Beyond Inference-Only Deployment: Comparing Weight-Based Consolidation Against Cascading Compaction
The article discusses a new approach to deploying large language models (LLMs) that goes beyond inference-only configurations. It compares weight-based consolidation with cascading compaction, highlighting the benefits of consolidating interaction knowledge into model weights. The findings suggest that this method significantly improves knowledge retention compared to traditional compaction methods.
- ▪Current LLM platforms operate in an inference-only mode, requiring users to repeatedly teach preferences and context.
- ▪Cascading compaction retains only 36.8% of knowledge, while nightly consolidation retains 80.4%, marking a significant improvement.
- ▪The study shows that procedural corrections and episodic project facts see the largest gains in knowledge retention.
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Computer Science > Artificial Intelligence arXiv:2605.24657 (cs) [Submitted on 23 May 2026] Title:Beyond Inference-Only Deployment: Comparing Weight-Based Consolidation Against Cascading Compaction Authors:Simon Dennis, Kevin Shabahang, Hao Guo, Rivaan Patil View a PDF of the paper titled Beyond Inference-Only Deployment: Comparing Weight-Based Consolidation Against Cascading Compaction, by Simon Dennis and 3 other authors View PDF HTML (experimental) Abstract: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.
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