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arXiv:2603.22343v1 Announce Type: new Abstract: Photovoltaic (PV) power forecasting in edge-enabled grids requires balancing forecasting accuracy, robustness under weather-driven distribution shifts, and strict latency constraints. …
arXiv:2603.22346v1 Announce Type: new Abstract: We isolate and empirically characterize first-mover bias -- a path-dependent concentration of feature importance caused by sequential residual fitting in …
arXiv:2603.22348v1 Announce Type: new Abstract: Online learning algorithms often faces a fundamental trilemma: balancing regret guarantees between adversarial and stochastic settings and providing baseline safety …
arXiv:2603.22352v1 Announce Type: new Abstract: Recent progress in reinforcement learning with verifiable rewards (RLVR) offers a practical path to self-improvement of language models, but existing …
arXiv:2603.22362v1 Announce Type: new Abstract: Full-waveform inversion (FWI) estimates physical parameters in the wave equation from limited measurements and has been widely applied in geophysical …
arXiv:2603.22364v1 Announce Type: new Abstract: Diffusion models have achieved state-of-the-art performance in generative modeling, but their success often relies heavily on classifier-free guidance (CFG), an …
arXiv:2603.22370v1 Announce Type: new Abstract: Deploying large language models (LLMs) on edge devices requires extremely low-bit quantization. Ultra-low precision formats such as NVFP4 offer a …
arXiv:2603.22372v1 Announce Type: new Abstract: Recent advances in multimodal learning have motivated the integration of auxiliary modalities such as text or vision into time series …
arXiv:2603.22375v1 Announce Type: new Abstract: Diffusion models deliver high-fidelity generation but remain slow at inference time due to many sequential network evaluations. We find that …
arXiv:2603.22379v1 Announce Type: new Abstract: Adapters are often selected and deployed based on nominal labels (e.g., instruction-tuned), which implicitly suggest what capability improves after adaptation. …
arXiv:2603.22380v1 Announce Type: new Abstract: Data-driven discovery of partial differential equations (PDEs) offers a promising paradigm for uncovering governing physical laws from observational data. However, …
arXiv:2603.22384v1 Announce Type: new Abstract: Autonomous agents operating in continuous environments must decide not only what to do, but when to act. We introduce a …
