Can LLMs Perceive Time? An Empirical Investigation

An essential control of AI agents is their ability to manage runtime. This ability requires a sense of time-awareness, to predict and estimate wall-clock time and to control their own actions. Prior work has focused on time-awareness, but duration-following and control in native agent harnesses remain unexplored. We present AgentTime, a benchmark for testing whether agents can work for a requested duration, predict their runtime, and estimate elapsed time afterward. It comprises 222 tasks from 18 sources spanning coding, computer use, agentic work, and automated research. Duration-following experiments append a single instruction specifying how long to work, with requests ranging from about a minute to multiple days. Accuracy on these instructions varies substantially: Fable 5.1 in Claude Code deviates from requested runtimes by a typical factor of 2.9$\times$, compared with only 1.2$\times$ for GPT-6 Astra in Codex. However, matching the requested runtime does not, by itself, establish continued work on the task. Among 158 reviewed Astra runs with classifiable transcripts, 14 explicitly slept after appearing to finish. In forecasting experiments, predictions tend to overestimate natural runtimes. In retrospective experiments, removing temporal information more than doubles deviation for Sol and Astra and nearly doubles it for Fable. An agent's ability to complete a task does not guarantee that it can control its own time or work for the whole requested duration. For agents to run reliably, safely, and autonomously over long horizons, we require the evaluation of both.

AI agents use large language models (LLMs) to plan and act over multiple steps, yet models are usually compared per call rather than per completed task. This paper asks: which model should an agent use to complete tasks reliably while minimising energy, cost and time? We show that per-call comparisons can be misleading for agentic AI. Because a task succeeds only if all required steps succeed, small differences in per-step reliability can produce large differences in overall task success. Failed attempts also consume resources. We therefore evaluate models using energy, cost and time per successfully completed task, treating the completed task as the functional unit. On this basis, we develop a decision model for choosing LLMs in agentic workflows. The model applies to tasks with binary or graded outcomes and to settings with or without retries. We show that each resource has a crossover task length at which a smaller, less reliable model ceases to be the greener, cheaper or faster option. These crossovers divide tasks into three regimes: for short tasks smaller models are preferred, for long tasks larger models are preferred, and only between these extremes do preferences among objectives matter. We embed these results in a two-stage framework that separates efficiency from preference. A Pareto screen first removes models that are never competitive; a weighted decision is required only when a genuine trade-off remains. A pilot study of 15 open-weight models from five developers, ranging from 0.6B to 27B parameters, on two multi-step agent tasks with measured GPU energy supports the predicted structure. The model with the lowest energy per completed task changes with task length and task type, and observed crossover points generally match those predicted from per-step measurements. These findings provide a practical framework for right-sizing language models in agentic AI.

Many efforts to ensure frontier AI models are safe rely on monitoring their chain-of-thought (CoT) reasoning. If models become able to perform sufficiently complex reasoning internally, without explicit thinking tokens, this would undermine such oversight. We measure how well frontier models reason without CoT across a suite of over 30,000 questions spanning 43 benchmarks in domains including math, coding, puzzles, causality, theory-of-mind, and strategic reasoning. To compare models against humans, we estimate the $50\%$-task-completion time horizon (TH): the human time required for tasks a model completes with $50\%$ success rate. We complement this with a $50\%$ reasoning token horizon: the minimum number of o3-mini reasoning tokens needed for tasks a model solves with $50\%$ success rate. We find that the no-CoT $50\%$ TH of frontier models has been doubling roughly every year over the past six years, with GPT-5.5's TH reaching over 3 minutes and reasoning token horizon exceeding 1,500 tokens. Our median estimates predict that frontier no-CoT THs could exceed 7 minutes by 2028, and 25 minutes by 2030, though these projections carry substantial uncertainty. We recommend frontier developers track this explicitly.