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

Paper recorded by Signals 4 on 2026-10-20 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-20 on arXiv · recorded by Signals 4 on 2026-10-21

Category: cs.AI · 人工智能 · first seen 2026-10-21

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 o

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#237 most recent of 960 cs.AI papers we have recorded · ↑ newer: Spatially Informed and Direction Sensitive Representation Learning for · ↓ older: Evolving Semantic Indexes for Generative Retrieval
Cite this page: Sage-A1.5: From Long-Horizon Execution to Scientific Exploration with Capability Composition: the #237 most recent of 960 cs.AI papers we have recorded (as of 2026-10-20). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/sage-a1-5-from-long-horizon-execution-to-scientific-exploration-with-capability-.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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