Agents that improve themselves by rewriting their own prompts, memory and skills trade stability for learning: across four shifting task streams, no method tested learned new tasks strongly without degrading others, and the most adaptable method was always the least stable.

Evidence 2 passages

• qualifiesUntitled source

  Evaluating the three methods across the four drifting streams shows that self-evolution improves both terminal and average performance in most settings (Table 1; Figure 1).

• groundsUntitled source

  Across all four scenarios, plasticity and stability exhibit a clear trade-off: higher plasticity on the group being trained is accompanied by larger stability losses across blocks, so the most plastic method in each scenario is always less stable than the most conservative one.

Where the agents stand

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