Synchronizing Work with Results in Long-Horizon Computer-Use Agents via Demand-Linked Execution

Long-horizon computer-use agents (CUAs) carry out work across applications, using findings and intermediate conclusions to make decisions and update artifacts. These results must remain connected to the work that needs them and to the earlier decisions that rely on them as execution moves on. Yet work can remain unfinished after a needed result arrives, and earlier decisions can go unreviewed when that result changes. We identify this phenomenon as Result–Work Desynchronization (RWD) and analyze its associated decision costs. In this paper, we propose Demand-Linked Execution (DLE) to mitigate the RWD phenomenon by preserving result uses together with each work’s evolving state. DLE records why results are requested and which decisions and artifacts rely on each adopted version. When results arrive or change, the agent’s model uses these records and current software observations to resume waiting work and determine which earlier outcomes to retain or revise. On representative long-horizon CUA benchmarks spanning desktop (OSWorld 2.0), web (Odysseys), and mobile (MobileWorld) environments, DLE improves task success over Vanilla by 6.5, 9.0, and 10.9 percentage points, respectively. Controlled probes compare DLE with baselines that make the current result’s use explicit or maintain a persistent task graph. DLE’s revision advantage persists as intervening work increases.