arXiv:2509.22442v1 Announce Type: cross Abstract: Learning a control policy for a multi-phase, long-horizon task, such as basketball maneuvers, remains challenging for reinforcement learning approaches due to the need for seamless policy composition and transitions between skills. A long-horizon task typically…
arXiv:2509.21474v1 Announce Type: new Abstract: While diffusion language models (DLMs) have achieved competitive performance in text generation, improving their reasoning ability with reinforcement learning remains an active research area. Here, we introduce d2, a reasoning framework tailored for masked DLMs.…
arXiv:2406.00276v2 Announce Type: replace Abstract: The sustainable utilization of lithium-ion batteries (LIBs) is crucial to the global energy transition and carbon neutrality, yet data scarcity and heterogeneity remain major barriers across remanufacturing, reusing, and recycling. This dissertation develops a machine…
arXiv:2509.21477v1 Announce Type: new Abstract: Reconstructing subsurface ocean dynamics, such as vertical velocity fields, from incomplete surface observations poses a critical challenge in Earth science, a field long hampered by the lack of standardized, analysis-ready benchmarks. To systematically address this…
arXiv:2504.18591v2 Announce Type: replace Abstract: Advances in neural operators have introduced discretization invariant surrogate models for PDEs on general geometries, yet many approaches struggle to encode local geometric structure and variable domains efficiently. We introduce enf2enf, a neural field approach…
arXiv:2509.21479v1 Announce Type: new Abstract: With promising empirical performance across a wide range of applications, synthetic data augmentation appears a viable solution to data scarcity and the demands of increasingly data-intensive models. Its effectiveness lies in expanding the training set…
arXiv:2506.02724v2 Announce Type: replace Abstract: The widespread utilization of language models in modern applications is inconceivable without Parameter-Efficient Fine-Tuning techniques, such as low-rank adaptation ($texttt{LoRA}$), which adds trainable adapters to selected layers. Although $texttt{LoRA}$ may obtain accurate solutions, it requires…
arXiv:2509.21484v1 Announce Type: new Abstract: We study distributed learning in the setting of gradient-free zero-order optimization and introduce FedZero, a federated zero-order algorithm that delivers sharp theoretical guarantees. Specifically, FedZero: (1) achieves near-optimal optimization error bounds with high probability in…
arXiv:2509.06580v3 Announce Type: replace Abstract: Artificial intelligence promises to accelerate scientific discovery, yet its benefits remain unevenly distributed. While technical obstacles such as scarce data, fragmented standards, and unequal access to computation are significant, we argue that the primary barriers…
arXiv:2509.21485v1 Announce Type: new Abstract: This paper presents a method for modeling transient fluid flow in subsurface reservoir systems based on the developed neural operator architecture (TFNO-opt). Reservoir systems are complex dynamic objects with distributed parameters described by systems of…
