Enterprise AI Analysis
Timely Machine: Awareness of Time Makes Test-Time Scaling Agentic
As large language models (LLMs) increasingly tackle complex reasoning tasks, test-time scaling has become critical. Traditional methods struggle in agentic scenarios due to unpredictable tool latency. We propose Timely Machine, redefining test-time as wall-clock time, enabling models to dynamically adjust strategies based on time budgets. Our benchmark, Timely-Eval, shows model performance shifts with tool latency, revealing existing models' inability to adapt. We introduce Timely-RL, a reinforcement learning approach that teaches time-aware reasoning, consistently boosting performance. This work offers a new perspective on test-time scaling for the agentic era, emphasizing intrinsic time awareness and strategic planning.
Executive Impact at a Glance
Our analysis reveals tangible benefits for enterprise operations:
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Performance Boost
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Adaptability Improvement
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On-Time Completion
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Problem Statement
Solution Proposed
Experimental Insights
Problem with Traditional Test-Time Scaling
The traditional definition of test-time scaling, based on generation length, breaks down in agentic scenarios with frequent tool calls. Tool latency decouples inference time from generation length, making total time unpredictable. Existing methods overlook tool latency, treating budget as tool call count rather than actual wall-clock time.
Timely Machine & Timely-RL
We introduce Timely Machine, redefining test-time as wall-clock time. Models dynamically adjust strategies based on time budgets. Timely-RL, a reinforcement learning approach, is proposed to train models for time-aware reasoning. After cold-start SFT, RL enhances temporal planning, improving time budget awareness and boosting performance.
Model Performance vs. Latency
Our benchmark, Timely-Eval, reveals that smaller models excel with fast feedback through more interactions, while larger models dominate high-latency settings via superior interaction quality. Existing models fail to adapt reasoning to time budgets, highlighting the need for dynamic strategy adjustment.
93.9%
TimelyLM-8B on-time completion on ML tasks (93.9% vs 51.8% for Qwen3-32B)
TimelyLM-8B consistently outperforms other models in achieving high on-time completion rates on Machine Learning tasks, demonstrating its superior ability to manage time budgets effectively. This directly translates to more reliable and efficient AI deployments in time-sensitive operational environments.
Timely-RL Reasoning Pipeline
Initial Assessment
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Cognitive Budgeting
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Dynamic Adjustment
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Convergence
| Feature | Traditional LLMs | Timely Machine (Timely-RL) |
|---|---|---|
| Time Definition | Generation Length | Wall-Clock Time (Physical) |
| Tool Latency | Ignored/Implicit | Dynamically Considered |
| Strategy Adjustment | Fixed/Static | Dynamic/Agentic |
| Budget Control | Token-based | Time-based (Real-time Feedback) |
Interactive Games: Adapting to Latency
In interactive text games, Timely Machine demonstrates superior adaptability to varying tool latencies. Unlike static models, TimelyLM-8B dynamically shifts its strategy to either maximize interactions in low-latency environments or focus on high-quality turns in high-latency scenarios.
This flexibility leads to consistently higher game scores and more efficient exploration within the given time budgets, proving its agentic intelligence in dynamic, real-world-like environments. For instance, in low-latency settings, smaller TimelyLM models can even outperform larger, less time-aware counterparts by making more, quicker decisions.
Highlight: Optimal strategy adapts based on observed tool latency, maximizing outcomes under diverse conditions.
Calculate Your Potential AI Savings
Discover how much time and cost your enterprise could reclaim by adopting time-aware AI agents.
Annual Savings
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Annual Hours Reclaimed
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Our Implementation Roadmap
A phased approach to integrating Timely Machine capabilities into your enterprise.
Phase 1: Discovery & Strategy
Assess current AI workflows, identify time-sensitive tasks, and define initial success metrics.
Phase 2: Pilot & Customization
Implement Timely-RL on a subset of critical tasks, fine-tune models with enterprise-specific data, and integrate existing tools.
Phase 3: Deployment & Optimization
Roll out across target departments, establish continuous monitoring, and iterate for maximal efficiency gains.
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