shown in Tigure[OOte examine whether the provided context is sufficient to disambiguate the answer. If the question solvable by human reviewers, their reasoning chains are further polished by the LLM Rewriter to follow our structured templates, and the resulting samples are collected as Step 2 CoT data.
Series:
LBC
D855
Time Folres intervao)
Makro Stoteat SBLD Aldioaattrrr
bera
atber
Tapun
Irger Dadls lrwes
fer
Prggess 9u000 P1
Tandee Made
Figure 10: Screenshot of the human evaluation interface used in Step 2 of Hierarchical CoT Annota- tion.
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E
PROMPT USED IN THIS PAPER
E.1 HUMAN-GUIDED REASONING TEMPLATE FOR HIERARCHICAL COT ANNOTATION.
As detailed in Section 3.2 our hierarchical annotation pipeline relies on structured reasoning templates (CoT) generation process. These templates
to ensure consistency and quality in the serve as explicit guidelines for the LLM Analyzer in the initial solving phase, as well as for human experts during verification and the LLM Rewriter in the refinement phase. The templates defined in this section are specifically designed for CoT annotation only. They provide a systematic framework for breaking down each reasoning task into logical steps, ensuring that all annotated traces follow a consistent structure while capturing the essential temporal reasoning processes. This approach guarantees that the resulting CoT data maintains high quality and facilitates effective model learning.
Human-guided Reasoning Template for Taskl: Scenario Understanding
Step 1. Series length check
Observed length=(L). Expected per option: A(exp.len.A); Beexp_len.BB; C(exp_len.C): De) Retain option(s) whose expected length x L.
Step 2. Magnitude & unit sanity
Value (mnge=(min--mmall D(range_D) D). Eliminate options whose units/ranges mismatch.
Typical ranges: Arrange_At A}; B B}; Crrange.Ch-
Step Shape & temporal pattern
Note trends/sassonaliyy/siic.. {key patterns). Compare to option narratives: A {match or not}; B (mmathh or not): C (match or not); D (match or not). Keep hest-matching narratives.
Step 4. External-event alignment Identify clear events (e.g., single-day surge, mid-series drop): {events). Which option explicitly explains this?
Step 5. Final elimination plausibility Remaining candidates: (remaining). Choose the scenario that satisfies all of length, magnitude, pattern, and event consistency.
Step 6. Double-check length consistency Confirm (tentative.chee cchoicechhceeeeeee expected length (L]] {yes/no). If "no", revert next best candidate; else accept. Final choice: {chosen.option).
Human-guided Reasoning Template for Task3: Event-aware Forecasting
Step 1. Identify the baseline patterns You should first identify the baseline patterns and trend from the historical series.
Step 2. Estimate the impact of any events
Next, estimate the incremental impact of any special events (pre-event buildup, during-event lift, post-event dispersal) as an overlay on the baseline.
Step Combine the baseline and event effects
Finally, given the current context, combine the baseline and event effects to generate the forecast sequence.
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Human-guided Reasoning Template for Task2: Causality Discovery
Step 1. Trend Consistency
Check whether the two series demonstrate structurally consistent trends, such as shared "stable rise fall" shapes, both rise/fall similar points (within =1-2 time steps), and flat/stable periods aligned in time. It's OK for their absolute values to differ - match shape, not magnitude. Red Flag: If one rises while the other stays flat or falls + Stop in the option: are not causal. Be perceptually flexible. Flatness doesn't It require perfect constancy - as long as fluctuations are very small relative to the scale of the full time series, they still be considered flat.
Step Key Fluctuation Alignment
Check whether the two time series have notable peaks, dips, inflection points at the same or nearly the same time.
You must ensure: Spikes/dips occur within +1-2 steps (which means <24 hours lag if sampling 12h). For time series with low overall discharge (maybe near 0), even modest changes can be meaningful if they represent clear pattern change relative to baseline. If peaks differ by 23 steps, it's too much lag to infer causality + the two time series are not causal. Do not confuse visually similar shapes with causality if key changes happen at clearly different times.
Step 3. Direction of Causality Only perform this step if both Step and 2 pass. Use the domain principle: "Small rivers flow into big rivers" not the reverse.
Rule: If 'mean(A) < mean(B)', then 'A + B If "mean(B) < mean(A)', then 'B + A'. Notes: If Step 1 or fails, skip Step 3. Timing matters: steps of lag (36h) is already too much. Matching is about structure and timing, not numbers.
Human-guided Reasoning Template for Task4: Decision Making
Step Forecast the next 24-hour load
Use the historical 48-hour load pattern to generate forecast for tomorrow's s 24-hour load. Pay special attention the peak-price hours and estimate the likely loads during those hours.
Step Principles for evaluating strategies
Charging should take place during off-peat hours when electricity price is low. Discharging should take place during peak hours when electricity price is high and forecasted load is significant. Avoid charging during peak hours discharging during off- fff-peak hours, these operations increase cost instead of saving it.
Step 3. Cost calculation and strategy comparison For each strategy, compute the expected saving using:
Saving
Σ
min min(
(Ppeak
Pvalley)
hεpeak
where i(h)) is the forecasted load at hour h. pdis is the maximum discharging power, and ppeak. Pvalley are the peak and valley electricity prices. Select the strategy with the highest saving.
E.2 SYSTEM PROMPT FOR TRAINING AND EVALUATION
This section presents the system prompts used in the ablation study on training stages (Section 4.2). The prompts categorized into two types: Chain-of- Toiih-ohooTooghh(CTCT prompts that require models to generate reasoning traces before answers, and Answer-only(ANS) prompts that directly output final answers without explicit reasoning.
For Tasks 1, 2, and 4, the CoT prompts enforce a structured output format where models must provide step-by-step reasoning within <think> tags before the final answer in <answer> tags. The ANS prompts for these tasks skip the reasoning step and output only the final answer. For Task 3, the prompts are adapted to accommodate sequence predictions while maintaining the same CoT/ANS
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distinction. These prompts ensure consistent evaluation across different training configurations: ANS-SFT uses ANS prompts, CoT-SFT uses CoT prompts, and CoT-SFT+RL uses CoT prompts during both training stages.
