Pack Light, CarryOn: Object‑Oriented Context Boosts Long‑Horizon Agents
agents
| Source: HF Papers | Original article
Researchers propose a new method named Pack Light, CarryOn that structures long‑horizon agent context around reusable progress instead of full interaction histories.
A new research paper unveils **CarryOn**, an object‑oriented context runtime designed for long‑horizon AI agents. The authors argue that current agents waste resources by repeatedly re‑processing full interaction histories to reconstruct progress. Instead, CarryOn treats completed work, findings and application states as reusable “task objects” that can be inspected directly, while maintaining a lightweight tail of recent steps for continuity.
The approach was tested with Claude Opus 4.7 on the OSWorld 2.0 benchmark. Compared with the conventional history‑replay method, CarryOn lifted the partial‑score metric from 43.9 % to 49.7 %, a gain of nearly six points. At the same time it slashed token consumption by 52 % and reduced inference cost by 36 %. The paper frames these improvements as a step toward more efficient, scalable agents that can carry forward reusable artefacts rather than re‑generating them on each turn.
Why it matters is twofold. First, the token and cost savings directly address the economic bottlenecks that have limited the deployment of sophisticated autonomous agents in production settings. Second, the object‑oriented view aligns with recent work on hybrid reasoning agents, which blend latent‑space thinking with textual action (see our coverage on 19 Oct 2026). By structuring context around durable objects, CarryOn could simplify the integration of agents into complex workflows such as contract negotiation or multi‑step data‑center management.
What to watch next includes broader adoption of the CarryOn runtime in commercial LLM platforms, further benchmarking on diverse task suites, and potential extensions that let agents share task objects across different users or services. If the efficiency gains hold at scale, CarryOn may become a foundational building block for the next generation of long‑running AI assistants.
Sources
Back to AIPULSEN