Long-Horizon-Terminal-Bench 2.0: Automating Diverse Task Construction and Measuring Sustained Agent Progress
Long-horizon terminal benchmarks measure whether an agent can sustain progress through extended workflows with process rewards, not only whether its final state passes a binary check.
The article introduces Long-Horizon-Terminal-Bench 2.0 (LHTB 2.0), an enhanced benchmark for evaluating AI agents' ability to sustain progress through extended workflows with process rewards.
What happened
The article presents LHTB 2.0, an updated benchmark for assessing AI agents' long-term task execution. It includes 77 tasks across 13 domains, with a focus on measuring sustained progress through process rewards. The benchmark introduces a systematic pipeline for task generation and extends the evaluation period to three hours with frequent checkpoints.
Why it matters
LHTB 2.0 provides a comprehensive and process-aware testbed for evaluating AI agents' reliability over extended periods, which is crucial for real-world applications requiring sustained performance.
- LHTB 2.0 offers a more robust framework for evaluating AI agents' long-term task execution.
- The focus on process rewards and sustained progress improves the assessment of AI performance beyond final outcomes.
- Extended evaluation periods and checkpoints enable better measurement of agent reliability over time.
The bigger picture
The benchmark enhances the ability to assess AI agents' sustained progress, offering a more accurate measure of their performance in complex, long-term tasks. This can lead to improvements in AI systems requiring reliability over extended periods.
LHTB 2.0 represents a significant advancement in benchmarking AI agents, providing a comprehensive and process-aware testbed for evaluating sustained progress in extended workflows.
Technical details
- Long-Horizon-Terminal-Bench 2.0 (LHTB 2.0)
- Systematic reconstruction-task pipeline
- Dense progress ladders for rewarding partial progress
- Evaluation horizon extended to three hours with 30-minute checkpoints
- Parameterized reference implementation for generating scenarios
- Binary verifier scores vs. dense ladder scores
FAQ
What is Long-Horizon-Terminal-Bench 2.0 (LHTB 2.0)?
LHTB 2.0 is an enhanced benchmark for evaluating AI agents' ability to sustain progress through extended workflows with process rewards, building on the original Long-Horizon-Terminal-Bench.
How does LHTB 2.0 differ from the original benchmark?
LHTB 2.0 extends the original by adding 31 new tasks, increasing the total to 77 tasks across 13 domains and eight task types. It also extends the evaluation horizon to three hours and introduces a systematic pipeline for task generation.
What are the key features of LHTB 2.0?
Key features include a systematic reconstruction-task pipeline, dense progress ladders for rewarding partial progress, extended evaluation periods, and checkpoints every 30 minutes to measure sustained progress.
This summary and analysis were generated with AI from the original article and may contain errors. Read the original for the full story.
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Originally published by arXiv - cs.AI on Oct 14, 2026.
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