Papers
arxiv:2610.28578

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

Published on Oct 20
ยท Submitted by
shiyang
on Oct 21
Authors:
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,

Abstract

Sage-A1.5 is a 35B mixture-of-experts agentic model that improves long-horizon problem solving via adaptive task delegation, verification, and open scientific exploration.

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.

Community

๐Ÿš€ We've open-sourced Sage-A1.5, a 35B MoE agentic model from Shanghai AI Laboratory! Its key strengths include:

  • ๐Ÿ’ป Coding: Tackles complex, multi-step coding tasks with coordinated execution and verification.
  • ๐Ÿค Agentic orchestration: Delegates work to parallel subagents with isolated contexts.
  • โœ… Autonomous verification: Checks progress, validates evidence, and revises strategies when needed.
  • ๐Ÿ”ฌ Scientific discovery: Supports exploratory research in neuroscience and electrochemistry, with real scientific feedback driving recursive self-improvement.

We hope Sage-A1.5 makes long-horizon coding and scientific exploration more accessible to the community!

You can also check out our previous release, Agents-A1, which laid the foundation for Sage-A1.5.

๐Ÿค— Open model:
https://huggingface.co/InternScience/Sage-A1.5

๐ŸŽ Limited free access:
https://discovery.intern-ai.org.cn/token-plan/home?tabIndex=0

This is an automated message from the ResearchStudio team.

We created an interactive ResearchStudio Reel for this paper. It includes a visual poster, a video, and a blog, all available for download in editable formats.

Visual poster for this paper

Open the ResearchStudio Reel โ†’

Download all files from Hugging Face

Please give this comment a thumbs up if you find the Reel helpful!

Want to explore or create Reels for more papers? Visit the ResearchStudio demo.

Sign up or log in to comment

Get this paper in your agent:

hf papers read 2610.28578
Don't have the latest CLI?
curl -LsSf https://hf.co/cli/install.sh | bash

Models citing this paper 6

Browse 6 models citing this paper

Datasets citing this paper 0

No dataset linking this paper

Cite arxiv.org/abs/2610.28578 in a dataset README.md to link it from this page.

Spaces citing this paper 2

Collections including this paper 2