Sculptor: Empowering LLMs with Cognitive Agency via Active Context
  Management
Authors: name: Mo Li, name: L. H. Xu, name: Qitai Tan, name: Ting Cao, name: Yunxin Liu

Large Language Models (LLMs) suffer from significant performance degradation
when processing long contexts due to proactive interference, where irrelevant
information in earlier parts of the context disrupts reasoning and memory
recall. While most research focuses on external memory systems to augment LLMs'
capabilities, we propose a complementary approach: empowering LLMs with Active
Context Management (ACM) tools to actively sculpt their internal working
memory. We introduce Sculptor, a framework that equips LLMs with three
categories of tools: (1) context fragmentation, (2) summary, hide, and restore,
and (3) intelligent search. Our approach enables LLMs to proactively manage
their attention and working memory, analogous to how humans selectively focus
on relevant information while filtering out distractions. Experimental
evaluation on information-sparse benchmarks-PI-LLM (proactive interference) and
NeedleBench Multi-Needle Reasoning-demonstrates that Sculptor significantly
improves performance even without specific training, leveraging LLMs' inherent
tool calling generalization capabilities. By enabling Active Context
Management, Sculptor not only mitigates proactive interference but also
provides a cognitive foundation for more reliable reasoning across diverse
long-context tasks-highlighting that explicit context-control strategies,
rather than merely larger token windows, are key to robustness at scale.