Introduces the TSCognition benchmark for cognitive time series reasoning tasks and the TSAlign alignment framework, reporting outperformance over LLM, VLM, and time-series baselines on TSCognition and TimerBed with lower compute.
TimeOmni-1: Incentivizing Complex Reasoning with Time Series in Large Language Models
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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.
years
2026 6verdicts
UNVERDICTED 6representative citing papers
LLaTiSA is a vision-language model trained on a new 83k-sample hierarchical time series reasoning dataset that shows superior performance and out-of-distribution generalization on stratified TSR tasks.
STReasoner uses S-GRPO reinforcement learning to let LLMs integrate time series, graphs, and text for spatio-temporal reasoning, delivering 17-135% accuracy gains over baselines on a new four-task benchmark at 0.004X the cost of proprietary models.
TSQAgent uses three collaborative LLM agents with analytical tools to identify relevant quality dimensions and enable quantitative comparisons for time series data, improving on standard LLM methods and leading to better downstream data selection.
KairosAgent fuses LLM semantic reasoning with TSFM numerical forecasting through dynamic tool use and RL-based multi-turn refinement to deliver superior zero-shot multimodal time series predictions.
StockR1 unifies LLM-based financial reasoning and time-series forecasting by emitting verifiable forecast actions that condition a decoder, optimized via consistency-grounded RL to improve accuracy on QA and prediction tasks.
citing papers explorer
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From Recognition to Understanding: Unlocking Cognitive Time Series Reasoning with LLMs
Introduces the TSCognition benchmark for cognitive time series reasoning tasks and the TSAlign alignment framework, reporting outperformance over LLM, VLM, and time-series baselines on TSCognition and TimerBed with lower compute.
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LLaTiSA: Towards Difficulty-Stratified Time Series Reasoning from Visual Perception to Semantics
LLaTiSA is a vision-language model trained on a new 83k-sample hierarchical time series reasoning dataset that shows superior performance and out-of-distribution generalization on stratified TSR tasks.
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STReasoner: Empowering LLMs for Spatio-Temporal Reasoning in Time Series via Spatial-Aware Reinforcement Learning
STReasoner uses S-GRPO reinforcement learning to let LLMs integrate time series, graphs, and text for spatio-temporal reasoning, delivering 17-135% accuracy gains over baselines on a new four-task benchmark at 0.004X the cost of proprietary models.
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TSQAgent: Rating Time Series Data Quality via Dedicated Agentic Reasoning
TSQAgent uses three collaborative LLM agents with analytical tools to identify relevant quality dimensions and enable quantitative comparisons for time series data, improving on standard LLM methods and leading to better downstream data selection.
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KairosAgent: Agentic Time Series Forecasting with Fused Semantic Reasoning
KairosAgent fuses LLM semantic reasoning with TSFM numerical forecasting through dynamic tool use and RL-based multi-turn refinement to deliver superior zero-shot multimodal time series predictions.
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Reasoning through Verifiable Forecast Actions: Consistency-Grounded RL for Financial LLMs
StockR1 unifies LLM-based financial reasoning and time-series forecasting by emitting verifiable forecast actions that condition a decoder, optimized via consistency-grounded RL to improve accuracy on QA and prediction tasks.