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新论文 · v1arXiv:2611.14962

FIRE: Failure Isolation and Reliability for Production-Connected Experiments at Scale

Shripad Nadgowda, Gaurav Mitra, Sing Sing Ma, Vipul Chaskar, Pradipta Ghosh, Canturk Isci, Onkar Deshpande, Edgardo Aviles-Lopez 等 10 位作者

原文摘要

Pre-production changes are usually tested in isolated environments to prevent their side effects from reaching production. Some record-and-replay platforms allow a production-connected mode: candidate changes receive recorded production requests while selected downstream calls reach production dependencies. This improves fidelity before deployment, but it also introduces reliability risks. At hyperscale, experimental traffic can fan out across shared services, trigger latent defects, or send malformed input to systems serving user requests. In this paper, we show how production-connected experimentation can operate reliably in a large-scale recommendation serving system. To contain downstream effects without sacrificing realistic dependency behavior, the platform uses three complementary guardrails. Isolated tiers redirect replay traffic away from shared dependencies where replication is practical. Dependency monitors observe downstream health and experiment-attributed errors and terminate experiments after sustained violations. Traffic pinning limits experimental calls to a designated subset of downstream workers when full replication would require scarce capacity. Isolated ranking tiers process billions of replayed requests per day using under 1% of the corresponding serving capacity. Over 90 days, these tiers experienced more than 200,000 abnormal worker exits without directly affecting production-serving workers. During a separate three-month window, dependency monitors cancelled approximately 5% of experiments for the measured ranking service and accounted for 87% of its cancellations. Dependency-aware triggering also reduced code-review experiment executions by 25%. These results show that high-fidelity, production-connected experimentation can operate at scale when downstream effects are isolated where possible, bounded where necessary, and continuously monitored and attributed.

cs.SE · cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12426

dNaViT: Discrete Codes are General-Purpose Visual Representations

Xiaotong Li, Zhengrong Yue, Xu Huang, Jialv Zou, Minhao Jing, Wenhao Yan, Haodong Li, Hai Huang 等 12 位作者

原文摘要

Discrete autoregressive modeling, notably through next-token prediction, has become a cornerstone of modern language models. Extending this paradigm to vision allows visual and language models to converge on a unified framework, thereby fostering shared methodology and infrastructure. This work investigates a central question: how far can a finite discrete vocabulary go in representing the richness of the visual world? We present dNaViT, a Discrete Native-resolution Vision Transformer that produces discrete visual primitives analogous to language tokens. Built upon a frozen pretrained encoder, dNaViT applies lightweight residual modulation with a dual-branch objective to inject reconstruction-aware cues into semantic features. By leveraging grouped product quantization, it scales discrete capacity without inflating spatial sequence lengths, while a balanced training recipe maintains both semantic and pixel-level information. Consequently, dNaViT functions as a versatile discrete visual tokenizer, enabling unified encoding across downstream tasks alongside high-fidelity image decoding. Extensive evaluations show that dNaViT matches or exceeds task-specific baselines across 12 visual understanding benchmarks, 4 generation benchmarks, and an image editing benchmark. These findings demonstrate that a compact discrete vocabulary can serve as a universal visual currency and unlock the full potential of fully unified, native multimodal methodology.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39497

Does Scaling Toward AGI Depend on What System-1 Lets System-2 Execute?

Qi Wang, Yun Qu, Lizhou Cai, Heming Zou, Haoang Chi, Yiqin Lv, Xiang Li, Haiyan Feng 等 13 位作者

原文摘要

Progress toward artificial general intelligence (AGI) has largely relied on scaling a resource-intensive executive, System-2, through larger models, more data and compute, longer reasoning, and more agents. We ask whether the next stage may come instead from co-designing it with System-1, a lightweight, semantically aware predictor that evaluates candidate interactions before execution, so that System-2 acts only on those worth their cost. Our answer is a qualified yes. We study \textit{conditional interaction}, a predict--gate--execute--update loop: System-1 estimates each candidate's utility, System-2 executes those admitted by an adaptive threshold, and the outcomes update System-1. The loop spans training, test-time reasoning, adaptive decision-making, and agentic tool use, with utility and cost defined by the application. Under ideal screening and equal execution costs, execution compute falls by a factor approaching $1/ρ_u$, where $ρ_u$ is the density of utility-relevant interactions (cost-weighted when costs differ); prediction errors, exploration, and distribution shift reduce the saving. We bound selection regret by predictor error and the utility margin. These results motivate our central hypothesis: $ρ_u$ is a measurable coordinate of scaling efficiency and makes informative compute a better description of scaling than total compute.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37563

Pneuma: A Large-Scale Real-World Time Series Benchmark for Commercial Aircraft Predictive Maintenance

Jiafeng Lin, Yutian Zhang, Huan Zhang, Yuxuan Wang, Jinyan Feng, Yuhui Liu, Zhongyi Pei, Jianmin Wang

原文摘要

Predictive maintenance is vital for commercial aviation, yet progress remains hindered by the lack of public, large-scale real-world datasets. As a result, most methods are developed and evaluated on proprietary data, limiting reproducibility. We introduce Pneuma, the first publicly available large-scale benchmark for predictive maintenance using onboard time series from in-service commercial aircraft. Built around bleed air systems, Pneuma contains over 920 million multivariate time steps across four aircraft types and 9 verified fault scenarios, directly aligned with real maintenance logs. The dataset exhibits key real-world challenges, including substantial class imbalance, subtle fault evolution patterns, and significant variance across aircraft configurations. We structure the benchmark around three tasks: multivariate forecasting, supervised fault classification, and unsupervised anomaly detection, with labels derived from actual maintenance records. Under unified settings, we evaluate over 25 models from four methodological families on 9 carefully curated subsets. The results reveal that even strong contemporary models struggle with subtle fault signatures. Further analyses show consistent gains from additional in-context examples and multivariate inference, while forecasting quality is strongly associated with anomaly detection performance. Together, these findings provide practical insights into effectively modeling complex real-world aviation time series. We hope Pneuma will serve as a rigorous and reproducible foundation for advancing data-driven predictive maintenance toward real-world deployment in commercial aviation.

cs.LG

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arXiv ↗PDF ↗