TemporalBench: A Benchmark for Evaluating LLM-Based Agents on Contextual and Event-Informed Time Series Tasks
arXiv:2602.13272v1 Announce Type: new Abstract: It is unclear whether strong forecasting performance reflects genuine temporal understanding or the ability to reason under contextual and event-driven conditions. We introduce TemporalBench, a multi-domain benchmark designed to evaluate temporal reasoning behavior under progressively richer informational settings. TemporalBench adopts a four-tier task taxonomy that examines historical structure interpretation, context-free forecasting, contextual temporal reasoning, and event-conditioned prediction across four real-world domains: retail, healthcare, energy, and physical systems. By controlling access to future targets and contextual information, the benchmark enables a diagnostic analysis of whether models can correctly interpret temporal patterns, align them with external context, and adapt predictions when conditions change. Extensive baseline experiments show that strong numerical forecasting accuracy does not reliably translate into
arXiv:2602.13272v1 Announce Type: new Abstract: It is unclear whether strong forecasting performance reflects genuine temporal understanding or the ability to reason under contextual and event-driven conditions. We introduce TemporalBench, a multi-domain benchmark designed to evaluate temporal reasoning behavior under progressively richer informational settings. TemporalBench adopts a four-tier task taxonomy that examines historical structure interpretation, context-free forecasting, contextual temporal reasoning, and event-conditioned prediction across four real-world domains: retail, healthcare, energy, and physical systems. By controlling access to future targets and contextual information, the benchmark enables a diagnostic analysis of whether models can correctly interpret temporal patterns, align them with external context, and adapt predictions when conditions change. Extensive baseline experiments show that strong numerical forecasting accuracy does not reliably translate into robust contextual or event-aware temporal reasoning; instead, existing agent frameworks exhibit fragmented strengths and systematic failure modes that remain largely hidden under forecasting-only benchmarks. The TemporalBench dataset is publicly available at https://huggingface.co/datasets/Melady/TemporalBench, and we additionally provide a public leaderboard at https://huggingface.co/spaces/Melady/TemporalBench_Leaderboard.
Executive Summary
The article introduces TemporalBench, a novel benchmark designed to evaluate the temporal reasoning capabilities of large language model (LLM)-based agents. Unlike traditional forecasting benchmarks, TemporalBench assesses models' ability to interpret historical data, reason contextually, and adapt predictions under event-driven conditions across four real-world domains. The study reveals that high forecasting accuracy does not necessarily indicate robust temporal reasoning, highlighting fragmented strengths and systematic failures in current agent frameworks. The benchmark is publicly available, fostering transparency and further research in the field.
Key Points
- ▸ TemporalBench evaluates temporal reasoning under progressively richer informational settings.
- ▸ The benchmark uses a four-tier task taxonomy across four real-world domains.
- ▸ Existing agent frameworks show fragmented strengths and systematic failure modes.
- ▸ High forecasting accuracy does not reliably translate into robust contextual reasoning.
Merits
Comprehensive Evaluation Framework
TemporalBench provides a nuanced evaluation of temporal reasoning by incorporating contextual and event-driven conditions, offering a more holistic assessment than traditional forecasting benchmarks.
Multi-Domain Applicability
The benchmark's application across retail, healthcare, energy, and physical systems enhances its relevance and utility in various real-world scenarios.
Public Availability
The public release of the dataset and leaderboard promotes transparency and encourages further research and development in the field.
Demerits
Complexity and Implementation
The complexity of the benchmark may pose challenges for implementation and interpretation, requiring significant expertise and resources.
Limited Scope of Domains
While the benchmark covers four diverse domains, it may not encompass all possible scenarios where temporal reasoning is critical, potentially limiting its generalizability.
Baseline Limitations
The baseline experiments reveal systematic failures, but the study does not provide comprehensive solutions or frameworks to address these issues, leaving room for further investigation.
Expert Commentary
The introduction of TemporalBench marks a significant advancement in the evaluation of LLM-based agents, addressing a critical gap in the assessment of temporal reasoning. Traditional benchmarks often focus on numerical forecasting accuracy, which, as this study demonstrates, does not necessarily translate into robust contextual or event-aware reasoning. The four-tier task taxonomy and multi-domain approach provide a nuanced and comprehensive framework for understanding the capabilities and limitations of current models. The findings highlight the need for further research into improving the reasoning abilities of AI agents, particularly in dynamic and complex environments. The public availability of the benchmark fosters transparency and collaboration, which are essential for advancing the field. However, the complexity of the benchmark and the limited scope of domains may pose challenges for widespread adoption and implementation. Future research should aim to address these limitations and develop more generalized frameworks that can be applied across a broader range of scenarios. Additionally, the ethical implications of deploying AI models with varying levels of temporal reasoning capabilities must be carefully considered, particularly in domains where decisions have significant real-world impacts.
Recommendations
- ✓ Developers should prioritize improving the contextual and event-aware reasoning capabilities of LLM-based agents, leveraging frameworks like TemporalBench for evaluation.
- ✓ Researchers should explore the ethical implications of AI decision-making in dynamic environments and develop guidelines for ensuring fairness and transparency.