TimeOmni-1: A Unified Framework for Endowing Large Language Models with Temporal Reasoning Capabilities (Introduction)

TimeOmni-1, a research achievement accepted by ICLR 2026, is the first unified temporal reasoning model. It aims to address the problem that traditional large language models (LLMs) lack deep reasoning capabilities when processing temporal data. Through the TSR-Suite Temporal Reasoning Dataset and phased training strategy, this model significantly improves the performance of LLMs in temporal perception, prediction, and decision-making tasks.

• Original author team: Tong Guan (AntonGuan), Zijie Meng, Dianqi Li, etc.
• Release information: The paper was published on September 29, 2025, updated in February 2026, and included in ICLR 2026.
• Open-source resources: Model weights, test datasets, online demos, and code have been open-sourced (on platforms like GitHub and Hugging Face).

Research Background and Motivation

Temporal data analysis is crucial in fields such as finance, industry, and healthcare. However, traditional LLMs excel at language understanding and generation but lack deep reasoning capabilities for temporal patterns. Existing multimodal temporal datasets have limitations: most stay at surface-level alignment and question-answering tasks without touching deep reasoning, leading to two major issues—lack of clear definitions for temporal reasoning tasks and scarcity of high-quality reasoning data. Research team's goal: Redefine the task framework, build a specialized dataset that supports the cultivation of reasoning capabilities, and promote the development of practical temporal reasoning models (TSRM).

TSR-Suite: Formal Definition of Temporal Reasoning Capabilities

The research team proposed TSR-Suite (Temporal Reasoning Suite), which formalizes temporal reasoning capabilities into four atomic tasks covering three core dimensions:

1. Perception Capability: Basic capability, including scenario understanding (identifying the scenario/business meaning behind temporal data) and causal discovery (identifying causal relationships between variables rather than statistical correlations);
2. Extrapolation Capability: Event-aware prediction (predicting future values while considering influencing events, such as the impact of financial reports/policies on stock prices);
3. Decision-Making Capability: Advanced application, making comprehensive judgments based on perception and extrapolation, and making reasonable decisions by weighing uncertainties.

TimeOmni-1 Model Architecture and Training Strategy

TimeOmni-1 is the first unified model for diverse real-world temporal reasoning problems, adopting a multi-stage training strategy:

1. Basic Pre-training: General representation learning on large-scale temporal data;
2. Task-Specific Fine-Tuning: Optimization for the four atomic tasks in TSR-Suite;
3. Reinforcement Learning Optimization: Introducing a reward function to encourage longer and more coherent reasoning chains;
4. Comprehensive Reasoning Integration: Fusing various capabilities to form a unified framework. Core innovation: Deeply integrating temporal data with LLM reasoning, converting temporal data into LLM-understandable representations through a specialized encoder, then using LLM reasoning capabilities for analysis, prediction, and decision-making.

Experimental Results and Performance Evaluation

TimeOmni-1 demonstrates strong out-of-distribution generalization ability and high effective response rate:

• Causal discovery task: Accuracy of 64.0%, significantly higher than GPT-4.1's 35.9%;
• Event-aware prediction task: Effective response rate is more than 6% higher than GPT-4.1;
• TSR-Suite dataset: Contains over 23,000 samples, of which 2,300 are manually stratified and annotated to ensure authority and reliability.

Application Prospects and Industry Impact

TimeOmni-1 has far-reaching significance for multiple industries:

• Financial sector: Accurate price prediction, risk assessment, trading strategy formulation (event-aware prediction adapts to financial report/policy impact analysis);
• Industrial IoT: Predictive maintenance of equipment (identifying abnormal patterns, predicting failures, causal discovery helps find root causes of failures);
• Healthcare: Analyzing trends in physiological indicators, identifying danger signals, assisting clinical decision-making;
• Meteorology and environment: Integrating the impact of extreme weather events to improve forecast accuracy.

Technical Insights and Future Directions

TimeOmni-1 reveals directions for multimodal learning: True intelligence requires deep reasoning capabilities, and temporal data needs to understand causal relationships and dynamic evolution; it provides ideas for the specialized application of LLMs—transforming general LLMs into domain expert models through domain reasoning datasets and training frameworks. Future research directions:

• Extend TSR-Suite to more domain scenarios;
• Deeply integrate temporal data with multimodal data such as images and text;
• Improve the efficiency of real-time reasoning for streaming data;
• Enhance the interpretability of the model's reasoning process.