On the Clock: Towards Punctual and Productive Time-Budgeted AI Agents
Quick Answer
The study evaluates Qwen3.6-27B and Qwen3-4B AI agents under strict time budgets, revealing gaps in time awareness and performance.
Quick Take
While harness-based timing feedback improves budget adherence, agents struggle to utilize extra time effectively, indicating a challenge in balancing punctuality and productivity.
Key Points
- Qwen3.6-27B and Qwen3-4B tested on MLE-Bench Lite and Zork I benchmarks.
- Agents fail to effectively manage time without explicit timing feedback.
- Reinforcement learning with budget-aware rewards improves adherence but not task performance.
- Timing information injection significantly enhances budget adherence for Qwen3.6-27B.
- A gap exists between time adherence and productive time allocation in budget-conditioned agents.
DeepSignal Analysis
What happened
The study assesses the performance of Qwen3.6-27B and Qwen3-4B AI agents under strict time constraints. It finds that while timing feedback improves adherence to time budgets, the agents still struggle to utilize additional time effectively, highlighting a disconnect between punctuality and productivity.
Key evidence
- Qwen3.6-27B and Qwen3-4B were evaluated on MLE-Bench Lite and Zork I, where additional time could enhance performance.
- Agents failed to manage time effectively when budgets were only stated in prompts, indicating a lack of time awareness.
- Reinforcement learning with budget-aware rewards improved budget adherence for Qwen3.6-27B but did not enhance task performance on MLE-Bench.
Why it matters
Understanding how AI agents manage time is crucial for developing systems that can operate efficiently under constraints. The findings suggest that while current interventions can improve adherence to time budgets, they do not guarantee effective use of time, which is essential for maximizing productivity. This gap presents ongoing challenges for the design of budget-conditioned AI agents.
What to watch
Paper Resources
📖 Reader Mode
~2 min readAbstract:We study whether small LLM agents can operate effectively under explicit wall-clock time budgets by both respecting the allocated runtime and using available time productively. We evaluate Qwen3.6-27B on five competitions from MLE-Bench Lite and Qwen3-4B on Zork I (Jericho), two agentic benchmarks where additional computational time can meaningfully improve performance. In the simplest setting, where the budget is stated only in the prompt, agents fail to translate the stated budget into controlled use of time. These failures arise from gaps in time awareness, since the harness provides no timing feedback, but also because they cannot reliably anticipate the duration of actions, and do not have a learned mapping from available time to an appropriate strategy. We investigate two complementary classes of interventions: harness-based mechanisms that expose timing information and enforce deadlines, and reinforcement learning with budget-aware rewards. Injecting timing information through the harness substantially improves budget adherence for Qwen3.6-27B without measurable loss in performance, while enforcement hooks tighten adherence further. RL with GRPO achieves near-perfect budget adherence on Zork I and generalizes to held-out budgets not seen during training, but does not improve task performance over the untrained harness on MLE-Bench. Once agents are made to respect the budget, they still fail to use additional time to improve task performance. RL-trained policies learn when to stop but often fill extra time with repeated actions, and GRPO training on multiple budgets tends to collapse toward the strategy learned for the shortest budget. Our results reveal a gap between time adherence and productive time allocation, which remains a central challenge for budget-conditioned agents.
| Comments: | Accepted at the NeurIPS 2026 Workshop on Resource-Aware Agentic AI. 23 pages |
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.10833 [cs.AI] |
| (or arXiv:2610.10833v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.10833 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Vlad Sobal [view email]
[v1]
Wed, 7 Oct 2026 19:35:08 UTC (152 KB)
— Originally published at arxiv.org
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