System Prompt of CoT for Task1, 2 and
Output Format:
<think>>on Your step-by-step reasoning process that justifies your answer</think>
<answer> Your final answer(Note: Only output a single uppercase letter of the correct op- tion)//answer>
System Prompt of CoT for Task3
You should think the impact of the event first, then output the predicted sequence. Output Format:
<think> Your step-by-step reasoning process</think>
<answer> (Your predicted sequence)<ansserz
System Prompt of ANS for Task1, and 4
Output Format:
<answer> Your final answer(Note: Only output a single uppercase letter of the correct op-
tion)//answer>
System Prompt of ANS for Task3
You should output the predicted sequence directly, Output Format:
<answer> (Your predicted
E.3 MODEL ROBUSTNESS TO PROMPT VARIATIONS
In this section, we investigate the robustness of TIMEOMNI- prompt variations. We design three prompt perturbations:
Paraphrased Question. We use ChatGPT to rewrite the original question while keeping the semantic meaning unchanged.
Paraphrased System Prompt. We use ChatGPT to paraphrase the system prompt while keeping the semantic meaning unchanged.
w/o System Prompt. We entirely remove the system prompt simulate the extreme case.
The experimental results are summarized in Table 7 The results show that paraphrasing either the question or the system prompt leads to minimal performance degradation, indicating that the model does not rely specific wording phrasing.
Surprisingly, even in the absence of the system prompt, the model remains highly robust. Although it longer outputs the explicit <think> and <answer> tags, adjust the evaluation script to parse the fire--form responses and observe that TIMEOMNI-1 still maintains strong performance, sometimes outperforming the original prompt. Manual inspection further reveals that the model continues to follow the reasoning template injected during Stage 1 training, suggesting that the temporal prior has been deeply internalized.
Overall, these results demonstrate that TIMEOMNI-1 1 is highly robust to prompt variations and resistant to perturbed instructions.
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Table 7: Evaluation of TIMEOMNI-I under prompt variations. Red: the best, Blue: the 2nd best. denotes the w/o System Prompt setting, in which the model no longer produces the explicit <think> and <answer> tags; therefore we do not report SR for this condition.
Scenario Understanding
Causality Discovery
Event-aware Forecasting
Decision Making
Prompt Variations
ID
OOD
ID
0OD
ID
OOD
ID
0OD
ACC
SR%
ACC
SR%
ACC
S8%
ACC
SR%
MAE
SR%
MAE
SR%
ACC
SR%
ACC
SR%
Paraphrase Question
86.2
97.8
85.3
97.5
65.8
99.5
60.2
98.8
13,90 90.1
150.9
81.5
47.1
99.8
56.1
99.5
Paraphrase System Prompt
99.5
66.8
99.8
62.8
99.5
152.1
43.6
100.0
System Prompt
68.5
150.1
40.4
Original Prompt
99.8
145.5
F IMPLEMENTATION DETAILS OF THE TRAINING STAGES
Since there currently lacks pre-trained time series encoder analogous to Vision Transformers (ViT) (Dosovisskyy et al., 2021) the computer vision domain, we follow the common practice of tokenizing time series into text inputs, which aligns with the approaches adopted in Time-R1 (Luo ct al. 2025) and Time-MQA (Kong et al. 2025).
F.1 STAGE SUPERVISED FINE-TUNING (SFT)
SFT is process where preitrrined model is further trained on labeled dataset adapt it for specific task. This is achieved by minimizing the negative log- ligeliil of the output given the input data. In the context of time series reasoning, the model learns to generate intermediate rationales and final answers based on observed time series data and auxiliary context. Specifically, based on carefully curated dataset D {(X,, C₁ R4, W the model's parameters 0 are updated by minimizing the loss function:
N
1 L(0:D) -
Σ log ne (R) (X,, C.))
where R, and v/ are the predicted rationale and final answer. respectively. And for the i-th example, X; and C2 represent the observed time series and auxiliary context.
F.2 STAGE REINFORCEMENT LEARNING (RI.
In reinforcement learning, we employ the group relative policy optimization (GRPO) algorithm (Shao et al. 2024) to refine our post-SFT model using carefully designed reward functions. Given an input pair (), GRPO samples N rationale-answer trajectories {(R,, y:) M from the policy model ge. and organizes them into groups [ The reward for trajectory i Go is computed
b=
Å y:)
Σ R(Rg,
JEG.
where combines both the correctness the answer and the quality the response format. The policy is then updated using the following objective:
1
N
To Ri, Yi
(X,)) A.
GRPO (8)
min
(5)
N
Theee
[R;, X)C)
T88
e, +c) A DKL ll Teuan))
clip
Here, Th indicates the SFT model. and 3 are hyperparameters that control the clipping threshold of the PPO update and the weight of the Sellback-Leiblee (KL) divergence penalty, respectively.
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F.3 DISCUSSION OF TOKENIZING TIME SERIES INTO TEXT INPUTS
TIMEOMNI-1 I employs a text-based strategy, representing time series as textual sequences. For direct comparison, also implement embedding-basee approach by training time series encoder from scratch following the CharTS (Xie et al., (025) architecture, which is conceptually similar OSM (Langer al. 2025) and Time-LLM (Jin al. 2024a). The same base model Siwen2.5-Instrutt-1ee is used in both settings to ensure a fair comparison. Results across all four tasks are summarized Table while inference efficiency under identical test conditions provided in Table
Our results indicate that a simple MLP-style encoder, similar to those used in CharTS, OpenTSLM, and Time-LLM, does not provide clear advantage over text serialization. Although the encoder improves Task accuracy in-distribution, it leads to substantially lower success rates for Tasks and 4, suggesting interference with the base model's cnntruction-followinn ability. A potential reason for the performance gap is the higher data requirement of the embedding- based method. Training a time series encoder from scratch is data-intensive, and our 2.3k CoT-Data might be inadequate.
The embedding- design also slows inference by a factor of three because introduces an additional neural path that cannot benefit from the kerne-levell optimizations available in vLLM. For memory usage, the text-based approach requires a larger KV cache due to its longer tokenized representation; however, this overhead remains manageable and scales reasonably with input length. In our comparison in Table | the average time series length exceeds 300 time steps, yet the peak GPU memory remains below 52GB.
Taken together, the text-based strategy is both effective and efficient for the settings considered in this work, where sequence lengths and input dimensionality is modest. While dedicated time series encoders may offer advantages for currently lacks a widely adopted pretrained encoder analogous to ViT (Dosovitskiy al. | [202T) for vision. Developing such general purpose time series encoder is promising direction for our future work.
or extremely long sequences, the field
Table 8: Comparison of Text- based and Embedding based Approaches. The same base model (Owen2.5-Instruct- 14B) is used both settings to ensure fair comparison. Red: the best, Blue: the 2nd best.
Scenario Understanding
Causality Discovery
Event-aware Forecasting
Decision Making
Method
ID
0OD
ID
OOD
ID
OOD
ID
OOD
ACC
SR%
ACC
SR%
ACC
SR
ACC
SR%
MAE
SR%
MAE
SRS
ACC
SRS
ACC
SRS.
