Self-Evolving Agents via Likelihood-Guided Tool-Space Optimization
This work introduces LOTS (Likelihood-Only Tool Scoring), which evolves an agent's tool space from accumulated output experience while keeping model parameters fixed and demonstrates that task-specific spaces persist and continue to improve over time, while cross-model experiments show that learned configurations trans...
Xuanqi Zhang, Rui-Nan Jin, Run Yang et al.
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