arXiv:2607. 01537v1 Announce Type: new Abstract: Certified world models estimate how long their predictions remain valid.
The deployment of Vision-Language Models (VLMs) in autonomous driving (AD) systems is constrained by on-board computing power, restricting vehicles to small VLMs (SVLMs) with limited perception and reasoning capabilities. Infrastructure-assisted AD alleviates this resource constraint by enabling collaboration with large VLMs (LVLMs) at edge servers.
arXiv:2606. 13092v3 Announce Type: replace Abstract: Scale buys interpolation; structure buys certifiable transfer.
arXiv:2607. 27626v1 Announce Type: new Abstract: Safety-critical IoT systems such as industrial closed-loop control, V2X coordination, and remote teleoperation require every sensor's peak Age of Information (peak AoI, also abbreviated PAoI) to stay below a hard per-slot deadline, not merely an average bound.
arXiv:2608. 10433v2 Announce Type: replace Abstract: Temporal reports are increasingly emitted alongside numerical forecasts and are often interpreted as statements about the computation producing those forecasts.
arXiv:2607. 02544v1 Announce Type: cross Abstract: DiLoCo-style training reduces communication by letting learner islands train locally before occasional outer synchronization, making it attractive for fragmented industrial AI fleets where training shares hardware with latency-sensitive serving.
arXiv:2607. 12928v1 Announce Type: new Abstract: We study the online binary sequential calibration problem.
arXiv:2606. 24946v1 Announce Type: new Abstract: Learned world models are useful only over horizons on which their rollout error remains controlled.
arXiv:2608. 06893v1 Announce Type: new Abstract: Schr\"odinger bridge models restore a clean signal from a degraded observation by following the conditional bridges of a reference process, yet this reference is chosen heuristically, typically white noise with a hand-tuned schedule.
arXiv:2511. 18191v2 Announce Type: replace Abstract: Time series forecasting drives operational decisions under tight latency budgets, and autoregressive time series foundation models (TSFMs) increasingly deliver the most accurate forecasts.
arXiv:2607. 21910v2 Announce Type: replace Abstract: World models let agents plan against predicted physical state, but that state drifts; re-observation is costly and delayed, and repair can fail.
arXiv:2606. 25068v1 Announce Type: new Abstract: Online time-series forecasters receive labels only after horizon-dependent delays, while every adaptation step spends limited compute.
Training-free feature forecasting accelerates diffusion sampling by predicting features at skipped denoising steps. Recent work has mainly focused on designing stronger forecasters.