MemoBrain: Executive Memory as an Agentic Brain for Reasoning
Complex reasoning in tool-augmented agent frameworks is inherently long-horizon, causing reasoning traces and transient tool artifacts to accumulate and strain the bounded working context of large language models. Without explicit memory mechanisms, such accumulation disrupts logical continuity and undermines task alignment. This positions memory not as an auxiliary efficiency concern, but as a core component for sustaining coherent, goal-directed reasoning over long horizons.
  We propose MemoBrain, an executive memory model for tool-augmented agents that constructs a dependency-aware memory over reasoning steps, capturing salient intermediate states and their logical relations. Operating as a co-pilot alongside the reasoning agent, MemoBrain organizes reasoning progress without blocking execution and actively manages the working context. Specifically, it prunes invalid steps, folds completed sub-trajectories, and preserves a compact, high-salience reasoning backbone under a fixed context budget. Together, these mechanisms enable explicit cognitive control over reasoning trajectories rather than passive context accumulation.
  We evaluate MemoBrain on challenging long-horizon benchmarks, including GAIA, WebWalker, and BrowseComp-Plus, demonstrating consistent improvements over strong baselines.
6966fc3cc5e371f6b235d02e
Hongjin Qian
admin_assigned
2026-01-14T12:50:24.206Z
64a627232944e255ef574dda
/avatars/4c2fd5bf922013fe691c6a3e3fa138a2.svg
Hongjin Qian
TommyChien
user
TommyChien
6966fc3cc5e371f6b235d02f
Zhao Cao
6966fc3cc5e371f6b235d030
Zheng Liu
admin_assigned
2026-01-14T12:50:47.540Z
64a38c590111d5ff6c3d5f2b
/avatars/ef13dc7ce243819bc0da9b04e778b432.svg
zhengliu
lz1001
user
lz1001
MemoBrain: Executive Memory as an Agentic Brain for Reasoning
Complex reasoning in tool-augmented agent frameworks is inherently long-horizon, causing reasoning traces and transient tool artifacts to accumulate and strain the bounded working context of large language models. Without explicit memory mechanisms, such accumulation disrupts logical continuity and undermines task alignment. This positions memory not as an auxiliary efficiency concern, but as a core component for sustaining coherent, goal-directed reasoning over long horizons.
  We propose MemoBrain, an executive memory model for tool-augmented agents that constructs a dependency-aware memory over reasoning steps, capturing salient intermediate states and their logical relations. Operating as a co-pilot alongside the reasoning agent, MemoBrain organizes reasoning progress without blocking execution and actively manages the working context. Specifically, it prunes invalid steps, folds completed sub-trajectories, and preserves a compact, high-salience reasoning backbone under a fixed context budget. Together, these mechanisms enable explicit cognitive control over reasoning trajectories rather than passive context accumulation.
  We evaluate MemoBrain on challenging long-horizon benchmarks, including GAIA, WebWalker, and BrowseComp-Plus, demonstrating consistent improvements over strong baselines.
Memory management in tool-augmented agents is crucial for maintaining coherent, goal-directed reasoning over extended tasks, requiring explicit mechanisms to track and organize reasoning steps within constrained contexts.
tool-augmented agents long-horizon reasoning working context executive memory model dependency-aware memory reasoning steps cognitive control reasoning trajectories context budget memory pruning sub-trajectory folding