Base Model
54.0
100.0
54.5
100.0
30.5
100.0
31.4
99.8
20.13
88.0
131.05
64.1
35.1
100.0
34.8
100.0
Text- base (Ours)
74.2
100.0
69.4
99.3
49.5
100.0
47.9
98.2
15.22
90.2
139.44
84.9
38,9
100.0
51.1
99.0
smbedding-basse
88.5
92.9
82.4
97.7
40.3
68.7
38.7
62.9
22.15
75.1
162.23
58.4
18.2
59.5
20.0
51.9
Table 9: Inference efficiency comparison.
Method
Samples
Avg Inference Speed per Sample (8)
Peak GPU Memory (GB)
Text-based (Ours)
100
7.07
51.77
Embeddingg-used
100
22.01
32.04
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Published as a conference paper at ICLR 2026
G TIME SERIES TASK-GROUNDED REWARD DESIGN
All samples are required to follow a basic format reward Rooman which checks whether outputs ply with the schema <think>.. wtt.///tinaaassweeee...e.e . answer>> For discrete-outpe tasks (scenario understanding, causality discovery, decision-making), correctness is directly measur- able by
1,
= y,
diiscrete
0, otherwise.
For sequence-output task (event-aware forecasting), we define a continuous reward based on the exponential decay of mean absolute error (MAE):
1 MAE
V/
T
1=1
0,
if (0) * len(y):
Reqquence
(8)
eepp(=a a MАE) + Reounts
if len len(y).
together with horizon-matching bonus Reount 0.1. And the final per-sample reward integrates all components as:
R₂ Aormat + Riskk
where Rusk refers to Rdiscrete for discrete-output tasks and Reequence for sequence-output task. We set 0.1 in all experiments.
G.1 REWARD SENSITIVITY ANALYSIS
In this section, conduct controlled sensitivity sweeps the key reward components (a, R A), and evaluate their effects on training stability. All experiments are trained for epoch. For a and Reounts which only affect the reward design of Task 3, we report the MAE curves for Task 3 over 1 epoch. For X. we evaluate the proportion of responses with a positive task reward Rusk over epoch, where Rusk denotes Rediscrnne for discrete-output tasks and Requeece for the sequence-output task.
a and Reonat exhibit low sensitivity, Varying these coefficients leads to only mild changes in MAE curves, indicating that the RL process remains stable under a broad range of settings. Format reward is essential. When disabling the format component (A = 0), the model collapses after 70 steps, showing sharp drop the positive ratio of Rusk This confirms that format consistency necessary for stable RL optimization.
Overall, the current hyperparameter choices yield the better trade-off between stability and perfor- mance. These analyses provide clear guidance for reproducing and tuning the reward design.
Sensitivity
Recent Sensitivity
Sensitivity
21
21
0.2
Roan
0.1
Rour =02
20
0.025
20
Ruai 00.0
Ruaa Positive Ratio
0.0.05
Roaen +0.1
2 0.1
(MA(Ta)k)))
MAE(TBSia)
19
199
18
188
17
17
16
16
20
60
80
100
120
Training Stop
Training Step
Training Step
Figure 11: Reward sensitivity across a. Reounts and X. The model is robust to and Reounts whereas disabling the format reward (A = 0) causes training collapse.
25

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Published as a conference paper at ICLR 2026
H TRAINING CONFIGURATION
Our training process follows a two-stage procedure, consisting supervised fine-tuning (SFT) followed by reinforcement learning (RL). For the SFT stage, we begin by fine-tuning the Qwen2.5- 7B-Instruct (Team) 2024) with full-parameter updates for total of epoch, utilizing DeepSpeed ZeRO-3 (Raleyy et al., 2020) for efficient training. The fine-tuning is performed in BF16 precision with FlashAttention-2 enabled to accelerate attention operations. The maximum sequence length is to 8192, and the per-device batch size is 1, with gradient accumulation of 32. The optimization procedure uses a peak learning rate of 1.0 x 10-5 with a cosine learning rate scheduler and the warm-up ratio as 0.1. All training is carried out using the L.aMM-Pacttry repository (Zheng et al., 2024 system equipped with single NVIDIA 1200-140G GPU. In the RL stage, continue training from the Stage checkpoint using the verl repository (Sheng et al. 2024) and FSDP (Zhao al. 2023) under BF16 precision. The maximum sequence length reduced 2048, and training performed across 8 NVIDIA A A100-80G GPUs. The training batch size is 128, with RL minibatches of size 32 and per-GPU micro-batches of 8. Gradient clipping is applied with maximum global norm of 3.0, and a KL-penalty coefficient of 0.04 is used to regulate the model. For rollouts, trajectories collected per update, with sampling temperature of 0.7. The learning rate set to 1.0 x 10-6, and training continues for epochs.
1 REINFORCEMENT LEARNING TRAINING DYNAMICS
This section reports the full reinforcement learning (RL) training dynamics in Figure 12 The plots track both the overall reward and the format correct ratio for the training split and the OOD testbed throughout the RL stage. To reduce evaluation cost during RL, cap the maximum response length 2048 tokens during training. As a result, the performance these curves may appear slightly lower than the full results reported in Table [ which is expected. Overall, the curves provide clear view of the optimization behavior and demonstrate that RL training for TIMEOMNI-D remains stable and continues to improve over training steps.
Training Overall Reward
Training Format Correct Ratio
Overall Reward
0.70
0.98
0.65
0.96
100
200
300
390
100
200
300
390
Training Step
Training Step
Format
0.56
0.94
Overall Roward
Ratio
0.52
0.50
0.88
Training Step
Training
Figure 12: Full reinforcement learning training dynamics showing reward progression and format correctness for the training split and the OOD testhed.
26

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Published as a conference paper at ICLR 2026
J SCALING OF TRAINING DATASET
In this section, we analyze the scaling behavior of TIMEOMNI-I by varying the amount of training data used in Stage (SFT) and Stage 2 (RL). All experiments strictly follow the training configurations reported in Appendix H All Stage (SFT) data scaling runs are trained for epoch, and all Stage (RL) data scaling runs trained for epochs. Figures 13 present the scaling behavior of Stage 1 (SFT) as we vary the amount of CoT--Data (25%, 50%, 75%, 100%). Figures [4 present the scaling
behavior of Stage 2 (RL) as we increase the amount of RL-Data (25%, 50%, 75%, 100%).
