The authors constructed long-lived tasks from chronologically replayed news, financial-market and social-media streams.
Long-lived agents depend strongly on deciding when to act
ReLiveGym tests agents over replayed weeks of real-world information and finds that action timing, model choice and hindsight feedback all shape performance.
Independent
Xisen Jin · Jingheng Li · Zhenglun Chen · Junyi Du · Xiang Ren
Research Digest··3 min read
Jin and colleagues introduce ReLiveGym, a diagnostic environment for evaluating language-model agents that monitor evolving information streams and act only when needed.
Why this paper
Independent
In one line
Long-lived agents' performance depends on action timing mechanisms and continuous feedback, which vary across tasks and models.
What we could check
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