Information-Constrained Memory Evolution for Self-Improving Language Agents

External memory helps language agents improve through experience without retraining their acting models, but seemingly useful lessons can harm decisions and repeated feedback can encourage overfitting. We aim to learn memory edits that improve task performance while controlling how much information the editor receives from reused feedback tasks. We propose Information-Constrained Memory Evolution (ICME), which evaluates each edit through its effect on the entire memory bank, keeping the acting model and retriever fixed. A limited-bit feedback channel trains the editor from evaluations of the same tasks before and after each edit, preserving the expected learning signal; after training, the editor updates fresh memory banks without evaluator feedback. In matched experiments with Qwen3-14B, ICME improves success rates over Auto-Dreamer by 3.9 percentage points on unseen ALFWorld tasks and 5.9 points on ScienceWorld. By linking feedback capacity to a bound on the final memory bank's expected overestimation of performance, this work provides a principled basis for learning from memory edits under repeated feedback reuse.