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Sage-A1.5: From Long-Horizon Execution to Scientific Exploration with Capability Composition

Wenjie Lou, Tianshuo Peng, Shiyang Feng, Shengji Tang, Runmin Ma, Ruizhe Chen, Jinxin Shi, Haoyang Peng, Fangchen Yu, Zhijie Zhong, Yuhao Zhou, Yue Fan, Yixin Chen, Yiming Wu, Yiheng Wang, Yigeng Jiang, Yichen Jiang, Yi Xie, Xu Guo, Xiaohan He · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28578 · cited by 0 works (OpenAlex) · open-access PDF

German

In diesem Bericht präsentieren wir Sage-A1.5, ein 35B Mix-of-Expert-Agent-Modell, das auf A1 aufbaut, um die Lösung von langen Agentenproblemen durch adaptive Kollaborationskapazitäten voranzutreiben. Im Vergleich zu A1 erweitert Sage-A1.5 nicht nur die Lösung einzelner Aufgaben, sondern auch die Organisation der Arbeit und die Überarbeitung seines Ansatzes während der Ausführung. Sage-A1.5 hebt drei Verbesserungen hervor: praktische Orchestrierung, Verifizierung und offene wissenschaftliche Entdeckung. Erstens erweitert die Agent-Discestrierung die interaktive Ausführung eines einzelnen Agenten durch Aufgabendelegation und parallele Zusammenarbeit zwischen Subagenten mit isolierten Kontexten, wodurch komplexe Aufgaben während der Ausführung zerlegt und gleichzeitig angegangen

Machine translation (Meta M2M100); the English original follows.

Abstract

In this report, we introduce Sage-A1.5, a 35B Mixture-of-Experts agentic model that builds on A1 to advance long-horizon problem solving through adaptive capability composition. Compared with A1, Sage-A1.5 learns not only to solve individual tasks, but also to organize work and revise its approach during execution. Sage-A1.5 highlights three improvements: agentic orchestration, verification, and open scientific discovery. First, agentic orchestration extends single-agent execution through task delegation and parallel collaboration among subagents with isolated contexts, enabling complex tasks to be decomposed and addressed concurrently and supporting efficient execution while reducing interference across concurrent subagents. Second, agentic verification strengthens long-horizon reliability through progress checking, evidence validation, and reflective strategy revision, allowing the agent to recognize unproductive search paths and pursue alternative approaches. Third, open scientific discovery extends agentic capabilities beyond answering predefined questions to conducting exploratory research, connecting scientific inquiry with interactive analysis and inspectable research artifacts. We showcase these capabilities through complicated agentic tasks and multidisciplinary scientific applications. Besides, we hope Sage-A1.5 provides the community with an accessible foundation for open scientific discovery, offering a practical path from long-horizon task execution to collaborative, verifiable, and inspectable scientific exploration.

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Topics: Orchestration, Computer science, Task (project management), Data science, Multidisciplinary approach, Reliability (semiconductor)

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