arXiv: cs.AI preprintsInternational5 November 2026
CompressionBench: How can your coding agents compress LLMs?
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arXiv:2611.06858v1 Announce Type: new Abstract: Recursive self-improvement requires AI systems to improve the models and infrastructure used by subsequent systems. LLM compression offers a concrete efficiency objective: retain a pretrained model's behavior within a smaller model storage budget. We study whether coding agents can carry out this step autonomously. We formulate compression as a terminal task in which an agent receives a source checkpoint, storage limit, inference environment, and time budget, then constructs compressed weights and executable inference code. The agent controls its
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