Lost in Compaction: LLM Context Compression Drops 83% of User Constraints, Penn State Study Finds
As large language models take on longer multi-turn workflows and agentic tasks, systems increasingly rely on context compaction to summarize past conversation histories and keep prompt lengths within memory budgets. However, this compression comes at a severe cost to user control. A new study by Pennsylvania State University researchers Zhiqi Wang, Yichi Zhang, Dongwon Lee, and Yuchen Yang reveals that standard context compaction drops an average of 83 percent of user-defined operational constr
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