AI

On the Clock: Towards Punctual and Productive Time-Budgeted AI Agents

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 09/10/2026, 04:03:24 UTC.

Briefings

This article investigates whether small LLM agents can effectively use explicit wall-clock time budgets. The study evaluates Qwen3.6-27B on MLE-Bench Lite and Qwen3-4B on Zork I, finding that agents fail to translate stated budgets into controlled time use due to lack of timing feedback and inability to anticipate action durations. Interventions including harness-based timing exposure and reinforcement learning with budget-aware rewards were tested. While RL with GRPO achieved near-perfect budget adherence on Zork I and generalized to unseen budgets, it did not improve task performance on MLE-Bench. The research highlights a persistent gap between respecting time limits and allocating productive time.

Key points

• Qwen3.6-27B agents fail to translate explicit wall-clock time budgets into controlled time use because they lack timing feedback and cannot reliably anticipate action durations.
• Reinforcement learning with GRPO achieves near-perfect budget adherence on Zork I and generalizes to held-out budgets, but does not improve task performance on MLE-Bench.

Context and limitations

The study covers only two specific benchmarks (MLE-Bench Lite and Zork I) and uses Qwen3.6-27B and Qwen3-4B models. Results are limited to the period of the arXiv preprint and do not include long-term performance data or real-world deployment metrics.

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