Stage1 (SFT) Taak1 (ID)
Stage1 (SFT) Task2 (ID)
Stage1 (SFT) Task3 (ID)
Stage1 (SFT) Taske (ID)
sn
AC
$4.00
SA 18)
54 PM
206
30th
1m.m
Tririn Data
.4th
12:55
70% Thanning
Tahing Data
udw
Stagel (SFT) Teak1 (OOD)
Stage1 (SFT) Task2 (OOD)
Stage1 (SFT) Teak3 (OOD)
Stage1 (SFT) Task4 (OOD)
ACC (N
ACC PM
SWC
A00 19)
20%
122%
25%
1975
Prining Data Scaling
Thaining Data Sceling
Taining Data Ing
Pakiing Data Scaling
Figure 13: Scaling of Stage 1 (SFT) Training Dataset.
Stage2 (RL) Tack1 (ID)
Stage2 (RL) Task2 (ID)
Stage2 (RL) Task3 (ID)
Stage2 (RL) Task4 (ID)
(h
200
MIC
SR PND
ACC
(k)
ADC
ACC
25%
59%
25%
300%
356
50%
15%
10.%
25%
50%
10%
100m
25%
55%
70%
100%
Sariing Data Scaring
Training Dala Scaling
Trening Scaling
Faaning Dala Scaling
Stage2 Taskl (OOD)
Stage2 (RL) (OOD)
Stage2 (RL) (OOD)
Stage2 (OOD)
300
300
155
ADC PN
N0C
SR
SR
AGC 50. 2r
ACC 58 P9
50 14
143
54 150
Taaing Data Soaling
Training Data Scating
Data
Trsniing Data Scating
Figure 14: Scaling of Stage 2 (RL) Training Dataset.
Across all four tasks and both ID/OOD testbeds, the primary ACC/MAE metries follow a clear larger training sets monotonically improve Task 1, Task 2, Task 4 ACC and reduce Task 3 MAE. Notably, Task 3 MAE does not perfectly follow a scaling trend during SFT, which suggests that sample quality may influence the SFT phase more strongly. However, once the model enters Stage 2 where performs its own exploration and refinement, Task 3 MAE follows a more clear scaling pattern and continues to decrease. In Stage 1, SR for Task 1, Task 2, Task 4 is already saturated near 1 100%. In Stage 2, RL continues to improve the SR for Task 3. Together, these curves show that both training stages benefit from increased data.
scaling pattern:
27

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Published as a conference paper at ICLR 2026
K ADDITIONAL EVALUATION ON EXTERNAL TIME SERIES QA BENCHMARKS
To further validate the generalization ability of TIMEOMNI-I, we conduct experiments on three external time series QA benchmarks spanning 13 tasks and 3,406 samples:
MTBench (Chen et al.
2025
(real-world time series; 4 tasks; 2,380 samples),
Time CCai ct al.
2024b) (synthetic time series; 5 tasks; 746 samples),
CaTh-Bench (Zhou et al.
2025 (real-world time series; 4 tasks; 280 samples).
We evaluate TIMEOMNI-I alongside two baselines: (1) Qwen2.5-Instruct-7B. the base model of TIMEOMNI-1, and (2) the original results reported in the original paper.
Table 10: MTBench results (real-world dataset). Red: the best, Blue: the 2nd best.
Method
FinanceQA (7-Day)
WeutherQA (7-Day)
FinanceQA (30-Duy)
WeatherQA (14-Day)
AVG
Num. Samples
484
666
523
707
Base Model (iveen..-Instructt777
62.2
14.7
53.5
36.2
49.2
Original Paper (GPT-44)
44.8
TIMEOMNI-1
72.1
$1.4
66.2
42.6
58.1
Table 11: TimeSeriesExam results (synthetic dataset), Red: the best, Blue: the 2nd best.
Method
Amomaly Delection
Causality
Noise Understaniing
Pallern Recognition
Similarity Analysis
AVG
Num. Samples
108
72
362
120
Base Model Fire22.-IInstruct-o
37.0
44.4
44.0
50.0
59.2
46.9
Original Puper (Phl-3.5)
28.0
52.0
47.0
39.6
TIMEOSIN-1
42.6
37.5
56.00
56.4
46.7
47.8
Table 12: CaTS-Bench results (real-world dataset). Red: the best, Blue: the 2nd best.
Method
Caption Retrieval
TimeSeries Retrieval
Amplitude Comparison
Mean Compurison
AVG
Num. Samples
Buse Model (Qwen2.5..m Dowee2.--Instuue-711
69.0
46.0
87.5
57.5
65.0
Original Paper (LLaVA v1.6 Mistral)
44.0
29.0
43.0
35.0
37.8
TIMEOMNN-1
57.0
37.0
92.5
63.5
MTBench is a multimodal time series QA benchmark where the auxiliary text C contains task- relevant information (e.g. external events) rather than only task instructions. This setting closely matches the formulation of TSR-SUITE, where the joint distribution
RR y) ~ RR, y X, = Po X, poly X,
ensures that both the reasoning path R and the final answer y depend on the interaction between the time series imput X and the contextual information C.. Owing to this strong alignment, TIMEOMNI-I achieves the largest gains on MTBennh, even surpassing the originally reported GPT-4o performance.
By contrast, the textual input in TimeSSerisEExm and CaTS-Bench serves primarily as task instruc- tions, meaning that the reasoning is dominated by the time series modality and is less aligned with the multimodal reasoning design of TSR-SUITE. Even under this more restricted setup, TIMEOMNI-1 outperforms the base model, demonstrating that it has acquired time series reasoning abilities that transfer robustly across domains and tasks.
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Published as a conference paper at ICLR 2026
L
CASE STUDY
Table 13: Task1 (Scenario Understanding) example with options and reasoning process.
Task1: Scenario Understanding
User
You are given time series. Please identify the scenario that most likely created it.
[39, 58, 51, 45, 40, 39, 57, 45, 45, 34, 40, 32.64, 62, 92, 37, 31, 29.52, 46, 69, 79, 43, 51,
50, 50, 56, 45, 57, 32, 35, 55, 38, 41, 38, 54, 33, 48, 54, 40.45, 30.31, 56, 49, 37.41, 45, 39,
49, 31.52, 47, 51, 33, 53, 38.49, 33, 30, 35.52, 41, 32.61, 52, 44, 29.68, 41, 32.44, 46.5,
48, 38, 46, 38, 49, 45, 52, 47, 45, 39, 47, 44.47, 28, 36, 39, 27.67, 52,
30, 30. 27.4, 40. 35.51, 38, 35.43, 37,
28.49.
