Interesting new approach to enable memory in long-running agents.

Weighted Memory Tree organizes execution into tasks, subtasks, and actions, then gives every memory a retention score that moves. Event based updates raise it, selection based decay lowers it.

When a subtask finishes, its step by step detail collapses into a short summary and the full version stays retrievable. If a later step needs the details, the agent pulls them back.

Context trimming is usually permanent. Drop the wrong turn and the agent has no way to recover it. Folding gives you the same token savings with a way back.

On GAIA-Text with Qwen3-8B, Gemma 4 E4B, and Llama-3.1-8B, it beats linear memory by 9.97 points on average while using 32.8% fewer prompt tokens. Memory poisoning experiments show retention scoring limits how far unreliable information spreads.

Paper: https://arxiv.org/abs/2608.20631

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