Agents Explore but Agents Ignore: LLMs Lack Environmental Curiosity
Current large language model agents struggle to proactively identify and leverage unexpected yet critical information in their environments, exhibiting a notable lack of exploratory responsiveness to environmental stimuli. This work introduces the novel concept of “environmental curiosity,” defining it as an agent’s capacity to recognize, reflect upon, and act on serendipitous relevant cues. To systematically evaluate this capability, the authors embed complete task solutions within three established benchmarks—Terminal-Bench, SWE-Bench, and AppWorld—and assess agents’ utilization of such embedded information. Experimental results reveal that while agents frequently encounter these cues (up to 81% detection rate), their actual usage remains strikingly low (as low as 7%). Although optimized configurations yield modest improvements, most agents still overlook crucial environmental signals, exposing a fundamental limitation in current architectures regarding environmental curiosity.