32.45, 30, 32,33,,,,,
27, 29.55, 32, 46, 44, 29.42, 26.72, 32, 44. 34.61, 37, 37, 45, 38.44, 42. 30, 51,
30. 31.56, 34, 42, 45, 39. 30, 42, 34, 50, 27.33, 38, 41.55, 32.43, 40, 40, 50, 39, 33.53, 26.
25.73, .... 27, 30, 22.59, 38, 33, 26.58, 23.48, 32,62, 40, 20.33, 27.4, 33.44, 35.42, 33.34,
31, 38, 21, 21, 38, 36).
60
50
WM m/l Th iw Myr My My
40
30
50
100
150
200
250
300
Options:
A. ATM daily cash withdrawals over a year with an annual festival (365 daily samples).
B. Weekend music festival causing a surge in noise levels (96 hourly samples).
C. Hourly restaurant food orders during 2-week local food festival (336 hourly samples).
NYC daily taxi pick-ups over a year with subway strike (365 daily samples).
TIMEOMNI-1
<think>
1) Length check.
Observed length is 336. Options expect: A= 365, 96, C= 336, D= 365. Only
matches exactly.
2) Magnitude sanity.
Values span z 15-92. reasonable for hourly food orders in busy restaurant (C). NYC daily taxi rides (D) are typically in thousands; A and B already fail length.
3) Shape/pattern.
Fluctuating baseline with intermittent surges (c.g., 92) and no single prolonged spike lorke--oonsstent with repeated demand bursts during local festival (C).
4) Event alignment.
A 2-week festival plausibly produces multiple hourly peaks across days (C), rather than one annual or weekend-only spike (A/B), and not daily-year scale (D).
5) Double-check.
weeks x 24 hours 336 consistent with (C).
</think>
Canswer>C</ann Canawer>>
29

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Published as a conference paper at ICLR 2026
Table 14: Task2 (Causality Discovery) example with options and reasoning process.
Task2: Causal Discovery
User
You are given two time series related to river discharge measurements, expressed m /s.
Through causal discovery methods, we aim to identity potential causal relationships
between different measuring stations from time series data alone. The time series of A463
is: [4.12, 3.8, 3.44, 3.11, 2.97, 2.85, 2.79, 2.76, 2.63, 2.74, 2.69, 2.77, 2.75, 2.79, 2.97, 3, 3.09, 3.18, 3.24, 3,16, 3.16, 3.37, 3.37, 3.54, 3.56, 3.51, 3.43, 3.37, 3.37, 3.21, 3.12, 3.07,
3.16, 3.12, 3.16, 3.15, 2.95, 3, 2.95, 2.84, 2.75, 2.74, 2.74, 2.74, 2.74, 2.74, 2.6, 2.56, 2.56,
2.53, 2.54, 2.52, 2.33, 2.33, 2.33, 2.4, 2.61, 2.9, 3.22, 3.45, 3.58, 3,95], the time series of 4PRY is [7.55, 7.31, 6.78, 6.5, 6.2. 6,08, 6, 6. 5,91, 5,82, 5.78, 5,84, 5.92, 5,88, 5.89, 5.82, 6.25, 5,94, 5.9, 5,89, 5,89. 6, 6, 5.9, 5.6,, 5.7. 5.55, 5.35, 5.29, 5.14, 5.11. 5,18, 5.12, 5.11, 5.07, 4.89, 4.81, 4.82, 4.7, 4.69, 4.61.4.61. 4.58, 4.45, 4.55, 4.42, 4,44, 4.32, 4.28, 4.37, 4.53, 4.5, 4.62, 4.84, 5, 5.43, 5.74]. Please identify the causal relationships between the two mcasurement stations? The data is collected every 12 hours from 2020-01-00 01-01 to 2020-01-31 totally 62 points cach series.
Discharge (mtss)
A463
2020-01-01
2020-01-10 00:00
2020-01-20 01-00
2020-01-31 00:00
00:00
00:00
Options:
A. A463 is the and 4PRY is the effect
B. A463 and 4PRY
causal.
C. 4PRY is the cause and A463 is effect
TIMEOMNI-1
<think>
1) Trend consistency (perceptual matching).
Both A463 and 4PRY start with a gradual decrease over the first 10-12 points. Both show rise around point 15 15-20, peaking at similar positions (A463 rises from 2.7 to
3.5, 4PRY from 5.8 6).
Both then decline after their peaks, with secondary smaller rise around point 50 (A463: 2.5 3.9; 4PRY: 4.3 to 5.7).
The overall shapes match: hoth series are "decline - flat - rise + fall - rise" with similar timing.
No major segment where one rises while the other falls stays flat.
2) Key fluctuation alignment (peak synchrony).
Major peaks occur nearly simultaneously (points ~~9-20: A463 = 3.37; 4PRY ~ 5.9). Secondary peaks/dips are aligned within 1-2 steps.
No meaningful lag (>> 2 steps) between corresponding changes.
3) Direction of causality (hydrologic magnitude heuristic).
Mean level: mean A463) < meean(4PRY (A463 mostly 2.5-3.5; 4PRY mostly 4.5-6.5). Hydrology rule-of-thumb: smaller tributaries feed larger channels + upstream (smaller) downstream (larger). </think>
Eanswer>A</answes
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Published as a conference paper at ICLR 2026
Table 15: Task3 (Event-aware Forecasting) example with reasoning process.
Task3: Event-aware forecasting
User
Over the past hours (from 2015-05-24 00:00:00 to 2015-05-22500
23: :0: 00), hourly taxi pickups were:
8, 11. 18. 28, 22, 22, 26, 33, 49. 76, 85. 53, 69, 133, 116, 64. 28, 8, 7. 3, 12, 10, 22, 22, 35, 51, 37, 59, 57, 61, 50, 87, 54, 72, 56, 49].
[26, 22,
Significant event in this window:
201 2015-05-29 17:00 BROOKLYN BOXING: Amir Khan vs. Chris Algieri &
Fortuna vs. Vasquez.
In the next 24 hours: 2015-05-31 19:30 Ed Sheeran.
150
Taxi Pick-ups
Event1: BROOKLYN BOKING
75
Day1
Day2
Day3
TIMEOMNI-I
<think>
1) Identify Baseline Patterns and Trends.
The data covers two days (48 hourly values). There is clear daily pattern: low pickups ovemight (midnight-6ann
rising through morning. peaking late afternoon/evening.. On days, there is pronounced spike around 17:00-20:00, likely After the spike, pickups drop sharply, then gradually decrease into the night.
the boxing event.
2) Estimate Event Impact.
