arXiv:2610.09940v1 Announce Type: cross Abstract: Joint-embedding predictive architectures (JEPAs) predict masked or future observations in representation space, offering a natural source of predicti...
Jev-Mobile introduces a new approach for mobile GUI agents by separating high‑frequency lightweight execution from low‑frequency vision‑language model (VLM) planning. The VLM sets local goals, the accessibility tree provides a structured action space, and Jev, a fast typed decision model, repeatedly selects actions within this space, allowing multiple GUI actions per VLM decision. On the AndroidWorld task suite, Jev-Mobile achieves 79% task success, reduces mean end‑to‑end execution time by 32.7%, and cuts model API cost by 73.4% compared to a step‑wise VLM baseline.
The paper investigates how a small grounding model for a GUI agent should receive the action type. Using Qwen2‑VL‑2B fine‑tuned with LoRA on Android, the authors compare a flat baseline with five conditioning methods—auxiliary loss, hard‑routed action word, additive learned embedding, prepended learned token, and prompt word—under matched data, compute, and decoding settings. Results show that auxiliary loss, additive embedding, and prompt word each improve hit@0.10 by five to seven points over the baseline, while hard routing and prepended token perform similarly to the baseline; however, removing a problematic off‑screen touch point class lifts the baseline by nearly seven points, after which no method outperforms it. "whyItMatters":"The study identifies effective strategies for encoding action type in small grounding models, which could improve the accuracy of GUI agents in real‑world Android applications."
The paper introduces SLIM, a pushing benchmark that tests language‑based goal specification for latent world models. It shows that a vanilla LeWM fails almost entirely due to an action‑insensitive encoder, but adding an inverse‑dynamics auxiliary loss restores encoder sensitivity and dramatically improves success rates. With the repaired latent, a lightweight language‑goal head can plan from natural language sentences, achieving high performance on navigation and pushing tasks without retraining the world model.
SWE-VERIFIER is a lightweight generative model that selectively triggers candidate sampling and verification for coding agents, scoring proposed actions to decide when additional sampling is needed and ranking the resulting actions before execution. Trained on Qwen3.5-4B with preference data, it improves resolution on MiniMax-M2.7 from 75.47% to 77.20% at 1.09× cost and on DeepSeek-V4-Flash from 72.60% to 74.27% at 1.13× cost, while verifying fewer than 4% of steps. The approach demonstrates that selective sampling and lightweight verification can enhance coding‑agent performance and inference efficiency with minimal overhead.
arXiv:2609.37250v1 Announce Type: cross Abstract: World-action models (WAMs) couple future visual-state prediction with action generation. By adapting video generators or image-editing models pretrai...
arXiv:2610.14228v1 Announce Type: cross Abstract: Pixel-space world models have recently emerged as a promising approach for GUI agents, predicting future screens prior to execution and subsequently...
Q-Reflector is a standalone GUI action reflector built on Qwen3‑VL‑8B that learns to generate pre‑ and post‑conditions for each executed action and then evaluates execution success and task validity. It is trained with supervised fine‑tuning, teacher‑student distillation, and reward‑based refinement, and achieves 82.16% accuracy on 1,082 AndroidWorld transitions, outperforming baseline models. In MobileWorld tasks, it improves task success rates by 17.09 and 14.53 percentage points over no external reflection, and surpasses GPT‑5.2 and Claude Haiku 4.5 by 3.42 and 5.98 percentage points respectively.
arXiv:2607. 05238v1 Announce Type: new Abstract: JEPA world models predict the next latent state with a single deterministic predictor trained by latent regression.
arXiv:2607. 24112v1 Announce Type: new Abstract: We introduce State Transition Pretraining (STP) as a new scaling axis for GUI agents.
Agents end many steps in a typed decision over a set of candidates: which screen element to click, which product to pick. System One models answer in one forward pass, and Jeva showed that four proper...
MobileGym is a browser-hosted, lightweight simulation platform designed for mobile GUI agent research. It offers verifiable outcome signals via deterministic, JSON-based state judging and supports scalable online reinforcement learning with hundreds of parallel instances on a single server. The platform includes a declarative task-definition framework, a structured AnswerSheet protocol, and a benchmark of 416 parameterized tasks across 28 apps, demonstrating strong sim-to-real transfer in a case study.