# Before You Continue: Evaluating Time-Sensitive Safety Awareness in Conversational Assistants

**Authors:** Peidi Dong, Yunze Xiao, Yu-Lin Tsai, Yichen Zheng, Heyuan Huang, Weihao Xuan, Mark Dredze, Jen-tse Huang  
**Lab:** Carnegie Mellon · **Published:** 4 Nov 2026  
**Links:** [arXiv](https://arxiv.org/abs/2611.06839) · [PDF](https://arxiv.org/pdf/2611.06839) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2611.06839.html)

## Key numbers

- **30.5%**: assistants account for time-dependent constraints in only 30.5% of cases where the proposed action violates a…
- **62.1%**: models fail to apply these constraints in 62.1% of cases despite correctly calculating the elapsed time
- **33**: along with a separate set of 33 scenarios designed to compare models' ability to calculate elapsed…

## Abstract

Large language model (LLM) assistants increasingly help users with everyday activities that involve time-dependent constraints. However, users may not mention these constraints in later requests, requiring assistants to recognize and adjust their advice accordingly. To evaluate such capability, we introduce TASC (Temporal Action Safety in Conversation), a benchmark that evaluates whether LLMs apply time-dependent constraints when responding to conversational requests. TASC consists of 3,418 inputs derived from 128 templates, along with a separate set of 33 scenarios designed to compare models' ability to calculate elapsed time with their ability to apply that information. Across 13 models, assistants account for time-dependent constraints in only 30.5% of cases where the proposed action violates a rule, while rarely warning users unnecessarily. More importantly, models fail to apply these constraints in 62.1% of cases despite correctly calculating the elapsed time. Explicitly providing the timing resolves only about half of these failures, revealing a gap between temporal reasoning and its application in conversational advice.

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