AI RESEARCH
Beyond Inference-Only Deployment: Comparing Weight-Based Consolidation Against Cascading Compaction
arXiv CS.AI
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ArXi:2605.24657v1 Announce Type: new Major LLM platforms deploy models in an inference-only configuration: the model serves requests but never updates per-user weights. Users must repeatedly re-teach preferences, corrections, and project context, and context-based workarounds consume context-window space and degrade under cascading compaction. We evaluate an alternative: nightly consolidation of interaction knowledge into model weights via reflection, synthesis, and Low-Rank Adaptation (LoRA) fine-tuning on a single consumer.