The 2211-05--2 91, 21: 85, 53, 23: 69, then post-event dispersal 0-3h: 133, 116, 28; well above baseline (>220-0/hh.
17:00 boxing event caused large surge: 17: 44, 18: 49, 19: 76, 20:
3) Context for Next Day.
Next event: 2015-05-31 19:30 Ed Sheeran concert. Expect similar pre-event buildup. sharp spike during/after, then decline. No other events.
4) Forecast Sequence.
Use the prior non-event pattern as baseline and overlay an event-shaped spike at the concert time: Overnight (0-6) low (1 (1-8); Morning (7-11) rising (8-18); Farly afternoon (12-16) moderate (18-33); Pre-event (17-18) increase (33-44); Event spike (19-21) large (76-91);
Post-event (22-23) decline (53-69).
5) Finalize Sequence.
For each hour, use baseline + event effect applicable. </think>
<answer>
[7. 3, 10, 13, 15, 18, 20, 22, 25, 30, 35, 44, 49, 76, 91, 85, 53, 69]
</ </answer>
31

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Published as a conference paper at ICLR 2026
Table 16: Task4 (Decision Making) example with reasoning process.
Task4: Decision-making
User
You manage home energy storage system with battery capacity 18 kWh, current
state-of-charge (SoC) 5 kWh, max charging power 5 kW. max discharging power kW.. Historical 48-hour hourly load (kWh) from 2023-07-10 00:00 2023-07-11 23:00 given. Tomorrow's 's 24-hour pricing: off-peak $0.22/kWh (hours 0-14, 20-23), peak 50.54/kWh (hours 15-19) 19). Based on historical usage, predict tomorrow's 's load and select the optimal 24rhour battery strategy from Options A D (hour indices 0-23).
0.54
Price (5/kWh)
0.22
5.0
Observed Forecast
Charge h=
Load (kWh)
Discharge th=15,17)
2.5
0.0 0h
24h
48h
72h
Options:
A. Charge:
5, };
Discharge: { 2. 18
B. Charge:
1,2 )
Discharge: 15, 17
C. Charge: 2, 10 };
Discharge: 18, 19
D. Charge: 14,
Discharge: 16, 18
TIMEÔMNI-1
<think>
1) Load forecast.
Mirror the most recent [1.42, 3.16, 2.06, L.03, 1.81, 1.02, 0.71, 0.34, 0.62, 0.75, 0.87, 2.44, 3.71, 1.98, 4.86, 1.37, 1.00, 0.65, 2.40, 0.40, 2.32, 2.51, 1.59, 0.76).
day
(hours 24-47) as tomorrow's profile:
2) Prices principle.
Off-peak: hours 0-14, 20-23 23 ($0.22/kWh) Peak: hours 15 19 (50.54/kwh). Charge in off -eak, discharge in peak maximize spread Ap 0.54 0.22 0.32
3) Feasibility & SoC constraints.
Capacity Wh; initial SoC 5 k/W; max charge kWh/; max discharge k0kWh/
A: Discharge at hour 2 (off-peak) = violates principle + exclude.
B: Charge {1,2} (off-peak) > SoC: - 10 15: Discharge (15.17} (peak).
C: Charge 22.10) SoC: 5-+ 10
Discharge (18,19) (peak).
D: Charge [11.14] = SoC: 5 10 15; Discharge {16,18) (peak).
4) Discharge amounts savings (bounded by load and khhhh/))
Pcak-hour loads (forecast):
h15= 4.86, h16= 1.37, h17= 1.00, h18= 0.65, h19= 2.40.
B: discharge (15,17) 4.86 1.00 5.86 kWhh saving 5.86 0.32 $1.8752.
(If discharging also h16 allowed as listed. then 4.86 1.37 1.00 7.23 kWh
= $2.3113.)
C: (18,19) + 0.65 + 2.40 3.05kWh + $0.976.
D: {16,18} = 1.37 0.65 2.02 + $0.6464.
All satisfy SoC bounds (start 5 kWh, charge 15 kWh, discharge & available). </think>
<answer> B
32


Reviews:
[{"review_id": "DAl8CTeErJ", "reviewer": "Reviewer_YkbD", "summary": "The paper presents a reasoning model for time series data. The authors develop a new dataset by augmenting existing time series data and train a model with SFT and RL to conduct multistep reasoning on a variety of tasks. The results show that this prior enables successful training and increased performance on the proposed dataset.", "questions": "- \"It remains unclear which tasks genuinely demand reasoning capabilities over time series.\" I'm not sure what this means. Several recent works (including those you cite like Merrill et al 2024 and Cow et al. 2024) define tasks that require reasoning over time series. \n- Line 157: \"Many questions are overly simple and straightforward, where invoking reasoning leads to over-thinking\" and Line 185 \"QA-pairs must reward reasoning\". Why is it a problem that reasoning doesn't provide an increase in performance? Is this really an issue with the benchmark, or does it say more about the models themselves?  If the models are overthinking a reasonable, realistic question then my instinct would be to improve the models, not find some set of questions where this wasn't the case.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 1, "presentation": 3, "contribution": 2, "strengths": "- It's true that the majority of prior work focused more on evaluating models than training them to be better at these tasks\n- The ablations are appropriate and useful for understanding how the model works\n- The human-generated reasoning traces are a valuable contribution to the field", "weaknesses": "A serious weakness of the paper is that the authors only evaluate on the dataset they propose. It's not particularly surprising that training on a dataset improves in-distribution performance. I'd be more impressed if the authors showed that their model improves performance on third party benchmarks (including those they reference), or that it mitigates this \"overthinking\" problem that they motivate their dataset with. \n\nThe evaluation section and tables mention an \"OOD\" dataset, but it's not clear how this is constructed or in what sense it is out of distribution.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "rMb5wawXqw", "reviewer": "Reviewer_XR2U", "summary": "This paper introduces TSR-Suite, a comprehensive time series reasoning dataset containing 23K+ QA pairs across four reasoning-critical tasks (scenario understanding, causality discovery, event-aware forecasting, and decision-making), and TIMEOMNI-1, a unified reasoning model trained via a two-stage curriculum (supervised fine-tuning followed by reinforcement learning). The work addresses important limitations of existing time series QA datasets by systematically designing tasks that genuinely require reasoning capabilities and ensuring context sufficiency. The hierarchical Chain-of-Thought annotation process is well-designed, and the experimental results demonstrate strong performance improvements over baseline models, particularly in causality discovery (64.0% vs 35.9% with GPT-4.1).\n\nHowever, the paper has several significant limitations that prevent it from meeting the ICLR acceptance threshold: (1) The model architecture is not clearly described, particularly how time series data is encoded and processed. The paper appears to use text-based serialization of numerical sequences, but this design choice is not explicitly justified or compared with embedding-based approaches. (2) The scalability concerns are not adequately addressed—the dataset appears limited to short sequences and low-dimensional data, with no evidence of handling long sequences (hundreds/thousands of time steps) or high-dimensional multivariate signals (dozens of dimensions). (3) Related work coverage is insufficient, missing important recent work such as ChatTime and ITFormer that directly address time series QA tasks.", "questions": "## Questions\n\n1. **Architecture Clarification**: Can the authors provide a clear description of how time series data is encoded and input to the model? Specifically:\n\n   - Is numerical data converted to text strings? If so, what is the exact format?\n   - Are there any learnable embeddings or encoders for time series, or is it purely text-based?