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About this paper
Hayder Tirmazi, Sam Markelon, Allison Bishop, Michael Mitzenmacher
Tirmazi et al. establish a formal theoretical framework for context compaction in Large Language Model (LLM) agents, demonstrating an equivalence between generative compaction and one-way communication complexity. Their work also empirically shows that current LLM-based compaction methods can perform near random chance for tasks like set membership queries, indicating substantial information loss.
Key insights
- Generative compaction (allowing arbitrary summaries) can be significantly more information-efficient than selection-based compaction (retaining subsets of items), showing a $\Theta(\log n)$ factor budget advantage for specific tasks.
- The proven equivalence between generative compaction and one-way communication complexity allows leveraging decades of communication complexity research to quantify fundamental limits on context management.
- Current deployed LLM-based context compaction mechanisms can be highly sub-optimal, with empirical results showing near-random performance for certain query types even with explicit instructions.
Summary by alphaXiv.
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