aiqbench analysis
This arXiv paper proposes CarryOn, a context-management runtime for LLM agents that organizes continuation around persistent "carrier" records of task progress instead of compressed interaction histories. The central technical problem is keeping those records valid as older history is discarded, which the authors address by revising only affected records against evidence and environment checks. Reported gains on OSWorld 2.0 and BrowseComp-Plus come with large token reductions (51.9% and 22.9%), but these are single-paper benchmark results, so readers should check how the approach holds up under other agent tasks and implementations.
Pack Light, CarryOn: Building Object-Oriented Context for Long-Horizon Agents
arXiv:2610.18784v1 Announce Type: cross Abstract: Long-horizon agents accumulate progress in persistent task state, yet repeatedly process interaction histories describing how that progress was achieved. This raises a natural question: can long-horizon context instead be organized around reusable progress rather than the history that produced it? In this paper, we introduce CarryOn, an object-oriented context runtime that organizes continuation around reusable task objects. Rather than treating compressed histories or reconstructed accounts of progress as the primary basis for continuation, CarryOn builds a context view from continuation carriers, complementary execution context, and a live tail of recent interactions. A core challenge is keeping this reusable basis valid after its surrounding history is retired: how can CarryOn update the basis without reconstructing the full representation at every fold? CarryOn maintains carrier records across folds, revising affected records against bound evidence and applicable environment checks while carrying forward unaffected records and jointly updating execution context. Experiments on OSWorld 2.0 and BrowseComp-Plus show improved gains of 5.8% and 7.0% while reducing total token usage by 51.9% and 22.9%, respectively. Project page: https://ethannju.github.io/carryon
Abstract:Long-horizon agents accumulate progress in persistent task state, yet repeatedly process interaction histories describing how that progress was achieved. This raises a natural question: can long-horizon context instead be organized around reusable progress rather than the history that produced it? In this paper, we introduce CarryOn, an object-oriented context runtime that organizes continuation around reusable task objects. Rather than treating compressed histories or reconstructed accounts of progress as the primary basis for continuation, CarryOn builds a context view from continuation carriers, complementary execution context, and a live tail of recent interactions. A core challenge is keeping this reusable basis valid after its surrounding history is retired: how can CarryOn update the basis without reconstructing the full representation at every fold? CarryOn maintains carrier records across folds, revising affected records against bound evidence and applicable environment checks while carrying forward unaffected records and jointly updating execution context. Experiments on OSWorld 2.0 and BrowseComp-Plus show improved gains of 5.8% and 7.0% while reducing total token usage by 51.9% and 22.9%, respectively. Project page: this https URL
24 pages. Project page: this https URL
Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
arXiv:2610.18784 [cs.AI]
(or arXiv:2610.18784v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.18784 Focus to learn more arXiv-issued DOI via DataCite
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 277 words
Related coverage

[AINews] The Future of Latent Space
Latent Space · Sep 25, 2026

[AINews] Meta Muse Wonder & Wonder Full: MSL’s Claude 5.5-tier Frontier Model
Latent Space · Oct 13, 2026

[AINews] not much happened today
Latent Space · Sep 18, 2026

[AINews] Opus 5.5 is good at explainer videos
Latent Space · Sep 29, 2026