# PRIMETIME : Limits of LLMs in Temporal Primitives

arXiv: https://arxiv.org/abs/2504.16155 · HTML: https://mlsanity.com/paper/2504.16155/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning.
- First posted on arXiv 22 Apr 2025.

## Placed in

- LLM reasoning / [Knowledge- and tool-augmented reasoning](https://mlsanity.com/lr/knowledge-and-tool-augmented-reasoning/)

## Built on by (most cited first, 1 of 1)

- [Can Language Models Handle a Non-Gregorian Calendar? The Case of the Japanese wareki](https://mlsanity.com/paper/2509.04432/) (2025)

## Abstract (arXiv)

This paper introduces PRIMETIME, a synthetic generator that supports both benchmarking and fine-tuning of two primitive operations underlying temporal reasoning in Large Language Models (LLMs): parsing and arithmetic on datetimes. Existing temporal benchmarks assume simplified canonical datetime forms, conflate arithmetic, composition, and world knowledge into a single aggregate score, and offer no direct path to remediation. The first contribution is methodological: the PRIMETIME synthetic generator delivers non-conflated, uncontaminated, and unlimited datetime exemplars that enable a decompositional evaluation strategy for each primitive in isolation. The generator is extensible to support complex datetime tasks and is publicly released, alongside generated benchmarks. The second contribution is diagnostic: under this evaluation strategy, the primitives themselves prove individually unreliable, with per-primitive accuracy ranging from near-zero to perfect across models and prompting conditions. The third contribution is constructive: the same generator used for diagnosis also produces new training exemplars for fine-tuning, and the resulting models show that the primitives are fully learnable and the composed Event Planning task reaches frontier-level accuracy using small quantized LoRA transformers. The broader takeaway is that a single synthetic generator can serve both diagnosis and production-ready deployment. This methodological pattern may apply beyond temporal reasoning.