arXiv:2601.16622v2 Announce Type: replace-cross
Abstract: Equivariant Graph Neural Networks (EGNNs) have become a widely used approach for modeling 3D atomistic systems. However, mainstream…
arXiv:2603.02970v2 Announce Type: replace
Abstract: We introduce LAGO, a LocAl-Global Optimization framework coupling Bayesian Optimization (BO) and gradient-based trust region local refinement…
arXiv:2605.00369v4 Announce Type: replace
Abstract: We study how large language models can be used to generate inventory policies in online settings with non-stationary demand. Our work is motivated…
arXiv:2508.02039v2 Announce Type: replace
Abstract: Increasing concerns for data privacy and other difficulties associated with retrieving source data for model training have created the need for…
arXiv:2606.06214v1 Announce Type: cross
Abstract: Correctness and readability are key measures of code quality, respectively ensuring functional fidelity and ease of comprehension. While most…
arXiv:2606.04672v2 Announce Type: replace-cross
Abstract: Continuous-time dynamic graphs (CTDGs) provide a richer framework to capture fine-grained temporal patterns in evolving relational data….
arXiv:2606.06021v1 Announce Type: cross
Abstract: On-policy distillation (OPD) supervises the student only in output space by matching next-token probabilities. This output-only paradigm has two…
arXiv:2603.24747v2 Announce Type: replace
Abstract: The emergence of large language model agents capable of invoking external tools has created urgent need for formal verification of agent…
arXiv:2304.10891v3 Announce Type: replace-cross
Abstract: Transformer-based models are becoming a central paradigm in autonomous driving because they can capture long-range spatial dependencies,…
arXiv:2606.05008v1 Announce Type: cross
Abstract: As multi-modal models advance towards long-form video understanding, memory emerges as a critical capability. Despite substantial efforts in…