PATH-Bench: New Benchmark for Path-Dependent Evaluation of Lifelong AI Agents
Researchers have unveiled PATH-Bench, a new benchmark aimed at assessing lifelong LLM agents by considering the trajectory of accumulated experiences. Unlike traditional benchmarks, PATH-Bench emphasizes the impact of previous interactions on the transfer and retention of knowledge by agents. It evaluates task relationships through multi-model in-context learning, creates probe-centered sequences with both beneficial and detrimental histories, and conducts repeated assessments of probe tasks to gauge average performance, forward transfer, backward transfer, and forgetting. The benchmark was applied to eight representative agents in single-turn code generation and multi-turn tool-use tasks, revealing that the utility of experience is influenced by both its representation and the interaction structure of the task. The study can be found on arXiv with the identifier 2608.01149.
Key facts
- PATH-Bench is a benchmark for path-dependent evaluation of lifelong agents.
- It estimates directed task relationships via multi-model in-context learning.
- It constructs probe-centered sequences with controlled helpful and interfering histories.
- It measures average performance, forward transfer, backward transfer, and forgetting.
- Eight representative agents were evaluated on single-turn code generation and multi-turn tool-use tasks.
- Experiments included positive- and negative-dominant histories.
- Results show experience utility depends on representation and task interaction structure.
- The paper is available on arXiv (2608.01149).
Entities
Institutions
- arXiv