arXiv:2601.08079v1 Announce Type: cross 
Abstract: 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.

تم تقديم MemoBrain، وهو نموذج ذاكرة تنفيذية لوكلاء معززين بالأدوات، لمعالجة التحديات المتعلقة بالتفكير على المدى الطويل في أطر الذكاء الاصطناعي. يقوم هذا النموذج بالتقاط الحالات الوسيطة البارزة وعلاقاتها المنطقية، مما يعزز من تماسك وهدفية عمليات التفكير.

La introducción de MemoBrain, un modelo de memoria ejecutiva para agentes aumentados por herramientas, aborda los desafíos del razonamiento a largo plazo en marcos de IA. Este modelo captura estados intermedios destacados y sus relaciones lógicas, mejorando la coherencia y la orientación hacia objetivos de los procesos de razonamiento.

L'introduction de MemoBrain, un modèle de mémoire exécutive pour les agents augmentés par des outils, répond aux défis du raisonnement à long terme dans les cadres d'IA. Ce modèle capture des états intermédiaires saillants et leurs relations logiques, améliorant la cohérence et l'orientation vers les objectifs des processus de raisonnement.

The introduction of MemoBrain, an executive memory model for tool-augmented agents, addresses the challenges of long-horizon reasoning in AI frameworks. This model captures salient intermediate states and their logical relations, enhancing the coherence and goal-directedness of reasoning processes.

MemoBrain: Executive Memory as an Agentic Brain for Reasoning

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MemoBrain: Executive Memory as an Agentic Brain for Reasoning

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