让下一篇重要论文，不再错过。

World-action models adapt pretrained video generators, and vision-language-action models build on pretrained vision-language backbones; both tie policy architecture and inference cost to large transformers designed for generation rather than control. It remains unclear how much these generation-oriented choices affect control. In this work, we introduce Latent Action JEPA (La-JEPA), a compact world-action model whose policy transformer is trained from scratch in the feature space of a pretrained V-JEPA 2.1 encoder. Following Fast-WAM, La-JEPA learns to predict future features jointly with actions during training and generates only actions at inference. Among policies without embodied pretraining, La-JEPA achieves state-of-the-art performance in clean and randomized RoboTwin 2.0 scenes and on LIBERO-Plus with correct instructions, while using far fewer parameters than the strongest competing policies. It runs substantially faster than Fast-WAM at comparable performance and outperforms the closest JEPA-based methods, LeapBot-WA and JEPA-WAM, on LIBERO-Plus and RoboTwin 2.0 without their pretrained policy backbones. On four real-robot tasks, its average success is comparable to pi0.5 and above non-embodied-pretrained baselines. Through a design study of the choices behind La-JEPA, we show that JEPA features outperform video VAE latents, most clearly out of distribution, and that training-time future prediction choices significantly impact the policy even when future prediction is skipped at inference. We correct the LIBERO-Plus instructions, as the officially released evaluator corrupts them for most variants, and re-evaluate several strong open baselines. Without a generative pretrained policy backbone, La-JEPA is better and faster than far larger policies, suggesting that robot foundation models may benefit from architectures designed for control rather than inherited from generation.

Information Retrieval-based Bug Localization (IRBL) leverages bug reports to identify faulty source files. Evaluations of IRBL approaches primarily rely on labeled issues from issue tracking systems. However, prior research has shown that issue labels are frequently incorrect in open-source projects, threatening the validity of IRBL evaluations. Existing studies have primarily examined traditional IRBL techniques on small datasets, overlooking modern approaches and the partial overlap between noisy and cleaned datasets. In this paper, we conduct a large-scale empirical study to investigate the prevalence and impact of mislabeled issues in IRBL benchmarks. We analyze five benchmark datasets comprising over 55,000 issue reports and find that, on average, 34.82% of issues are mislabeled. We further evaluate six IRBL techniques, spanning traditional, deep learning-based, and large language model-based approaches using the widely adopted Ye et al. benchmark and statistical methods that account for dataset overlap. Our results show that removing mislabeled issues from evaluation datasets significantly reduces measured performance across all six techniques, with Mean Reciprocal Rank decreasing by 4.68%-21.74%. Although mislabeling does not change the relative ranking of techniques, it affects the magnitude of their performance differences. These findings highlight the importance of reliable evaluation data for ensuring valid and meaningful comparisons of IRBL techniques.