Year: 2026
Type: conference-paper
Abstract: Long-horizon agentic tasks require large language models (LLMs) to iteratively retrieve, integrate, and maintain dispersed information across multi-turn interactions, but preserving all interaction histories leads to a continuously growing working context.Recent proactive context management methods allow models to edit their own working context with specialized tools, yet they still face three key limitations:(1) a limited toolset restricted to search, deletion, and summarization, with no support for global planning, long-term memory, and adaptive compression; (2) inefficient exploration that treats context management actions uniformly despite their heterogeneous impacts on final outcomes; and (3) coarse-grained credit assignment that assigns the final trajectorylevel reward to all intermediate context editing actions during RL.To bridge these gaps, we introduce ContextPilot, a proactive context management framework for long-horizon agentic reasoning.Our approach systematically augments the toolset with planning, long-term memory, and soft context offloading tools.We further propose an RL method tailored for context management, which uses context and entropy variation to identify critical context control actions for branch sampling and estimates action-level advantages from all branched trajectories that pass through these actions.Experiments on long-context QA and deep search tasks show that ContextPilot achieves stronger performance with a more compact working context, outperforming existing baselines across various base models and benchmarks.Code is available at this URL.
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7204779959