\n   - How are multiple time series (e.g., Task 2) represented in the input prompt?\n2. **Scalability Validation**:\n\n   - Have the authors tested their approach on longer sequences (e.g., 500+ time steps)? What are the limitations?\n   - Can the approach handle multivariate time series with 10+ dimensions? How would this be encoded?\n   - What is the computational efficiency compared to embedding-based approaches (e.g., OpenTSLM)?\n3. **Design Choice Justification**:\n\n   - Why was text serialization chosen over learned embeddings? What are the trade-offs?\n   - How does this approach compare to methods like Time-LLM or OpenTSLM in terms of efficiency and effectiveness for long sequences?\n4. **Dataset Characteristics**:\n\n   - What are the typical sequence lengths in TSR-Suite? What is the distribution?\n   - Are there examples with sequences longer than 100 time steps? If not, why not?\n   - Why were only univariate or paired sequences chosen? Were multivariate scenarios considered?\n5. **Related Work Comparison**:\n\n   - How does TIMEOMNI-1 compare with ChatTime, which also addresses multi-step time series reasoning?\n   - What are the key differences from ITFormer and similar methods?\n   - Why should text-based serialization be preferred over architectural innovations like those in OpenTSLM?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "* **Systematic Task Design**: The paper provides a principled approach to formulating reasoning-critical time series tasks through two design principles: requiring genuine reasoning and ensuring context sufficiency. The four tasks form a progressive pathway from perception to extrapolation to decision-making, which is well-motivated.\n* **High-Quality Dataset Construction**: TSR-Suite represents a substantial contribution with 23K+ curated samples. The hierarchical CoT annotation pipeline (LLM Analyzer → Human Reviewer → LLM Rewriter) is innovative and ensures both quality and scalability in data construction.\n* **Comprehensive Evaluation**: The experimental evaluation is thorough, covering both in-distribution and out-of-distribution generalization across diverse domains. The joint training experiments demonstrating mutual gains across capabilities provide valuable insights.\n* **Clear Problem Identification**: The analysis of limitations in existing datasets (Figure 1) is well-executed and provides strong motivation for the proposed work.", "weaknesses": "### 1. Model Architecture Omission\n\n**Critical Issue**: The paper fails to clearly describe how time series data is processed and encoded into the language model. From the examples (e.g., sequences shown as `[1.2, 3.4, 5.6, ...]`), it appears that numerical time series are simply serialized as text strings and fed directly to the LLM. However, this design choice is never explicitly stated, justified, or compared with alternative approaches.\n\n**Why This Matters**:\n\n- **Reproducibility**: Without clear specification of input encoding, it is difficult to reproduce the results.\n- **Novelty Assessment**: It is unclear whether the approach introduces architectural innovations or simply relies on LLM text understanding capabilities.\n- **Comparison Baseline**: The choice of text serialization vs. embedding-based approaches (as used in OpenTSLM, ChatTS, etc.) should be explicitly discussed.\n\n**Specific Questions Unanswered**:\n\n- How are long sequences handled? Are they truncated, sampled, or aggregated?\n- What is the token budget for time series data vs. context?\n- How are multiple time series (Task 2) represented in the input?\n- Is there any preprocessing, normalization, or feature extraction?\n\n**Comparison with Related Work**:\n\n- **OpenTSLM** uses explicit architectural components (SoftPrompt or Flamingo-style Perceiver Resampler) for time series encoding.\n- **Time-LLM** uses PatchTST-style tokenization with reprogramming layers.\n- The current paper should justify why a simpler text-based approach is sufficient or preferred.\n\n### 2. Scalability and Efficiency Concerns\n\nThe paper does not address scalability limitations that are critical for practical deployment:\n\n**Sequence Length Limitations**:\n\n- All examples show short sequences (12-24 time steps).\n- There is no evidence of handling longer sequences (hundreds or thousands of steps), which are common in real-world applications (e.g., hourly data over years).\n- With text serialization, long sequences would consume substantial token budgets and face LLM context window limitations.\n\n**Dimensionality Limitations**:\n\n- The paper focuses on univariate time series (single-dimensional) or at most pairs of sequences.\n- Real-world applications often involve multivariate signals (e.g., 12-lead ECG with 12 dimensions, sensor arrays with 50+ channels).\n- No discussion of how high-dimensional multivariate time series would be handled.\n\n**Efficiency Concerns**:\n\n- Text serialization of numerical data is token-inefficient compared to learned embeddings.\n- For a sequence of length T, text serialization requires ~T tokens (one per value), whereas embedding-based approaches can compress this.\n- The computational cost of processing long text sequences through LLMs is quadratic in sequence length.\n\n**Missing Analysis**:\n\n- No ablation studies on sequence length or dimensionality.\n- No efficiency/computation cost analysis comparing text-based vs. embedding-based approaches.\n- No discussion of context window limitations or how they were handled.\n\n### 3. Dataset Scope Limitations\n\nWhile TSR-Suite is substantial in sample size (23K+), the scope appears limited:\n\n**Short Sequences**: Examples suggest sequences are typically short (12-24 steps). Real-world time series reasoning often requires much longer histories.\n\n**Low Dimensionality**: Tasks focus on univariate sequences or pairs. High-dimensional multivariate scenarios are not addressed.\n\n**Limited Complexity**: The chosen tasks (scenario understanding, causality discovery) may not fully capture the complexity of reasoning over diverse time series modalities (e.g., sensor fusion, multi-scale temporal patterns).\n\n**Missing Validation**: There is no evidence that the proposed approach would scale to:\n\n- Sequences with 1000+ time steps\n- Multivariate signals with 20+ dimensions\n- Complex sensor fusion scenarios\n\n### 4. Insufficient Related Work Coverage\n\nSeveral important recent works on time series QA and reasoning are not adequately discussed:\n\n**Missing Works**:\n\n- **ChatTime** (Ye et al., 2025): \"When LLM Meets Time Series: Can LLMs Perform Multi-Step Time Series Reasoning and Inference\" - directly addresses time series reasoning with LLMs, including multi-step reasoning capabilities.\n- **ITFormer** (and related work): Methods for bridging time series and natural language for multi-task temporal-textual QA should be compared.\n- **Time-R1** (Luo et al., 2025): While cited, the comparison could be more detailed, especially regarding reasoning frameworks.\n\n**Why This Matters**:\n\n- These works represent the current state-of-the-art in time series + LLM integration.\n- Without proper comparison, it is unclear what novel contributions TIMEOMNI-1 provides beyond existing methods.