隆重推出 SimuLang：桌面版 Playwright
Palo Alto, California • Oct 16, 2026• by Qingni Wang
Computer-use agents increasingly tackle long-horizon tasks, where running a frontier model at every step is expensive and a small open-weight model alone often fails.
Weak-to-strong escalation offers a middle ground: a weak agent acts by default and hands control to a strong one only when needed. However, practical escalation faces a dilemma:
- Deciding when to escalate: This requires an uncertainty signal cheap enough to check at every step.
- Post-handoff risk: After handoff, the strong agent can still go astray, and a black-box API exposes no internals to monitor.
To solve this, Simular’s research team proposes LEASH, a training-free framework that unleashes the strong agent only when the weak agent's latent uncertainty signals it is needed, and keeps it on a leash afterward.
Latent Uncertainty: Zero Extra Passes, Zero Monitor Training
At the core of LEASH is latent uncertainty:
- Hidden-state comparison: LEASH compares the weak model's hidden states with those from successful and failed trajectories.
- Failure-aligned uncertainty: A state that looks more like the failures signals higher uncertainty.
- Zero extra forward pass: During weak-agent execution, runtime uncertainty reuses activations already produced during action generation, requiring no extra forward pass and no dedicated monitor training.
Because it reuses existing activations, latent uncertainty provides a signal cheap enough to check at every step.
The Three Layers of LEASH
LEASH coordinates execution across three layers, all powered by latent uncertainty:
- Initial Task Uncertainty (Early Routing): Evaluates latent uncertainty at the beginning of the task to enable early routing directly to the strong agent when needed.
- Step-Level Uncertainty (Runtime Escalation): Monitors latent uncertainty at every step during weak-agent execution, escalating control to the strong agent when high uncertainty is detected.
- Shadow Observer (Continued Black-Box Oversight): After escalation, the weak agent acts as a shadow observer to provide continued oversight of the strong black-box agent, overcoming the limitation that black-box APIs expose no internals to monitor.
Evaluation on OSWorld
Experiments on the OSWorld benchmark across four contemporary weak-agent backbones demonstrate:
- Consistent improvements: LEASH consistently improves over standalone weak agents across all four evaluated backbones.
- Outperforming random escalation: At matched escalation rates, LEASH outperforms random escalation by 6.55 to 11.94 points, validating the effectiveness of the latent uncertainty signal.
- Matching frontier performance at lower cost: With Qwen3.5-35B-A3B as the weak backbone, LEASH achieves 73.66% task success at $0.4117 per task, matching always-on Claude Opus 5 (72.90%) while reducing inference cost by 19.2%.
"Each model provider wants you to use only their model. Sai frees you from such lock-in: it calibrates the different strengths and weaknesses of various models, to deliver greatest efficiency," said Eric Wang, head of research at Simular.
LEASH demonstrates this principle by combining the efficiency of weak open-weight agents with the capability of strong frontier models, using cheap latent uncertainty to route and supervise execution without lock-in.
For more details, read the paper here: https://arxiv.org/abs/2610.22280
