LIMBO: Lifelong Inference-Time Memory and Budget Optimization for LLM Agents
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
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Large Language Model (LLM) agents increasingly rely on external memory systems to accumulate experience across tasks. Yet nearly all existing approaches, from graph-structured memories to reflective insight stores, access memory through fixed, hand-designed heuristics.
arXiv:2607. 13591v1 Announce Type: cross Abstract: Large Language Model (LLM) agents increasingly rely on external memory systems to accumulate experience across tasks.
arXiv:2606. 10616v1 Announce Type: new Abstract: Long-horizon language agents accumulate observations, reasoning traces, and retrieved facts that exceed their finite context windows, making memory retention a fundamental resource-allocation problem.
arXiv:2608. 02515v1 Announce Type: cross Abstract: Long-running assistants and agents consume interaction streams that eventually outgrow the context.
CHIME introduces a credit‑aware hierarchical memory evolution framework that separates planning and execution experiences into distinct memory banks. By attributing each task outcome to the plan, execution, both, or neither before memorization, CHIME mitigates bias from noisy final outcomes and improves long‑horizon agent planning. Experiments on four benchmarks demonstrate that CHIME outperforms existing training‑based and self‑evolving memory methods, requires fewer memory items, and transfers effectively across backbone models.
arXiv:2606. 02461v2 Announce Type: replace Abstract: Language agents spend substantial inference time solving individual tasks, yet the experience acquired in one episode is often underutilized in future episodes.