\n- The architectural choices (text-based vs. embedding-based) should be positioned relative to these alternatives.\n\n### 5. Technical Details Omitted\n\n**Missing Information**:\n\n- Input preprocessing pipeline (normalization, scaling, handling of missing values)\n- Tokenization strategy for numerical values (precision, formatting)\n- How task-specific prompts are constructed\n- Details of the reward function design (mentioned but not fully specified in main text)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "4Eh5TJG6ZS", "reviewer": "Reviewer_2VxW", "summary": "The paper introduces a complete framework for time series reasoning that combines a multi task benchmark and a unified training recipe for large language models. The benchmark, called TSR Suite, covers perception, extrapolation, and decision making with four atomic tasks. The dataset contains more than twenty three thousand examples with a carefully curated subset of hierarchical chain of thought traces for supervision. The model, called TIMEOMNI 1, is trained in two stages. First, supervised fine tuning injects time aware priors using a small set of high quality reasoning trajectories. Second, reinforcement learning encourages correct and well structured solutions using task grounded rewards. Discrete tasks are scored by accuracy. Sequence prediction uses an error based reward that maps the mean absolute error into a bounded score and a length counting reward to ensure the model outputs the correct number of steps. Joint training across the four task families is shown to improve transfer. The model reports sizable gains on causality and decision oriented tasks while preserving general reasoning strength on non time series benchmarks.", "questions": "1. Can you provide the training curves related to RL part?\n\n2. What is the effect of the reward design choices? Do different changes alter stability during reinforcement learning?\n\n3. How sensitive is performance to the number and quality of verified chain of thought trajectories used in supervised fine tuning? can you provide a scaling plot and a minimum viable count that preserves most of the gains.\n\n4. How does TIMEOMNI 1 compare to recent live or interactive benchmarks such as FutureX and TSAIA in settings that require forward prediction with delayed feedback? What design choices would be needed to support interactive or tool based decision loops?\n\n[1] Zeng, Zhiyuan, et al. \"Futurex: An advanced live benchmark for llm agents in future prediction.\" arXiv preprint arXiv:2508.11987 (2025).\n\n[2] Ye, Wen, et al. \"When LLM Meets Time Series: Can LLMs Perform Multi-Step Time Series Reasoning and Inference.\" arXiv preprint arXiv:2509.01822 (2025).", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper tackles time series as a reasoning problem and operationalizes this view with a benchmark that spans perception, extrapolation, and decision making. This scope is broader than pure forecasting and motivates models that can explain and decide.\n\n2. In this paper, task design is aligned to evaluation. Multiple choice tasks allow objective grading and sequence prediction uses metric aligned rewards. This makes the reinforcement signal faithful to the intended competence.\n\n3. The training pipeline is relative simple to adopt. A small number of high quality trajectories teaches decomposition. Task rewards then refine the behavior without requiring domain specific architectures. The division of labor between supervised fine tuning and reinforcement learning is clear and effective.\n\n4. In the paper, evidence for cross task transfer is convincing. The model benefits from learning perception and extrapolation before decision tasks. Zero shot improvements on harder tasks suggest that the benchmark composition is synergistic rather than siloed.\n\n5. The model does not only fit a single dataset but shows robustness when the distribution shifts. The paper also checks general reasoning benchmarks to show that specialization does not degrade broader ability.", "weaknesses": "1. The contributions mix dataset construction, task design, training recipe, and model evaluation without a clear hierarchy. A contribution map that separates what is new in task formulation from what is new in learning would improve the paper.\n\n2. Although the benchmark size is large, the carefully verified chain-of-thought portion is relatively small. The paper would be stronger with an ablation that quantifies how many verified trajectories are needed and how quality trades off with quantity.\n\n3. Several reported gains are sizable, but the paper does not consistently provide confidence intervals or significance tests for both in-domain and out-of-domain settings.\n\n3. In this paper, reward sensitivity is under explored. A sensitivity sweep and a report of failure cases would help others reproduce the effect.\n\n4. The approach still depends on strict output formatting with explicit think and answer tags. This is practical for research but can be brittle in production. The paper does not explore robustness to prompt variations or noisy instructions.\n\n5. Comparisons to adjacent time series agent benchmarks and live decision settings are brief. Positioning against systems that test forward looking planning or interactive tools would help define the boundary of the proposed benchmark.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "KmGyOFIvtp", "reviewer": "Reviewer_ruC5", "summary": "This paper introduces TSR-Suite, a dataset covering time series perception, extrapolation and decision-making tasks. This dataset is large in size so that it could support both evaluation and training of time series reasoning models. With the collected training trajectories, the paper introduces TIMEOMNI-1, the first unified reasoning model to tackle time series reasoning tasks. TIMEOMNI-1 achieves superior performance over current landscape of time series reasoning models. Extensive ablation and analysis studies confirms the effectiveness of CoT SFT + RL and multi task joint training paradigm.", "questions": "For event aware-forecasting, how is the reward designed since the output should be a variable length sequence. \n\nHow do you split in vs out of distribution data? is it based on data domain?\n\nare different time series domain roughly evenly distributed across task types to prevent certain task types biased towards certain time series application domains?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper is well-positioned and well-motivated, easy to follow and very clear in structure. \nThe paper clearly identifies limitations in current time series benchmarks for time series reasoning and introduces the new dataset with clear goals and well-established data annotation pipeline. \nThough the training paradigm is not novel, the adaptation to time series domain demonstrated great execution. \nAnalysis and ablation studies reveal insights that are very useful for the community.", "weaknesses": "The experimental results on time series reasoning is purely performed on the newly proposed dataset. Although there are certain limitations on existing time series reasoning datasets, it would be helpful to see TIMEOMNI-1's performance on some established time series reasoning benchmarks/datasets.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)